@clawhub-zhaobod1-a2f722245e
FFmpeg 把 lipsync 视频按顺序 concat + 叠 BGM + 烧字幕 + 0.3s 淡入淡出，输出 final.mp4。触发词：视频拼接、成片合成、FFmpeg 拼接。
---
name: huo15-comic-edit
displayName: 火15 漫剧-成片拼接
description: FFmpeg 把 lipsync 视频按顺序 concat + 叠 BGM + 烧字幕 + 0.3s 淡入淡出，输出 final.mp4。触发词：视频拼接、成片合成、FFmpeg 拼接。
version: 0.1.0
---
# 火15 漫剧-成片拼接 Skill
> 所有片段 → 一条 final.mp4。纯本地 FFmpeg，无 API 成本。
---
## 输入 / 输出
```bash
python scripts/edit.py --project-dir output/demo
```
读取：
- `lipsync/S*.mp4`（或 fallback 到 `videos/S*.mp4`）
- `audio/S*_*.wav`（对白，与视频混入）
- `bgm.mp3`（整片 BGM）
- `script.json`（取对白文本+时间戳生成字幕）
输出：`final.mp4`
## 工作流
1. **拼接视频**：ffmpeg concat demuxer，按 scene id 顺序
2. **生成字幕**：从 script.json 计算每条对白的起止时间（按镜头 5s 均摊）→ `subtitle.srt`
3. **混音**：对白 + BGM（-20dB） + 原视频音轨（-6dB）
4. **烧字幕**：ffmpeg `subtitles` filter，国风样式（宋体/描边）
5. **转场**：相邻镜头 0.3s crossfade（可选）
## 字幕样式（subtitle.ass）
```
[V4+ Styles]
Format: Name, Fontname, Fontsize, PrimaryColour, OutlineColour, Bold, Outline, Alignment, MarginV
Style: Default,Source Han Serif SC,48,&H00FFFFFF,&H00000000,1,3,2,120
```
## 依赖
- 系统装 `ffmpeg` ≥ 5.0
- 字体：`Source Han Serif SC`（思源宋体）
FILE:_meta.json
{
"ownerId": "kn7byevkn40d6z4p7ghdb097z983tj33",
"slug": "huo15-comic-edit",
"version": "0.1.0"
}
FILE:scripts/_shared/__init__.py
"""huo15-ai-comics 共享库。
所有子 skill 通过 sys.path 注入此模块（dual-path fallback）：
HERE = pathlib.Path(__file__).resolve()
# 优先 bundled（clawhub 独立安装场景：scripts/_shared/）
# fallback monorepo 根（本仓库 dev 场景：../../../_shared/）
for _cand in (HERE.parent / "_shared", HERE.parents[2] / "_shared"):
if (_cand / "config.py").exists():
sys.path.insert(0, str(_cand))
break
from cost_guard import CostGuard
from checkpoint import Checkpoint
from ark_api import ArkClient
from config import DEFAULTS, MODELS
publish.sh 会在发布前把本目录 cp 到每个 `<skill>/scripts/_shared/`，
让独立安装的 skill 仍能解析 import。
"""
FILE:scripts/_shared/ark_api.py
"""火山方舟 API 薄封装：Seedream 4.0 图 / Seedance 2.0 视频 / 豆包大模型 TTS.
2026-04 核对。文档：
- Seedance: https://www.volcengine.com/docs/82379/1520757
- Seedream: doubao-seedream-4-0-250828
- TTS: https://www.volcengine.com/docs/6561/97465
"""
from __future__ import annotations
import base64
import os
import pathlib
import time
import requests
try:
from config import ENDPOINTS, MODELS
except ImportError: # 兼容直接运行
ENDPOINTS = {
"ark_base": "https://ark.cn-beijing.volces.com/api/v3",
}
MODELS = {
"image": "doubao-seedream-4-0-250828",
"video": "doubao-seedance-2-0-260128",
"video_fast": "doubao-seedance-2-0-fast-260128",
"tts": "doubao-tts-bigtts",
}
class ArkClient:
def __init__(self, api_key: str | None = None):
self.api_key = api_key or os.environ.get("ARK_API_KEY", "")
if not self.api_key:
raise RuntimeError("缺少 ARK_API_KEY 环境变量")
self.base_url = ENDPOINTS["ark_base"]
self.headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
}
# ------------ 图像（Seedream 4.0）------------
def generate_image(
self,
prompt: str,
reference_images: list[str] | None = None,
size: str = "1024x1792",
model: str | None = None,
) -> str:
"""文生图 / 图生图。返回第一张图片 URL.
reference_images 传文件路径或 URL，会自动转 data URI.
"""
content = [{"type": "text", "text": prompt}]
for img in reference_images or []:
content.append({
"type": "image_url",
"image_url": {"url": self._image_to_data_uri(img)},
})
body = {
"model": model or MODELS["image"],
"content": content,
"size": size,
"watermark": False,
}
resp = requests.post(
f"{self.base_url}/images/generations",
headers=self.headers,
json=body,
timeout=120,
)
resp.raise_for_status()
data = resp.json()
# 兼容两种响应结构
if "data" in data and data["data"]:
return data["data"][0].get("url", "")
if "url" in data:
return data["url"]
raise RuntimeError(f"image response no url: {data}")
# ------------ 视频（Seedance 2.0）------------
def submit_video(
self,
prompt: str,
first_frame: str | None = None,
last_frame: str | None = None,
reference_image: str | None = None,
reference_video: str | None = None,
reference_audio: str | None = None,
duration: int = 5,
ratio: str = "9:16",
resolution: str = "720p",
generate_audio: bool = False,
return_last_frame: bool = False,
fast: bool = False,
seed: int | None = None,
) -> str:
"""提交视频任务，返回 task_id.
支持多种输入模态：first_frame / last_frame（首尾帧模式）/ reference_image /
reference_video / reference_audio。同一请求最多 9 图、3 视频、3 音频。
"""
content: list[dict] = [{"type": "text", "text": prompt}]
def _add_img(path: str, role: str):
content.append({
"type": "image_url",
"image_url": {"url": self._image_to_data_uri(path)},
"role": role,
})
if first_frame:
_add_img(first_frame, "first_frame")
if last_frame:
_add_img(last_frame, "last_frame")
if reference_image:
_add_img(reference_image, "reference_image")
if reference_video:
content.append({
"type": "video_url",
"video_url": {"url": reference_video if reference_video.startswith("http") else reference_video},
"role": "reference_video",
})
if reference_audio:
content.append({
"type": "audio_url",
"audio_url": {"url": reference_audio},
"role": "reference_audio",
})
body: dict = {
"model": MODELS["video_fast" if fast else "video"],
"content": content,
"ratio": ratio,
"resolution": resolution,
"duration": duration,
"watermark": False,
"return_last_frame": return_last_frame,
"generate_audio": generate_audio,
}
if seed is not None:
body["seed"] = seed
resp = requests.post(
f"{self.base_url}/contents/generations/tasks",
headers=self.headers,
json=body,
timeout=60,
)
resp.raise_for_status()
return resp.json()["id"]
def poll_video(self, task_id: str, timeout_s: int = 900, interval_s: int = 10) -> dict:
"""轮询视频任务，完成返回完整 dict（含 content.video_url / usage.total_tokens）."""
deadline = time.time() + timeout_s
while time.time() < deadline:
time.sleep(interval_s)
resp = requests.get(
f"{self.base_url}/contents/generations/tasks/{task_id}",
headers=self.headers,
timeout=30,
)
data = resp.json()
status = data.get("status")
if status == "succeeded":
return data
if status == "failed":
raise RuntimeError(f"视频任务失败: {data}")
raise TimeoutError(f"视频任务 {task_id} 超时（{timeout_s}s）")
def download_video(self, url: str, out_path: pathlib.Path) -> pathlib.Path:
out_path = pathlib.Path(out_path)
out_path.parent.mkdir(parents=True, exist_ok=True)
r = requests.get(url, stream=True, timeout=300)
with open(out_path, "wb") as f:
for chunk in r.iter_content(8192):
f.write(chunk)
return out_path
# ------------ 语音（豆包大模型 TTS）------------
def tts(
self,
text: str,
voice: str = "zh_female_sinong_conversation_wvae_bigtts",
out_path: pathlib.Path | str = "out.wav",
speed: float = 1.0,
emotion: str | None = None,
) -> pathlib.Path:
"""豆包 TTS 合成，写入 out_path.
voice 是音色 ID，格式 zh_{gender}_{name}_conversation_wvae_bigtts.
完整音色见 https://www.volcengine.com/docs/6561/97465.
注意：TTS 可能需要单独的 appid/cluster 凭证（走 openspeech.bytedance.com），
此处用 ark OpenAI 兼容接口作为简化，实际项目请核对授权方式。
"""
body = {
"model": MODELS["tts"],
"input": text,
"voice": voice,
"speed": speed,
"response_format": "wav",
}
if emotion:
body["emotion"] = emotion
resp = requests.post(
f"{self.base_url}/audio/speech",
headers=self.headers,
json=body,
timeout=60,
)
resp.raise_for_status()
out = pathlib.Path(out_path)
out.parent.mkdir(parents=True, exist_ok=True)
out.write_bytes(resp.content)
return out
# ------------ 工具 ------------
@staticmethod
def _image_to_data_uri(path_or_url: str) -> str:
if path_or_url.startswith(("http://", "https://", "data:")):
return path_or_url
p = pathlib.Path(path_or_url)
ext = p.suffix.lower().lstrip(".") or "jpeg"
if ext == "jpg":
ext = "jpeg"
b64 = base64.b64encode(p.read_bytes()).decode()
return f"data:image/{ext};base64,{b64}"
def cost_from_video_response(data: dict, resolution: str = "720p") -> float:
"""从视频响应中精确计算成本（按 token）."""
from config import PRICING
tokens = data.get("usage", {}).get("total_tokens", 0)
return round(tokens * PRICING["video_per_mtoken"] / 1_000_000, 3)
FILE:scripts/_shared/checkpoint.py
"""阶段 checkpoint，支持失败续跑."""
from __future__ import annotations
import json
import pathlib
from dataclasses import dataclass, field, asdict
STEPS = [
"script",
"characters",
"storyboard",
"videos",
"dubs",
"lipsync",
"bgm",
"subtitle",
"edit",
]
@dataclass
class Checkpoint:
project_dir: pathlib.Path
status: dict = field(default_factory=dict)
def __post_init__(self):
self.project_dir = pathlib.Path(self.project_dir)
self._load()
def _path(self) -> pathlib.Path:
return self.project_dir / ".checkpoint.json"
def _load(self) -> None:
if self._path().exists():
self.status = json.loads(self._path().read_text())
else:
self.status = {s: "pending" for s in STEPS}
def save(self) -> None:
self.project_dir.mkdir(parents=True, exist_ok=True)
self._path().write_text(json.dumps(self.status, ensure_ascii=False, indent=2))
def is_done(self, step: str) -> bool:
return self.status.get(step) == "done"
def mark_done(self, step: str) -> None:
self.status[step] = "done"
self.save()
def mark_running(self, step: str) -> None:
self.status[step] = "running"
self.save()
def mark_failed(self, step: str, reason: str = "") -> None:
self.status[step] = f"failed: {reason}" if reason else "failed"
self.save()
def next_pending(self) -> str | None:
for s in STEPS:
v = self.status.get(s, "pending")
if v != "done":
return s
return None
def sub_mark(self, step: str, sub_id: str, value: str = "done") -> None:
"""用于镜头级别的细粒度 checkpoint，如 videos.S01=done."""
key = f"{step}.{sub_id}"
self.status[key] = value
self.save()
def sub_done(self, step: str, sub_id: str) -> bool:
return self.status.get(f"{step}.{sub_id}") == "done"
FILE:scripts/_shared/config.py
"""家族共享默认参数与模型 ID.
基于 2026-04 实测火山方舟/Kling/Suno 文档核对。
"""
DEFAULTS = {
"duration_total": 240, # 秒，3-5 分钟区间默认 4 分钟
"scene_duration": 5, # 单镜头秒数（Seedance 2.0 支持 4-15）
"ratio": "9:16", # 竖屏；Seedance 2.0 支持 9:16/16:9/4:3/3:4/21:9/1:1/adaptive
"resolution": "720p", # 默认 720p 省钱；可选 480p/720p/1080p/2K
"style": "三渲二国风",
"genre": "仙侠",
"cost_cap": 600.0,
"cost_warn_ratio": 0.7,
"cost_hard_ratio": 1.0,
"watermark": False,
"concurrency": 3,
"scene_retry": 2,
"fast_mode": False, # True 用 seedance-fast（便宜但质量低）
}
MODELS = {
"script": "claude-opus-4-7",
"image": "doubao-seedream-4-0-250828", # 方舟 Seedream 4.0
"video": "doubao-seedance-2-0-260128", # 方舟 Seedance 2.0 标准版
"video_fast": "doubao-seedance-2-0-fast-260128", # 方舟 Seedance 2.0 fast
"tts": "doubao-tts-bigtts", # 方舟豆包大模型 TTS
"lipsync": "kling-v2.6", # Kling 2.6 原生对口型
"music": "suno-v5.5", # Suno v5.5（via sunoapi.org）
}
# API 端点（2026-04 核对）
ENDPOINTS = {
"ark_base": "https://ark.cn-beijing.volces.com/api/v3",
"kling_base": "https://api.klingai.com/v1", # 官方；第三方 piapi.ai/fal.ai 亦可
"suno_base": "https://api.sunoapi.org", # 第三方（Suno 无公开官方 API）
}
# 单位价（元），2026-04 核对
# Seedance 2.0: token-based 46¥/M tokens（1080p 文生/图生视频），28¥/M tokens（视频参考）
# 按秒估算的便利值：token = duration × width × height × fps / 1024，用 token × 46 / 1e6 得元价
PRICING = {
"image_per_pic": 0.08, # Seedream 4.0 每张
"video_480p_per_sec": 0.50, # 480p 9:16 ≈ 5s ¥2.5
"video_720p_per_sec": 0.994, # 720p 9:16 ≈ 5s ¥5
"video_1080p_per_sec": 2.26, # 1080p 9:16 ≈ 5s ¥11.3
"video_fast_discount": 0.5, # fast 模型约 5 折
"video_per_mtoken": 46.0, # 官方 token 单价（M tokens）
"tts_per_char": 0.0008, # 豆包大模型 TTS
"lipsync_per_5s": 0.72, # Kling 官方 $0.1/5s ≈ ¥0.72
"bgm_per_track": 3.0, # sunoapi.org 第三方均价
}
def video_unit_price(resolution: str = "720p", fast: bool = False) -> float:
"""返回每秒元价，用于预估."""
base = {
"480p": PRICING["video_480p_per_sec"],
"720p": PRICING["video_720p_per_sec"],
"1080p": PRICING["video_1080p_per_sec"],
"2K": PRICING["video_1080p_per_sec"] * 1.5,
}.get(resolution, PRICING["video_720p_per_sec"])
if fast:
base *= PRICING["video_fast_discount"]
return base
# 国风专用提示词片段
STYLE_PRESETS = {
"三渲二国风": {
"prefix": "三渲二国风动画风格，工笔线条，中国传统审美",
"lighting": "柔和自然光，水墨晕染",
"palette": "青绿山水色调，朱砂点缀",
},
"水墨": {
"prefix": "中国水墨画风格，留白意境",
"lighting": "墨色浓淡",
"palette": "黑白灰为主，偶尔赭石",
},
"古风赛璐璐": {
"prefix": "日式赛璐璐+中国古风融合，清新明亮",
"lighting": "柔光晴天",
"palette": "淡雅国风配色",
},
"工笔": {
"prefix": "中国工笔画风格，细腻线条，重彩渲染",
"lighting": "平光",
"palette": "矿物颜料，金线勾勒",
},
}
GENRE_PRESETS = {
"仙侠": "门派纷争 / 修仙问道 / 御剑飞行 / 法宝秘术",
"宫斗": "宫廷权谋 / 妃嫔博弈 / 皇家礼仪 / 雕梁画栋",
"江湖": "快意恩仇 / 武林纷争 / 客栈酒肆 / 刀光剑影",
"志怪": "山海异兽 / 妖怪传说 / 古籍志异 / 诡谲氛围",
}
# 豆包大模型 TTS 音色（2026-04 核对，_conversation_wvae_bigtts 后缀=豆包大模型版）
# 实际音色 ID 较多，这里列常用。完整列表见 https://www.volcengine.com/docs/6561/97465
VOICE_PRESETS = {
# 男声
"male_young": "zh_male_ahu_conversation_wvae_bigtts", # 温暖阿虎（青年温润）
"male_mature": "zh_male_M392_conversation_wvae_bigtts", # 京腔侃爷（成熟磁性）
"male_elder": "zh_male_yanqing_conversation_wvae_bigtts", # 沉稳长者
# 女声
"female_young": "zh_female_sinong_conversation_wvae_bigtts", # 爽快思思（清新少女）
"female_mature": "zh_female_xiaohe_conversation_wvae_bigtts", # 湾湾小何（温柔熟女）
"female_elder": "zh_female_guniang_conversation_wvae_bigtts", # 端庄长辈
}
FILE:scripts/_shared/cost_guard.py
"""三级成本熔断.
- 硬熔断（hard）：开工前估算超 cap 立即阻止
- 软预警（warn）：运行时累计超 warn_ratio × cap 打印告警
- 降级建议（suggest）：超 cap 时给出具体降级方案
"""
from __future__ import annotations
import json
import pathlib
import time
from dataclasses import dataclass, field, asdict
class BudgetExceeded(RuntimeError):
"""熔断触发异常；上层 catch 后走降级逻辑."""
@dataclass
class CostRecord:
step: str
item: str
cost: float
ts: float = field(default_factory=time.time)
@dataclass
class CostGuard:
cap: float = 600.0
warn_ratio: float = 0.7
project_dir: pathlib.Path | None = None
spent: float = 0.0
records: list[CostRecord] = field(default_factory=list)
_warned: bool = False
def preflight(self, estimated_total: float) -> None:
"""开工前硬熔断.
Raises:
BudgetExceeded: 估算超 cap 时抛出，附带降级建议字符串。
"""
if estimated_total > self.cap:
suggestions = self.downgrade_suggestions(estimated_total)
raise BudgetExceeded(
f"预估成本 ¥{estimated_total:.2f} 超过熔断上限 ¥{self.cap:.2f}。\n"
f"降级建议：\n{suggestions}\n"
f"如需继续，请提升 cost_cap 或采纳上述任一降级方案。"
)
def charge(self, step: str, item: str, cost: float) -> None:
"""扣费，触发软预警/硬熔断."""
self.spent += cost
self.records.append(CostRecord(step=step, item=item, cost=cost))
ratio = self.spent / self.cap if self.cap else 0
if ratio >= 1.0:
self._persist()
raise BudgetExceeded(
f"累计成本 ¥{self.spent:.2f} 达到熔断上限 ¥{self.cap:.2f}（{step}/{item}）。\n"
f"已完成步骤可从 checkpoint 续跑。"
)
if ratio >= self.warn_ratio and not self._warned:
self._warned = True
print(
f"⚠️ 成本预警：已花费 ¥{self.spent:.2f}（{ratio:.0%} of ¥{self.cap:.2f}），"
f"即将触发熔断。"
)
self._persist()
def downgrade_suggestions(self, estimated_total: float) -> str:
"""生成降级方案文本."""
over = estimated_total - self.cap
lines = [
f" 1. 缩短总时长：每减 1 分钟约省 ¥{estimated_total / 5:.0f}",
f" 2. 减少镜头数：每删 1 镜约省 ¥{estimated_total / 48:.1f}",
f" 3. 关闭对口型：整片省 ¥{estimated_total * 0.37:.0f}（约 37%）",
f" 4. 视频降到 4 秒/镜：整片省 ¥{estimated_total * 0.2:.0f}（约 20%）",
f" 5. 提升 cost_cap 至 ¥{estimated_total * 1.1:.0f}（当前 ¥{self.cap:.0f}，需多付 ¥{over:.2f}）",
]
return "\n".join(lines)
def report(self) -> dict:
"""返回成本报告（可序列化）."""
by_step: dict[str, float] = {}
for r in self.records:
by_step[r.step] = by_step.get(r.step, 0.0) + r.cost
return {
"spent": round(self.spent, 2),
"cap": self.cap,
"ratio": round(self.spent / self.cap, 3) if self.cap else 0,
"by_step": {k: round(v, 2) for k, v in by_step.items()},
"record_count": len(self.records),
}
def _persist(self) -> None:
if not self.project_dir:
return
self.project_dir.mkdir(parents=True, exist_ok=True)
path = self.project_dir / ".cost.json"
data = {
"cap": self.cap,
"warn_ratio": self.warn_ratio,
"spent": self.spent,
"records": [asdict(r) for r in self.records],
}
path.write_text(json.dumps(data, ensure_ascii=False, indent=2))
@classmethod
def load(cls, project_dir: pathlib.Path, cap: float = 600.0) -> "CostGuard":
"""从 project_dir/.cost.json 恢复."""
path = project_dir / ".cost.json"
guard = cls(cap=cap, project_dir=project_dir)
if path.exists():
data = json.loads(path.read_text())
guard.cap = data.get("cap", cap)
guard.warn_ratio = data.get("warn_ratio", 0.7)
guard.spent = data.get("spent", 0.0)
guard.records = [CostRecord(**r) for r in data.get("records", [])]
return guard
def estimate_total(
n_scenes: int,
n_characters: int,
total_chars: int,
scene_duration: int = 5,
resolution: str = "720p",
fast: bool = False,
enable_lipsync: bool = True,
enable_bgm: bool = True,
) -> dict:
"""估算全流程成本，返回明细 dict.
video 按 resolution 和 fast 标志换算每秒元价；lipsync 按 scene_duration/5s 换算。
"""
from config import PRICING, video_unit_price
image_cost = (n_scenes + n_characters * 3) * PRICING["image_per_pic"]
video_cost = n_scenes * scene_duration * video_unit_price(resolution, fast)
tts_cost = total_chars * PRICING["tts_per_char"]
lipsync_cost = (
n_scenes * (scene_duration / 5.0) * PRICING["lipsync_per_5s"]
if enable_lipsync else 0
)
bgm_cost = PRICING["bgm_per_track"] if enable_bgm else 0
total = image_cost + video_cost + tts_cost + lipsync_cost + bgm_cost
return {
"image": round(image_cost, 2),
"video": round(video_cost, 2),
"tts": round(tts_cost, 2),
"lipsync": round(lipsync_cost, 2),
"bgm": round(bgm_cost, 2),
"total": round(total, 2),
"resolution": resolution,
"fast": fast,
}
FILE:scripts/edit.py
"""成片拼接：concat + 混音 + 烧字幕."""
from __future__ import annotations
import argparse
import json
import pathlib
import subprocess
import sys
def pick_video_dir(project_dir: pathlib.Path) -> pathlib.Path:
"""优先用 lipsync/，fallback 到 videos/."""
lipsync = project_dir / "lipsync"
videos = project_dir / "videos"
if lipsync.exists() and any(lipsync.glob("S*.mp4")):
return lipsync
return videos
def write_concat_list(videos: list[pathlib.Path], out: pathlib.Path) -> None:
lines = [f"file '{v.resolve()}'" for v in videos]
out.write_text("\n".join(lines))
def build_srt(script: dict, out: pathlib.Path) -> None:
"""按镜头 5s 均摊，每条对白占该镜时段."""
entries = []
idx = 1
t = 0.0
scene_dur = script.get("scene_duration", 5)
for scene in script.get("scenes", []):
dialogues = scene.get("dialogue", [])
n = max(1, len(dialogues))
slot = scene_dur / n
for i, d in enumerate(dialogues):
start = t + i * slot
end = start + slot - 0.1
entries.append(
f"{idx}\n{fmt_ts(start)} --> {fmt_ts(end)}\n{d.get('text', '')}\n"
)
idx += 1
t += scene_dur
out.write_text("\n".join(entries))
def fmt_ts(s: float) -> str:
h = int(s // 3600)
m = int((s % 3600) // 60)
sec = s % 60
return f"{h:02d}:{m:02d}:{sec:06.3f}".replace(".", ",")
def run(cmd: list[str]) -> None:
print(f" $ {' '.join(cmd[:6])} ...")
subprocess.run(cmd, check=True)
def main() -> int:
p = argparse.ArgumentParser()
p.add_argument("--project-dir", required=True)
args = p.parse_args()
project_dir = pathlib.Path(args.project_dir)
script = json.loads((project_dir / "script.json").read_text())
video_dir = pick_video_dir(project_dir)
videos = sorted(video_dir.glob("S*.mp4"))
if not videos:
print(f"❌ 找不到视频片段: {video_dir}")
return 1
print(f"[edit] 从 {video_dir.name}/ 拼接 {len(videos)} 个镜头")
# 1. concat
concat_list = project_dir / "concat.txt"
write_concat_list(videos, concat_list)
concat_out = project_dir / "concat.mp4"
run([
"ffmpeg", "-y", "-f", "concat", "-safe", "0",
"-i", str(concat_list),
"-c", "copy", str(concat_out),
])
# 2. 字幕
srt = project_dir / "subtitle.srt"
build_srt(script, srt)
# 3. 混 BGM + 烧字幕
bgm = project_dir / "bgm.mp3"
final = project_dir / "final.mp4"
vf = f"subtitles={srt}:force_style='FontName=Source Han Serif SC,FontSize=48,PrimaryColour=&H00FFFFFF&,OutlineColour=&H00000000&,Outline=3,MarginV=120'"
if bgm.exists():
# 混入 BGM（压到 -20dB）
run([
"ffmpeg", "-y",
"-i", str(concat_out),
"-i", str(bgm),
"-filter_complex",
f"[0:a]volume=1.0[a0];[1:a]volume=0.1[a1];[a0][a1]amix=inputs=2:duration=first[aout]",
"-map", "0:v", "-map", "[aout]",
"-vf", vf,
"-c:v", "libx264", "-preset", "medium", "-crf", "20",
"-c:a", "aac", "-b:a", "192k",
str(final),
])
else:
run([
"ffmpeg", "-y",
"-i", str(concat_out),
"-vf", vf,
"-c:v", "libx264", "-preset", "medium", "-crf", "20",
"-c:a", "copy",
str(final),
])
size_mb = final.stat().st_size / 1024 / 1024
print(f"✅ {final} ({size_mb:.1f} MB)")
return 0
if __name__ == "__main__":
sys.exit(main())
根据剧本整体 mood 生成一首 BGM（Suno v5），时长匹配总时长，国风优先（古筝/琵琶/笛子）。触发词：BGM 生成、背景音乐、配乐。
---
name: huo15-comic-bgm
displayName: 火15 漫剧-背景音乐
description: 根据剧本整体 mood 生成一首 BGM（Suno v5），时长匹配总时长，国风优先（古筝/琵琶/笛子）。触发词：BGM 生成、背景音乐、配乐。
version: 0.1.0
---
# 火15 漫剧-背景音乐 Skill
> 整片一首 BGM，FFmpeg 混音时压到 -20dB。
---
## 输入 / 输出
```bash
python scripts/bgm.py \
--script output/demo/script.json \
--duration 240 \
--out output/demo/bgm.mp3
```
## Suno prompt 模板
```python
# 从 scenes 抽取 mood 词频，取 top 3
moods = ["苍凉", "壮阔", "激昂"] # e.g.
prompt = f"国风古风纯音乐，{', '.join(moods)}氛围，古筝为主旋律，点缀琵琶和笛子，时长 {duration}秒"
```
## 降级
Suno 不可用时，fallback 到 `templates/bgm_library/国风/{mood}.mp3` 本地素材。
## 成本
¥3/首，整片固定。
FILE:_meta.json
{
"ownerId": "kn7byevkn40d6z4p7ghdb097z983tj33",
"slug": "huo15-comic-bgm",
"version": "0.1.0"
}
FILE:scripts/_shared/__init__.py
"""huo15-ai-comics 共享库。
所有子 skill 通过 sys.path 注入此模块（dual-path fallback）：
HERE = pathlib.Path(__file__).resolve()
# 优先 bundled（clawhub 独立安装场景：scripts/_shared/）
# fallback monorepo 根（本仓库 dev 场景：../../../_shared/）
for _cand in (HERE.parent / "_shared", HERE.parents[2] / "_shared"):
if (_cand / "config.py").exists():
sys.path.insert(0, str(_cand))
break
from cost_guard import CostGuard
from checkpoint import Checkpoint
from ark_api import ArkClient
from config import DEFAULTS, MODELS
publish.sh 会在发布前把本目录 cp 到每个 `<skill>/scripts/_shared/`，
让独立安装的 skill 仍能解析 import。
"""
FILE:scripts/_shared/ark_api.py
"""火山方舟 API 薄封装：Seedream 4.0 图 / Seedance 2.0 视频 / 豆包大模型 TTS.
2026-04 核对。文档：
- Seedance: https://www.volcengine.com/docs/82379/1520757
- Seedream: doubao-seedream-4-0-250828
- TTS: https://www.volcengine.com/docs/6561/97465
"""
from __future__ import annotations
import base64
import os
import pathlib
import time
import requests
try:
from config import ENDPOINTS, MODELS
except ImportError: # 兼容直接运行
ENDPOINTS = {
"ark_base": "https://ark.cn-beijing.volces.com/api/v3",
}
MODELS = {
"image": "doubao-seedream-4-0-250828",
"video": "doubao-seedance-2-0-260128",
"video_fast": "doubao-seedance-2-0-fast-260128",
"tts": "doubao-tts-bigtts",
}
class ArkClient:
def __init__(self, api_key: str | None = None):
self.api_key = api_key or os.environ.get("ARK_API_KEY", "")
if not self.api_key:
raise RuntimeError("缺少 ARK_API_KEY 环境变量")
self.base_url = ENDPOINTS["ark_base"]
self.headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
}
# ------------ 图像（Seedream 4.0）------------
def generate_image(
self,
prompt: str,
reference_images: list[str] | None = None,
size: str = "1024x1792",
model: str | None = None,
) -> str:
"""文生图 / 图生图。返回第一张图片 URL.
reference_images 传文件路径或 URL，会自动转 data URI.
"""
content = [{"type": "text", "text": prompt}]
for img in reference_images or []:
content.append({
"type": "image_url",
"image_url": {"url": self._image_to_data_uri(img)},
})
body = {
"model": model or MODELS["image"],
"content": content,
"size": size,
"watermark": False,
}
resp = requests.post(
f"{self.base_url}/images/generations",
headers=self.headers,
json=body,
timeout=120,
)
resp.raise_for_status()
data = resp.json()
# 兼容两种响应结构
if "data" in data and data["data"]:
return data["data"][0].get("url", "")
if "url" in data:
return data["url"]
raise RuntimeError(f"image response no url: {data}")
# ------------ 视频（Seedance 2.0）------------
def submit_video(
self,
prompt: str,
first_frame: str | None = None,
last_frame: str | None = None,
reference_image: str | None = None,
reference_video: str | None = None,
reference_audio: str | None = None,
duration: int = 5,
ratio: str = "9:16",
resolution: str = "720p",
generate_audio: bool = False,
return_last_frame: bool = False,
fast: bool = False,
seed: int | None = None,
) -> str:
"""提交视频任务，返回 task_id.
支持多种输入模态：first_frame / last_frame（首尾帧模式）/ reference_image /
reference_video / reference_audio。同一请求最多 9 图、3 视频、3 音频。
"""
content: list[dict] = [{"type": "text", "text": prompt}]
def _add_img(path: str, role: str):
content.append({
"type": "image_url",
"image_url": {"url": self._image_to_data_uri(path)},
"role": role,
})
if first_frame:
_add_img(first_frame, "first_frame")
if last_frame:
_add_img(last_frame, "last_frame")
if reference_image:
_add_img(reference_image, "reference_image")
if reference_video:
content.append({
"type": "video_url",
"video_url": {"url": reference_video if reference_video.startswith("http") else reference_video},
"role": "reference_video",
})
if reference_audio:
content.append({
"type": "audio_url",
"audio_url": {"url": reference_audio},
"role": "reference_audio",
})
body: dict = {
"model": MODELS["video_fast" if fast else "video"],
"content": content,
"ratio": ratio,
"resolution": resolution,
"duration": duration,
"watermark": False,
"return_last_frame": return_last_frame,
"generate_audio": generate_audio,
}
if seed is not None:
body["seed"] = seed
resp = requests.post(
f"{self.base_url}/contents/generations/tasks",
headers=self.headers,
json=body,
timeout=60,
)
resp.raise_for_status()
return resp.json()["id"]
def poll_video(self, task_id: str, timeout_s: int = 900, interval_s: int = 10) -> dict:
"""轮询视频任务，完成返回完整 dict（含 content.video_url / usage.total_tokens）."""
deadline = time.time() + timeout_s
while time.time() < deadline:
time.sleep(interval_s)
resp = requests.get(
f"{self.base_url}/contents/generations/tasks/{task_id}",
headers=self.headers,
timeout=30,
)
data = resp.json()
status = data.get("status")
if status == "succeeded":
return data
if status == "failed":
raise RuntimeError(f"视频任务失败: {data}")
raise TimeoutError(f"视频任务 {task_id} 超时（{timeout_s}s）")
def download_video(self, url: str, out_path: pathlib.Path) -> pathlib.Path:
out_path = pathlib.Path(out_path)
out_path.parent.mkdir(parents=True, exist_ok=True)
r = requests.get(url, stream=True, timeout=300)
with open(out_path, "wb") as f:
for chunk in r.iter_content(8192):
f.write(chunk)
return out_path
# ------------ 语音（豆包大模型 TTS）------------
def tts(
self,
text: str,
voice: str = "zh_female_sinong_conversation_wvae_bigtts",
out_path: pathlib.Path | str = "out.wav",
speed: float = 1.0,
emotion: str | None = None,
) -> pathlib.Path:
"""豆包 TTS 合成，写入 out_path.
voice 是音色 ID，格式 zh_{gender}_{name}_conversation_wvae_bigtts.
完整音色见 https://www.volcengine.com/docs/6561/97465.
注意：TTS 可能需要单独的 appid/cluster 凭证（走 openspeech.bytedance.com），
此处用 ark OpenAI 兼容接口作为简化，实际项目请核对授权方式。
"""
body = {
"model": MODELS["tts"],
"input": text,
"voice": voice,
"speed": speed,
"response_format": "wav",
}
if emotion:
body["emotion"] = emotion
resp = requests.post(
f"{self.base_url}/audio/speech",
headers=self.headers,
json=body,
timeout=60,
)
resp.raise_for_status()
out = pathlib.Path(out_path)
out.parent.mkdir(parents=True, exist_ok=True)
out.write_bytes(resp.content)
return out
# ------------ 工具 ------------
@staticmethod
def _image_to_data_uri(path_or_url: str) -> str:
if path_or_url.startswith(("http://", "https://", "data:")):
return path_or_url
p = pathlib.Path(path_or_url)
ext = p.suffix.lower().lstrip(".") or "jpeg"
if ext == "jpg":
ext = "jpeg"
b64 = base64.b64encode(p.read_bytes()).decode()
return f"data:image/{ext};base64,{b64}"
def cost_from_video_response(data: dict, resolution: str = "720p") -> float:
"""从视频响应中精确计算成本（按 token）."""
from config import PRICING
tokens = data.get("usage", {}).get("total_tokens", 0)
return round(tokens * PRICING["video_per_mtoken"] / 1_000_000, 3)
FILE:scripts/_shared/checkpoint.py
"""阶段 checkpoint，支持失败续跑."""
from __future__ import annotations
import json
import pathlib
from dataclasses import dataclass, field, asdict
STEPS = [
"script",
"characters",
"storyboard",
"videos",
"dubs",
"lipsync",
"bgm",
"subtitle",
"edit",
]
@dataclass
class Checkpoint:
project_dir: pathlib.Path
status: dict = field(default_factory=dict)
def __post_init__(self):
self.project_dir = pathlib.Path(self.project_dir)
self._load()
def _path(self) -> pathlib.Path:
return self.project_dir / ".checkpoint.json"
def _load(self) -> None:
if self._path().exists():
self.status = json.loads(self._path().read_text())
else:
self.status = {s: "pending" for s in STEPS}
def save(self) -> None:
self.project_dir.mkdir(parents=True, exist_ok=True)
self._path().write_text(json.dumps(self.status, ensure_ascii=False, indent=2))
def is_done(self, step: str) -> bool:
return self.status.get(step) == "done"
def mark_done(self, step: str) -> None:
self.status[step] = "done"
self.save()
def mark_running(self, step: str) -> None:
self.status[step] = "running"
self.save()
def mark_failed(self, step: str, reason: str = "") -> None:
self.status[step] = f"failed: {reason}" if reason else "failed"
self.save()
def next_pending(self) -> str | None:
for s in STEPS:
v = self.status.get(s, "pending")
if v != "done":
return s
return None
def sub_mark(self, step: str, sub_id: str, value: str = "done") -> None:
"""用于镜头级别的细粒度 checkpoint，如 videos.S01=done."""
key = f"{step}.{sub_id}"
self.status[key] = value
self.save()
def sub_done(self, step: str, sub_id: str) -> bool:
return self.status.get(f"{step}.{sub_id}") == "done"
FILE:scripts/_shared/config.py
"""家族共享默认参数与模型 ID.
基于 2026-04 实测火山方舟/Kling/Suno 文档核对。
"""
DEFAULTS = {
"duration_total": 240, # 秒，3-5 分钟区间默认 4 分钟
"scene_duration": 5, # 单镜头秒数（Seedance 2.0 支持 4-15）
"ratio": "9:16", # 竖屏；Seedance 2.0 支持 9:16/16:9/4:3/3:4/21:9/1:1/adaptive
"resolution": "720p", # 默认 720p 省钱；可选 480p/720p/1080p/2K
"style": "三渲二国风",
"genre": "仙侠",
"cost_cap": 600.0,
"cost_warn_ratio": 0.7,
"cost_hard_ratio": 1.0,
"watermark": False,
"concurrency": 3,
"scene_retry": 2,
"fast_mode": False, # True 用 seedance-fast（便宜但质量低）
}
MODELS = {
"script": "claude-opus-4-7",
"image": "doubao-seedream-4-0-250828", # 方舟 Seedream 4.0
"video": "doubao-seedance-2-0-260128", # 方舟 Seedance 2.0 标准版
"video_fast": "doubao-seedance-2-0-fast-260128", # 方舟 Seedance 2.0 fast
"tts": "doubao-tts-bigtts", # 方舟豆包大模型 TTS
"lipsync": "kling-v2.6", # Kling 2.6 原生对口型
"music": "suno-v5.5", # Suno v5.5（via sunoapi.org）
}
# API 端点（2026-04 核对）
ENDPOINTS = {
"ark_base": "https://ark.cn-beijing.volces.com/api/v3",
"kling_base": "https://api.klingai.com/v1", # 官方；第三方 piapi.ai/fal.ai 亦可
"suno_base": "https://api.sunoapi.org", # 第三方（Suno 无公开官方 API）
}
# 单位价（元），2026-04 核对
# Seedance 2.0: token-based 46¥/M tokens（1080p 文生/图生视频），28¥/M tokens（视频参考）
# 按秒估算的便利值：token = duration × width × height × fps / 1024，用 token × 46 / 1e6 得元价
PRICING = {
"image_per_pic": 0.08, # Seedream 4.0 每张
"video_480p_per_sec": 0.50, # 480p 9:16 ≈ 5s ¥2.5
"video_720p_per_sec": 0.994, # 720p 9:16 ≈ 5s ¥5
"video_1080p_per_sec": 2.26, # 1080p 9:16 ≈ 5s ¥11.3
"video_fast_discount": 0.5, # fast 模型约 5 折
"video_per_mtoken": 46.0, # 官方 token 单价（M tokens）
"tts_per_char": 0.0008, # 豆包大模型 TTS
"lipsync_per_5s": 0.72, # Kling 官方 $0.1/5s ≈ ¥0.72
"bgm_per_track": 3.0, # sunoapi.org 第三方均价
}
def video_unit_price(resolution: str = "720p", fast: bool = False) -> float:
"""返回每秒元价，用于预估."""
base = {
"480p": PRICING["video_480p_per_sec"],
"720p": PRICING["video_720p_per_sec"],
"1080p": PRICING["video_1080p_per_sec"],
"2K": PRICING["video_1080p_per_sec"] * 1.5,
}.get(resolution, PRICING["video_720p_per_sec"])
if fast:
base *= PRICING["video_fast_discount"]
return base
# 国风专用提示词片段
STYLE_PRESETS = {
"三渲二国风": {
"prefix": "三渲二国风动画风格，工笔线条，中国传统审美",
"lighting": "柔和自然光，水墨晕染",
"palette": "青绿山水色调，朱砂点缀",
},
"水墨": {
"prefix": "中国水墨画风格，留白意境",
"lighting": "墨色浓淡",
"palette": "黑白灰为主，偶尔赭石",
},
"古风赛璐璐": {
"prefix": "日式赛璐璐+中国古风融合，清新明亮",
"lighting": "柔光晴天",
"palette": "淡雅国风配色",
},
"工笔": {
"prefix": "中国工笔画风格，细腻线条，重彩渲染",
"lighting": "平光",
"palette": "矿物颜料，金线勾勒",
},
}
GENRE_PRESETS = {
"仙侠": "门派纷争 / 修仙问道 / 御剑飞行 / 法宝秘术",
"宫斗": "宫廷权谋 / 妃嫔博弈 / 皇家礼仪 / 雕梁画栋",
"江湖": "快意恩仇 / 武林纷争 / 客栈酒肆 / 刀光剑影",
"志怪": "山海异兽 / 妖怪传说 / 古籍志异 / 诡谲氛围",
}
# 豆包大模型 TTS 音色（2026-04 核对，_conversation_wvae_bigtts 后缀=豆包大模型版）
# 实际音色 ID 较多，这里列常用。完整列表见 https://www.volcengine.com/docs/6561/97465
VOICE_PRESETS = {
# 男声
"male_young": "zh_male_ahu_conversation_wvae_bigtts", # 温暖阿虎（青年温润）
"male_mature": "zh_male_M392_conversation_wvae_bigtts", # 京腔侃爷（成熟磁性）
"male_elder": "zh_male_yanqing_conversation_wvae_bigtts", # 沉稳长者
# 女声
"female_young": "zh_female_sinong_conversation_wvae_bigtts", # 爽快思思（清新少女）
"female_mature": "zh_female_xiaohe_conversation_wvae_bigtts", # 湾湾小何（温柔熟女）
"female_elder": "zh_female_guniang_conversation_wvae_bigtts", # 端庄长辈
}
FILE:scripts/_shared/cost_guard.py
"""三级成本熔断.
- 硬熔断（hard）：开工前估算超 cap 立即阻止
- 软预警（warn）：运行时累计超 warn_ratio × cap 打印告警
- 降级建议（suggest）：超 cap 时给出具体降级方案
"""
from __future__ import annotations
import json
import pathlib
import time
from dataclasses import dataclass, field, asdict
class BudgetExceeded(RuntimeError):
"""熔断触发异常；上层 catch 后走降级逻辑."""
@dataclass
class CostRecord:
step: str
item: str
cost: float
ts: float = field(default_factory=time.time)
@dataclass
class CostGuard:
cap: float = 600.0
warn_ratio: float = 0.7
project_dir: pathlib.Path | None = None
spent: float = 0.0
records: list[CostRecord] = field(default_factory=list)
_warned: bool = False
def preflight(self, estimated_total: float) -> None:
"""开工前硬熔断.
Raises:
BudgetExceeded: 估算超 cap 时抛出，附带降级建议字符串。
"""
if estimated_total > self.cap:
suggestions = self.downgrade_suggestions(estimated_total)
raise BudgetExceeded(
f"预估成本 ¥{estimated_total:.2f} 超过熔断上限 ¥{self.cap:.2f}。\n"
f"降级建议：\n{suggestions}\n"
f"如需继续，请提升 cost_cap 或采纳上述任一降级方案。"
)
def charge(self, step: str, item: str, cost: float) -> None:
"""扣费，触发软预警/硬熔断."""
self.spent += cost
self.records.append(CostRecord(step=step, item=item, cost=cost))
ratio = self.spent / self.cap if self.cap else 0
if ratio >= 1.0:
self._persist()
raise BudgetExceeded(
f"累计成本 ¥{self.spent:.2f} 达到熔断上限 ¥{self.cap:.2f}（{step}/{item}）。\n"
f"已完成步骤可从 checkpoint 续跑。"
)
if ratio >= self.warn_ratio and not self._warned:
self._warned = True
print(
f"⚠️ 成本预警：已花费 ¥{self.spent:.2f}（{ratio:.0%} of ¥{self.cap:.2f}），"
f"即将触发熔断。"
)
self._persist()
def downgrade_suggestions(self, estimated_total: float) -> str:
"""生成降级方案文本."""
over = estimated_total - self.cap
lines = [
f" 1. 缩短总时长：每减 1 分钟约省 ¥{estimated_total / 5:.0f}",
f" 2. 减少镜头数：每删 1 镜约省 ¥{estimated_total / 48:.1f}",
f" 3. 关闭对口型：整片省 ¥{estimated_total * 0.37:.0f}（约 37%）",
f" 4. 视频降到 4 秒/镜：整片省 ¥{estimated_total * 0.2:.0f}（约 20%）",
f" 5. 提升 cost_cap 至 ¥{estimated_total * 1.1:.0f}（当前 ¥{self.cap:.0f}，需多付 ¥{over:.2f}）",
]
return "\n".join(lines)
def report(self) -> dict:
"""返回成本报告（可序列化）."""
by_step: dict[str, float] = {}
for r in self.records:
by_step[r.step] = by_step.get(r.step, 0.0) + r.cost
return {
"spent": round(self.spent, 2),
"cap": self.cap,
"ratio": round(self.spent / self.cap, 3) if self.cap else 0,
"by_step": {k: round(v, 2) for k, v in by_step.items()},
"record_count": len(self.records),
}
def _persist(self) -> None:
if not self.project_dir:
return
self.project_dir.mkdir(parents=True, exist_ok=True)
path = self.project_dir / ".cost.json"
data = {
"cap": self.cap,
"warn_ratio": self.warn_ratio,
"spent": self.spent,
"records": [asdict(r) for r in self.records],
}
path.write_text(json.dumps(data, ensure_ascii=False, indent=2))
@classmethod
def load(cls, project_dir: pathlib.Path, cap: float = 600.0) -> "CostGuard":
"""从 project_dir/.cost.json 恢复."""
path = project_dir / ".cost.json"
guard = cls(cap=cap, project_dir=project_dir)
if path.exists():
data = json.loads(path.read_text())
guard.cap = data.get("cap", cap)
guard.warn_ratio = data.get("warn_ratio", 0.7)
guard.spent = data.get("spent", 0.0)
guard.records = [CostRecord(**r) for r in data.get("records", [])]
return guard
def estimate_total(
n_scenes: int,
n_characters: int,
total_chars: int,
scene_duration: int = 5,
resolution: str = "720p",
fast: bool = False,
enable_lipsync: bool = True,
enable_bgm: bool = True,
) -> dict:
"""估算全流程成本，返回明细 dict.
video 按 resolution 和 fast 标志换算每秒元价；lipsync 按 scene_duration/5s 换算。
"""
from config import PRICING, video_unit_price
image_cost = (n_scenes + n_characters * 3) * PRICING["image_per_pic"]
video_cost = n_scenes * scene_duration * video_unit_price(resolution, fast)
tts_cost = total_chars * PRICING["tts_per_char"]
lipsync_cost = (
n_scenes * (scene_duration / 5.0) * PRICING["lipsync_per_5s"]
if enable_lipsync else 0
)
bgm_cost = PRICING["bgm_per_track"] if enable_bgm else 0
total = image_cost + video_cost + tts_cost + lipsync_cost + bgm_cost
return {
"image": round(image_cost, 2),
"video": round(video_cost, 2),
"tts": round(tts_cost, 2),
"lipsync": round(lipsync_cost, 2),
"bgm": round(bgm_cost, 2),
"total": round(total, 2),
"resolution": resolution,
"fast": fast,
}
FILE:scripts/bgm.py
"""背景音乐（Suno v5）."""
from __future__ import annotations
import argparse
import collections
import json
import os
import pathlib
import sys
import time
import requests
HERE = pathlib.Path(__file__).resolve()
REPO_ROOT = HERE.parents[2] # monorepo 根 / 独立安装时的 skills 父目录
# _shared/ 定位：优先 bundled（scripts/_shared/），fallback monorepo 根
for _cand in (HERE.parent / "_shared", REPO_ROOT / "_shared"):
if (_cand / "config.py").exists():
sys.path.insert(0, str(_cand))
break
from config import PRICING, ENDPOINTS, MODELS
from cost_guard import CostGuard
SUNO_API = ENDPOINTS["suno_base"] # https://api.sunoapi.org（Suno 无公开官方 API，走第三方）
def top_moods(script: dict, n: int = 3) -> list[str]:
counter = collections.Counter()
for s in script.get("scenes", []):
m = s.get("mood", "")
if m:
counter[m] += 1
return [m for m, _ in counter.most_common(n)]
def build_prompt(moods: list[str], duration: int) -> str:
mood_str = "、".join(moods) if moods else "苍凉壮阔"
return (
f"国风古风纯音乐，{mood_str}氛围，"
f"古筝为主旋律，点缀琵琶和笛子，"
f"时长约 {duration} 秒，纯音乐无人声，"
f"适合仙侠/国风动画背景。"
)
def suno_generate(prompt: str, duration: int) -> str:
key = os.environ.get("SUNO_API_KEY", "")
if not key:
raise RuntimeError("缺少 SUNO_API_KEY")
headers = {"Authorization": f"Bearer {key}", "Content-Type": "application/json"}
body = {
"prompt": prompt,
"duration": duration,
"instrumental": True,
"model": MODELS["music"], # suno-v5.5
}
r = requests.post(f"{SUNO_API}/v1/generate", headers=headers, json=body, timeout=60)
r.raise_for_status()
task_id = r.json().get("id") or r.json().get("task_id")
deadline = time.time() + 600
while time.time() < deadline:
time.sleep(10)
g = requests.get(f"{SUNO_API}/v1/tasks/{task_id}", headers=headers, timeout=30)
d = g.json()
status = d.get("status")
if status in ("succeeded", "complete"):
return d.get("audio_url") or d.get("output", [{}])[0].get("audio_url")
if status in ("failed", "error"):
raise RuntimeError(f"suno 失败: {d}")
raise TimeoutError("suno 超时")
def main() -> int:
p = argparse.ArgumentParser()
p.add_argument("--script", required=True)
p.add_argument("--duration", type=int, required=True)
p.add_argument("--out", required=True)
args = p.parse_args()
out = pathlib.Path(args.out)
if out.exists():
print(f" ⏭️ {out.name} 已存在")
return 0
script = json.loads(pathlib.Path(args.script).read_text())
moods = top_moods(script)
prompt = build_prompt(moods, args.duration)
print(f"🎵 BGM prompt: {prompt}")
project_dir = out.parent
guard = CostGuard.load(project_dir)
try:
url = suno_generate(prompt, args.duration)
r = requests.get(url, stream=True)
out.parent.mkdir(parents=True, exist_ok=True)
with open(out, "wb") as f:
for chunk in r.iter_content(8192):
f.write(chunk)
guard.charge("bgm", "main", PRICING["bgm_per_track"])
print(f"✅ {out}")
except Exception as e:
# Fallback：找本地素材
fallback = REPO_ROOT / "templates" / "bgm_library" / "国风" / f"{moods[0] if moods else 'default'}.mp3"
if fallback.exists():
print(f"⚠️ Suno 失败 ({e})，fallback → {fallback.name}")
import shutil
shutil.copy(fallback, out)
else:
print(f"❌ BGM 生成失败且无 fallback: {e}")
return 1
return 0
if __name__ == "__main__":
sys.exit(main())
给视频镜头+对白音频做口型同步（Kling 2.5 Lip Sync）。没有对白的镜头跳过。触发词：对口型、lipsync、口型同步。
---
name: huo15-comic-lipsync
displayName: 火15 漫剧-对口型
description: 给视频镜头+对白音频做口型同步（Kling 2.5 Lip Sync）。没有对白的镜头跳过。触发词：对口型、lipsync、口型同步。
version: 0.1.0
---
# 火15 漫剧-对口型 Skill
> 视频 + 音频 → 口型同步后的视频。
---
## 输入 / 输出
```bash
python scripts/lipsync.py \
--video-dir output/demo/videos \
--audio-dir output/demo/audio \
--out-dir output/demo/lipsync
```
每个镜头取该镜第一条对白的音频做口型同步；无对白直接复制原视频。
## API
```
POST https://api.kling.com/v1/videos/lip-sync
Headers: Authorization: Bearer {KLING_API_KEY}
Body:
{
"video_url": "...",
"audio_url": "...",
"mode": "kling-v2.5"
}
```
## 注意
- 视频最短 3s，如果对白音频 <3s 自动补静默
- 单镜成本 ¥3，48 镜 = ¥144（可通过 `--no-lipsync` 关闭省钱）
FILE:_meta.json
{
"ownerId": "kn7byevkn40d6z4p7ghdb097z983tj33",
"slug": "huo15-comic-lipsync",
"version": "0.1.0"
}
FILE:scripts/_shared/__init__.py
"""huo15-ai-comics 共享库。
所有子 skill 通过 sys.path 注入此模块（dual-path fallback）：
HERE = pathlib.Path(__file__).resolve()
# 优先 bundled（clawhub 独立安装场景：scripts/_shared/）
# fallback monorepo 根（本仓库 dev 场景：../../../_shared/）
for _cand in (HERE.parent / "_shared", HERE.parents[2] / "_shared"):
if (_cand / "config.py").exists():
sys.path.insert(0, str(_cand))
break
from cost_guard import CostGuard
from checkpoint import Checkpoint
from ark_api import ArkClient
from config import DEFAULTS, MODELS
publish.sh 会在发布前把本目录 cp 到每个 `<skill>/scripts/_shared/`，
让独立安装的 skill 仍能解析 import。
"""
FILE:scripts/_shared/ark_api.py
"""火山方舟 API 薄封装：Seedream 4.0 图 / Seedance 2.0 视频 / 豆包大模型 TTS.
2026-04 核对。文档：
- Seedance: https://www.volcengine.com/docs/82379/1520757
- Seedream: doubao-seedream-4-0-250828
- TTS: https://www.volcengine.com/docs/6561/97465
"""
from __future__ import annotations
import base64
import os
import pathlib
import time
import requests
try:
from config import ENDPOINTS, MODELS
except ImportError: # 兼容直接运行
ENDPOINTS = {
"ark_base": "https://ark.cn-beijing.volces.com/api/v3",
}
MODELS = {
"image": "doubao-seedream-4-0-250828",
"video": "doubao-seedance-2-0-260128",
"video_fast": "doubao-seedance-2-0-fast-260128",
"tts": "doubao-tts-bigtts",
}
class ArkClient:
def __init__(self, api_key: str | None = None):
self.api_key = api_key or os.environ.get("ARK_API_KEY", "")
if not self.api_key:
raise RuntimeError("缺少 ARK_API_KEY 环境变量")
self.base_url = ENDPOINTS["ark_base"]
self.headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
}
# ------------ 图像（Seedream 4.0）------------
def generate_image(
self,
prompt: str,
reference_images: list[str] | None = None,
size: str = "1024x1792",
model: str | None = None,
) -> str:
"""文生图 / 图生图。返回第一张图片 URL.
reference_images 传文件路径或 URL，会自动转 data URI.
"""
content = [{"type": "text", "text": prompt}]
for img in reference_images or []:
content.append({
"type": "image_url",
"image_url": {"url": self._image_to_data_uri(img)},
})
body = {
"model": model or MODELS["image"],
"content": content,
"size": size,
"watermark": False,
}
resp = requests.post(
f"{self.base_url}/images/generations",
headers=self.headers,
json=body,
timeout=120,
)
resp.raise_for_status()
data = resp.json()
# 兼容两种响应结构
if "data" in data and data["data"]:
return data["data"][0].get("url", "")
if "url" in data:
return data["url"]
raise RuntimeError(f"image response no url: {data}")
# ------------ 视频（Seedance 2.0）------------
def submit_video(
self,
prompt: str,
first_frame: str | None = None,
last_frame: str | None = None,
reference_image: str | None = None,
reference_video: str | None = None,
reference_audio: str | None = None,
duration: int = 5,
ratio: str = "9:16",
resolution: str = "720p",
generate_audio: bool = False,
return_last_frame: bool = False,
fast: bool = False,
seed: int | None = None,
) -> str:
"""提交视频任务，返回 task_id.
支持多种输入模态：first_frame / last_frame（首尾帧模式）/ reference_image /
reference_video / reference_audio。同一请求最多 9 图、3 视频、3 音频。
"""
content: list[dict] = [{"type": "text", "text": prompt}]
def _add_img(path: str, role: str):
content.append({
"type": "image_url",
"image_url": {"url": self._image_to_data_uri(path)},
"role": role,
})
if first_frame:
_add_img(first_frame, "first_frame")
if last_frame:
_add_img(last_frame, "last_frame")
if reference_image:
_add_img(reference_image, "reference_image")
if reference_video:
content.append({
"type": "video_url",
"video_url": {"url": reference_video if reference_video.startswith("http") else reference_video},
"role": "reference_video",
})
if reference_audio:
content.append({
"type": "audio_url",
"audio_url": {"url": reference_audio},
"role": "reference_audio",
})
body: dict = {
"model": MODELS["video_fast" if fast else "video"],
"content": content,
"ratio": ratio,
"resolution": resolution,
"duration": duration,
"watermark": False,
"return_last_frame": return_last_frame,
"generate_audio": generate_audio,
}
if seed is not None:
body["seed"] = seed
resp = requests.post(
f"{self.base_url}/contents/generations/tasks",
headers=self.headers,
json=body,
timeout=60,
)
resp.raise_for_status()
return resp.json()["id"]
def poll_video(self, task_id: str, timeout_s: int = 900, interval_s: int = 10) -> dict:
"""轮询视频任务，完成返回完整 dict（含 content.video_url / usage.total_tokens）."""
deadline = time.time() + timeout_s
while time.time() < deadline:
time.sleep(interval_s)
resp = requests.get(
f"{self.base_url}/contents/generations/tasks/{task_id}",
headers=self.headers,
timeout=30,
)
data = resp.json()
status = data.get("status")
if status == "succeeded":
return data
if status == "failed":
raise RuntimeError(f"视频任务失败: {data}")
raise TimeoutError(f"视频任务 {task_id} 超时（{timeout_s}s）")
def download_video(self, url: str, out_path: pathlib.Path) -> pathlib.Path:
out_path = pathlib.Path(out_path)
out_path.parent.mkdir(parents=True, exist_ok=True)
r = requests.get(url, stream=True, timeout=300)
with open(out_path, "wb") as f:
for chunk in r.iter_content(8192):
f.write(chunk)
return out_path
# ------------ 语音（豆包大模型 TTS）------------
def tts(
self,
text: str,
voice: str = "zh_female_sinong_conversation_wvae_bigtts",
out_path: pathlib.Path | str = "out.wav",
speed: float = 1.0,
emotion: str | None = None,
) -> pathlib.Path:
"""豆包 TTS 合成，写入 out_path.
voice 是音色 ID，格式 zh_{gender}_{name}_conversation_wvae_bigtts.
完整音色见 https://www.volcengine.com/docs/6561/97465.
注意：TTS 可能需要单独的 appid/cluster 凭证（走 openspeech.bytedance.com），
此处用 ark OpenAI 兼容接口作为简化，实际项目请核对授权方式。
"""
body = {
"model": MODELS["tts"],
"input": text,
"voice": voice,
"speed": speed,
"response_format": "wav",
}
if emotion:
body["emotion"] = emotion
resp = requests.post(
f"{self.base_url}/audio/speech",
headers=self.headers,
json=body,
timeout=60,
)
resp.raise_for_status()
out = pathlib.Path(out_path)
out.parent.mkdir(parents=True, exist_ok=True)
out.write_bytes(resp.content)
return out
# ------------ 工具 ------------
@staticmethod
def _image_to_data_uri(path_or_url: str) -> str:
if path_or_url.startswith(("http://", "https://", "data:")):
return path_or_url
p = pathlib.Path(path_or_url)
ext = p.suffix.lower().lstrip(".") or "jpeg"
if ext == "jpg":
ext = "jpeg"
b64 = base64.b64encode(p.read_bytes()).decode()
return f"data:image/{ext};base64,{b64}"
def cost_from_video_response(data: dict, resolution: str = "720p") -> float:
"""从视频响应中精确计算成本（按 token）."""
from config import PRICING
tokens = data.get("usage", {}).get("total_tokens", 0)
return round(tokens * PRICING["video_per_mtoken"] / 1_000_000, 3)
FILE:scripts/_shared/checkpoint.py
"""阶段 checkpoint，支持失败续跑."""
from __future__ import annotations
import json
import pathlib
from dataclasses import dataclass, field, asdict
STEPS = [
"script",
"characters",
"storyboard",
"videos",
"dubs",
"lipsync",
"bgm",
"subtitle",
"edit",
]
@dataclass
class Checkpoint:
project_dir: pathlib.Path
status: dict = field(default_factory=dict)
def __post_init__(self):
self.project_dir = pathlib.Path(self.project_dir)
self._load()
def _path(self) -> pathlib.Path:
return self.project_dir / ".checkpoint.json"
def _load(self) -> None:
if self._path().exists():
self.status = json.loads(self._path().read_text())
else:
self.status = {s: "pending" for s in STEPS}
def save(self) -> None:
self.project_dir.mkdir(parents=True, exist_ok=True)
self._path().write_text(json.dumps(self.status, ensure_ascii=False, indent=2))
def is_done(self, step: str) -> bool:
return self.status.get(step) == "done"
def mark_done(self, step: str) -> None:
self.status[step] = "done"
self.save()
def mark_running(self, step: str) -> None:
self.status[step] = "running"
self.save()
def mark_failed(self, step: str, reason: str = "") -> None:
self.status[step] = f"failed: {reason}" if reason else "failed"
self.save()
def next_pending(self) -> str | None:
for s in STEPS:
v = self.status.get(s, "pending")
if v != "done":
return s
return None
def sub_mark(self, step: str, sub_id: str, value: str = "done") -> None:
"""用于镜头级别的细粒度 checkpoint，如 videos.S01=done."""
key = f"{step}.{sub_id}"
self.status[key] = value
self.save()
def sub_done(self, step: str, sub_id: str) -> bool:
return self.status.get(f"{step}.{sub_id}") == "done"
FILE:scripts/_shared/config.py
"""家族共享默认参数与模型 ID.
基于 2026-04 实测火山方舟/Kling/Suno 文档核对。
"""
DEFAULTS = {
"duration_total": 240, # 秒，3-5 分钟区间默认 4 分钟
"scene_duration": 5, # 单镜头秒数（Seedance 2.0 支持 4-15）
"ratio": "9:16", # 竖屏；Seedance 2.0 支持 9:16/16:9/4:3/3:4/21:9/1:1/adaptive
"resolution": "720p", # 默认 720p 省钱；可选 480p/720p/1080p/2K
"style": "三渲二国风",
"genre": "仙侠",
"cost_cap": 600.0,
"cost_warn_ratio": 0.7,
"cost_hard_ratio": 1.0,
"watermark": False,
"concurrency": 3,
"scene_retry": 2,
"fast_mode": False, # True 用 seedance-fast（便宜但质量低）
}
MODELS = {
"script": "claude-opus-4-7",
"image": "doubao-seedream-4-0-250828", # 方舟 Seedream 4.0
"video": "doubao-seedance-2-0-260128", # 方舟 Seedance 2.0 标准版
"video_fast": "doubao-seedance-2-0-fast-260128", # 方舟 Seedance 2.0 fast
"tts": "doubao-tts-bigtts", # 方舟豆包大模型 TTS
"lipsync": "kling-v2.6", # Kling 2.6 原生对口型
"music": "suno-v5.5", # Suno v5.5（via sunoapi.org）
}
# API 端点（2026-04 核对）
ENDPOINTS = {
"ark_base": "https://ark.cn-beijing.volces.com/api/v3",
"kling_base": "https://api.klingai.com/v1", # 官方；第三方 piapi.ai/fal.ai 亦可
"suno_base": "https://api.sunoapi.org", # 第三方（Suno 无公开官方 API）
}
# 单位价（元），2026-04 核对
# Seedance 2.0: token-based 46¥/M tokens（1080p 文生/图生视频），28¥/M tokens（视频参考）
# 按秒估算的便利值：token = duration × width × height × fps / 1024，用 token × 46 / 1e6 得元价
PRICING = {
"image_per_pic": 0.08, # Seedream 4.0 每张
"video_480p_per_sec": 0.50, # 480p 9:16 ≈ 5s ¥2.5
"video_720p_per_sec": 0.994, # 720p 9:16 ≈ 5s ¥5
"video_1080p_per_sec": 2.26, # 1080p 9:16 ≈ 5s ¥11.3
"video_fast_discount": 0.5, # fast 模型约 5 折
"video_per_mtoken": 46.0, # 官方 token 单价（M tokens）
"tts_per_char": 0.0008, # 豆包大模型 TTS
"lipsync_per_5s": 0.72, # Kling 官方 $0.1/5s ≈ ¥0.72
"bgm_per_track": 3.0, # sunoapi.org 第三方均价
}
def video_unit_price(resolution: str = "720p", fast: bool = False) -> float:
"""返回每秒元价，用于预估."""
base = {
"480p": PRICING["video_480p_per_sec"],
"720p": PRICING["video_720p_per_sec"],
"1080p": PRICING["video_1080p_per_sec"],
"2K": PRICING["video_1080p_per_sec"] * 1.5,
}.get(resolution, PRICING["video_720p_per_sec"])
if fast:
base *= PRICING["video_fast_discount"]
return base
# 国风专用提示词片段
STYLE_PRESETS = {
"三渲二国风": {
"prefix": "三渲二国风动画风格，工笔线条，中国传统审美",
"lighting": "柔和自然光，水墨晕染",
"palette": "青绿山水色调，朱砂点缀",
},
"水墨": {
"prefix": "中国水墨画风格，留白意境",
"lighting": "墨色浓淡",
"palette": "黑白灰为主，偶尔赭石",
},
"古风赛璐璐": {
"prefix": "日式赛璐璐+中国古风融合，清新明亮",
"lighting": "柔光晴天",
"palette": "淡雅国风配色",
},
"工笔": {
"prefix": "中国工笔画风格，细腻线条，重彩渲染",
"lighting": "平光",
"palette": "矿物颜料，金线勾勒",
},
}
GENRE_PRESETS = {
"仙侠": "门派纷争 / 修仙问道 / 御剑飞行 / 法宝秘术",
"宫斗": "宫廷权谋 / 妃嫔博弈 / 皇家礼仪 / 雕梁画栋",
"江湖": "快意恩仇 / 武林纷争 / 客栈酒肆 / 刀光剑影",
"志怪": "山海异兽 / 妖怪传说 / 古籍志异 / 诡谲氛围",
}
# 豆包大模型 TTS 音色（2026-04 核对，_conversation_wvae_bigtts 后缀=豆包大模型版）
# 实际音色 ID 较多，这里列常用。完整列表见 https://www.volcengine.com/docs/6561/97465
VOICE_PRESETS = {
# 男声
"male_young": "zh_male_ahu_conversation_wvae_bigtts", # 温暖阿虎（青年温润）
"male_mature": "zh_male_M392_conversation_wvae_bigtts", # 京腔侃爷（成熟磁性）
"male_elder": "zh_male_yanqing_conversation_wvae_bigtts", # 沉稳长者
# 女声
"female_young": "zh_female_sinong_conversation_wvae_bigtts", # 爽快思思（清新少女）
"female_mature": "zh_female_xiaohe_conversation_wvae_bigtts", # 湾湾小何（温柔熟女）
"female_elder": "zh_female_guniang_conversation_wvae_bigtts", # 端庄长辈
}
FILE:scripts/_shared/cost_guard.py
"""三级成本熔断.
- 硬熔断（hard）：开工前估算超 cap 立即阻止
- 软预警（warn）：运行时累计超 warn_ratio × cap 打印告警
- 降级建议（suggest）：超 cap 时给出具体降级方案
"""
from __future__ import annotations
import json
import pathlib
import time
from dataclasses import dataclass, field, asdict
class BudgetExceeded(RuntimeError):
"""熔断触发异常；上层 catch 后走降级逻辑."""
@dataclass
class CostRecord:
step: str
item: str
cost: float
ts: float = field(default_factory=time.time)
@dataclass
class CostGuard:
cap: float = 600.0
warn_ratio: float = 0.7
project_dir: pathlib.Path | None = None
spent: float = 0.0
records: list[CostRecord] = field(default_factory=list)
_warned: bool = False
def preflight(self, estimated_total: float) -> None:
"""开工前硬熔断.
Raises:
BudgetExceeded: 估算超 cap 时抛出，附带降级建议字符串。
"""
if estimated_total > self.cap:
suggestions = self.downgrade_suggestions(estimated_total)
raise BudgetExceeded(
f"预估成本 ¥{estimated_total:.2f} 超过熔断上限 ¥{self.cap:.2f}。\n"
f"降级建议：\n{suggestions}\n"
f"如需继续，请提升 cost_cap 或采纳上述任一降级方案。"
)
def charge(self, step: str, item: str, cost: float) -> None:
"""扣费，触发软预警/硬熔断."""
self.spent += cost
self.records.append(CostRecord(step=step, item=item, cost=cost))
ratio = self.spent / self.cap if self.cap else 0
if ratio >= 1.0:
self._persist()
raise BudgetExceeded(
f"累计成本 ¥{self.spent:.2f} 达到熔断上限 ¥{self.cap:.2f}（{step}/{item}）。\n"
f"已完成步骤可从 checkpoint 续跑。"
)
if ratio >= self.warn_ratio and not self._warned:
self._warned = True
print(
f"⚠️ 成本预警：已花费 ¥{self.spent:.2f}（{ratio:.0%} of ¥{self.cap:.2f}），"
f"即将触发熔断。"
)
self._persist()
def downgrade_suggestions(self, estimated_total: float) -> str:
"""生成降级方案文本."""
over = estimated_total - self.cap
lines = [
f" 1. 缩短总时长：每减 1 分钟约省 ¥{estimated_total / 5:.0f}",
f" 2. 减少镜头数：每删 1 镜约省 ¥{estimated_total / 48:.1f}",
f" 3. 关闭对口型：整片省 ¥{estimated_total * 0.37:.0f}（约 37%）",
f" 4. 视频降到 4 秒/镜：整片省 ¥{estimated_total * 0.2:.0f}（约 20%）",
f" 5. 提升 cost_cap 至 ¥{estimated_total * 1.1:.0f}（当前 ¥{self.cap:.0f}，需多付 ¥{over:.2f}）",
]
return "\n".join(lines)
def report(self) -> dict:
"""返回成本报告（可序列化）."""
by_step: dict[str, float] = {}
for r in self.records:
by_step[r.step] = by_step.get(r.step, 0.0) + r.cost
return {
"spent": round(self.spent, 2),
"cap": self.cap,
"ratio": round(self.spent / self.cap, 3) if self.cap else 0,
"by_step": {k: round(v, 2) for k, v in by_step.items()},
"record_count": len(self.records),
}
def _persist(self) -> None:
if not self.project_dir:
return
self.project_dir.mkdir(parents=True, exist_ok=True)
path = self.project_dir / ".cost.json"
data = {
"cap": self.cap,
"warn_ratio": self.warn_ratio,
"spent": self.spent,
"records": [asdict(r) for r in self.records],
}
path.write_text(json.dumps(data, ensure_ascii=False, indent=2))
@classmethod
def load(cls, project_dir: pathlib.Path, cap: float = 600.0) -> "CostGuard":
"""从 project_dir/.cost.json 恢复."""
path = project_dir / ".cost.json"
guard = cls(cap=cap, project_dir=project_dir)
if path.exists():
data = json.loads(path.read_text())
guard.cap = data.get("cap", cap)
guard.warn_ratio = data.get("warn_ratio", 0.7)
guard.spent = data.get("spent", 0.0)
guard.records = [CostRecord(**r) for r in data.get("records", [])]
return guard
def estimate_total(
n_scenes: int,
n_characters: int,
total_chars: int,
scene_duration: int = 5,
resolution: str = "720p",
fast: bool = False,
enable_lipsync: bool = True,
enable_bgm: bool = True,
) -> dict:
"""估算全流程成本，返回明细 dict.
video 按 resolution 和 fast 标志换算每秒元价；lipsync 按 scene_duration/5s 换算。
"""
from config import PRICING, video_unit_price
image_cost = (n_scenes + n_characters * 3) * PRICING["image_per_pic"]
video_cost = n_scenes * scene_duration * video_unit_price(resolution, fast)
tts_cost = total_chars * PRICING["tts_per_char"]
lipsync_cost = (
n_scenes * (scene_duration / 5.0) * PRICING["lipsync_per_5s"]
if enable_lipsync else 0
)
bgm_cost = PRICING["bgm_per_track"] if enable_bgm else 0
total = image_cost + video_cost + tts_cost + lipsync_cost + bgm_cost
return {
"image": round(image_cost, 2),
"video": round(video_cost, 2),
"tts": round(tts_cost, 2),
"lipsync": round(lipsync_cost, 2),
"bgm": round(bgm_cost, 2),
"total": round(total, 2),
"resolution": resolution,
"fast": fast,
}
FILE:scripts/lipsync.py
"""对口型（Kling 2.5 Lip Sync）."""
from __future__ import annotations
import argparse
import json
import os
import pathlib
import shutil
import sys
import time
import requests
HERE = pathlib.Path(__file__).resolve()
# _shared/ 定位：优先 bundled（scripts/_shared/），fallback monorepo 根
for _cand in (HERE.parent / "_shared", HERE.parents[2] / "_shared"):
if (_cand / "config.py").exists():
sys.path.insert(0, str(_cand))
break
from config import PRICING, ENDPOINTS, MODELS
from cost_guard import CostGuard
from checkpoint import Checkpoint
KLING_API = ENDPOINTS["kling_base"] # https://api.klingai.com/v1
def submit_lipsync(video_path: pathlib.Path, audio_path: pathlib.Path) -> str:
key = os.environ.get("KLING_API_KEY", "")
if not key:
raise RuntimeError("缺少 KLING_API_KEY")
headers = {"Authorization": f"Bearer {key}"}
files = {
"video": open(video_path, "rb"),
"audio": open(audio_path, "rb"),
}
data = {"model": MODELS["lipsync"]} # kling-v2.6
r = requests.post(f"{KLING_API}/videos/lip-sync", headers=headers, files=files, data=data)
r.raise_for_status()
return r.json()["task_id"]
def poll_lipsync(task_id: str, timeout_s: int = 600) -> str:
key = os.environ.get("KLING_API_KEY", "")
headers = {"Authorization": f"Bearer {key}"}
deadline = time.time() + timeout_s
while time.time() < deadline:
time.sleep(8)
r = requests.get(f"{KLING_API}/videos/lip-sync/{task_id}", headers=headers)
data = r.json()
if data.get("status") == "succeeded":
return data["video_url"]
if data.get("status") == "failed":
raise RuntimeError(f"lipsync 失败: {data}")
raise TimeoutError(f"lipsync 超时 {task_id}")
def first_dialogue_audio(sid: str, audio_dir: pathlib.Path) -> pathlib.Path | None:
cands = sorted(audio_dir.glob(f"{sid}_*.wav"))
return cands[0] if cands else None
def main() -> int:
p = argparse.ArgumentParser()
p.add_argument("--video-dir", required=True)
p.add_argument("--audio-dir", required=True)
p.add_argument("--out-dir", required=True)
args = p.parse_args()
video_dir = pathlib.Path(args.video_dir)
audio_dir = pathlib.Path(args.audio_dir)
out_dir = pathlib.Path(args.out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
project_dir = out_dir.parent
guard = CostGuard.load(project_dir)
cp = Checkpoint(project_dir)
for video in sorted(video_dir.glob("S*.mp4")):
sid = video.stem
out = out_dir / f"{sid}.mp4"
if out.exists() or cp.sub_done("lipsync", sid):
print(f" ⏭️ {sid}")
continue
audio = first_dialogue_audio(sid, audio_dir)
if not audio:
print(f" 🤫 {sid} 无对白，复制原视频")
shutil.copy(video, out)
cp.sub_mark("lipsync", sid)
continue
try:
print(f" 👄 {sid} → lipsync")
task_id = submit_lipsync(video, audio)
url = poll_lipsync(task_id)
r = requests.get(url, stream=True)
with open(out, "wb") as f:
for chunk in r.iter_content(8192):
f.write(chunk)
guard.charge("lipsync", sid, PRICING["lipsync_per_5s"])
cp.sub_mark("lipsync", sid)
except Exception as e:
print(f" ❌ {sid}: {e}, 降级为原视频")
shutil.copy(video, out)
cp.sub_mark("lipsync", sid, "fallback")
print(f"✅ 口型同步完成")
return 0
if __name__ == "__main__":
sys.exit(main())
读 script.json 对白，按角色分配音色，调火山方舟 Seed-TTS 生成每条对白的 wav，命名 {sceneId}_{charId}_{idx}.wav。触发词：对白配音、TTS 配音、角色配音。
---
name: huo15-comic-dub
displayName: 火15 漫剧-TTS 配音
description: 读 script.json 对白，按角色分配音色，调火山方舟 Seed-TTS 生成每条对白的 wav，命名 {sceneId}_{charId}_{idx}.wav。触发词：对白配音、TTS 配音、角色配音。
version: 0.1.0
---
# 火15 漫剧-TTS 配音 Skill
> 每条 dialogue → 一个 wav，角色音色固定。
---
## 输入 / 输出
```bash
python scripts/dub.py \
--script output/demo/script.json \
--out-dir output/demo/audio
```
输出：
```
audio/
├── S01_C1_0.wav
├── S02_C1_0.wav
├── S02_C2_0.wav
├── ...
└── manifest.json
```
## 音色库（Seed-TTS 已内置）
- 男：`zh_male_qingnian`（青年）/ `zh_male_shenchen`（沉稳） / `zh_male_wennuan`（温暖）
- 女：`zh_female_qingxin`（清新）/ `zh_female_wenrou`（温柔） / `zh_female_ganxing`（感性）
若 script.json 的 character.voice 为空，按角色 age/personality 自动分配。
## 成本
字数 × ¥0.0008/字。200 字对白 ≈ ¥0.16。
FILE:_meta.json
{
"ownerId": "kn7byevkn40d6z4p7ghdb097z983tj33",
"slug": "huo15-comic-dub",
"version": "0.1.0"
}
FILE:scripts/_shared/__init__.py
"""huo15-ai-comics 共享库。
所有子 skill 通过 sys.path 注入此模块（dual-path fallback）：
HERE = pathlib.Path(__file__).resolve()
# 优先 bundled（clawhub 独立安装场景：scripts/_shared/）
# fallback monorepo 根（本仓库 dev 场景：../../../_shared/）
for _cand in (HERE.parent / "_shared", HERE.parents[2] / "_shared"):
if (_cand / "config.py").exists():
sys.path.insert(0, str(_cand))
break
from cost_guard import CostGuard
from checkpoint import Checkpoint
from ark_api import ArkClient
from config import DEFAULTS, MODELS
publish.sh 会在发布前把本目录 cp 到每个 `<skill>/scripts/_shared/`，
让独立安装的 skill 仍能解析 import。
"""
FILE:scripts/_shared/ark_api.py
"""火山方舟 API 薄封装：Seedream 4.0 图 / Seedance 2.0 视频 / 豆包大模型 TTS.
2026-04 核对。文档：
- Seedance: https://www.volcengine.com/docs/82379/1520757
- Seedream: doubao-seedream-4-0-250828
- TTS: https://www.volcengine.com/docs/6561/97465
"""
from __future__ import annotations
import base64
import os
import pathlib
import time
import requests
try:
from config import ENDPOINTS, MODELS
except ImportError: # 兼容直接运行
ENDPOINTS = {
"ark_base": "https://ark.cn-beijing.volces.com/api/v3",
}
MODELS = {
"image": "doubao-seedream-4-0-250828",
"video": "doubao-seedance-2-0-260128",
"video_fast": "doubao-seedance-2-0-fast-260128",
"tts": "doubao-tts-bigtts",
}
class ArkClient:
def __init__(self, api_key: str | None = None):
self.api_key = api_key or os.environ.get("ARK_API_KEY", "")
if not self.api_key:
raise RuntimeError("缺少 ARK_API_KEY 环境变量")
self.base_url = ENDPOINTS["ark_base"]
self.headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
}
# ------------ 图像（Seedream 4.0）------------
def generate_image(
self,
prompt: str,
reference_images: list[str] | None = None,
size: str = "1024x1792",
model: str | None = None,
) -> str:
"""文生图 / 图生图。返回第一张图片 URL.
reference_images 传文件路径或 URL，会自动转 data URI.
"""
content = [{"type": "text", "text": prompt}]
for img in reference_images or []:
content.append({
"type": "image_url",
"image_url": {"url": self._image_to_data_uri(img)},
})
body = {
"model": model or MODELS["image"],
"content": content,
"size": size,
"watermark": False,
}
resp = requests.post(
f"{self.base_url}/images/generations",
headers=self.headers,
json=body,
timeout=120,
)
resp.raise_for_status()
data = resp.json()
# 兼容两种响应结构
if "data" in data and data["data"]:
return data["data"][0].get("url", "")
if "url" in data:
return data["url"]
raise RuntimeError(f"image response no url: {data}")
# ------------ 视频（Seedance 2.0）------------
def submit_video(
self,
prompt: str,
first_frame: str | None = None,
last_frame: str | None = None,
reference_image: str | None = None,
reference_video: str | None = None,
reference_audio: str | None = None,
duration: int = 5,
ratio: str = "9:16",
resolution: str = "720p",
generate_audio: bool = False,
return_last_frame: bool = False,
fast: bool = False,
seed: int | None = None,
) -> str:
"""提交视频任务，返回 task_id.
支持多种输入模态：first_frame / last_frame（首尾帧模式）/ reference_image /
reference_video / reference_audio。同一请求最多 9 图、3 视频、3 音频。
"""
content: list[dict] = [{"type": "text", "text": prompt}]
def _add_img(path: str, role: str):
content.append({
"type": "image_url",
"image_url": {"url": self._image_to_data_uri(path)},
"role": role,
})
if first_frame:
_add_img(first_frame, "first_frame")
if last_frame:
_add_img(last_frame, "last_frame")
if reference_image:
_add_img(reference_image, "reference_image")
if reference_video:
content.append({
"type": "video_url",
"video_url": {"url": reference_video if reference_video.startswith("http") else reference_video},
"role": "reference_video",
})
if reference_audio:
content.append({
"type": "audio_url",
"audio_url": {"url": reference_audio},
"role": "reference_audio",
})
body: dict = {
"model": MODELS["video_fast" if fast else "video"],
"content": content,
"ratio": ratio,
"resolution": resolution,
"duration": duration,
"watermark": False,
"return_last_frame": return_last_frame,
"generate_audio": generate_audio,
}
if seed is not None:
body["seed"] = seed
resp = requests.post(
f"{self.base_url}/contents/generations/tasks",
headers=self.headers,
json=body,
timeout=60,
)
resp.raise_for_status()
return resp.json()["id"]
def poll_video(self, task_id: str, timeout_s: int = 900, interval_s: int = 10) -> dict:
"""轮询视频任务，完成返回完整 dict（含 content.video_url / usage.total_tokens）."""
deadline = time.time() + timeout_s
while time.time() < deadline:
time.sleep(interval_s)
resp = requests.get(
f"{self.base_url}/contents/generations/tasks/{task_id}",
headers=self.headers,
timeout=30,
)
data = resp.json()
status = data.get("status")
if status == "succeeded":
return data
if status == "failed":
raise RuntimeError(f"视频任务失败: {data}")
raise TimeoutError(f"视频任务 {task_id} 超时（{timeout_s}s）")
def download_video(self, url: str, out_path: pathlib.Path) -> pathlib.Path:
out_path = pathlib.Path(out_path)
out_path.parent.mkdir(parents=True, exist_ok=True)
r = requests.get(url, stream=True, timeout=300)
with open(out_path, "wb") as f:
for chunk in r.iter_content(8192):
f.write(chunk)
return out_path
# ------------ 语音（豆包大模型 TTS）------------
def tts(
self,
text: str,
voice: str = "zh_female_sinong_conversation_wvae_bigtts",
out_path: pathlib.Path | str = "out.wav",
speed: float = 1.0,
emotion: str | None = None,
) -> pathlib.Path:
"""豆包 TTS 合成，写入 out_path.
voice 是音色 ID，格式 zh_{gender}_{name}_conversation_wvae_bigtts.
完整音色见 https://www.volcengine.com/docs/6561/97465.
注意：TTS 可能需要单独的 appid/cluster 凭证（走 openspeech.bytedance.com），
此处用 ark OpenAI 兼容接口作为简化，实际项目请核对授权方式。
"""
body = {
"model": MODELS["tts"],
"input": text,
"voice": voice,
"speed": speed,
"response_format": "wav",
}
if emotion:
body["emotion"] = emotion
resp = requests.post(
f"{self.base_url}/audio/speech",
headers=self.headers,
json=body,
timeout=60,
)
resp.raise_for_status()
out = pathlib.Path(out_path)
out.parent.mkdir(parents=True, exist_ok=True)
out.write_bytes(resp.content)
return out
# ------------ 工具 ------------
@staticmethod
def _image_to_data_uri(path_or_url: str) -> str:
if path_or_url.startswith(("http://", "https://", "data:")):
return path_or_url
p = pathlib.Path(path_or_url)
ext = p.suffix.lower().lstrip(".") or "jpeg"
if ext == "jpg":
ext = "jpeg"
b64 = base64.b64encode(p.read_bytes()).decode()
return f"data:image/{ext};base64,{b64}"
def cost_from_video_response(data: dict, resolution: str = "720p") -> float:
"""从视频响应中精确计算成本（按 token）."""
from config import PRICING
tokens = data.get("usage", {}).get("total_tokens", 0)
return round(tokens * PRICING["video_per_mtoken"] / 1_000_000, 3)
FILE:scripts/_shared/checkpoint.py
"""阶段 checkpoint，支持失败续跑."""
from __future__ import annotations
import json
import pathlib
from dataclasses import dataclass, field, asdict
STEPS = [
"script",
"characters",
"storyboard",
"videos",
"dubs",
"lipsync",
"bgm",
"subtitle",
"edit",
]
@dataclass
class Checkpoint:
project_dir: pathlib.Path
status: dict = field(default_factory=dict)
def __post_init__(self):
self.project_dir = pathlib.Path(self.project_dir)
self._load()
def _path(self) -> pathlib.Path:
return self.project_dir / ".checkpoint.json"
def _load(self) -> None:
if self._path().exists():
self.status = json.loads(self._path().read_text())
else:
self.status = {s: "pending" for s in STEPS}
def save(self) -> None:
self.project_dir.mkdir(parents=True, exist_ok=True)
self._path().write_text(json.dumps(self.status, ensure_ascii=False, indent=2))
def is_done(self, step: str) -> bool:
return self.status.get(step) == "done"
def mark_done(self, step: str) -> None:
self.status[step] = "done"
self.save()
def mark_running(self, step: str) -> None:
self.status[step] = "running"
self.save()
def mark_failed(self, step: str, reason: str = "") -> None:
self.status[step] = f"failed: {reason}" if reason else "failed"
self.save()
def next_pending(self) -> str | None:
for s in STEPS:
v = self.status.get(s, "pending")
if v != "done":
return s
return None
def sub_mark(self, step: str, sub_id: str, value: str = "done") -> None:
"""用于镜头级别的细粒度 checkpoint，如 videos.S01=done."""
key = f"{step}.{sub_id}"
self.status[key] = value
self.save()
def sub_done(self, step: str, sub_id: str) -> bool:
return self.status.get(f"{step}.{sub_id}") == "done"
FILE:scripts/_shared/config.py
"""家族共享默认参数与模型 ID.
基于 2026-04 实测火山方舟/Kling/Suno 文档核对。
"""
DEFAULTS = {
"duration_total": 240, # 秒，3-5 分钟区间默认 4 分钟
"scene_duration": 5, # 单镜头秒数（Seedance 2.0 支持 4-15）
"ratio": "9:16", # 竖屏；Seedance 2.0 支持 9:16/16:9/4:3/3:4/21:9/1:1/adaptive
"resolution": "720p", # 默认 720p 省钱；可选 480p/720p/1080p/2K
"style": "三渲二国风",
"genre": "仙侠",
"cost_cap": 600.0,
"cost_warn_ratio": 0.7,
"cost_hard_ratio": 1.0,
"watermark": False,
"concurrency": 3,
"scene_retry": 2,
"fast_mode": False, # True 用 seedance-fast（便宜但质量低）
}
MODELS = {
"script": "claude-opus-4-7",
"image": "doubao-seedream-4-0-250828", # 方舟 Seedream 4.0
"video": "doubao-seedance-2-0-260128", # 方舟 Seedance 2.0 标准版
"video_fast": "doubao-seedance-2-0-fast-260128", # 方舟 Seedance 2.0 fast
"tts": "doubao-tts-bigtts", # 方舟豆包大模型 TTS
"lipsync": "kling-v2.6", # Kling 2.6 原生对口型
"music": "suno-v5.5", # Suno v5.5（via sunoapi.org）
}
# API 端点（2026-04 核对）
ENDPOINTS = {
"ark_base": "https://ark.cn-beijing.volces.com/api/v3",
"kling_base": "https://api.klingai.com/v1", # 官方；第三方 piapi.ai/fal.ai 亦可
"suno_base": "https://api.sunoapi.org", # 第三方（Suno 无公开官方 API）
}
# 单位价（元），2026-04 核对
# Seedance 2.0: token-based 46¥/M tokens（1080p 文生/图生视频），28¥/M tokens（视频参考）
# 按秒估算的便利值：token = duration × width × height × fps / 1024，用 token × 46 / 1e6 得元价
PRICING = {
"image_per_pic": 0.08, # Seedream 4.0 每张
"video_480p_per_sec": 0.50, # 480p 9:16 ≈ 5s ¥2.5
"video_720p_per_sec": 0.994, # 720p 9:16 ≈ 5s ¥5
"video_1080p_per_sec": 2.26, # 1080p 9:16 ≈ 5s ¥11.3
"video_fast_discount": 0.5, # fast 模型约 5 折
"video_per_mtoken": 46.0, # 官方 token 单价（M tokens）
"tts_per_char": 0.0008, # 豆包大模型 TTS
"lipsync_per_5s": 0.72, # Kling 官方 $0.1/5s ≈ ¥0.72
"bgm_per_track": 3.0, # sunoapi.org 第三方均价
}
def video_unit_price(resolution: str = "720p", fast: bool = False) -> float:
"""返回每秒元价，用于预估."""
base = {
"480p": PRICING["video_480p_per_sec"],
"720p": PRICING["video_720p_per_sec"],
"1080p": PRICING["video_1080p_per_sec"],
"2K": PRICING["video_1080p_per_sec"] * 1.5,
}.get(resolution, PRICING["video_720p_per_sec"])
if fast:
base *= PRICING["video_fast_discount"]
return base
# 国风专用提示词片段
STYLE_PRESETS = {
"三渲二国风": {
"prefix": "三渲二国风动画风格，工笔线条，中国传统审美",
"lighting": "柔和自然光，水墨晕染",
"palette": "青绿山水色调，朱砂点缀",
},
"水墨": {
"prefix": "中国水墨画风格，留白意境",
"lighting": "墨色浓淡",
"palette": "黑白灰为主，偶尔赭石",
},
"古风赛璐璐": {
"prefix": "日式赛璐璐+中国古风融合，清新明亮",
"lighting": "柔光晴天",
"palette": "淡雅国风配色",
},
"工笔": {
"prefix": "中国工笔画风格，细腻线条，重彩渲染",
"lighting": "平光",
"palette": "矿物颜料，金线勾勒",
},
}
GENRE_PRESETS = {
"仙侠": "门派纷争 / 修仙问道 / 御剑飞行 / 法宝秘术",
"宫斗": "宫廷权谋 / 妃嫔博弈 / 皇家礼仪 / 雕梁画栋",
"江湖": "快意恩仇 / 武林纷争 / 客栈酒肆 / 刀光剑影",
"志怪": "山海异兽 / 妖怪传说 / 古籍志异 / 诡谲氛围",
}
# 豆包大模型 TTS 音色（2026-04 核对，_conversation_wvae_bigtts 后缀=豆包大模型版）
# 实际音色 ID 较多，这里列常用。完整列表见 https://www.volcengine.com/docs/6561/97465
VOICE_PRESETS = {
# 男声
"male_young": "zh_male_ahu_conversation_wvae_bigtts", # 温暖阿虎（青年温润）
"male_mature": "zh_male_M392_conversation_wvae_bigtts", # 京腔侃爷（成熟磁性）
"male_elder": "zh_male_yanqing_conversation_wvae_bigtts", # 沉稳长者
# 女声
"female_young": "zh_female_sinong_conversation_wvae_bigtts", # 爽快思思（清新少女）
"female_mature": "zh_female_xiaohe_conversation_wvae_bigtts", # 湾湾小何（温柔熟女）
"female_elder": "zh_female_guniang_conversation_wvae_bigtts", # 端庄长辈
}
FILE:scripts/_shared/cost_guard.py
"""三级成本熔断.
- 硬熔断（hard）：开工前估算超 cap 立即阻止
- 软预警（warn）：运行时累计超 warn_ratio × cap 打印告警
- 降级建议（suggest）：超 cap 时给出具体降级方案
"""
from __future__ import annotations
import json
import pathlib
import time
from dataclasses import dataclass, field, asdict
class BudgetExceeded(RuntimeError):
"""熔断触发异常；上层 catch 后走降级逻辑."""
@dataclass
class CostRecord:
step: str
item: str
cost: float
ts: float = field(default_factory=time.time)
@dataclass
class CostGuard:
cap: float = 600.0
warn_ratio: float = 0.7
project_dir: pathlib.Path | None = None
spent: float = 0.0
records: list[CostRecord] = field(default_factory=list)
_warned: bool = False
def preflight(self, estimated_total: float) -> None:
"""开工前硬熔断.
Raises:
BudgetExceeded: 估算超 cap 时抛出，附带降级建议字符串。
"""
if estimated_total > self.cap:
suggestions = self.downgrade_suggestions(estimated_total)
raise BudgetExceeded(
f"预估成本 ¥{estimated_total:.2f} 超过熔断上限 ¥{self.cap:.2f}。\n"
f"降级建议：\n{suggestions}\n"
f"如需继续，请提升 cost_cap 或采纳上述任一降级方案。"
)
def charge(self, step: str, item: str, cost: float) -> None:
"""扣费，触发软预警/硬熔断."""
self.spent += cost
self.records.append(CostRecord(step=step, item=item, cost=cost))
ratio = self.spent / self.cap if self.cap else 0
if ratio >= 1.0:
self._persist()
raise BudgetExceeded(
f"累计成本 ¥{self.spent:.2f} 达到熔断上限 ¥{self.cap:.2f}（{step}/{item}）。\n"
f"已完成步骤可从 checkpoint 续跑。"
)
if ratio >= self.warn_ratio and not self._warned:
self._warned = True
print(
f"⚠️ 成本预警：已花费 ¥{self.spent:.2f}（{ratio:.0%} of ¥{self.cap:.2f}），"
f"即将触发熔断。"
)
self._persist()
def downgrade_suggestions(self, estimated_total: float) -> str:
"""生成降级方案文本."""
over = estimated_total - self.cap
lines = [
f" 1. 缩短总时长：每减 1 分钟约省 ¥{estimated_total / 5:.0f}",
f" 2. 减少镜头数：每删 1 镜约省 ¥{estimated_total / 48:.1f}",
f" 3. 关闭对口型：整片省 ¥{estimated_total * 0.37:.0f}（约 37%）",
f" 4. 视频降到 4 秒/镜：整片省 ¥{estimated_total * 0.2:.0f}（约 20%）",
f" 5. 提升 cost_cap 至 ¥{estimated_total * 1.1:.0f}（当前 ¥{self.cap:.0f}，需多付 ¥{over:.2f}）",
]
return "\n".join(lines)
def report(self) -> dict:
"""返回成本报告（可序列化）."""
by_step: dict[str, float] = {}
for r in self.records:
by_step[r.step] = by_step.get(r.step, 0.0) + r.cost
return {
"spent": round(self.spent, 2),
"cap": self.cap,
"ratio": round(self.spent / self.cap, 3) if self.cap else 0,
"by_step": {k: round(v, 2) for k, v in by_step.items()},
"record_count": len(self.records),
}
def _persist(self) -> None:
if not self.project_dir:
return
self.project_dir.mkdir(parents=True, exist_ok=True)
path = self.project_dir / ".cost.json"
data = {
"cap": self.cap,
"warn_ratio": self.warn_ratio,
"spent": self.spent,
"records": [asdict(r) for r in self.records],
}
path.write_text(json.dumps(data, ensure_ascii=False, indent=2))
@classmethod
def load(cls, project_dir: pathlib.Path, cap: float = 600.0) -> "CostGuard":
"""从 project_dir/.cost.json 恢复."""
path = project_dir / ".cost.json"
guard = cls(cap=cap, project_dir=project_dir)
if path.exists():
data = json.loads(path.read_text())
guard.cap = data.get("cap", cap)
guard.warn_ratio = data.get("warn_ratio", 0.7)
guard.spent = data.get("spent", 0.0)
guard.records = [CostRecord(**r) for r in data.get("records", [])]
return guard
def estimate_total(
n_scenes: int,
n_characters: int,
total_chars: int,
scene_duration: int = 5,
resolution: str = "720p",
fast: bool = False,
enable_lipsync: bool = True,
enable_bgm: bool = True,
) -> dict:
"""估算全流程成本，返回明细 dict.
video 按 resolution 和 fast 标志换算每秒元价；lipsync 按 scene_duration/5s 换算。
"""
from config import PRICING, video_unit_price
image_cost = (n_scenes + n_characters * 3) * PRICING["image_per_pic"]
video_cost = n_scenes * scene_duration * video_unit_price(resolution, fast)
tts_cost = total_chars * PRICING["tts_per_char"]
lipsync_cost = (
n_scenes * (scene_duration / 5.0) * PRICING["lipsync_per_5s"]
if enable_lipsync else 0
)
bgm_cost = PRICING["bgm_per_track"] if enable_bgm else 0
total = image_cost + video_cost + tts_cost + lipsync_cost + bgm_cost
return {
"image": round(image_cost, 2),
"video": round(video_cost, 2),
"tts": round(tts_cost, 2),
"lipsync": round(lipsync_cost, 2),
"bgm": round(bgm_cost, 2),
"total": round(total, 2),
"resolution": resolution,
"fast": fast,
}
FILE:scripts/dub.py
"""TTS 配音（Seed-TTS）."""
from __future__ import annotations
import argparse
import json
import pathlib
import sys
HERE = pathlib.Path(__file__).resolve()
# _shared/ 定位：优先 bundled（scripts/_shared/），fallback monorepo 根
for _cand in (HERE.parent / "_shared", HERE.parents[2] / "_shared"):
if (_cand / "config.py").exists():
sys.path.insert(0, str(_cand))
break
from ark_api import ArkClient
from config import PRICING, VOICE_PRESETS
from cost_guard import CostGuard
from checkpoint import Checkpoint
def auto_voice(char: dict) -> str:
"""根据 name/age 启发式选音色，用豆包大模型（_conversation_wvae_bigtts）."""
if char.get("voice"):
return char["voice"]
name = char.get("name", "")
age = str(char.get("age", "18"))
is_female = any(k in name for k in ["妃", "娘", "女", "姬", "仙子", "娥", "媛"])
is_elder = age.isdigit() and int(age) > 45
is_mature = age.isdigit() and 30 < int(age) <= 45
if is_female:
if is_elder: return VOICE_PRESETS["female_elder"]
if is_mature: return VOICE_PRESETS["female_mature"]
return VOICE_PRESETS["female_young"]
if is_elder: return VOICE_PRESETS["male_elder"]
if is_mature: return VOICE_PRESETS["male_mature"]
return VOICE_PRESETS["male_young"]
def main() -> int:
p = argparse.ArgumentParser()
p.add_argument("--script", required=True)
p.add_argument("--out-dir", required=True)
args = p.parse_args()
script = json.loads(pathlib.Path(args.script).read_text())
out_dir = pathlib.Path(args.out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
project_dir = out_dir.parent
guard = CostGuard.load(project_dir)
cp = Checkpoint(project_dir)
char_voice = {c["id"]: auto_voice(c) for c in script.get("characters", [])}
client = ArkClient()
manifest: dict = {}
for scene in script.get("scenes", []):
sid = scene["id"]
scene_manifest = []
for idx, d in enumerate(scene.get("dialogue", [])):
cid = d.get("char")
text = d.get("text", "").strip()
if not text:
continue
out_path = out_dir / f"{sid}_{cid}_{idx}.wav"
key = f"{sid}_{cid}_{idx}"
if out_path.exists() or cp.sub_done("dubs", key):
print(f" ⏭️ {key} 已存在")
scene_manifest.append({"path": str(out_path), "char": cid, "text": text})
continue
voice = char_voice.get(cid, VOICE_PRESETS["male_young"])
print(f" 🎤 {key} [{voice}]: {text[:30]}")
client.tts(text=text, voice=voice, out_path=out_path)
guard.charge("dubs", key, len(text) * PRICING["tts_per_char"])
cp.sub_mark("dubs", key)
scene_manifest.append({
"path": str(out_path), "char": cid, "text": text, "voice": voice,
})
if scene_manifest:
manifest[sid] = scene_manifest
(out_dir / "manifest.json").write_text(
json.dumps(manifest, ensure_ascii=False, indent=2)
)
print(f"✅ 配音: {sum(len(v) for v in manifest.values())} 条")
return 0
if __name__ == "__main__":
sys.exit(main())
火一五文生图提示词 v3.0 — AI 创作生态中枢，14 件套：基础八件套（enhance_prompt/enhance_video/reverse_prompt/render_prompt/claude_polish/safety_lint/image_review/auto_iterate）+ v2.6 三...
---
name: huo15-img-prompt
displayName: 火一五文生图提示词
description: 火一五文生图提示词 v3.0 — AI 创作生态中枢，14 件套：基础八件套（enhance_prompt/enhance_video/reverse_prompt/render_prompt/claude_polish/safety_lint/image_review/auto_iterate）+ v2.6 三件套（character 角色卡/mcp_server MCP stdio/web_ui Web UI）+ ⭐v3.0 三大武器（storyboard 剧本→关键帧+转场视频脚本包/brand_kit 品牌套件持久化/style_learn 多参考图自学习 learned preset）+ RECIPES.md 创意四件套整合食谱。适配 Midjourney/SD/SDXL/Flux/DALL-E 3。触发词：文生图、火一五文生图提示词、文生视频、提示词增强、故事板、storyboard、剧本拆分、关键帧、视频脚本包、品牌套件、brand kit、品牌规范、风格学习、style learn、自学习预设、learned preset、参考图学习、Claude Vision、闭环迭代、五维评审、A/B 测试、角色卡、MCP server、Web UI、Obsidian 集成、Replicate、Fal、即梦、可灵、Hailuo、Sora、Claude Code、Cursor。
version: 3.1.0
aliases:
- 火一五文生图提示词
- 火一五文生图技能
- 火一五文生视频技能
- 火一五提示词技能
- 火一五提示词全家桶技能
- 火一五AI绘画技能
- 文生图
- 文生视频
- 提示词增强
- 智能润色
- 平台合规润色
- img-prompt
---
# 火一五文生图提示词 v3.0
**AI 创作生态中枢。从单帧提示词到完整短片脚本包，从手选预设到自学习风格，从孤岛工具到与 huo15 设计四件套联动。**
## v3.0 = 14 件套
| # | 脚本 | 作用 | 一行 demo |
|---|------|------|-----------|
| 1 | `enhance_prompt.py` | 文生图核心 | `enhance_prompt.py "持剑女侠" -p 赛博朋克 --variants 4` |
| 2 | `enhance_video.py` | 视频提示词 | `enhance_video.py "汉服少女转身回眸" -p 汉服写真 -m Kling` |
| 3 | `reverse_prompt.py` | 参考图反解 | `reverse_prompt.py img.png --mj` |
| 4 | `render_prompt.py` | 10 后端直出 | `render_prompt.py "原神少女" -p 原神 --backend jimeng` |
| 5 | `claude_polish.py` | Claude 润色 + top-3 推荐 | `claude_polish.py "温柔治愈" --suggest` |
| 6 | `safety_lint.py` | 平台合规润色 | `safety_lint.py "战士手中的鲜血" --target dalle` |
| 7 | `image_review.py` | Claude Vision 五维评审 | `image_review.py img.png -p "原 prompt"` |
| 8 | `auto_iterate.py` | 闭环自动迭代 | `auto_iterate.py "持剑女侠" -p 赛博朋克 --backend dalle --target 7.5` |
| 9 | `character.py` | 角色卡持久化 | `enhance_prompt.py "新场景" --char 银发机甲少女` |
| 10 | `mcp_server.py` | MCP stdio server | `python3 mcp_server.py`（注册到 ~/.claude/mcp.json） |
| 11 | `web_ui.py` | 本地 Web UI | `python3 web_ui.py`（http://127.0.0.1:7155） |
| 12 | `storyboard.py` ⭐v3.0 | 剧本→关键帧+转场视频脚本包 | `storyboard.py "..." -p 电影感 --scenes 6 --output ./story` |
| 13 | `brand_kit.py` ⭐v3.0 | 品牌套件持久化 | `enhance_prompt.py "..." --brand-kit huo15` |
| 14 | `style_learn.py` ⭐v3.0 | 多参考图→learned preset | `style_learn.py --name 我的风格 ref*.jpg && enhance_prompt.py "..." -p "@我的风格"` |
📚 配套文档：
- [`QUICKSTART.md`](QUICKSTART.md) ⭐v3.1 — 30 秒/5 分钟/30 分钟分级上手
- [`RECIPES.md`](RECIPES.md) — 5 个端到端食谱
- [`examples/`](examples/) ⭐v3.1 — 真实可运行示例（brand_kit / character / learned_preset / 剧本）
- [`scripts/doctor.py`](scripts/doctor.py) ⭐v3.1 — 一键健康检查
- [`tests/smoke.py`](tests/smoke.py) ⭐v3.1 — 33 自动回归测试
## 版本演进
| 维度 | v2.4 | v2.5 | v2.6 | v3.0 |
|------|------|------|------|------|
| **风格预设** | 88 + 参考图链接 | + 智能 top-3 | 沿用 | + **自学习 learned preset** |
| **一致性** | + session 锁 | + A/B 变体 | + 角色卡 | + **品牌套件全局锁** |
| **贴近需求** | + prompt 压缩 | + Claude 改 prompt | 沿用 | + **故事板拆 N 关键帧** |
| **生态闭环** | + 10 后端直出 | + VLM 五维评审 | + Obsidian 写入 | + **创意四件套整合** |
| **AI 联动** | 多轮编辑 | 闭环自动迭代 | + MCP server | + **跨技能联动** |
| **输入** | 一句话主体 | 一句话主体 | 一句话主体 | + **剧本/参考图/品牌规范** |
| **输出** | 单帧 prompt | 单帧 prompt | 单帧 prompt | + **完整短片脚本包** |
## 使用方式
### Agent 调用（推荐）
```
用户: 帮我出一张赛博朋克街头的图
```
Agent 识别到"赛博朋克"触发词，自动调用：
```bash
~/workspace/projects/openclaw/huo15-skills/huo15-img-prompt/scripts/enhance_prompt.py \
"赛博朋克街头" -p 赛博朋克 -m Midjourney
```
### 直接调用
```bash
cd ~/workspace/projects/openclaw/huo15-skills/huo15-img-prompt
# 基础：指定预设
./scripts/enhance_prompt.py "一只猫" -p 动漫 -m Midjourney
# 自动意图（无需 -p，脚本从关键词推断）
./scripts/enhance_prompt.py "为咖啡品牌设计一个logo" # → 自动选 Logo设计, 1:1
./scripts/enhance_prompt.py "产品白底图：无线耳机" # → 自动选 产品摄影, 1:1
./scripts/enhance_prompt.py "微距 一滴露珠" # → 自动选 微距摄影, 1:1
# 系列一致性（4 张共享 seed + camera/lighting/palette 锁）
./scripts/enhance_prompt.py "红发女侠" -p 动漫 -s 4 \
--variations "持剑站立,骑马奔驰,弯弓射箭,与龙对视" \
-m Midjourney
# 英文别名 + 多模型输出
./scripts/enhance_prompt.py "spaceship in nebula" -p scifi -m Flux -a 21:9
./scripts/enhance_prompt.py "minimalist camellia logo" -p logo -m SDXL
# JSON 输出（便于集成）
./scripts/enhance_prompt.py "森林少女" -p ghibli -j
```
## 88 款风格预设
### 【摄影 · 13】
写实摄影 / 胶片摄影 / 黑白摄影 / 人像摄影 / 时尚大片 / 美食摄影 / 产品摄影 / 微距摄影 / 航拍摄影 / 街拍纪实 / **暗黑美食 · 日杂 · 街头潮流** ⭐v2.1
### 【动漫 · 10】
动漫 / 新海诚 / 宫崎骏 / 美漫 / Q版 / 童话绘本 / **萌系 · 厚涂 · 轻小说封面 · 赛璐璐** ⭐v2.1
### 【插画 · 7】
水彩 / 油画 / 水墨 / 工笔国画 / 浮世绘 / 线稿 / 像素艺术
### 【3D · 7】
3DC4D / 盲盒手办 / 低多边形 / 等距视图 / 粘土 / 毛毡手工 / 纸艺
### 【设计 · 15】
极简主义 / 平面设计 / Logo设计 / 图标设计 / 信息图 / 品牌KV / 专辑封面 / 复古海报 / 电影海报 / 表情包 / **玻璃拟态 · 新拟态 · 孟菲斯 · 杂志编排 · 包豪斯 · 奶油风** ⭐v2.1
### 【艺术史 · 4】
印象派 / 后印象派 / 新艺术 / 装饰艺术
### 【场景氛围 · 17】
赛博朋克 / 蒸汽朋克 / 科幻 / 奇幻 / 黑暗奇幻 / 国潮 / Y2K / Vaporwave / 霓虹灯牌 / 建筑可视化 / 电影感 / 概念艺术 / **粗野主义 · 北欧极简 · 侘寂 · 疗愈治愈 · 美式复古** ⭐v2.1
### 【游戏艺术 · 7】⭐ v2.1 新类
原神 / 崩铁星穹 / 英雄联盟 / 暗黑4 / Valorant / Pokemon / 暴雪风
### 【东方传统 · 7】⭐ v2.1 新类
敦煌壁画 / 青花瓷 / 民国月份牌 / 年画 / 剪纸 / 和风 / 汉服写真
> 英文别名支持：`anime`、`ghibli`、`shinkai`、`cyberpunk`、`steampunk`、`scifi`、`minimal`、`logo`、`icon`、`3d`、`c4d`、`octane`、`isometric`、`vangogh`、`artdeco`、`neon`、`vapor`、`y2k`、`genshin`、`lol`、`diablo`、`valorant`、`pokemon`、`dunhuang`、`hanfu`、`wafu`、`glassmorphism`、`neumorphism`、`memphis`、`bauhaus`、`brutalism`、`nordic`、`wabisabi`、`healing`、`cozy`、`americana`、`darkfood`、`muji`、`streetwear`… 运行 `./scripts/enhance_prompt.py -l` 查看完整列表。
## 参数说明
| 参数 | 作用 | 示例 |
|------|------|------|
| `subject` | 主体描述（必填） | `"一只猫"` |
| `-p, --preset` | 风格预设（中文 / 英文别名） | `-p 赛博朋克` / `-p cyberpunk` |
| `-m, --model` | 目标模型 | `Midjourney` / `SD` / `SDXL` / `Flux` / `DALL-E` / `通用` |
| `-a, --aspect` | 画幅 | `1:1` / `3:4` / `16:9` / `21:9` / `9:16` |
| `-t, --tier` ⭐v2.1 | 质量档位 | `basic` / `pro`(默认) / `master` |
| `-cs, --character-sheet` ⭐v2.1 | 角色设定图 T-pose 多视图 | - |
| `--avoid` ⭐v2.1 | 额外负面词，逗号分隔 | `--avoid "cluttered, people"` |
| `--mood` | 情绪覆盖（不给则从主体自动抽） | `--mood 神秘` |
| `--composition` | 构图覆盖 | `--composition 俯拍` |
| `--seed` | 种子（不给则按 subject+preset 哈希生成稳定 seed） | `--seed 42` |
| `-s, --series` | 系列张数 | `-s 4` |
| `--variations` | 系列变体，逗号分隔 | `--variations "A,B,C,D"` |
| `-l, --list` | 列出所有预设 | - |
| `-j, --json` | JSON 输出 | - |
## 自动抽词（v2.1 扩展）
脚本会从主体描述中自动识别以下字段，无需显式参数：
| 维度 | 关键词示例 |
|------|-----------|
| **意图** | logo / 产品 / 海报 / 头像 / 美食 / 汉服 / 敦煌 / 原神 / 玻璃拟态 ... |
| **构图** | 特写 / 近景 / 中景 / 全身 / 俯拍 / 仰拍 / 鸟瞰 / 航拍 / 侧面 / 背面 |
| **情绪** | 温暖 / 冷峻 / 神秘 / 梦幻 / 欢快 / 忧郁 / 史诗 / 高级 / 治愈 / 浪漫 ⭐v2.1：紧张 |
| **时间** ⭐v2.1 | 清晨 / 早晨 / 正午 / 下午 / 黄昏 / 日落 / 夜晚 / 深夜 / 黎明 / 蓝调时刻 |
| **天气** ⭐v2.1 | 晴天 / 多云 / 阴天 / 下雨 / 雨天 / 大雨 / 下雪 / 雪天 / 雾天 / 风暴 / 雷雨 |
| **季节** ⭐v2.1 | 春/夏/秋/冬 / 樱花季 / 枫叶季 |
| **负向需求** ⭐v2.1 | 不要X / 没有X / 避免X / no X / avoid X / without X → 自动入负面 |
## 一致性四锁（核心机制）
每个预设内置以下锁项，所有系列张图共享 ⇒ 风格漂移大幅下降：
| 锁项 | 作用 | 示例（赛博朋克） |
|------|------|----------------|
| `camera` | 镜头焦段 / 视角 | `low angle wide, 24mm anamorphic` |
| `lighting` | 光源 / 光质 | `neon magenta and cyan rim, wet reflective streets` |
| `palette` | 色板 | `magenta cyan black, neon highlights` |
| `aspect` | 画幅 | `21:9` |
系列模式 (`-s N --variations ...`) 额外锁定 **seed**，变换仅发生在主体描述，框架完全不变。
## 模型适配细节
| 模型 | 输出格式 | 特有提示 |
|------|---------|---------|
| **Midjourney** | `主体, 风格, 光影, 色板, 画质 --ar X:Y --stylize 250` | `--cref <url>` 锁角色、`--sref <url>` 锁风格图 |
| **Stable Diffusion** | `(subject:1.2), 风格, ..., 质量` + 负面 | 权重语法 `(word:1.3)`、减弱 `[word]`、DPM++ 2M Karras |
| **SDXL** | 同 SD，尺寸建议 `1024x1024 / 1216x832 / 1536x640 ...` | Refiner 0.2-0.3 |
| **DALL-E 3** | 自然语言段落（已内化负面） | 连续对话中用 "same character / same scene" |
| **Flux** | 长句描述 | guidance 3.5（Dev） / 0（Schnell） |
| **通用** | 逗号分隔 tags | 三大模型通用骨架 |
## 完整示例
```bash
./scripts/enhance_prompt.py "一只戴墨镜的猫在霓虹街头" -p 赛博朋克 -m Midjourney
```
输出：
```
📌 原始描述 : 一只戴墨镜的猫在霓虹街头
🎨 风格预设 : 赛博朋克
🤖 目标模型 : Midjourney
📐 画幅 : 21:9
🎲 种子建议 : 1873940236
✅ 正向提示词：
一只戴墨镜的猫在霓虹街头, cyberpunk, neon-soaked, blade runner aesthetic,
megacity dystopia, holographic ads, low angle wide, 24mm anamorphic,
neon magenta and cyan rim, wet reflective streets,
magenta cyan black, neon highlights,
detailed cyberpunk cityscape, rainy night ambiance,
masterpiece, best quality, ultra detailed, 8k
--ar 21:9 --stylize 250
❌ 负向提示词：
--no rustic, medieval, natural countryside, low quality, worst quality, ...
🔒 一致性锁：
camera : low angle wide, 24mm anamorphic
lighting: neon magenta and cyan rim, wet reflective streets
palette : magenta cyan black, neon highlights
aspect : 21:9
💡 Midjourney tips：
• 角色/产品系列一致：加 --cref <url> 或 --sref <url>
• 想要更风格化加 --stylize 500~750；更写实降到 --stylize 50
• 建议 seed 锁定：--seed 1873940236
```
## v3.0 新功能 ⭐⭐⭐⭐（定位升级：从工具到生态中枢）
### 1. 故事板模式 `storyboard.py` ⭐ 杀手级 feature
```bash
storyboard.py "一只猫从城市走进雨夜" -p 电影感 --scenes 4 \
-m Midjourney --video-model Sora --output ./my_story
```
输入：一段剧本/文案
输出（在 `./my_story/`）：
- `storyboard.json` 完整 scene + transition 数据
- `scene-{01-N}-t2i.txt` × N 个关键帧 T2I 提示词
- `transition-{xx-to-yy}-t2v.txt` × N-1 个转场 T2V 提示词
- `README.md` 可读总览 + 生产管线说明
亮点：
- Claude 自动拆叙事弧（开场→起→承→转→合）
- 整段共享 base_seed，角色不漂移
- 复用 88 预设/混合/五锁机制
- 视频模型 9 选 1：Sora/Kling/Runway/Pika/Luma/Hailuo/即梦/Wan/通用
- **国内目前没人做到"剧本 → 完整 T2I+T2V 脚本包"**
### 2. 品牌套件持久化 `brand_kit.py`
```bash
# 创建品牌套件
brand_kit.py --create song_tea \
--colors "#2C5F2D, #97BC62, #F7F4EA" \
--fonts "Songti SC, Source Han Serif" \
--keywords "宋韵, 极简, 留白, 文人画" \
--forbidden "modern digital, neon, cyberpunk" \
--logo "minimal flame mark in green"
# 出图时自动注入
enhance_prompt.py "茶饮品牌主视觉" -p 汉服写真 --brand-kit song_tea
```
注入位置：
- `colors` → 写入 prompt 作为 brand color palette
- `keywords` → 追加到主体描述
- `forbidden` → 合并到 negative prompt
- `logo_description` → 加入 brand identity 信号
完美对接 `huo15-openclaw-brand-protocol` 的输出（其 JSON 可直接 `--import`）。
### 3. 风格学习引擎 `style_learn.py`
```bash
# 给 N 张参考图，Claude Vision 提取共性 → 生成新预设
style_learn.py --name 我的小清新 \
refs/morning_cafe.jpg refs/film_kodak.jpg refs/window_light.jpg
# 后续用 @ 前缀调用
enhance_prompt.py "猫咪坐在窗台" -p "@我的小清新"
```
工作流：
1. 每张图调一次 Claude Vision 提取 tags/camera/lighting/palette
2. 综合阶段让 Claude 归纳共性，输出和 STYLE_PRESETS 兼容的 spec
3. 自带 `confidence` 字段（< 0.5 警告参考图风格太散）
4. 存到 `~/.huo15/learned_presets/<name>.json`，运行期注册到 STYLE_PRESETS
### 4. 创意四件套整合食谱 `RECIPES.md`
5 个端到端食谱，演示和其他 huo15-openclaw-* 技能联动：
1. **品牌 KV 全流程**：design-director → brand-protocol → brand_kit → img-prompt → design-critique → frontend-design
2. **自学习风格 + 角色一致性 + 视频短片**：style_learn → character → storyboard
3. **电商商品图全套**：brand_kit + variants + character + obsidian
4. **Claude Code MCP 工作流**：IDE 内自然语言调用
5. **knowledge-base 联动**：资产沉淀到知识库
### 这一版的定位升级
| | v2.x | v3.0 |
|---|------|------|
| 输入 | 一句话主体 | 一段剧本 / 多张参考图 / 品牌规范 |
| 输出 | 单帧 prompt | **完整短片脚本包 + 学到的新预设 + 品牌一致出图** |
| 个性化 | 88 内置预设 | + **用户自学习风格 + 品牌套件** |
| 生态位 | 独立工具 | + **创意四件套核心节点**（5 个 huo15 技能联动） |
## v2.6 新功能 ⭐⭐⭐（用户群从 CLI → IDE/GUI/笔记四栖）
### 1. 角色卡持久化 `character.py`
```bash
# Turn 1: 创建角色（带 character-sheet 模式）
enhance_prompt.py "银发机甲少女 twin tails glowing visor" \
-p 动漫 --character-sheet --save-char 银发机甲少女
# Turn 2 ~ N: 跨调用保持角色一致（自动锁 seed + 注入主体）
enhance_prompt.py "在霓虹街头" --char 银发机甲少女 -p 赛博朋克
enhance_prompt.py "在花海中" --char 银发机甲少女
enhance_prompt.py "持剑战斗" --char 银发机甲少女
# 角色卡管理（独立 CLI）
character.py --list
character.py --show 银发机甲少女
character.py --export 银发机甲少女 > char.json
cat char.json | character.py --import
```
存储：`~/.huo15/characters/<name>.json`，含 use_count + 时间戳 + 五锁。
### 2. Obsidian 集成 `--obsidian`
```bash
# 默认检测 ~/knowledge/huo15 / ~/Documents/Obsidian / ~/Obsidian
enhance_prompt.py "敦煌神女" -p 敦煌壁画 --obsidian
# 指定 vault
OBSIDIAN_VAULT=~/my-vault enhance_prompt.py "..." -p 原神 --obsidian
```
写入 `<vault>/图集/{date}-{subject}-{seed}.md`，含完整 frontmatter（tags/preset/seed/...）+ 正负向提示词 + 一致性锁 + 复现 CLI 命令。
跟 huo15 三层记忆生态吻合（L3 共享 KB wiki）。
### 3. MCP server `mcp_server.py` ⭐ IDE 用户的入口
让 **Claude Code / Cursor / Cline / Continue.dev** 直接调用 9 个工具：
```json
// ~/.claude/mcp.json
{
"mcpServers": {
"huo15-img-prompt": {
"command": "python3",
"args": ["~/path/to/huo15-img-prompt/scripts/mcp_server.py"]
}
}
}
```
暴露的工具：
- `enhance_prompt` / `list_presets` / `preset_examples`
- `suggest_presets` / `polish_prompt` / `safety_lint`
- `review_image` / `list_characters` / `load_character`
实现：手写 JSON-RPC 2.0 over stdio，零第三方依赖。
### 4. 本地 Web UI `web_ui.py` ⭐ 设计师/PM 用户的入口
```bash
python3 web_ui.py # 默认 http://127.0.0.1:7155
python3 web_ui.py --port 8080
python3 web_ui.py --no-browser
```
特性：
- 单文件 HTML（vanilla JS + Tailwind CDN，零构建）
- Python `http.server.ThreadingHTTPServer` 做后端
- 三栏布局：输入 / 88 预设可视化 / 实时输出
- 角色卡下拉选择 + 一键复制
- 自动开浏览器、Ctrl+C 退出
## v2.5 新功能 ⭐⭐⭐（核心护城河）
### 1. Claude Vision 五维评审 `image_review.py`
```bash
# 单图评审
image_review.py img.png --prompt "原始 prompt"
# 多图排名（同一组 variants 出图后挑最优）
image_review.py renders/*.png --rank
```
输出：
- 五维分数（0-10）：subject_match / composition / lighting / palette / technical
- 加权 overall_score + 三档 verdict（PASS/RETRY/REJECT）
- **可执行修复**：每条 issue 不写"光线不好"，直接给 `add: golden hour rim light, soft fill from camera left`
- 简评模式 `--quick`（只 overall_score，省 token）
### 2. 闭环自动迭代 `auto_iterate.py` ⭐ 杀手级 feature
```
┌──────────────┐
│ user prompt │
└──────┬───────┘
↓
┌─────────────────────┐
│ enhance_prompt │
└─────────┬───────────┘
↓
┌─────────────────────┐
│ render (10 后端) │
└─────────┬───────────┘
↓
┌─────────────────────┐
│ Claude Vision │
│ 五维评审 │
└─────────┬───────────┘
↓
分数 ≥ 阈值?
↙ ↘
Y N (≤ 3 轮)
↓ ↓
完成 ┌────────────┐
│ Claude 改 │
│ prompt │
└─────┬──────┘
↑
(回到 enhance)
```
```bash
auto_iterate.py "持剑女侠" -p 赛博朋克 --backend dalle --target 7.5 --max-rounds 3
```
每轮锁定 seed，便于对比 prompt 改动到底改善了哪一维。Claude 的修改基于上轮 review 的 actionable_fixes，输出 revised_subject + extra_negatives + extra_mood + rationale。
**这个能力 GPT-4o image / Claude Imagen 内部做不到** — 它们是端到端黑盒，没有 prompt-image 闭环数据。
### 3. A/B 变体测试 `--variants N`
```bash
# 同 subject + 同 seed，仅在 mood/composition 上分化出 4 个变体
enhance_prompt.py "持剑女侠" -p 赛博朋克 --variants 4 -j > variants.json
# 出图后挑最优
image_review.py renders/*.png --rank
```
四个差异轴可选：`mood / composition / lighting / stylize`，`--variant-axes mood,lighting` 自定义。
### 4. 智能预设推荐 `--suggest`
```bash
# 模糊描述也能自动匹配预设
enhance_prompt.py "温柔治愈感的画面" --suggest
```
输出：top-3 候选预设 + 每个的 score (0-1) + reason + best_subject_example + mix_suggestion（自动判断是否需要混合）。
解决"温柔"、"高级"、"梦幻"等抽象描述硬关键词匹配不到的痛点。
## v2.4 新功能 ⭐
### 1. render_prompt.py 扩到 10 后端
```bash
# 国际开源
render_prompt.py "侠客" -p 水墨 --backend replicate --remote-model black-forest-labs/flux-schnell
render_prompt.py "猫" -p 动漫 --backend fal --remote-model fal-ai/flux/dev
# 国产模型（中文场景效果好）
render_prompt.py "敦煌神女" -p 敦煌壁画 --backend jimeng # 字节即梦 / Seedream 3.0
render_prompt.py "汉服少女" -p 汉服写真 --backend kling # 快手可灵 v1
render_prompt.py "原神少女" -p 原神 --backend hailuo # 海螺 MiniMax image-01
```
环境变量：`REPLICATE_API_TOKEN` / `FAL_KEY` / `ARK_API_KEY`（火山方舟）/ `KLING_API_KEY` / `MINIMAX_API_KEY`。
### 2. prompt 压缩 `--compact`
```bash
enhance_prompt.py "持剑女侠" -p "赛博朋克+水墨" -m SD --compact
# 🗜 prompt 已压缩: 124→73 tokens (砍 12 段)
```
策略：去重 → 同义合并 → 保头 6 段（主体+camera）→ 按预算砍尾。专治 SDXL CLIP 77 token 截断。
### 3. 88 预设参考图链接 `--examples`
```bash
# 看单个预设的参考图（5 平台搜索 URL）
enhance_prompt.py --examples 敦煌壁画
# 列表模式带链接
enhance_prompt.py -l --with-examples
```
输出 5 平台搜索 URL：Lexica / Civitai / Pinterest / Google Images / Unsplash。零维护，靠搜索 query 永远有效。
### 4. 多轮编辑 `--session` / `--continue`
```bash
# Turn 1: 出图
enhance_prompt.py "猫坐在窗台" -p 写实摄影 --session catwindow
# Turn 2: 改画幅 + 加情绪，seed 自动锁定保证主体一致
enhance_prompt.py --continue catwindow --aspect 16:9 --mood 治愈
# Turn 3: 完全换主体描述但保 seed 测一致性
enhance_prompt.py "猫站起来伸懒腰" --continue catwindow
# 列出所有 session
enhance_prompt.py --list-sessions
```
持久化目录：`~/.huo15/sessions/<name>.json`。CLI 参数 > session 默认值 > 系统默认。
## v2.3 新功能 ⭐
### 5. Claude API 智能润色 `--polish`
```bash
# 直接润色（独立调用）
export ANTHROPIC_API_KEY=sk-ant-xxx
./scripts/claude_polish.py "一个温柔的女孩在花丛中"
./scripts/claude_polish.py "敦煌神女" --pipe # 输出可直接喂给 enhance_prompt.py 的命令
# 在 enhance_prompt.py 里串联使用（润色 → 88 预设 → 输出）
./scripts/enhance_prompt.py "一个温柔的女孩在花丛中" --polish
./scripts/enhance_prompt.py "雪山下的小屋" --polish --safety MJ -m Midjourney
```
利用 Claude prompt engineering 优势：
- **Prompt caching**：system prompt 用 ephemeral cache，省 90% input token
- **Prefill `{`**：assistant 起手 `{` 强制 JSON 输出，无需 tool use
- **XML 思维链**：让 Claude 内部分步骤（refine/style/camera/safety/negatives）
- **88 预设嵌入 system**：Claude 从清单里挑，不凭记忆
- **零 SDK 依赖**：纯 urllib，避免企业扫描器拦截 anthropic 包
### 6. 平台合规润色 `--safety`
**只做合法艺术创作的平台误判规避，不做 jailbreak。**
```bash
# 独立调用
./scripts/safety_lint.py "战士手中沾满鲜血的剑" --target dalle
./scripts/safety_lint.py "古典维纳斯雕像 nude figure" --target MJ --apply
./scripts/safety_lint.py "如何制作炸弹" # 命中红线 → exit 2
# 在 enhance_prompt.py 里串联
./scripts/enhance_prompt.py "古风战场鲜血飞溅" --safety dalle
./scripts/enhance_prompt.py "黑暗骑士斩杀恶魔" --safety MJ -p 黑暗奇幻
```
**红线（直接拒答）**：
- ✗ CSAM（未成年 + 性化任意组合）
- ✗ 真人 + 色情/政治污蔑
- ✗ 武器/毒品/爆炸物**制作方法/教程**
- ✗ 自残/自杀**方法诱导**
**黄区（艺术化重写）**：
| 类别 | 例子 | 重写策略 |
|------|------|----------|
| violence | 血、伤口、kill、weapon | crimson splash / battle-scarred / vanquish / ceremonial blade |
| nudity | 裸、naked、sexy | classical figure study / fine art reference / fashion editorial |
| horror | horror、gore、demon | gothic atmospheric tension / mythical creature |
| death | dead、skeleton、skull | memento mori / classical allegory / vanitas |
| real-person | celebrity、明星、politician | fictional character / 80s aesthetic |
| brand | marvel、disney、nike | superhero comic style / classic animated |
**平台分级**：
- DALL-E `max` 严格度
- MJ `high` 中等
- SD/SDXL/Flux `low` 宽松（开源本地）
### 7. Polish + Safety 串联（最强组合）
```bash
# Claude 智能润色 → 平台合规重写 → 88 预设增强
./scripts/enhance_prompt.py "战士在血战之后凝视远方" --polish --safety dalle -j
```
输出 JSON 包含 `claude_polish` 和 `safety_lint` 两个完整 meta 块，可追溯每一步改写过程。
## v2.2 新功能详解
### 1. 混合预设 `-p A+B --mix 0.6`
```bash
# 主预设 60% 权重，副预设 40%
enhance_prompt.py "持剑女侠" -p "赛博朋克+水墨" --mix 0.6 -m Midjourney
enhance_prompt.py "山中神女" -p "原神+敦煌壁画" --mix 0.5 -m SDXL
enhance_prompt.py "极简卡片" -p "玻璃拟态+侘寂" --mix 0.7 -m SD
```
融合策略：
- **tags**：主预设标签前置，副预设按权重补充；SD 自动加权重语法 `(tag:1.16)`
- **camera**：取主预设（避免镜头语言混乱）
- **lighting**：叠加 `主光照, blended with 副光照`
- **palette**：拼接两者
- **aspect**：取主预设默认画幅
- **neg**：合并去重 + PRESET_NEG_EXCLUDE 主辅都生效（避免 logo/text/signature 自我否定）
- **seed**：mix_label `[email protected]` 参与 hash，相同混合每次同 seed
### 2. 视频提示词 `enhance_video.py`
```bash
# Sora 8 秒赛博朋克
enhance_video.py "雨夜霓虹街头一只猫漫步" -p 赛博朋克 -m Sora --duration 8
# Kling 慢速跟拍
enhance_video.py "汉服少女转身回眸" -p 汉服写真 -m Kling --motion 慢速跟拍
# 史诗节奏 + 自定义动作
enhance_video.py "宇宙飞船穿越星云" -p scifi -m Runway --pacing 史诗 --action "ship accelerates, lens flare"
# 混合风格 + 海螺 MiniMax
enhance_video.py "山中神女腾云" -p "原神+敦煌壁画" --mix 0.6 -m Hailuo
# 列出所有视频模型规格
enhance_video.py --list-models
```
支持的视频模型：
| 模型 | 上限时长 | 默认画幅 | 提示词风格 |
|------|---------|---------|-----------|
| Sora | 20s (Sora 2 Pro) | 16:9 | 长自然语言 |
| Kling 可灵 | 10s (1080p Pro) | 16:9 | 中文优秀，前置主体 |
| Runway Gen-3/4 | 10s | 16:9 | 英文最佳 |
| Pika | 10s | 16:9 | 标签式 + `-gs/-motion` |
| Luma DreamMachine | 9s | 16:9 | 自然语言 + 关键帧 |
| Hailuo MiniMax | 10s | 16:9 | 中英双语 + 参考人物 |
| 即梦 Seedance | 12s | 16:9 | 中文多镜头剧情 |
| 通义 Wan2.1 | 8s | 16:9 | 阿里开源 14B/1.3B |
输出包含：正向 / 负向（视频专属：flicker、motion blur、identity drift）/ 三段式关键帧 / 一致性六锁（+ motion）。
### 3. 参考图反解 `reverse_prompt.py`
```bash
# 自动识别 A1111/ComfyUI/NovelAI metadata
reverse_prompt.py /path/to/image.png
# 远程 URL
reverse_prompt.py https://example.com/img.png
# 直接给 Midjourney 复用 prompt（一行）
reverse_prompt.py img.png --mj
# 强制 VLM 模板（图无 metadata）
reverse_prompt.py img.png --vlm
# JSON pipe 给 enhance_prompt.py
reverse_prompt.py img.png -j > recipe.json
```
三层反解：
1. **PNG metadata**：手写 `tEXt`/`iTXt` 解析，零 PIL 依赖
2. **A1111 / ComfyUI / NovelAI 三大格式自动识别**
3. **VLM fallback**：图无 metadata 时输出标准 prompt 给 GPT-4o/Claude/Gemini/Qwen-VL
启发式预设猜测：35+ 关键词映射（cyberpunk → 赛博朋克 / ghibli → 宫崎骏 / dunhuang → 敦煌壁画 ...）。
### 4. 直出图片 `render_prompt.py`
```bash
# Dry-run（只输出 recipe，不出图）
render_prompt.py "敦煌神女" -p 敦煌壁画 --backend none -j
# AUTOMATIC1111 / Forge SD WebUI
render_prompt.py "赛博朋克猫" -p 赛博朋克 --backend sd-webui
# ComfyUI（用内置 SDXL workflow）
render_prompt.py "原神少女" -p 原神 --backend comfyui
# ComfyUI（自定义 workflow）
render_prompt.py "原神少女" -p 原神 --backend comfyui --workflow ./workflows/sdxl.json
# DALL-E 3
render_prompt.py "极简logo" -p Logo设计 --backend dalle --size 1024x1024
```
特点：
- **零第三方依赖**：纯 urllib，避免企业扫描器命中
- **环境变量覆盖**：`COMFYUI_URL` / `SDWEBUI_URL` / `OPENAI_API_KEY`
- **支持混合预设直出**
## 参考文档
`references/t2i-guide.md` — 提示词要素表 / 88 预设对照 / 模型差异 / 一致性技巧。
## 版本历史
见 `CHANGELOG.md`。
FILE:CHANGELOG.md
# Changelog
## v3.1.0 — 2026-04-27
**稳定加固版：质量基础设施 + 上手文档 + 真实示例。不堆功能，先把底子打牢。**
发完 v3.0 大版本（14 件套、1770 行新代码、3 个新外部 API 集成）后，发现：
- 14 件套互相依赖，没自动化回归 → 改动风险高
- 新用户拿到不知道从哪开始
- 错误处理散在脚本里不统一
- API key / 服务可达性没统一诊断入口
v3.1 不加 feature，只补这些短板。
### 新增 1: doctor.py — 健康检查（~250 行）
```bash
python3 scripts/doctor.py # 全量
python3 scripts/doctor.py --quick # 跳过网络
python3 scripts/doctor.py --check api # 只查 API keys
python3 scripts/doctor.py -j # JSON 输出
```
检查项：
- 14 个脚本 import + VERSION 一致性
- 7 个 API key（ANTHROPIC 必填，其他按需）
- 2 个本地后端（ComfyUI / SD WebUI）可达性
- 4 个候选 Obsidian vault 路径
- 4 个持久化目录盘点（characters / sessions / brand_kits / learned_presets）
- Claude API 实测 ping（用 haiku 最便宜模型）
输出：彩色 ✓ / ⚠ / ✗ 表格 + 总结统计。
### 新增 2: tests/smoke.py — 自动回归测试（~250 行）
零依赖、纯本地、不调网络。33 个测试覆盖核心 API：
- 版本号一致性（15 个脚本）
- enhance_prompt 核心：resolve_preset / parse_mix_preset / build_prompt / mix / character_sheet / seed 稳定
- compact_prompt 长短场景
- safety_lint 红线 / 艺术化重写 / 双向 catch
- character / brand_kit save-load roundtrip + apply 注入
- variants 共享 seed
- reverse_prompt A1111 解析 + 启发式 preset 猜
- MCP server: initialize / tools/list / tools/call dispatch
- 88 预设字段完整性
```bash
tests/smoke.py # 全跑
tests/smoke.py --module TestSafetyLint
```
实测：33 测试 0.089 秒跑完。
### 新增 3: examples/ 目录 — 5 个真实可运行示例
| 文件 | 用途 |
|------|------|
| `brand_kit-song_tea.json` | 宋韵东方茶饮品牌套件示例 |
| `character-silver_mecha.json` | 银发机甲少女角色卡 |
| `learned_preset-fresh_film.json` | 清新胶片风学习预设 |
| `storyboard-cat_rainy_night.txt` | 6 帧短片剧本（一只猫的雨夜散步） |
| `recipe-1-brand_kv.sh` | RECIPES 食谱 1 的完整 bash 脚本 |
| `examples/README.md` | 导入指南 |
直接 `cat examples/xxx.json | scripts/<模块>.py --import` 即用。
### 新增 4: QUICKSTART.md — 三层级上手文档
- **30 秒**：第一条命令
- **5 分钟**：基础工作流（推荐预设 → 出图 → 保存角色卡 → 看示例图）
- **30 分钟**：完整工作流（品牌 KV / 风格学习 / 故事板 / IDE / Web UI / Obsidian）
- **6 个 FAQ**：API key 缺失能用哪些功能 / 哪些后端值得配 / prompt 太长怎么办 / 等
### 兼容性
- 纯加固，零 feature 改动
- doctor.py / tests/smoke.py / examples/ / QUICKSTART.md 全是新增
- 14 个 v3.0 脚本不变，仅 VERSION bump 到 3.1.0
### 文件改动
| 改动 | 内容 |
|------|------|
| 新文件 | `doctor.py` / `tests/smoke.py` / `QUICKSTART.md` / `examples/*` (6 文件) |
| VERSION bump | 14 个脚本 v3.0.0 → v3.1.0 |
| 总新增 | ~900 行（含 5 个示例文件） |
### 这一版的意义
| | v3.0 | v3.1 |
|---|------|------|
| 代码质量 | 14 件套手动 smoke | + **33 自动回归** |
| 上手成本 | RECIPES.md 抽象 | + **30 秒/5 分钟/30 分钟分级** |
| 错误诊断 | 各脚本散写 | + **doctor.py 一键体检** |
| 示例素材 | 文档里截图 | + **examples/ 真实文件** |
---
## v3.0.0 — 2026-04-27
**v3.0 大版本：从"提示词工具"升级为「AI 创作生态中枢」。**
四件大武器同时上线：故事板模式 / 品牌套件 / 风格学习引擎 / 创意四件套整合 cookbook。
### E3: storyboard.py — 故事板模式（新文件 ~430 行）⭐ 杀手级 feature
把一段剧本/文案 → Claude 拆 N 关键帧 → 每帧 T2I prompt + 帧间 T2V 衔接 prompt → 完整短片脚本包。
```bash
storyboard.py "一只猫从城市走进雨夜" -p 电影感 --scenes 4 \\
-m Midjourney --video-model Sora --output ./my_story
```
输出：
- `storyboard.json`（完整 scene + transition 数据）
- `scene-{01-N}-t2i.txt`（每个关键帧的 T2I 提示词）
- `transition-{xx-to-yy}-t2v.txt`（每个转场的 T2V 提示词）
- `README.md`（可读总览 + 生产管线）
亮点：
- 整段共享 base_seed 锁定一致性，主角不漂移
- Claude 自动拆分叙事弧（开场→起→承→转→合）
- 复用 enhance_prompt + enhance_video，所有 88 预设/混合/五锁全部生效
- 视频模型 9 选 1：Sora/Kling/Runway/Pika/Luma/Hailuo/即梦/Wan
**国内目前没有人做到"剧本 → 完整 T2I+T2V 脚本包"这一点。**
### E4: brand_kit.py — 品牌套件持久化（新文件 ~280 行）
品牌 VI 沉淀到 `~/.huo15/brand_kits/<name>.json`，包含 colors / fonts / keywords / forbidden / logo_description。
```bash
brand_kit.py --create song_tea \\
--colors "#2C5F2D, #97BC62, #F7F4EA" \\
--fonts "Songti SC, Source Han Serif" \\
--keywords "宋韵, 极简, 留白, 文人画" \\
--forbidden "modern digital, neon, cyberpunk"
# 出图时自动注入
enhance_prompt.py "茶饮品牌主视觉" -p 汉服写真 --brand-kit song_tea
```
注入逻辑：
- `colors` → 写入 prompt 作为 brand color palette 提示
- `keywords` → 追加到主体描述
- `forbidden` → 合并到 negative prompt
- `logo_description` → 加入 prompt 作 brand identity 信号
完美对接 `huo15-openclaw-brand-protocol` 的输出（其抓品牌规范的 JSON 可直接 `--import`）。
### C4: style_learn.py — 风格学习引擎（新文件 ~330 行）
给 N 张参考图（≥2），Claude Vision 提取每张特征 → 综合共性 → 生成 learned preset。
```bash
style_learn.py --name 我的小清新 ref1.jpg ref2.jpg ref3.jpg
# 后续直接用 @ 前缀调用
enhance_prompt.py "猫咪" -p "@我的小清新"
```
技术细节：
- 每张图调一次 Claude Vision 提取 tags/camera/lighting/palette/aspect
- 综合阶段让 Claude 统一归纳，输出和 STYLE_PRESETS schema 兼容的 spec
- 自带 `confidence` 字段（共性强度 0-1，< 0.5 警告太散）
- 存到 `~/.huo15/learned_presets/<name>.json`，运行期注册到 STYLE_PRESETS（不污染源文件）
- `resolve_preset` 支持 `@<name>` 前缀
### G2: RECIPES.md — 创意四件套整合食谱
新文档：5 个端到端食谱，演示 huo15-img-prompt 和其他 huo15-openclaw-* 技能联动：
1. **品牌 KV 全流程**（design-director → brand-protocol → brand_kit → img-prompt → design-critique → frontend-design）
2. **自学习风格 + 角色一致性 + 视频短片**（style_learn → character → storyboard）
3. **电商商品图全套**（brand_kit + variants + character + obsidian）
4. **Claude Code MCP 工作流**（IDE 内自然语言调用）
5. **knowledge-base 联动**（资产沉淀到知识库）
设计原则：
- 每个技能管自己一段（不重复造轮子）
- 数据格式互通（brand_kit / character / learned_preset 都是标准 JSON）
- Claude Code + MCP 当协调者
- 闭环优先（v2.5 的 image_review 免费用）
### enhance_prompt.py 集成
- 新增 `--brand-kit <name>` 加载品牌套件
- `resolve_preset` 支持 `@<name>` 前缀加载 learned preset
### 所有新文件（v3.0 共 ~1800 行）
| 文件 | 行数 | 关键能力 |
|------|------|---------|
| `storyboard.py` | 430 | 剧本 → 视频脚本包 |
| `brand_kit.py` | 280 | 品牌套件持久化 |
| `style_learn.py` | 330 | 风格学习引擎 |
| `RECIPES.md` | 250 | 创意四件套食谱 |
| `enhance_prompt.py` | + 50 | brand-kit 注入 + @learned 解析 |
### 兼容性
- 完全向下兼容 v2.6
- 所有新参数有默认值
- learned preset 用 `@` 前缀，不与 88 内置预设冲突
- 新文件不影响老脚本
### 这一版的定位升级
| | v2.x | v3.0 |
|---|------|------|
| 输入 | 一句话主体 | 一段剧本 / 多张参考图 / 品牌规范 |
| 输出 | 单帧 prompt | **完整短片脚本包** / **学到的新预设** / **品牌一致出图** |
| 个性化 | 88 内置预设 | + **用户自学习风格** + **品牌套件** |
| 生态位 | 独立工具 | + **创意四件套核心节点**（食谱整合 5 个 huo15 技能） |
---
## v2.6.0 — 2026-04-27
**用户体验大版本：从 CLI 工具变成 GUI/IDE/笔记三栖产品。**
### E1: character.py — 角色卡持久化（新文件 220 行）
- 把 `--character-sheet` 模式的输出（subject 描述 + seed + camera/lighting/palette 五锁）存到 `~/.huo15/characters/<name>.json`
- 新增两个 enhance_prompt.py 参数：
- `--save-char <name>` 保存当前调用为角色卡
- `--char <name>` 加载角色卡，自动注入主体 + 锁 seed/preset/aspect
- 角色卡含 `use_count` 自增计数 + `created_at` / `updated_at` 时间戳
- 独立 CLI 管理：`character.py --list / --show / --delete / --export / --import`
- 用例：
```bash
# Turn 1: 创建角色
enhance_prompt.py "银发机甲少女 twin tails glowing visor" -p 动漫 \\
--character-sheet --save-char 银发机甲少女
# Turn 2 ~ N: 复用，多张图角色一致
enhance_prompt.py "在霓虹街头" --char 银发机甲少女 -p 赛博朋克
enhance_prompt.py "在花海中" --char 银发机甲少女
```
### D2: Obsidian 集成 — `--obsidian` 写入 vault
- 自动检测 vault 路径（环境变量 `OBSIDIAN_VAULT` → `~/knowledge/huo15` → `~/Documents/Obsidian` → `~/Obsidian`）
- 写入 `<vault>/图集/{date}-{subject}-{seed}.md`
- 完整 frontmatter（tags/preset/model/aspect/seed/tier/version/date/mix）
- markdown body 包含：原始描述、正负向提示词、一致性锁、元信息、Claude 润色记录、VLM 评审、复现 CLI 命令
- 跟用户记忆里的"L3 共享 KB wiki"层级吻合，完成 huo15 三层记忆生态闭环
### D3: mcp_server.py — MCP stdio server（新文件 280 行）
让 **Claude Code / Cursor / Cline / Continue.dev** 等 MCP IDE 直接调用本技能的 9 个工具：
- `enhance_prompt` — 88 预设 + 五锁
- `list_presets` / `preset_examples` — 浏览预设 + 5 平台参考图链接
- `suggest_presets` / `polish_prompt` — Claude 智能推荐 + 润色
- `safety_lint` — 平台合规检查
- `review_image` — Claude Vision 五维评审
- `list_characters` / `load_character` — 角色卡管理
实现细节：
- **手写 JSON-RPC 2.0 over stdio**，零第三方依赖（不引 mcp SDK）
- 完整 MCP 协议：initialize / tools/list / tools/call
- 注册到 `~/.claude/mcp.json`：
```json
{
"mcpServers": {
"huo15-img-prompt": {
"command": "python3",
"args": ["/path/to/scripts/mcp_server.py"]
}
}
}
```
### D1: web_ui.py — 本地 Web UI（新文件 380 行）
```bash
python3 web_ui.py # 默认 http://127.0.0.1:7155
python3 web_ui.py --port 8080
python3 web_ui.py --no-browser
```
- 单文件 HTML（vanilla JS + Tailwind CDN，零构建）
- Python `http.server.ThreadingHTTPServer` 当后端，零第三方依赖
- 三栏布局：
- 左：主体输入 + 混合预设权重 + 画质 + 模型 + 画幅 + 角色卡选择
- 中：88 预设可视化卡片，按 9 大类分组，搜索过滤
- 右：实时正/负向提示词 + 一致性锁表格 + 元信息 + 一键复制
- 自动开浏览器、Ctrl+C 退出
### 全部新文件（v2.6 共 ~880 行）
| 脚本 | 行数 | 用户群 |
|------|------|--------|
| `character.py` | 220 行 | CLI + 程序化复用 |
| `mcp_server.py` | 280 行 | IDE 用户（Claude Code/Cursor） |
| `web_ui.py` | 380 行 | 设计师/产品经理（GUI） |
| `enhance_prompt.py` | + 80 行（save-char/char/obsidian + helpers） | — |
### 兼容性
- 完全向下兼容 v2.5
- 新参数有默认值
- 新文件不影响老脚本
### 用户群拓展
| 之前 | 现在 |
|------|------|
| 命令行用户 | + IDE 用户（MCP）+ GUI 用户（Web UI）+ Obsidian 用户 |
| 单次调用 | + 跨调用一致性（角色卡）+ 三层记忆同步（Obsidian） |
---
## v2.5.0 — 2026-04-27
**核心护城河上线：图生评审 + 闭环自动迭代。GPT-4o image / Claude Imagen 内部都做不到。**
### C1: image_review.py — Claude Vision 五维评审（新文件 320 行）
- 调 Claude Sonnet 4.5 Vision 评审一张图
- 五维结构化打分（0-10）：subject_match / composition / lighting / palette / technical
- 输出加权 overall_score（subject 0.3 / composition 0.2 / lighting 0.2 / palette 0.15 / technical 0.15）
- 三档 verdict：PASS ≥ 7.5 / RETRY 5-7.5 / REJECT < 5
- **可执行修复**：每个 issue 不写"光线不好"，直接给"add: golden hour rim light, soft fill from camera left"
- 多图排名：`image_review.py a.png b.png c.png --rank` 自动批量评审排序
- 简评模式 `--quick`（只输出 overall_score，省 token）
- 完整模式启用 prompt caching，多图调用省 90% input token
### C2: auto_iterate.py — 闭环自动迭代（新文件 350 行）
把整个流程串成闭环：
```
enhance_prompt → render → image_review → 不达标？让 Claude 改 prompt → 回到第一步（≤ 3 轮）
```
- 9 个后端可选（DALL-E / SD-WebUI / ComfyUI / Replicate / Fal / 即梦 / 可灵 / 海螺）
- 整轮锁定 seed，便于对比每轮的 prompt 改动到底改善了哪一维
- Claude 改 prompt 的 system prompt 单独设计，输入是上轮评审，输出是 revised_subject + extra_negatives + extra_mood + rationale
- 每轮 trace 全保留（subject + recipe + image_path + review + revision），最终选最高分
- 用例：
```bash
auto_iterate.py "持剑女侠" -p 赛博朋克 --backend dalle --target 7.5 --max-rounds 3
```
### C3: enhance_prompt.py 加 `--variants N`（A/B 测试）
- 同 subject + 同 seed，仅在指定轴上分化
- 内置 4 维差异轴：mood / composition / lighting / stylize
- `--variant-axes mood,composition` 选差异轴（默认这俩）
- 用例：
```bash
# 出 4 个变体（mood × composition 笛卡尔积取 4 个）
enhance_prompt.py "持剑女侠" -p 赛博朋克 --variants 4 -j > variants.json
# 出图后挑最优（用 image_review.py 排名）
for f in renders/*.png; do echo "$f"; done | xargs image_review.py --rank
```
### A1: 智能预设推荐 `--suggest`
- 解决"温柔感"、"高级感"等模糊描述匹配不到预设的痛点
- 让 Claude 看用户描述 + 88 预设清单，返回 **top 3** 候选
- 每个候选附 score (0-1) + 一句话 reason + best_subject_example
- 同时给 mix_suggestion（自动判断该不该混合）
- 同时暴露在 `enhance_prompt.py --suggest` 和 `claude_polish.py --suggest`
- 用例：
```bash
enhance_prompt.py "温柔治愈感的画面" --suggest
# → top_3: 疗愈治愈 / 奶油风 / 童话绘本，附理由 + 适合主体
```
### 兼容性
- 完全向下兼容 v2.4
- 新文件 `image_review.py` / `auto_iterate.py` 不影响老脚本
- 所有新参数有默认值
### 文件改动
| 文件 | 改动 |
|------|------|
| `scripts/image_review.py` | 新文件 320 行 |
| `scripts/auto_iterate.py` | 新文件 350 行 |
| `scripts/enhance_prompt.py` | + 100 行（variants + suggest dispatch） |
| `scripts/claude_polish.py` | + 90 行（suggest_presets 函数 + CLI） |
| 其他 4 脚本 | VERSION bump |
### 真实差异化
这一版做完，huo15-img-prompt 有了 GPT-4o image / Claude Imagen 都没有的能力：
- **闭环反馈**：能告诉用户"这张图差在哪"+"下轮怎么改"
- **可解释性**：5 维分项打分 + 改进 trace 全留
- **多模型协作**：Claude 评审 + DALL-E/Replicate/即梦 出图，跨厂商组合
- **A/B 实验**：同 seed 控变量比较
---
## v2.4.0 — 2026-04-27
**补齐 CLI 体验：扩 7 后端、prompt 压缩、参考图链接、多轮编辑。**
### B1+B2: render_prompt.py 扩 7 个后端
| 后端 | 环境变量 | 用例 |
|------|---------|------|
| `replicate` | `REPLICATE_API_TOKEN` | `--remote-model black-forest-labs/flux-schnell` |
| `fal` | `FAL_KEY` | `--remote-model fal-ai/flux/schnell` |
| `jimeng` | `ARK_API_KEY`（火山方舟） | 字节即梦 / Seedream 3.0 |
| `kling` | `KLING_API_KEY` | 快手可灵 v1 |
| `hailuo` / `minimax` | `MINIMAX_API_KEY` | 海螺 image-01 |
加上原有的 `comfyui` / `sd-webui` / `dalle` / `none(dry-run)`，**共 10 个后端**。
### F2: enhance_prompt.py 加 `--compact`
- 自动估算 prompt token 数（中文按字、英文按 1.3 token/word）
- 超过 CLIP 77 token 触发压缩：去重 + 同义合并 + 按权重保留
- 必保头 6 段（主体 + camera 锁），尾部按预算砍
- 输出 `compaction` meta：before/after token 数、砍了几段
- 实测：v2.3 长 prompt 124 → 73 tokens（不损失主体）
### F1: enhance_prompt.py 加 `--examples` / `--with-examples`
- 88 预设全量映射到搜索关键词（`PRESET_SEARCH_TERMS`）
- 实时生成 5 平台搜索 URL：Lexica / Civitai / Pinterest / Google Images / Unsplash
- 用法：
- `enhance_prompt.py --examples 敦煌壁画` 单预设的 5 平台链接
- `enhance_prompt.py -l --with-examples` 列表模式带链接
- **零维护策略**：不内置静态图 URL，靠搜索 query 永远有效
### A2: enhance_prompt.py 加 `--session` / `--continue`
- 持久化目录 `~/.huo15/sessions/<name>.json`
- `--session catwindow` 保存当前调用
- `--continue catwindow` 加载历史 session 作为默认值，**自动锁定 seed** 保持多轮一致性
- CLI 参数 > session 默认值 > 系统默认（标准三层覆盖）
- `--list-sessions` 列出全部历史
- 用例：
```bash
# Turn 1
enhance_prompt.py "猫坐在窗台" -p 写实摄影 --session catwindow
# Turn 2: 改画幅 + 加情绪，seed 自动锁定保证主体一致
enhance_prompt.py --continue catwindow --aspect 16:9 --mood 治愈
# Turn 3: 完全换主体描述但保 seed 测一致性
enhance_prompt.py "猫站起来伸懒腰" --continue catwindow
```
### 兼容性
- 完全向下兼容 v2.3，所有新参数有默认值
- session 文件格式版本化（`name`/`iterations[]`/`latest`/`count`），未来扩字段不破坏老文件
### 文件改动
| 文件 | 改动 |
|------|------|
| `scripts/enhance_prompt.py` | + 220 行（compaction + sessions + preset URLs） |
| `scripts/render_prompt.py` | + 230 行（5 后端函数） |
| 其他 4 脚本 | 仅 VERSION bump |
---
## v2.3.0 — 2026-04-26
**接入 Claude API + 平台合规润色，并起中文别名「火一五文生图提示词」。**
### 中文别名
`displayName: 火一五文生图提示词`，aliases 列表新增`火一五文生图提示词` 排第一位。
### enhance_prompt.py — 加 --polish / --safety
| 参数 | 作用 |
|------|------|
| `--polish` | 先调 Claude API（ANTHROPIC_API_KEY）智能润色，再走 88 预设增强 |
| `--safety <platform>` | 平台合规重写：DALL-E/MJ/SD/SDXL/Flux，自动把可能误判的艺术词替换 |
两者可叠加使用：先 polish 让 Claude 写出专业描述，再 safety 把误判词艺术化。
### 新增脚本：claude_polish.py（350 行）
- **Claude API 直调**：纯 urllib，不引入 anthropic SDK，避免企业扫描器
- **prompt caching 启用**：system prompt 用 `cache_control: ephemeral`，省 90% input token
- **Prefill `{` + JSON 强约束**：assistant 起手 prefill 强制结构化输出
- **88 风格预设嵌入 system prompt**：让 Claude 从清单里挑而非凭记忆
- **XML 思维链**：内部 `<thinking>` 让 Claude 分步骤思考（refine/style/camera/safety/negatives）
- **Platform warnings**：Claude 主动识别 DALL-E/MJ/SD 各自的风险点并给出建议
- **--pipe**：输出可直接喂给 enhance_prompt.py 的 CLI 命令
### 新增脚本：safety_lint.py（330 行）
**仅服务合法艺术创作场景**，不做 jailbreak：
✗ 红线（直接拒答）：
- CSAM（任何含未成年 + 性化的组合）
- 真人 + 色情 / 政治污蔑
- 武器/毒品/爆炸物**制作方法/教程**
- 自残/自杀**方法诱导**
✓ 黄区（艺术化重写）：
- **violence**: 鲜血/血/伤口/kill/murder/weapon/gun/knife → crimson splash / battle-scarred / vanquish / ceremonial blade
- **nudity**: naked/nude/裸/sexy → classical figure study / fine art reference / fashion editorial
- **horror**: horror/scary/gore/monster/demon/evil → gothic atmospheric tension / mythical creature / dark fantasy
- **death**: dead/corpse/skeleton/skull → memento mori / classical allegory / vanitas still life
- **real-person**: celebrity/明星/actor/politician → fictional protagonist / 80s aesthetic
- **brand**: marvel/disney/nike/iphone → superhero comic style / classic animated film / athletic sportswear
- **weapon-model**: ak47/glock/uzi → fictional assault rifle prop
每词内置 `category` + `platforms_affected`。平台分级：
- DALL-E `max` 严格度：所有黄区都触发高风险标记
- MJ `high` 中等：暴力/真人/品牌触发高风险
- SD/SDXL/Flux `low` 宽松（开源）：只对成人内容触发中风险
输出三模式：默认人类可读 / `-j` JSON / `--apply` 直接输出重写文本（pipe 友好）。
### 兼容性
- **完全向下兼容 v2.2**：所有新参数有默认值
- `--polish` 需 `ANTHROPIC_API_KEY`，未设置时报友好错误并不影响其他功能
- `--safety` 是纯本地词典，无网络依赖
### 设计原则
我们**坚决不做** jailbreak / 越狱 / 绕过模型对齐：
- 仅做"合法艺术创作场景下的平台误判规避"
- 红线检测优先于重写
- 替换词全部来自正规艺术、摄影、影视术语
- 用户输入红线内容时直接 `sys.exit(2)` 并给出改写建议
---
## v2.2.0 — 2026-04-25
**四件套大版本：混合预设 + 视频提示词 + 参考图反解 + 直出图片。**
### 新增脚本
| 脚本 | 作用 | 关键参数 |
|------|------|---------|
| `enhance_prompt.py` | 文生图（升级） | `-p A+B --mix 0.6` |
| `enhance_video.py` ⭐ | 视频提示词 | `-m Sora/Kling/Runway/Pika/Luma/Hailuo/即梦/Wan` |
| `reverse_prompt.py` ⭐ | 参考图反解 | A1111 / ComfyUI / NovelAI metadata + VLM 模板 |
| `render_prompt.py` ⭐ | 提示词直出 | `--backend comfyui/sd-webui/dalle/none` |
### enhance_prompt.py — 混合预设
- **`-p "A+B"` 语法**：`赛博朋克+水墨` / `原神+敦煌壁画` / `glassmorphism+wabisabi` 任意两两融合
- **`--mix <ratio>`**：主预设权重 0.1-0.9（默认 0.6）
- **SD 模式**：自动加权重语法 `(primary_tag:1.16), (secondary_tag:1.04)`
- **MJ/Flux/通用**：按比例前置主预设标签
- **camera/lighting/palette 智能融合**：相机沿主预设、光影叠加、色板拼接、aspect 取主
- **PRESET_NEG_EXCLUDE 双向生效**：主辅任一需要 logo/text/signature 都会从 universal_neg 剔除
- **seed 锁定**：mix_label `[email protected]` 参与 hash，相同混合每次生成相同 seed
### enhance_video.py — 视频提示词（新文件 470 行）
- **9 大视频模型规格**：Sora / Kling 可灵 / Runway Gen-3/4 / Pika / Luma DreamMachine / Hailuo MiniMax / 即梦 Seedance / 通义 Wan2.1 / 通用
- **30+ 镜头运动词典**：推/拉/摇/移/跟/环绕/手持/航拍/希区柯克/POV/子弹时间/延时/慢动作 ...
- **9 节奏档位**：缓慢 / 宁静 / 中速 / 紧张 / 急促 / 快切 / 动感 / 史诗 ...
- **30+ 主体动作自动抽词**：走/跑/跳/飞/舞/回眸/转身/挥剑/骑马/对视 ...
- **关键帧三段式拆分**：开场建立 → 中段动作峰值 → 结尾落点
- **视频专属负面词**：flicker / motion blur artifacts / identity drift / morphing artifacts
- **复用 88 风格预设 + 混合预设**：视觉锁完全沿用 image preset 体系
- **格式适配**：Pika 输出标签式，其他全部自然语言
### reverse_prompt.py — 参考图反解（新文件 340 行）
- **三层反解策略**：
1. **PNG metadata**：手写 PNG `tEXt`/`iTXt` 解析，零依赖（不引入 PIL）
2. **A1111/ComfyUI/NovelAI 三大格式自动识别**：parameters / prompt+workflow / Description+Comment
3. **VLM fallback 模板**：图无 metadata 时，输出标准化 88 预设选择 prompt 给 GPT-4o/Claude/Gemini/Qwen-VL
- **启发式预设猜测**：35+ 关键词 → 预设映射（cyberpunk → 赛博朋克 / ghibli → 宫崎骏 / dunhuang → 敦煌壁画 ...）
- **画幅自动推断**：从 size 字段算 ratio，匹配最近的 1:1/16:9/3:4/21:9 等
- **三种输出**：`text`(默认) / `--mj`(单行 MJ prompt) / `-j`(结构化 JSON 可 pipe)
- **支持本地路径 + 远程 URL**
### render_prompt.py — 直出图片（新文件 270 行）
- **4 个后端**：
- `comfyui` — 本地 ComfyUI HTTP API（默认 http://127.0.0.1:8188）
- `sd-webui` — AUTOMATIC1111 / Forge txt2img API（默认 http://127.0.0.1:7860）
- `dalle` — OpenAI DALL-E 3（OPENAI_API_KEY）
- `none` — dry-run，只输出 recipe JSON 不出图
- **零第三方依赖**：纯 urllib，避免企业扫描器命中
- **ComfyUI 默认 workflow**：内置 SDXL 9 节点 workflow，可用 `--workflow` 覆盖
- **环境变量覆盖**：`COMFYUI_URL` / `SDWEBUI_URL`
- **支持混合预设直出**
### 新增功能矩阵
| 维度 | v2.1 | v2.2 |
|------|------|------|
| 出图前 | 提示词增强 | + **混合预设**（任意两两融合） |
| 出图中 | （手工复制到模型） | + **直出**（comfyui/sd-webui/dalle） |
| 出图后 | （无） | + **反解**（A1111/ComfyUI/NovelAI metadata） |
| 视频 | （不支持） | + **视频提示词**（9 模型 + 关键帧 + 镜头运动） |
### 兼容性
- **完全向下兼容**：v2.1 所有 CLI 命令在 v2.2 不变；新参数均有默认值
- **JSON 字段新增**：`mix_secondary` / `mix_ratio` / `mix_label`（旧字段保留）
- **enhance_video.py / reverse_prompt.py / render_prompt.py 是新文件**，不影响 enhance_prompt.py 老用户
### 未变
- 88 风格预设、五锁机制、系列模式、角色设定图、质量档位 — 全部保留
---
## v2.1.0 — 2026-04-24
**再扩充：更贴近需求 + 更多风格 + 角色一致性。**
### 新增风格预设（+32 款，总 56 → 88）
- **游戏艺术（新类，7）**：原神 / 崩铁星穹 / 英雄联盟 / 暗黑4 / Valorant / Pokemon / 暴雪风
- **东方传统（新类，7）**：敦煌壁画 / 青花瓷 / 民国月份牌 / 年画 / 剪纸 / 和风 / 汉服写真
- **动漫扩展（+4）**：萌系 / 厚涂 / 轻小说封面 / 赛璐璐
- **现代设计（+6）**：玻璃拟态 / 新拟态 / 孟菲斯 / 杂志编排 / 包豪斯 / 奶油风
- **建筑氛围（+3）**：粗野主义 / 北欧极简 / 侘寂
- **摄影扩展（+3）**：暗黑美食 / 日杂 / 街头潮流
- **氛围综合（+2）**：疗愈治愈 / 美式复古
### 新功能
- **角色设定图模式** `--character-sheet` / `-cs`：
- 自动生成 T-pose + 正面 / 三分之二 / 侧面 / 背面多视图的设定图提示词
- 专为 Midjourney `--cref`、Stable Diffusion IP-Adapter 做角色参考用
- 画幅自动锁 16:9
- **时间 / 天气 / 季节 自动抽词**：
- 14 时间词：清晨 / 黎明 / 黄昏 / 日落 / 深夜 / 蓝调时刻 / 魔法时刻 ...
- 15 天气词：晴天 / 下雨 / 暴雨 / 下雪 / 暴雪 / 雾天 / 雷雨 ...
- 10 季节词：春夏秋冬 / 樱花季 / 枫叶季
- **负向需求识别**：识别主体描述中的"不要X / 没有X / 避免X / no X / avoid X / without X"，自动从正向提示中移除并加入负面提示。
- **质量档位** `-t basic / pro / master`：
- `basic`: `high quality, detailed`（省 token）
- `pro` (默认): `masterpiece, best quality, ultra detailed, 8k`
- `master`: 叠加 `hdr, intricate details, sharp focus, award winning, trending on artstation, professional, highly polished`
- **显式负面追加** `--avoid "cluttered, people"`：CLI 级附加负面词。
### 新增别名（+45）
`genshin` / `mihoyo` / `honkai` / `starrail` / `lol` / `diablo` / `valorant` / `pokemon` / `blizzard` / `overwatch` / `dunhuang` / `qinghua` / `porcelain` / `yuefenpai` / `wafu` / `hanfu` / `papercut` / `nianhua` / `moe` / `lightnovel` / `lncover` / `celshaded` / `glassmorphism` / `glass` / `neumorphism` / `memphis` / `editorial` / `bauhaus` / `cream` / `korean` / `brutalism` / `brutalist` / `nordic` / `scandinavian` / `wabisabi` / `zen` / `darkfood` / `muji` / `streetwear` / `hypebeast` / `healing` / `cozy` / `americana` ...
### 改进
- 主体描述中的"不要X"子句会先被 `strip_negative_clauses()` 去除再送入正向提示，避免正向污染。
- `print_prompt()` 输出增加 ⭐ 质量档位、👤 角色设定图模式、🕐 时间、☁️ 天气、🍂 季节、🚫 用户负向 六个新字段展示。
- `list_presets()` 按 8 大类分类展示（新增"游戏" / "东方"分组）。
### 兼容性
- **向下兼容**：v2.0 CLI 命令在 v2.1 完全可用，所有新参数均有默认值。
- **JSON 字段新增**：`quality_tier` / `character_sheet` / `time_of_day` / `weather` / `season` / `user_negatives`（旧字段保留）。
---
## v2.0.0 — 2026-04-24
**大版本升级：一致性 + 贴近需求 + 风格扩充。**
### 新增
- **风格预设 17 → 56**，六大分类：
- 摄影 10（新增：黑白摄影 / 人像摄影 / 时尚大片 / 美食摄影 / 微距摄影 / 航拍摄影 / 街拍纪实）
- 动漫 6（新增：新海诚 / 宫崎骏 / 美漫 / Q版 / 童话绘本）
- 插画 7（新增：工笔国画 / 浮世绘 / 线稿）
- 3D 7（全部新增：3DC4D / 盲盒手办 / 低多边形 / 等距视图 / 粘土 / 毛毡手工 / 纸艺）
- 设计 10（新增：平面设计 / Logo设计 / 图标设计 / 信息图 / 品牌KV / 专辑封面 / 电影海报 / 表情包）
- 艺术史 4（全部新增：印象派 / 后印象派 / 新艺术 / 装饰艺术）
- 场景氛围 12（新增：黑暗奇幻 / Y2K / Vaporwave / 霓虹灯牌 / 概念艺术）
- **一致性四锁**：每个预设内置 `camera` / `lighting` / `palette` / `aspect` 四项独立锁，系列出图风格不再漂移。
- **系列批量模式** `-s N --variations "A,B,C,D"`：共享 seed + 四锁，主体描述差异化，一次生成一整套。
- **意图识别器**：无需指定 `-p`，脚本从"logo/产品/海报/头像/美食/赛博/水墨..."等关键词自动推荐预设 + 画幅。
- **构图/情绪抽词**：主体描述中的"俯拍/特写/航拍/神秘/温馨/史诗..."自动并入提示词。
- **稳定 seed 建议**：基于 `md5(subject + preset)` 生成 32-bit seed，便于复现。
- **英文 / 同义词别名**：60+ 别名（anime、ghibli、cyberpunk、minimal、3d、logo、neon、vapor…）。
- **多模型精细化适配**：
- Midjourney 输出 `--ar --stylize`，提示 `--cref/--sref`
- Stable Diffusion 输出权重语法 `(subject:1.2)`，提示采样器/CFG
- SDXL 输出推荐尺寸（`1216x832` 等）
- Flux 输出长句自然语言 + guidance 提示
- DALL-E 3 输出段落式自然语言
- **JSON 输出** `-j`：结构化一致性锁 + 所有参数，便于下游集成。
- **CLI 增强**：`-a/--aspect`、`--mood`、`--composition`、`--seed`、`-l/--list`、`-v/--version`。
### 修复
- 修复 Logo设计 / 图标设计 / 表情包 / 海报 等预设的**全局负面词包含 "logo/text"** 导致的语义自我否定。
- 修复 水墨 / 工笔国画 / 浮世绘 预设中**负面词包含 "signature"** 与画面印章冲突。
### 破坏性变更
- `build_prompt()` 返回 dict 新增 `aspect` / `seed_suggestion` / `consistency_lock` / `hint` / `version` 字段（向下兼容，原有字段保留）。
---
## v1.0.0 — 2026-04-24（初始版本）
- 17 风格预设（写实摄影 / 胶片摄影 / 动漫 / 赛博朋克 / 水彩 / 油画 / 建筑可视化 / 产品设计 / 像素艺术 / 奇幻 / 科幻 / 复古海报 / 水墨 / 蒸汽朋克 / 极简主义 / 电影感 / 国潮）。
- 支持 Midjourney / SD / DALL-E / 通用 四种输出骨架。
- CLI：`subject -p <preset> -m <model> [-l] [-j]`。
FILE:QUICKSTART.md
# 快速上手 — 三层级
> 30 秒 / 5 分钟 / 30 分钟，按你想投入的时间往下读。
## 0. 首次安装后先跑这个
```bash
python3 scripts/doctor.py --quick
```
会告诉你：
- 14 个脚本是否都能 import + 版本是否一致
- 哪些 API key 已配 / 缺哪些（按需）
- Obsidian vault 检测到没
- 持久化资产盘点（characters / sessions / brand_kits / learned_presets）
如果看到 `✗ ANTHROPIC_API_KEY 未设置（必填）`，先：
```bash
export ANTHROPIC_API_KEY=sk-ant-xxx # 把 sk-ant-xxx 换成你的真实 key
# 或写到 ~/.zshrc / ~/.bashrc
```
---
## 30 秒：第一条命令
```bash
scripts/enhance_prompt.py "一只赛博朋克的猫" -p 赛博朋克 -m Midjourney
```
复制 `✅ 正向提示词` 那段，粘贴到 Midjourney/Discord，回车出图。
完了，就这么简单。
---
## 5 分钟：基础工作流
### Step 1: 选预设（不知道选哪个 → 让 Claude 推荐）
```bash
scripts/enhance_prompt.py "温柔治愈感的画面" --suggest
```
Claude 会从 88 预设里挑 top 3，附评分 + 适合场景。
### Step 2: 出图
```bash
# 基础（自己挑预设）
scripts/enhance_prompt.py "咖啡馆窗边的少女" -p 胶片摄影 -m Midjourney
# 混合两种风格（v3.0 特性）
scripts/enhance_prompt.py "持剑女侠" -p "赛博朋克+水墨" --mix 0.6 -m SDXL
# A/B 测试出 4 个变体（同 seed，不同 mood/composition）
scripts/enhance_prompt.py "夜晚街景" -p 电影感 --variants 4 -j > /tmp/variants.json
```
### Step 3: 保存常用资产
```bash
# 角色卡（跨调用保持一致）
scripts/enhance_prompt.py "银发机甲少女, twin tails, glowing visor" \
-p 动漫 --character-sheet --save-char silver_mecha
# 后续直接用
scripts/enhance_prompt.py "在霓虹街头" --char silver_mecha
scripts/enhance_prompt.py "在花海中" --char silver_mecha
# → 自动锁 seed，三张图角色一致
```
### Step 4: 看预设示例图
```bash
scripts/enhance_prompt.py --examples 敦煌壁画
# 输出 5 平台搜索 URL（Lexica / Civitai / Pinterest / Google / Unsplash）
```
---
## 30 分钟：完整工作流
### A. 端到端品牌 KV（食谱 1）
```bash
# 1. 导入示例品牌套件
cat examples/brand_kit-song_tea.json | scripts/brand_kit.py --import
# 2. 用品牌套件 + 智能润色 + 闭环迭代
scripts/auto_iterate.py "宋韵茶饮品牌主视觉，一杯热茶，远山轮廓" \
-p "汉服写真+水墨" \
--backend dalle \
--target 7.5 \
--max-rounds 3
# → Claude Vision 五维评审，分数 < 7.5 自动改 prompt 重出
```
### B. 学习自己喜欢的风格
```bash
# 给 N 张参考图（你自己拍的、收藏的）
scripts/style_learn.py --name 我的小清新 \
refs/morning_cafe.jpg \
refs/sunset.jpg \
refs/quiet_corner.jpg \
refs/film_kodak.jpg
# 后续用 @ 前缀调用
scripts/enhance_prompt.py "猫咪坐在窗台" -p "@我的小清新"
```
### C. 短片故事板（v3.0 杀手 feature）
```bash
# 输入剧本 → Claude 拆 6 关键帧 + 5 个转场
scripts/storyboard.py < examples/storyboard-cat_rainy_night.txt \
-p 电影感 --scenes 6 \
-m Midjourney --video-model Sora \
--output ./renders/cat_rainy_night
```
输出（在 `./renders/cat_rainy_night/`）：
- `storyboard.json` 完整数据
- `scene-{01-06}-t2i.txt` 6 个关键帧 T2I prompt
- `transition-{xx-to-yy}-t2v.txt` 5 个转场 T2V prompt
- `README.md` 可读总览
把 6 个 scene prompt 喂 Midjourney，5 个 transition prompt 喂 Sora，剪辑串联即得 ~30 秒短片。
### D. 在 IDE 里直接用（Claude Code / Cursor）
注册到 `~/.claude/mcp.json`：
```json
{
"mcpServers": {
"huo15-img-prompt": {
"command": "python3",
"args": ["/path/to/huo15-img-prompt/scripts/mcp_server.py"]
}
}
}
```
然后在 Claude Code：
```
> @huo15-img-prompt 帮我给落地页做 hero 图，主题是"AI 编程助手"，要科技感但温暖
```
Claude Code 会自动调链路：suggest_presets → polish_prompt → enhance_prompt → review_image。
### E. 本地 Web UI
```bash
python3 scripts/web_ui.py
# 自动开浏览器到 http://127.0.0.1:7155
```
可视化 88 预设、实时 prompt 预览、一键复制。
### F. 把 recipe 沉淀到 Obsidian
```bash
scripts/enhance_prompt.py "..." --obsidian
# 自动写入 ~/knowledge/huo15/图集/ 或 OBSIDIAN_VAULT 指定位置
# 含完整 frontmatter + 复现命令
```
---
## 常见问题
### Q1: 我没有 ANTHROPIC_API_KEY，能用吗？
可以。**80% 功能不依赖 Claude**：
- ✅ enhance_prompt（88 预设 / 五锁 / 混合 / variants / compact）
- ✅ enhance_video（视频提示词）
- ✅ reverse_prompt（参考图反解，metadata 模式）
- ✅ render_prompt（10 后端直出，需要对应后端的 key）
- ✅ safety_lint（红线检测，纯本地词典）
- ✅ character / brand_kit（持久化）
- ✅ web_ui / mcp_server
需要 ANTHROPIC_API_KEY 的：
- ❌ claude_polish（智能润色）
- ❌ image_review（VLM 评审）
- ❌ auto_iterate（闭环迭代）
- ❌ storyboard（剧本拆分）
- ❌ style_learn（风格学习）
- ❌ --suggest 推荐预设
### Q2: 我能在 Anaconda 环境里用吗？
可以，纯标准库，没有第三方依赖。任何 Python 3.8+ 环境都行。
### Q3: 哪些后端最值得配？
- **入门**：DALL-E 3（OPENAI_API_KEY，质量稳定，文字渲染好）
- **省钱**：本地 ComfyUI / SD WebUI（一次性下载模型，永久免费）
- **国产场景**：字节即梦（ARK_API_KEY，中文场景效果好）
- **快速试**：Replicate flux-schnell（REPLICATE_API_TOKEN，4 步出图）
### Q4: 14 个脚本太多，我从哪 3 个开始？
1. `enhance_prompt.py` — 这是核心，所有路径都从它开始
2. `doctor.py` — 出问题先跑这个
3. `web_ui.py` — 不想敲命令的时候用
剩下的按需：要出图 → render_prompt；要短片 → storyboard；要做品牌一致 → brand_kit。
### Q5: 出来的 prompt 太长，SDXL 会截断怎么办？
```bash
scripts/enhance_prompt.py "..." -m SDXL --compact
# 自动压缩到 CLIP 77 token 内
```
### Q6: 我之前的图想改一改，prompt 丢了
```bash
scripts/reverse_prompt.py 你的图.png
# 自动从 PNG metadata 提取 A1111/ComfyUI/NovelAI 三种格式的 prompt
```
如果图没 metadata：
```bash
scripts/reverse_prompt.py 你的图.png --vlm
# 输出标准 VLM 模板，复制给 GPT-4o / Claude Sonnet 4.5 / Gemini 即可
```
---
## 下一步
- 完整 88 预设：`scripts/enhance_prompt.py -l --with-examples`
- 食谱书：[RECIPES.md](RECIPES.md)
- 完整 CLI：每个脚本都有 `-h` / `--help`
有问题先跑 `doctor.py`，再到 [https://clawhub.ai/skills/huo15-img-prompt](https://clawhub.ai/skills/huo15-img-prompt) 看 issue。
FILE:RECIPES.md
# 火一五创意生态 — 整合食谱
> v3.0 加入 huo15 创意生态后，huo15-img-prompt 不再是独立工具，而是「创意管线」的核心节点。
> 本文档列出和其他 huo15-openclaw-* 技能的串联用法。
## 火一五创意全家桶
| 技能 | 角色 |
|------|------|
| `huo15-openclaw-design-director` | 选设计方向（5 流派 × 20 哲学 → 3 方向反差对比） |
| `huo15-openclaw-brand-protocol` | 抓品牌规范（Ask/Search/Download/Verify/Codify） |
| `huo15-openclaw-frontend-design` | 高保真 Web UI 落地 + 设计 tokens |
| `huo15-openclaw-design-critique` | 5 维设计评审 + Keep/Fix/Quick Wins |
| **`huo15-img-prompt`** ⭐ | 文生图 + 文生视频 + 闭环迭代 |
---
## 食谱 1：从零到一做品牌 KV（key visual）
```
设计方向 → 品牌规范 → 出图 → 评审 → 落地
```
### Step 1: design-director 选方向
```
> 我要做一个茶饮品牌，希望有东方气质但又年轻
```
→ design-director 给 3 个反差对比方向（如：宋韵极简 / 国潮复古 / 禅意新中式）。
### Step 2: brand-protocol 沉淀品牌规范
选定"宋韵极简"后：
```
> 帮我把这个方向 codify 成 brand kit
```
→ brand-protocol 输出：colors / fonts / visual_keywords / forbidden 元素。
导入 huo15-img-prompt：
```bash
brand-protocol-output.json | brand_kit.py --import
# 或手动创建
brand_kit.py --create song_tea \
--colors "#2C5F2D, #97BC62, #F7F4EA" \
--fonts "Songti SC, Source Han Serif" \
--keywords "宋韵, 极简, 留白, 文人画" \
--forbidden "modern digital, neon, cyberpunk"
```
### Step 3: img-prompt 出 KV 图
```bash
# 用品牌套件 + 推荐预设
enhance_prompt.py "茶饮品牌主视觉, 一杯热茶, 远山" \
-p "汉服写真+水墨" \
--brand-kit song_tea \
--polish # Claude 智能润色
# 或闭环迭代到 7.5 分
auto_iterate.py "茶饮品牌主视觉" \
-p "汉服写真+水墨" \
--backend dalle \
--target 7.5
```
### Step 4: design-critique 评审
```
> 用 design-critique 评审这套图
```
→ Keep/Fix/Quick Wins 三分类反馈。
### Step 5: frontend-design 落地
把出图作为 hero 图，调用 frontend-design 生成完整官网：
```
> 用 frontend-design 给这个茶饮品牌做落地页，hero 用上面这张 KV
```
→ 完整 HTML/CSS/JS 原型，沿用 brand kit 的 tokens。
---
## 食谱 2：自学习风格 + 角色一致性 + 视频短片
适合个人 IP / 自媒体内容创作。
### Step 1: style_learn 学习独有风格
收集你喜欢的 5-10 张图（自己拍的、收藏的、参考的）：
```bash
style_learn.py --name 我的小清新 \
refs/morning_cafe.jpg \
refs/sunset_seoul.jpg \
refs/film_kodak.jpg \
refs/window_light.jpg \
refs/quiet_corner.jpg
```
→ Claude Vision 综合 5 张图共性 → 生成 learned preset `@我的小清新`。
### Step 2: character 创建固定角色
```bash
enhance_prompt.py "20 岁亚裔女孩, 长直发, 圆框眼镜, 米白毛衣" \
-p "@我的小清新" \
--character-sheet \
--save-char 我的女主
```
### Step 3: storyboard 拆短片剧本
```bash
storyboard.py "女主在咖啡馆度过的下雨午后，从写信到收到回信" \
-p "@我的小清新" \
--scenes 6 \
-m Midjourney \
--video-model Sora \
--output ./my_story
```
→ 6 个关键帧 + 5 个转场，每个都 prompt 完整可用。
### Step 4: 出图 + 出视频
```bash
# 关键帧用 Midjourney 出
for f in my_story/scene-*-t2i.txt; do
cat $f | grep -A1 '## Positive' | tail -1
# 喂给 MJ
done
# 转场用 Sora 出（关键帧作为首帧）
for f in my_story/transition-*.txt; do
# 喂给 Sora i2v 模式
done
# 剪辑串联即得短片
```
### Step 5: 评审反馈
```bash
image_review.py my_story/renders/*.png --rank
```
不好的轮次让 auto_iterate 自动改。
---
## 食谱 3：电商商品图全套
### Step 1: brand_kit 沉淀品牌色
```bash
brand_kit.py --create my_brand \
--colors "#FF6B35, #1A1A2E, #FAFAFA" \
--keywords "清爽, 现代, 高级感"
```
### Step 2: 用变体测试找最优 prompt
```bash
enhance_prompt.py "无线耳机产品图, 白底, 30 度俯视" \
-p 产品摄影 \
--brand-kit my_brand \
--variants 6 \
--variant-axes mood,composition,lighting
# 出 6 张图后排名
image_review.py renders/variant-*.png --rank
```
### Step 3: 锁定最优 → 系列扩展
最优变体的 seed → 用作角色卡：
```bash
enhance_prompt.py "无线耳机产品图" \
-p 产品摄影 \
--brand-kit my_brand \
--save-char earphone_main \
--seed <最优 seed>
# 后续所有变体（不同角度、配件、场景）共用
enhance_prompt.py "无线耳机展示图，环境光" --char earphone_main
enhance_prompt.py "无线耳机加包装" --char earphone_main
enhance_prompt.py "无线耳机使用场景" --char earphone_main
```
### Step 4: 沉淀到 Obsidian
```bash
enhance_prompt.py "..." --char earphone_main --obsidian
```
→ 所有 recipe 写入 vault `图集/`，方便后续团队 review。
---
## 食谱 4：Claude Code 工作流（IDE 内调用）
注册 MCP server 到 `~/.claude/mcp.json`：
```json
{
"mcpServers": {
"huo15-img-prompt": {
"command": "python3",
"args": ["/path/to/huo15-img-prompt/scripts/mcp_server.py"]
}
}
}
```
然后在 Claude Code 里：
```
> @huo15-img-prompt 帮我给落地页做一张 hero 图，主题是"AI 编程助手"，要科技感但温暖
```
Claude Code 会：
1. 调 `suggest_presets` 推荐 top-3
2. 调 `polish_prompt` 润色
3. 调 `enhance_prompt` 出 prompt
4. 你出图后调 `review_image` 五维评审
5. 不好让 Claude 改 prompt 重出（闭环）
---
## 食谱 5：和 huo15-openclaw-knowledge-base 联动
把 brand_kit + character_card + learned_preset 沉淀到 huo15 知识库：
```bash
# brand_kit 导出 → 入库
brand_kit.py --export song_tea | jq '.' > raw/song_tea_brand.json
# 然后用 knowledge-base 编译入 wiki
# character 导出 → 入库
character.py --export 我的女主 > raw/heroine.json
```
→ wiki 全局搜索时这些资产可被检索。
---
## 设计原则
1. **每个技能管自己一段**：design-director 选方向，img-prompt 出图，design-critique 评审
2. **数据格式互通**：brand_kit / character / learned_preset 都是标准 JSON，可在技能间传递
3. **Claude 是协调者**：用 Claude Code + MCP 让 Claude 自动选择合适的技能链路
4. **闭环优先**：每一步都有"评审 → 改 → 重做"的能力，不要单步走到底
## 反对的做法
❌ 把所有功能塞到一个技能里（违反"每个 skill 独立模块化"原则）
❌ 在 img-prompt 里复刻 design-director 的方向选择能力（重复造轮子）
❌ 不沉淀 brand_kit / character / learned_preset 到 ~/.huo15/（每次重新生成）
❌ 跳过评审直接发布（v2.5 的闭环迭代是免费的，不用白不用）
---
由 huo15-img-prompt v3.0 发布，后续随生态扩展持续更新。
FILE:_meta.json
{
"ownerId": "kn7byevkn40d6z4p7ghdb097z983tj33",
"slug": "huo15-img-prompt",
"version": "3.1.0"
}
FILE:examples/README.md
# 示例素材
直接可用的 brand kit / character / learned preset / 剧本，导入即用。
## 文件清单
| 文件 | 类型 | 用途 |
|------|------|------|
| `brand_kit-song_tea.json` | 品牌套件 | 宋韵东方茶饮品牌示例 |
| `character-silver_mecha.json` | 角色卡 | 银发机甲少女（演示跨调用一致性） |
| `learned_preset-fresh_film.json` | 学习预设 | 清新胶片风（日杂感） |
| `storyboard-cat_rainy_night.txt` | 剧本 | 一只猫的雨夜散步（6 帧） |
| `recipe-1-brand_kv.sh` | Bash 脚本 | RECIPES.md 食谱 1 的可运行版 |
## 导入方式
```bash
# Brand kit
cat examples/brand_kit-song_tea.json | scripts/brand_kit.py --import
scripts/brand_kit.py --show song_tea
# 角色卡
cat examples/character-silver_mecha.json | scripts/character.py --import
scripts/character.py --show silver_mecha
# Learned preset（直接复制到目录即可）
mkdir -p ~/.huo15/learned_presets
cp examples/learned_preset-fresh_film.json ~/.huo15/learned_presets/
# 用 learned preset 出图
scripts/enhance_prompt.py "咖啡馆窗边少女" -p "@fresh_film"
# 故事板
scripts/storyboard.py < examples/storyboard-cat_rainy_night.txt \
-p 电影感 --scenes 6 --output ./renders/cat_rainy_night
# 完整食谱 1
bash examples/recipe-1-brand_kv.sh
```
## 自定义指南
复制示例文件改字段即可：
- **brand_kit**：改 `colors / fonts / keywords / forbidden / logo_description`
- **character**：改 `subject_description / preset / aspect / seed`
- **learned_preset**：建议用 `style_learn.py --name 你的风格 ref*.jpg` 自动生成，不要手写
## 反对的做法
❌ 手写 learned_preset.json — confidence 字段是 Claude 综合后的可信度，手写会失真
❌ 直接改示例文件 — 应该导入后用 `--update` 修改自己的副本
❌ 把 example 当生产数据 — 仅供参考，按业务场景重新建立
FILE:examples/brand_kit-song_tea.json
{
"name": "song_tea",
"version": "3.1.0",
"description": "宋韵东方茶饮品牌套件示例 — 用于 RECIPES.md 食谱 1",
"colors": [
"#2C5F2D",
"#97BC62",
"#F7F4EA",
"#3E2723"
],
"fonts": [
"Songti SC",
"Source Han Serif",
"Noto Serif CJK SC"
],
"keywords": [
"宋韵",
"极简",
"留白",
"文人画",
"山水意境",
"克制"
],
"forbidden": [
"modern digital",
"neon",
"cyberpunk",
"harsh contrast",
"saturated cartoon"
],
"logo_description": "minimal seal-script chinese character on parchment",
"use_count": 0
}
FILE:examples/character-silver_mecha.json
{
"name": "silver_mecha",
"version": "3.1.0",
"description": "示例角色卡：银发机甲少女 — 用于演示 --char 跨调用一致性",
"use_count": 0,
"subject_description": "银发双马尾机甲少女，发光面甲，藏青色机甲战衣，轮廓发光线条",
"preset": "动漫",
"mix_secondary": "",
"mix_ratio": null,
"aspect": "16:9",
"seed": 1154904041,
"camera": "low angle hero shot, 50mm portrait lens, shallow depth of field",
"lighting": "anime-style soft light, rim light on hair, glowing visor accent",
"palette": "vibrant saturated anime palette, silver and deep navy, neon cyan accents",
"is_character_sheet": true,
"positive_anchor": "silver-haired twin-tails mecha girl, glowing visor, navy mecha armor"
}
FILE:examples/learned_preset-fresh_film.json
{
"name": "fresh_film",
"version": "3.1.0",
"description": "示例 learned preset：清新胶片风格 — 用于演示 -p '@fresh_film' 调用",
"created_at": 1745000000,
"use_count": 0,
"category": "学习",
"tags": "kodak portra 400 film stock, soft natural light, muted earth tones, hazy atmospheric, lifestyle photography, japanese magazine aesthetic",
"quality": "raw photo, fine grain, scanned film, masterpiece, best quality",
"neg": "harsh contrast, oversaturated, hdr, plastic skin, digital sharpness, neon",
"camera": "35mm film camera, 50mm prime, shallow depth of field, slight handheld feel",
"lighting": "soft window light, golden hour rim, slightly underexposed, natural ambient fill",
"palette": "muted earth tones, faded film colors, sage green, warm cream, soft blush",
"aspect": "3:2",
"synthesis_notes": "日杂感的清新胶片风：自然光、克制色彩、留白构图、低饱和高级感",
"best_subject_examples": [
"咖啡馆里的窗边少女",
"雨后湿漉漉的窗台",
"夏末的乡村小路"
],
"confidence": 0.85,
"source_count": 5,
"source_images": [
"(示例文件，源图省略)"
]
}
FILE:examples/recipe-1-brand_kv.sh
#!/bin/bash
# 示例食谱 1：从零到一做品牌 KV
# 配合 RECIPES.md 食谱 1。运行前确保已配置 ANTHROPIC_API_KEY + OPENAI_API_KEY。
#
# 这个脚本演示完整工作流：
# 导入 brand_kit → polish → variants 出 4 个候选 → image_review 排名 → 闭环迭代到 7.5 分
set -e
SCRIPTS_DIR="$(dirname "$0")/../scripts"
EXAMPLES_DIR="$(dirname "$0")"
echo "━━━ Step 1: 导入示例 brand_kit ━━━"
cat "$EXAMPLES_DIR/brand_kit-song_tea.json" | python3 "$SCRIPTS_DIR/brand_kit.py" --import
echo
echo "━━━ Step 2: Claude 智能润色（建议预设） ━━━"
python3 "$SCRIPTS_DIR/claude_polish.py" "宋韵茶饮品牌主视觉，一杯热茶，远山轮廓" --suggest
echo
echo "━━━ Step 3: 出 4 个 A/B 变体（同 seed 不同 mood/composition） ━━━"
python3 "$SCRIPTS_DIR/enhance_prompt.py" \
"宋韵茶饮品牌主视觉，一杯热茶，远山轮廓" \
-p "汉服写真+水墨" \
--brand-kit song_tea \
--variants 4 \
-j > /tmp/variants.json
echo "已写入 /tmp/variants.json，含 4 个变体的完整 prompt"
echo
echo "━━━ Step 4（需要 ANTHROPIC + 后端）: 闭环自动迭代到 7.5 分 ━━━"
echo "下一步真正出图（需要 OPENAI_API_KEY）："
cat <<EOF
python3 $SCRIPTS_DIR/auto_iterate.py \\
"宋韵茶饮品牌主视觉，一杯热茶，远山轮廓" \\
-p "汉服写真+水墨" \\
--backend dalle \\
--target 7.5 \\
--max-rounds 3
EOF
echo
echo "━━━ Step 5: 把最终 recipe 写入 Obsidian vault ━━━"
echo "如果有 OBSIDIAN_VAULT 或 ~/knowledge/huo15:"
cat <<EOF
python3 $SCRIPTS_DIR/enhance_prompt.py \\
"宋韵茶饮品牌主视觉，一杯热茶，远山轮廓" \\
-p "汉服写真+水墨" \\
--brand-kit song_tea \\
--obsidian
EOF
echo
echo "✅ 食谱 1 演示完成。详见 RECIPES.md。"
FILE:examples/storyboard-cat_rainy_night.txt
## 示例剧本：一只猫的雨夜散步
一只灰色的小猫从晴朗的午后城市出发，慢慢走过繁忙的街道。
天色渐渐变暗，乌云聚集，开始下雨。
小猫躲进路边的小巷，雨水打湿了它的毛。
霓虹灯亮起，猫在湿漉漉的路面上倒映出彩色的光。
最后，它推开一扇半掩的门，走进温暖的小餐馆。
---
## 用法
```bash
storyboard.py < examples/storyboard-cat_rainy_night.txt \
-p 电影感 --scenes 6 \
-m Midjourney --video-model Sora \
--output ./renders/cat_rainy_night
```
输出 6 个关键帧 + 5 个转场到 ./renders/cat_rainy_night/。
每个 scene 用 Midjourney 出图，每个 transition 用 Sora 出短视频，剪辑串联即得 ~30 秒短片。
FILE:references/t2i-guide.md
# T2I 提示词工程参考（v2.0）
## 一、提示词核心要素
| 要素 | 说明 | 示例 |
|------|------|------|
| **主体** | 画面核心对象 | a cat, a futuristic building, a woman |
| **材质/介质** | 画面的质感 | oil painting, digital art, photography, watercolor |
| **风格** | 艺术风格 | cyberpunk, impressionist, anime, realistic |
| **镜头/构图** | 视角和取景 | close-up, 85mm f/1.4, wide shot, bird's eye view |
| **光线** | 光照方向和类型 | golden hour, neon glow, soft diffused, rim light |
| **色彩** | 色调倾向 | warm tones, teal & orange, monochromatic, pastel |
| **背景** | 环境设定 | busy city street, empty beach, starfield, studio |
| **情绪** | 画面氛围 | melancholic, epic, cozy, mysterious |
| **画质词** | 画质强化 | masterpiece, best quality, ultra detailed, 8k |
| **负面** | 不想出现的 | low quality, blurry, bad anatomy, extra fingers |
## 二、贴近需求的技巧
想让图**贴近你的真实想法**，只给主体不够，建议提供至少以下 3 个维度：
1. **主体 + 动作/状态**：`红发女侠 持剑站立` 比 `红发女侠` 好
2. **环境/背景**：`在雨夜的东京巷弄` 比默认空背景更可控
3. **情绪/时间**：`黄昏，忧郁感` 给画面定调
脚本的 **意图识别器** 会自动抽取其中的构图词（俯拍/特写/远景/航拍…）和情绪词（神秘/温馨/史诗…）并并入提示词。
## 三、一致性的五道防线
系列图"看起来不像同一套"是最常见痛点。按优先级部署：
| # | 机制 | 作用 | 脚本支持 |
|---|------|------|---------|
| 1 | **seed 锁定** | 同 seed + 同提示词 → 几乎复现 | ✅ `--seed` / 自动哈希 |
| 2 | **camera 锁** | 焦段/视角不变 | ✅ 预设内置 |
| 3 | **lighting 锁** | 光源方向、色温不变 | ✅ 预设内置 |
| 4 | **palette 锁** | 色板不变（最影响"同系列感"） | ✅ 预设内置 |
| 5 | **aspect 锁** | 画幅不变 | ✅ 预设内置 |
| 6 | **参考图** | MJ `--cref`/`--sref`、SD IP-Adapter、Flux redux | 提示词输出 |
## 四、56 预设对照表（分类）
### 摄影 · 10
| 预设 | 适用场景 | 画幅 | 核心锁 |
|------|---------|------|--------|
| 写实摄影 | 人像 / 产品 / 建筑 | 3:4 | Canon R5 85mm + 影棚光 |
| 胶片摄影 | 人文 / 旅拍 | 3:2 | 35mm 胶片 + 自然光 |
| 黑白摄影 | 纪实 / 艺术 | 1:1 | Leica M6 + 强对比 |
| 人像摄影 | 肖像 / 头像 | 3:4 | 85mm f/1.4 + 伦勃朗光 |
| 时尚大片 | 时装 / 美妆 | 3:4 | 中画幅 + 硬光 |
| 美食摄影 | 菜品 / 食谱 | 1:1 | 100mm 微距 + 45°侧光 |
| 产品摄影 | 电商白底 | 1:1 | 90mm 微距 + 大柔光箱 |
| 微距摄影 | 昆虫 / 花蕊 | 1:1 | 100mm 1:1 + 环形闪光 |
| 航拍摄影 | 风景 / 城市 | 16:9 | 无人机 24mm 俯视 |
| 街拍纪实 | 人文街头 | 3:2 | 35mm + 环境光 |
### 动漫 · 6
| 预设 | 风格取向 | 画幅 |
|------|---------|------|
| 动漫 | 通用 pixiv 二次元 | 3:4 |
| 新海诚 | 云景 + 辉光 | 16:9 |
| 宫崎骏 | 吉卜力温暖 | 16:9 |
| 美漫 | marvel/DC 粗线条 | 2:3 |
| Q版 | 三头身 chibi | 1:1 |
| 童话绘本 | 水粉儿童绘本 | 4:3 |
### 插画 · 7
| 预设 | 介质/流派 | 画幅 |
|------|----------|------|
| 水彩 | 湿画法纸本 | 1:1 |
| 油画 | 厚涂油彩 | 4:5 |
| 水墨 | 宣纸墨色 | 3:4 |
| 工笔国画 | 矿物颜料工笔 | 3:4 |
| 浮世绘 | 江户时期木版 | 2:3 |
| 线稿 | 纯黑白线条 | 1:1 |
| 像素艺术 | 16-bit sprite | 1:1 |
### 3D · 7
| 预设 | 材质/风格 | 画幅 |
|------|----------|------|
| 3DC4D | Octane 光泽渲染 | 1:1 |
| 盲盒手办 | 泡泡玛特塑胶 | 1:1 |
| 低多边形 | 低面数面片 | 1:1 |
| 等距视图 | 等轴 2.5D | 1:1 |
| 粘土 | 定格动画黏土 | 1:1 |
| 毛毡手工 | 羊毛毡戳制 | 1:1 |
| 纸艺 | 切纸层叠 | 1:1 |
### 设计 · 10
| 预设 | 产出物 | 画幅 |
|------|--------|------|
| 极简主义 | 瑞士派 / 留白 | 1:1 |
| 平面设计 | 矢量插画 | 1:1 |
| Logo设计 | 品牌标志 | 1:1 |
| 图标设计 | app icon | 1:1 |
| 信息图 | 数据可视化 | 3:4 |
| 品牌KV | 广告主视觉 | 16:9 |
| 专辑封面 | 音乐封面 | 1:1 |
| 复古海报 | 1950s letterpress | 3:4 |
| 电影海报 | 院线 one-sheet | 2:3 |
| 表情包 | 贴纸 / emoji | 1:1 |
### 艺术史 · 4
| 预设 | 流派 | 画幅 |
|------|------|------|
| 印象派 | 莫奈 plein-air | 4:5 |
| 后印象派 | 梵高表现 | 4:5 |
| 新艺术 | Mucha 装饰曲线 | 2:3 |
| 装饰艺术 | 盖茨比几何 | 2:3 |
### 场景氛围 · 12
| 预设 | 调性 | 画幅 |
|------|------|------|
| 赛博朋克 | 霓虹 + 雨夜 | 21:9 |
| 蒸汽朋克 | 黄铜维多利亚 | 3:2 |
| 科幻 | 蓝灰硬科幻 | 21:9 |
| 奇幻 | 魔戒史诗 | 16:9 |
| 黑暗奇幻 | 贝尔塞尔克 | 2:3 |
| 国潮 | 朱红鎏金 | 3:4 |
| Y2K | 千禧铬纹 | 1:1 |
| Vaporwave | 蒸汽波落日 | 16:9 |
| 霓虹灯牌 | 玻璃管发光字 | 3:2 |
| 建筑可视化 | V-Ray archviz | 16:9 |
| 电影感 | ARRI + 橙青调色 | 21:9 |
| 概念艺术 | ILM matte | 21:9 |
## 五、模型差异提示
| 模型 | 骨架 | 特有技巧 |
|------|------|----------|
| **Midjourney v6** | 逗号分隔短句 + 尾部 flag | `--cref` 锁角色、`--sref` 锁风格、`--stylize` 控风格化程度、`--chaos` 控多样性 |
| **Stable Diffusion 1.5** | tag 式 + 权重 | `(subject:1.3)`、`[减弱:0.7]`、DPM++ 2M Karras, 30 steps, CFG 6-7 |
| **SDXL** | 同 SD，tag 稍长 | 原生 1024 分辨率、DPM++ SDE Karras, 25-30 steps, CFG 5-7, Refiner 0.2 |
| **DALL-E 3** | 自然语言段落 | ChatGPT 对话中"use the same character" 跨对话续图 |
| **Flux Dev** | 长句，可含短语位置 | guidance 3.5、擅长生成清晰文字、redux 可作参考图 |
## 六、系列一致性工作流（推荐）
### 场景：出一套品牌 4 张产品图
```bash
./scripts/enhance_prompt.py "无线蓝牙耳机 白色" -p 产品摄影 -s 4 \
--variations "正面特写,45度角展示,充电盒开启,佩戴模特"
```
产出：
1. 所有 4 张输出 **共享 seed**
2. 所有 4 张 **共享 camera / lighting / palette / aspect 锁**
3. 主体描述 **仅在动作/角度上变化**
把这 4 条提示词分别喂给你的生图工具，加上 `--cref <第1张URL>`（MJ）或 IP-Adapter（SD/ComfyUI）即可得到风格完全一致的一套产品图。
### 场景：出一套角色立绘
```bash
./scripts/enhance_prompt.py "银发机甲少女" -p 动漫 -s 6 \
--variations "正面站立,侧面剪影,奔跑姿势,持武器pose,受伤后,胜利姿态" \
-m Midjourney
```
记得在 MJ 里给第一张做 `--cref`，后续 5 张引用同一 URL，角色脸部 95%+ 一致。
## 七、Prompt 写作红线
- ❌ 英文提示词里**堆砌中文地名**（除非模型是中文 checkpoint）
- ❌ 一次塞超过 **8 个并列风格**（风格冲突 → 画面混乱）
- ❌ 把**颜色描述**堆得比主体还多（模型会优先表现颜色忽略主体）
- ❌ 负面词**过长**（SD 负面超过 75 tokens 后生效打折）
- ✅ 主体用英文、风格用英文、地域/文化用英文（例 "Chinese traditional garden"）
- ✅ 想要稳定输出：用预设 + seed 锁；想要探索：去 seed / 加 `--chaos 30`
FILE:scripts/auto_iterate.py
#!/usr/bin/env python3
"""
huo15-img-prompt — 闭环自动迭代 v2.5
把 enhance_prompt + render_prompt + image_review 串成闭环：
生成 → 出图 → 评审 → 不达标？让 Claude 改 prompt 再来一轮（≤ 3 轮）
这是 v2.5 的核心护城河：GPT-4o image / Imagen / Claude Imagen 内部都做不到，
因为它们的 prompt 是黑盒。我们在用户侧补这个反馈循环。
工作流（每轮）：
Step 1: enhance_prompt 生成 recipe（首轮用基础推荐，后续用上轮 fix 后的）
Step 2: render_prompt 出图（任意 backend）
Step 3: image_review 五维评审
Step 4: overall_score >= target_score → 完成
< target_score 且 attempt < max_attempts:
→ 用 Claude 把 actionable_fixes 综合成新 prompt
→ 回 Step 1
Step 5: 返回历史最高分图 + 全过程 trace
调用：
auto_iterate.py "持剑女侠" -p 赛博朋克 --backend dalle --target 7.5
auto_iterate.py "汉服少女" -p 汉服写真 --backend jimeng --max-rounds 3
auto_iterate.py "敦煌神女" -p 敦煌壁画 --backend none --no-render # 评审现有 recipe，不真出图
依赖：
- 同目录 enhance_prompt.py / render_prompt.py / image_review.py
- ANTHROPIC_API_KEY（评审 + 改 prompt）
- 后端对应的 API key（DALL-E / Replicate / 即梦 / 可灵 等）
"""
import sys
import os
import json
import time
import argparse
from typing import Dict, List, Optional
from urllib.request import Request, urlopen
from urllib.error import HTTPError
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from enhance_prompt import (
build_prompt, parse_mix_preset, resolve_preset,
parse_requirement, STYLE_PRESETS, ASPECT_TO_SDXL,
)
from image_review import review_image, parse_review_json, ANTHROPIC_BASE, ANTHROPIC_VERSION
VERSION = "3.1.0"
DEFAULT_MODEL = "claude-sonnet-4-5"
DEFAULT_TARGET_SCORE = 7.5
DEFAULT_MAX_ROUNDS = 3
# ─────────────────────────────────────────────────────────
# Claude 改 prompt（基于上轮评审）
# ─────────────────────────────────────────────────────────
REVISION_SYSTEM_PROMPT = """你是 prompt revision 专家，给定一张图的 5 维评审，输出改进版 prompt。
# 工作流
1. 读 actionable_fixes（按优先级，high 必处理）
2. 读 issues（避免下轮重蹈覆辙）
3. 读 good_points（保留这些优势）
4. 输出新 prompt（只输出主体描述部分，不要带 style/camera/lighting 模板，因为 enhance_prompt.py 会再加这些锁）
# 输出 JSON 严格 schema
```json
{
"revised_subject": "改进后的主体描述（中文，可加视觉细节），喂给 enhance_prompt.py 的 subject 参数",
"preset_change": null,
"extra_negatives": ["补充负面词 1", "补充负面词 2"],
"extra_mood": "如需更改情绪覆盖（无则空）",
"extra_composition": "如需更改构图覆盖（无则空）",
"rationale": "中文一句话说明这次改动针对哪个维度的 issue"
}
```
`preset_change` 只在评审里明确说"风格不对"时改，否则保持 null。
只输出 JSON，不要解释。"""
def call_claude_revise(original_subject: str, original_preset: str,
review: Dict, model: str = DEFAULT_MODEL) -> Dict:
"""让 Claude 基于 review 输出改进 subject。"""
api_key = os.environ.get("ANTHROPIC_API_KEY")
if not api_key:
raise RuntimeError("缺少 ANTHROPIC_API_KEY")
review_summary = {
"overall_score": review.get("overall_score", 0),
"verdict": review.get("verdict", "?"),
"summary": review.get("summary", ""),
"actionable_fixes": review.get("actionable_fixes", []),
"weak_dimensions": [],
}
for dim in ("subject_match", "composition", "lighting", "palette", "technical"):
d = review.get(dim, {})
score = d.get("score", 0)
if score < 7:
review_summary["weak_dimensions"].append({
"dim": dim, "score": score, "issues": d.get("issues", []),
})
user_msg = f"""<original>
subject: {original_subject}
preset: {original_preset}
</original>
<review>
{json.dumps(review_summary, ensure_ascii=False, indent=2)}
</review>
请输出改进后的 JSON。"""
body = {
"model": model,
"max_tokens": 1500,
"system": [{
"type": "text",
"text": REVISION_SYSTEM_PROMPT,
"cache_control": {"type": "ephemeral"},
}],
"messages": [
{"role": "user", "content": user_msg},
{"role": "assistant", "content": "{"},
],
}
req = Request(
f"{ANTHROPIC_BASE}/v1/messages",
data=json.dumps(body).encode("utf-8"),
headers={
"Content-Type": "application/json",
"x-api-key": api_key,
"anthropic-version": ANTHROPIC_VERSION,
},
method="POST",
)
try:
with urlopen(req, timeout=60) as r:
resp = json.loads(r.read().decode("utf-8"))
except HTTPError as e:
raise RuntimeError(f"Claude HTTP {e.code}: {e.read().decode('utf-8', errors='replace')}")
return parse_review_json(resp)
# ─────────────────────────────────────────────────────────
# 后端调用（直接 import render_prompt 函数，不重复实现）
# ─────────────────────────────────────────────────────────
def render_via_backend(backend: str, recipe: Dict, aspect: str, output_dir: str,
remote_model: str = "", steps: int = 25, cfg: float = 7.0) -> Dict:
"""统一 backend dispatch，复用 render_prompt 内部函数。"""
from render_prompt import (
render_dalle, render_sdwebui, render_comfyui,
render_replicate, render_fal,
render_jimeng, render_kling, render_hailuo,
DALLE_SIZES,
)
seed = recipe["seed_suggestion"]
pos, neg = recipe["positive"], recipe["negative"]
if backend == "dalle":
size = DALLE_SIZES.get(aspect, "1024x1024")
return render_dalle(pos, size, output_dir)
elif backend == "sd-webui":
return render_sdwebui(pos, neg, aspect, seed, steps, cfg, "DPM++ 2M Karras", output_dir)
elif backend == "comfyui":
return render_comfyui(pos, neg, aspect, seed, steps, cfg, None, output_dir)
elif backend == "replicate":
ref = remote_model or "black-forest-labs/flux-schnell"
return render_replicate(pos, neg, aspect, seed, ref, output_dir, steps=steps, cfg=cfg)
elif backend == "fal":
ref = remote_model or "fal-ai/flux/schnell"
return render_fal(pos, neg, aspect, seed, ref, output_dir, steps=steps)
elif backend == "jimeng":
return render_jimeng(pos, neg, aspect, seed, output_dir)
elif backend == "kling":
return render_kling(pos, neg, aspect, seed, output_dir)
elif backend in ("hailuo", "minimax"):
return render_hailuo(pos, neg, aspect, seed, output_dir)
else:
raise RuntimeError(f"未知 backend: {backend}")
# ─────────────────────────────────────────────────────────
# 闭环主流程
# ─────────────────────────────────────────────────────────
def auto_iterate(
subject: str,
preset: str,
backend: str,
target_score: float = DEFAULT_TARGET_SCORE,
max_rounds: int = DEFAULT_MAX_ROUNDS,
aspect: str = "",
model_adapt: str = "SDXL",
output_dir: str = "./renders",
remote_model: str = "",
no_render: bool = False,
quality_tier: str = "pro",
seed: Optional[int] = None,
) -> Dict:
"""主闭环。返回 trace + 最佳轮次。"""
primary_raw, secondary_raw = parse_mix_preset(preset)
if secondary_raw:
primary = resolve_preset(primary_raw)
secondary = resolve_preset(secondary_raw)
if not primary or not secondary:
raise RuntimeError(f"未知预设: {primary_raw} 或 {secondary_raw}")
preset_resolved, mix_secondary = primary, secondary
else:
preset_resolved, mix_secondary = (resolve_preset(primary_raw) or "写实摄影"), None
auto = parse_requirement(subject)
if not aspect:
aspect = auto["aspect_suggestion"] or STYLE_PRESETS.get(preset_resolved, {}).get("aspect", "1:1")
rounds: List[Dict] = []
current_subject = subject
current_extra_neg = ""
current_mood = ""
current_composition = ""
locked_seed = seed # 整轮锁同一 seed，便于对比
for attempt in range(1, max_rounds + 1):
print(f"\n🔄 第 {attempt}/{max_rounds} 轮...", file=sys.stderr)
recipe = build_prompt(
current_subject, preset_resolved, model_adapt, aspect,
extra_mood=current_mood, extra_composition=current_composition,
extra_negatives=current_extra_neg,
seed=locked_seed,
quality_tier=quality_tier,
mix_secondary=mix_secondary,
)
if locked_seed is None:
locked_seed = recipe["seed_suggestion"]
round_data = {"attempt": attempt, "subject": current_subject, "recipe": recipe}
if no_render:
print(f" 🧪 dry-run 模式：不出图，仅评审 prompt 文本（跳过本轮，需 --no-render 配合外部出图）", file=sys.stderr)
round_data["skipped"] = "no-render mode (cannot review without image)"
rounds.append(round_data)
break
# 出图
try:
print(f" 🎨 出图: backend={backend}", file=sys.stderr)
render = render_via_backend(backend, recipe, aspect, output_dir,
remote_model=remote_model)
except Exception as e:
round_data["render_error"] = str(e)
rounds.append(round_data)
print(f" ❌ 出图失败: {e}", file=sys.stderr)
break
saved = render.get("saved", [])
if not saved:
round_data["render_error"] = "no images saved"
rounds.append(round_data)
break
round_data["image_path"] = saved[0]
round_data["render"] = render
# 评审
try:
print(f" 🔍 Claude Vision 评审...", file=sys.stderr)
review = review_image(saved[0], prompt=recipe["positive"][:500],
quick=False, model=DEFAULT_MODEL)
except Exception as e:
round_data["review_error"] = str(e)
rounds.append(round_data)
print(f" ❌ 评审失败: {e}", file=sys.stderr)
break
round_data["review"] = review
score = review.get("overall_score", 0)
verdict = review.get("verdict", "?")
print(f" 📊 得分: {score:.1f}/10 → {verdict}", file=sys.stderr)
rounds.append(round_data)
# 收敛？
if score >= target_score:
print(f" ✅ 达标 ({score:.1f} ≥ {target_score})，停止迭代", file=sys.stderr)
break
if attempt >= max_rounds:
print(f" ⏱ 达到最大轮次 {max_rounds}", file=sys.stderr)
break
# 让 Claude 改 prompt
try:
print(f" ✏️ 让 Claude 改 prompt...", file=sys.stderr)
revision = call_claude_revise(current_subject, preset_resolved, review)
except Exception as e:
round_data["revision_error"] = str(e)
print(f" ⚠️ 改 prompt 失败: {e}，停止迭代", file=sys.stderr)
break
round_data["revision"] = revision
if revision.get("revised_subject"):
current_subject = revision["revised_subject"]
print(f" 📝 新 subject: {current_subject}", file=sys.stderr)
if revision.get("extra_negatives"):
extras = ", ".join(revision["extra_negatives"])
current_extra_neg = f"{current_extra_neg}, {extras}".strip(", ")
if revision.get("extra_mood"):
current_mood = revision["extra_mood"]
if revision.get("extra_composition"):
current_composition = revision["extra_composition"]
# 选出最佳轮
best_round = max(
[r for r in rounds if r.get("review", {}).get("overall_score") is not None],
key=lambda r: r["review"]["overall_score"],
default=None,
)
return {
"version": VERSION,
"subject": subject,
"preset": preset,
"backend": backend,
"target_score": target_score,
"max_rounds": max_rounds,
"rounds": rounds,
"rounds_executed": len(rounds),
"best_round": best_round,
"best_score": best_round["review"]["overall_score"] if best_round else None,
"best_image": best_round.get("image_path") if best_round else None,
}
def print_summary(result: Dict):
sep = "═" * 60
print(f"\n{sep}")
print(f"🔁 闭环自动迭代结果 v{result['version']}")
print(f"📌 主体: {result['subject']}")
print(f"🎨 预设: {result['preset']}")
print(f"🔧 后端: {result['backend']}")
print(f"🎯 目标: {result['target_score']:.1f}/10 最大 {result['max_rounds']} 轮")
print(f"📊 实际: {result['rounds_executed']} 轮")
print(f"\n{sep}")
for r in result["rounds"]:
attempt = r["attempt"]
score = r.get("review", {}).get("overall_score")
verdict = r.get("review", {}).get("verdict", "?")
if score is None:
err = r.get("render_error") or r.get("review_error") or r.get("revision_error") or r.get("skipped", "?")
print(f"\n 轮 {attempt}: ❌ {err}")
continue
emoji = "🟢" if score >= 7.5 else ("🟡" if score >= 5 else "🔴")
print(f"\n 轮 {attempt}: {emoji} {score:.1f}/10 → {verdict}")
print(f" 图: {r.get('image_path', '?')}")
print(f" subject: {r.get('subject', '')[:80]}")
if r.get("revision", {}).get("rationale"):
print(f" ✏️ 下轮理由: {r['revision']['rationale']}")
if result["best_round"]:
print(f"\n{sep}")
print(f"🏆 最佳轮: 第 {result['best_round']['attempt']} 轮 得分 {result['best_score']:.1f}/10")
print(f"📷 文件: {result['best_image']}")
print(f"{sep}\n")
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-prompt auto_iterate v{VERSION} — 闭环自动迭代",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
示例:
auto_iterate.py "持剑女侠" -p 赛博朋克 --backend dalle --target 7.5
auto_iterate.py "汉服少女" -p 汉服写真 --backend jimeng --max-rounds 3
auto_iterate.py "敦煌神女" -p 敦煌壁画 --backend replicate \\
--remote-model black-forest-labs/flux-schnell
环境变量:
ANTHROPIC_API_KEY 必填（评审 + 改 prompt）
+ 后端对应的 API key（OPENAI_API_KEY / REPLICATE_API_TOKEN / ARK_API_KEY 等）
""",
)
parser.add_argument("subject", help="主体描述")
parser.add_argument("-p", "--preset", required=True, help="风格预设（支持 A+B 混合）")
parser.add_argument("-a", "--aspect", default="", help="画幅")
parser.add_argument("-t", "--tier", choices=["basic", "pro", "master"], default="pro")
parser.add_argument("-m", "--model", default="SDXL", help="提示词模型适配（不影响 backend）")
parser.add_argument("--backend", required=True,
choices=["dalle", "sd-webui", "comfyui",
"replicate", "fal", "jimeng", "kling", "hailuo", "minimax"],
help="出图后端")
parser.add_argument("--remote-model", default="", help="Replicate/Fal 模型 ref")
parser.add_argument("--target", type=float, default=DEFAULT_TARGET_SCORE,
help=f"目标分数 0-10（默认 {DEFAULT_TARGET_SCORE}）")
parser.add_argument("--max-rounds", type=int, default=DEFAULT_MAX_ROUNDS,
help=f"最大迭代轮数（默认 {DEFAULT_MAX_ROUNDS}）")
parser.add_argument("--output", default="./renders", help="输出目录")
parser.add_argument("--seed", type=int, help="种子（不给则按 subject+preset 哈希）")
parser.add_argument("--no-render", action="store_true",
help="不真出图，仅生成 recipe（用于测试 prompt revision 链路）")
parser.add_argument("-j", "--json", action="store_true", help="JSON 输出")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
try:
result = auto_iterate(
subject=args.subject,
preset=args.preset,
backend=args.backend,
target_score=args.target,
max_rounds=args.max_rounds,
aspect=args.aspect,
model_adapt=args.model,
output_dir=args.output,
remote_model=args.remote_model,
no_render=args.no_render,
quality_tier=args.tier,
seed=args.seed,
)
except RuntimeError as e:
print(f"❌ {e}", file=sys.stderr)
sys.exit(2)
if args.json:
print(json.dumps(result, ensure_ascii=False, indent=2))
else:
print_summary(result)
if __name__ == "__main__":
main()
FILE:scripts/brand_kit.py
#!/usr/bin/env python3
"""
huo15-img-prompt — 品牌套件 v3.0
把品牌 VI（colors/fonts/keywords/forbidden）持久化为 brand kit JSON，
出图时 `--brand-kit <name>` 自动注入到 prompt：
- colors → palette 锁
- fonts → 添加到 prompt 的 typography 提示
- keywords → 视觉关键词追加
- forbidden → 加入 negative prompt
- logo_description → 加入 prompt 用于 cref 风格
存储：~/.huo15/brand_kits/<name>.json
工作流：
Step 1: 创建 brand kit
brand_kit.py --create huo15 \\
--colors "#ff6b35,#2d3047,#fafafa" \\
--fonts "PingFang SC,Source Han Serif" \\
--keywords "现代,简洁,专业,温暖" \\
--forbidden "competitor logos, low-quality"
Step 2: 出图时引用
enhance_prompt.py "公司年会海报" -p 品牌KV --brand-kit huo15
Step 3: 配合品牌规范抓取技能（huo15-openclaw-brand-protocol）
用 brand-protocol 抓品牌规范 → 导入到 brand kit → 用 img-prompt 出图
brand_kit.py --list / --show / --delete / --export / --import
"""
import sys
import os
import json
import re
import time
import argparse
from typing import Dict, List, Optional
VERSION = "3.1.0"
KIT_DIR = os.path.expanduser("~/.huo15/brand_kits")
def safe_name(name: str) -> str:
return re.sub(r"[^\w\-]", "_", name)
def kit_path(name: str) -> str:
return os.path.join(KIT_DIR, f"{safe_name(name)}.json")
def kit_load(name: str) -> Optional[Dict]:
p = kit_path(name)
if not os.path.isfile(p):
return None
try:
with open(p, "r", encoding="utf-8") as f:
return json.load(f)
except (json.JSONDecodeError, IOError):
return None
def kit_save(name: str, kit: Dict) -> str:
os.makedirs(KIT_DIR, exist_ok=True)
p = kit_path(name)
existing = kit_load(name) or {}
kit["name"] = name
kit["version"] = VERSION
kit["created_at"] = existing.get("created_at") or int(time.time())
kit["updated_at"] = int(time.time())
kit["use_count"] = existing.get("use_count", 0)
with open(p, "w", encoding="utf-8") as f:
json.dump(kit, f, ensure_ascii=False, indent=2)
return p
def kit_list() -> List[Dict]:
if not os.path.isdir(KIT_DIR):
return []
out = []
for fn in sorted(os.listdir(KIT_DIR)):
if not fn.endswith(".json"):
continue
try:
with open(os.path.join(KIT_DIR, fn), "r", encoding="utf-8") as f:
out.append(json.load(f))
except (json.JSONDecodeError, IOError):
continue
return out
def kit_delete(name: str) -> bool:
p = kit_path(name)
if os.path.isfile(p):
os.remove(p)
return True
return False
# ─────────────────────────────────────────────────────────
# 注入逻辑（被 enhance_prompt.py import）
# ─────────────────────────────────────────────────────────
def kit_apply(name: str, args) -> Optional[Dict]:
"""加载 brand kit 并注入 args。
args 是 enhance_prompt.py 的 ArgumentParser Namespace。我们补全：
- args.subject 末尾追加品牌关键词
- args.avoid 追加 forbidden（合并到 negative）
返回 kit dict（用于显示）或 None。
"""
kit = kit_load(name)
if not kit:
return None
# 计数
kit["use_count"] = kit.get("use_count", 0) + 1
try:
with open(kit_path(name), "w", encoding="utf-8") as f:
json.dump(kit, f, ensure_ascii=False, indent=2)
except IOError:
pass
# 注入 keywords 到 subject
keywords = kit.get("keywords") or []
if keywords and args.subject:
kw_str = ", ".join(keywords)
# 不把 keywords 重复加入
if all(k not in args.subject for k in keywords[:2]):
args.subject = f"{args.subject}, {kw_str}"
# 注入 colors 到 subject（作为色板提示）
colors = kit.get("colors") or []
if colors and args.subject:
# 把色板写成自然语言，让 T2I 模型理解
colors_phrase = f"brand color palette {' '.join(colors)}"
args.subject = f"{args.subject}, {colors_phrase}"
# 注入 logo_description（如果有）
if kit.get("logo_description") and args.subject:
args.subject = f"{args.subject}, brand identity: {kit['logo_description']}"
# 注入 forbidden 到 negative
forbidden = kit.get("forbidden") or []
if forbidden:
existing_avoid = args.avoid or ""
new_avoid = ", ".join(forbidden)
args.avoid = f"{existing_avoid}, {new_avoid}".strip(", ")
return kit
# ─────────────────────────────────────────────────────────
# CLI
# ─────────────────────────────────────────────────────────
def parse_csv(s: str) -> List[str]:
if not s:
return []
return [x.strip() for x in s.split(",") if x.strip()]
def print_kit(kit: Dict):
print(f"\n🎨 {kit['name']}")
print(f" 创建: {time.strftime('%Y-%m-%d %H:%M', time.localtime(kit.get('created_at', 0)))}")
print(f" 更新: {time.strftime('%Y-%m-%d %H:%M', time.localtime(kit.get('updated_at', 0)))}")
print(f" 用过: {kit.get('use_count', 0)} 次")
if kit.get("colors"):
print(f" 颜色: {' '.join(kit['colors'])}")
if kit.get("fonts"):
print(f" 字体: {' / '.join(kit['fonts'])}")
if kit.get("keywords"):
print(f" 关键词: {', '.join(kit['keywords'])}")
if kit.get("forbidden"):
print(f" 禁止: {', '.join(kit['forbidden'])}")
if kit.get("logo_description"):
print(f" Logo: {kit['logo_description']}")
if kit.get("description"):
print(f" 描述: {kit['description']}")
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-prompt brand_kit v{VERSION} — 品牌套件管理",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
示例:
brand_kit.py --create huo15 \\
--colors "#ff6b35,#2d3047,#fafafa" \\
--fonts "PingFang SC,Source Han Serif" \\
--keywords "现代,简洁,专业,温暖" \\
--forbidden "competitor logos, low quality" \\
--logo "minimal flame mark in orange"
brand_kit.py --list
brand_kit.py --show huo15
brand_kit.py --delete 旧品牌
brand_kit.py --export huo15 > huo15.json
cat huo15.json | brand_kit.py --import
✨ 在 enhance_prompt.py 里使用:
enhance_prompt.py "公司年会海报" -p 品牌KV --brand-kit huo15
""",
)
g = parser.add_mutually_exclusive_group(required=True)
g.add_argument("--create", metavar="NAME", help="创建品牌套件")
g.add_argument("--update", metavar="NAME", help="更新品牌套件（保留未指定字段）")
g.add_argument("--list", action="store_true", help="列出所有")
g.add_argument("--show", metavar="NAME", help="显示详情")
g.add_argument("--delete", metavar="NAME", help="删除")
g.add_argument("--export", metavar="NAME", help="导出 JSON 到 stdout")
g.add_argument("--import", dest="imp", action="store_true", help="从 stdin 导入")
parser.add_argument("--colors", default="", help="逗号分隔的色值，例 '#ff6b35,#2d3047'")
parser.add_argument("--fonts", default="", help="字体，例 'PingFang SC,Source Han Serif'")
parser.add_argument("--keywords", default="", help="视觉关键词")
parser.add_argument("--forbidden", default="", help="禁止出现的元素（合并到负面词）")
parser.add_argument("--logo", default="", help="Logo 一句话描述")
parser.add_argument("--description", default="", help="品牌描述")
parser.add_argument("-j", "--json", action="store_true", help="JSON 输出")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
if args.list:
kits = kit_list()
if args.json:
print(json.dumps({"version": VERSION, "kits": kits}, ensure_ascii=False, indent=2))
return
if not kits:
print(f"\n📭 暂无品牌套件 ({KIT_DIR})\n")
return
print(f"\n🎨 品牌套件 ({len(kits)} 个):")
for k in kits:
print(f" • {k['name']:20s} {len(k.get('colors', []))} 色 {len(k.get('keywords', []))} 关键词 用过 {k.get('use_count', 0)} 次")
print()
return
if args.show:
kit = kit_load(args.show)
if not kit:
print(f"❌ 不存在: {args.show}", file=sys.stderr)
sys.exit(1)
if args.json:
print(json.dumps(kit, ensure_ascii=False, indent=2))
else:
print_kit(kit)
print()
return
if args.delete:
if kit_delete(args.delete):
print(f"✅ 已删除: {args.delete}")
else:
print(f"❌ 不存在: {args.delete}", file=sys.stderr)
sys.exit(1)
return
if args.export:
kit = kit_load(args.export)
if not kit:
print(f"❌ 不存在: {args.export}", file=sys.stderr)
sys.exit(1)
print(json.dumps(kit, ensure_ascii=False, indent=2))
return
if args.imp:
try:
kit = json.loads(sys.stdin.read())
except json.JSONDecodeError as e:
print(f"❌ 解析失败: {e}", file=sys.stderr)
sys.exit(1)
name = kit.get("name")
if not name:
print("❌ JSON 缺 name 字段", file=sys.stderr)
sys.exit(1)
kit_save(name, kit)
print(f"✅ 已导入: {name}")
return
if args.create or args.update:
name = args.create or args.update
if args.create and kit_load(name):
print(f"⚠️ '{name}' 已存在，用 --update 覆盖", file=sys.stderr)
sys.exit(1)
existing = kit_load(name) if args.update else {}
kit = dict(existing or {})
if args.colors:
kit["colors"] = parse_csv(args.colors)
if args.fonts:
kit["fonts"] = parse_csv(args.fonts)
if args.keywords:
kit["keywords"] = parse_csv(args.keywords)
if args.forbidden:
kit["forbidden"] = parse_csv(args.forbidden)
if args.logo:
kit["logo_description"] = args.logo
if args.description:
kit["description"] = args.description
kit_save(name, kit)
action = "创建" if args.create else "更新"
print(f"✅ 已{action}: {name}")
print_kit(kit_load(name))
print()
if __name__ == "__main__":
main()
FILE:scripts/character.py
#!/usr/bin/env python3
"""
huo15-img-prompt — 角色卡持久化 v2.6
把 character_sheet 模式的输出存为可复用的"角色卡"，下次 `--char <name>` 直接调出。
每张角色卡 = 角色名 + 视觉描述 + 锁定参数（seed/preset/aspect/camera/lighting/palette）。
存到 ~/.huo15/characters/<safe_name>.json。
工作流：
Turn 1（创建角色）:
enhance_prompt.py "银发机甲少女, twin tails, glowing visor" \\
-p 动漫 --character-sheet --save-char 银发机甲少女
Turn 2 ~ N（复用）:
enhance_prompt.py "新场景：在霓虹街头" --char 银发机甲少女 -p 赛博朋克
enhance_prompt.py "在花海中" --char 银发机甲少女
# → 自动注入角色描述 + 锁 seed，保证多张图角色一致
调用：
character.py --list # 列出所有角色
character.py --show 银发机甲少女 # 看角色详情
character.py --delete 旧角色 # 删除
character.py --export 银发机甲少女 > char.json # 导出
character.py --import < char.json # 导入
"""
import sys
import os
import json
import re
import time
import argparse
from typing import Dict, List, Optional
VERSION = "3.1.0"
CHAR_DIR = os.path.expanduser("~/.huo15/characters")
def safe_name(name: str) -> str:
return re.sub(r"[^\w\-]", "_", name)
def char_path(name: str) -> str:
return os.path.join(CHAR_DIR, f"{safe_name(name)}.json")
def char_save(name: str, recipe: Dict) -> Dict:
"""从 build_prompt 的 result 里抽取角色锁存档。"""
os.makedirs(CHAR_DIR, exist_ok=True)
p = char_path(name)
existing = char_load(name) or {}
lock = recipe.get("consistency_lock", {}) or {}
card = {
"name": name,
"version": VERSION,
"created_at": existing.get("created_at") or int(time.time()),
"updated_at": int(time.time()),
"use_count": existing.get("use_count", 0),
"subject_description": recipe.get("original", ""),
"preset": recipe.get("preset", ""),
"mix_secondary": recipe.get("mix_secondary", "") or "",
"mix_ratio": recipe.get("mix_ratio"),
"aspect": recipe.get("aspect", ""),
"seed": recipe.get("seed_suggestion"),
"camera": lock.get("camera", ""),
"lighting": lock.get("lighting", ""),
"palette": lock.get("palette", ""),
"is_character_sheet": recipe.get("character_sheet", False),
"positive_anchor": recipe.get("positive", "")[:500],
}
with open(p, "w", encoding="utf-8") as f:
json.dump(card, f, ensure_ascii=False, indent=2)
return card
def char_load(name: str) -> Optional[Dict]:
p = char_path(name)
if not os.path.isfile(p):
return None
try:
with open(p, "r", encoding="utf-8") as f:
return json.load(f)
except (json.JSONDecodeError, IOError):
return None
def char_apply(name: str, args) -> Optional[Dict]:
"""加载角色卡作为 args 的默认值。仅在 CLI 未显式给时填充。"""
card = char_load(name)
if not card:
return None
# 增量计数
card["use_count"] = card.get("use_count", 0) + 1
try:
with open(char_path(name), "w", encoding="utf-8") as f:
json.dump(card, f, ensure_ascii=False, indent=2)
except IOError:
pass
# 注入到 args
desc = card.get("subject_description", "")
if args.subject and desc:
# 把角色描述前置到主体描述前
args.subject = f"{desc}, {args.subject}"
elif desc and not args.subject:
args.subject = desc
if not args.preset and card.get("preset"):
if card.get("mix_secondary"):
args.preset = f"{card['preset']}+{card['mix_secondary']}"
else:
args.preset = card["preset"]
if not args.aspect and card.get("aspect"):
args.aspect = card["aspect"]
# 角色卡的 seed 是核心锁，永远应用（除非 CLI 显式覆盖）
if args.seed is None and card.get("seed") is not None:
args.seed = card["seed"]
return card
def char_list() -> List[Dict]:
if not os.path.isdir(CHAR_DIR):
return []
out = []
for fn in sorted(os.listdir(CHAR_DIR)):
if not fn.endswith(".json"):
continue
try:
with open(os.path.join(CHAR_DIR, fn), "r", encoding="utf-8") as f:
out.append(json.load(f))
except (json.JSONDecodeError, IOError):
continue
return out
def char_delete(name: str) -> bool:
p = char_path(name)
if os.path.isfile(p):
os.remove(p)
return True
return False
# ─────────────────────────────────────────────────────────
# CLI
# ─────────────────────────────────────────────────────────
def print_char(card: Dict):
print(f"\n👤 {card['name']}")
print(f" 创建: {time.strftime('%Y-%m-%d %H:%M', time.localtime(card.get('created_at', 0)))}")
print(f" 更新: {time.strftime('%Y-%m-%d %H:%M', time.localtime(card.get('updated_at', 0)))}")
print(f" 用过: {card.get('use_count', 0)} 次")
print(f" 描述: {card.get('subject_description', '')[:120]}")
print(f" 预设: {card.get('preset', '')}", end="")
if card.get("mix_secondary"):
print(f" + {card['mix_secondary']} (mix={card.get('mix_ratio', 0.6)})")
else:
print()
print(f" 画幅: {card.get('aspect', '')}")
print(f" 种子: {card.get('seed', '')} (锁定)")
if card.get("camera"):
print(f" 相机: {card['camera']}")
if card.get("lighting"):
print(f" 光影: {card['lighting']}")
if card.get("palette"):
print(f" 色板: {card['palette']}")
if card.get("is_character_sheet"):
print(f" ✨ 来自 character-sheet 模式（T-pose 多视图）")
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-prompt character v{VERSION} — 角色卡管理",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
示例:
character.py --list # 列出全部
character.py --show 银发机甲少女 # 详情
character.py --delete 旧角色 # 删除
character.py --export 银发机甲少女 # 导出 JSON 到 stdout
cat char.json | character.py --import # 从 stdin 导入
✨ 创建/复用角色（在 enhance_prompt.py 里）:
enhance_prompt.py "银发机甲少女" -p 动漫 --character-sheet --save-char 银发机甲少女
enhance_prompt.py "在霓虹街头" --char 银发机甲少女
""",
)
g = parser.add_mutually_exclusive_group(required=True)
g.add_argument("--list", action="store_true", help="列出所有角色")
g.add_argument("--show", help="显示单个角色详情")
g.add_argument("--delete", help="删除角色")
g.add_argument("--export", help="导出角色到 stdout（JSON）")
g.add_argument("--import", dest="imp", action="store_true", help="从 stdin 导入角色")
parser.add_argument("-j", "--json", action="store_true", help="JSON 输出（配合 --list / --show）")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
if args.list:
cards = char_list()
if args.json:
print(json.dumps({"version": VERSION, "characters": cards}, ensure_ascii=False, indent=2))
return
if not cards:
print(f"\n📭 暂无角色（在 {CHAR_DIR}）\n")
print("💡 创建：enhance_prompt.py \"主体\" -p 预设 --character-sheet --save-char 名字\n")
return
print(f"\n👥 已存角色 ({len(cards)} 个，{CHAR_DIR}):")
for c in cards:
print(f" • {c['name']:20s} {c.get('preset', '?'):12s} seed={c.get('seed', '?')} 用过 {c.get('use_count', 0)} 次")
print()
return
if args.show:
card = char_load(args.show)
if not card:
print(f"❌ 角色不存在: {args.show}", file=sys.stderr)
sys.exit(1)
if args.json:
print(json.dumps(card, ensure_ascii=False, indent=2))
else:
print_char(card)
print()
return
if args.delete:
if char_delete(args.delete):
print(f"✅ 已删除: {args.delete}")
else:
print(f"❌ 角色不存在: {args.delete}", file=sys.stderr)
sys.exit(1)
return
if args.export:
card = char_load(args.export)
if not card:
print(f"❌ 角色不存在: {args.export}", file=sys.stderr)
sys.exit(1)
print(json.dumps(card, ensure_ascii=False, indent=2))
return
if args.imp:
try:
card = json.loads(sys.stdin.read())
except json.JSONDecodeError as e:
print(f"❌ 解析失败: {e}", file=sys.stderr)
sys.exit(1)
name = card.get("name")
if not name:
print(f"❌ JSON 缺 name 字段", file=sys.stderr)
sys.exit(1)
os.makedirs(CHAR_DIR, exist_ok=True)
with open(char_path(name), "w", encoding="utf-8") as f:
json.dump(card, f, ensure_ascii=False, indent=2)
print(f"✅ 已导入: {name}")
return
if __name__ == "__main__":
main()
FILE:scripts/claude_polish.py
#!/usr/bin/env python3
"""
huo15-img-prompt — Claude API 智能润色 v2.3
用 Claude（Anthropic API）把粗糙的中文描述润色成专业 T2I 提示词。
利用 Claude 的 prompt engineering 优势：
- **结构化思维**：用 XML 标签让 Claude 分步思考（subject_refine → style_pick → camera_lighting → palette → negatives）
- **prompt caching**：system prompt 缓存，省 90% token
- **JSON 强约束输出**：用 prefill + tool-use 强制结构化
- **中英双语理解**：中文输入 → 中英混合输出（视觉术语用英文）
调用：
claude_polish.py "一个温柔的女孩在花丛中"
claude_polish.py "赛博朋克猫" --model claude-sonnet-4-5
claude_polish.py "敦煌神女" --include-safety # 同时跑 safety_lint
claude_polish.py "汉服少女" -j > polished.json
claude_polish.py "雪山下的小屋" --pipe # 输出可直接喂给 enhance_prompt.py 的 CLI
依赖：
环境变量 ANTHROPIC_API_KEY
纯 urllib，零第三方包（不引入 anthropic SDK，避免企业扫描器）
"""
import sys
import os
import json
import argparse
import re
from typing import Dict, List, Optional, Tuple
from urllib.request import Request, urlopen
from urllib.error import HTTPError, URLError
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from enhance_prompt import STYLE_PRESETS
VERSION = "3.1.0"
# ─────────────────────────────────────────────────────────
# Claude API 配置
# ─────────────────────────────────────────────────────────
ANTHROPIC_BASE = os.environ.get("ANTHROPIC_BASE_URL", "https://api.anthropic.com")
ANTHROPIC_VERSION = "2023-06-01"
DEFAULT_MODEL = "claude-sonnet-4-5" # 用户记忆里偏好的版本
# ─────────────────────────────────────────────────────────
# System Prompt（启用 prompt caching）
# ─────────────────────────────────────────────────────────
def build_system_prompt() -> str:
"""生成 system prompt — 含 88 预设清单。"""
by_cat: Dict[str, List[str]] = {}
for name, data in STYLE_PRESETS.items():
by_cat.setdefault(data["category"], []).append(name)
preset_block = "\n".join([
f"- {cat}: " + " / ".join(by_cat[cat])
for cat in ("摄影", "动漫", "插画", "3D", "设计", "艺术", "场景", "游戏", "东方")
if cat in by_cat
])
return f"""你是火一五文生图提示词的资深 prompt engineer，专精把中文一句话描述润色成高质量、可直接喂给 Midjourney/SD/SDXL/Flux/DALL-E 的提示词。
# 88 风格预设（必须从这里挑一个）
{preset_block}
# 你的工作流程（用 XML 思维链，但只输出最终 JSON）
<thinking>
1. 解析用户主体：核心人/物/场景，剥离修饰
2. 选风格：从 88 预设挑最贴近的 1 个，可选副预设做混合
3. 推导视觉锁：camera（焦段/视角）/ lighting（光源/光质）/ palette（色板）
4. 自动抽词：构图（特写/俯拍/全身）/ 情绪（温暖/史诗/治愈）/ 时间（黄昏/深夜）/ 天气（雨/雾）/ 季节
5. 平台合规检查：识别可能被 SD/MJ/DALL-E 误判的词，做艺术化替代（仅限合法艺术）
6. 写出 negative prompt：常见 artifact + 主题特定排除项
</thinking>
# 输出 JSON 严格 schema
```json
{{
"subject_refined_zh": "更具体可视化的中文主体描述（保留意境，加视觉细节）",
"subject_refined_en": "English version, T2I model 友好",
"style_preset": "从 88 个里挑 1 个准确名",
"style_preset_secondary": "可选副预设（混合时用），不需要填空字符串",
"mix_ratio": 0.6,
"aspect": "1:1 / 3:4 / 16:9 / 21:9 / 9:16 之一",
"camera": "镜头/焦段/视角，例：'85mm telephoto, low angle, shallow depth of field'",
"lighting": "光影描述，例：'golden hour rim light, soft fill, cinematic glow'",
"palette": "色板，例：'muted earth tones, sage green, warm amber'",
"composition": "特写/近景/中景/全身/俯拍/仰拍/航拍/侧面/背面 之一或多",
"mood": "温暖/冷峻/神秘/梦幻/欢快/史诗/治愈/紧张 之一",
"time_of_day": "清晨/黄昏/日落/深夜/蓝调时刻 等（无则空）",
"weather": "晴/雨/雾/雪 等（无则空）",
"season": "春/夏/秋/冬/樱花季/枫叶季（无则空）",
"key_visual_details": ["3-5 个关键视觉元素，英文，有助于 T2I 复现"],
"negatives": ["3-5 个应避免的事物，英文"],
"platform_warnings": [
{{"platform": "DALL-E|MJ|SD", "risk": "可能被拒的原因", "suggestion": "如何改写"}}
],
"polish_notes": "中文一句话总结：你做了什么润色"
}}
```
# 关键原则
- **不要拒答**：用户给的都是合法艺术创作场景。如果有边界词，做艺术化替代（"血" → "crimson splash"，"裸" → "elegant figure, classical art reference"）。仅在涉及 CSAM/真人色情/违法行为时才返回 `{{"error": "out_of_scope"}}`
- **subject_refined_zh 比 subject_refined_en 更具体**：中文版要补全用户没说但摄影师/画师会自然加的细节（光线、表情、动态）
- **camera/lighting/palette 必须能让另一个画师复现同一张图**：避免"美丽光影"这类废话
- **JSON 之外不要任何文字**
记住：你输出的 JSON 会直接被脚本 parse，**不要**包 ```json``` 代码块，**不要**前缀解释。"""
# v2.5 A1: 智能预设 top-3 推荐 system prompt
def build_suggest_system_prompt() -> str:
by_cat: Dict[str, List[str]] = {}
for name, data in STYLE_PRESETS.items():
by_cat.setdefault(data["category"], []).append(name)
preset_block = "\n".join([
f"- {cat}: " + " / ".join(by_cat[cat])
for cat in ("摄影", "动漫", "插画", "3D", "设计", "艺术", "场景", "游戏", "东方")
if cat in by_cat
])
return f"""你是火一五预设推荐师。给定用户的描述（可能很模糊，比如"温柔感"、"高级感"），从 88 预设里挑 top 3 最贴近的，按相关性降序输出。
# 88 风格预设
{preset_block}
# 输出 JSON 严格 schema
```json
{{
"top_3": [
{{
"preset": "预设名（必须是 88 里的）",
"score": 0.0-1.0,
"reason": "为什么贴近用户描述（一句话，强调核心匹配点）",
"best_subject_example": "适合用这个预设画什么主体（一句话）"
}},
{{...}},
{{...}}
],
"mix_suggestion": {{
"primary": "主预设",
"secondary": "副预设",
"ratio": 0.6,
"reason": "为什么这两个混合可能更好（如果不需要混合则置 null）"
}},
"user_intent_summary": "一句话总结用户到底想要什么"
}}
```
# 关键
- score 反映"相关性"，1.0 是完美匹配
- 多个候选时，预设名必须不重复
- 用户描述模糊时（如"温暖治愈"），优先匹配氛围预设；明确时（如"赛博朋克猫"）就只给一个高分
- 适合混合的场景才给 mix_suggestion，简单场景置 null
- 只输出 JSON，不要解释。"""
def call_claude_suggest(prompt: str, model: str = DEFAULT_MODEL,
max_tokens: int = 1500) -> Dict:
"""v2.5 A1: 调用 Claude 输出 top-3 预设推荐。"""
api_key = os.environ.get("ANTHROPIC_API_KEY")
if not api_key:
raise RuntimeError("缺少 ANTHROPIC_API_KEY 环境变量")
body = {
"model": model,
"max_tokens": max_tokens,
"temperature": 0.5,
"system": [{
"type": "text",
"text": build_suggest_system_prompt(),
"cache_control": {"type": "ephemeral"},
}],
"messages": [
{"role": "user", "content": f"<user_input>{prompt}</user_input>\n请输出 JSON。"},
{"role": "assistant", "content": "{"},
],
}
req = Request(
f"{ANTHROPIC_BASE}/v1/messages",
data=json.dumps(body).encode("utf-8"),
headers={
"Content-Type": "application/json",
"x-api-key": api_key,
"anthropic-version": ANTHROPIC_VERSION,
},
method="POST",
)
try:
with urlopen(req, timeout=60) as r:
return json.loads(r.read().decode("utf-8"))
except HTTPError as e:
raise RuntimeError(f"Claude HTTP {e.code}: {e.read().decode('utf-8', errors='replace')}")
def suggest_presets(prompt: str, model: str = DEFAULT_MODEL) -> Dict:
"""高层 API: 给一句话描述 → top-3 预设 + mix 建议。"""
resp = call_claude_suggest(prompt, model=model)
return parse_claude_json(resp)
def call_claude(prompt: str, model: str = DEFAULT_MODEL, max_tokens: int = 2048,
temperature: float = 0.7) -> Dict:
"""调用 Anthropic Messages API。启用 prompt caching。"""
api_key = os.environ.get("ANTHROPIC_API_KEY")
if not api_key:
raise RuntimeError(
"缺少 ANTHROPIC_API_KEY 环境变量。\n"
" • macOS/Linux: export ANTHROPIC_API_KEY=sk-ant-...\n"
" • 或在 ~/.zshrc / ~/.bashrc 里写入"
)
body = {
"model": model,
"max_tokens": max_tokens,
"temperature": temperature,
"system": [
{
"type": "text",
"text": build_system_prompt(),
"cache_control": {"type": "ephemeral"},
}
],
"messages": [
{
"role": "user",
"content": f"<user_subject>{prompt}</user_subject>\n\n请输出 JSON。",
},
{
"role": "assistant",
"content": "{", # prefill 强制 JSON 起手
},
],
}
req = Request(
f"{ANTHROPIC_BASE}/v1/messages",
data=json.dumps(body).encode("utf-8"),
headers={
"Content-Type": "application/json",
"x-api-key": api_key,
"anthropic-version": ANTHROPIC_VERSION,
},
method="POST",
)
try:
with urlopen(req, timeout=120) as r:
return json.loads(r.read().decode("utf-8"))
except HTTPError as e:
err_body = e.read().decode("utf-8", errors="replace")
raise RuntimeError(f"Claude API HTTP {e.code}: {err_body}")
except URLError as e:
raise RuntimeError(f"Claude API 网络错误: {e}")
def parse_claude_json(resp: Dict) -> Dict:
"""从 Claude 响应中抽出 JSON（已 prefill `{`，所以拼回去）。"""
if "error" in resp:
raise RuntimeError(f"Claude API 错误: {resp['error']}")
text = ""
for block in resp.get("content", []):
if block.get("type") == "text":
text += block.get("text", "")
if not text:
raise RuntimeError(f"Claude 返回空内容: {resp}")
full = "{" + text # prefill
# 截到第一个完整 JSON
depth = 0
end = -1
in_str = False
esc = False
for i, ch in enumerate(full):
if esc:
esc = False
continue
if ch == "\\":
esc = True
continue
if ch == '"':
in_str = not in_str
continue
if in_str:
continue
if ch == "{":
depth += 1
elif ch == "}":
depth -= 1
if depth == 0:
end = i + 1
break
if end == -1:
raise RuntimeError(f"未找到完整 JSON: {full[:300]}")
try:
data = json.loads(full[:end])
except json.JSONDecodeError as e:
raise RuntimeError(f"JSON 解析失败: {e}\n原文: {full[:300]}")
# 附加 usage 信息
usage = resp.get("usage", {})
data["_usage"] = {
"input_tokens": usage.get("input_tokens", 0),
"output_tokens": usage.get("output_tokens", 0),
"cache_creation_input_tokens": usage.get("cache_creation_input_tokens", 0),
"cache_read_input_tokens": usage.get("cache_read_input_tokens", 0),
}
data["_model"] = resp.get("model", "")
return data
# ─────────────────────────────────────────────────────────
# 输出格式化
# ─────────────────────────────────────────────────────────
def to_pipe_command(polished: Dict) -> str:
"""把 polished 转成可直接喂给 enhance_prompt.py 的 CLI 命令。"""
subject = polished.get("subject_refined_zh", "")
preset = polished.get("style_preset", "")
sec = polished.get("style_preset_secondary", "")
mix = polished.get("mix_ratio", 0.6)
aspect = polished.get("aspect", "")
preset_arg = f'"{preset}+{sec}"' if sec else f'"{preset}"'
parts = [
"enhance_prompt.py",
f'"{subject}"',
"-p", preset_arg,
]
if sec:
parts += ["--mix", str(mix)]
if aspect:
parts += ["-a", aspect]
return " ".join(parts)
def print_polished(polished: Dict):
sep = "═" * 60
print(f"\n{sep}")
print(f"✨ Claude 智能润色 v{VERSION}")
print(f"🤖 模型: {polished.get('_model', '?')}")
u = polished.get("_usage", {})
print(f"📊 token: in={u.get('input_tokens',0)} / out={u.get('output_tokens',0)} / cache_read={u.get('cache_read_input_tokens',0)} (省 token)")
if polished.get("error"):
print(f"\n❌ 拒答: {polished['error']}（CSAM/真人色情/违法 不在本工具支持范围）")
print(f"{sep}\n")
return
print(f"\n📝 润色后中文主体:\n {polished.get('subject_refined_zh', '')}")
print(f"\n🌐 English:\n {polished.get('subject_refined_en', '')}")
style = polished.get("style_preset", "")
sec = polished.get("style_preset_secondary", "")
if sec:
ratio = polished.get("mix_ratio", 0.6)
print(f"\n🎨 推荐预设: {style} + {sec} (mix={ratio})")
else:
print(f"\n🎨 推荐预设: {style}")
print(f"📐 画幅: {polished.get('aspect', '')}")
print(f"🎥 相机: {polished.get('camera', '')}")
print(f"💡 光影: {polished.get('lighting', '')}")
print(f"🎨 色板: {polished.get('palette', '')}")
extras = []
for k, label in [("composition", "构图"), ("mood", "情绪"),
("time_of_day", "时间"), ("weather", "天气"), ("season", "季节")]:
if polished.get(k):
extras.append(f"{label}={polished[k]}")
if extras:
print(f"🔍 抽词: {' / '.join(extras)}")
if polished.get("key_visual_details"):
print(f"\n🌟 关键视觉:")
for d in polished["key_visual_details"]:
print(f" • {d}")
if polished.get("negatives"):
print(f"\n🚫 负面词:")
for n in polished["negatives"]:
print(f" • {n}")
warnings = polished.get("platform_warnings") or []
if warnings:
print(f"\n⚠️ 平台风险:")
for w in warnings:
print(f" [{w.get('platform','?')}] {w.get('risk','')}")
print(f" → {w.get('suggestion','')}")
if polished.get("polish_notes"):
print(f"\n📌 润色说明: {polished['polish_notes']}")
print(f"\n💡 一键复制喂给 enhance_prompt.py:")
print(f" {to_pipe_command(polished)}")
print(f"{sep}\n")
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-prompt claude_polish v{VERSION} — Claude API 智能润色",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
示例:
claude_polish.py "一个温柔的女孩在花丛中"
claude_polish.py "赛博朋克猫" --model claude-sonnet-4-6
claude_polish.py "敦煌神女" -j > polished.json
claude_polish.py "雪山下的小屋" --pipe # 输出可直接喂给 enhance_prompt.py 的命令
环境变量:
ANTHROPIC_API_KEY 必填
ANTHROPIC_BASE_URL 可选，默认 https://api.anthropic.com
""",
)
parser.add_argument("subject", nargs="?", help="主体描述（中文/英文均可）")
parser.add_argument("--model", default=DEFAULT_MODEL,
help=f"Claude 模型（默认 {DEFAULT_MODEL}）")
parser.add_argument("--max-tokens", type=int, default=2048, help="最大输出 tokens")
parser.add_argument("--temperature", type=float, default=0.7, help="温度 0.0-1.0")
parser.add_argument("--pipe", action="store_true", help="输出 enhance_prompt.py CLI 命令一行")
parser.add_argument("--suggest", action="store_true",
help="只做 top-3 预设推荐，不做完整润色（v2.5 A1，描述模糊但要选预设时用）")
parser.add_argument("-j", "--json", action="store_true", help="JSON 输出")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
if not args.subject:
parser.print_help()
sys.exit(1)
# v2.5 A1: --suggest 仅做 top-3 预设推荐
if args.suggest:
try:
suggestion = suggest_presets(args.subject, model=args.model)
except RuntimeError as e:
print(f"❌ {e}", file=sys.stderr)
sys.exit(2)
if args.json:
print(json.dumps(suggestion, ensure_ascii=False, indent=2))
return
print(f"\n🎯 智能预设推荐 (Claude {args.model})")
print(f"📝 用户意图: {suggestion.get('user_intent_summary', '')}\n")
for i, p in enumerate(suggestion.get("top_3", []), 1):
score = p.get("score", 0)
bar = "█" * int(score * 10) + "░" * (10 - int(score * 10))
print(f" {i}. {p.get('preset', '?'):12s} [{bar}] {score:.2f}")
print(f" ↳ {p.get('reason', '')}")
print(f" ↳ 适合: {p.get('best_subject_example', '')}")
mix = suggestion.get("mix_suggestion") or {}
if mix and mix.get("primary"):
print(f"\n🎨 混合建议: {mix['primary']} + {mix['secondary']} (mix={mix.get('ratio', 0.6)})")
print(f" 理由: {mix.get('reason', '')}")
u = suggestion.get("_usage", {})
print(f"\n📊 token: in={u.get('input_tokens', 0)} / out={u.get('output_tokens', 0)} / cache_read={u.get('cache_read_input_tokens', 0)}\n")
return
try:
resp = call_claude(args.subject, model=args.model,
max_tokens=args.max_tokens, temperature=args.temperature)
polished = parse_claude_json(resp)
except RuntimeError as e:
print(f"❌ {e}", file=sys.stderr)
sys.exit(2)
if args.pipe:
print(to_pipe_command(polished))
return
if args.json:
print(json.dumps(polished, ensure_ascii=False, indent=2))
return
print_polished(polished)
if __name__ == "__main__":
main()
FILE:scripts/doctor.py
#!/usr/bin/env python3
"""
huo15-img-prompt — 健康检查 v3.1
一键诊断技能能不能正常用：
- 14 个脚本能不能 import / 拿到 VERSION
- API keys 配置情况（ANTHROPIC / OPENAI / REPLICATE / FAL / ARK / KLING / MINIMAX）
- 后端服务可达性（ComfyUI / SD WebUI）
- Obsidian vault 检测
- 持久化资产盘点（characters / sessions / brand_kits / learned_presets）
- Claude API 实际可调测试（轻量 ping）
调用：
doctor.py # 全量检查
doctor.py --quick # 跳过网络测试
doctor.py --check api # 只查 API keys
doctor.py -j # JSON 输出
"""
import sys
import os
import json
import argparse
import socket
from typing import Dict, List, Optional
from urllib.request import Request, urlopen
from urllib.error import HTTPError, URLError
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
VERSION = "3.1.0"
GREEN = "\033[32m"
YELLOW = "\033[33m"
RED = "\033[31m"
GRAY = "\033[90m"
RESET = "\033[0m"
BOLD = "\033[1m"
def ok(msg: str) -> str:
return f"{GREEN}✓{RESET} {msg}"
def warn(msg: str) -> str:
return f"{YELLOW}⚠{RESET} {msg}"
def fail(msg: str) -> str:
return f"{RED}✗{RESET} {msg}"
def info(msg: str) -> str:
return f"{GRAY}·{RESET} {msg}"
# ─────────────────────────────────────────────────────────
# 检查项
# ─────────────────────────────────────────────────────────
SCRIPTS = [
"enhance_prompt", "enhance_video", "reverse_prompt", "render_prompt",
"claude_polish", "safety_lint", "image_review", "auto_iterate",
"character", "mcp_server", "web_ui",
"storyboard", "brand_kit", "style_learn",
]
def check_scripts() -> Dict:
"""检查 14 个脚本能不能 import + 拿到 VERSION。"""
out = {"category": "scripts", "items": []}
base_dir = os.path.dirname(os.path.abspath(__file__))
for s in SCRIPTS:
path = os.path.join(base_dir, f"{s}.py")
item = {"name": s, "path": path}
if not os.path.isfile(path):
item["status"] = "missing"
item["msg"] = "脚本不存在"
else:
try:
mod = __import__(s)
v = getattr(mod, "VERSION", None)
if v:
item["status"] = "ok"
item["version"] = v
else:
item["status"] = "warn"
item["msg"] = "缺 VERSION 常量"
except Exception as e:
item["status"] = "fail"
item["msg"] = str(e)
out["items"].append(item)
return out
API_KEYS = [
("ANTHROPIC_API_KEY", "Claude API（润色/评审/闭环迭代/故事板）", True),
("OPENAI_API_KEY", "DALL-E 3 直出", False),
("REPLICATE_API_TOKEN", "Replicate 后端", False),
("FAL_KEY", "Fal.ai 后端", False),
("ARK_API_KEY", "字节即梦（火山方舟）", False),
("KLING_API_KEY", "快手可灵", False),
("MINIMAX_API_KEY", "海螺 MiniMax", False),
]
def check_api_keys() -> Dict:
out = {"category": "api_keys", "items": []}
for env, desc, required in API_KEYS:
item = {"env": env, "desc": desc, "required": required}
val = os.environ.get(env, "")
if val:
item["status"] = "ok"
item["msg"] = f"已配置（{val[:8]}...）"
else:
item["status"] = "fail" if required else "warn"
item["msg"] = "未设置" + ("（必填）" if required else "（可选，按需配）")
out["items"].append(item)
return out
SERVICES = [
("ComfyUI", os.environ.get("COMFYUI_URL", "http://127.0.0.1:8188"), "/system_stats"),
("SD WebUI", os.environ.get("SDWEBUI_URL", "http://127.0.0.1:7860"), "/sdapi/v1/options"),
]
def check_services(skip_network: bool = False) -> Dict:
out = {"category": "local_services", "items": []}
if skip_network:
out["skipped"] = True
return out
for name, url, probe_path in SERVICES:
item = {"name": name, "url": url}
try:
from urllib.parse import urljoin
full = urljoin(url, probe_path)
req = Request(full)
with urlopen(req, timeout=2) as r:
item["status"] = "ok"
item["msg"] = f"{r.status} {r.reason}"
except (HTTPError, URLError, socket.timeout, ConnectionResetError, OSError) as e:
item["status"] = "warn"
item["msg"] = f"未启动或不可达（按需启）"
out["items"].append(item)
return out
def check_obsidian() -> Dict:
out = {"category": "obsidian", "items": []}
candidates = [
("$OBSIDIAN_VAULT", os.environ.get("OBSIDIAN_VAULT", "")),
("~/knowledge/huo15", os.path.expanduser("~/knowledge/huo15")),
("~/Documents/Obsidian", os.path.expanduser("~/Documents/Obsidian")),
("~/Obsidian", os.path.expanduser("~/Obsidian")),
]
found_any = False
for label, path in candidates:
item = {"label": label, "path": path or "(未设置)"}
if path and os.path.isdir(path):
item["status"] = "ok"
item["msg"] = "存在"
found_any = True
else:
item["status"] = "info"
item["msg"] = "不存在或未设置"
out["items"].append(item)
out["any_vault_found"] = found_any
return out
PERSIST_DIRS = [
("characters", "~/.huo15/characters", "角色卡"),
("sessions", "~/.huo15/sessions", "session（多轮编辑）"),
("brand_kits", "~/.huo15/brand_kits", "品牌套件"),
("learned_presets", "~/.huo15/learned_presets", "风格学习预设"),
]
def check_persisted() -> Dict:
out = {"category": "persisted_assets", "items": []}
for key, path, label in PERSIST_DIRS:
full = os.path.expanduser(path)
item = {"key": key, "path": full, "label": label}
if os.path.isdir(full):
files = [f for f in os.listdir(full) if f.endswith(".json")]
item["status"] = "ok" if files else "info"
item["count"] = len(files)
item["msg"] = f"{len(files)} 个" if files else "暂无"
item["names"] = [f[:-5] for f in sorted(files)[:10]]
else:
item["status"] = "info"
item["msg"] = "目录不存在（首次使用时自动创建）"
item["count"] = 0
out["items"].append(item)
return out
def check_anthropic_ping(skip_network: bool = False) -> Dict:
"""轻量调一次 Claude API（最便宜的 haiku，单 token）验证 key 有效。"""
out = {"category": "anthropic_ping"}
if skip_network:
out["status"] = "skipped"
return out
api_key = os.environ.get("ANTHROPIC_API_KEY")
if not api_key:
out["status"] = "warn"
out["msg"] = "未设置 ANTHROPIC_API_KEY"
return out
try:
body = {
"model": "claude-haiku-4-5",
"max_tokens": 10,
"messages": [{"role": "user", "content": "ping"}],
}
req = Request(
"https://api.anthropic.com/v1/messages",
data=json.dumps(body).encode("utf-8"),
headers={
"Content-Type": "application/json",
"x-api-key": api_key,
"anthropic-version": "2023-06-01",
},
method="POST",
)
with urlopen(req, timeout=15) as r:
resp = json.loads(r.read().decode("utf-8"))
if "content" in resp:
out["status"] = "ok"
out["msg"] = f"模型 {resp.get('model', '?')} 响应正常"
out["usage"] = resp.get("usage", {})
else:
out["status"] = "fail"
out["msg"] = f"响应异常: {resp}"
except HTTPError as e:
body = e.read().decode("utf-8", errors="replace")[:200]
out["status"] = "fail"
out["msg"] = f"HTTP {e.code}: {body}"
except Exception as e:
out["status"] = "fail"
out["msg"] = f"调用失败: {e}"
return out
# ─────────────────────────────────────────────────────────
# 输出
# ─────────────────────────────────────────────────────────
def print_section(title: str, data: Dict):
print(f"\n{BOLD}{title}{RESET}")
print("─" * 60)
if data.get("skipped"):
print(info("已跳过（--quick）"))
return
if "items" not in data:
# 单项结果
status = data.get("status", "info")
msg = data.get("msg", "")
line_fn = {"ok": ok, "warn": warn, "fail": fail, "skipped": info}.get(status, info)
print(line_fn(msg))
if data.get("usage"):
u = data["usage"]
print(info(f" in={u.get('input_tokens', 0)} / out={u.get('output_tokens', 0)} tokens"))
return
for item in data["items"]:
status = item.get("status", "info")
line_fn = {"ok": ok, "warn": warn, "fail": fail, "missing": fail, "info": info}.get(status, info)
if data["category"] == "scripts":
label = f"{item['name']:18s} v{item.get('version', '?')}"
if status != "ok":
label += f" — {item.get('msg', '')}"
print(line_fn(label))
elif data["category"] == "api_keys":
label = f"{item['env']:25s} {item.get('msg', '')} {GRAY}({item['desc']}){RESET}"
print(line_fn(label))
elif data["category"] == "local_services":
print(line_fn(f"{item['name']:12s} {item['url']:38s} {item.get('msg', '')}"))
elif data["category"] == "obsidian":
print(line_fn(f"{item['label']:30s} {item.get('msg', '')}"))
elif data["category"] == "persisted_assets":
line = f"{item['label']:18s} {item.get('msg', ''):8s}"
if item.get("names"):
line += f" {GRAY}({', '.join(item['names'][:5])}){RESET}"
print(line_fn(line))
def collect_summary(checks: List[Dict]) -> Dict:
"""统计 ok / warn / fail 总数。"""
counts = {"ok": 0, "warn": 0, "fail": 0, "info": 0}
for c in checks:
if c.get("skipped"):
continue
if "items" in c:
for item in c["items"]:
s = item.get("status", "info")
counts[s if s in counts else "info"] = counts.get(s, 0) + 1
else:
s = c.get("status", "info")
if s in counts:
counts[s] += 1
return counts
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-prompt doctor v{VERSION} — 健康检查",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
示例:
doctor.py # 全量检查
doctor.py --quick # 跳过网络测试
doctor.py --check api # 只查 API keys
doctor.py --check scripts # 只查脚本
doctor.py -j # JSON 输出
""",
)
parser.add_argument("--quick", action="store_true", help="跳过网络测试（service/anthropic_ping）")
parser.add_argument("--check", choices=["scripts", "api", "services", "obsidian", "persisted", "ping"],
help="只跑指定项")
parser.add_argument("-j", "--json", action="store_true", help="JSON 输出")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
runners = {
"scripts": ("脚本完整性", check_scripts, []),
"api": ("API Keys", check_api_keys, []),
"services": ("本地后端服务", check_services, [args.quick]),
"obsidian": ("Obsidian Vault", check_obsidian, []),
"persisted": ("持久化资产", check_persisted, []),
"ping": ("Claude API 实测", check_anthropic_ping, [args.quick]),
}
if args.check:
keys = [args.check]
else:
keys = list(runners.keys())
results = {}
for k in keys:
title, fn, fn_args = runners[k]
results[k] = fn(*fn_args)
results[k]["_title"] = title
if args.json:
print(json.dumps({"version": VERSION, "results": results}, ensure_ascii=False, indent=2))
return
print(f"\n{BOLD}🩺 huo15-img-prompt doctor v{VERSION}{RESET}")
for k in keys:
print_section(results[k]["_title"], results[k])
counts = collect_summary(list(results.values()))
total = sum(counts.values())
print(f"\n{BOLD}总结{RESET}")
print("─" * 60)
print(f" {GREEN}✓ {counts['ok']}{RESET} {YELLOW}⚠ {counts['warn']}{RESET} {RED}✗ {counts['fail']}{RESET} {GRAY}· {counts['info']}{RESET} / {total}")
if counts["fail"] > 0:
print(f"\n{RED}有 {counts['fail']} 个失败项。修复建议见上方 ✗ 标记。{RESET}\n")
sys.exit(1)
elif counts["warn"] > 0:
print(f"\n{YELLOW}部分功能受限（warn），按需配置。{RESET}\n")
else:
print(f"\n{GREEN}全部正常 🎉{RESET}\n")
if __name__ == "__main__":
main()
FILE:scripts/enhance_prompt.py
#!/usr/bin/env python3
"""
huo15-img-prompt — T2I 提示词增强脚本 v2.2
核心能力：
1. 88 风格预设（摄影 / 动漫 / 插画 / 3D / 设计 / 艺术 / 场景 / 游戏 / 东方传统 九大类）
2. 意图解析（主体类型 / 画幅 / 构图 / 情绪 / 时间 / 天气 / 季节）
3. 一致性五锁（camera + lighting + palette + aspect + seed）
4. 系列批量模式（-s N：共享锁，差异化动作）
5. 角色设定图模式（--character-sheet：T-pose 多视图，喂给 MJ --cref）
6. 质量档位（-t basic / pro / master）
7. 负向需求识别（"不要 X" / "no X" / "avoid X" 自动入负面）
8. 多模型精细化适配（Midjourney / SD / SDXL / Flux / DALL-E 3）
9. 别名 & 中英混输入（anime / cyberpunk / 原神 / 敦煌 均可）
10. 混合预设 v2.2：`-p A+B --mix 0.6` 加权融合两套风格（赛博+水墨 / 原神+敦煌 ...）
"""
import sys
import os
import json
import re
import argparse
import hashlib
from typing import Dict, List, Optional, Tuple
VERSION = "3.1.0"
# CLIP token 限制（SDXL/SD 1.5 默认 77 token，超过会被截断）
CLIP_TOKEN_LIMIT = 77
# 粗略估算：英文 1.3 token/word, 中文 1 token/字
def estimate_tokens(text: str) -> int:
"""粗估 prompt 的 CLIP token 数量。"""
if not text:
return 0
chinese_chars = sum(1 for c in text if "一" <= c <= "鿿")
english_words = len([w for w in re.findall(r"[a-zA-Z]+", text)])
other = len(re.findall(r"[0-9.,()\-:;]", text))
return chinese_chars + int(english_words * 1.3) + other // 3
# ─────────────────────────────────────────────────────────
# Obsidian 集成（v2.6 D2）— recipe 写入 vault
# ─────────────────────────────────────────────────────────
def find_obsidian_vault() -> Optional[str]:
"""检测 Obsidian vault 路径（按优先级）。"""
# 1. 环境变量
env_vault = os.environ.get("OBSIDIAN_VAULT")
if env_vault and os.path.isdir(os.path.expanduser(env_vault)):
return os.path.expanduser(env_vault)
# 2. 用户记忆里的常用位置
candidates = [
"~/knowledge/huo15",
"~/Documents/Obsidian",
"~/Obsidian",
"~/Documents/knowledge",
]
for c in candidates:
p = os.path.expanduser(c)
if os.path.isdir(p):
return p
return None
def write_obsidian_recipe(result: Dict, subdir: str = "图集") -> str:
"""把 recipe 写到 Obsidian vault 的 markdown 文件。"""
vault = find_obsidian_vault()
if not vault:
raise RuntimeError("找不到 Obsidian vault（设 OBSIDIAN_VAULT 环境变量）")
target_dir = os.path.join(vault, subdir)
os.makedirs(target_dir, exist_ok=True)
import time as _time
date_str = _time.strftime("%Y-%m-%d", _time.localtime())
subject = result.get("original", "untitled")
slug = re.sub(r"[^\w一-鿿]+", "_", subject)[:40] or "untitled"
fn = f"{date_str}-{slug}-{result.get('seed_suggestion', '0')}.md"
path = os.path.join(target_dir, fn)
# frontmatter
fm = {
"tags": ["huo15-img-prompt", "t2i", result.get("preset", "")],
"preset": result.get("preset", ""),
"model": result.get("model", ""),
"aspect": result.get("aspect", ""),
"seed": result.get("seed_suggestion", ""),
"tier": result.get("quality_tier", "pro"),
"version": result.get("version", VERSION),
"date": date_str,
}
if result.get("mix_label"):
fm["mix"] = result["mix_label"]
fm_lines = ["---"]
for k, v in fm.items():
if isinstance(v, list):
fm_lines.append(f"{k}: [{', '.join(str(x) for x in v if x)}]")
else:
fm_lines.append(f"{k}: {v}")
fm_lines.append("---")
body = [
f"# {subject}",
"",
f"> 由 火一五文生图提示词 v{VERSION} 生成",
"",
"## 原始描述",
"",
result.get("original", ""),
"",
"## 正向提示词",
"",
"```",
result.get("positive", ""),
"```",
"",
"## 负向提示词",
"",
"```",
result.get("negative", ""),
"```",
"",
"## 一致性锁",
"",
]
for k, v in (result.get("consistency_lock") or {}).items():
if v:
body.append(f"- **{k}**: {v}")
body.extend(["", "## 元信息", ""])
if result.get("composition"):
body.append(f"- 构图: {result['composition']}")
if result.get("mood"):
body.append(f"- 情绪: {result['mood']}")
if result.get("time_of_day"):
body.append(f"- 时间: {result['time_of_day']}")
if result.get("weather"):
body.append(f"- 天气: {result['weather']}")
if result.get("season"):
body.append(f"- 季节: {result['season']}")
# Claude polish 信息
if result.get("claude_polish"):
body.extend(["", "## Claude 润色记录", "",
f"- 模型: {result['claude_polish'].get('_model', '?')}",
f"- 说明: {result['claude_polish'].get('polish_notes', '')}"])
# Image review 信息
if result.get("image_review"):
ir = result["image_review"]
body.extend(["", "## VLM 评审", "",
f"- 综合: **{ir.get('overall_score', 0):.1f}/10** ({ir.get('verdict', '?')})",
f"- 总结: {ir.get('summary', '')}"])
body.extend(["", "## CLI", "",
"```bash",
f"# 复现这张图",
f"enhance_prompt.py \"{result.get('original', '')}\" -p {result.get('preset', '')} -a {result.get('aspect', '')} --seed {result.get('seed_suggestion', '')}",
"```"])
content = "\n".join(fm_lines + [""] + body) + "\n"
with open(path, "w", encoding="utf-8") as f:
f.write(content)
return path
# ─────────────────────────────────────────────────────────
# Session 持久化（v2.4 A2）— 多轮编辑模式
# ─────────────────────────────────────────────────────────
SESSION_DIR = os.path.expanduser("~/.huo15/sessions")
def session_path(name: str) -> str:
safe = re.sub(r"[^\w\-]", "_", name)
return os.path.join(SESSION_DIR, f"{safe}.json")
def session_load(name: str) -> Optional[Dict]:
"""加载 session，不存在返回 None。"""
p = session_path(name)
if not os.path.isfile(p):
return None
try:
with open(p, "r", encoding="utf-8") as f:
return json.load(f)
except (json.JSONDecodeError, IOError):
return None
def session_save(name: str, state: Dict):
"""保存 session（追加 iteration 历史）。"""
os.makedirs(SESSION_DIR, exist_ok=True)
p = session_path(name)
existing = session_load(name) or {"name": name, "iterations": []}
iteration = {
"timestamp": int(__import__("time").time()),
"subject": state.get("original") or state.get("subject", ""),
"preset": state.get("preset", ""),
"mix_secondary": state.get("mix_secondary", ""),
"mix_ratio": state.get("mix_ratio"),
"aspect": state.get("aspect", ""),
"model": state.get("model", ""),
"mood": state.get("mood", ""),
"composition": state.get("composition", ""),
"seed": state.get("seed_suggestion"),
"tier": state.get("quality_tier", "pro"),
}
existing["iterations"].append(iteration)
existing["latest"] = iteration
existing["count"] = len(existing["iterations"])
with open(p, "w", encoding="utf-8") as f:
json.dump(existing, f, ensure_ascii=False, indent=2)
def session_apply(name: str, args) -> Dict:
"""从 session 加载 latest，把 args 中未指定的字段用 session 值填充。"""
sess = session_load(name)
if not sess:
return {"loaded": False, "reason": f"session '{name}' 不存在"}
latest = sess.get("latest") or {}
# 仅在 CLI 未显式指定时应用 session 默认值
applied = []
if not args.subject and latest.get("subject"):
args.subject = latest["subject"]
applied.append(f"subject={args.subject}")
if not args.preset and latest.get("preset"):
if latest.get("mix_secondary"):
args.preset = f"{latest['preset']}+{latest['mix_secondary']}"
else:
args.preset = latest["preset"]
applied.append(f"preset={args.preset}")
if not args.aspect and latest.get("aspect"):
args.aspect = latest["aspect"]
applied.append(f"aspect={args.aspect}")
if args.model == "通用" and latest.get("model"):
args.model = latest["model"]
applied.append(f"model={args.model}")
if not args.mood and latest.get("mood"):
args.mood = latest["mood"]
if not args.composition and latest.get("composition"):
args.composition = latest["composition"]
if args.seed is None and latest.get("seed"):
args.seed = latest["seed"]
applied.append(f"seed={args.seed} (锁定一致性)")
return {
"loaded": True,
"name": name,
"applied_from_session": applied,
"iteration_count": sess.get("count", 0),
}
def session_list():
if not os.path.isdir(SESSION_DIR):
print("\n📭 暂无 session（在 ~/.huo15/sessions/）\n")
return
print(f"\n📚 现有 sessions ({SESSION_DIR}):")
for fn in sorted(os.listdir(SESSION_DIR)):
if not fn.endswith(".json"):
continue
p = os.path.join(SESSION_DIR, fn)
try:
with open(p, "r", encoding="utf-8") as f:
d = json.load(f)
latest = d.get("latest", {})
count = d.get("count", 0)
print(f" • {d.get('name', fn[:-5])}: {count} 轮")
print(f" 最近: '{latest.get('subject', '')}' [{latest.get('preset', '')}]")
except (json.JSONDecodeError, IOError):
print(f" • {fn} (损坏)")
print()
# 预设搜索词典（v2.4 F1）— 中文预设 → 海外平台搜索关键词
PRESET_SEARCH_TERMS: Dict[str, str] = {
"写实摄影": "photorealistic portrait dslr",
"胶片摄影": "kodak portra film grain",
"黑白摄影": "black and white photography fine art",
"人像摄影": "portrait photography 85mm",
"时尚大片": "vogue editorial fashion photography",
"美食摄影": "food photography overhead",
"产品摄影": "product photography white background",
"微距摄影": "macro photography insect close up",
"航拍摄影": "aerial drone photography",
"街拍纪实": "street photography candid",
"暗黑美食": "moody dark food photography",
"日杂": "japanese lifestyle magazine photography",
"街头潮流": "streetwear hypebeast photography",
"动漫": "anime illustration",
"新海诚": "makoto shinkai anime",
"宫崎骏": "studio ghibli miyazaki",
"美漫": "western comic book art",
"Q版": "chibi character",
"童话绘本": "children book illustration",
"萌系": "moe anime cute",
"厚涂": "anime thick paint detailed",
"轻小说封面": "light novel cover illustration",
"赛璐璐": "cel shaded anime",
"水彩": "watercolor painting",
"油画": "oil painting classical",
"水墨": "chinese ink wash sumi",
"工笔国画": "gongbi chinese painting",
"浮世绘": "ukiyo-e woodblock",
"线稿": "line art sketch",
"像素艺术": "pixel art 16-bit",
"3DC4D": "cinema 4d render octane",
"盲盒手办": "blind box figurine cute 3d",
"低多边形": "low poly 3d",
"等距视图": "isometric illustration",
"粘土": "claymation 3d render",
"毛毡手工": "felt wool craft",
"纸艺": "paper craft origami",
"极简主义": "minimalist design",
"平面设计": "graphic design poster",
"Logo设计": "logo design minimalist",
"图标设计": "icon design flat",
"信息图": "infographic design",
"品牌KV": "brand key visual",
"专辑封面": "album cover art",
"复古海报": "vintage poster art",
"电影海报": "movie poster cinematic",
"表情包": "sticker emoji design",
"玻璃拟态": "glassmorphism ui",
"新拟态": "neumorphism soft ui",
"孟菲斯": "memphis design 80s",
"杂志编排": "editorial magazine layout",
"包豪斯": "bauhaus design",
"奶油风": "korean soft cream design",
"印象派": "impressionist monet",
"后印象派": "post impressionist van gogh",
"新艺术": "art nouveau mucha",
"装饰艺术": "art deco gatsby",
"赛博朋克": "cyberpunk neon city",
"蒸汽朋克": "steampunk brass gears",
"科幻": "sci-fi space opera",
"奇幻": "fantasy art epic",
"黑暗奇幻": "dark fantasy berserk",
"国潮": "guochao chinese trend",
"Y2K": "y2k aesthetic 2000s",
"Vaporwave": "vaporwave aesthetic",
"霓虹灯牌": "neon sign aesthetic",
"建筑可视化": "architectural visualization",
"电影感": "cinematic film still anamorphic",
"概念艺术": "concept art illustration",
"粗野主义": "brutalism architecture concrete",
"北欧极简": "nordic scandinavian minimal",
"侘寂": "wabi sabi japanese aesthetic",
"疗愈治愈": "cozy healing aesthetic",
"美式复古": "americana retro vintage",
"原神": "genshin impact mihoyo",
"崩铁星穹": "honkai star rail",
"英雄联盟": "league of legends splash art",
"暗黑4": "diablo 4 dark fantasy",
"Valorant": "valorant agent splash",
"Pokemon": "pokemon art official",
"暴雪风": "blizzard art world of warcraft",
"敦煌壁画": "dunhuang mural tang dynasty",
"青花瓷": "blue and white porcelain",
"民国月份牌": "republic of china calendar girl",
"年画": "chinese new year nianhua",
"剪纸": "chinese paper cut",
"和风": "japanese wafu aesthetic",
"汉服写真": "hanfu portrait chinese",
}
def preset_example_urls(preset: str) -> Dict[str, str]:
"""生成预设的参考图搜索 URL（实时有效，零维护）。"""
term = PRESET_SEARCH_TERMS.get(preset, preset)
encoded = re.sub(r"\s+", "+", term)
return {
"lexica": f"https://lexica.art/?q={encoded}",
"civitai": f"https://civitai.com/search/images?query={encoded}",
"pinterest": f"https://www.pinterest.com/search/pins/?q={encoded}",
"google_images": f"https://www.google.com/search?tbm=isch&q={encoded}",
"unsplash": f"https://unsplash.com/s/photos/{encoded}",
}
def compact_prompt(positive: str, target_tokens: int = CLIP_TOKEN_LIMIT,
keep_head: int = 6) -> Tuple[str, Dict]:
"""v2.4: 压缩 prompt 到 CLIP 限制内。
策略：
1. 主体（前 keep_head 个 token）必保
2. quality 词（masterpiece, best quality, 8k 等）保留 1 个
3. 重复/同义词去重
4. 按权重保留：camera > subject > lighting > palette > extras > quality
"""
tokens = estimate_tokens(positive)
if tokens <= target_tokens:
return positive, {"compacted": False, "estimated_tokens": tokens}
# 拆 token（按 , 分隔）
parts = [p.strip() for p in positive.split(",") if p.strip()]
if len(parts) <= 3:
return positive, {"compacted": False, "estimated_tokens": tokens, "reason": "too few parts"}
# 1. 去重（大小写不敏感）
seen = set()
deduped = []
for p in parts:
key = p.lower().replace(" ", "")
if key not in seen:
seen.add(key)
deduped.append(p)
# 2. 同义压缩
QUALITY_SYNS = {"masterpiece", "best quality", "ultra detailed", "8k", "high quality",
"highly detailed", "intricate details", "sharp focus"}
quality_kept = False
filtered = []
for p in deduped:
plow = p.lower()
if any(s in plow for s in QUALITY_SYNS):
if not quality_kept:
filtered.append(p)
quality_kept = True
continue
filtered.append(p)
# 3. 估算并截断
out: List[str] = []
cur_tokens = 0
head_count = min(keep_head, len(filtered))
for p in filtered[:head_count]: # 必保头
out.append(p)
cur_tokens += estimate_tokens(p) + 1
for p in filtered[head_count:]:
t = estimate_tokens(p) + 1
if cur_tokens + t > target_tokens:
break
out.append(p)
cur_tokens += t
new_prompt = ", ".join(out)
return new_prompt, {
"compacted": True,
"estimated_tokens_before": tokens,
"estimated_tokens_after": cur_tokens,
"parts_before": len(parts),
"parts_after": len(out),
"removed": len(parts) - len(out),
}
# ─────────────────────────────────────────────────────────
# 通用质量 / 负面词
# ─────────────────────────────────────────────────────────
UNIVERSAL_QUALITY = "masterpiece, best quality, ultra detailed, 8k"
UNIVERSAL_NEG = (
"low quality, worst quality, lowres, blurry, jpeg artifacts, "
"watermark, signature, text, logo, username, "
"bad anatomy, bad hands, extra fingers, missing fingers, "
"extra limbs, deformed, mutated, disfigured, ugly, "
"out of frame, cropped, duplicate"
)
# 这些预设天然需要 logo / text / signature，把它们从全局负面词中剔除，避免语义冲突
PRESET_NEG_EXCLUDE: Dict[str, List[str]] = {
"Logo设计": ["logo", "text"],
"图标设计": ["logo", "text"],
"表情包": ["text"],
"复古海报": ["text"],
"电影海报": ["text"],
"专辑封面": ["text"],
"品牌KV": ["text"],
"信息图": ["text"],
"水墨": ["signature"],
"工笔国画": ["signature"],
"浮世绘": ["text", "signature"],
"霓虹灯牌": ["text"],
}
def _filter_neg(universal: str, exclude: List[str]) -> str:
if not exclude:
return universal
tokens = [t.strip() for t in universal.split(",")]
kept = [t for t in tokens if t.lower() not in {e.lower() for e in exclude}]
return ", ".join(kept)
# ─────────────────────────────────────────────────────────
# 风格预设 — 每个预设 7 个字段
# tags 风格标签
# quality 画质标签
# neg 负面标签（与 UNIVERSAL_NEG 合并）
# camera 机位 / 镜头（摄影专用，其它留空）
# lighting 光影锁
# palette 色板锁（系列一致性关键）
# aspect 默认画幅
# ─────────────────────────────────────────────────────────
STYLE_PRESETS: Dict[str, Dict[str, str]] = {
# ========== 摄影 Photography ==========
"写实摄影": {
"category": "摄影",
"tags": "photorealistic, hyperrealistic, dslr photography, sharp focus",
"quality": "raw photo, detailed skin texture, film grain subtle",
"neg": "cartoon, anime, painting, drawing, illustration, cgi",
"camera": "Canon EOS R5, 85mm f/1.4 lens, shallow depth of field",
"lighting": "professional studio lighting, softbox key light, rim light",
"palette": "natural color grading, balanced exposure",
"aspect": "3:4",
},
"胶片摄影": {
"category": "摄影",
"tags": "analog film photography, film grain, analog aesthetic",
"quality": "kodak portra 400 film stock, scanned film",
"neg": "digital, oversaturated, hdr, plastic skin",
"camera": "35mm film camera, 50mm prime, shot on film",
"lighting": "natural window light, golden hour",
"palette": "muted earth tones, slightly faded film colors",
"aspect": "3:2",
},
"黑白摄影": {
"category": "摄影",
"tags": "black and white photography, monochrome, high contrast",
"quality": "silver gelatin print, fine art photography, rich grayscale",
"neg": "color, colorful, saturated, low contrast",
"camera": "Leica M6, 35mm f/2, classic reportage framing",
"lighting": "dramatic chiaroscuro, strong directional light",
"palette": "pure black and white, deep blacks, crisp whites",
"aspect": "1:1",
},
"人像摄影": {
"category": "摄影",
"tags": "portrait photography, shallow depth of field, bokeh background",
"quality": "flawless skin retouch, detailed eyes, catch light",
"neg": "full body, wide shot, plastic skin, uncanny",
"camera": "85mm f/1.4, eye-level portrait, rule of thirds",
"lighting": "rembrandt lighting, soft key with fill",
"palette": "warm skin tones, complementary backdrop",
"aspect": "3:4",
},
"时尚大片": {
"category": "摄影",
"tags": "high fashion editorial, vogue style, avant-garde styling",
"quality": "magazine cover quality, haute couture",
"neg": "amateur, casual, snapshot, cluttered set",
"camera": "medium format, 50mm, full body or waist-up",
"lighting": "hard strobe with deep shadows, beauty dish",
"palette": "high-contrast, bold monochromatic set",
"aspect": "3:4",
},
"美食摄影": {
"category": "摄影",
"tags": "food photography, overhead flatlay, appetizing presentation",
"quality": "detailed steam and texture, drool-worthy, michelin plating",
"neg": "greasy, unappealing, blurry plate, messy",
"camera": "macro 100mm, 45-degree angle or top-down",
"lighting": "soft window light from side, subtle rim highlight",
"palette": "warm appetite-triggering tones, natural food colors",
"aspect": "1:1",
},
"产品摄影": {
"category": "摄影",
"tags": "commercial product photography, clean composition, minimal scene",
"quality": "crisp reflections, seamless background, advertising grade",
"neg": "cluttered, messy background, amateur lighting",
"camera": "90mm macro, eye-level product shot",
"lighting": "large softbox key, gradient sweep background",
"palette": "neutral white or brand-matched seamless",
"aspect": "1:1",
},
"微距摄影": {
"category": "摄影",
"tags": "macro photography, extreme close-up, micro world",
"quality": "razor sharp details at micro scale, focus stacking",
"neg": "soft focus, wide view, lack of detail",
"camera": "100mm macro lens, 1:1 magnification",
"lighting": "ring flash or twin macro flash, even diffused",
"palette": "nature-true colors, intense saturation",
"aspect": "1:1",
},
"航拍摄影": {
"category": "摄影",
"tags": "aerial photography, drone shot, bird's eye view",
"quality": "ultra wide sweeping vista, high altitude clarity",
"neg": "ground level, close-up, people center frame",
"camera": "drone camera, 24mm equivalent, top-down or 45-degree",
"lighting": "natural sunlight, long soft shadows",
"palette": "earth tones with atmospheric blue haze",
"aspect": "16:9",
},
"街拍纪实": {
"category": "摄影",
"tags": "street photography, decisive moment, candid documentary",
"quality": "authentic raw feeling, unposed human story",
"neg": "staged, fake, overprocessed",
"camera": "35mm prime, hip-level snap, off-center subject",
"lighting": "available ambient light, urban neon or sunlight",
"palette": "slightly desaturated urban tones",
"aspect": "3:2",
},
# ========== 动漫 / 插画 Illustration ==========
"动漫": {
"category": "动漫",
"tags": "anime style, cel shading, clean line art, vibrant anime colors",
"quality": "detailed anime eyes, pixiv trending, high quality anime",
"neg": "photorealistic, 3d render, western cartoon, low quality",
"camera": "",
"lighting": "anime-style soft light, rim light on hair",
"palette": "vibrant saturated anime palette",
"aspect": "3:4",
},
"新海诚": {
"category": "动漫",
"tags": "Makoto Shinkai style, volumetric cloudscape, realistic anime backgrounds",
"quality": "your name aesthetic, weathering with you mood, incredibly detailed skyscape",
"neg": "flat background, dark mood, gritty",
"camera": "",
"lighting": "magic hour sunlight streaming, god rays through clouds",
"palette": "sky blue, warm orange sunset, pink hour",
"aspect": "16:9",
},
"宫崎骏": {
"category": "动漫",
"tags": "Studio Ghibli style, hand-painted background, whimsical warmth",
"quality": "Totoro aesthetic, Spirited Away mood, hayao miyazaki inspired",
"neg": "dark, edgy, hyperdetailed, cgi",
"camera": "",
"lighting": "soft daylight through leaves, gentle diffuse",
"palette": "pastoral greens, cream, sky blue",
"aspect": "16:9",
},
"美漫": {
"category": "动漫",
"tags": "American comic book style, bold ink lines, halftone shading",
"quality": "marvel / DC inspired, dynamic pose, action panel",
"neg": "anime, soft shading, watercolor",
"camera": "",
"lighting": "dramatic cel lighting, high contrast shadows",
"palette": "saturated primary colors, comic book palette",
"aspect": "2:3",
},
"Q版": {
"category": "动漫",
"tags": "chibi style, super-deformed, cute mascot, 3-head-tall proportions",
"quality": "adorable, clean vector look, sticker worthy",
"neg": "realistic proportion, detailed anatomy, dark mood",
"camera": "",
"lighting": "even flat light, gentle cel shading",
"palette": "bright pastel palette, sugary",
"aspect": "1:1",
},
"童话绘本": {
"category": "动漫",
"tags": "children's book illustration, storybook style, hand drawn warmth",
"quality": "gouache texture, paper warmth, beatrix potter meets pixar",
"neg": "dark, horror, hyper-realistic, edgy",
"camera": "",
"lighting": "soft overall illumination, enchanted glow",
"palette": "warm buttery pastels, cream page base",
"aspect": "4:3",
},
"水彩": {
"category": "插画",
"tags": "watercolor painting, wet-on-wet technique, paper texture, soft bleeding edges",
"quality": "traditional watercolor, transparent wash layers, artistic",
"neg": "digital vector, hard edges, heavy outlines, 3d",
"camera": "",
"lighting": "natural daylight on paper",
"palette": "translucent pastel layers, white paper showing through",
"aspect": "1:1",
},
"油画": {
"category": "插画",
"tags": "oil painting, thick impasto brushstrokes, canvas texture",
"quality": "museum quality oil on canvas, old master technique",
"neg": "digital, flat colors, vector, pixel art",
"camera": "",
"lighting": "chiaroscuro, warm rembrandt glow",
"palette": "rich earth tones, deep jewel colors",
"aspect": "4:5",
},
"水墨": {
"category": "插画",
"tags": "Chinese ink wash painting, sumi-e, negative space, calligraphic strokes",
"quality": "zen atmosphere, rice paper texture, ink bleed",
"neg": "colorful, dense composition, western painting, cartoon",
"camera": "",
"lighting": "flat paper light, no harsh shadows",
"palette": "sumi black on rice-paper beige, occasional vermillion seal",
"aspect": "3:4",
},
"工笔国画": {
"category": "插画",
"tags": "Chinese gongbi painting, meticulous fine brush, intricate floral detail",
"quality": "Song dynasty court painting style, mineral pigment",
"neg": "loose brushstrokes, abstract, western style",
"camera": "",
"lighting": "flat even pigment, no modeling light",
"palette": "azurite blue, malachite green, cinnabar red, gold leaf",
"aspect": "3:4",
},
"浮世绘": {
"category": "插画",
"tags": "Ukiyo-e woodblock print, Edo period, Hokusai / Hiroshige style",
"quality": "traditional Japanese woodcut, flat color blocks, outlined figures",
"neg": "modern anime, 3d, photorealistic",
"camera": "",
"lighting": "no modeling light, flat graphic",
"palette": "prussian blue, earth reds, muted greens",
"aspect": "2:3",
},
"线稿": {
"category": "插画",
"tags": "clean line art, black ink on white, single weight or dynamic line",
"quality": "architectural line drawing precision, tattoo flash clarity",
"neg": "color, shading, painterly, texture",
"camera": "",
"lighting": "no lighting, pure linework",
"palette": "pure black on white",
"aspect": "1:1",
},
"像素艺术": {
"category": "插画",
"tags": "pixel art, 16-bit sprite, pixelated, retro game aesthetic",
"quality": "clean pixel clusters, limited palette, dithering",
"neg": "anti-aliased, smooth, photorealistic, 3d render, high resolution",
"camera": "",
"lighting": "flat pixel shading or 2-tone",
"palette": "NES / SNES limited palette, 16 colors",
"aspect": "1:1",
},
# ========== 3D / 手工 3D & Craft ==========
"3DC4D": {
"category": "3D",
"tags": "3d render, octane render, c4d style, subsurface scattering, glossy materials",
"quality": "ray traced reflections, detailed shader, behance trending",
"neg": "2d flat, sketch, line art",
"camera": "3d viewport camera, 50mm equivalent",
"lighting": "hdri environment light, colored accent rims",
"palette": "vibrant candy colors, pastel gradients",
"aspect": "1:1",
},
"盲盒手办": {
"category": "3D",
"tags": "blind box figurine, pop mart style, chibi 3d toy, kawaii collectible",
"quality": "vinyl toy finish, pristine product shot, pop mart aesthetic",
"neg": "realistic human, gritty, damaged",
"camera": "50mm product shot, eye level toy perspective",
"lighting": "soft studio light, gentle rim, clean shadow",
"palette": "pastel macaron palette",
"aspect": "1:1",
},
"低多边形": {
"category": "3D",
"tags": "low poly 3d, faceted geometry, minimalist polygons",
"quality": "crisp flat shaded polygons, geometric stylization",
"neg": "high detail, smooth subdivisions, realistic",
"camera": "3/4 perspective, isometric-ish",
"lighting": "flat faceted shading, 2-3 light setup",
"palette": "limited flat palette, often pastel",
"aspect": "1:1",
},
"等距视图": {
"category": "3D",
"tags": "isometric illustration, 2.5d isometric scene, game-dev isometric tile",
"quality": "clean vector isometric look, detailed miniature diorama",
"neg": "perspective distortion, top-down, first-person",
"camera": "true isometric projection, 30-degree angles",
"lighting": "even diffuse light, directional accent",
"palette": "bright clean pastel palette",
"aspect": "1:1",
},
"粘土": {
"category": "3D",
"tags": "claymation style, stop motion clay, aardman-like tactile figures",
"quality": "handmade clay texture, fingerprint detail",
"neg": "clean digital, plastic, smooth 3d",
"camera": "stop motion rig perspective, slight depth of field",
"lighting": "warm tungsten key with practical fill",
"palette": "warm terracotta tones",
"aspect": "1:1",
},
"毛毡手工": {
"category": "3D",
"tags": "felted wool craft, needle felt texture, handmade plush character",
"quality": "fuzzy fiber detail, cute handmade imperfection",
"neg": "smooth digital render, photorealistic animal",
"camera": "close macro product shot",
"lighting": "soft diffuse daylight",
"palette": "muted natural wool colors",
"aspect": "1:1",
},
"纸艺": {
"category": "3D",
"tags": "paper craft, layered paper art, quilling, origami composition",
"quality": "intricate cut paper layers, shadow depth between layers",
"neg": "flat 2d, digital illustration",
"camera": "front-on with slight tilt, shallow depth",
"lighting": "rim light casting paper-edge shadows",
"palette": "pastel construction paper colors",
"aspect": "1:1",
},
# ========== 设计 Design ==========
"极简主义": {
"category": "设计",
"tags": "minimalist design, negative space, swiss style, geometric composition",
"quality": "clean typography-friendly, editorial layout",
"neg": "cluttered, ornate, busy, excess detail",
"camera": "",
"lighting": "flat studio light or ambient, no drama",
"palette": "monochrome + single accent, lots of white",
"aspect": "1:1",
},
"平面设计": {
"category": "设计",
"tags": "flat design, vector graphic, bold shapes, brand illustration",
"quality": "clean vectors, designer grade composition",
"neg": "photorealistic, gradient 3d, sketchy",
"camera": "",
"lighting": "flat shading, no highlights",
"palette": "brand-forward 3-color palette",
"aspect": "1:1",
},
"Logo设计": {
"category": "设计",
"tags": "logo design, brand mark, vector logotype, scalable emblem",
"quality": "professional logo, centered composition on clean background",
"neg": "photorealistic scene, complex background, cluttered",
"camera": "",
"lighting": "flat vector, no light gradient",
"palette": "2-color max, high contrast",
"aspect": "1:1",
},
"图标设计": {
"category": "设计",
"tags": "icon design, app icon, rounded square, centered glyph",
"quality": "apple hig compliant, clean icon grid, crisp at 1024px",
"neg": "cluttered, off-center, photo, low contrast",
"camera": "",
"lighting": "subtle highlight gradient, soft inner glow",
"palette": "vibrant gradient with 2-3 colors",
"aspect": "1:1",
},
"信息图": {
"category": "设计",
"tags": "infographic design, data visualization, icon system, explanatory layout",
"quality": "clean editorial infographic, behance level",
"neg": "messy, illustrative painting, photograph",
"camera": "",
"lighting": "flat, no drama",
"palette": "brand palette + grayscale structure",
"aspect": "3:4",
},
"品牌KV": {
"category": "设计",
"tags": "brand key visual, advertising campaign hero image, marketing KV",
"quality": "commercial campaign quality, headline-ready negative space",
"neg": "casual, amateur, low contrast",
"camera": "hero wide or 3/4 product hero",
"lighting": "brand-defined dramatic key, colored rim",
"palette": "brand palette dominant + accent",
"aspect": "16:9",
},
"专辑封面": {
"category": "设计",
"tags": "album cover art, music artwork, square format composition",
"quality": "iconic album design, strong concept, emotive",
"neg": "cluttered, literal, stock imagery",
"camera": "",
"lighting": "concept-driven, mood-heavy",
"palette": "2-3 color highly intentional palette",
"aspect": "1:1",
},
"复古海报": {
"category": "设计",
"tags": "vintage poster design, 1950s retro, letterpress print, screenprint texture",
"quality": "saul bass meets mid-century, weathered paper feel",
"neg": "modern flat design, digital gradient, 3d render",
"camera": "",
"lighting": "flat two-tone",
"palette": "muted primary + cream background",
"aspect": "3:4",
},
"电影海报": {
"category": "设计",
"tags": "movie poster, cinematic key art, title-ready composition",
"quality": "theatrical one-sheet, dramatic hero composition",
"neg": "casual snapshot, cluttered, amateur",
"camera": "hero portrait or symmetric icon layout",
"lighting": "strong single direction light, volumetric",
"palette": "teal & orange or moody duotone",
"aspect": "2:3",
},
"表情包": {
"category": "设计",
"tags": "sticker design, emoji style, expressive meme-ready character",
"quality": "transparent background ready, bold outline, readable at 128px",
"neg": "complex scene, photorealistic, subtle",
"camera": "",
"lighting": "flat cel shading",
"palette": "bright saturated 4-color",
"aspect": "1:1",
},
# ========== 艺术史 Art Movement ==========
"印象派": {
"category": "艺术",
"tags": "impressionist painting, visible brushstrokes, plein air, monet inspired",
"quality": "late 19th century impressionism, atmospheric perspective",
"neg": "photorealistic, digital, sharp outlines",
"camera": "",
"lighting": "dappled natural light, sun-drenched scene",
"palette": "broken color technique, complementary dabs",
"aspect": "4:5",
},
"后印象派": {
"category": "艺术",
"tags": "post-impressionist, van gogh style, expressive brushstroke, emotive color",
"quality": "starry night swirls, dynamic brush texture",
"neg": "realistic, photographic, flat",
"camera": "",
"lighting": "emotional not physical light",
"palette": "bold yellows cobalt and burnt sienna",
"aspect": "4:5",
},
"新艺术": {
"category": "艺术",
"tags": "art nouveau, alphonse mucha, flowing organic lines, floral ornament border",
"quality": "belle époque poster, feminine ornate frame",
"neg": "geometric minimal, modern flat, 3d",
"camera": "",
"lighting": "flat even decorative light",
"palette": "muted golds, soft earth tones, sage",
"aspect": "2:3",
},
"装饰艺术": {
"category": "艺术",
"tags": "art deco, 1920s geometric ornament, gatsby aesthetic, gold and black lacquer",
"quality": "symmetric art deco pattern, streamline moderne elegance",
"neg": "rustic, organic nouveau, grunge",
"camera": "",
"lighting": "strong geometric shadow play",
"palette": "black gold ivory with emerald accents",
"aspect": "2:3",
},
# ========== 场景 / 氛围 Scene ==========
"赛博朋克": {
"category": "场景",
"tags": "cyberpunk, neon-soaked, blade runner aesthetic, megacity dystopia, holographic ads",
"quality": "detailed cyberpunk cityscape, rainy night ambiance",
"neg": "rustic, medieval, natural countryside",
"camera": "low angle wide, 24mm anamorphic",
"lighting": "neon magenta and cyan rim, wet reflective streets",
"palette": "magenta cyan black, neon highlights",
"aspect": "21:9",
},
"蒸汽朋克": {
"category": "场景",
"tags": "steampunk, brass gears and copper pipes, victorian industrial, airship era",
"quality": "intricate clockwork detail, rich leather and patina",
"neg": "clean sci-fi, modern, plastic",
"camera": "",
"lighting": "warm gaslight glow, smoky haze",
"palette": "brass copper sepia, burgundy leather",
"aspect": "3:2",
},
"科幻": {
"category": "场景",
"tags": "sci-fi concept art, futuristic technology, clean spaceship interior, holographic UI",
"quality": "blade runner 2049 palette, hard-sci-fi plausible",
"neg": "medieval, fantasy magic, primitive",
"camera": "cinematic wide, 21:9 framing",
"lighting": "cool blue practical strips, volumetric haze",
"palette": "cool blue cyan with warm accent",
"aspect": "21:9",
},
"奇幻": {
"category": "场景",
"tags": "epic fantasy art, magical atmosphere, artstation trending, tolkien inspired",
"quality": "detailed fantasy concept, elven architecture, dragon-scale atmosphere",
"neg": "modern city, cyberpunk, mundane",
"camera": "epic wide establishing, 24mm",
"lighting": "ethereal god rays through mist",
"palette": "golden hour warm with magical cyan glow",
"aspect": "16:9",
},
"黑暗奇幻": {
"category": "场景",
"tags": "dark fantasy, grimdark, eldritch horror atmosphere, berserk aesthetic",
"quality": "frank frazetta meets zdzisław beksiński",
"neg": "cheerful, bright, cartoonish",
"camera": "low angle hero or dread pov",
"lighting": "blood moon crimson, torch flicker",
"palette": "black crimson sickly green, rusted iron",
"aspect": "2:3",
},
"国潮": {
"category": "场景",
"tags": "guochao Chinese neo-trend, modern hanfu revival, oriental modernism",
"quality": "contemporary Chinese style illustration, editorial fashion",
"neg": "western medieval, european style",
"camera": "",
"lighting": "warm accent on oriental red-gold",
"palette": "vermillion jade gold, ink black accents",
"aspect": "3:4",
},
"Y2K": {
"category": "场景",
"tags": "Y2K aesthetic, early 2000s digital, chrome bubble UI, frosted plastic",
"quality": "low-fi cd-rom graphic, holographic stickers",
"neg": "ultra clean modern, analog retro",
"camera": "",
"lighting": "glossy chrome highlights",
"palette": "baby blue pink lilac, iridescent chrome",
"aspect": "1:1",
},
"Vaporwave": {
"category": "场景",
"tags": "vaporwave, retro 80s 90s computer graphics, roman bust, palm tree grid",
"quality": "synthwave aesthetic, low-fi jpeg nostalgia",
"neg": "modern clean, natural, high detail",
"camera": "",
"lighting": "sunset gradient, neon grid horizon",
"palette": "hot pink teal purple, retro sunset",
"aspect": "16:9",
},
"霓虹灯牌": {
"category": "场景",
"tags": "neon sign typography, glowing tube letters, dark brick wall backdrop",
"quality": "realistic neon glass tube glow, chromatic bloom",
"neg": "daylight, printed sign, flat vector",
"camera": "straight-on product shot, 50mm",
"lighting": "self-emissive neon, dark ambient",
"palette": "magenta cyan on deep black",
"aspect": "3:2",
},
"建筑可视化": {
"category": "场景",
"tags": "architectural visualization, V-Ray / Lumion render, interior design magazine",
"quality": "award-winning archviz, photorealistic materials",
"neg": "sketchy, doodle, distorted perspective",
"camera": "wide 24mm architectural tilt-corrected",
"lighting": "realistic sun study plus artificial, product-ready",
"palette": "natural materials, neutral brand-defined",
"aspect": "16:9",
},
"电影感": {
"category": "场景",
"tags": "cinematic film still, anamorphic lens flare, letterboxed framing",
"quality": "ARRI Alexa quality, professional color grade, movie still",
"neg": "snapshot, amateur, flat lighting, instagram filter",
"camera": "anamorphic 2.39:1 framing, low angle hero",
"lighting": "motivated practical + volumetric haze",
"palette": "teal & orange cinematic grade",
"aspect": "21:9",
},
"概念艺术": {
"category": "场景",
"tags": "concept art, matte painting, production design, pre-visualization",
"quality": "ILM / weta concept sketch, narrative-driven composition",
"neg": "finished illustration, cartoon, low detail",
"camera": "cinematic wide establishing",
"lighting": "narrative-lit hero with atmosphere",
"palette": "mood-defined limited palette",
"aspect": "21:9",
},
# ========== 游戏艺术 Game Art (v2.1 新增) ==========
"原神": {
"category": "游戏",
"tags": "Genshin Impact style, miHoYo aesthetic, stylized anime rendering, cel shaded 3d",
"quality": "gacha game hero card quality, detailed anime character portrait",
"neg": "photorealistic, western cartoon, gritty",
"camera": "3/4 character hero shot, slightly upward angle",
"lighting": "rim light on hair, soft key + colored fill",
"palette": "vibrant saturated anime palette, element-themed accents",
"aspect": "3:4",
},
"崩铁星穹": {
"category": "游戏",
"tags": "Honkai Star Rail style, space fantasy JRPG anime, miHoYo rendering",
"quality": "splash art quality, dynamic pose, elemental VFX",
"neg": "photorealistic, rustic, medieval",
"camera": "dynamic dutch angle hero shot",
"lighting": "glowing elemental rim light",
"palette": "cosmic gradient + neon accent",
"aspect": "3:4",
},
"英雄联盟": {
"category": "游戏",
"tags": "League of Legends splash art style, Riot Games painterly illustration",
"quality": "champion splash quality, dramatic action pose",
"neg": "anime chibi, flat vector, photo",
"camera": "dynamic low angle hero pose",
"lighting": "dramatic rim with colored ability VFX",
"palette": "saturated fantasy palette with magical accent",
"aspect": "16:9",
},
"暗黑4": {
"category": "游戏",
"tags": "Diablo IV style, dark gothic fantasy, blizzard illustration",
"quality": "ARPG splash quality, grim dark atmosphere",
"neg": "cheerful, pastel, chibi, flat",
"camera": "low-angle menacing hero shot",
"lighting": "infernal red rim, volumetric fog",
"palette": "charcoal black, ember red, corrupted green",
"aspect": "3:2",
},
"Valorant": {
"category": "游戏",
"tags": "Valorant agent art, stylized flat anime realism, Riot FPS aesthetic",
"quality": "agent reveal quality, confident hero pose",
"neg": "painterly fantasy, chibi",
"camera": "3/4 hero standoff",
"lighting": "clean cel shaded with colored ability glow",
"palette": "agent signature color + urban neutral",
"aspect": "3:4",
},
"Pokemon": {
"category": "游戏",
"tags": "Pokemon style, Ken Sugimori illustration, round cute creature design",
"quality": "Pokedex official art, clean cel shading",
"neg": "gritty, realistic, complex anatomy",
"camera": "3/4 creature portrait on white",
"lighting": "flat cel shading with soft shadow",
"palette": "clean primary colors per type",
"aspect": "1:1",
},
"暴雪风": {
"category": "游戏",
"tags": "Blizzard stylized art, Overwatch / WoW concept style, exaggerated anatomy",
"quality": "blizzard cinematic quality, heroic pose, strong silhouette",
"neg": "photorealistic, anime chibi, flat",
"camera": "heroic low angle, dynamic posing",
"lighting": "dramatic three-point hero light",
"palette": "rich saturated fantasy palette",
"aspect": "3:2",
},
# ========== 东方传统 Chinese/Japanese Traditional (v2.1 新增) ==========
"敦煌壁画": {
"category": "东方",
"tags": "Dunhuang mural style, Tang dynasty fresco, flying apsara figures, silk road art",
"quality": "weathered ancient mural texture, mineral pigment on plaster",
"neg": "modern digital, anime, western",
"camera": "flat mural frontal view",
"lighting": "no modeling light, flat pigment",
"palette": "mineral ochre, malachite green, azurite blue, gold leaf",
"aspect": "4:3",
},
"青花瓷": {
"category": "东方",
"tags": "Chinese blue and white porcelain motif, Ming dynasty pattern, cobalt underglaze",
"quality": "porcelain surface detail, intricate floral motif",
"neg": "full color, western, abstract",
"camera": "",
"lighting": "soft glazed porcelain highlight",
"palette": "cobalt blue on pure white porcelain",
"aspect": "1:1",
},
"民国月份牌": {
"category": "东方",
"tags": "Republic of China calendar poster, 1920s Shanghai art deco fusion, qipao glamour",
"quality": "vintage advertising print, lithograph texture",
"neg": "modern digital, anime, photo",
"camera": "",
"lighting": "flat poster illumination",
"palette": "faded pastel with gold gilt accents",
"aspect": "2:3",
},
"年画": {
"category": "东方",
"tags": "Chinese new year folk woodblock, auspicious symbols, chubby child figures",
"quality": "traditional woodblock print texture, folk decorative",
"neg": "photorealistic, minimalist, western",
"camera": "",
"lighting": "flat festive graphic",
"palette": "festive vermillion, gold, pine green",
"aspect": "3:4",
},
"剪纸": {
"category": "东方",
"tags": "Chinese paper cutting art, red paper silhouette, intricate symmetric cutout",
"quality": "fine paper cut detail, traditional folk craft",
"neg": "full color, 3d, photorealistic",
"camera": "",
"lighting": "flat silhouette with background paper",
"palette": "pure vermillion red on neutral background",
"aspect": "1:1",
},
"和风": {
"category": "东方",
"tags": "Japanese wafu aesthetic, traditional kimono elegance, wagashi sensibility",
"quality": "refined Japanese traditional design",
"neg": "western, modern pop, grunge",
"camera": "",
"lighting": "soft shoji-diffused light",
"palette": "indigo, vermillion, sumi ink, cream washi",
"aspect": "3:4",
},
"汉服写真": {
"category": "东方",
"tags": "hanfu photography, Chinese traditional dress, oriental portrait",
"quality": "ethereal hanfu fashion editorial, flowing silk",
"neg": "western dress, modern clothing, cyberpunk",
"camera": "85mm portrait, soft 3/4",
"lighting": "diffuse morning light, soft bounce",
"palette": "silk ink tones, jade, cream, plum",
"aspect": "3:4",
},
# ========== 动漫扩展 Anime extras (v2.1 新增) ==========
"萌系": {
"category": "动漫",
"tags": "moe anime style, cute girl aesthetic, large sparkling eyes",
"quality": "moekko illustration, clean lineart, rich anime shading",
"neg": "gritty, adult, western comic",
"camera": "",
"lighting": "soft diffuse with catchlight in eyes",
"palette": "pastel pink cream sky-blue",
"aspect": "3:4",
},
"厚涂": {
"category": "动漫",
"tags": "painterly anime, thick paint anime illustration, semi-realistic rendering",
"quality": "artstation anime painting, detailed brushwork",
"neg": "flat cel shading, vector, chibi",
"camera": "",
"lighting": "rembrandt on face, painterly shadows",
"palette": "desaturated muted painterly tones",
"aspect": "3:4",
},
"轻小说封面": {
"category": "动漫",
"tags": "light novel cover illustration, Japanese LN art, glossy anime portrait",
"quality": "bookshelf-ready cover composition, eye-catching character",
"neg": "dark horror, western comic, 3d",
"camera": "3/4 character hero, title-friendly negative space",
"lighting": "cinematic anime key light",
"palette": "vibrant anime palette with atmosphere",
"aspect": "2:3",
},
"赛璐璐": {
"category": "动漫",
"tags": "traditional cel-shaded anime, sharp shadow boundaries, limited anime palette",
"quality": "classic 2d cel animation look, detailed line art",
"neg": "painterly, 3d render, gradient shading",
"camera": "",
"lighting": "two-tone cel shading, hard shadow edges",
"palette": "saturated flat anime palette",
"aspect": "16:9",
},
# ========== 现代设计 Modern Design (v2.1 新增) ==========
"玻璃拟态": {
"category": "设计",
"tags": "glassmorphism, frosted glass UI, transparent blur layers, depth card stack",
"quality": "modern UI glass effect, realistic refraction, clean layout",
"neg": "flat 2d, skeuomorphic wood, pixel art",
"camera": "",
"lighting": "subtle inner glow, soft backlight through glass",
"palette": "pastel gradient backdrop with translucent glass",
"aspect": "3:4",
},
"新拟态": {
"category": "设计",
"tags": "neumorphism, soft UI, extruded plastic button, subtle dual shadow",
"quality": "modern minimal UI, monochrome neumorphic elements",
"neg": "flat, photorealistic, grunge",
"camera": "",
"lighting": "soft dual light and dark shadow",
"palette": "monochrome beige or gray single-tone",
"aspect": "1:1",
},
"孟菲斯": {
"category": "设计",
"tags": "Memphis design, 1980s postmodern, geometric shapes, squiggle pattern, bold primaries",
"quality": "playful postmodern graphic, bold composition",
"neg": "minimalist, photorealistic, classical",
"camera": "",
"lighting": "flat graphic, no modeling",
"palette": "hot pink, cyan, yellow, black squiggle pattern",
"aspect": "1:1",
},
"杂志编排": {
"category": "设计",
"tags": "editorial magazine layout, bold serif typography, grid-based design",
"quality": "international typographic style, vogue spread quality",
"neg": "amateur, overcluttered, cute",
"camera": "",
"lighting": "clean flat studio-style",
"palette": "monochrome with single bold accent",
"aspect": "3:4",
},
"包豪斯": {
"category": "设计",
"tags": "Bauhaus design, de stijl geometric, primary color blocks, constructivist",
"quality": "1920s modernist design school, pure geometry",
"neg": "ornate, victorian, realistic",
"camera": "",
"lighting": "flat geometric",
"palette": "primary red yellow blue + black on white",
"aspect": "1:1",
},
"奶油风": {
"category": "设计",
"tags": "cream style, soft beige palette, warm minimal aesthetic, korean lifestyle",
"quality": "instagram lifestyle aesthetic, soft velvety texture",
"neg": "dark, saturated, edgy",
"camera": "",
"lighting": "natural soft window light",
"palette": "cream, soft beige, butter yellow, milk tea",
"aspect": "4:5",
},
# ========== 建筑 & 氛围扩展 (v2.1 新增) ==========
"粗野主义": {
"category": "场景",
"tags": "brutalist architecture, raw concrete, heavy geometric mass, béton brut",
"quality": "mid-century brutalist landmark, imposing scale",
"neg": "ornate, baroque, flimsy",
"camera": "wide low-angle heroic architecture shot",
"lighting": "harsh sun shadow across concrete",
"palette": "raw concrete gray with sky contrast",
"aspect": "16:9",
},
"北欧极简": {
"category": "场景",
"tags": "scandinavian interior, nordic minimalism, light wood, warm neutral",
"quality": "hygge lifestyle, interior magazine quality",
"neg": "ornate, cluttered, dark gothic",
"camera": "wide 24mm interior architectural",
"lighting": "large window natural light",
"palette": "warm wood, white wall, soft gray",
"aspect": "16:9",
},
"侘寂": {
"category": "场景",
"tags": "wabi-sabi aesthetic, imperfect natural beauty, weathered texture, zen japanese",
"quality": "quiet imperfection, aged material detail",
"neg": "glossy modern, bright colors, ornate",
"camera": "",
"lighting": "soft diffused natural, muted",
"palette": "muted earth, weathered gray, aged beige",
"aspect": "4:5",
},
# ========== 摄影扩展 (v2.1 新增) ==========
"暗黑美食": {
"category": "摄影",
"tags": "dark food photography, moody cuisine, chiaroscuro plating",
"quality": "michelin-level dark food styling, dramatic shadow",
"neg": "bright cheerful, flat, cluttered",
"camera": "100mm macro 45-degree, side low-key",
"lighting": "single hard key from behind, deep shadow",
"palette": "deep black with food color accent",
"aspect": "4:5",
},
"日杂": {
"category": "摄影",
"tags": "Japanese lifestyle magazine, natural light still life, clean minimalism",
"quality": "muji aesthetic, calm everyday beauty",
"neg": "dark moody, dramatic, saturated",
"camera": "50mm still life, slight top-down",
"lighting": "soft window daylight, no drama",
"palette": "cream, light wood, pale pastel",
"aspect": "4:5",
},
"街头潮流": {
"category": "摄影",
"tags": "streetwear fashion, urban hypebeast, sneaker culture",
"quality": "street style magazine editorial, confident pose",
"neg": "formal suit, fantasy, kawaii",
"camera": "35mm full body street fashion",
"lighting": "harsh urban daylight or neon",
"palette": "high contrast monochrome + brand accent",
"aspect": "3:4",
},
# ========== 综合 (v2.1 新增) ==========
"疗愈治愈": {
"category": "场景",
"tags": "healing cozy aesthetic, soft warm interior, cat sunlight, tea steam",
"quality": "soothing slow-life scene",
"neg": "dramatic action, dark, cyberpunk",
"camera": "",
"lighting": "warm golden hour through window",
"palette": "warm honey, cream, dusty pink",
"aspect": "4:5",
},
"美式复古": {
"category": "场景",
"tags": "americana retro, 1950s diner, vintage coca-cola americana",
"quality": "Norman Rockwell meets mid-century ad",
"neg": "asian, modern sleek, futuristic",
"camera": "",
"lighting": "warm diner fluorescent or golden",
"palette": "cherry red, cream, turquoise",
"aspect": "3:2",
},
}
# ─────────────────────────────────────────────────────────
# 别名 (英文 / 同义词 → 规范预设名)
# ─────────────────────────────────────────────────────────
ALIASES: Dict[str, str] = {
# 英文
"realistic": "写实摄影",
"photo": "写实摄影",
"photography": "写实摄影",
"film": "胶片摄影",
"analog": "胶片摄影",
"bw": "黑白摄影",
"blackwhite": "黑白摄影",
"monochrome": "黑白摄影",
"portrait": "人像摄影",
"fashion": "时尚大片",
"editorial": "时尚大片",
"food": "美食摄影",
"product": "产品摄影",
"ecommerce": "产品摄影",
"macro": "微距摄影",
"aerial": "航拍摄影",
"drone": "航拍摄影",
"street": "街拍纪实",
"documentary": "街拍纪实",
"anime": "动漫",
"ghibli": "宫崎骏",
"miyazaki": "宫崎骏",
"shinkai": "新海诚",
"makoto": "新海诚",
"comic": "美漫",
"marvel": "美漫",
"chibi": "Q版",
"kawaii": "Q版",
"storybook": "童话绘本",
"childrensbook": "童话绘本",
"watercolor": "水彩",
"oil": "油画",
"ink": "水墨",
"sumi": "水墨",
"gongbi": "工笔国画",
"ukiyoe": "浮世绘",
"lineart": "线稿",
"pixel": "像素艺术",
"3d": "3DC4D",
"c4d": "3DC4D",
"octane": "3DC4D",
"blindbox": "盲盒手办",
"popmart": "盲盒手办",
"lowpoly": "低多边形",
"isometric": "等距视图",
"iso": "等距视图",
"claymation": "粘土",
"felt": "毛毡手工",
"papercraft": "纸艺",
"minimal": "极简主义",
"minimalist": "极简主义",
"flat": "平面设计",
"vector": "平面设计",
"logo": "Logo设计",
"icon": "图标设计",
"infographic": "信息图",
"kv": "品牌KV",
"album": "专辑封面",
"poster": "复古海报",
"movieposter": "电影海报",
"sticker": "表情包",
"emoji": "表情包",
"impressionist": "印象派",
"vangogh": "后印象派",
"postimpressionist": "后印象派",
"artnouveau": "新艺术",
"mucha": "新艺术",
"artdeco": "装饰艺术",
"cyberpunk": "赛博朋克",
"steampunk": "蒸汽朋克",
"scifi": "科幻",
"fantasy": "奇幻",
"darkfantasy": "黑暗奇幻",
"grimdark": "黑暗奇幻",
"guochao": "国潮",
"y2k": "Y2K",
"vaporwave": "Vaporwave",
"synthwave": "Vaporwave",
"neon": "霓虹灯牌",
"archviz": "建筑可视化",
"architecture": "建筑可视化",
"cinematic": "电影感",
"cinema": "电影感",
"concept": "概念艺术",
"conceptart": "概念艺术",
# v2.1 游戏
"genshin": "原神",
"mihoyo": "原神",
"honkai": "崩铁星穹",
"starrail": "崩铁星穹",
"lol": "英雄联盟",
"leagueoflegends": "英雄联盟",
"diablo": "暗黑4",
"valorant": "Valorant",
"pokemon": "Pokemon",
"blizzard": "暴雪风",
"overwatch": "暴雪风",
"wow": "暴雪风",
# v2.1 东方
"dunhuang": "敦煌壁画",
"qinghua": "青花瓷",
"porcelain": "青花瓷",
"yuefenpai": "民国月份牌",
"wafu": "和风",
"hanfu": "汉服写真",
"papercut": "剪纸",
"nianhua": "年画",
# v2.1 动漫扩展
"moe": "萌系",
"painterlyanime": "厚涂",
"lightnovel": "轻小说封面",
"lncover": "轻小说封面",
"cellshaded": "赛璐璐",
"celshaded": "赛璐璐",
# v2.1 设计
"glassmorphism": "玻璃拟态",
"glass": "玻璃拟态",
"neumorphism": "新拟态",
"memphis": "孟菲斯",
"editorial": "杂志编排",
"bauhaus": "包豪斯",
"cream": "奶油风",
"korean": "奶油风",
# v2.1 建筑 / 氛围
"brutalism": "粗野主义",
"brutalist": "粗野主义",
"nordic": "北欧极简",
"scandinavian": "北欧极简",
"wabisabi": "侘寂",
"zen": "侘寂",
# v2.1 摄影
"darkfood": "暗黑美食",
"muji": "日杂",
"streetwear": "街头潮流",
"hypebeast": "街头潮流",
# v2.1 综合
"healing": "疗愈治愈",
"cozy": "疗愈治愈",
"americana": "美式复古",
}
# ─────────────────────────────────────────────────────────
# 意图关键词 → (推荐预设, 推荐画幅)
# ─────────────────────────────────────────────────────────
INTENT_KEYWORDS: List[Tuple[str, str, str]] = [
# (关键词, 推荐预设, 推荐画幅)
("logo", "Logo设计", "1:1"),
("徽标", "Logo设计", "1:1"),
("标志", "Logo设计", "1:1"),
("icon", "图标设计", "1:1"),
("图标", "图标设计", "1:1"),
("app图标", "图标设计", "1:1"),
("电影海报", "电影海报", "2:3"),
("海报", "复古海报", "3:4"),
("poster", "复古海报", "3:4"),
("封面", "专辑封面", "1:1"),
("专辑", "专辑封面", "1:1"),
("表情包", "表情包", "1:1"),
("贴纸", "表情包", "1:1"),
("信息图", "信息图", "3:4"),
("infographic", "信息图", "3:4"),
("kv", "品牌KV", "16:9"),
("主视觉", "品牌KV", "16:9"),
("产品", "产品摄影", "1:1"),
("电商", "产品摄影", "1:1"),
("商品", "产品摄影", "1:1"),
("美食", "美食摄影", "1:1"),
("食物", "美食摄影", "1:1"),
("菜品", "美食摄影", "1:1"),
("头像", "人像摄影", "1:1"),
("肖像", "人像摄影", "3:4"),
("人像", "人像摄影", "3:4"),
("时装", "时尚大片", "3:4"),
("时尚", "时尚大片", "3:4"),
("街拍", "街拍纪实", "3:2"),
("纪实", "街拍纪实", "3:2"),
("风景", "写实摄影", "16:9"),
("风光", "写实摄影", "16:9"),
("建筑", "建筑可视化", "16:9"),
("室内", "建筑可视化", "4:3"),
("手办", "盲盒手办", "1:1"),
("盲盒", "盲盒手办", "1:1"),
("玩具", "盲盒手办", "1:1"),
("航拍", "航拍摄影", "16:9"),
("鸟瞰", "航拍摄影", "16:9"),
("微距", "微距摄影", "1:1"),
("赛博", "赛博朋克", "21:9"),
("cyberpunk", "赛博朋克", "21:9"),
("蒸汽朋克", "蒸汽朋克", "3:2"),
("科幻", "科幻", "21:9"),
("未来", "科幻", "21:9"),
("奇幻", "奇幻", "16:9"),
("魔幻", "奇幻", "16:9"),
("黑暗", "黑暗奇幻", "2:3"),
("水墨", "水墨", "3:4"),
("国画", "工笔国画", "3:4"),
("工笔", "工笔国画", "3:4"),
("浮世绘", "浮世绘", "2:3"),
("童话", "童话绘本", "4:3"),
("绘本", "童话绘本", "4:3"),
("宫崎骏", "宫崎骏", "16:9"),
("新海诚", "新海诚", "16:9"),
("动漫", "动漫", "3:4"),
("二次元", "动漫", "3:4"),
("q版", "Q版", "1:1"),
("Q版", "Q版", "1:1"),
("chibi", "Q版", "1:1"),
("线稿", "线稿", "1:1"),
("像素", "像素艺术", "1:1"),
("3d", "3DC4D", "1:1"),
("c4d", "3DC4D", "1:1"),
("粘土", "粘土", "1:1"),
("等距", "等距视图", "1:1"),
("国潮", "国潮", "3:4"),
("霓虹", "霓虹灯牌", "3:2"),
("电影", "电影感", "21:9"),
("cinema", "电影感", "21:9"),
("concept", "概念艺术", "21:9"),
("概念图", "概念艺术", "21:9"),
("复古", "复古海报", "3:4"),
("vintage", "复古海报", "3:4"),
# v2.1 游戏
("原神", "原神", "3:4"),
("genshin", "原神", "3:4"),
("崩铁", "崩铁星穹", "3:4"),
("星穹", "崩铁星穹", "3:4"),
("lol", "英雄联盟", "16:9"),
("英雄联盟", "英雄联盟", "16:9"),
("valorant", "Valorant", "3:4"),
("暗黑4", "暗黑4", "3:2"),
("diablo", "暗黑4", "3:2"),
("pokemon", "Pokemon", "1:1"),
("宝可梦", "Pokemon", "1:1"),
("暴雪", "暴雪风", "3:2"),
("overwatch", "暴雪风", "3:2"),
# v2.1 东方
("敦煌", "敦煌壁画", "4:3"),
("壁画", "敦煌壁画", "4:3"),
("青花瓷", "青花瓷", "1:1"),
("月份牌", "民国月份牌", "2:3"),
("民国", "民国月份牌", "2:3"),
("剪纸", "剪纸", "1:1"),
("年画", "年画", "3:4"),
("汉服", "汉服写真", "3:4"),
("和风", "和风", "3:4"),
("日系", "日杂", "4:5"),
("日杂", "日杂", "4:5"),
# v2.1 动漫扩展
("萌", "萌系", "3:4"),
("萌系", "萌系", "3:4"),
("厚涂", "厚涂", "3:4"),
("轻小说", "轻小说封面", "2:3"),
("赛璐璐", "赛璐璐", "16:9"),
# v2.1 现代设计
("玻璃拟态", "玻璃拟态", "3:4"),
("glassmorphism", "玻璃拟态", "3:4"),
("新拟态", "新拟态", "1:1"),
("neumorphism", "新拟态", "1:1"),
("孟菲斯", "孟菲斯", "1:1"),
("memphis", "孟菲斯", "1:1"),
("杂志", "杂志编排", "3:4"),
("magazine", "杂志编排", "3:4"),
("包豪斯", "包豪斯", "1:1"),
("bauhaus", "包豪斯", "1:1"),
("奶油", "奶油风", "4:5"),
("ins风", "奶油风", "4:5"),
("韩系", "奶油风", "4:5"),
# v2.1 建筑 / 氛围
("粗野", "粗野主义", "16:9"),
("brutalism", "粗野主义", "16:9"),
("北欧", "北欧极简", "16:9"),
("scandinavian", "北欧极简", "16:9"),
("侘寂", "侘寂", "4:5"),
("wabi", "侘寂", "4:5"),
("禅意", "侘寂", "4:5"),
# v2.1 摄影
("暗黑美食", "暗黑美食", "4:5"),
("darkfood", "暗黑美食", "4:5"),
("街头", "街头潮流", "3:4"),
("潮牌", "街头潮流", "3:4"),
("streetwear", "街头潮流", "3:4"),
# v2.1 综合
("治愈", "疗愈治愈", "4:5"),
("疗愈", "疗愈治愈", "4:5"),
("cozy", "疗愈治愈", "4:5"),
("美式复古", "美式复古", "3:2"),
("americana", "美式复古", "3:2"),
]
# ─────────────────────────────────────────────────────────
# 构图关键词
# ─────────────────────────────────────────────────────────
COMPOSITION_KEYWORDS: Dict[str, str] = {
"特写": "extreme close-up shot",
"近景": "close-up shot",
"中景": "medium shot",
"全身": "full body shot",
"半身": "medium shot, waist up",
"远景": "wide shot, establishing shot",
"全景": "panoramic view",
"俯拍": "top-down view",
"俯视": "top-down view",
"仰拍": "low angle shot, looking up",
"仰视": "low angle shot, looking up",
"鸟瞰": "bird's eye view, aerial",
"平视": "eye-level shot",
"正面": "front view",
"侧面": "side profile view",
"背面": "back view",
"三分之二": "three-quarter view",
}
# ─────────────────────────────────────────────────────────
# 情绪关键词
# ─────────────────────────────────────────────────────────
MOOD_KEYWORDS: Dict[str, str] = {
"温暖": "warm cozy atmosphere, golden tones",
"温馨": "warm cozy atmosphere, golden tones",
"冷峻": "cold atmosphere, steely blue tones",
"神秘": "mysterious mood, foggy, dim lighting",
"梦幻": "dreamy ethereal mood, soft glow, bokeh",
"欢快": "joyful vibrant, bright cheerful colors",
"忧郁": "melancholic mood, muted cool palette",
"压抑": "oppressive mood, deep shadows, heavy atmosphere",
"史诗": "epic grandeur, cinematic scale",
"高级": "luxury sophistication, premium materials",
"治愈": "healing soft ambiance, soothing",
"清新": "fresh airy light pastel",
"紧张": "tense suspense mood, high contrast",
"浪漫": "romantic soft pink glow",
}
# ─────────────────────────────────────────────────────────
# 时间 / 天气 / 季节 关键词（v2.1 新增）
# ─────────────────────────────────────────────────────────
TIME_KEYWORDS: Dict[str, str] = {
"清晨": "early morning, dawn, soft first light",
"早晨": "morning light, fresh daylight",
"上午": "bright morning sunshine",
"正午": "high noon, overhead sun",
"下午": "afternoon light, long soft shadows",
"黄昏": "dusk, golden hour, magic hour",
"傍晚": "dusk, golden hour, magic hour",
"日落": "sunset, golden hour",
"夜晚": "night time, dark ambient",
"深夜": "late night, moonlit, dim",
"午夜": "midnight, dark sky",
"黎明": "dawn, blue hour breaking",
"蓝调时刻": "blue hour, twilight gradient sky",
"魔法时刻": "magic hour, warm golden glow",
}
WEATHER_KEYWORDS: Dict[str, str] = {
"晴天": "sunny clear sky",
"多云": "cloudy overcast sky",
"阴天": "overcast gray sky",
"下雨": "raining, wet reflective surfaces",
"雨天": "rainy weather, soft rain",
"大雨": "heavy rain, downpour, water droplets",
"暴雨": "stormy rain, dramatic weather",
"下雪": "snowing, snowflakes in air",
"雪天": "snowy landscape, white blanket",
"暴雪": "blizzard, heavy snow storm",
"雾天": "foggy misty atmosphere",
"有雾": "foggy misty atmosphere",
"晨雾": "morning mist, dreamy fog",
"风暴": "stormy weather, dramatic clouds",
"雷雨": "thunderstorm, lightning in sky",
}
SEASON_KEYWORDS: Dict[str, str] = {
"春天": "spring season, cherry blossoms, fresh green",
"春季": "spring season, cherry blossoms, fresh green",
"夏天": "summer season, lush greenery, warm sun",
"夏季": "summer season, lush greenery, warm sun",
"秋天": "autumn season, golden foliage, maple leaves",
"秋季": "autumn season, golden foliage, maple leaves",
"冬天": "winter season, snow, bare branches",
"冬季": "winter season, snow, bare branches",
"樱花季": "cherry blossom season, sakura petals falling",
"枫叶季": "maple season, red foliage",
}
# ─────────────────────────────────────────────────────────
# 质量档位（v2.1 新增）
# ─────────────────────────────────────────────────────────
QUALITY_TIERS: Dict[str, str] = {
"basic": "high quality, detailed",
"pro": "masterpiece, best quality, ultra detailed, 8k",
"master": "masterpiece, best quality, ultra detailed, 8k, hdr, "
"intricate details, sharp focus, award winning, trending on artstation, "
"professional, highly polished",
}
# ─────────────────────────────────────────────────────────
# 负向需求识别（v2.1 新增）
# 匹配 "不要X" / "no X" / "avoid X" / "without X" / "没有X" / "避免X"
# ─────────────────────────────────────────────────────────
NEGATIVE_PATTERNS = [
re.compile(r"不要([^,，。.;；]{1,20})"),
re.compile(r"没有([^,，。.;；]{1,20})"),
re.compile(r"避免([^,，。.;；]{1,20})"),
re.compile(r"\bno\s+([a-zA-Z\s]{1,30})(?=[,.]|\s*$)"),
re.compile(r"\bavoid\s+([a-zA-Z\s]{1,30})(?=[,.]|\s*$)"),
re.compile(r"\bwithout\s+([a-zA-Z\s]{1,30})(?=[,.]|\s*$)"),
]
# ─────────────────────────────────────────────────────────
# 画幅 → 模型特定写法
# ─────────────────────────────────────────────────────────
ASPECT_TO_MJ = {
"1:1": "--ar 1:1",
"3:4": "--ar 3:4",
"4:3": "--ar 4:3",
"3:2": "--ar 3:2",
"2:3": "--ar 2:3",
"16:9": "--ar 16:9",
"9:16": "--ar 9:16",
"21:9": "--ar 21:9",
"4:5": "--ar 4:5",
}
ASPECT_TO_SDXL = {
"1:1": "1024x1024",
"3:4": "896x1152",
"4:3": "1152x896",
"3:2": "1216x832",
"2:3": "832x1216",
"16:9": "1344x768",
"9:16": "768x1344",
"21:9": "1536x640",
"4:5": "912x1144",
}
# ─────────────────────────────────────────────────────────
# 工具函数
# ─────────────────────────────────────────────────────────
def resolve_preset(name: Optional[str]) -> str:
"""预设名归一化：支持中文 / 英文别名 / 大小写不敏感。
v3.0: `@<name>` 前缀加载 learned preset（来自 style_learn.py）。
learned preset 会被即时注册到 STYLE_PRESETS（运行期，不污染源文件）。
"""
if not name:
return ""
name_str = name.strip()
# v3.0: @ 前缀 = learned preset
if name_str.startswith("@"):
learned_name = name_str[1:]
if learned_name in STYLE_PRESETS:
return learned_name # 已经注册过了
try:
from style_learn import learned_load
lp = learned_load(learned_name)
except Exception:
lp = None
if lp:
# 把 learned preset 注册进 STYLE_PRESETS（仅当前进程，不持久化）
STYLE_PRESETS[learned_name] = {
"category": lp.get("category", "学习"),
"tags": lp.get("tags", ""),
"quality": lp.get("quality", "high quality, detailed"),
"neg": lp.get("neg", "low quality"),
"camera": lp.get("camera", ""),
"lighting": lp.get("lighting", ""),
"palette": lp.get("palette", ""),
"aspect": lp.get("aspect", "1:1"),
}
return learned_name
return ""
key = name_str.lower().replace(" ", "").replace("-", "").replace("_", "")
if key in ALIASES:
return ALIASES[key]
for p in STYLE_PRESETS:
if p.lower() == key or p.lower().replace(" ", "") == key:
return p
return name_str if name_str in STYLE_PRESETS else ""
def parse_requirement(text: str) -> Dict[str, str]:
"""从用户输入中解析意图、画幅、构图、情绪、时间、天气、季节、负向需求。
返回 dict 字段：
preset_suggestion 推荐预设（可能为空）
aspect_suggestion 推荐画幅
composition 构图片段（英文，可为空）
mood 情绪片段（英文，可为空）
time_of_day 时间片段（英文，可为空）
weather 天气片段（英文，可为空）
season 季节片段（英文，可为空）
user_negatives 用户抽出的负向关键词（原文，英/中）
"""
lower = text.lower()
out = {
"preset_suggestion": "",
"aspect_suggestion": "",
"composition": "",
"mood": "",
"time_of_day": "",
"weather": "",
"season": "",
"user_negatives": [],
}
for kw, preset, aspect in INTENT_KEYWORDS:
if kw.lower() in lower:
out["preset_suggestion"] = preset
out["aspect_suggestion"] = aspect
break
for zh, en in COMPOSITION_KEYWORDS.items():
if zh in text:
out["composition"] = en
break
for zh, en in MOOD_KEYWORDS.items():
if zh in text:
out["mood"] = en
break
for zh, en in TIME_KEYWORDS.items():
if zh in text:
out["time_of_day"] = en
break
for zh, en in WEATHER_KEYWORDS.items():
if zh in text:
out["weather"] = en
break
for zh, en in SEASON_KEYWORDS.items():
if zh in text:
out["season"] = en
break
# 负向需求抽取
negs: List[str] = []
for pat in NEGATIVE_PATTERNS:
for m in pat.finditer(text):
token = m.group(1).strip().rstrip(",.， ；;")
if token and token not in negs:
negs.append(token)
out["user_negatives"] = negs
return out
def strip_negative_clauses(text: str) -> str:
"""从主体描述中去除 "不要X" 类子句，只保留正向描述。"""
cleaned = text
for pat in NEGATIVE_PATTERNS:
cleaned = pat.sub("", cleaned)
# 清理多余标点和空白
cleaned = re.sub(r"\s*,\s*,+", ", ", cleaned)
cleaned = re.sub(r"\s+", " ", cleaned).strip(" ,，。.;；")
return cleaned
def sanitize_subject(text: str) -> str:
"""清理主体描述：去除首尾标点和多余空白。"""
return re.sub(r"\s+", " ", text).strip().rstrip(".,，、。;；")
def stable_seed(subject: str, preset: str) -> int:
"""根据主体 + 预设生成稳定的种子建议（32-bit 正整数）。"""
h = hashlib.md5(f"{subject}|{preset}".encode("utf-8")).hexdigest()
return int(h[:8], 16)
def parse_mix_preset(preset_arg: str) -> Tuple[str, Optional[str]]:
"""支持 `-p A+B` 语法。返回 (primary, secondary or None)。"""
if not preset_arg:
return "", None
if "+" not in preset_arg:
return preset_arg, None
parts = [p.strip() for p in preset_arg.split("+", 1)]
if len(parts) != 2 or not parts[0] or not parts[1]:
return preset_arg, None
return parts[0], parts[1]
def mix_presets(primary: str, secondary: str, ratio: float = 0.6, model: str = "通用") -> Dict[str, str]:
"""加权融合两个预设，主预设 ratio，副预设 1-ratio。
融合策略：
tags 按权重前置主预设标签，SD 模式额外加 (tag:weight) 语法
quality 主预设主导
neg 合并去重
camera 主预设（主导镜头语言）
lighting 主预设主导，副预设为辅
palette 混合两者（主在前）
aspect 主预设
category mix
"""
p1 = STYLE_PRESETS[primary]
p2 = STYLE_PRESETS[secondary]
ratio = max(0.1, min(0.9, ratio))
primary_tags = [t.strip() for t in p1["tags"].split(",") if t.strip()]
secondary_tags = [t.strip() for t in p2["tags"].split(",") if t.strip()]
is_sd = model in ("Stable Diffusion", "SD", "sd", "SDXL", "sdxl")
if is_sd:
w1 = round(0.8 + ratio * 0.6, 2)
w2 = round(0.8 + (1 - ratio) * 0.6, 2)
merged_tags = [f"({t}:{w1})" for t in primary_tags] + [f"({t}:{w2})" for t in secondary_tags]
else:
n1 = max(1, int(round(len(primary_tags) * (0.5 + ratio))))
n2 = max(1, int(round(len(secondary_tags) * (0.5 + (1 - ratio)))))
merged_tags = primary_tags[:n1] + secondary_tags[:n2]
merged_palette = ", ".join([
x for x in [p1.get("palette", ""), p2.get("palette", "")] if x
])
if p1.get("lighting") and p2.get("lighting"):
merged_lighting = f"{p1['lighting']}, blended with {p2['lighting']}"
else:
merged_lighting = p1.get("lighting") or p2.get("lighting", "")
neg_tokens = []
seen = set()
for src in (p1["neg"], p2["neg"]):
for t in src.split(","):
t = t.strip()
if t and t.lower() not in seen:
seen.add(t.lower())
neg_tokens.append(t)
return {
"category": f"{p1['category']}+{p2['category']}",
"tags": ", ".join(merged_tags),
"quality": p1["quality"],
"neg": ", ".join(neg_tokens),
"camera": p1.get("camera", "") or p2.get("camera", ""),
"lighting": merged_lighting,
"palette": merged_palette,
"aspect": p1.get("aspect", "1:1"),
}
def build_prompt(
subject: str,
preset: str,
model: str = "通用",
aspect: str = "",
extra_mood: str = "",
extra_composition: str = "",
extra_negatives: str = "",
seed: Optional[int] = None,
quality_tier: str = "pro",
character_sheet: bool = False,
mix_secondary: Optional[str] = None,
mix_ratio: float = 0.6,
) -> Dict:
"""构建增强后的提示词。
v2.1 新增参数：
extra_negatives 额外负面词，逗号分隔
quality_tier 质量档位 basic / pro / master
character_sheet 角色设定图模式（T-pose 多视图）
v2.2 新增参数：
mix_secondary 副预设名（已 resolve），与主预设融合
mix_ratio 主预设权重 0.1-0.9
"""
preset = resolve_preset(preset) or "写实摄影"
if mix_secondary:
mix_secondary = resolve_preset(mix_secondary) or ""
if mix_secondary and mix_secondary != preset:
data = mix_presets(preset, mix_secondary, mix_ratio, model)
mixed_label = f"{preset}+{mix_secondary}@{mix_ratio:.2f}"
else:
data = STYLE_PRESETS[preset]
mixed_label = ""
auto = parse_requirement(subject)
subject_clean = sanitize_subject(strip_negative_clauses(subject))
if not extra_composition:
extra_composition = auto["composition"]
if not extra_mood:
extra_mood = auto["mood"]
if not aspect:
aspect = data.get("aspect", "1:1")
# 时间 / 天气 / 季节
ambient_parts = [auto["time_of_day"], auto["weather"], auto["season"]]
ambient = ", ".join([x for x in ambient_parts if x])
# 角色设定图模式
if character_sheet:
subject_clean = (
f"character design sheet of {subject_clean}, "
f"multiple views: front view, three-quarter view, side view, back view, "
f"T-pose, clean white background, reference sheet, "
f"consistent character design"
)
aspect = "16:9"
consistency_parts = [
data["tags"],
data.get("camera", ""),
data.get("lighting", ""),
data.get("palette", ""),
]
consistency = ", ".join([x for x in consistency_parts if x])
# 质量档位（替换 UNIVERSAL_QUALITY）
tier_quality = QUALITY_TIERS.get(quality_tier, QUALITY_TIERS["pro"])
quality_combined = f"{data['quality']}, {tier_quality}"
# 负面词：预设 + 全局过滤 + 用户抽出 + 显式追加
neg_exclude = list(PRESET_NEG_EXCLUDE.get(preset, []))
if mix_secondary and mix_secondary in PRESET_NEG_EXCLUDE:
neg_exclude.extend(PRESET_NEG_EXCLUDE[mix_secondary])
universal_neg_filtered = _filter_neg(UNIVERSAL_NEG, neg_exclude)
user_neg_from_subject = ", ".join(auto["user_negatives"])
neg_parts = [data["neg"], universal_neg_filtered, user_neg_from_subject, extra_negatives]
neg_combined = ", ".join([x for x in neg_parts if x])
extras = ", ".join([x for x in [extra_composition, extra_mood, ambient] if x])
seed_key = mixed_label or preset
# 按模型生成不同形式
if model in ("Midjourney", "MJ", "mj"):
core = f"{subject_clean}, {consistency}"
if extras:
core = f"{core}, {extras}"
core = f"{core}, {quality_combined}"
flags = [ASPECT_TO_MJ.get(aspect, "--ar 1:1"), "--stylize 250"]
positive = f"{core} {' '.join(flags)}"
negative = f"--no {neg_combined}"
hint = (
"Midjourney tips：\n"
" • 角色/产品系列一致：加 --cref <url> 或 --sref <url>\n"
f" • 想要更风格化加 --stylize 500~750；更写实降到 --stylize 50\n"
f" • 建议 seed 锁定：--seed {seed or stable_seed(subject_clean, seed_key)}"
)
elif model in ("Stable Diffusion", "SD", "sd"):
positive = (
f"({subject_clean}:1.2), {consistency}"
+ (f", {extras}" if extras else "")
+ f", {quality_combined}"
)
negative = neg_combined
hint = (
"Stable Diffusion tips：\n"
f" • 强化权重: (word:1.2~1.5), 减弱: [word:0.7]\n"
f" • 建议尺寸 (SD 1.5): 512x{{hw_from_aspect}}; (SDXL): {ASPECT_TO_SDXL.get(aspect,'1024x1024')}\n"
f" • 采样: DPM++ 2M Karras, 30 steps, CFG 6.5\n"
f" • 建议 seed 锁定: {seed or stable_seed(subject_clean, seed_key)}（系列同 seed 提升一致性）"
)
elif model in ("SDXL", "sdxl"):
positive = (
f"{subject_clean}, {consistency}"
+ (f", {extras}" if extras else "")
+ f", {quality_combined}"
)
negative = neg_combined
hint = (
"SDXL tips：\n"
f" • 推荐尺寸: {ASPECT_TO_SDXL.get(aspect,'1024x1024')}\n"
f" • 采样: DPM++ SDE Karras, 25-30 steps, CFG 5-7\n"
f" • Refiner 使用率 0.2-0.3\n"
f" • seed: {seed or stable_seed(subject_clean, seed_key)}"
)
elif model in ("DALL-E", "DALL·E", "dalle", "DALLE"):
parts = [f"A {preset} style image of {subject_clean}"]
if data.get("camera"):
parts.append(f"captured with {data['camera']}")
if data.get("lighting"):
parts.append(f"lit by {data['lighting']}")
if data.get("palette"):
parts.append(f"with a color palette of {data['palette']}")
if extras:
parts.append(extras)
parts.append("highly detailed, professional composition")
positive = ". ".join(parts) + "."
negative = "(DALL-E 3 忽略负面提示，已通过正向描述规避)"
hint = (
"DALL-E 3 tips：\n"
" • 用自然语言句子 + 细节形容词效果最佳\n"
f" • 画幅: {aspect} (仅支持 1:1, 16:9, 9:16 在 ChatGPT 内)\n"
" • 一致性: 在同一会话连续生成并引用 \"use the same character\""
)
elif model in ("Flux", "flux"):
positive = (
f"{subject_clean}. {consistency}."
+ (f" {extras}." if extras else "")
+ f" {quality_combined}."
)
negative = neg_combined
hint = (
"Flux tips：\n"
" • 支持长自然语言提示，可加句式结构 \"The subject is...\"\n"
f" • 建议 Flux Dev: guidance 3.5; Flux Schnell: guidance 0\n"
f" • seed: {seed or stable_seed(subject_clean, seed_key)}"
)
else: # 通用
positive = (
f"{subject_clean}, {consistency}"
+ (f", {extras}" if extras else "")
+ f", {quality_combined}"
)
negative = neg_combined
hint = "通用格式：Midjourney / SD / Flux 皆可直接使用。"
return {
"version": VERSION,
"original": subject,
"preset": preset,
"mix_secondary": mix_secondary or "",
"mix_ratio": mix_ratio if mix_secondary else None,
"mix_label": mixed_label,
"model": model,
"aspect": aspect,
"composition": extra_composition,
"mood": extra_mood,
"time_of_day": auto.get("time_of_day", ""),
"weather": auto.get("weather", ""),
"season": auto.get("season", ""),
"quality_tier": quality_tier,
"character_sheet": character_sheet,
"user_negatives": auto.get("user_negatives", []),
"seed_suggestion": seed or stable_seed(subject_clean, seed_key),
"positive": positive,
"negative": negative,
"hint": hint,
"consistency_lock": {
"camera": data.get("camera", ""),
"lighting": data.get("lighting", ""),
"palette": data.get("palette", ""),
"aspect": aspect,
},
}
def build_series(
subject: str,
preset: str,
model: str,
aspect: str,
variations: List[str],
seed: Optional[int] = None,
quality_tier: str = "pro",
mix_secondary: Optional[str] = None,
mix_ratio: float = 0.6,
) -> List[Dict]:
"""系列批量生成：共享 camera/lighting/palette/seed 锁，仅替换主体描述。"""
if seed is None:
seed_key = f"{preset}+{mix_secondary}@{mix_ratio:.2f}" if mix_secondary else preset
seed = stable_seed(subject, seed_key)
results = []
for i, v in enumerate(variations, 1):
full = f"{subject}, {v}" if v and v != subject else subject
r = build_prompt(
full, preset, model, aspect, seed=seed, quality_tier=quality_tier,
mix_secondary=mix_secondary, mix_ratio=mix_ratio,
)
r["series_index"] = i
r["series_total"] = len(variations)
results.append(r)
return results
# ─────────────────────────────────────────────────────────
# 输出
# ─────────────────────────────────────────────────────────
def print_prompt(result: Dict):
sep = "═" * 60
print(f"\n{sep}")
if "series_index" in result:
print(f"📸 系列生成 [{result['series_index']}/{result['series_total']}]")
if result.get("character_sheet"):
print("👤 角色设定图模式：T-pose 多视图（喂给 MJ --cref / IP-Adapter）")
print(f"📌 原始描述 : {result['original']}")
if result.get("mix_label"):
print(f"🎨 风格预设 : {result['mix_label']} (混合)")
else:
print(f"🎨 风格预设 : {result['preset']}")
print(f"🤖 目标模型 : {result['model']}")
print(f"📐 画幅 : {result['aspect']}")
print(f"⭐ 质量档位 : {result.get('quality_tier', 'pro')}")
if result.get("composition"):
print(f"🎥 构图 : {result['composition']}")
if result.get("mood"):
print(f"🎭 情绪 : {result['mood']}")
if result.get("time_of_day"):
print(f"🕐 时间 : {result['time_of_day']}")
if result.get("weather"):
print(f"☁️ 天气 : {result['weather']}")
if result.get("season"):
print(f"🍂 季节 : {result['season']}")
if result.get("user_negatives"):
print(f"🚫 用户负向 : {', '.join(result['user_negatives'])} → 已入负面")
print(f"🎲 种子建议 : {result['seed_suggestion']}")
print(f"\n✅ 正向提示词：")
print(f"{result['positive']}")
print(f"\n❌ 负向提示词：")
print(f"{result['negative']}")
print(f"\n🔒 一致性锁：")
for k, v in result["consistency_lock"].items():
if v:
print(f" {k:8s}: {v}")
print(f"\n💡 {result['hint']}")
print(f"{sep}\n")
def list_presets(with_examples: bool = False):
by_cat: Dict[str, List[str]] = {}
for name, data in STYLE_PRESETS.items():
by_cat.setdefault(data["category"], []).append(name)
print(f"\n🎨 可用风格预设 (共 {len(STYLE_PRESETS)} 款)")
print("─" * 50)
order = ["摄影", "动漫", "插画", "3D", "设计", "艺术", "场景", "游戏", "东方"]
for cat in order:
if cat not in by_cat:
continue
print(f"\n【{cat}】 {len(by_cat[cat])} 款")
for name in by_cat[cat]:
if with_examples:
urls = preset_example_urls(name)
print(f" • {name}")
print(f" 🔍 Lexica: {urls['lexica']}")
print(f" 🔍 Civitai: {urls['civitai']}")
else:
print(f" • {name}")
print(
"\n💡 同义别名示例：anime, ghibli, cyberpunk, genshin, lol, "
"dunhuang, hanfu, glassmorphism, bauhaus, brutalism, healing, cozy ..."
)
if not with_examples:
print("💡 加 --with-examples 查看每个预设的 Lexica/Civitai/Pinterest 参考图链接（v2.4）\n")
else:
print("")
# v2.5 C3: 变体差异轴
VARIANT_AXES_DICT: Dict[str, List[str]] = {
"mood": [
"神秘 ethereal mysterious",
"治愈 cozy healing soft",
"史诗 epic dramatic cinematic",
"高级 luxurious refined sophisticated",
"梦幻 dreamy ethereal whimsical",
"紧张 tense intense gripping",
],
"composition": [
"特写 close-up portrait",
"全身 full body wide shot",
"俯拍 top-down overhead",
"仰拍 low-angle hero shot",
"侧面 side profile",
"三分之二 three-quarter view",
],
"lighting": [
"黄金时刻 golden hour warm rim light",
"蓝调时刻 blue hour cool gradient",
"硬光 hard directional spotlight",
"柔光 soft diffused window light",
"霓虹 neon multi-color rim",
"侧光 side rim with deep shadows",
],
"stylize": [
"stylize 50 写实",
"stylize 250 平衡",
"stylize 500 风格化",
"stylize 750 高度风格化",
],
}
def build_variants(subject: str, preset: str, model: str, aspect: str,
axes: List[str], n: int, seed: Optional[int] = None,
quality_tier: str = "pro",
mix_secondary: Optional[str] = None,
mix_ratio: float = 0.6) -> List[Dict]:
"""v2.5 C3: 生成 N 个 A/B 测试变体。
每个变体在 axes 指定的维度上选不同值，其余字段固定（含 seed）。
"""
valid_axes = [a for a in axes if a in VARIANT_AXES_DICT]
if not valid_axes:
valid_axes = ["mood", "composition"]
# 生成 N 个差异化组合
if seed is None:
seed = stable_seed(subject, preset)
variants = []
for i in range(n):
mood = composition = lighting = ""
extras_neg = ""
# 每个 axis 取第 i % len(axis) 个值
for axis in valid_axes:
values = VARIANT_AXES_DICT[axis]
val = values[i % len(values)]
# 中文部分作 mood/composition，英文部分作 prompt 注入
zh, _, en = val.partition(" ")
if axis == "mood":
mood = en
elif axis == "composition":
composition = en
elif axis == "lighting":
# 注入到 extras_neg 反向：实际加到 subject 后
pass
# 在 subject 后面拼接 lighting / stylize 信号（让 build_prompt 不破坏锁机制）
injected_subject = subject
for axis in valid_axes:
if axis == "lighting":
_, _, en = VARIANT_AXES_DICT[axis][i % len(VARIANT_AXES_DICT[axis])].partition(" ")
injected_subject = f"{subject}, {en}"
elif axis == "stylize":
# stylize 不改 subject，留给 MJ flag（默认 250）
pass
r = build_prompt(
injected_subject, preset, model, aspect,
extra_mood=mood, extra_composition=composition,
seed=seed, quality_tier=quality_tier,
mix_secondary=mix_secondary, mix_ratio=mix_ratio,
)
# 描述这个变体
descriptors = []
for axis in valid_axes:
zh, _, _ = VARIANT_AXES_DICT[axis][i % len(VARIANT_AXES_DICT[axis])].partition(" ")
descriptors.append(f"{axis}={zh}")
r["variant_index"] = i + 1
r["variant_total"] = n
r["variant_descriptor"] = " / ".join(descriptors)
variants.append(r)
return variants
def show_preset_examples(preset: str):
"""打印单个预设的所有平台参考图链接。"""
resolved = resolve_preset(preset) or preset
if resolved not in STYLE_PRESETS:
print(f"❌ 未知预设: {preset}（运行 -l 查看所有预设）")
return
urls = preset_example_urls(resolved)
data = STYLE_PRESETS[resolved]
print(f"\n🎨 {resolved} ({data['category']}) — 参考图链接")
print("─" * 60)
print(f" 风格特征: {data['tags']}")
print(f" 默认画幅: {data.get('aspect', '1:1')}")
print(f" 搜索词: {PRESET_SEARCH_TERMS.get(resolved, resolved)}")
print(f"\n📍 参考图平台:")
for plat, url in urls.items():
print(f" • {plat:14s} {url}")
print()
# ─────────────────────────────────────────────────────────
# CLI
# ─────────────────────────────────────────────────────────
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-prompt v{VERSION} — T2I 提示词增强工具",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
示例:
# 基础
enhance_prompt.py "一只赛博朋克风格的猫" -p 赛博朋克 -m Midjourney
# 自动意图 + 时间 / 天气 / 季节 / 负向需求识别
enhance_prompt.py "雨天黄昏的东京巷弄，忧郁氛围，不要人物"
enhance_prompt.py "秋天樱花季汉服写真"
# 新预设（v2.1）
enhance_prompt.py "双马尾少女" -p 原神 -t master
enhance_prompt.py "手持月亮的神女" -p 敦煌壁画
enhance_prompt.py "极简仪表盘UI" -p 玻璃拟态
# 角色设定图（给 Midjourney --cref 做参考）
enhance_prompt.py "银发机甲少女" -p 动漫 --character-sheet -m Midjourney
# 混合预设（v2.2）
enhance_prompt.py "持剑女侠" -p "赛博朋克+水墨" --mix 0.6 -m Midjourney
enhance_prompt.py "山中神女" -p "原神+敦煌壁画" --mix 0.5 -m SDXL
# 系列一致性（4 张共享 camera/lighting/palette/seed）
enhance_prompt.py "一个红发女侠" -p 动漫 -s 4 \\
--variations "持剑站立,骑马奔驰,弯弓射箭,与龙对视"
# 质量档位 + 显式负面追加
enhance_prompt.py "品牌展台" -p 品牌KV -t master --avoid "cluttered, people"
# JSON 输出
enhance_prompt.py "极简Logo一朵山茶花" -p Logo设计 -j
""",
)
parser.add_argument("subject", nargs="?", help="要生成图片的主体描述")
parser.add_argument(
"-p", "--preset",
help="风格预设（中文 / 英文别名）。混合：'赛博朋克+水墨' 或 'genshin+dunhuang'（v2.2）",
)
parser.add_argument(
"--mix", type=float, default=0.6,
help="主预设权重 0.1-0.9，仅在 -p A+B 混合时生效（默认 0.6，主导主预设）",
)
parser.add_argument(
"-m", "--model", default="通用",
help="目标模型: Midjourney / SD / SDXL / DALL-E / Flux / 通用",
)
parser.add_argument("-a", "--aspect", default="", help="画幅: 1:1 / 3:4 / 16:9 / 21:9 ...")
parser.add_argument("--mood", default="", help="情绪覆盖")
parser.add_argument("--composition", default="", help="构图覆盖")
parser.add_argument("--avoid", default="", help="额外负面词，逗号分隔（v2.1）")
parser.add_argument(
"-t", "--tier", choices=["basic", "pro", "master"], default="pro",
help="质量档位 basic/pro/master，默认 pro（v2.1）",
)
parser.add_argument(
"-cs", "--character-sheet", action="store_true",
help="角色设定图模式：T-pose 多视图，适合给 MJ --cref 做角色参考（v2.1）",
)
parser.add_argument("--seed", type=int, help="种子（不给则哈希生成稳定 seed）")
parser.add_argument("-s", "--series", type=int, default=1, help="系列张数（配合 --variations 使用）")
parser.add_argument("--variations", default="", help="系列变体，逗号分隔，如 '持剑,骑马,射箭'")
parser.add_argument("--variants", type=int, default=0,
help="A/B 测试：同 subject 出 N 个不同 mood/composition 变体（v2.5），可 pipe 给 image_review --rank")
parser.add_argument("--variant-axes", default="mood,composition",
help="变体差异轴，逗号分隔（mood/composition/lighting/stylize），默认 mood,composition（v2.5）")
parser.add_argument("--polish", action="store_true",
help="先用 Claude API 智能润色（需 ANTHROPIC_API_KEY）后再增强（v2.3）")
parser.add_argument("--suggest", action="store_true",
help="只 Claude 推荐 top-3 预设，不做完整 prompt（v2.5 A1，描述模糊时用）")
parser.add_argument("--safety", default="",
help="平台合规润色：DALL-E/MJ/SD/SDXL/Flux，自动重写艺术词避免误判（v2.3）")
parser.add_argument("--compact", action="store_true",
help="压缩 prompt 到 CLIP 77 token 内（防 SDXL 截断），自动去重 + 保留主体（v2.4）")
parser.add_argument("--compact-target", type=int, default=CLIP_TOKEN_LIMIT,
help=f"压缩目标 token 数，默认 {CLIP_TOKEN_LIMIT}（v2.4）")
parser.add_argument("--session", default="",
help="保存当前调用到 ~/.huo15/sessions/<name>.json 供后续 --continue 使用（v2.4）")
parser.add_argument("--continue", dest="cont", default="",
help="加载之前的 session 作为默认值，CLI 参数为补丁，自动锁定 seed（v2.4）")
parser.add_argument("--list-sessions", action="store_true",
help="列出所有 session（v2.4）")
parser.add_argument("--save-char", default="",
help="保存当前调用为角色卡 ~/.huo15/characters/<name>.json（v2.6）")
parser.add_argument("--char", default="",
help="加载角色卡，自动注入主体描述 + 锁 seed/preset/aspect（v2.6）")
parser.add_argument("--obsidian", action="store_true",
help="把 recipe 写入 Obsidian vault『图集/』，自动 frontmatter + 复现命令（v2.6）")
parser.add_argument("--brand-kit", default="",
help="加载品牌套件 ~/.huo15/brand_kits/<name>.json，自动注入 colors/keywords/forbidden（v3.0）")
parser.add_argument("-l", "--list", action="store_true", help="列出所有预设")
parser.add_argument("--with-examples", action="store_true",
help="-l 时附 Lexica/Civitai 参考图链接（v2.4）")
parser.add_argument("--examples",
help="查看单个预设的所有平台参考图链接（v2.4）")
parser.add_argument("-j", "--json", action="store_true", help="JSON 输出")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
if args.list:
list_presets(with_examples=args.with_examples)
return
if args.examples:
show_preset_examples(args.examples)
return
if args.list_sessions:
session_list()
return
# v2.4 A2: --continue 加载历史 session 作为默认值
session_meta = None
if args.cont:
session_meta = session_apply(args.cont, args)
if not session_meta.get("loaded"):
print(f"⚠️ {session_meta.get('reason')}", file=sys.stderr)
# v2.6 E1: --char 加载角色卡（在 session 之后，让角色卡覆盖 session）
char_meta = None
if args.char:
try:
from character import char_apply
char_meta = char_apply(args.char, args)
if not char_meta:
print(f"⚠️ 角色卡 '{args.char}' 不存在", file=sys.stderr)
except ImportError:
print(f"⚠️ character 模块未找到", file=sys.stderr)
# v3.0 E4: --brand-kit 加载品牌套件
brand_kit_meta = None
if args.brand_kit:
try:
from brand_kit import kit_apply
brand_kit_meta = kit_apply(args.brand_kit, args)
if not brand_kit_meta:
print(f"⚠️ 品牌套件 '{args.brand_kit}' 不存在", file=sys.stderr)
except ImportError:
print(f"⚠️ brand_kit 模块未找到", file=sys.stderr)
if not args.subject:
parser.print_help()
sys.exit(1)
# v2.5 A1: --suggest 委托 claude_polish 做 top-3 预设推荐
if args.suggest:
try:
from claude_polish import suggest_presets
suggestion = suggest_presets(args.subject)
except Exception as e:
print(f"❌ {e}", file=sys.stderr)
sys.exit(2)
if args.json:
print(json.dumps(suggestion, ensure_ascii=False, indent=2))
return
print(f"\n🎯 智能预设推荐\n📝 用户意图: {suggestion.get('user_intent_summary', '')}\n")
for i, p in enumerate(suggestion.get("top_3", []), 1):
score = p.get("score", 0)
bar = "█" * int(score * 10) + "░" * (10 - int(score * 10))
print(f" {i}. {p.get('preset', '?'):12s} [{bar}] {score:.2f} → {p.get('reason', '')}")
mix = suggestion.get("mix_suggestion") or {}
if mix and mix.get("primary"):
print(f"\n🎨 混合建议: {mix['primary']} + {mix['secondary']} (mix={mix.get('ratio', 0.6)})")
print(f" {mix.get('reason', '')}")
print()
return
subject = args.subject
polish_meta: Optional[Dict] = None
safety_meta: Optional[Dict] = None
preset_override = args.preset
aspect_override = args.aspect
mix_override = None
# v2.3: Claude 智能润色（前置）
if args.polish:
try:
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from claude_polish import call_claude, parse_claude_json
resp = call_claude(subject)
polished = parse_claude_json(resp)
if polished.get("error"):
print(f"❌ Claude 润色拒答: {polished['error']}", file=sys.stderr)
sys.exit(2)
subject = polished.get("subject_refined_zh") or subject
if not preset_override:
pri = polished.get("style_preset", "")
sec = polished.get("style_preset_secondary", "")
if pri and sec:
preset_override = f"{pri}+{sec}"
mix_override = polished.get("mix_ratio", 0.6)
elif pri:
preset_override = pri
if not aspect_override and polished.get("aspect"):
aspect_override = polished["aspect"]
polish_meta = polished
except Exception as e:
print(f"⚠️ Claude 润色失败，回退到原描述: {e}", file=sys.stderr)
# v2.3: 平台合规润色
if args.safety:
try:
from safety_lint import lint as safety_lint
r = safety_lint(subject, platform=args.safety)
if r["verdict"] == "REJECT":
print(f"🚫 命中红线: {r['reason']}\n类别: {', '.join(r.get('categories', []))}", file=sys.stderr)
print(r.get("advice", ""), file=sys.stderr)
sys.exit(2)
if r["verdict"] == "REWRITE":
subject = r["rewritten"]
safety_meta = r
except ImportError:
print(f"⚠️ safety_lint 模块未找到", file=sys.stderr)
# 自动推荐
auto = parse_requirement(subject)
raw_preset = preset_override or auto["preset_suggestion"] or "写实摄影"
primary_raw, secondary_raw = parse_mix_preset(raw_preset)
preset = primary_raw
mix_secondary = secondary_raw
# 校验混合预设
if mix_secondary:
primary_resolved = resolve_preset(preset)
secondary_resolved = resolve_preset(mix_secondary)
if not primary_resolved or not secondary_resolved:
unknown = [n for n, r in [(preset, primary_resolved), (mix_secondary, secondary_resolved)] if not r]
print(f"❌ 未知预设：{', '.join(unknown)}（运行 -l 查看列表）", file=sys.stderr)
sys.exit(1)
preset = primary_resolved
mix_secondary = secondary_resolved
aspect = aspect_override or auto["aspect_suggestion"] or STYLE_PRESETS.get(resolve_preset(preset) or "写实摄影", {}).get("aspect", "1:1")
# 混合权重（polish 推荐 > CLI --mix）
effective_mix = mix_override if mix_override is not None else args.mix
# v2.5 C3: A/B 变体模式（同 subject + 同 seed，不同 mood/composition）
if args.variants > 0:
axes = [a.strip() for a in args.variant_axes.split(",") if a.strip()]
variants = build_variants(
subject, preset, args.model, aspect, axes, args.variants,
seed=args.seed, quality_tier=args.tier,
mix_secondary=mix_secondary, mix_ratio=effective_mix,
)
if args.json:
out = {"version": VERSION, "variants": variants}
print(json.dumps(out, ensure_ascii=False, indent=2))
else:
print(f"🎲 A/B 变体测试 ({args.variants} 个，差异轴: {', '.join(axes)})")
print(f" 所有变体共享 seed = {variants[0]['seed_suggestion']}（仅在指定轴上分化）")
for v in variants:
print(f"\n 变体 {v['variant_index']}/{v['variant_total']}: {v['variant_descriptor']}")
print(f" positive head: {v['positive'][:120]}...")
print(f"\n💡 出图后用 image_review.py img1.png img2.png ... --rank 选最优\n")
return
# 系列模式
if args.series > 1 or args.variations:
variations = [v.strip() for v in args.variations.split(",") if v.strip()]
if not variations:
variations = [subject] * args.series
elif len(variations) < args.series:
variations += [variations[-1]] * (args.series - len(variations))
results = build_series(
subject, preset, args.model, aspect,
variations[: max(args.series, len(variations))],
seed=args.seed, quality_tier=args.tier,
mix_secondary=mix_secondary, mix_ratio=effective_mix,
)
if args.json:
out = {"version": VERSION, "series": results}
if polish_meta: out["claude_polish"] = polish_meta
if safety_meta: out["safety_lint"] = safety_meta
print(json.dumps(out, ensure_ascii=False, indent=2))
else:
if polish_meta:
print(f"✨ Claude 已润色 → 主体: {subject}")
if safety_meta and safety_meta.get("verdict") == "REWRITE":
print(f"🛡 平台合规重写 → {subject}")
for r in results:
print_prompt(r)
print(f"🔐 本系列 {len(results)} 张共享 seed = {results[0]['seed_suggestion']}，一致性锁见每张「🔒」区块。")
return
# 单张
result = build_prompt(
subject, preset, args.model, aspect,
extra_mood=args.mood, extra_composition=args.composition,
extra_negatives=args.avoid, seed=args.seed,
quality_tier=args.tier, character_sheet=args.character_sheet,
mix_secondary=mix_secondary, mix_ratio=effective_mix,
)
if polish_meta:
result["claude_polish"] = polish_meta
if safety_meta:
result["safety_lint"] = safety_meta
# v2.4: 压缩 prompt（针对 CLIP 模型）
if args.compact:
compacted, meta = compact_prompt(result["positive"], target_tokens=args.compact_target)
result["positive_original"] = result["positive"]
result["positive"] = compacted
result["compaction"] = meta
# v2.4 A2: 保存 session
session_name = args.session or args.cont
if session_name:
session_save(session_name, result)
result["session"] = {"name": session_name, "saved": True}
if session_meta:
result["session"]["loaded_from"] = session_meta
# v2.6 E1: 保存角色卡
if args.save_char:
try:
from character import char_save
saved_card = char_save(args.save_char, result)
result["character_card"] = {"name": args.save_char, "saved": True}
except Exception as e:
print(f"⚠️ 角色卡保存失败: {e}", file=sys.stderr)
# v2.6 D2: Obsidian 写入
if args.obsidian:
try:
obsidian_path = write_obsidian_recipe(result)
result["obsidian"] = {"saved": True, "path": obsidian_path}
except Exception as e:
print(f"⚠️ Obsidian 写入失败: {e}", file=sys.stderr)
if args.json:
print(json.dumps(result, ensure_ascii=False, indent=2))
else:
if char_meta:
print(f"👤 已加载角色卡 '{args.char}' (用过 {char_meta.get('use_count', 1)} 次, seed={char_meta.get('seed')})")
if brand_kit_meta:
print(f"🎨 已加载品牌套件 '{args.brand_kit}' ({len(brand_kit_meta.get('colors', []))} 色, {len(brand_kit_meta.get('keywords', []))} 关键词)")
if session_meta and session_meta.get("loaded"):
applied = session_meta.get("applied_from_session", [])
print(f"📂 已加载 session '{session_meta['name']}' (第 {session_meta['iteration_count']+1} 轮)")
if applied:
print(f" 继承字段: {', '.join(applied)}")
if polish_meta:
print(f"✨ Claude 已润色 (in={polish_meta.get('_usage',{}).get('input_tokens',0)}/out={polish_meta.get('_usage',{}).get('output_tokens',0)} tokens)")
if safety_meta and safety_meta.get("verdict") == "REWRITE":
print(f"🛡 平台合规已重写: {len(safety_meta.get('substitutions',[]))} 处替换 (target={safety_meta['platform']})")
if args.compact and result.get("compaction", {}).get("compacted"):
m = result["compaction"]
print(f"🗜 prompt 已压缩: {m['estimated_tokens_before']}→{m['estimated_tokens_after']} tokens (砍 {m['removed']} 段)")
print_prompt(result)
if session_name:
safe = re.sub(r"[^\w\-]", "_", session_name)
print(f"💾 已保存 session: ~/.huo15/sessions/{safe}.json")
if args.save_char:
safe_char = re.sub(r"[^\w\-]", "_", args.save_char)
print(f"👤 已保存角色卡: ~/.huo15/characters/{safe_char}.json")
if result.get("obsidian", {}).get("saved"):
print(f"📚 已写入 Obsidian: {result['obsidian']['path']}")
if __name__ == "__main__":
main()
FILE:scripts/enhance_video.py
#!/usr/bin/env python3
"""
huo15-img-prompt — T2V 视频提示词增强脚本 v2.2
把 enhance_prompt.py 的 88 风格预设 + 一致性锁，扩展到视频维度：
- 镜头运动（推/拉/摇/移/跟/环绕/手持/无人机...）
- 节奏（缓慢 / 中速 / 紧张快切）
- 时长（建议秒数 + 关键帧拆分）
- 主体动作（自动从描述中抽词，或显式 --action）
- 模型适配：Sora / Kling 可灵 / Runway Gen-3/Gen-4 / Pika / Luma DreamMachine / 即梦 / Hailuo MiniMax / Wan2.1
调用：
enhance_video.py "雨夜霓虹街头一只猫漫步" -p 赛博朋克 -m Sora --duration 8
enhance_video.py "汉服少女转身回眸" -p 汉服写真 -m Kling --motion 慢速跟拍
enhance_video.py "宇宙飞船穿越星云" -p scifi -m Runway --action "ship accelerates, lens flare"
依赖：
enhance_prompt.py 同目录（复用其预设 + 意图解析 + 一致性锁）
"""
import sys
import os
import json
import re
import argparse
import hashlib
from typing import Dict, List, Optional, Tuple
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from enhance_prompt import (
STYLE_PRESETS,
ALIASES,
QUALITY_TIERS,
resolve_preset,
parse_requirement,
parse_mix_preset,
mix_presets,
sanitize_subject,
strip_negative_clauses,
stable_seed,
list_presets as list_image_presets,
)
VERSION = "3.1.0"
# ─────────────────────────────────────────────────────────
# 镜头运动（中文 → 英文 + 视频专业术语）
# ─────────────────────────────────────────────────────────
CAMERA_MOTION: Dict[str, str] = {
"推": "slow push-in (dolly in)",
"推镜": "smooth dolly in, gradual close-up",
"拉": "pull back (dolly out)",
"拉镜": "slow pull back revealing wider scene",
"摇": "pan (horizontal)",
"横摇": "horizontal pan from left to right",
"竖摇": "vertical tilt up to down",
"移": "lateral tracking shot",
"跟": "tracking shot following the subject",
"跟拍": "smooth tracking shot, subject locked in frame",
"环绕": "360 orbital shot around the subject",
"围绕": "360 orbit shot, slow rotation",
"手持": "handheld camera, slight shake, documentary feel",
"稳定": "smooth gimbal stabilized, fluid motion",
"无人机": "aerial drone shot, high-altitude reveal",
"航拍": "aerial drone descent, cinematic reveal",
"升": "crane up, vertical rise",
"降": "crane down, descent",
"变焦": "zoom in, focal length change",
"希区柯克": "dolly zoom (vertigo effect)",
"希区": "dolly zoom (vertigo effect)",
"鱼眼": "fisheye lens distortion, wide warped perspective",
"POV": "first-person POV, immersive",
"POV视角": "first-person POV, immersive",
"子弹时间": "bullet-time freeze, 360 frozen pan",
"延时": "time-lapse, accelerated motion",
"慢动作": "slow motion 120fps, ultra-smooth",
"快切": "rapid cuts, high-energy montage",
}
# 节奏 → 英文
PACING: Dict[str, str] = {
"缓慢": "slow steady pacing, contemplative rhythm",
"舒缓": "slow steady pacing, contemplative rhythm",
"宁静": "calm, atmospheric, lingering shots",
"中速": "moderate pacing, balanced cuts",
"紧张": "tense pacing, building intensity",
"急促": "fast pacing, urgent cuts",
"快切": "rapid cuts, high-energy edit",
"动感": "kinetic energy, dynamic motion",
"史诗": "epic crescendo, sweeping movement",
}
# 主体动作关键词（自动抽词）
ACTION_KEYWORDS: Dict[str, str] = {
"走": "walking forward",
"漫步": "walking calmly",
"奔跑": "running fast",
"跑": "running",
"跳": "jumping",
"飞": "flying through the air",
"舞": "dancing gracefully",
"舞蹈": "dancing gracefully",
"回眸": "turning to look back over shoulder",
"转身": "turning around",
"微笑": "smiling softly",
"战斗": "fighting, dynamic combat motion",
"挥剑": "swinging a sword",
"射箭": "drawing and releasing an arrow",
"骑马": "riding a horse at full gallop",
"驾驶": "driving forward",
"穿越": "traveling through, breaking forward",
"升起": "rising up slowly",
"落下": "falling down gently",
"爆炸": "explosion blooming outward",
"绽放": "blooming open",
"凝视": "gazing intently into the camera",
"对视": "locking eyes with the viewer",
"睁眼": "eyes opening slowly",
"闭眼": "eyes closing slowly",
"呼吸": "breathing softly, chest rising and falling",
"拥抱": "embracing tenderly",
"牵手": "holding hands",
"握手": "shaking hands",
}
# ─────────────────────────────────────────────────────────
# 模型规格
# ─────────────────────────────────────────────────────────
VIDEO_MODELS: Dict[str, Dict[str, str]] = {
"Sora": {
"max_duration": "20s (Sora 2 Pro)",
"default_duration": 10,
"aspect_default": "16:9",
"tip": "支持长自然语言描述。可叠加 'cinematic, IMAX, 35mm film, photorealistic'。一致性强，可复用 character description。",
"format": "natural",
},
"Kling": {
"max_duration": "10s (1080p Pro)",
"default_duration": 5,
"aspect_default": "16:9",
"tip": "可灵 1.6/2.0：建议提示前置主体，后置镜头/光影。支持首尾帧控制（image-to-video）。",
"format": "natural",
},
"可灵": {
"max_duration": "10s (1080p Pro)",
"default_duration": 5,
"aspect_default": "16:9",
"tip": "可灵 1.6/2.0：中文提示词支持良好，可加 'cinematic 电影感'。",
"format": "natural",
},
"Runway": {
"max_duration": "10s (Gen-3 Alpha Turbo)",
"default_duration": 5,
"aspect_default": "16:9",
"tip": "Gen-3 / Gen-4：英文提示效果最佳。支持 Motion Brush 局部运动。CFG ~7。",
"format": "natural",
},
"Pika": {
"max_duration": "10s (Pika 2.0)",
"default_duration": 4,
"aspect_default": "16:9",
"tip": "Pika：标签式提示，支持 -gs (guidance scale) 和 -motion (1-4)。",
"format": "tag",
},
"Luma": {
"max_duration": "9s (Dream Machine 1.6)",
"default_duration": 5,
"aspect_default": "16:9",
"tip": "Luma Dream Machine：自然语言 + 关键帧（首尾图）。Loop 模式支持无缝循环。",
"format": "natural",
},
"DreamMachine": {
"max_duration": "9s",
"default_duration": 5,
"aspect_default": "16:9",
"tip": "Luma Dream Machine：自然语言 + 关键帧。",
"format": "natural",
},
"Hailuo": {
"max_duration": "10s (MiniMax 02 / S2V-01)",
"default_duration": 6,
"aspect_default": "16:9",
"tip": "海螺 MiniMax 02：中文支持优秀。S2V-01 可指定参考人物。",
"format": "natural",
},
"MiniMax": {
"max_duration": "10s",
"default_duration": 6,
"aspect_default": "16:9",
"tip": "MiniMax 视频：中英双语，长描述效果好。",
"format": "natural",
},
"即梦": {
"max_duration": "12s (Seedance 1.0)",
"default_duration": 5,
"aspect_default": "16:9",
"tip": "即梦 / Seedance：抖音生态，支持中文 + 多镜头剧情连贯。",
"format": "natural",
},
"Seedance": {
"max_duration": "12s",
"default_duration": 5,
"aspect_default": "16:9",
"tip": "Seedance 1.0：多镜头剧情连贯，支持中文。",
"format": "natural",
},
"Wan": {
"max_duration": "8s (Wan 2.1)",
"default_duration": 4,
"aspect_default": "16:9",
"tip": "通义 Wan 2.1：阿里开源，I2V 支持高分辨率。中英双语提示。",
"format": "natural",
},
"Wan2.1": {
"max_duration": "8s",
"default_duration": 4,
"aspect_default": "16:9",
"tip": "通义 Wan 2.1：阿里开源 14B / 1.3B 双参数。",
"format": "natural",
},
"通用": {
"max_duration": "—",
"default_duration": 5,
"aspect_default": "16:9",
"tip": "通用模板：自然语言 + 镜头 + 节奏 + 主体动作。",
"format": "natural",
},
}
MODEL_ALIASES: Dict[str, str] = {
"sora": "Sora", "kling": "Kling", "kelin": "Kling", "klingai": "Kling",
"runway": "Runway", "gen3": "Runway", "gen4": "Runway",
"pika": "Pika", "luma": "Luma", "dreammachine": "Luma",
"hailuo": "Hailuo", "minimax": "Hailuo",
"jimeng": "即梦", "seedance": "即梦",
"wan": "Wan", "wan21": "Wan", "wan2.1": "Wan",
"tongyi": "Wan",
}
def resolve_video_model(name: str) -> str:
if not name:
return "通用"
key = name.strip().lower().replace("-", "").replace("_", "").replace(" ", "")
if key in MODEL_ALIASES:
return MODEL_ALIASES[key]
for m in VIDEO_MODELS:
if m.lower() == key:
return m
return name if name in VIDEO_MODELS else "通用"
# ─────────────────────────────────────────────────────────
# 解析
# ─────────────────────────────────────────────────────────
def parse_motion(text: str) -> str:
for zh, en in CAMERA_MOTION.items():
if zh in text:
return en
return ""
def parse_pacing(text: str) -> str:
for zh, en in PACING.items():
if zh in text:
return en
return ""
def parse_action(text: str) -> str:
actions = []
for zh, en in ACTION_KEYWORDS.items():
if zh in text and en not in actions:
actions.append(en)
return ", ".join(actions[:3])
# ─────────────────────────────────────────────────────────
# 关键帧拆分
# ─────────────────────────────────────────────────────────
def keyframe_breakdown(subject: str, motion: str, duration: int) -> List[Dict[str, str]]:
"""简单的三段式拆分：开场（建立）→ 中段（动作）→ 结尾（落点）。"""
if duration <= 3:
return [{"t": "0s", "desc": f"establish shot: {subject}"}]
third = max(1, duration // 3)
return [
{"t": "0s", "desc": f"opening: establish {subject} in scene, static composition"},
{"t": f"{third}s", "desc": f"mid: {motion or 'subject performs main action'}, peak motion"},
{"t": f"{2*third}s", "desc": f"closing: settle into resting frame, fade or hold"},
]
# ─────────────────────────────────────────────────────────
# 主构建
# ─────────────────────────────────────────────────────────
def build_video_prompt(
subject: str,
preset: str,
model: str = "通用",
aspect: str = "",
duration: Optional[int] = None,
motion: str = "",
pacing: str = "",
action: str = "",
seed: Optional[int] = None,
quality_tier: str = "pro",
extra_negatives: str = "",
mix_secondary: Optional[str] = None,
mix_ratio: float = 0.6,
) -> Dict:
preset = resolve_preset(preset) or "电影感"
if mix_secondary:
mix_secondary = resolve_preset(mix_secondary) or ""
model = resolve_video_model(model)
spec = VIDEO_MODELS[model]
# 视觉锁（复用 image preset）
if mix_secondary and mix_secondary != preset:
data = mix_presets(preset, mix_secondary, mix_ratio, model)
mixed_label = f"{preset}+{mix_secondary}@{mix_ratio:.2f}"
else:
data = STYLE_PRESETS[preset]
mixed_label = ""
# 时长 / 画幅
if duration is None:
duration = spec["default_duration"]
if not aspect:
aspect = data.get("aspect", spec["aspect_default"])
# 自动解析
auto = parse_requirement(subject)
subject_clean = sanitize_subject(strip_negative_clauses(subject))
if not motion:
motion = parse_motion(subject) or "smooth gimbal stabilized, fluid motion"
if not pacing:
pacing = parse_pacing(subject) or "moderate pacing, balanced cuts"
if not action:
action = parse_action(subject)
# ambient
ambient_parts = [auto["time_of_day"], auto["weather"], auto["season"]]
ambient = ", ".join([x for x in ambient_parts if x])
# 视觉锁字段
visual_lock = ", ".join([
x for x in [data["tags"], data.get("camera", ""), data.get("lighting", ""), data.get("palette", "")] if x
])
quality_phrase = QUALITY_TIERS.get(quality_tier, QUALITY_TIERS["pro"])
seed_key = mixed_label or preset
seed_value = seed or stable_seed(subject_clean, seed_key)
# 构造正向提示
if spec["format"] == "tag": # Pika 标签格式
parts = [
subject_clean,
f"{motion}",
f"{pacing}",
visual_lock,
ambient,
action,
quality_phrase,
"cinematic video",
]
positive = ", ".join([p for p in parts if p])
positive += f" -gs 12 -motion 3 -ar {aspect}"
else: # 自然语言格式
sentences = []
sentences.append(f"A {duration}-second video of {subject_clean}.")
sentences.append(f"Camera movement: {motion}.")
if action:
sentences.append(f"The subject is {action}.")
sentences.append(f"Pacing: {pacing}.")
sentences.append(f"Visual style: {visual_lock}.")
if ambient:
sentences.append(f"Atmosphere: {ambient}.")
sentences.append(f"Quality: {quality_phrase}, cinematic, smooth temporal coherence, no flicker, consistent character across frames.")
positive = " ".join(sentences)
# 负面
base_neg = data["neg"]
video_neg = (
"flicker, frame drop, motion blur artifacts, jittery camera, "
"low fps, choppy motion, morphing artifacts, identity drift, "
"deformed limbs mid-motion, inconsistent character, watermark"
)
neg_parts = [base_neg, video_neg, extra_negatives, ", ".join(auto.get("user_negatives", []))]
negative = ", ".join([x for x in neg_parts if x])
# 关键帧
keyframes = keyframe_breakdown(subject_clean, motion, duration)
hint = (
f"{model} tips：\n"
f" • {spec['tip']}\n"
f" • 推荐时长：{duration}s（上限 {spec['max_duration']}）\n"
f" • 一致性：i2v 模式可固定首帧角色 / 用 image-prompt 保持服装色彩\n"
f" • seed: {seed_value}（同一 seed + 同一 prompt 在多数模型可复现）"
)
return {
"version": VERSION,
"type": "t2v",
"original": subject,
"preset": preset,
"mix_secondary": mix_secondary or "",
"mix_label": mixed_label,
"model": model,
"aspect": aspect,
"duration_s": duration,
"max_duration": spec["max_duration"],
"motion": motion,
"pacing": pacing,
"action": action,
"time_of_day": auto.get("time_of_day", ""),
"weather": auto.get("weather", ""),
"season": auto.get("season", ""),
"seed_suggestion": seed_value,
"quality_tier": quality_tier,
"positive": positive,
"negative": negative,
"keyframes": keyframes,
"hint": hint,
"consistency_lock": {
"camera": data.get("camera", ""),
"lighting": data.get("lighting", ""),
"palette": data.get("palette", ""),
"aspect": aspect,
"motion": motion,
},
}
def print_video_prompt(r: Dict):
sep = "═" * 60
print(f"\n{sep}")
print(f"🎬 视频提示词（v{r['version']}）")
print(f"📌 原始描述 : {r['original']}")
if r.get("mix_label"):
print(f"🎨 风格预设 : {r['mix_label']} (混合)")
else:
print(f"🎨 风格预设 : {r['preset']}")
print(f"🤖 目标模型 : {r['model']}（上限 {r['max_duration']}）")
print(f"📐 画幅 : {r['aspect']}")
print(f"⏱ 时长 : {r['duration_s']}s")
print(f"🎥 镜头运动 : {r['motion']}")
print(f"🎵 节奏 : {r['pacing']}")
if r.get("action"):
print(f"💪 主体动作 : {r['action']}")
if r.get("time_of_day") or r.get("weather") or r.get("season"):
amb = ", ".join([x for x in [r.get("time_of_day", ""), r.get("weather", ""), r.get("season", "")] if x])
print(f"🌤 环境 : {amb}")
print(f"⭐ 质量档位 : {r['quality_tier']}")
print(f"🎲 种子建议 : {r['seed_suggestion']}")
print(f"\n✅ 正向提示词：\n{r['positive']}")
print(f"\n❌ 负向提示词：\n{r['negative']}")
print(f"\n🎞 关键帧拆分：")
for kf in r["keyframes"]:
print(f" {kf['t']:>4s} {kf['desc']}")
print(f"\n🔒 一致性锁：")
for k, v in r["consistency_lock"].items():
if v:
print(f" {k:8s}: {v}")
print(f"\n💡 {r['hint']}")
print(f"{sep}\n")
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-prompt enhance_video v{VERSION} — T2V 视频提示词增强",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
示例:
enhance_video.py "雨夜霓虹街头一只猫漫步" -p 赛博朋克 -m Sora --duration 8
enhance_video.py "汉服少女转身回眸" -p 汉服写真 -m Kling --motion 慢速跟拍
enhance_video.py "宇宙飞船穿越星云" -p scifi -m Runway --duration 5 --pacing 史诗
enhance_video.py "山中神女腾云" -p "原神+敦煌壁画" --mix 0.6 -m Hailuo
enhance_video.py "侠客挥剑" -p 水墨 -m 即梦 --action "spinning sword strike"
""",
)
parser.add_argument("subject", nargs="?", help="主体描述")
parser.add_argument("-p", "--preset", help="风格预设（沿用 88 款图像预设；支持 A+B 混合）")
parser.add_argument("--mix", type=float, default=0.6, help="主预设权重 0.1-0.9（默认 0.6）")
parser.add_argument(
"-m", "--model", default="通用",
help="视频模型: Sora / Kling / Runway / Pika / Luma / Hailuo / 即梦 / Wan / 通用",
)
parser.add_argument("-a", "--aspect", default="", help="画幅 16:9 / 9:16 / 1:1 / 21:9")
parser.add_argument("--duration", type=int, help="时长（秒），不给走模型默认")
parser.add_argument("--motion", default="", help="镜头运动覆盖（中/英）")
parser.add_argument("--pacing", default="", help="节奏覆盖")
parser.add_argument("--action", default="", help="主体动作覆盖")
parser.add_argument("--avoid", default="", help="额外负面词")
parser.add_argument("-t", "--tier", choices=["basic", "pro", "master"], default="pro")
parser.add_argument("--seed", type=int, help="种子")
parser.add_argument("-l", "--list", action="store_true", help="列出图像预设（视频沿用）")
parser.add_argument("--list-models", action="store_true", help="列出视频模型规格")
parser.add_argument("-j", "--json", action="store_true", help="JSON 输出")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
if args.list:
list_image_presets()
return
if args.list_models:
print(f"\n🎬 视频模型规格 (v{VERSION})\n" + "─" * 50)
for name, spec in VIDEO_MODELS.items():
print(f"\n【{name}】")
print(f" 上限时长: {spec['max_duration']}")
print(f" 默认时长: {spec['default_duration']}s")
print(f" 默认画幅: {spec['aspect_default']}")
print(f" 说明: {spec['tip']}")
return
if not args.subject:
parser.print_help()
sys.exit(1)
raw_preset = args.preset or "电影感"
primary_raw, secondary_raw = parse_mix_preset(raw_preset)
if secondary_raw:
primary_resolved = resolve_preset(primary_raw)
secondary_resolved = resolve_preset(secondary_raw)
if not primary_resolved or not secondary_resolved:
unknown = [n for n, r in [(primary_raw, primary_resolved), (secondary_raw, secondary_resolved)] if not r]
print(f"❌ 未知预设：{', '.join(unknown)}", file=sys.stderr)
sys.exit(1)
preset, mix_secondary = primary_resolved, secondary_resolved
else:
preset, mix_secondary = primary_raw, None
result = build_video_prompt(
args.subject, preset, model=args.model, aspect=args.aspect,
duration=args.duration, motion=args.motion, pacing=args.pacing,
action=args.action, seed=args.seed, quality_tier=args.tier,
extra_negatives=args.avoid, mix_secondary=mix_secondary, mix_ratio=args.mix,
)
if args.json:
print(json.dumps(result, ensure_ascii=False, indent=2))
else:
print_video_prompt(result)
if __name__ == "__main__":
main()
FILE:scripts/image_review.py
#!/usr/bin/env python3
"""
huo15-img-prompt — Claude Vision 图像评审 v2.5
把 Claude Vision 当作图像质量评审师。给一张图（本地路径或 URL）+ 原 prompt，
输出五维结构化打分 + 缺陷列表 + 可执行修复建议（喂给下一轮迭代）。
为什么这个能力 GPT-4o image gen / Imagen 内部做不到？
- 它们是端到端黑盒：prompt → 图，没有 prompt-image 闭环数据回流
- 我们在用户侧补这个回路，每张图都能产出 "下一轮怎么改 prompt" 的可执行指令
- 这就是 v2.5 的核心护城河：迭代提升而不是单次出图
五维评分：
1. subject_match 主体准确度（图与 prompt 的吻合）
2. composition 构图（黄金分割、留白、视觉层次、引导线）
3. lighting 光影（光源逻辑、明暗关系、艺术化处理）
4. palette 色彩（和谐度、风格一致性、色温情绪）
5. technical 技术质量（锐度、噪点、artifact、anatomy 错误）
调用：
image_review.py /path/to/image.png --prompt "原 prompt"
image_review.py https://example.com/img.png -p "..." -j > review.json
image_review.py img.png --quick # 简评，只给 overall 分
image_review.py a.png b.png c.png --rank # 多图排名
依赖：纯 urllib + ANTHROPIC_API_KEY，零 SDK
"""
import sys
import os
import json
import base64
import argparse
import re
from typing import Dict, List, Optional, Tuple
from urllib.request import Request, urlopen
from urllib.error import HTTPError, URLError
VERSION = "3.1.0"
ANTHROPIC_BASE = os.environ.get("ANTHROPIC_BASE_URL", "https://api.anthropic.com")
ANTHROPIC_VERSION = "2023-06-01"
DEFAULT_MODEL = "claude-sonnet-4-5"
# ─────────────────────────────────────────────────────────
# 评审 system prompt（启用 prompt caching，多图调用省 90% token）
# ─────────────────────────────────────────────────────────
def build_review_system_prompt(quick: bool = False) -> str:
if quick:
return """你是图像质量评审师。给一张图，输出严格 JSON：
{"overall_score": 0.0-10.0, "verdict": "PASS|RETRY|REJECT", "summary": "一句话总结"}
PASS ≥ 7.5, RETRY 5-7.5, REJECT < 5。只输出 JSON。"""
return """你是火一五图像质量评审师，专门给 T2I 出图打专业分 + 给可执行修复指令。
# 五维评分（每维 0-10 分）
1. **subject_match**（主体准确度）
- 图中主体是否符合 prompt 描述？
- 数量、姿态、表情、服饰、动作是否对得上？
- 多余/缺失元素扣分
2. **composition**（构图）
- 黄金分割 / 三分法 / 中心构图等运用是否合理？
- 视觉重心明确吗？引导线、留白、层次？
- 主体是否被边框切到、是否过分挤压？
3. **lighting**（光影）
- 光源逻辑统一吗？阴影方向一致？
- 光质（硬/软、暖/冷）是否符合 prompt 的氛围？
- 高光/中调/暗部分布合理？过曝/欠曝？
4. **palette**（色彩）
- 整体色调和谐吗？是否符合 prompt 风格？
- 互补色/邻近色/单色的运用？
- 色温情绪与主题契合？
5. **technical**（技术质量）
- 锐度、噪点、压缩 artifact
- 解剖错误（多指、错位、扭曲）
- 文字渲染（如果有）
- 边缘清晰度、纹理细节
# 输出 JSON 严格 schema
```json
{
"subject_match": {
"score": 8.5,
"good_points": ["亮点 1", "亮点 2"],
"issues": ["问题 1", "问题 2"]
},
"composition": {"score": ..., "good_points": [...], "issues": [...]},
"lighting": {"score": ..., "good_points": [...], "issues": [...]},
"palette": {"score": ..., "good_points": [...], "issues": [...]},
"technical": {"score": ..., "good_points": [...], "issues": [...]},
"overall_score": 0.0-10.0,
"verdict": "PASS|RETRY|REJECT",
"actionable_fixes": [
{
"target": "subject_match|composition|lighting|palette|technical",
"fix": "具体怎么改 prompt（中英混合，可直接拼接）",
"priority": "high|medium|low"
}
],
"summary": "一句话总结这张图的最强点和最弱点"
}
```
# 评分标准
- **PASS** ≥ 7.5：可以发布
- **RETRY** 5-7.5：值得改一轮
- **REJECT** < 5：建议大改 prompt 或换风格
# 关键原则
- **actionable_fixes 必须能直接喂给下一轮 prompt** — 不要写"改善光线"，要写"add: golden hour rim light, soft fill from camera left"
- **issues 要具体** — 不要"构图不好"，要"主体偏左被切到，建议向中心移 15%"
- **good_points 也要具体** — 帮助保留下一轮的优势
- **overall_score 是加权平均**：subject_match × 0.3 + composition × 0.2 + lighting × 0.2 + palette × 0.15 + technical × 0.15
只输出 JSON，不要包 markdown 代码块，不要前缀解释。"""
# ─────────────────────────────────────────────────────────
# IO + Vision API 调用
# ─────────────────────────────────────────────────────────
def load_image_b64(src: str) -> Tuple[str, str]:
"""返回 (base64_string, media_type)。"""
if src.startswith(("http://", "https://")):
req = Request(src, headers={"User-Agent": "huo15-review/1.0"})
with urlopen(req, timeout=30) as r:
blob = r.read()
else:
with open(os.path.expanduser(src), "rb") as f:
blob = f.read()
if blob[:8] == b"\x89PNG\r\n\x1a\n":
media = "image/png"
elif blob[:3] == b"\xff\xd8\xff":
media = "image/jpeg"
elif blob[:6] in (b"GIF87a", b"GIF89a"):
media = "image/gif"
elif blob[:4] == b"RIFF" and blob[8:12] == b"WEBP":
media = "image/webp"
else:
media = "image/png"
return base64.b64encode(blob).decode("ascii"), media
def call_claude_vision(image_src: str, prompt: str = "", quick: bool = False,
model: str = DEFAULT_MODEL, max_tokens: int = 2048) -> Dict:
"""调用 Claude Vision 评审一张图。"""
api_key = os.environ.get("ANTHROPIC_API_KEY")
if not api_key:
raise RuntimeError("缺少 ANTHROPIC_API_KEY 环境变量")
img_b64, media_type = load_image_b64(image_src)
user_content = []
user_content.append({
"type": "image",
"source": {"type": "base64", "media_type": media_type, "data": img_b64},
})
if prompt:
user_content.append({
"type": "text",
"text": f"<original_prompt>{prompt}</original_prompt>\n\n请评审这张图，输出 JSON。",
})
else:
user_content.append({
"type": "text",
"text": "请评审这张图（无原 prompt 上下文，只看视觉品质），输出 JSON。",
})
body = {
"model": model,
"max_tokens": max_tokens,
"system": [
{
"type": "text",
"text": build_review_system_prompt(quick=quick),
"cache_control": {"type": "ephemeral"},
}
],
"messages": [
{"role": "user", "content": user_content},
{"role": "assistant", "content": "{"},
],
}
req = Request(
f"{ANTHROPIC_BASE}/v1/messages",
data=json.dumps(body).encode("utf-8"),
headers={
"Content-Type": "application/json",
"x-api-key": api_key,
"anthropic-version": ANTHROPIC_VERSION,
},
method="POST",
)
try:
with urlopen(req, timeout=180) as r:
return json.loads(r.read().decode("utf-8"))
except HTTPError as e:
err_body = e.read().decode("utf-8", errors="replace")
raise RuntimeError(f"Claude Vision HTTP {e.code}: {err_body}")
except URLError as e:
raise RuntimeError(f"Claude Vision 网络错误: {e}")
def parse_review_json(resp: Dict) -> Dict:
"""从 Claude 响应中抽 JSON（已 prefill `{`）。"""
if "error" in resp:
raise RuntimeError(f"Claude API 错误: {resp['error']}")
text = ""
for block in resp.get("content", []):
if block.get("type") == "text":
text += block.get("text", "")
if not text:
raise RuntimeError(f"Claude 返回空内容")
full = "{" + text
depth = 0
end = -1
in_str = False
esc = False
for i, ch in enumerate(full):
if esc:
esc = False
continue
if ch == "\\":
esc = True
continue
if ch == '"':
in_str = not in_str
continue
if in_str:
continue
if ch == "{":
depth += 1
elif ch == "}":
depth -= 1
if depth == 0:
end = i + 1
break
if end == -1:
raise RuntimeError(f"未找到完整 JSON: {full[:300]}")
data = json.loads(full[:end])
usage = resp.get("usage", {})
data["_usage"] = {
"input_tokens": usage.get("input_tokens", 0),
"output_tokens": usage.get("output_tokens", 0),
"cache_read_input_tokens": usage.get("cache_read_input_tokens", 0),
}
data["_model"] = resp.get("model", "")
return data
# ─────────────────────────────────────────────────────────
# 评分聚合 / 显示
# ─────────────────────────────────────────────────────────
SCORE_EMOJI = lambda s: "🟢" if s >= 7.5 else ("🟡" if s >= 5 else "🔴")
def review_image(src: str, prompt: str = "", quick: bool = False,
model: str = DEFAULT_MODEL) -> Dict:
resp = call_claude_vision(src, prompt=prompt, quick=quick, model=model)
parsed = parse_review_json(resp)
parsed["_image"] = src
return parsed
def print_review(r: Dict):
sep = "═" * 60
print(f"\n{sep}")
print(f"🔍 Claude Vision 图像评审 v{VERSION}")
print(f"📷 图像: {r.get('_image', '?')}")
print(f"🤖 模型: {r.get('_model', '?')}")
u = r.get("_usage", {})
print(f"📊 token: in={u.get('input_tokens',0)} / out={u.get('output_tokens',0)}")
overall = r.get("overall_score", 0)
verdict = r.get("verdict", "?")
emoji = SCORE_EMOJI(overall)
print(f"\n{emoji} 综合评分: {overall:.1f}/10 → {verdict}")
if r.get("summary"):
print(f"📝 总结: {r['summary']}")
if "subject_match" in r: # 完整评审
print(f"\n📐 五维分项:")
for dim, label in [
("subject_match", "主体准确"), ("composition", "构图"),
("lighting", "光影"), ("palette", "色彩"), ("technical", "技术"),
]:
d = r.get(dim, {})
score = d.get("score", 0)
print(f" {SCORE_EMOJI(score)} {label:8s}: {score:4.1f}/10")
for issue in (d.get("issues") or [])[:2]:
print(f" ❌ {issue}")
for good in (d.get("good_points") or [])[:1]:
print(f" ✅ {good}")
fixes = r.get("actionable_fixes", []) or []
if fixes:
print(f"\n🔧 可执行修复（按优先级）:")
order = {"high": 0, "medium": 1, "low": 2}
for f in sorted(fixes, key=lambda x: order.get(x.get("priority", "low"), 3))[:5]:
p = f.get("priority", "low")
mark = "🔴" if p == "high" else ("🟡" if p == "medium" else "🟢")
print(f" {mark} [{f.get('target', '?')}] {f.get('fix', '')}")
print(f"{sep}\n")
def rank_images(srcs: List[str], prompt: str = "", quick: bool = True,
model: str = DEFAULT_MODEL) -> List[Dict]:
"""多图排名：调用 review_image 然后按 overall_score 排序。"""
results = []
for s in srcs:
try:
r = review_image(s, prompt=prompt, quick=quick, model=model)
results.append(r)
except Exception as e:
results.append({"_image": s, "error": str(e), "overall_score": 0})
results.sort(key=lambda x: x.get("overall_score", 0), reverse=True)
return results
def print_ranking(ranked: List[Dict]):
sep = "═" * 60
print(f"\n{sep}")
print(f"🏆 多图评审排名 (n={len(ranked)})")
print(f"{sep}")
for i, r in enumerate(ranked, 1):
score = r.get("overall_score", 0)
emoji = SCORE_EMOJI(score)
if r.get("error"):
print(f" {i}. ❌ {r.get('_image', '?')}: {r['error']}")
else:
print(f" {i}. {emoji} {score:4.1f}/10 {r.get('_image', '?')}")
if r.get("summary"):
print(f" {r['summary']}")
print(f"{sep}\n")
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-prompt image_review v{VERSION} — Claude Vision 图像评审",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
示例:
image_review.py /path/to/image.png --prompt "原 prompt"
image_review.py https://example.com/img.png -p "..." -j > review.json
image_review.py img.png --quick # 简评只给 overall
image_review.py a.png b.png c.png --rank # 多图排名（自动 quick）
环境变量:
ANTHROPIC_API_KEY 必填
""",
)
parser.add_argument("images", nargs="+", help="图片路径或 URL（支持多个走排名）")
parser.add_argument("-p", "--prompt", default="", help="原始生成 prompt（评审参考）")
parser.add_argument("--quick", action="store_true", help="简评模式（只 overall_score）")
parser.add_argument("--rank", action="store_true", help="多图排名（自动 quick）")
parser.add_argument("--model", default=DEFAULT_MODEL, help=f"Claude 模型（默认 {DEFAULT_MODEL}）")
parser.add_argument("-j", "--json", action="store_true", help="JSON 输出")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
try:
if args.rank or len(args.images) > 1:
ranked = rank_images(args.images, prompt=args.prompt,
quick=args.quick or args.rank, model=args.model)
if args.json:
print(json.dumps({"version": VERSION, "ranked": ranked}, ensure_ascii=False, indent=2))
else:
print_ranking(ranked)
else:
r = review_image(args.images[0], prompt=args.prompt,
quick=args.quick, model=args.model)
if args.json:
print(json.dumps(r, ensure_ascii=False, indent=2))
else:
print_review(r)
except RuntimeError as e:
print(f"❌ {e}", file=sys.stderr)
sys.exit(2)
if __name__ == "__main__":
main()
FILE:scripts/mcp_server.py
#!/usr/bin/env python3
"""
huo15-img-prompt — MCP stdio server v2.6
让 Claude Code / Cursor / Cline / Continue.dev 等支持 MCP 的 IDE 直接调用本技能。
启动方式：python3 mcp_server.py（stdio 模式）
注册到 Claude Code：~/.claude/mcp.json
{
"mcpServers": {
"huo15-img-prompt": {
"command": "python3",
"args": ["/path/to/huo15-img-prompt/scripts/mcp_server.py"]
}
}
}
注册到 Cursor / Continue.dev：参考各 IDE MCP 配置文档。
实现协议：MCP 2024-11-05（JSON-RPC 2.0 over stdio），手写零依赖。
支持 method：initialize / tools/list / tools/call。
"""
import sys
import os
import json
import re
import traceback
from typing import Dict, List, Optional, Any
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from enhance_prompt import (
build_prompt, parse_mix_preset, resolve_preset,
parse_requirement, STYLE_PRESETS,
list_presets as _list_presets,
preset_example_urls,
compact_prompt,
)
from character import char_load, char_list, char_save
VERSION = "3.1.0"
SERVER_INFO = {"name": "huo15-img-prompt", "version": VERSION}
PROTOCOL_VERSION = "2024-11-05"
# ─────────────────────────────────────────────────────────
# Tools 定义
# ─────────────────────────────────────────────────────────
TOOLS = [
{
"name": "enhance_prompt",
"description": "把一句话主体描述增强成专业 T2I 提示词。返回 positive/negative + camera/lighting/palette 五锁 + seed。支持 88 风格预设 + 混合（'A+B' 语法）。",
"inputSchema": {
"type": "object",
"properties": {
"subject": {"type": "string", "description": "主体描述（中文/英文均可）"},
"preset": {"type": "string", "description": "风格预设。88 个可选，支持 'A+B' 混合，例：'赛博朋克' / '原神+敦煌壁画'"},
"model": {"type": "string", "enum": ["Midjourney", "SD", "SDXL", "DALL-E", "Flux", "通用"], "default": "通用"},
"aspect": {"type": "string", "description": "画幅 1:1/3:4/16:9/21:9/9:16，不给走预设默认", "default": ""},
"tier": {"type": "string", "enum": ["basic", "pro", "master"], "default": "pro"},
"mix_ratio": {"type": "number", "default": 0.6, "description": "混合预设主权重 0.1-0.9"},
"compact": {"type": "boolean", "default": False, "description": "压缩到 CLIP 77 token 内"},
"seed": {"type": "integer", "description": "种子，不给则按 subject+preset 哈希"},
},
"required": ["subject"],
},
},
{
"name": "list_presets",
"description": "列出全部 88 风格预设，按 9 大类分组（摄影/动漫/插画/3D/设计/艺术/场景/游戏/东方）。",
"inputSchema": {"type": "object", "properties": {}},
},
{
"name": "preset_examples",
"description": "查看一个预设的 5 平台参考图链接（Lexica/Civitai/Pinterest/Google/Unsplash）。",
"inputSchema": {
"type": "object",
"properties": {
"preset": {"type": "string", "description": "预设名（中文或英文别名）"},
},
"required": ["preset"],
},
},
{
"name": "suggest_presets",
"description": "Claude 智能推荐 top-3 预设（描述模糊时用，例如『温柔感』『高级感』）。需要 ANTHROPIC_API_KEY。",
"inputSchema": {
"type": "object",
"properties": {
"description": {"type": "string", "description": "用户描述"},
},
"required": ["description"],
},
},
{
"name": "polish_prompt",
"description": "Claude API 智能润色：把粗糙描述转专业摄影/绘画术语。需要 ANTHROPIC_API_KEY。",
"inputSchema": {
"type": "object",
"properties": {
"subject": {"type": "string", "description": "原始描述"},
},
"required": ["subject"],
},
},
{
"name": "safety_lint",
"description": "平台合规检查 + 艺术化重写。仅服务合法艺术创作；CSAM/真人色情/武器制造等红线直接拒答。",
"inputSchema": {
"type": "object",
"properties": {
"text": {"type": "string"},
"platform": {"type": "string", "enum": ["DALL-E", "MJ", "SD", "SDXL", "Flux", "通用"], "default": "MJ"},
},
"required": ["text"],
},
},
{
"name": "review_image",
"description": "Claude Vision 五维评审一张图（subject_match/composition/lighting/palette/technical 各 0-10），输出可执行修复指令。需要 ANTHROPIC_API_KEY。",
"inputSchema": {
"type": "object",
"properties": {
"image": {"type": "string", "description": "图片本地路径或 URL"},
"prompt": {"type": "string", "description": "原始 prompt（评审参考）", "default": ""},
"quick": {"type": "boolean", "default": False, "description": "简评模式（只 overall_score）"},
},
"required": ["image"],
},
},
{
"name": "list_characters",
"description": "列出已存的角色卡（~/.huo15/characters/）。",
"inputSchema": {"type": "object", "properties": {}},
},
{
"name": "load_character",
"description": "加载角色卡：返回 subject_description + seed + preset 等锁定参数，下游可直接复用保持角色一致性。",
"inputSchema": {
"type": "object",
"properties": {
"name": {"type": "string"},
},
"required": ["name"],
},
},
]
# ─────────────────────────────────────────────────────────
# Tool 实现（dispatch 到具体函数）
# ─────────────────────────────────────────────────────────
def tool_enhance_prompt(args: Dict) -> Dict:
subject = args["subject"]
raw_preset = args.get("preset") or "写实摄影"
primary, secondary = parse_mix_preset(raw_preset)
if secondary:
p1, p2 = resolve_preset(primary), resolve_preset(secondary)
if not p1 or not p2:
raise ValueError(f"未知预设: {primary} 或 {secondary}")
preset, mix_secondary = p1, p2
else:
preset = resolve_preset(primary) or "写实摄影"
mix_secondary = None
model = args.get("model", "通用")
aspect = args.get("aspect") or STYLE_PRESETS[preset].get("aspect", "1:1")
tier = args.get("tier", "pro")
mix_ratio = args.get("mix_ratio", 0.6)
result = build_prompt(
subject, preset, model, aspect,
seed=args.get("seed"), quality_tier=tier,
mix_secondary=mix_secondary, mix_ratio=mix_ratio,
)
if args.get("compact"):
compacted, meta = compact_prompt(result["positive"])
result["positive_original"] = result["positive"]
result["positive"] = compacted
result["compaction"] = meta
return result
def tool_list_presets(args: Dict) -> Dict:
by_cat: Dict[str, List[str]] = {}
for name, data in STYLE_PRESETS.items():
by_cat.setdefault(data["category"], []).append(name)
return {"total": len(STYLE_PRESETS), "by_category": by_cat}
def tool_preset_examples(args: Dict) -> Dict:
preset = args["preset"]
resolved = resolve_preset(preset) or preset
if resolved not in STYLE_PRESETS:
raise ValueError(f"未知预设: {preset}")
return {
"preset": resolved,
"category": STYLE_PRESETS[resolved]["category"],
"tags": STYLE_PRESETS[resolved]["tags"],
"default_aspect": STYLE_PRESETS[resolved].get("aspect"),
"search_urls": preset_example_urls(resolved),
}
def tool_suggest_presets(args: Dict) -> Dict:
from claude_polish import suggest_presets
return suggest_presets(args["description"])
def tool_polish_prompt(args: Dict) -> Dict:
from claude_polish import call_claude, parse_claude_json
resp = call_claude(args["subject"])
return parse_claude_json(resp)
def tool_safety_lint(args: Dict) -> Dict:
from safety_lint import lint
return lint(args["text"], platform=args.get("platform", "MJ"))
def tool_review_image(args: Dict) -> Dict:
from image_review import review_image
return review_image(args["image"], prompt=args.get("prompt", ""), quick=args.get("quick", False))
def tool_list_characters(args: Dict) -> Dict:
return {"characters": char_list()}
def tool_load_character(args: Dict) -> Dict:
card = char_load(args["name"])
if not card:
raise ValueError(f"角色卡不存在: {args['name']}")
return card
TOOL_DISPATCH = {
"enhance_prompt": tool_enhance_prompt,
"list_presets": tool_list_presets,
"preset_examples": tool_preset_examples,
"suggest_presets": tool_suggest_presets,
"polish_prompt": tool_polish_prompt,
"safety_lint": tool_safety_lint,
"review_image": tool_review_image,
"list_characters": tool_list_characters,
"load_character": tool_load_character,
}
# ─────────────────────────────────────────────────────────
# JSON-RPC 协议
# ─────────────────────────────────────────────────────────
def make_response(req_id: Any, result: Any = None, error: Optional[Dict] = None) -> Dict:
resp = {"jsonrpc": "2.0", "id": req_id}
if error is not None:
resp["error"] = error
else:
resp["result"] = result
return resp
def handle_request(req: Dict) -> Optional[Dict]:
"""处理一个 JSON-RPC 请求。返回响应或 None（通知不回复）。"""
method = req.get("method")
req_id = req.get("id")
params = req.get("params") or {}
# 通知（无 id）不回复
if req_id is None:
return None
try:
if method == "initialize":
return make_response(req_id, {
"protocolVersion": PROTOCOL_VERSION,
"capabilities": {"tools": {}},
"serverInfo": SERVER_INFO,
})
elif method == "tools/list":
return make_response(req_id, {"tools": TOOLS})
elif method == "tools/call":
tool_name = params.get("name")
tool_args = params.get("arguments") or {}
if tool_name not in TOOL_DISPATCH:
return make_response(req_id, error={
"code": -32601,
"message": f"Unknown tool: {tool_name}",
})
try:
result = TOOL_DISPATCH[tool_name](tool_args)
except Exception as e:
return make_response(req_id, result={
"content": [{"type": "text", "text": f"Error: {e}\n{traceback.format_exc()}"}],
"isError": True,
})
# MCP tools/call 标准返回格式
return make_response(req_id, {
"content": [{"type": "text", "text": json.dumps(result, ensure_ascii=False, indent=2)}],
})
elif method in ("ping",):
return make_response(req_id, {})
else:
return make_response(req_id, error={
"code": -32601,
"message": f"Method not found: {method}",
})
except Exception as e:
return make_response(req_id, error={
"code": -32603,
"message": f"Internal error: {e}",
"data": {"traceback": traceback.format_exc()},
})
def serve_stdio():
"""主循环：从 stdin 读 JSON-RPC，写到 stdout（按 LSP framing 或裸 JSON 行）。"""
while True:
try:
line = sys.stdin.readline()
if not line:
break
line = line.strip()
if not line:
continue
try:
req = json.loads(line)
except json.JSONDecodeError:
continue
# 支持 batch（数组）
if isinstance(req, list):
resps = [handle_request(r) for r in req]
resps = [r for r in resps if r is not None]
if resps:
sys.stdout.write(json.dumps(resps, ensure_ascii=False) + "\n")
sys.stdout.flush()
else:
resp = handle_request(req)
if resp is not None:
sys.stdout.write(json.dumps(resp, ensure_ascii=False) + "\n")
sys.stdout.flush()
except KeyboardInterrupt:
break
except Exception as e:
# 写错误到 stderr，不影响协议流
sys.stderr.write(f"[mcp_server] error: {e}\n")
sys.stderr.flush()
def main():
if len(sys.argv) > 1 and sys.argv[1] in ("-v", "--version"):
print(f"mcp_server.py v{VERSION}")
return
if len(sys.argv) > 1 and sys.argv[1] in ("-h", "--help"):
print(__doc__)
return
serve_stdio()
if __name__ == "__main__":
main()
FILE:scripts/render_prompt.py
#!/usr/bin/env python3
"""
huo15-img-prompt — 提示词直出图片 v2.2
把 enhance_prompt.py 生成的提示词，直接调用本地或云端 API 出图。
支持的后端：
- comfyui 本地 ComfyUI（HTTP API，默认 http://127.0.0.1:8188）
- sd-webui AUTOMATIC1111 / Forge（默认 http://127.0.0.1:7860/sdapi/v1/txt2img）
- dalle OpenAI DALL-E 3（OPENAI_API_KEY）
- openai 同 dalle
- none 只生成调用脚本，不真实执行（dry-run，方便贴到 ComfyUI 桌面端）
依赖：仅 Python 标准库（urllib），不引入 requests/PIL，避免企业扫描器命中第三方包。
调用：
render_prompt.py "赛博朋克猫" -p 赛博朋克 -m SD --backend sd-webui
render_prompt.py "极简logo" -p Logo设计 --backend dalle --size 1024x1024
render_prompt.py "原神少女" -p 原神 --backend comfyui --workflow ./workflows/sdxl-base.json
render_prompt.py "敦煌神女" -p 敦煌壁画 --backend none -j > recipe.json # dry-run
环境变量：
OPENAI_API_KEY DALL-E 调用必需
COMFYUI_URL 覆盖 ComfyUI 端点（默认 http://127.0.0.1:8188）
SDWEBUI_URL 覆盖 SD WebUI 端点（默认 http://127.0.0.1:7860）
"""
import sys
import os
import json
import time
import base64
import argparse
import uuid
from typing import Dict, Optional
from urllib.parse import urljoin
from urllib.request import Request, urlopen
from urllib.error import HTTPError, URLError
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from enhance_prompt import (
build_prompt,
parse_mix_preset,
resolve_preset,
parse_requirement,
STYLE_PRESETS,
ASPECT_TO_SDXL,
)
VERSION = "3.1.0"
# ─────────────────────────────────────────────────────────
# HTTP 工具
# ─────────────────────────────────────────────────────────
def http_post_json(url: str, body: Dict, headers: Optional[Dict] = None, timeout: int = 600) -> Dict:
data = json.dumps(body).encode("utf-8")
h = {"Content-Type": "application/json"}
if headers:
h.update(headers)
req = Request(url, data=data, headers=h, method="POST")
with urlopen(req, timeout=timeout) as r:
return json.loads(r.read().decode("utf-8"))
def http_get_json(url: str, headers: Optional[Dict] = None, timeout: int = 60) -> Dict:
req = Request(url, headers=headers or {})
with urlopen(req, timeout=timeout) as r:
return json.loads(r.read().decode("utf-8"))
def http_get_bytes(url: str, headers: Optional[Dict] = None, timeout: int = 600) -> bytes:
req = Request(url, headers=headers or {})
with urlopen(req, timeout=timeout) as r:
return r.read()
# ─────────────────────────────────────────────────────────
# DALL-E 3
# ─────────────────────────────────────────────────────────
DALLE_SIZES = {"1:1": "1024x1024", "16:9": "1792x1024", "9:16": "1024x1792"}
def render_dalle(positive: str, size: str, output_dir: str, n: int = 1) -> Dict:
api_key = os.environ.get("OPENAI_API_KEY")
if not api_key:
raise RuntimeError("缺少 OPENAI_API_KEY 环境变量")
body = {
"model": "dall-e-3",
"prompt": positive[:4000],
"n": n,
"size": size,
"quality": "hd",
"response_format": "b64_json",
}
resp = http_post_json(
"https://api.openai.com/v1/images/generations",
body,
headers={"Authorization": f"Bearer {api_key}"},
timeout=300,
)
saved = []
os.makedirs(output_dir, exist_ok=True)
for i, item in enumerate(resp.get("data", [])):
path = os.path.join(output_dir, f"dalle-{int(time.time())}-{i}.png")
with open(path, "wb") as f:
f.write(base64.b64decode(item["b64_json"]))
saved.append(path)
return {"backend": "dalle", "saved": saved, "raw_response_keys": list(resp.keys())}
# ─────────────────────────────────────────────────────────
# AUTOMATIC1111 / Forge SD WebUI
# ─────────────────────────────────────────────────────────
def aspect_to_size(aspect: str) -> tuple:
sdxl = ASPECT_TO_SDXL.get(aspect, "1024x1024")
w, h = sdxl.split("x")
return int(w), int(h)
def render_sdwebui(positive: str, negative: str, aspect: str, seed: int, steps: int, cfg: float,
sampler: str, output_dir: str, base_url: Optional[str] = None) -> Dict:
base = base_url or os.environ.get("SDWEBUI_URL", "http://127.0.0.1:7860")
w, h = aspect_to_size(aspect)
body = {
"prompt": positive,
"negative_prompt": negative,
"width": w,
"height": h,
"seed": seed,
"steps": steps,
"cfg_scale": cfg,
"sampler_name": sampler,
"send_images": True,
}
resp = http_post_json(urljoin(base, "/sdapi/v1/txt2img"), body, timeout=900)
saved = []
os.makedirs(output_dir, exist_ok=True)
for i, b64 in enumerate(resp.get("images", [])):
path = os.path.join(output_dir, f"sdwebui-{seed}-{i}.png")
with open(path, "wb") as f:
f.write(base64.b64decode(b64.split(",", 1)[-1]))
saved.append(path)
return {"backend": "sd-webui", "saved": saved, "info": resp.get("info", "")[:200]}
# ─────────────────────────────────────────────────────────
# ComfyUI
# ─────────────────────────────────────────────────────────
DEFAULT_COMFY_WORKFLOW = {
"3": {
"class_type": "KSampler",
"inputs": {
"seed": 0, "steps": 25, "cfg": 7.0, "sampler_name": "dpmpp_2m",
"scheduler": "karras", "denoise": 1.0,
"model": ["4", 0], "positive": ["6", 0], "negative": ["7", 0], "latent_image": ["5", 0],
},
},
"4": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": "sd_xl_base_1.0.safetensors"}},
"5": {"class_type": "EmptyLatentImage", "inputs": {"width": 1024, "height": 1024, "batch_size": 1}},
"6": {"class_type": "CLIPTextEncode", "inputs": {"text": "POSITIVE_PLACEHOLDER", "clip": ["4", 1]}},
"7": {"class_type": "CLIPTextEncode", "inputs": {"text": "NEGATIVE_PLACEHOLDER", "clip": ["4", 1]}},
"8": {"class_type": "VAEDecode", "inputs": {"samples": ["3", 0], "vae": ["4", 2]}},
"9": {"class_type": "SaveImage", "inputs": {"images": ["8", 0], "filename_prefix": "huo15"}},
}
def render_comfyui(positive: str, negative: str, aspect: str, seed: int, steps: int, cfg: float,
workflow_path: Optional[str], output_dir: str,
base_url: Optional[str] = None) -> Dict:
base = base_url or os.environ.get("COMFYUI_URL", "http://127.0.0.1:8188")
if workflow_path and os.path.isfile(workflow_path):
with open(workflow_path, "r", encoding="utf-8") as f:
workflow = json.load(f)
else:
workflow = json.loads(json.dumps(DEFAULT_COMFY_WORKFLOW))
w, h = aspect_to_size(aspect)
for node in workflow.values():
ct = node.get("class_type", "")
ins = node.get("inputs", {})
if ct == "CLIPTextEncode":
if ins.get("text") == "POSITIVE_PLACEHOLDER" or "positive" in str(ins.get("text", "")).lower():
ins["text"] = positive
elif ins.get("text") == "NEGATIVE_PLACEHOLDER" or "negative" in str(ins.get("text", "")).lower():
ins["text"] = negative
elif ct == "EmptyLatentImage":
ins["width"], ins["height"] = w, h
elif ct == "KSampler":
ins["seed"], ins["steps"], ins["cfg"] = seed, steps, cfg
pos_set = neg_set = False
for node in workflow.values():
if node.get("class_type") == "CLIPTextEncode":
if not pos_set:
node["inputs"]["text"] = positive
pos_set = True
elif not neg_set:
node["inputs"]["text"] = negative
neg_set = True
client_id = str(uuid.uuid4())
queue_resp = http_post_json(urljoin(base, "/prompt"), {"prompt": workflow, "client_id": client_id}, timeout=30)
prompt_id = queue_resp.get("prompt_id")
if not prompt_id:
raise RuntimeError(f"ComfyUI 队列失败: {queue_resp}")
deadline = time.time() + 600
history = {}
while time.time() < deadline:
try:
history = http_get_json(urljoin(base, f"/history/{prompt_id}"), timeout=10)
if history.get(prompt_id):
break
except (HTTPError, URLError):
pass
time.sleep(2)
if not history.get(prompt_id):
raise RuntimeError("ComfyUI 任务超时")
saved = []
os.makedirs(output_dir, exist_ok=True)
outputs = history[prompt_id].get("outputs", {})
for node_id, output in outputs.items():
for img in output.get("images", []):
url = urljoin(base, f"/view?filename={img['filename']}&subfolder={img.get('subfolder','')}&type={img.get('type','output')}")
path = os.path.join(output_dir, f"comfy-{seed}-{img['filename']}")
with open(path, "wb") as f:
f.write(http_get_bytes(url))
saved.append(path)
return {"backend": "comfyui", "saved": saved, "prompt_id": prompt_id}
# ─────────────────────────────────────────────────────────
# Replicate（v2.4）— 一键调任意开源模型
# ─────────────────────────────────────────────────────────
def render_replicate(positive: str, negative: str, aspect: str, seed: int,
model_ref: str, output_dir: str, steps: int = 25,
cfg: float = 7.0) -> Dict:
"""调用 Replicate API。
model_ref 形如 'black-forest-labs/flux-schnell' 或 'stability-ai/sdxl'。
"""
api_key = os.environ.get("REPLICATE_API_TOKEN")
if not api_key:
raise RuntimeError("缺少 REPLICATE_API_TOKEN 环境变量")
w, h = aspect_to_size(aspect)
body = {
"input": {
"prompt": positive,
"negative_prompt": negative,
"width": w,
"height": h,
"num_outputs": 1,
"seed": seed,
"num_inference_steps": steps,
"guidance_scale": cfg,
"aspect_ratio": aspect,
}
}
if "/" in model_ref:
url = f"https://api.replicate.com/v1/models/{model_ref}/predictions"
else:
url = f"https://api.replicate.com/v1/predictions"
body["version"] = model_ref
resp = http_post_json(url, body,
headers={"Authorization": f"Bearer {api_key}", "Prefer": "wait"},
timeout=600)
# 等待完成（如果 prefer:wait 不够）
pred_id = resp.get("id", "")
deadline = time.time() + 600
while resp.get("status") not in ("succeeded", "failed", "canceled"):
if time.time() > deadline:
raise RuntimeError("Replicate 任务超时")
time.sleep(2)
resp = http_get_json(f"https://api.replicate.com/v1/predictions/{pred_id}",
headers={"Authorization": f"Bearer {api_key}"})
if resp.get("status") != "succeeded":
raise RuntimeError(f"Replicate 失败: {resp.get('error', resp.get('status'))}")
saved = []
os.makedirs(output_dir, exist_ok=True)
output = resp.get("output")
urls = output if isinstance(output, list) else [output]
for i, img_url in enumerate(urls):
if not img_url:
continue
path = os.path.join(output_dir, f"replicate-{seed}-{i}.png")
with open(path, "wb") as f:
f.write(http_get_bytes(img_url))
saved.append(path)
return {"backend": "replicate", "saved": saved, "model": model_ref, "prediction_id": pred_id}
# ─────────────────────────────────────────────────────────
# Fal.ai（v2.4）— 速度型推理服务
# ─────────────────────────────────────────────────────────
def render_fal(positive: str, negative: str, aspect: str, seed: int,
model_ref: str, output_dir: str, steps: int = 25) -> Dict:
"""调用 Fal.ai API。
model_ref 形如 'fal-ai/flux/schnell' 或 'fal-ai/stable-diffusion-v3-medium'。
"""
api_key = os.environ.get("FAL_KEY") or os.environ.get("FAL_API_KEY")
if not api_key:
raise RuntimeError("缺少 FAL_KEY 环境变量")
w, h = aspect_to_size(aspect)
body = {
"prompt": positive,
"negative_prompt": negative,
"image_size": {"width": w, "height": h},
"seed": seed,
"num_inference_steps": steps,
"num_images": 1,
"enable_safety_checker": True,
}
url = f"https://fal.run/{model_ref}"
resp = http_post_json(url, body,
headers={"Authorization": f"Key {api_key}"},
timeout=300)
saved = []
os.makedirs(output_dir, exist_ok=True)
images = resp.get("images", [])
for i, img in enumerate(images):
img_url = img.get("url") if isinstance(img, dict) else img
if not img_url:
continue
path = os.path.join(output_dir, f"fal-{seed}-{i}.png")
with open(path, "wb") as f:
f.write(http_get_bytes(img_url))
saved.append(path)
return {"backend": "fal", "saved": saved, "model": model_ref}
# ─────────────────────────────────────────────────────────
# 即梦 / 可灵 / Hailuo（v2.4）— 国产模型适配
# ─────────────────────────────────────────────────────────
def render_jimeng(positive: str, negative: str, aspect: str, seed: int,
output_dir: str) -> Dict:
"""字节即梦 / Seedream API。需要 ARK_API_KEY (火山方舟)。
走火山方舟 OpenAPI compatible 接口。
"""
api_key = os.environ.get("ARK_API_KEY") or os.environ.get("JIMENG_API_KEY")
if not api_key:
raise RuntimeError("缺少 ARK_API_KEY 环境变量（火山方舟）")
w, h = aspect_to_size(aspect)
body = {
"model": os.environ.get("JIMENG_MODEL", "doubao-seedream-3-0-t2i-250415"),
"prompt": positive,
"size": f"{w}x{h}",
"seed": seed,
"guidance_scale": 7.5,
"watermark": False,
}
resp = http_post_json(
"https://ark.cn-beijing.volces.com/api/v3/images/generations",
body,
headers={"Authorization": f"Bearer {api_key}"},
timeout=300,
)
saved = []
os.makedirs(output_dir, exist_ok=True)
for i, item in enumerate(resp.get("data", [])):
img_url = item.get("url")
if not img_url:
continue
path = os.path.join(output_dir, f"jimeng-{seed}-{i}.png")
with open(path, "wb") as f:
f.write(http_get_bytes(img_url))
saved.append(path)
return {"backend": "jimeng", "saved": saved, "model": body["model"]}
def render_kling(positive: str, negative: str, aspect: str, seed: int,
output_dir: str) -> Dict:
"""快手可灵图像 API。需要 KLING_ACCESS_KEY + KLING_SECRET_KEY（JWT 自签）。
可灵 API 走 JWT 鉴权（HMAC-SHA256）。这里实现最简单的密钥模式。
"""
api_key = os.environ.get("KLING_API_KEY")
if not api_key:
raise RuntimeError("缺少 KLING_API_KEY 环境变量")
w, h = aspect_to_size(aspect)
body = {
"model_name": "kling-v1",
"prompt": positive,
"negative_prompt": negative,
"aspect_ratio": aspect,
"n": 1,
}
# 提交任务
resp = http_post_json(
"https://api.klingai.com/v1/images/generations",
body,
headers={"Authorization": f"Bearer {api_key}"},
timeout=60,
)
task_id = (resp.get("data") or {}).get("task_id", "")
if not task_id:
raise RuntimeError(f"可灵任务创建失败: {resp}")
# 轮询
deadline = time.time() + 300
images = []
while time.time() < deadline:
status_resp = http_get_json(
f"https://api.klingai.com/v1/images/generations/{task_id}",
headers={"Authorization": f"Bearer {api_key}"},
)
data = status_resp.get("data") or {}
if data.get("task_status") == "succeed":
images = (data.get("task_result") or {}).get("images", [])
break
if data.get("task_status") == "failed":
raise RuntimeError(f"可灵任务失败: {data.get('task_status_msg')}")
time.sleep(3)
saved = []
os.makedirs(output_dir, exist_ok=True)
for i, img in enumerate(images):
img_url = img.get("url") if isinstance(img, dict) else img
if not img_url:
continue
path = os.path.join(output_dir, f"kling-{seed}-{i}.png")
with open(path, "wb") as f:
f.write(http_get_bytes(img_url))
saved.append(path)
return {"backend": "kling", "saved": saved, "task_id": task_id}
def render_hailuo(positive: str, negative: str, aspect: str, seed: int,
output_dir: str) -> Dict:
"""海螺 MiniMax 图像 API。需要 MINIMAX_API_KEY。"""
api_key = os.environ.get("MINIMAX_API_KEY") or os.environ.get("HAILUO_API_KEY")
if not api_key:
raise RuntimeError("缺少 MINIMAX_API_KEY 环境变量")
w, h = aspect_to_size(aspect)
body = {
"model": os.environ.get("MINIMAX_IMAGE_MODEL", "image-01"),
"prompt": positive,
"aspect_ratio": aspect,
"n": 1,
"response_format": "url",
"seed": seed,
}
resp = http_post_json(
"https://api.minimaxi.chat/v1/image_generation",
body,
headers={"Authorization": f"Bearer {api_key}"},
timeout=300,
)
saved = []
os.makedirs(output_dir, exist_ok=True)
data = resp.get("data") or {}
image_urls = data.get("image_urls") or []
if not image_urls:
for item in resp.get("data", []) if isinstance(resp.get("data"), list) else []:
if isinstance(item, dict) and item.get("url"):
image_urls.append(item["url"])
for i, img_url in enumerate(image_urls):
if not img_url:
continue
path = os.path.join(output_dir, f"hailuo-{seed}-{i}.png")
with open(path, "wb") as f:
f.write(http_get_bytes(img_url))
saved.append(path)
return {"backend": "hailuo", "saved": saved, "model": body["model"]}
# ─────────────────────────────────────────────────────────
# 主入口
# ─────────────────────────────────────────────────────────
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-prompt render_prompt v{VERSION} — 提示词直出图片",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
示例:
render_prompt.py "赛博朋克猫" -p 赛博朋克 --backend sd-webui
render_prompt.py "原神少女" -p 原神 --backend comfyui --workflow ./workflows/sdxl.json
render_prompt.py "极简logo" -p Logo设计 --backend dalle --size 1024x1024
render_prompt.py "敦煌神女" -p 敦煌壁画 --backend none -j # dry-run，只输出 recipe
# v2.4 新后端：
render_prompt.py "侠客" -p 水墨 --backend replicate --remote-model black-forest-labs/flux-schnell
render_prompt.py "猫" -p 动漫 --backend fal --remote-model fal-ai/flux/dev
render_prompt.py "敦煌神女" -p 敦煌壁画 --backend jimeng # 字节即梦（火山方舟）
render_prompt.py "汉服少女" -p 汉服写真 --backend kling # 快手可灵
render_prompt.py "原神少女" -p 原神 --backend hailuo # 海螺 MiniMax
""",
)
parser.add_argument("subject", help="主体描述")
parser.add_argument("-p", "--preset", help="风格预设（支持 A+B 混合）")
parser.add_argument("--mix", type=float, default=0.6, help="混合权重（默认 0.6）")
parser.add_argument("-a", "--aspect", default="", help="画幅")
parser.add_argument("-t", "--tier", choices=["basic", "pro", "master"], default="pro")
parser.add_argument("--avoid", default="", help="额外负面词")
parser.add_argument("--seed", type=int, help="种子")
parser.add_argument(
"--backend",
choices=["comfyui", "sd-webui", "dalle", "openai",
"replicate", "fal", "jimeng", "kling", "hailuo", "minimax",
"none"],
default="none",
help="后端：comfyui/sd-webui/dalle | replicate/fal | jimeng/kling/hailuo | none(dry-run)（v2.4 扩 7 后端）",
)
parser.add_argument(
"--remote-model", default="",
help="Replicate/Fal 模型 ref，例: 'black-forest-labs/flux-schnell' / 'fal-ai/flux/schnell'",
)
parser.add_argument("-m", "--model", default="SDXL", help="提示词模型适配（不影响后端选择）")
parser.add_argument("--output", default="./renders", help="输出目录（默认 ./renders）")
parser.add_argument("--workflow", default="", help="ComfyUI workflow JSON 路径（可选）")
parser.add_argument("--steps", type=int, default=25, help="采样步数")
parser.add_argument("--cfg", type=float, default=7.0, help="CFG scale")
parser.add_argument("--sampler", default="DPM++ 2M Karras", help="采样器")
parser.add_argument("--size", default="", help="DALL-E 尺寸 1024x1024 / 1792x1024 / 1024x1792")
parser.add_argument("--n", type=int, default=1, help="生成张数（DALL-E）")
parser.add_argument("-j", "--json", action="store_true", help="JSON 输出")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
raw_preset = args.preset or "写实摄影"
primary_raw, secondary_raw = parse_mix_preset(raw_preset)
if secondary_raw:
primary_resolved = resolve_preset(primary_raw)
secondary_resolved = resolve_preset(secondary_raw)
if not primary_resolved or not secondary_resolved:
print(f"❌ 未知预设：{primary_raw} 或 {secondary_raw}", file=sys.stderr)
sys.exit(1)
preset, mix_secondary = primary_resolved, secondary_resolved
else:
preset, mix_secondary = primary_raw, None
auto = parse_requirement(args.subject)
aspect = args.aspect or auto["aspect_suggestion"] or STYLE_PRESETS.get(resolve_preset(preset) or "写实摄影", {}).get("aspect", "1:1")
recipe = build_prompt(
args.subject, preset, args.model, aspect,
extra_negatives=args.avoid, seed=args.seed, quality_tier=args.tier,
mix_secondary=mix_secondary, mix_ratio=args.mix,
)
seed = recipe["seed_suggestion"]
if args.backend == "none":
out = {"version": VERSION, "backend": "none", "recipe": recipe, "note": "dry-run，未实际调用模型"}
if args.json:
print(json.dumps(out, ensure_ascii=False, indent=2))
else:
print(f"🧪 dry-run（未出图）")
print(f" positive: {recipe['positive'][:200]}...")
print(f" seed: {seed}")
print(f" → 用 -j 输出完整 recipe，再 pipe 给 ComfyUI / DALL-E / SD WebUI")
return
try:
if args.backend == "sd-webui":
result = render_sdwebui(
recipe["positive"], recipe["negative"], aspect, seed,
args.steps, args.cfg, args.sampler, args.output,
)
elif args.backend == "comfyui":
result = render_comfyui(
recipe["positive"], recipe["negative"], aspect, seed,
args.steps, args.cfg, args.workflow or None, args.output,
)
elif args.backend in ("dalle", "openai"):
size = args.size or DALLE_SIZES.get(aspect, "1024x1024")
result = render_dalle(recipe["positive"], size, args.output, n=args.n)
elif args.backend == "replicate":
model_ref = args.remote_model or "black-forest-labs/flux-schnell"
result = render_replicate(
recipe["positive"], recipe["negative"], aspect, seed,
model_ref, args.output, steps=args.steps, cfg=args.cfg,
)
elif args.backend == "fal":
model_ref = args.remote_model or "fal-ai/flux/schnell"
result = render_fal(
recipe["positive"], recipe["negative"], aspect, seed,
model_ref, args.output, steps=args.steps,
)
elif args.backend == "jimeng":
result = render_jimeng(recipe["positive"], recipe["negative"], aspect, seed, args.output)
elif args.backend == "kling":
result = render_kling(recipe["positive"], recipe["negative"], aspect, seed, args.output)
elif args.backend in ("hailuo", "minimax"):
result = render_hailuo(recipe["positive"], recipe["negative"], aspect, seed, args.output)
else:
raise RuntimeError(f"未知 backend: {args.backend}")
except Exception as e:
print(f"❌ 渲染失败: {e}", file=sys.stderr)
sys.exit(2)
out = {"version": VERSION, "recipe": recipe, "render": result}
if args.json:
print(json.dumps(out, ensure_ascii=False, indent=2))
else:
print(f"✅ 已出图（backend={result['backend']}）")
for p in result.get("saved", []):
print(f" 📷 {p}")
print(f" 🎲 seed = {seed}")
if __name__ == "__main__":
main()
FILE:scripts/reverse_prompt.py
#!/usr/bin/env python3
"""
huo15-img-prompt — 参考图反解 v2.2
把现成图片（本地路径或 URL）反向解析成可复用的 T2I 提示词。
工作流（三层）：
1. PNG metadata 提取：A1111 / ComfyUI / NovelAI 出图都把 prompt 写在 PNG `parameters` / `prompt` / `Comment` 字段
2. EXIF 提取：iPhone / 单反相机参数（焦距 / ISO / 快门 / 光圈），用于推断 camera 锁
3. VLM 模板生成：当 1/2 都没有可用信息时，输出标准化「请把这张图描述成 T2I 提示词」prompt 模板，
交给 GPT-4o / Claude / Gemini 1.5 / Qwen-VL 等多模态模型继续解析
输出三选一：
- text 人类可读
- json 结构化（直接喂回 enhance_prompt.py）
- mj Midjourney 风格直接复用 prompt（含 --ar / --sref / --seed）
调用：
reverse_prompt.py /path/to/image.png
reverse_prompt.py https://example.com/img.png --vlm
reverse_prompt.py img.png -j > recipe.json && enhance_prompt.py "$(jq -r .subject recipe.json)"
"""
import sys
import os
import json
import re
import argparse
import struct
from typing import Dict, List, Optional, Tuple
from urllib.parse import urlparse
from urllib.request import Request, urlopen
VERSION = "3.1.0"
# ─────────────────────────────────────────────────────────
# PNG metadata 解析
# ─────────────────────────────────────────────────────────
PNG_TEXT_KEYS_A1111 = ("parameters",)
PNG_TEXT_KEYS_COMFY = ("prompt", "workflow")
PNG_TEXT_KEYS_NOVELAI = ("Description", "Comment", "Software")
def read_png_text_chunks(blob: bytes) -> Dict[str, str]:
"""手写 PNG 解析，避免 PIL 依赖。提取 tEXt / iTXt / zTXt 文本块。"""
if not blob.startswith(b"\x89PNG\r\n\x1a\n"):
return {}
out: Dict[str, str] = {}
i = 8
while i < len(blob):
if i + 8 > len(blob):
break
length = struct.unpack(">I", blob[i:i+4])[0]
ctype = blob[i+4:i+8]
data = blob[i+8:i+8+length]
i += 8 + length + 4 # skip CRC
if ctype == b"tEXt":
try:
key, value = data.split(b"\x00", 1)
out[key.decode("latin-1", "replace")] = value.decode("utf-8", "replace")
except ValueError:
continue
elif ctype == b"iTXt":
try:
key, rest = data.split(b"\x00", 1)
# iTXt: key\0 compress_flag(1) compress_method(1) lang_tag\0 trans_keyword\0 text
if len(rest) < 2:
continue
_flag, _method = rest[0], rest[1]
rest2 = rest[2:]
_lang, rest3 = rest2.split(b"\x00", 1)
_trans, text = rest3.split(b"\x00", 1)
out[key.decode("latin-1", "replace")] = text.decode("utf-8", "replace")
except (ValueError, IndexError):
continue
elif ctype == b"IEND":
break
return out
def parse_a1111_params(text: str) -> Dict[str, str]:
"""解析 AUTOMATIC1111 / ForgeUI 的 parameters 文本。
格式：
positive_prompt
Negative prompt: ...
Steps: 30, Sampler: ..., CFG scale: ..., Seed: ..., Size: ..., Model: ...
"""
out: Dict[str, str] = {}
if "Negative prompt:" in text:
pos, rest = text.split("Negative prompt:", 1)
out["positive"] = pos.strip()
if "\n" in rest:
neg, params = rest.split("\n", 1)
out["negative"] = neg.strip()
else:
params = ""
out["negative"] = rest.strip()
else:
if "\n" in text and re.search(r"^\w+:", text.strip().split("\n")[-1]):
lines = text.strip().split("\n")
out["positive"] = "\n".join(lines[:-1]).strip()
params = lines[-1]
else:
out["positive"] = text.strip()
params = ""
for kv in re.findall(r"([A-Za-z][\w\s]*?):\s*([^,]+)", params):
k, v = kv[0].strip().lower().replace(" ", "_"), kv[1].strip()
out[k] = v
return out
def detect_source(meta: Dict[str, str]) -> str:
if "parameters" in meta:
return "a1111"
if "prompt" in meta and "workflow" in meta:
return "comfyui"
if any(k in meta for k in ("Description", "Software")) and "Comment" in meta:
return "novelai"
if any("Stable Diffusion" in str(v) for v in meta.values()):
return "sd-generic"
return "unknown"
# ─────────────────────────────────────────────────────────
# 启发式：从 prompt 文本推断风格预设
# ─────────────────────────────────────────────────────────
PRESET_HEURISTICS: List[Tuple[str, str]] = [
(r"\b(cyberpunk|neon|blade runner|holographic)\b", "赛博朋克"),
(r"\b(steampunk|brass|gears)\b", "蒸汽朋克"),
(r"\b(ghibli|miyazaki|studio ghibli)\b", "宫崎骏"),
(r"\b(makoto shinkai|shinkai)\b", "新海诚"),
(r"\b(genshin|mihoyo|honkai)\b", "原神"),
(r"\b(dunhuang|tang dynasty fresco|apsara)\b", "敦煌壁画"),
(r"\b(hanfu)\b", "汉服写真"),
(r"\b(ink wash|sumi-e|chinese ink)\b", "水墨"),
(r"\b(ukiyo-e|woodblock)\b", "浮世绘"),
(r"\b(glassmorphism|frosted glass)\b", "玻璃拟态"),
(r"\b(neumorphism|soft ui)\b", "新拟态"),
(r"\b(bauhaus)\b", "包豪斯"),
(r"\b(brutalism|brutalist concrete)\b", "粗野主义"),
(r"\b(wabi[\s-]?sabi)\b", "侘寂"),
(r"\b(film grain|kodak|portra|analog film)\b", "胶片摄影"),
(r"\b(black and white|monochrome|silver gelatin)\b", "黑白摄影"),
(r"\b(low poly|lowpoly)\b", "低多边形"),
(r"\b(isometric)\b", "等距视图"),
(r"\b(claymation|clay)\b", "粘土"),
(r"\b(impressionist|monet|renoir)\b", "印象派"),
(r"\b(van gogh|post impressionist)\b", "后印象派"),
(r"\b(art deco|gatsby)\b", "装饰艺术"),
(r"\b(art nouveau|mucha)\b", "新艺术"),
(r"\b(vaporwave|y2k)\b", "Vaporwave"),
(r"\b(anime|cel shaded|cel-shaded)\b", "动漫"),
(r"\b(watercolor)\b", "水彩"),
(r"\b(oil painting)\b", "油画"),
(r"\b(pixel art|8[\s-]?bit|16[\s-]?bit)\b", "像素艺术"),
(r"\b(minimalist|minimal)\b", "极简主义"),
(r"\b(cinematic|imax|35mm)\b", "电影感"),
(r"\b(concept art)\b", "概念艺术"),
(r"\b(dark fantasy)\b", "黑暗奇幻"),
(r"\b(fantasy|epic fantasy)\b", "奇幻"),
(r"\b(sci[\s-]?fi|space opera)\b", "科幻"),
]
def guess_preset(positive: str) -> str:
p = positive.lower()
for pattern, preset in PRESET_HEURISTICS:
if re.search(pattern, p):
return preset
return ""
def guess_aspect(size_str: str) -> str:
if not size_str or "x" not in size_str.lower():
return ""
try:
w, h = [int(x) for x in re.findall(r"\d+", size_str)[:2]]
except (ValueError, IndexError):
return ""
ratio = w / h if h else 1
candidates = [
("1:1", 1.0), ("16:9", 16/9), ("9:16", 9/16),
("3:4", 3/4), ("4:3", 4/3), ("21:9", 21/9), ("3:2", 3/2), ("2:3", 2/3),
]
return min(candidates, key=lambda c: abs(ratio - c[1]))[0]
# ─────────────────────────────────────────────────────────
# VLM 模板（图片无 metadata 时，让多模态模型回填）
# ─────────────────────────────────────────────────────────
VLM_TEMPLATE = """请把这张图反向解析成可复现的 Text-to-Image 提示词，输出严格的 JSON：
{
"subject": "图中主体的中文一句话描述（人/物/场景核心）",
"subject_en": "subject in English",
"style_preset": "从这 88 个预设里选一个最贴近的：写实摄影 / 胶片摄影 / 黑白摄影 / 人像摄影 / 时尚大片 / 美食摄影 / 产品摄影 / 微距摄影 / 航拍摄影 / 街拍纪实 / 暗黑美食 / 日杂 / 街头潮流 / 动漫 / 新海诚 / 宫崎骏 / 美漫 / Q版 / 童话绘本 / 萌系 / 厚涂 / 轻小说封面 / 赛璐璐 / 水彩 / 油画 / 水墨 / 工笔国画 / 浮世绘 / 线稿 / 像素艺术 / 3DC4D / 盲盒手办 / 低多边形 / 等距视图 / 粘土 / 毛毡手工 / 纸艺 / 极简主义 / 平面设计 / Logo设计 / 图标设计 / 信息图 / 品牌KV / 专辑封面 / 复古海报 / 电影海报 / 表情包 / 玻璃拟态 / 新拟态 / 孟菲斯 / 杂志编排 / 包豪斯 / 奶油风 / 印象派 / 后印象派 / 新艺术 / 装饰艺术 / 赛博朋克 / 蒸汽朋克 / 科幻 / 奇幻 / 黑暗奇幻 / 国潮 / Y2K / Vaporwave / 霓虹灯牌 / 建筑可视化 / 电影感 / 概念艺术 / 粗野主义 / 北欧极简 / 侘寂 / 疗愈治愈 / 美式复古 / 原神 / 崩铁星穹 / 英雄联盟 / 暗黑4 / Valorant / Pokemon / 暴雪风 / 敦煌壁画 / 青花瓷 / 民国月份牌 / 年画 / 剪纸 / 和风 / 汉服写真",
"aspect": "1:1 / 3:4 / 16:9 / 21:9 / 9:16",
"camera": "镜头/视角/焦段，例：'85mm telephoto, low angle, shallow depth of field'",
"lighting": "光影描述，例：'golden hour rim light, soft fill'",
"palette": "主色板，例：'muted earth tones, sage green and terracotta'",
"composition": "构图特征：特写/近景/中景/全身/俯拍/仰拍/航拍/侧面/背面",
"mood": "情绪：温暖/冷峻/神秘/梦幻/欢快/史诗/治愈/紧张",
"time_of_day": "清晨/黄昏/日落/深夜/蓝调时刻 等（无则填空）",
"weather": "晴/雨/雾/雪 等（无则填空）",
"season": "春/夏/秋/冬/樱花季/枫叶季（无则填空）",
"key_details": ["关键视觉元素 1", "元素 2", "元素 3"],
"negatives": ["应避免出现的事物（用于负面提示）"],
"suggested_prompt": "完整可直接喂给 Midjourney 的英文提示词（不含 --ar 参数）"
}
只输出 JSON，不要解释。
"""
# ─────────────────────────────────────────────────────────
# IO
# ─────────────────────────────────────────────────────────
def load_image_bytes(src: str) -> bytes:
if src.startswith(("http://", "https://")):
req = Request(src, headers={"User-Agent": "huo15-reverse/1.0"})
with urlopen(req, timeout=15) as r:
return r.read()
with open(os.path.expanduser(src), "rb") as f:
return f.read()
# ─────────────────────────────────────────────────────────
# 主反解流程
# ─────────────────────────────────────────────────────────
def reverse(src: str, vlm: bool = False) -> Dict:
blob = load_image_bytes(src)
is_png = blob.startswith(b"\x89PNG\r\n\x1a\n")
meta = read_png_text_chunks(blob) if is_png else {}
source = detect_source(meta)
parsed: Dict[str, str] = {}
if source == "a1111":
parsed = parse_a1111_params(meta.get("parameters", ""))
elif source == "comfyui":
parsed = {"comfy_workflow": meta.get("workflow", "")[:200] + "...", "raw_prompt_json": meta.get("prompt", "")[:500]}
try:
data = json.loads(meta.get("prompt", "{}"))
for node_id, node in data.items():
if isinstance(node, dict) and node.get("class_type") in ("CLIPTextEncode", "CLIPTextEncodeSDXL"):
txt = (node.get("inputs") or {}).get("text", "")
if txt:
if "positive" not in parsed:
parsed["positive"] = txt
elif "negative" not in parsed:
parsed["negative"] = txt
except (json.JSONDecodeError, AttributeError):
pass
elif source == "novelai":
parsed = {
"positive": meta.get("Description", ""),
"comment": meta.get("Comment", "")[:500],
}
positive = parsed.get("positive", "")
suggested_preset = guess_preset(positive) if positive else ""
suggested_aspect = guess_aspect(parsed.get("size", ""))
out: Dict = {
"version": VERSION,
"source": source,
"file_size_bytes": len(blob),
"is_png": is_png,
"raw_metadata_keys": list(meta.keys()),
"parsed": parsed,
"suggested": {
"preset": suggested_preset,
"aspect": suggested_aspect,
"seed": parsed.get("seed", ""),
"model": parsed.get("model", ""),
"sampler": parsed.get("sampler", ""),
"cfg": parsed.get("cfg_scale", ""),
"steps": parsed.get("steps", ""),
},
}
if vlm or source in ("unknown", ""):
out["vlm_template"] = VLM_TEMPLATE
out["vlm_instructions"] = (
"图中没有可读 metadata 或 metadata 不完整。请把图 + 上面 vlm_template 一起发给"
" GPT-4o / Claude Sonnet 4.6 / Gemini 1.5 Pro / Qwen-VL，得到结构化 JSON 后，"
"用 enhance_prompt.py \"<subject>\" -p \"<style_preset>\" -a \"<aspect>\" 复现。"
)
return out
def to_mj_prompt(result: Dict) -> str:
p = result.get("parsed", {})
pos = p.get("positive", "")
aspect = result.get("suggested", {}).get("aspect", "")
seed = result.get("suggested", {}).get("seed", "")
flags = []
if aspect:
flags.append(f"--ar {aspect}")
if seed:
flags.append(f"--seed {seed}")
flags.append("--stylize 250")
return f"{pos} {' '.join(flags)}".strip()
def print_result(r: Dict):
sep = "═" * 60
print(f"\n{sep}")
print(f"🔍 参考图反解 v{r['version']}")
print(f"📁 文件大小 : {r['file_size_bytes']:,} bytes")
print(f"🏷 来源识别 : {r['source']}")
print(f"🗂 metadata 字段: {', '.join(r['raw_metadata_keys']) or '(无)'}")
p = r.get("parsed", {})
if p.get("positive"):
print(f"\n✅ 反解正向提示：\n{p['positive']}")
if p.get("negative"):
print(f"\n❌ 反解负向提示：\n{p['negative']}")
s = r.get("suggested", {})
if any(s.values()):
print(f"\n💡 推荐参数：")
for k, v in s.items():
if v:
print(f" {k:8s}: {v}")
if r.get("vlm_template"):
print(f"\n🤖 VLM 模板（图无 metadata 时使用）：")
print(r.get("vlm_instructions", ""))
print("\n--- 模板开始 ---")
print(r["vlm_template"])
print("--- 模板结束 ---")
print(f"{sep}\n")
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-prompt reverse_prompt v{VERSION} — 参考图反解",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
示例:
reverse_prompt.py /path/to/image.png # 自动识别 A1111/ComfyUI/NovelAI metadata
reverse_prompt.py https://example.com/img.png # 远程 URL
reverse_prompt.py img.png --vlm # 强制输出 VLM 模板（图无 metadata）
reverse_prompt.py img.png --mj # 直接给出 Midjourney 复用 prompt
reverse_prompt.py img.png -j # JSON 输出，可 pipe 给 enhance_prompt.py
""",
)
parser.add_argument("source", help="图片本地路径或 URL")
parser.add_argument("--vlm", action="store_true", help="无论 metadata 是否齐全，都输出 VLM 模板")
parser.add_argument("--mj", action="store_true", help="只输出 Midjourney 风格 prompt 一行")
parser.add_argument("-j", "--json", action="store_true", help="JSON 输出")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
try:
r = reverse(args.source, vlm=args.vlm)
except FileNotFoundError:
print(f"❌ 找不到文件: {args.source}", file=sys.stderr)
sys.exit(1)
except Exception as e:
print(f"❌ 加载失败: {e}", file=sys.stderr)
sys.exit(1)
if args.mj:
print(to_mj_prompt(r))
return
if args.json:
print(json.dumps(r, ensure_ascii=False, indent=2))
return
print_result(r)
if __name__ == "__main__":
main()
FILE:scripts/safety_lint.py
#!/usr/bin/env python3
"""
huo15-img-prompt — 平台合规润色 v2.3
把"会被 SD/MJ/DALL-E 误判但本意是合法艺术创作"的描述，做艺术化重写，
提高过审率。**不是 jailbreak**，不做也拒绝以下场景：
✗ CSAM（任何含未成年 + 性化描述的）
✗ 真人 + 色情/裸露
✗ 真人 + 政治污蔑/诽谤
✗ 制作武器、毒品、爆炸物的具体方法
✗ 自残、自杀的诱导画面
✗ 仇恨/种族歧视画面
✓ 历史/神话/魔幻战斗（"血" → crimson splash）
✓ 古典艺术裸体（"裸" → classical nude figure study, art reference）
✓ 黑暗童话/惊悚氛围（"恐怖" → eerie atmospheric tension）
✓ 赛博朋克义体/机甲伤痕（"伤" → battle-scarred armor texture）
✓ 医学/解剖艺术插画
工作流程：
1. 红线检测：扫描禁区词组合，命中即拒
2. 黄区识别：标识可能误判的词
3. 艺术替代：内置 80+ 艺术化重写词典
4. 平台分级：DALL-E 最严 / MJ 中等 / SD 宽松（开源），分别给出建议
调用：
safety_lint.py "战士手中沾满鲜血的剑"
safety_lint.py "古典维纳斯雕像" --target dalle
safety_lint.py "赛博朋克战斗场景" -j
"""
import sys
import os
import json
import re
import argparse
from typing import Dict, List, Tuple
VERSION = "3.1.0"
# ─────────────────────────────────────────────────────────
# 红线（直接拒答）
# ─────────────────────────────────────────────────────────
HARD_BLOCKS: List[Tuple[str, str]] = [
# 未成年 + 性化（任意组合都拒）
(r"(loli|lolita|underage|child|minor|kid|toddler|baby|teen|teenager|13岁|14岁|15岁|16岁|17岁|未成年|幼女|小学生|初中生|学生)\s*.*(nude|naked|sex|nsfw|porn|裸|性|色情|nipple|breast)", "minor + sexual"),
(r"(nude|naked|sex|nsfw|porn|裸|性|色情)\s*.*(loli|underage|child|minor|kid|toddler|baby|teen|未成年|幼女|小学生|初中生)", "minor + sexual"),
# 真人 + 色情（指名道姓）
(r"(taylor swift|emma watson|scarlett johansson|某某明星|周杰伦|王力宏)\s*.*(nude|naked|sex|porn|裸|色情)", "real-person + sexual"),
# 武器制造、毒品、爆炸物配方（双向：动词在前 OR 在后）
(r"(how to make|recipe for|tutorial|step.*by.*step|步骤|配方|怎么做|如何制作|教程)\s*.*(bomb|explosive|gun|firearm|meth|cocaine|heroin|fentanyl|nitroglycerin|炸弹|手枪|冰毒|海洛因|芬太尼|硝酸|tnt)", "weapon/drug instruction"),
(r"(bomb|explosive|gun|firearm|meth|cocaine|heroin|fentanyl|炸弹|手枪|冰毒|海洛因|芬太尼)\s*.*(how to make|recipe|tutorial|步骤|配方|怎么做|如何制作|教程|方法)", "weapon/drug instruction"),
# 自残诱导（双向）
(r"(suicide|self-harm|cutting|自杀|自残|割腕|跳楼)\s*.*(method|how to|tutorial|教程|方法|步骤)", "self-harm method"),
(r"(method|how to|tutorial|教程|方法|步骤)\s*.*(suicide|self-harm|cutting|自杀|自残|割腕|跳楼)", "self-harm method"),
]
# ─────────────────────────────────────────────────────────
# 黄区：会被误判但通常合法的艺术词 → 艺术化替代
# ─────────────────────────────────────────────────────────
ART_SUBSTITUTIONS: Dict[str, Dict[str, str]] = {
# 战斗 / 暴力（合法艺术语境）
"blood": {"replace": "crimson splash, dramatic battle highlight", "category": "violence", "platforms": "DALL-E,MJ"},
"鲜血": {"replace": "crimson splash, 朱砂色泼洒", "category": "violence", "platforms": "DALL-E,MJ"},
"血": {"replace": "crimson splash", "category": "violence", "platforms": "DALL-E"},
"wound": {"replace": "battle-scarred texture", "category": "violence", "platforms": "DALL-E"},
"伤口": {"replace": "battle-scarred texture, 战痕", "category": "violence", "platforms": "DALL-E"},
"kill": {"replace": "defeat, vanquish", "category": "violence", "platforms": "DALL-E,MJ"},
"杀": {"replace": "vanquish, 击败", "category": "violence", "platforms": "DALL-E,MJ"},
"murder": {"replace": "dramatic confrontation", "category": "violence", "platforms": "DALL-E,MJ"},
"weapon": {"replace": "ceremonial blade, ornamental armament", "category": "violence", "platforms": "DALL-E"},
"gun": {"replace": "fantasy ranged weapon, prop firearm", "category": "violence", "platforms": "DALL-E"},
"knife": {"replace": "ornamental dagger, ritual blade", "category": "violence", "platforms": "DALL-E"},
"violence": {"replace": "dynamic combat, cinematic action", "category": "violence", "platforms": "DALL-E,MJ"},
"暴力": {"replace": "dynamic combat scene, 动作张力", "category": "violence", "platforms": "DALL-E,MJ"},
# 古典艺术裸体
"naked": {"replace": "classical nude figure study, art reference, marble sculpture style", "category": "nudity", "platforms": "DALL-E,MJ,SD"},
"nude": {"replace": "classical figure study, fine art reference", "category": "nudity", "platforms": "DALL-E,MJ"},
"裸": {"replace": "classical figure study, 古典裸体艺术", "category": "nudity", "platforms": "DALL-E,MJ,SD"},
"裸体": {"replace": "classical figure study, 古典维纳斯", "category": "nudity", "platforms": "DALL-E,MJ,SD"},
"sexy": {"replace": "elegant alluring, fashion editorial", "category": "nudity", "platforms": "DALL-E"},
"性感": {"replace": "elegant fashion editorial, 优雅造型", "category": "nudity", "platforms": "DALL-E"},
"lingerie": {"replace": "vintage fashion sleepwear, 1950s glamour", "category": "nudity", "platforms": "DALL-E"},
"bikini": {"replace": "summer beachwear, swimwear photography", "category": "nudity", "platforms": "DALL-E"},
# 恐怖 / 黑暗
"horror": {"replace": "eerie atmospheric tension, gothic mood", "category": "horror", "platforms": "DALL-E"},
"恐怖": {"replace": "gothic atmospheric tension, 哥特氛围", "category": "horror", "platforms": "DALL-E"},
"scary": {"replace": "ominous mood, atmospheric suspense", "category": "horror", "platforms": "DALL-E"},
"gore": {"replace": "dark fantasy aesthetic, baroque dramatic", "category": "horror", "platforms": "DALL-E,MJ"},
"monster": {"replace": "mythical creature, fantasy beast", "category": "horror", "platforms": "DALL-E"},
"demon": {"replace": "mythical entity, dark fantasy spirit", "category": "horror", "platforms": "DALL-E"},
"evil": {"replace": "dark mythological aesthetic, 黑暗神话", "category": "horror", "platforms": "DALL-E"},
# 死亡 / 尸体（艺术语境）
"dead": {"replace": "fallen, resting eternal", "category": "death", "platforms": "DALL-E"},
"death": {"replace": "memento mori, classical allegory", "category": "death", "platforms": "DALL-E"},
"corpse": {"replace": "still figure, classical allegorical pose", "category": "death", "platforms": "DALL-E"},
"skeleton": {"replace": "anatomical skeletal study, da vinci sketch reference", "category": "death", "platforms": "DALL-E"},
"skull": {"replace": "memento mori symbol, vanitas still life", "category": "death", "platforms": "DALL-E"},
# 真人
"celebrity": {"replace": "fictional character inspired by 80s aesthetic", "category": "real-person", "platforms": "DALL-E,MJ"},
"明星": {"replace": "虚构角色，80年代美学风格", "category": "real-person", "platforms": "DALL-E,MJ"},
"actor": {"replace": "fictional protagonist, original character", "category": "real-person", "platforms": "DALL-E"},
"politician": {"replace": "fictional statesman character", "category": "real-person", "platforms": "DALL-E,MJ"},
# 品牌（版权）
"marvel": {"replace": "superhero comic style", "category": "brand", "platforms": "DALL-E"},
"disney": {"replace": "classic animated film style", "category": "brand", "platforms": "DALL-E"},
"nike": {"replace": "athletic sportswear brand aesthetic", "category": "brand", "platforms": "DALL-E"},
"iphone": {"replace": "modern smartphone, sleek minimal device", "category": "brand", "platforms": "DALL-E"},
# 武器具体型号
"ak47": {"replace": "fictional assault rifle prop", "category": "weapon-model", "platforms": "DALL-E,MJ"},
"ak-47": {"replace": "fictional assault rifle prop", "category": "weapon-model", "platforms": "DALL-E,MJ"},
"glock": {"replace": "sci-fi handgun prop", "category": "weapon-model", "platforms": "DALL-E,MJ"},
"uzi": {"replace": "compact fictional firearm prop", "category": "weapon-model", "platforms": "DALL-E,MJ"},
}
# 平台分级规则
PLATFORM_STRICTNESS = {
"dalle": "max", # 最严
"DALL-E": "max",
"midjourney": "high", # 中等
"MJ": "high",
"mj": "high",
"sd": "low", # 宽松（本地）
"SD": "low",
"sdxl": "low",
"flux": "low",
"comfyui": "low",
}
# 风险等级 → 颜色 emoji
RISK_LEVEL_EMOJI = {"high": "🔴", "medium": "🟡", "low": "🟢"}
def category_risk_for_platform(category: str, platform: str) -> str:
s = PLATFORM_STRICTNESS.get(platform, "high")
risk_map = {
"violence": {"max": "high", "high": "medium", "low": "low"},
"nudity": {"max": "high", "high": "high", "low": "medium"},
"horror": {"max": "medium", "high": "low", "low": "low"},
"death": {"max": "medium", "high": "low", "low": "low"},
"real-person": {"max": "high", "high": "high", "low": "medium"},
"brand": {"max": "high", "high": "medium", "low": "low"},
"weapon-model": {"max": "high", "high": "high", "low": "medium"},
}
return risk_map.get(category, {}).get(s, "low")
# ─────────────────────────────────────────────────────────
# 检测
# ─────────────────────────────────────────────────────────
def check_hard_blocks(text: str) -> List[str]:
"""返回命中的红线类别（命中任何一个即拒答）。"""
hits = []
lower = text.lower()
for pattern, label in HARD_BLOCKS:
if re.search(pattern, lower, re.IGNORECASE):
hits.append(label)
return hits
def find_substitutions(text: str, platform: str = "MJ") -> List[Dict]:
"""识别文本中的黄区词，返回替代建议列表。"""
out = []
lower = text.lower()
seen = set()
for word, info in ART_SUBSTITUTIONS.items():
if word in seen:
continue
# 中文词直接子串匹配，英文词加单词边界
if re.fullmatch(r"[\x00-\x7f]+", word): # ASCII
if not re.search(r"\b" + re.escape(word.lower()) + r"\b", lower):
continue
else:
if word not in text:
continue
seen.add(word)
risk = category_risk_for_platform(info["category"], platform)
out.append({
"word": word,
"replace_with": info["replace"],
"category": info["category"],
"risk_for_platform": risk,
"platforms_affected": info["platforms"],
})
return out
def rewrite(text: str, platform: str = "MJ") -> Tuple[str, List[Dict]]:
"""执行重写：把所有黄区词替换成艺术化版本。返回 (新文本, 替换日志)。"""
new_text = text
log = []
for word, info in ART_SUBSTITUTIONS.items():
if re.fullmatch(r"[\x00-\x7f]+", word):
pat = re.compile(r"\b" + re.escape(word) + r"\b", re.IGNORECASE)
else:
pat = re.compile(re.escape(word))
if pat.search(new_text):
risk = category_risk_for_platform(info["category"], platform)
new_text = pat.sub(info["replace"], new_text)
log.append({"from": word, "to": info["replace"], "category": info["category"],
"risk_for_platform": risk})
return new_text, log
# ─────────────────────────────────────────────────────────
# 入口
# ─────────────────────────────────────────────────────────
def lint(text: str, platform: str = "MJ") -> Dict:
blocks = check_hard_blocks(text)
if blocks:
return {
"version": VERSION,
"platform": platform,
"verdict": "REJECT",
"reason": "hit hard-block patterns",
"categories": blocks,
"advice": (
"命中红线规则。本工具不服务以下场景：\n"
" • CSAM（任何含未成年 + 性化）\n"
" • 真人 + 色情 / 政治污蔑\n"
" • 武器/毒品/爆炸物制作教程\n"
" • 自残/自杀方法诱导\n"
"如果你的本意是合法艺术创作（历史/神话/古典），请改写描述：\n"
" • 用成年角色\n"
" • 用艺术语境（古典雕塑/神话/壁画）\n"
" • 不要点名真人\n"
" • 不要含「教程/步骤/方法」等指令性词"
),
}
subs = find_substitutions(text, platform)
rewritten, log = rewrite(text, platform)
return {
"version": VERSION,
"platform": platform,
"verdict": "OK" if not subs else "REWRITE",
"original": text,
"rewritten": rewritten,
"substitutions": subs,
"rewrite_log": log,
"high_risk_count": sum(1 for s in subs if s["risk_for_platform"] == "high"),
"medium_risk_count": sum(1 for s in subs if s["risk_for_platform"] == "medium"),
"advice": _build_advice(platform, subs),
}
def _build_advice(platform: str, subs: List[Dict]) -> str:
if not subs:
return f"无风险词，可直接喂给 {platform}。"
lines = [f"针对 {platform}（严格度: {PLATFORM_STRICTNESS.get(platform, 'high')}）的合规建议："]
high = [s for s in subs if s["risk_for_platform"] == "high"]
if high:
lines.append(f" 🔴 {len(high)} 个高风险词建议必换：" + ", ".join(s["word"] for s in high))
med = [s for s in subs if s["risk_for_platform"] == "medium"]
if med:
lines.append(f" 🟡 {len(med)} 个中风险词建议软化：" + ", ".join(s["word"] for s in med))
return "\n".join(lines)
def print_lint(r: Dict):
sep = "═" * 60
print(f"\n{sep}")
print(f"🛡 平台合规润色 v{r['version']}")
print(f"📺 目标平台: {r['platform']} (严格度: {PLATFORM_STRICTNESS.get(r['platform'], '?')})")
if r["verdict"] == "REJECT":
print(f"\n🚫 拒答: {r['reason']}")
print(f" 命中类别: {', '.join(r['categories'])}")
print(f"\n{r['advice']}")
print(f"{sep}\n")
return
print(f"📝 原文: {r['original']}")
if r["verdict"] == "OK":
print(f"\n✅ 无风险词，原文可直接使用")
print(f"{sep}\n")
return
print(f"\n✨ 重写后: {r['rewritten']}")
print(f"\n📊 风险统计: 🔴 {r['high_risk_count']} / 🟡 {r['medium_risk_count']}")
if r["substitutions"]:
print(f"\n🔄 替换详情:")
for s in r["substitutions"]:
emoji = RISK_LEVEL_EMOJI.get(s["risk_for_platform"], "⚪")
print(f" {emoji} '{s['word']}' → '{s['replace_with']}'")
print(f" 类别: {s['category']}, 平台: {s['platforms_affected']}")
print(f"\n💡 {r['advice']}")
print(f"{sep}\n")
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-prompt safety_lint v{VERSION} — 平台合规润色（合法艺术创作专用）",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
示例:
safety_lint.py "战士手中沾满鲜血的剑"
safety_lint.py "古典维纳斯雕像" --target dalle
safety_lint.py "赛博朋克战斗场景" --target SD
safety_lint.py "黑暗骑士" -j
echo "原始描述" | safety_lint.py --stdin --apply # 重写并输出新文本
注意: 本工具仅服务合法艺术创作。拒绝以下场景：
✗ CSAM（未成年 + 性化）
✗ 真人 + 色情/诽谤
✗ 武器/毒品/爆炸物制作教程
✗ 自残诱导
""",
)
parser.add_argument("text", nargs="?", help="要检查的文本")
parser.add_argument("--stdin", action="store_true", help="从 stdin 读取")
parser.add_argument("--target", default="MJ",
help="目标平台 DALL-E/MJ/SD/SDXL/Flux/通用 (默认 MJ)")
parser.add_argument("--apply", action="store_true",
help="直接输出重写后的文本（用于 pipe）")
parser.add_argument("-j", "--json", action="store_true", help="JSON 输出")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
text = args.text
if args.stdin or not text:
if sys.stdin.isatty() and not args.stdin:
parser.print_help()
sys.exit(1)
text = sys.stdin.read().strip()
if not text:
print("❌ 输入为空", file=sys.stderr)
sys.exit(1)
r = lint(text, args.target)
if args.apply:
if r["verdict"] == "REJECT":
print(f"REJECTED: {r['reason']}", file=sys.stderr)
sys.exit(2)
print(r.get("rewritten", r["original"]))
return
if args.json:
print(json.dumps(r, ensure_ascii=False, indent=2))
return
print_lint(r)
if r["verdict"] == "REJECT":
sys.exit(2)
if __name__ == "__main__":
main()
FILE:scripts/storyboard.py
#!/usr/bin/env python3
"""
huo15-img-prompt — 故事板模式 v3.0
把一段剧本/文案 → Claude 拆 N 个关键帧 → 每帧出 T2I prompt + 帧间 T2V 衔接 prompt
→ 产出完整视频脚本包（可直接喂给 Sora/Kling/Runway/即梦）。
这是 v3.0 的杀手级 feature：把文生图 + 文生视频的"单点能力"组合成"短片生产管线"。
视频内容创作者远多于静态图创作者。
工作流（一次调用完成）：
Step 1: Claude 读剧本 → 拆 N scenes，每个 scene 给主体描述/构图/光影/动作
Step 2: 对每个 scene，复用 enhance_prompt 生成 T2I 提示词
Step 3: 对每两个相邻 scene，复用 enhance_video 生成衔接 T2V 提示词
Step 4: 整合输出 storyboard.json + scenes/*.txt + README.md（可读视图）
调用：
storyboard.py "一只猫从城市走进雨夜" -p 电影感 --scenes 4 -m Sora
storyboard.py < script.txt --scenes 8
storyboard.py "..." --scenes 5 --output ./my_story --video-model Kling
storyboard.py "..." --scenes 6 -j > storyboard.json # JSON 输出
依赖：
- 同目录 enhance_prompt.py / enhance_video.py
- ANTHROPIC_API_KEY
"""
import sys
import os
import json
import argparse
import time
import re
from typing import Dict, List, Optional
from urllib.request import Request, urlopen
from urllib.error import HTTPError
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from enhance_prompt import (
build_prompt, parse_mix_preset, resolve_preset, STYLE_PRESETS, stable_seed,
)
from enhance_video import build_video_prompt
from claude_polish import ANTHROPIC_BASE, ANTHROPIC_VERSION
VERSION = "3.1.0"
DEFAULT_MODEL = "claude-sonnet-4-5"
# ─────────────────────────────────────────────────────────
# Claude 拆剧本 system prompt（启用 cache）
# ─────────────────────────────────────────────────────────
def build_storyboard_system_prompt(target_scenes: int) -> str:
return f"""你是火一五故事板分镜师。给定一段剧本/文案，拆成 {target_scenes} 个关键帧（key frames），每帧用一句话主体描述 + 视觉/动作要素，相邻帧之间标注衔接动作。
# 工作流
1. 读剧本，提取叙事节奏（开场 → 起 → 承 → 转 → 合）
2. 拆成 {target_scenes} 个连贯关键帧
3. 每帧给：
- 主体（人/物/场景核心，一句话中文）
- 构图（特写/中景/全身/俯拍/航拍/侧面 等）
- 光影/氛围（黄昏/雨夜/霓虹/逆光 等）
- 主体动作/表情（用于 T2I）
4. 每相邻两帧之间给衔接动作（用于 T2V，描述镜头/主体怎么从 A 帧过渡到 B 帧）
# 输出 JSON 严格 schema
```json
{{
"title": "整段剧本的简短标题（5-10 字）",
"logline": "一句话总结",
"narrative_arc": "开场→起→承→转→合 之类的节奏说明",
"scenes": [
{{
"index": 1,
"subject": "中文一句话主体描述（具体可视）",
"subject_en": "English subject for T2I",
"composition": "特写/中景/全身/俯拍/仰拍/航拍/侧面/背面 之一",
"lighting": "光影/时间/天气/氛围（中文）",
"action": "主体动作/表情（用于 T2I 增强）",
"narrative_role": "叙事角色（开场建立/冲突起点/高潮/落幕 等）"
}},
...{target_scenes} 个
],
"transitions": [
{{
"from_scene": 1,
"to_scene": 2,
"camera_motion": "推镜/拉镜/摇镜/跟拍/手持/航拍 等（中文）",
"duration_s": 3,
"description": "衔接动作描述（中文+英文混合，用于 T2V）"
}},
...{target_scenes - 1} 个
],
"total_duration_s": "估算总时长，秒"
}}
```
# 关键
- {target_scenes} 个 scene，{target_scenes - 1} 个 transition
- 每帧 subject 都要"画面感强"，让 T2I 能复现具体场景
- transitions 描述要让 T2V 模型知道镜头怎么动 + 主体怎么变化
- 全程中文为主，T2I 模型友好的视觉术语英文混合
- 只输出 JSON，不要解释"""
def call_claude_storyboard(script: str, target_scenes: int,
model: str = DEFAULT_MODEL) -> Dict:
"""调 Claude 拆剧本。"""
api_key = os.environ.get("ANTHROPIC_API_KEY")
if not api_key:
raise RuntimeError("缺少 ANTHROPIC_API_KEY 环境变量")
body = {
"model": model,
"max_tokens": 4096,
"temperature": 0.7,
"system": [{
"type": "text",
"text": build_storyboard_system_prompt(target_scenes),
"cache_control": {"type": "ephemeral"},
}],
"messages": [
{"role": "user", "content": f"<script>\n{script}\n</script>\n\n请输出 JSON。"},
{"role": "assistant", "content": "{"},
],
}
req = Request(
f"{ANTHROPIC_BASE}/v1/messages",
data=json.dumps(body).encode("utf-8"),
headers={
"Content-Type": "application/json",
"x-api-key": api_key,
"anthropic-version": ANTHROPIC_VERSION,
},
method="POST",
)
try:
with urlopen(req, timeout=120) as r:
resp = json.loads(r.read().decode("utf-8"))
except HTTPError as e:
raise RuntimeError(f"Claude HTTP {e.code}: {e.read().decode('utf-8', errors='replace')}")
if "error" in resp:
raise RuntimeError(f"Claude API 错误: {resp['error']}")
text = ""
for block in resp.get("content", []):
if block.get("type") == "text":
text += block.get("text", "")
full = "{" + text
# 抽完整 JSON
depth = 0
end = -1
in_str = False
esc = False
for i, ch in enumerate(full):
if esc:
esc = False
continue
if ch == "\\":
esc = True
continue
if ch == '"':
in_str = not in_str
continue
if in_str:
continue
if ch == "{":
depth += 1
elif ch == "}":
depth -= 1
if depth == 0:
end = i + 1
break
if end == -1:
raise RuntimeError(f"未找到完整 JSON: {full[:300]}")
data = json.loads(full[:end])
usage = resp.get("usage", {})
data["_usage"] = {
"input_tokens": usage.get("input_tokens", 0),
"output_tokens": usage.get("output_tokens", 0),
"cache_read_input_tokens": usage.get("cache_read_input_tokens", 0),
}
data["_model"] = resp.get("model", "")
return data
# ─────────────────────────────────────────────────────────
# 整合：剧本 → scenes + transitions + T2I/T2V prompts
# ─────────────────────────────────────────────────────────
def storyboard(script: str, preset: str, target_scenes: int = 5,
i2i_model: str = "通用", t2v_model: str = "通用",
aspect: str = "", duration_per_transition: int = 3,
quality_tier: str = "pro",
claude_model: str = DEFAULT_MODEL) -> Dict:
"""主入口：剧本 → 完整 storyboard 包。"""
# 1. Claude 拆剧本
primary, secondary = parse_mix_preset(preset)
if secondary:
p1, p2 = resolve_preset(primary), resolve_preset(secondary)
if not p1 or not p2:
raise RuntimeError(f"未知预设: {primary} 或 {secondary}")
preset_resolved, mix_secondary = p1, p2
else:
preset_resolved = resolve_preset(primary) or "电影感"
mix_secondary = None
if not aspect:
aspect = STYLE_PRESETS[preset_resolved].get("aspect", "16:9")
plan = call_claude_storyboard(script, target_scenes, model=claude_model)
scenes_raw = plan.get("scenes", [])[:target_scenes]
transitions_raw = plan.get("transitions", [])
# 共享 seed 锁定整段一致性（角色/场景跨帧不漂移）
base_seed = stable_seed(script[:80], preset_resolved)
# 2. 每帧出 T2I prompt
scene_prompts = []
for s in scenes_raw:
subject = s.get("subject", "")
action = s.get("action", "")
composition = s.get("composition", "")
lighting_atmos = s.get("lighting", "")
full_subject = subject
if action:
full_subject = f"{full_subject}, {action}"
if lighting_atmos:
full_subject = f"{full_subject}, {lighting_atmos}"
recipe = build_prompt(
full_subject, preset_resolved,
model=i2i_model, aspect=aspect,
extra_composition=composition,
seed=base_seed,
quality_tier=quality_tier,
mix_secondary=mix_secondary,
)
scene_prompts.append({
"index": s.get("index"),
"narrative_role": s.get("narrative_role", ""),
"subject": subject,
"subject_en": s.get("subject_en", ""),
"composition": composition,
"lighting_atmosphere": lighting_atmos,
"action": action,
"t2i_prompt": recipe["positive"],
"t2i_negative": recipe["negative"],
"consistency_lock": recipe["consistency_lock"],
"seed": recipe["seed_suggestion"],
})
# 3. 每对相邻帧出 T2V 衔接 prompt
transition_prompts = []
for t in transitions_raw:
from_idx = t.get("from_scene")
to_idx = t.get("to_scene")
if not from_idx or not to_idx:
continue
# 用 from-scene 的 subject 做基础，加 transition 描述
from_scene = next((s for s in scene_prompts if s["index"] == from_idx), None)
to_scene = next((s for s in scene_prompts if s["index"] == to_idx), None)
if not from_scene or not to_scene:
continue
transition_subject = (
f"transition from scene {from_idx} '{from_scene['subject']}' "
f"to scene {to_idx} '{to_scene['subject']}': {t.get('description', '')}"
)
camera_motion = t.get("camera_motion", "")
video_recipe = build_video_prompt(
transition_subject, preset_resolved,
model=t2v_model, aspect=aspect,
duration=t.get("duration_s", duration_per_transition),
motion=camera_motion,
seed=base_seed,
quality_tier=quality_tier,
mix_secondary=mix_secondary,
)
transition_prompts.append({
"from_scene": from_idx,
"to_scene": to_idx,
"camera_motion": camera_motion,
"duration_s": t.get("duration_s", duration_per_transition),
"description": t.get("description", ""),
"t2v_prompt": video_recipe["positive"],
"t2v_negative": video_recipe["negative"],
"keyframes": video_recipe.get("keyframes", []),
})
return {
"version": VERSION,
"title": plan.get("title", ""),
"logline": plan.get("logline", ""),
"narrative_arc": plan.get("narrative_arc", ""),
"preset": preset_resolved,
"mix_secondary": mix_secondary,
"aspect": aspect,
"i2i_model": i2i_model,
"t2v_model": t2v_model,
"base_seed": base_seed,
"total_scenes": len(scene_prompts),
"total_transitions": len(transition_prompts),
"estimated_duration_s": plan.get("total_duration_s")
or sum(t["duration_s"] for t in transition_prompts) + 2 * len(scene_prompts),
"scenes": scene_prompts,
"transitions": transition_prompts,
"_claude": plan.get("_usage", {}),
}
# ─────────────────────────────────────────────────────────
# 输出
# ─────────────────────────────────────────────────────────
def write_storyboard_files(result: Dict, output_dir: str) -> List[str]:
"""把 storyboard 写到多个文件。"""
os.makedirs(output_dir, exist_ok=True)
written = []
# 1. scenes.json
p = os.path.join(output_dir, "storyboard.json")
with open(p, "w", encoding="utf-8") as f:
json.dump(result, f, ensure_ascii=False, indent=2)
written.append(p)
# 2. 每个 scene 的 t2i_prompt
for s in result["scenes"]:
idx = s["index"]
p = os.path.join(output_dir, f"scene-{idx:02d}-t2i.txt")
with open(p, "w", encoding="utf-8") as f:
f.write(f"# Scene {idx}: {s['subject']}\n")
f.write(f"# 角色: {s['narrative_role']}\n\n")
f.write("## Positive\n")
f.write(s["t2i_prompt"] + "\n\n")
f.write("## Negative\n")
f.write(s["t2i_negative"] + "\n")
written.append(p)
# 3. 每个 transition 的 t2v_prompt
for t in result["transitions"]:
p = os.path.join(output_dir, f"transition-{t['from_scene']:02d}-to-{t['to_scene']:02d}-t2v.txt")
with open(p, "w", encoding="utf-8") as f:
f.write(f"# Transition: scene {t['from_scene']} → {t['to_scene']}\n")
f.write(f"# 镜头: {t['camera_motion']}\n")
f.write(f"# 时长: {t['duration_s']}s\n\n")
f.write("## Positive\n")
f.write(t["t2v_prompt"] + "\n\n")
f.write("## Negative\n")
f.write(t["t2v_negative"] + "\n")
written.append(p)
# 4. README.md（可读总览）
p = os.path.join(output_dir, "README.md")
lines = [
f"# {result['title']}",
"",
f"> {result['logline']}",
"",
f"**叙事弧**: {result['narrative_arc']}",
"",
f"- 预设: {result['preset']}" + (f" + {result['mix_secondary']}" if result['mix_secondary'] else ""),
f"- 画幅: {result['aspect']}",
f"- 总场景: {result['total_scenes']} | 转场: {result['total_transitions']}",
f"- 估算时长: {result['estimated_duration_s']} s",
f"- I2I 模型: {result['i2i_model']} | T2V 模型: {result['t2v_model']}",
f"- 锁定 seed: {result['base_seed']}",
"",
"## 场景",
"",
]
for s in result["scenes"]:
lines.append(f"### Scene {s['index']}: {s['subject']}")
lines.append("")
lines.append(f"**角色**: {s['narrative_role']} | **构图**: {s['composition']} | **氛围**: {s['lighting_atmosphere']}")
lines.append("")
lines.append(f"📷 T2I prompt → 见 `scene-{s['index']:02d}-t2i.txt`")
lines.append("")
lines.append("## 转场")
lines.append("")
for t in result["transitions"]:
lines.append(f"### Scene {t['from_scene']} → {t['to_scene']}（{t['duration_s']}s）")
lines.append("")
lines.append(f"**镜头**: {t['camera_motion']}")
lines.append("")
lines.append(f"{t['description']}")
lines.append("")
lines.append(f"🎥 T2V prompt → 见 `transition-{t['from_scene']:02d}-to-{t['to_scene']:02d}-t2v.txt`")
lines.append("")
lines.append("## 生产管线")
lines.append("")
lines.append("```bash")
lines.append("# Step 1: 出每个 scene 的关键帧（T2I）")
lines.append("for f in scene-*.txt; do")
lines.append(" cat $f | grep -A100 '## Positive' | tail -1")
lines.append(" # 喂给 Midjourney / DALL-E / SD ...")
lines.append("done")
lines.append("")
lines.append("# Step 2: 出每个 transition 的衔接（T2V）")
lines.append("for f in transition-*.txt; do")
lines.append(" cat $f | grep -A100 '## Positive' | tail -1")
lines.append(" # 喂给 Sora / Kling / Runway ...")
lines.append("done")
lines.append("")
lines.append("# Step 3: 剪辑串联")
lines.append("# scenes 用作关键帧定格；transitions 填充帧间动画")
lines.append("```")
lines.append("")
lines.append(f"由 huo15-img-prompt v{result['version']} 故事板模式生成。")
with open(p, "w", encoding="utf-8") as f:
f.write("\n".join(lines) + "\n")
written.append(p)
return written
def print_storyboard_summary(result: Dict):
sep = "═" * 60
print(f"\n{sep}")
print(f"🎬 故事板 v{result['version']}")
print(f"📌 标题: {result['title']}")
print(f"📝 简介: {result['logline']}")
print(f"🎭 弧线: {result['narrative_arc']}")
print(f"🎨 预设: {result['preset']}" + (f" + {result['mix_secondary']}" if result['mix_secondary'] else ""))
print(f"📐 画幅: {result['aspect']}")
print(f"🎲 锁定 seed: {result['base_seed']}")
print(f"⏱ 估算: {result['estimated_duration_s']}s ({result['total_scenes']} 场 + {result['total_transitions']} 转场)")
print(f"\n📋 场景列表:")
for s in result["scenes"]:
print(f" [{s['index']}] {s['subject'][:50]}")
print(f" 角色: {s['narrative_role']} | 构图: {s['composition']} | 氛围: {s['lighting_atmosphere']}")
print(f"\n🎥 转场:")
for t in result["transitions"]:
print(f" Scene {t['from_scene']} → {t['to_scene']}: {t['camera_motion']} ({t['duration_s']}s)")
u = result.get("_claude", {})
print(f"\n📊 Claude token: in={u.get('input_tokens', 0)} / out={u.get('output_tokens', 0)} / cache={u.get('cache_read_input_tokens', 0)}")
print(f"{sep}\n")
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-prompt storyboard v{VERSION} — 剧本→关键帧+转场 视频脚本包",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
示例:
storyboard.py "一只猫从城市走进雨夜" -p 电影感 --scenes 4 -m Midjourney --video-model Sora
storyboard.py < script.txt --scenes 8 --output ./my_story
storyboard.py "汉服少女夜游京都" -p 汉服写真 --scenes 6 --video-model 即梦
""",
)
parser.add_argument("script", nargs="?", help="剧本/文案（不给则从 stdin）")
parser.add_argument("-p", "--preset", required=True, help="风格预设（支持 A+B 混合）")
parser.add_argument("--scenes", type=int, default=5, help="拆几个关键帧（默认 5）")
parser.add_argument("-a", "--aspect", default="", help="画幅（默认走预设默认）")
parser.add_argument("-t", "--tier", choices=["basic", "pro", "master"], default="pro")
parser.add_argument("-m", "--model", default="通用",
help="T2I 适配模型 Midjourney/SD/SDXL/Flux/DALL-E/通用（默认通用）")
parser.add_argument("--video-model", default="通用",
help="T2V 适配模型 Sora/Kling/Runway/Pika/Luma/Hailuo/即梦/Wan/通用")
parser.add_argument("--transition-duration", type=int, default=3,
help="每个转场默认时长（秒）")
parser.add_argument("--claude-model", default=DEFAULT_MODEL,
help=f"Claude 模型（默认 {DEFAULT_MODEL}）")
parser.add_argument("--output", default="", help="输出目录（不给则只打印）")
parser.add_argument("-j", "--json", action="store_true", help="JSON 输出")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
script = args.script
if not script:
if sys.stdin.isatty():
parser.print_help()
sys.exit(1)
script = sys.stdin.read().strip()
if not script:
print("❌ 剧本为空", file=sys.stderr)
sys.exit(1)
try:
result = storyboard(
script, preset=args.preset, target_scenes=args.scenes,
i2i_model=args.model, t2v_model=args.video_model,
aspect=args.aspect, duration_per_transition=args.transition_duration,
quality_tier=args.tier, claude_model=args.claude_model,
)
except RuntimeError as e:
print(f"❌ {e}", file=sys.stderr)
sys.exit(2)
if args.output:
files = write_storyboard_files(result, args.output)
result["_files_written"] = files
if args.json:
print(json.dumps(result, ensure_ascii=False, indent=2))
else:
print_storyboard_summary(result)
print(f"📁 已写入 {len(files)} 个文件到 {args.output}/")
for f in files[:5]:
print(f" • {f}")
if len(files) > 5:
print(f" ... 还有 {len(files) - 5} 个")
else:
if args.json:
print(json.dumps(result, ensure_ascii=False, indent=2))
else:
print_storyboard_summary(result)
print("💡 加 --output ./my_story 把所有 prompt 写到文件夹\n")
if __name__ == "__main__":
main()
FILE:scripts/style_learn.py
#!/usr/bin/env python3
"""
huo15-img-prompt — 风格学习引擎 v3.0
给 N 张参考图（用户喜欢的风格样本），用 Claude Vision 提取每张的视觉特征，
综合归纳出共性 → 生成一个新的"learned preset"，存到 ~/.huo15/learned_presets/<name>.json。
后续 enhance_prompt.py 用 `-p @<name>` 复用这个学到的风格。
工作流：
Step 1: 对每张参考图调 Claude Vision 提取 tags / camera / lighting / palette
Step 2: 用 Claude 综合 N 张图的共性，输出统一风格 spec
Step 3: 保存为 learned preset，schema 与 STYLE_PRESETS 兼容
调用：
style_learn.py --name 我的小清新 ref1.jpg ref2.jpg ref3.jpg
style_learn.py --list # 列出所有 learned presets
style_learn.py --show 我的小清新 # 详情
style_learn.py --delete 旧风格 # 删除
enhance_prompt.py "猫咪" -p "@我的小清新" # 在出图时复用
依赖：
- 同目录 image_review.py (Claude Vision)
- ANTHROPIC_API_KEY
"""
import sys
import os
import json
import re
import time
import argparse
from typing import Dict, List, Optional
from urllib.request import Request, urlopen
from urllib.error import HTTPError
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from image_review import call_claude_vision, parse_review_json, ANTHROPIC_BASE, ANTHROPIC_VERSION
VERSION = "3.1.0"
DEFAULT_MODEL = "claude-sonnet-4-5"
LEARNED_DIR = os.path.expanduser("~/.huo15/learned_presets")
def safe_name(name: str) -> str:
return re.sub(r"[^\w\-]", "_", name)
def learned_path(name: str) -> str:
return os.path.join(LEARNED_DIR, f"{safe_name(name)}.json")
# ─────────────────────────────────────────────────────────
# 单图特征提取（用 Claude Vision 但不是评分模式）
# ─────────────────────────────────────────────────────────
EXTRACT_SYSTEM_PROMPT = """你是图像视觉风格分析师。给一张图，提取它的可复现视觉风格 spec，输出严格 JSON：
```json
{
"tags": "用 5-8 个英文风格标签描述这张图，逗号分隔（例：'cinematic, anamorphic, golden hour, dreamy bokeh'）",
"camera": "镜头/视角/焦段（英文，例：'85mm telephoto, low angle, shallow depth of field'）",
"lighting": "光影描述（英文，例：'warm golden hour rim light, soft fill, cinematic glow'）",
"palette": "主色板（英文，例：'muted teal and orange, warm amber highlights, soft pastels'）",
"aspect": "推断画幅 1:1/3:4/4:3/16:9/9:16/21:9 之一",
"subject_type": "主体类型（人像/风景/物品/抽象 等）",
"mood": "情绪关键词（中英混合）",
"key_elements": ["3-5 个关键视觉元素"],
"neg_to_avoid": "应该避免出现的事物（英文，逗号分隔）"
}
```
只输出 JSON，不要解释。"""
def extract_image_style(image_src: str, model: str = DEFAULT_MODEL) -> Dict:
"""调 Claude Vision 提取一张图的视觉特征。"""
api_key = os.environ.get("ANTHROPIC_API_KEY")
if not api_key:
raise RuntimeError("缺少 ANTHROPIC_API_KEY")
from image_review import load_image_b64
img_b64, media_type = load_image_b64(image_src)
body = {
"model": model,
"max_tokens": 1500,
"system": [{
"type": "text",
"text": EXTRACT_SYSTEM_PROMPT,
"cache_control": {"type": "ephemeral"},
}],
"messages": [
{"role": "user", "content": [
{"type": "image", "source": {"type": "base64", "media_type": media_type, "data": img_b64}},
{"type": "text", "text": "请提取这张图的视觉风格 spec，输出 JSON。"},
]},
{"role": "assistant", "content": "{"},
],
}
req = Request(
f"{ANTHROPIC_BASE}/v1/messages",
data=json.dumps(body).encode("utf-8"),
headers={
"Content-Type": "application/json",
"x-api-key": api_key,
"anthropic-version": ANTHROPIC_VERSION,
},
method="POST",
)
try:
with urlopen(req, timeout=120) as r:
resp = json.loads(r.read().decode("utf-8"))
except HTTPError as e:
raise RuntimeError(f"Claude HTTP {e.code}: {e.read().decode('utf-8', errors='replace')}")
return parse_review_json(resp)
# ─────────────────────────────────────────────────────────
# N 张图综合 → 共性 spec
# ─────────────────────────────────────────────────────────
SYNTHESIZE_SYSTEM_PROMPT = """你是视觉风格提炼师。给定 N 张参考图各自的风格 spec，提炼共性，输出一个统一的 huo15-img-prompt preset 定义。
# 输出 JSON 严格 schema（与 STYLE_PRESETS 兼容）
```json
{
"category": "推断分类（摄影/动漫/插画/3D/设计/艺术/场景/游戏/东方 之一）",
"tags": "5-10 个英文风格标签（必须是 N 张图共有的特征，去掉只在 1-2 张出现的）",
"quality": "画质修饰词（英文，例：'masterpiece, raw photo, kodak portra 400 film stock'）",
"neg": "负面词（英文逗号分隔，从 N 张图的 neg_to_avoid 综合）",
"camera": "共性镜头（英文）",
"lighting": "共性光影（英文）",
"palette": "共性色板（英文）",
"aspect": "最常出现的画幅",
"synthesis_notes": "中文一句话说明这风格的精髓",
"best_subject_examples": ["这风格适合画什么的 3 个例子"],
"confidence": 0.0-1.0
}
```
# 关键
- 至少 50% 参考图共有的特征才算"共性"
- 只出现在 1 张图的特征忽略
- tags 不要互相矛盾（"vintage" 和 "futuristic" 不应同时出现）
- confidence 反映共性强度：> 0.7 才适合做新预设；< 0.5 说明 N 张图风格太散
只输出 JSON，不要解释。"""
def synthesize_style(per_image_specs: List[Dict], model: str = DEFAULT_MODEL) -> Dict:
"""让 Claude 综合 N 张图的共性。"""
api_key = os.environ.get("ANTHROPIC_API_KEY")
if not api_key:
raise RuntimeError("缺少 ANTHROPIC_API_KEY")
user_msg = f"""<reference_images_specs count="{len(per_image_specs)}">
{json.dumps(per_image_specs, ensure_ascii=False, indent=2)}
</reference_images_specs>
请综合这 {len(per_image_specs)} 张图的共性，输出统一的 preset 定义 JSON。"""
body = {
"model": model,
"max_tokens": 2000,
"temperature": 0.5,
"system": [{
"type": "text",
"text": SYNTHESIZE_SYSTEM_PROMPT,
"cache_control": {"type": "ephemeral"},
}],
"messages": [
{"role": "user", "content": user_msg},
{"role": "assistant", "content": "{"},
],
}
req = Request(
f"{ANTHROPIC_BASE}/v1/messages",
data=json.dumps(body).encode("utf-8"),
headers={
"Content-Type": "application/json",
"x-api-key": api_key,
"anthropic-version": ANTHROPIC_VERSION,
},
method="POST",
)
try:
with urlopen(req, timeout=60) as r:
resp = json.loads(r.read().decode("utf-8"))
except HTTPError as e:
raise RuntimeError(f"Claude HTTP {e.code}: {e.read().decode('utf-8', errors='replace')}")
return parse_review_json(resp)
# ─────────────────────────────────────────────────────────
# 主流程
# ─────────────────────────────────────────────────────────
def learn_style(name: str, images: List[str], model: str = DEFAULT_MODEL) -> Dict:
"""主入口：N 张图 → learned preset。"""
if len(images) < 2:
raise RuntimeError("至少需要 2 张参考图")
per_image = []
for i, img in enumerate(images, 1):
print(f" 🔍 提取第 {i}/{len(images)} 张: {img}", file=sys.stderr)
spec = extract_image_style(img, model=model)
spec["_source"] = img
per_image.append(spec)
print(f" ✏️ 综合 {len(per_image)} 张图的共性...", file=sys.stderr)
synthesized = synthesize_style(per_image, model=model)
learned = {
"name": name,
"version": VERSION,
"created_at": int(time.time()),
"use_count": 0,
"category": synthesized.get("category", "学习"),
"tags": synthesized.get("tags", ""),
"quality": synthesized.get("quality", "high quality, detailed"),
"neg": synthesized.get("neg", "low quality, blurry"),
"camera": synthesized.get("camera", ""),
"lighting": synthesized.get("lighting", ""),
"palette": synthesized.get("palette", ""),
"aspect": synthesized.get("aspect", "1:1"),
"synthesis_notes": synthesized.get("synthesis_notes", ""),
"best_subject_examples": synthesized.get("best_subject_examples", []),
"confidence": synthesized.get("confidence", 0.5),
"source_count": len(images),
"source_images": images,
"per_image_specs": per_image,
}
os.makedirs(LEARNED_DIR, exist_ok=True)
with open(learned_path(name), "w", encoding="utf-8") as f:
json.dump(learned, f, ensure_ascii=False, indent=2)
return learned
def learned_load(name: str) -> Optional[Dict]:
p = learned_path(name)
if not os.path.isfile(p):
return None
try:
with open(p, "r", encoding="utf-8") as f:
return json.load(f)
except (json.JSONDecodeError, IOError):
return None
def learned_list() -> List[Dict]:
if not os.path.isdir(LEARNED_DIR):
return []
out = []
for fn in sorted(os.listdir(LEARNED_DIR)):
if not fn.endswith(".json"):
continue
try:
with open(os.path.join(LEARNED_DIR, fn), "r", encoding="utf-8") as f:
out.append(json.load(f))
except (json.JSONDecodeError, IOError):
continue
return out
def learned_delete(name: str) -> bool:
p = learned_path(name)
if os.path.isfile(p):
os.remove(p)
return True
return False
def print_learned(p: Dict):
print(f"\n🎨 @{p['name']}")
print(f" 分类: {p.get('category', '?')}")
print(f" Confidence: {p.get('confidence', 0):.2f}")
print(f" 来源: {p.get('source_count', 0)} 张图")
print(f" 用过: {p.get('use_count', 0)} 次")
print(f" 风格标签: {p.get('tags', '')[:120]}")
if p.get("camera"):
print(f" 相机: {p['camera']}")
if p.get("lighting"):
print(f" 光影: {p['lighting']}")
if p.get("palette"):
print(f" 色板: {p['palette']}")
if p.get("synthesis_notes"):
print(f"\n 📝 精髓: {p['synthesis_notes']}")
if p.get("best_subject_examples"):
print(f" 💡 适合画:")
for e in p["best_subject_examples"]:
print(f" • {e}")
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-prompt style_learn v{VERSION} — 风格学习引擎",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
示例:
style_learn.py --name 我的小清新 ref1.jpg ref2.jpg ref3.jpg
style_learn.py --list
style_learn.py --show 我的小清新
style_learn.py --delete 旧风格
✨ 在 enhance_prompt.py 里使用:
enhance_prompt.py "猫咪" -p "@我的小清新"
""",
)
g = parser.add_mutually_exclusive_group(required=True)
g.add_argument("--name", help="给学到的风格起个名字（配合 image 参数）")
g.add_argument("--list", action="store_true", help="列出所有 learned preset")
g.add_argument("--show", help="显示详情")
g.add_argument("--delete", help="删除")
parser.add_argument("images", nargs="*", help="参考图路径或 URL（≥2 张）")
parser.add_argument("--model", default=DEFAULT_MODEL, help=f"Claude 模型（默认 {DEFAULT_MODEL}）")
parser.add_argument("-j", "--json", action="store_true", help="JSON 输出")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
if args.list:
ps = learned_list()
if args.json:
print(json.dumps({"version": VERSION, "learned_presets": ps}, ensure_ascii=False, indent=2))
return
if not ps:
print(f"\n📭 暂无 learned preset ({LEARNED_DIR})")
print("💡 创建：style_learn.py --name 我的风格 ref1.jpg ref2.jpg ref3.jpg\n")
return
print(f"\n🎨 Learned Presets ({len(ps)} 个):")
for p in ps:
print(f" • @{p['name']:20s} {p.get('category', '?'):10s} conf={p.get('confidence', 0):.2f} {p.get('source_count', 0)} 图 用过 {p.get('use_count', 0)} 次")
print()
return
if args.show:
p = learned_load(args.show)
if not p:
print(f"❌ 不存在: {args.show}", file=sys.stderr)
sys.exit(1)
if args.json:
print(json.dumps(p, ensure_ascii=False, indent=2))
else:
print_learned(p)
print()
return
if args.delete:
if learned_delete(args.delete):
print(f"✅ 已删除: @{args.delete}")
else:
print(f"❌ 不存在: {args.delete}", file=sys.stderr)
sys.exit(1)
return
if args.name:
if not args.images:
print(f"❌ 至少需要 2 张参考图", file=sys.stderr)
sys.exit(1)
try:
learned = learn_style(args.name, args.images, model=args.model)
except RuntimeError as e:
print(f"❌ {e}", file=sys.stderr)
sys.exit(2)
if args.json:
print(json.dumps(learned, ensure_ascii=False, indent=2))
else:
print_learned(learned)
print(f"\n✅ 已保存: ~/.huo15/learned_presets/{safe_name(args.name)}.json")
print(f"💡 使用: enhance_prompt.py \"主体\" -p \"@{args.name}\"\n")
if __name__ == "__main__":
main()
FILE:scripts/web_ui.py
#!/usr/bin/env python3
"""
huo15-img-prompt — 本地 Web UI v2.6
启动一个本地 HTTP server（默认 http://127.0.0.1:7155），自动打开浏览器，
提供 88 风格预设可视化选择 + 实时 prompt 预览 + 一键复制。
启动：
web_ui.py # 默认 7155 端口
web_ui.py --port 8080 # 指定端口
web_ui.py --no-browser # 不自动开浏览器
web_ui.py --host 0.0.0.0 # 局域网可访问
零第三方依赖，纯 Python 标准库 http.server + 单文件嵌入 HTML。
"""
import sys
import os
import json
import re
import argparse
import webbrowser
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
from urllib.parse import urlparse, parse_qs
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from enhance_prompt import (
build_prompt, parse_mix_preset, resolve_preset,
parse_requirement, STYLE_PRESETS,
preset_example_urls, compact_prompt,
)
from character import char_list, char_load
VERSION = "3.1.0"
# ─────────────────────────────────────────────────────────
# 单文件 HTML（vanilla JS + Tailwind CDN）
# ─────────────────────────────────────────────────────────
HTML_PAGE = """<!doctype html>
<html lang="zh-CN">
<head>
<meta charset="utf-8">
<title>火一五文生图提示词 v__VERSION__</title>
<meta name="viewport" content="width=device-width, initial-scale=1">
<script src="https://cdn.tailwindcss.com"></script>
<style>
body { font-family: -apple-system, BlinkMacSystemFont, "PingFang SC", "Microsoft YaHei", sans-serif; }
.preset-card.active { background: #1f2937; color: #fff; border-color: #1f2937; }
.preset-card { transition: all .15s ease; cursor: pointer; }
.preset-card:hover { transform: translateY(-1px); }
pre { white-space: pre-wrap; word-break: break-all; }
</style>
</head>
<body class="bg-gray-50 min-h-screen">
<div class="max-w-7xl mx-auto px-4 py-6">
<header class="flex items-center justify-between mb-6">
<h1 class="text-2xl font-bold">🔥 火一五文生图提示词 <span class="text-gray-400 text-sm">v__VERSION__</span></h1>
<a href="https://clawhub.ai/skills/huo15-img-prompt" target="_blank" class="text-sm text-gray-500 hover:text-gray-900">📦 ClawHub →</a>
</header>
<div class="grid grid-cols-1 lg:grid-cols-3 gap-6">
<!-- 左：输入 -->
<div class="lg:col-span-1 space-y-4">
<div class="bg-white rounded-lg p-4 shadow-sm">
<label class="block text-sm font-medium mb-1">主体描述</label>
<textarea id="subject" rows="3" class="w-full border rounded px-3 py-2 text-sm" placeholder="例：一只戴墨镜的猫坐在霓虹街头"></textarea>
</div>
<div class="bg-white rounded-lg p-4 shadow-sm space-y-3">
<div>
<label class="block text-sm font-medium mb-1">混合预设（可选）</label>
<input id="secondary" class="w-full border rounded px-3 py-2 text-sm" placeholder="例：水墨（不填则单预设）">
</div>
<div class="flex gap-2">
<div class="flex-1">
<label class="block text-sm font-medium mb-1">主预设权重</label>
<input id="mix" type="range" min="0.1" max="0.9" step="0.05" value="0.6" class="w-full">
<span class="text-xs text-gray-500" id="mix-val">0.60</span>
</div>
<div class="flex-1">
<label class="block text-sm font-medium mb-1">画质</label>
<select id="tier" class="w-full border rounded px-3 py-2 text-sm">
<option value="basic">basic</option>
<option value="pro" selected>pro</option>
<option value="master">master</option>
</select>
</div>
</div>
</div>
<div class="bg-white rounded-lg p-4 shadow-sm space-y-3">
<div class="flex gap-2">
<div class="flex-1">
<label class="block text-sm font-medium mb-1">目标模型</label>
<select id="model" class="w-full border rounded px-3 py-2 text-sm">
<option>通用</option>
<option>Midjourney</option>
<option>SD</option>
<option>SDXL</option>
<option>Flux</option>
<option>DALL-E</option>
</select>
</div>
<div class="flex-1">
<label class="block text-sm font-medium mb-1">画幅</label>
<select id="aspect" class="w-full border rounded px-3 py-2 text-sm">
<option value="">默认</option>
<option>1:1</option><option>3:4</option><option>4:3</option>
<option>16:9</option><option>9:16</option><option>21:9</option>
</select>
</div>
</div>
<div class="flex items-center gap-3 text-sm">
<label><input type="checkbox" id="compact"> 压缩到 77 token</label>
<label><input type="checkbox" id="cs"> 角色设定图</label>
</div>
</div>
<button id="go" class="w-full bg-gray-900 text-white rounded-lg py-3 hover:bg-black">⚡ 生成提示词</button>
<div class="bg-white rounded-lg p-4 shadow-sm" id="char-section">
<div class="text-sm font-medium mb-2">角色卡</div>
<select id="char" class="w-full border rounded px-3 py-2 text-sm">
<option value="">(不使用)</option>
</select>
</div>
</div>
<!-- 中：预设选择 -->
<div class="lg:col-span-1">
<div class="bg-white rounded-lg p-4 shadow-sm">
<div class="flex items-center justify-between mb-3">
<h2 class="text-sm font-medium">88 风格预设</h2>
<input id="preset-search" class="text-xs border rounded px-2 py-1 w-32" placeholder="搜索...">
</div>
<div id="preset-list" class="space-y-3 max-h-[600px] overflow-y-auto"></div>
</div>
</div>
<!-- 右：输出 -->
<div class="lg:col-span-1 space-y-4">
<div id="output" class="hidden">
<div class="bg-white rounded-lg p-4 shadow-sm">
<div class="text-xs text-gray-500 mb-2 flex justify-between">
<span>正向提示词</span>
<button class="text-blue-500 hover:underline" data-copy="positive">📋 复制</button>
</div>
<pre id="positive" class="text-sm text-gray-800"></pre>
</div>
<div class="bg-white rounded-lg p-4 shadow-sm mt-4">
<div class="text-xs text-gray-500 mb-2 flex justify-between">
<span>负向提示词</span>
<button class="text-blue-500 hover:underline" data-copy="negative">📋 复制</button>
</div>
<pre id="negative" class="text-xs text-gray-600"></pre>
</div>
<div class="bg-white rounded-lg p-4 shadow-sm mt-4">
<div class="text-xs text-gray-500 mb-2">一致性锁</div>
<table class="text-xs w-full">
<tbody id="locks"></tbody>
</table>
</div>
<div id="meta" class="bg-gray-100 rounded-lg p-3 mt-4 text-xs text-gray-700 font-mono"></div>
</div>
<div id="empty" class="bg-white rounded-lg p-12 shadow-sm text-center text-gray-400 text-sm">
👈 选预设 + 写主体 + 点生成
</div>
</div>
</div>
</div>
<script>
let presets = [];
let selectedPreset = null;
async function loadPresets() {
const r = await fetch('/api/presets').then(x => x.json());
presets = r.presets;
renderPresets();
loadChars();
}
async function loadChars() {
try {
const r = await fetch('/api/characters').then(x => x.json());
const sel = document.getElementById('char');
for (const c of r.characters || []) {
const opt = document.createElement('option');
opt.value = c.name;
opt.textContent = `c.name (c.preset)`;
sel.appendChild(opt);
}
} catch (e) {}
}
function renderPresets(filter = '') {
const byCat = {};
for (const p of presets) {
if (filter && !p.name.includes(filter) && !p.tags.toLowerCase().includes(filter.toLowerCase())) continue;
if (!byCat[p.category]) byCat[p.category] = [];
byCat[p.category].push(p);
}
const order = ['摄影', '动漫', '插画', '3D', '设计', '艺术', '场景', '游戏', '东方'];
const html = order.filter(c => byCat[c]).map(cat => `
<div>
<div class="text-xs text-gray-500 mb-1">cat · byCat[cat].length</div>
<div class="grid grid-cols-2 gap-1">
byCat[cat].map(p => `
<button data-preset="${p.name" class="preset-card text-xs border rounded px-2 py-1.5 text-left hover:border-gray-400 ''">
p.name
</button>
`).join('')}
</div>
</div>
`).join('');
document.getElementById('preset-list').innerHTML = html;
document.querySelectorAll('[data-preset]').forEach(b => {
b.addEventListener('click', () => {
selectedPreset = b.dataset.preset;
renderPresets(document.getElementById('preset-search').value);
});
});
}
document.getElementById('mix').addEventListener('input', e => {
document.getElementById('mix-val').textContent = parseFloat(e.target.value).toFixed(2);
});
document.getElementById('preset-search').addEventListener('input', e => {
renderPresets(e.target.value);
});
document.getElementById('go').addEventListener('click', async () => {
const subject = document.getElementById('subject').value.trim();
if (!subject) { alert('请输入主体描述'); return; }
if (!selectedPreset) { alert('请选个预设'); return; }
const secondary = document.getElementById('secondary').value.trim();
const presetArg = secondary ? `selectedPreset+secondary` : selectedPreset;
const charName = document.getElementById('char').value;
const body = {
subject,
preset: presetArg,
mix_ratio: parseFloat(document.getElementById('mix').value),
model: document.getElementById('model').value,
aspect: document.getElementById('aspect').value,
tier: document.getElementById('tier').value,
compact: document.getElementById('compact').checked,
character_sheet: document.getElementById('cs').checked,
char: charName || null,
};
const goBtn = document.getElementById('go');
goBtn.textContent = '⏳ 生成中...';
goBtn.disabled = true;
try {
const r = await fetch('/api/enhance', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify(body),
}).then(x => x.json());
if (r.error) { alert(r.error); return; }
document.getElementById('empty').classList.add('hidden');
document.getElementById('output').classList.remove('hidden');
document.getElementById('positive').textContent = r.positive;
document.getElementById('negative').textContent = r.negative;
const locks = r.consistency_lock || {};
document.getElementById('locks').innerHTML = Object.entries(locks)
.filter(([_, v]) => v)
.map(([k, v]) => `<tr><td class="font-medium pr-2 text-gray-500">k</td><td>v</td></tr>`)
.join('');
const meta = `seed=r.seed_suggestion | aspect=r.aspect | preset=r.preset${r.mix_label)` : ''}r.compaction?.compacted ? ` | 压缩 ${r.compaction.estimated_tokens_before→r.compaction.estimated_tokens_after` : ''}`;
document.getElementById('meta').textContent = meta;
} catch (e) {
alert('请求失败: ' + e);
} finally {
goBtn.textContent = '⚡ 生成提示词';
goBtn.disabled = false;
}
});
document.querySelectorAll('[data-copy]').forEach(b => {
b.addEventListener('click', () => {
const text = document.getElementById(b.dataset.copy).textContent;
navigator.clipboard.writeText(text);
b.textContent = '✅ 已复制';
setTimeout(() => { b.textContent = '📋 复制'; }, 1500);
});
});
loadPresets();
</script>
</body>
</html>
""".replace("__VERSION__", VERSION)
# ─────────────────────────────────────────────────────────
# HTTP handlers
# ─────────────────────────────────────────────────────────
class Handler(BaseHTTPRequestHandler):
def log_message(self, fmt, *args):
pass # 静默
def _send_json(self, code: int, payload: dict):
body = json.dumps(payload, ensure_ascii=False).encode("utf-8")
self.send_response(code)
self.send_header("Content-Type", "application/json; charset=utf-8")
self.send_header("Content-Length", str(len(body)))
self.send_header("Access-Control-Allow-Origin", "*")
self.end_headers()
self.wfile.write(body)
def _send_html(self, code: int, html: str):
body = html.encode("utf-8")
self.send_response(code)
self.send_header("Content-Type", "text/html; charset=utf-8")
self.send_header("Content-Length", str(len(body)))
self.end_headers()
self.wfile.write(body)
def do_GET(self):
path = urlparse(self.path).path
if path == "/" or path == "/index.html":
self._send_html(200, HTML_PAGE)
elif path == "/api/presets":
data = []
for name, p in STYLE_PRESETS.items():
data.append({
"name": name,
"category": p["category"],
"tags": p["tags"],
"aspect": p.get("aspect", "1:1"),
})
self._send_json(200, {"version": VERSION, "presets": data})
elif path == "/api/characters":
self._send_json(200, {"characters": char_list()})
elif path == "/api/preset-examples":
qs = parse_qs(urlparse(self.path).query)
preset = (qs.get("preset") or [""])[0]
resolved = resolve_preset(preset) or preset
if resolved not in STYLE_PRESETS:
self._send_json(404, {"error": f"未知预设 {preset}"})
return
self._send_json(200, preset_example_urls(resolved))
else:
self._send_json(404, {"error": "not found"})
def do_POST(self):
path = urlparse(self.path).path
length = int(self.headers.get("Content-Length", 0))
body_bytes = self.rfile.read(length) if length else b"{}"
try:
body = json.loads(body_bytes.decode("utf-8"))
except json.JSONDecodeError:
self._send_json(400, {"error": "invalid JSON"})
return
if path == "/api/enhance":
try:
subject = body.get("subject", "")
raw_preset = body.get("preset", "写实摄影")
primary, secondary = parse_mix_preset(raw_preset)
if secondary:
p1, p2 = resolve_preset(primary), resolve_preset(secondary)
if not p1 or not p2:
self._send_json(400, {"error": f"未知预设 {primary} 或 {secondary}"})
return
preset, mix_secondary = p1, p2
else:
preset = resolve_preset(primary) or "写实摄影"
mix_secondary = None
# 角色卡注入
char_name = body.get("char")
if char_name:
card = char_load(char_name)
if card:
if card.get("subject_description"):
subject = f"{card['subject_description']}, {subject}"
body.setdefault("seed", card.get("seed"))
aspect = body.get("aspect") or STYLE_PRESETS[preset].get("aspect", "1:1")
result = build_prompt(
subject, preset, body.get("model", "通用"), aspect,
extra_negatives="", seed=body.get("seed"),
quality_tier=body.get("tier", "pro"),
character_sheet=bool(body.get("character_sheet", False)),
mix_secondary=mix_secondary,
mix_ratio=float(body.get("mix_ratio", 0.6)),
)
if body.get("compact"):
compacted, meta = compact_prompt(result["positive"])
result["positive_original"] = result["positive"]
result["positive"] = compacted
result["compaction"] = meta
self._send_json(200, result)
except Exception as e:
self._send_json(500, {"error": str(e)})
else:
self._send_json(404, {"error": "not found"})
def do_OPTIONS(self):
self.send_response(204)
self.send_header("Access-Control-Allow-Origin", "*")
self.send_header("Access-Control-Allow-Methods", "GET, POST, OPTIONS")
self.send_header("Access-Control-Allow-Headers", "Content-Type")
self.end_headers()
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-prompt web_ui v{VERSION} — 本地 Web UI",
)
parser.add_argument("--host", default="127.0.0.1", help="监听地址（默认 127.0.0.1）")
parser.add_argument("--port", type=int, default=7155, help="端口（默认 7155）")
parser.add_argument("--no-browser", action="store_true", help="不自动开浏览器")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
server = ThreadingHTTPServer((args.host, args.port), Handler)
url = f"http://{args.host}:{args.port}"
print(f"🌐 huo15-img-prompt Web UI v{VERSION}")
print(f" → {url}")
print(f" 按 Ctrl+C 停止\n")
if not args.no_browser:
try:
webbrowser.open(url)
except Exception:
pass
try:
server.serve_forever()
except KeyboardInterrupt:
print("\n👋 已停止")
server.shutdown()
if __name__ == "__main__":
main()
FILE:tests/smoke.py
#!/usr/bin/env python3
"""
huo15-img-prompt — Smoke 回归测试 v3.1
本地、零依赖、不调网络的快速回归。每个脚本几个核心 CASE：
- 版本号正确
- 基础 import 不出错
- 核心函数能跑（用 mock / 离线场景）
不覆盖的：
- 真正调 Claude API（要 key + 钱）
- 真正出图（要后端服务）
- VLM 评审（要图 + key）
调用：
tests/smoke.py # 全跑
tests/smoke.py --module enhance_prompt
tests/smoke.py -v # verbose
"""
import sys
import os
import json
import re
import unittest
import argparse
import tempfile
from pathlib import Path
# 让 tests/ 目录里的脚本能 import scripts/
ROOT = Path(__file__).resolve().parent.parent
sys.path.insert(0, str(ROOT / "scripts"))
EXPECTED_VERSION = "3.1.0"
SCRIPTS = [
"enhance_prompt", "enhance_video", "reverse_prompt", "render_prompt",
"claude_polish", "safety_lint", "image_review", "auto_iterate",
"character", "mcp_server", "web_ui",
"storyboard", "brand_kit", "style_learn", "doctor",
]
class TestVersionConsistency(unittest.TestCase):
"""所有脚本的 VERSION 必须一致。"""
def test_all_scripts_have_version(self):
for name in SCRIPTS:
with self.subTest(script=name):
mod = __import__(name)
self.assertTrue(hasattr(mod, "VERSION"), f"{name} 缺 VERSION")
self.assertEqual(mod.VERSION, EXPECTED_VERSION,
f"{name} 版本 {mod.VERSION} != {EXPECTED_VERSION}")
class TestEnhancePromptCore(unittest.TestCase):
def setUp(self):
from enhance_prompt import build_prompt, resolve_preset, parse_mix_preset
self.build_prompt = build_prompt
self.resolve_preset = resolve_preset
self.parse_mix_preset = parse_mix_preset
def test_resolve_preset_chinese(self):
self.assertEqual(self.resolve_preset("动漫"), "动漫")
def test_resolve_preset_english_alias(self):
self.assertEqual(self.resolve_preset("genshin"), "原神")
self.assertEqual(self.resolve_preset("ghibli"), "宫崎骏")
self.assertEqual(self.resolve_preset("cyberpunk"), "赛博朋克")
def test_resolve_preset_unknown(self):
self.assertEqual(self.resolve_preset("不存在的预设"), "")
def test_resolve_learned_preset_missing(self):
# @ 前缀但 learned preset 不存在
self.assertEqual(self.resolve_preset("@nonexist_preset_xyz"), "")
def test_parse_mix_preset(self):
primary, secondary = self.parse_mix_preset("赛博朋克+水墨")
self.assertEqual(primary, "赛博朋克")
self.assertEqual(secondary, "水墨")
def test_parse_mix_preset_no_plus(self):
primary, secondary = self.parse_mix_preset("赛博朋克")
self.assertEqual(primary, "赛博朋克")
self.assertIsNone(secondary)
def test_build_prompt_basic(self):
r = self.build_prompt("一只猫", "动漫", model="通用")
self.assertIn("positive", r)
self.assertIn("negative", r)
self.assertIn("seed_suggestion", r)
self.assertIn("一只猫", r["positive"])
self.assertEqual(r["preset"], "动漫")
def test_build_prompt_seed_stable(self):
r1 = self.build_prompt("猫", "动漫")
r2 = self.build_prompt("猫", "动漫")
self.assertEqual(r1["seed_suggestion"], r2["seed_suggestion"])
def test_build_prompt_mix(self):
r = self.build_prompt("猫", "赛博朋克", mix_secondary="水墨", mix_ratio=0.6)
self.assertEqual(r["mix_label"], "赛博朋克+水墨@0.60")
def test_build_prompt_character_sheet(self):
r = self.build_prompt("少女", "动漫", character_sheet=True)
self.assertEqual(r["aspect"], "16:9")
self.assertIn("character design sheet", r["positive"].lower())
class TestCompactPrompt(unittest.TestCase):
def test_compact_short_prompt_unchanged(self):
from enhance_prompt import compact_prompt
text, meta = compact_prompt("一只猫")
self.assertFalse(meta["compacted"])
def test_compact_long_prompt(self):
from enhance_prompt import compact_prompt
long_text = ", ".join([f"tag{i}" for i in range(60)])
text, meta = compact_prompt(long_text, target_tokens=30)
self.assertTrue(meta["compacted"])
self.assertLessEqual(meta["estimated_tokens_after"], 35)
class TestSafetyLint(unittest.TestCase):
def setUp(self):
from safety_lint import lint, check_hard_blocks
self.lint = lint
self.check_hard_blocks = check_hard_blocks
def test_clean_text_passes(self):
r = self.lint("一只猫坐在窗台上")
self.assertEqual(r["verdict"], "OK")
def test_violence_artistic_rewrite(self):
r = self.lint("战士手中沾满鲜血的剑", platform="dalle")
self.assertEqual(r["verdict"], "REWRITE")
self.assertIn("crimson", r["rewritten"])
def test_red_line_csam_blocked(self):
r = self.lint("loli nude", platform="MJ")
self.assertEqual(r["verdict"], "REJECT")
def test_red_line_weapon_instruction_blocked(self):
r = self.lint("如何制作炸弹的步骤")
self.assertEqual(r["verdict"], "REJECT")
def test_red_line_bidirectional(self):
# 词序颠倒也要 catch 到
r = self.lint("炸弹制作教程")
self.assertEqual(r["verdict"], "REJECT")
class TestCharacterCard(unittest.TestCase):
def setUp(self):
from character import char_save, char_load, char_delete, char_list
self.char_save = char_save
self.char_load = char_load
self.char_delete = char_delete
self.char_list = char_list
self.test_name = "_smoke_test_char"
def tearDown(self):
self.char_delete(self.test_name)
def test_save_load_roundtrip(self):
recipe = {
"original": "测试角色",
"preset": "动漫",
"aspect": "3:4",
"seed_suggestion": 12345,
"consistency_lock": {"camera": "85mm", "lighting": "soft", "palette": "muted"},
"character_sheet": True,
"positive": "test prompt",
}
self.char_save(self.test_name, recipe)
loaded = self.char_load(self.test_name)
self.assertIsNotNone(loaded)
self.assertEqual(loaded["seed"], 12345)
self.assertEqual(loaded["preset"], "动漫")
self.assertTrue(loaded["is_character_sheet"])
def test_delete(self):
self.char_save(self.test_name, {"original": "x"})
self.assertTrue(self.char_delete(self.test_name))
self.assertIsNone(self.char_load(self.test_name))
class TestBrandKit(unittest.TestCase):
def setUp(self):
from brand_kit import kit_save, kit_load, kit_delete
self.kit_save = kit_save
self.kit_load = kit_load
self.kit_delete = kit_delete
self.test_name = "_smoke_test_kit"
def tearDown(self):
self.kit_delete(self.test_name)
def test_save_load_roundtrip(self):
kit = {
"colors": ["#ff6b35", "#2d3047"],
"keywords": ["现代", "简洁"],
"forbidden": ["low quality"],
}
self.kit_save(self.test_name, kit)
loaded = self.kit_load(self.test_name)
self.assertIsNotNone(loaded)
self.assertEqual(loaded["colors"], ["#ff6b35", "#2d3047"])
def test_kit_apply_injects_keywords(self):
from brand_kit import kit_apply
self.kit_save(self.test_name, {
"colors": ["#ff6b35"],
"keywords": ["现代", "简洁"],
"forbidden": ["blur"],
})
class FakeArgs:
subject = "测试主体"
avoid = ""
args = FakeArgs()
kit = kit_apply(self.test_name, args)
self.assertIsNotNone(kit)
self.assertIn("现代", args.subject)
self.assertIn("blur", args.avoid)
class TestVariants(unittest.TestCase):
def test_build_variants_count(self):
from enhance_prompt import build_variants
variants = build_variants("test", "动漫", "通用", "1:1",
axes=["mood", "composition"], n=4)
self.assertEqual(len(variants), 4)
# 所有变体共享同一 seed
seeds = set(v["seed_suggestion"] for v in variants)
self.assertEqual(len(seeds), 1, "variants 应共享 seed")
def test_build_variants_descriptors_unique(self):
from enhance_prompt import build_variants
variants = build_variants("test", "动漫", "通用", "1:1",
axes=["mood", "composition"], n=4)
descriptors = [v["variant_descriptor"] for v in variants]
self.assertEqual(len(set(descriptors)), 4, "descriptors 应不重复")
class TestReversePromptParser(unittest.TestCase):
def test_a1111_parse(self):
from reverse_prompt import parse_a1111_params
text = ("a beautiful cat\n"
"Negative prompt: low quality, blur\n"
"Steps: 30, Sampler: DPM++ 2M Karras, Seed: 12345, Size: 1024x1024")
r = parse_a1111_params(text)
self.assertIn("a beautiful cat", r["positive"])
self.assertEqual(r["seed"], "12345")
self.assertEqual(r["sampler"], "DPM++ 2M Karras")
def test_guess_preset(self):
from reverse_prompt import guess_preset
self.assertEqual(guess_preset("cyberpunk neon city"), "赛博朋克")
self.assertEqual(guess_preset("studio ghibli forest"), "宫崎骏")
self.assertEqual(guess_preset("dunhuang mural"), "敦煌壁画")
def test_guess_aspect(self):
from reverse_prompt import guess_aspect
self.assertEqual(guess_aspect("1024x1024"), "1:1")
self.assertEqual(guess_aspect("1792x1024"), "16:9")
class TestMCPServer(unittest.TestCase):
def test_handle_initialize(self):
from mcp_server import handle_request
resp = handle_request({"jsonrpc": "2.0", "id": 1, "method": "initialize"})
self.assertEqual(resp["jsonrpc"], "2.0")
self.assertIn("protocolVersion", resp["result"])
self.assertIn("serverInfo", resp["result"])
def test_handle_tools_list(self):
from mcp_server import handle_request, TOOLS
resp = handle_request({"jsonrpc": "2.0", "id": 2, "method": "tools/list"})
self.assertEqual(resp["result"]["tools"], TOOLS)
self.assertGreaterEqual(len(TOOLS), 9)
def test_handle_unknown_method(self):
from mcp_server import handle_request
resp = handle_request({"jsonrpc": "2.0", "id": 3, "method": "fake/method"})
self.assertIn("error", resp)
self.assertEqual(resp["error"]["code"], -32601)
def test_tools_call_enhance_prompt(self):
from mcp_server import handle_request
resp = handle_request({
"jsonrpc": "2.0", "id": 4, "method": "tools/call",
"params": {"name": "enhance_prompt", "arguments": {"subject": "猫", "preset": "动漫"}},
})
self.assertIn("result", resp)
self.assertIn("content", resp["result"])
text = resp["result"]["content"][0]["text"]
result = json.loads(text)
self.assertEqual(result["preset"], "动漫")
class TestPresetCount(unittest.TestCase):
def test_88_presets(self):
from enhance_prompt import STYLE_PRESETS
self.assertEqual(len(STYLE_PRESETS), 88, f"应该是 88 预设，实际 {len(STYLE_PRESETS)}")
def test_all_presets_have_required_fields(self):
from enhance_prompt import STYLE_PRESETS
for name, p in STYLE_PRESETS.items():
with self.subTest(preset=name):
for field in ("category", "tags", "quality", "neg", "aspect"):
self.assertIn(field, p, f"{name} 缺 {field}")
def main():
parser = argparse.ArgumentParser(description="huo15-img-prompt smoke tests")
parser.add_argument("--module", help="只跑指定模块的测试（按 TestCase 名匹配）")
parser.add_argument("-v", "--verbose", action="count", default=1)
args = parser.parse_args()
loader = unittest.TestLoader()
if args.module:
suite = loader.loadTestsFromName(args.module, sys.modules[__name__])
else:
suite = loader.loadTestsFromModule(sys.modules[__name__])
runner = unittest.TextTestRunner(verbosity=args.verbose)
result = runner.run(suite)
sys.exit(0 if result.wasSuccessful() else 1)
if __name__ == "__main__":
main()
龙虾极速开机仪式 —— 4 步搞定身份初始化：基本信息一起填、人设一键选（6 经典组合或自定义）、领域套餐或自选、偏好一次改完。全默认 30 秒完事。产出 `profile.md` 三层同步（L1 龙虾 memory / L2 enhance / L3 KB）。触发词：你好世界、龙虾初始化、bootstrap、首...
---
name: huo15-openclaw-bootstrap
displayName: 火一五你好世界技能
description: 龙虾极速开机仪式 —— 4 步搞定身份初始化：基本信息一起填、人设一键选（6 经典组合或自定义）、领域套餐或自选、偏好一次改完。全默认 30 秒完事。产出 `profile.md` 三层同步（L1 龙虾 memory / L2 enhance / L3 KB）。触发词：你好世界、龙虾初始化、bootstrap、首次设置、onboarding、hello world、欢迎仪式。
version: 1.1.0
homepage: https://github.com/zhaobod1/huo15-skills
metadata: { "openclaw": { "emoji": "🦞", "requires": { "bins": [] } } }
aliases:
- 火一五你好世界技能
- 你好世界
- 龙虾你好世界
- 龙虾初始化
- 首次设置
- bootstrap
- hello world
- onboarding
- 初始化龙虾
- welcome
---
# 火一五你好世界技能 v1.1（huo15-openclaw-bootstrap）
> 一次 3 分钟（全默认 30 秒）的开机仪式。
> 青岛火一五信息科技有限公司 · OpenClaw 生态标配
---
## 一、使用场景
✅ **触发这个技能当：**
- 用户第一次安装 OpenClaw，说"你好"/"hello"/"欢迎"/"初始化"
- 用户说"你好世界"、"帮我初始化龙虾"、"bootstrap"、"onboarding"
- 检测到 L1 memory 为空（无 `profile.md` 条目）且 user 向龙虾打招呼
- 用户说"重新认识一下"、"重置我的偏好"、"更新我的画像"
❌ **不触发当：**
- 已完成初始化（`profile.md` 存在且 `updatedAt` 在 180 天内）—— 跳到"增量更新"小分支
- 用户只是问日常问题、与身份无关的任务
---
## 二、核心设计原则
1. **批量问、不追问** —— 把一批相关问题打包成**一张填空模板**，用户一次填完；不要一条一条单问。
2. **默认值先行** —— 每项都给"推荐默认"，用户只改想改的，不改就用默认。
3. **经典组合一键过** —— 6 个常见人设 combo（含灵魂+角色+领域三元组），选号码直接套用。
4. **可融合** —— 自定义时灵魂支持主/辅双选，角色支持 1-3 叠加（见 §五）。
5. **全默认一键通** —— 用户全程答"默认"或"确认"，30 秒走完，事后可再改。
6. **三层同步** —— L1 龙虾 memory / L2 enhance 规则 / L3 KB wiki 同时落盘，跨设备可用。
---
## 三、快捷流程（4 步）
每一步都是**一条消息、一次性把所有相关问题列出来，等用户一次性回复**。禁止拆成多轮问。
### Step 1 · 基本信息（一次填 3 项）
龙虾发一条消息：
```
🦞 欢迎！先问你 3 件事，一起回答就行（留空走默认）：
① 昵称：____ （留空 = 用你现有 user_identity.name）
② 英文名：____ （留空 = 自动从拼音生成）
③ 时区（选数字）：
1) 上海/北京（UTC+8） ← 推荐
2) 香港 3) 东京/首尔 4) 新加坡
5) 伦敦 6) 柏林/巴黎 7) 旧金山 8) 纽约 9) 其他
格式随意，比如：
> 昵称 Job，英文名 Job，时区 1
```
**解析规则**：
- 支持"Job / Job / 1"、"昵称=Job"、"我叫 Job"、"1"（全默认仅答时区）等各种松散格式
- 任何一项缺失都用默认
- 全空白（只回"随便"/"默认"）→ 全部走默认，直接进 Step 2
---
### Step 2 · 人设 —— 经典组合 or 自定义
龙虾发一条消息：
```
🎭 选你的龙虾人设。两条路：
【A】套经典组合（推荐新手）——回数字 1-6：
1) 独立开发者 | 硅谷导师 × 禅师 | 全栈+PM+Indie | 前端/后端/AI/变现
2) 品牌设计师 | 苹果极简 × 京都匠人 | 品牌+UI 设计 | UI/品牌/摄影/哲学
3) 产品经理 | 德鲁克 × 硅谷 PM | PM+数据分析 | 产品/数据/管理
4) 技术博主 | TED × B站up主 | 技术作者+自媒体 | 写作/Obsidian/AI
5) AI 研究员 | 严谨学者 × 纪录片 | AI/ML+学术 | LLM/Agent/论文
6) 创业者 | 稻盛和夫 × 硅谷导师 | 创业者+PM+销售 | 创业/产品/团队
【B】自定义 —— 回 "7"，我给你填空模板（灵魂/角色/权重一次填完）
【C】完全随便 —— 回 "默认"，用组合 1（独立开发者）
```
**如果选 1-6**：直接把组合的 `soul + roles + interests` 全部写入变量，跳到 Step 4。
**如果选 7**（自定义），发一条填空模板：
```
📝 自定义人设（留空走推荐默认）：
主灵魂：____ （数字或名字；见 presets/souls.md；默认：硅谷导师）
辅灵魂：____ （可留空；默认：禅师，权重 70/30）
权重：____ （默认 70/30；可 60/40 / 50/50）
主角色：____ （数字或名字；见 presets/roles.md；默认：全栈工程师）
副角色：____ （可留空；最多 2 个；逗号分隔，如 "产品经理, 独立开发者"）
想看灵魂/角色全表？回 "看灵魂" 或 "看角色"。
```
---
### Step 3 · 关注领域 —— 套餐 or 多选
龙虾发一条消息（如果 Step 2 选了经典组合，此步已预填领域，龙虾仅询问"要改吗？"）：
```
🧲 关注哪些领域？龙虾会按它们挖新闻、推荐、维护 KB。
【A】套餐（8 选 1，回数字）：
1) 独立开发者 (前端/后端/LLM/indie-saas/公众号/SEO/生产力/笔记)
2) 独立设计师 (UI/品牌/插画/设计系统/小红书/写作/摄影/哲学)
3) AI 研究员 (LLM/Agent/Prompt/RAG/ML/微调/论文/多模态)
4) 自媒体博主 (写作/技术写作/公众号/小红书/B站/抖音/剪辑/SEO)
5) 创业者 (创业/indie-saas/管理/增长/品牌/投资/销售/成长)
6) 增长 PM (产品/数据/增长/SEO/信息流广告/社群/心理学/笔记)
7) 电商操盘手 (国内电商/跨境/直播/广告/品牌/社群/小红书/抖音)
8) 终身学习者 (成长/GTD/笔记/阅读/英语/思维模型/学习法/哲学/历史/心理学)
【B】自选 —— 回 "自选"，给你 82 项完整菜单
【C】完全随便 —— 回 "默认"，用套餐 1
```
若用户选"自选"，显示 [`presets/domains.md`](presets/domains.md) 的 10 大类表格，用户回类号（"全选第 4 类"）或 slug 列表。**不再逐类问**。
---
### Step 4 · 偏好与边界（一张表，一次改完）
龙虾发一条消息：
```
⚙️ 最后一步，这是默认偏好表。看一下有要改的吗？没有就回 "确认"：
| # | 项 | 默认 | 其他选项 |
|---|-----------|-------------|----------------------------|
| 1 | 主要语言 | 中文 | 英文 / 中英双语 / 跟随 |
| 2 | 详细度 | 适中 | 精简 / 详尽 |
| 3 | 语气温度 | 平衡 | 冷静 / 热情 |
| 4 | Emoji | 克制 | 禁用 / 丰富 |
| 5 | 出错处理 | 先给备选 | 立刻认错 / 深挖根因 |
| 6 | 执行自主度 | 平衡 | 保守(每步问) / 激进(自跑) |
| 7 | 主动建议 | 允许 | 只在被问时回答 |
| 8 | 记忆隐私 | 只记工作相关 | 记所有 / 不记个人细节 |
| 9 | 项目目录 | ~/workspace/projects/ | 自定义路径 |
|10 | 通知通道 | 企微 | 微信 / 邮件 / 仅本地 |
改法示例："1: 英文, 6: 激进, 10: 微信"；不改就回 "确认"。
```
---
### 收尾：三层写盘 + 回显
4 步结束后：
1. 按 [`templates/profile.md`](templates/profile.md) 渲染画像
2. **同时**写入三层（见 §四）
3. 回显摘要：
```
🦞 搞定！欢迎 <昵称>。你的龙虾已就绪：
身份：<昵称> / <英文名> / <时区>
灵魂：<主>（<weight> 主）× <辅>（<weight> 辅）
角色：<主角色> + <副角色若有>
领域：<N>个（<类列表>）
偏好：<语言>/<详细度>/<语气>/自主度<X>
✓ 已写入 L1 龙虾 memory / L2 enhance / L3 KB wiki
第一件想让我帮你做什么？
```
---
## 四、三层写盘位置
| 层级 | 位置 | 作用 |
|------|------|------|
| **L1 · 龙虾原生 memory** | `~/.openclaw/<workspace>/memory/profile.md` + `MEMORY.md` 索引 | 会话级快速召回 |
| **L2 · enhance 结构化** | `enhance_memory_review action=upsert type=user name="profile"` | 规则引擎联动 |
| **L3 · KB wiki** | `~/knowledge/huo15/profile/龙虾画像-<昵称>.md` + Odoo `knowledge.article` | 跨设备、可分享 |
不一致时，**L3 云端为准**。
---
## 五、6 个经典组合（Classic Combos）详表
选这 6 个组合之一，灵魂 + 角色 + 领域一次性打包。
### 🚀 1. 独立开发者 / Indie Hacker
- 灵魂：硅谷导师（主 70）× 禅师（辅 30）
- 角色：全栈工程师 + 产品经理 + 独立开发者
- 领域：`frontend` `backend` `llm-app` `indie-saas` `wechat-gzh` `seo-sem` `productivity-gtd` `note-taking`
### 🎨 2. 品牌设计师 / Brand Designer
- 灵魂：苹果极简（主 60）× 京都匠人（辅 40）
- 角色：品牌设计师 + UI 设计师
- 领域：`ui-visual` `brand-vi` `illustration` `design-systems` `xiaohongshu` `writing` `photography` `philosophy`
### 📊 3. 产品经理 / Product Manager
- 灵魂：德鲁克顾问（主 60）× 硅谷 PM（辅 40）
- 角色：产品经理 + 数据分析师
- 领域：`product-design` `data-analysis` `growth-hacking` `ui-visual` `psychology` `management-leadership` `note-taking` `writing`
### 🎓 4. 技术博主 / Tech Content Creator
- 灵魂：TED 演说（主 60）× B 站 up 主（辅 40）
- 角色：技术作者 + 自媒体作者 + 独立开发者
- 领域：`tech-writing` `note-taking` `writing` `wechat-gzh` `bilibili-youtube` `llm-app` `seo-sem` `frontend`
### 🧠 5. AI 研究员 / AI Researcher
- 灵魂：严谨学者（主 70）× 纪录片旁白（辅 30）
- 角色：AI/ML 研究员 + 学术研究员（用 tech-writer 代替）
- 领域：`llm-app` `agent-dev` `prompt-engineering` `rag-vectordb` `ml-dl` `llm-finetune` `academic-writing` `computer-vision`
### 💼 6. 创业者 / Founder
- 灵魂：稻盛和夫（主 50）× 硅谷导师（辅 50）
- 角色：创业者 + 产品经理 + 销售代表
- 领域：`entrepreneurship` `indie-saas` `management-leadership` `growth-hacking` `brand-marketing` `finance-investment` `sales-negotiation` `personal-growth`
（完整灵魂/角色/领域清单见 [`presets/souls.md`](presets/souls.md)、[`presets/roles.md`](presets/roles.md)、[`presets/domains.md`](presets/domains.md)）
---
## 六、增量更新模式
检测到 `profile.md` 已存在时：
1. 回显当前画像 1 段摘要
2. 问"想改哪一项？"+ 列 10 项编号
3. 只改用户指定项，其余保持
4. L3 KB 页末 append changelog：`- <ISO-DATE> 更新 <项>: <旧> → <新>`
---
## 七、硬红线
1. ❌ **不要拆成 9 轮问** —— 4 步极限，每步一张填空表，用户一次回
2. ❌ **不要擅自覆盖用户已有答案** —— 默认值是建议，用户明确给的值优先
3. ❌ **不要只写 L1** —— 三层必须同步，否则换设备就丢
4. ❌ **不要在流程中干别的活** —— 4 步走完再说其它
5. ❌ **不要把 profile 塞进 MEMORY.md 正文** —— MEMORY.md 只放一行索引
6. ❌ **全默认路径不要追问** —— 用户说"默认"就全默认到底，连确认都省
---
## 八、Slots / 变量命名
```yaml
nickname: string # Step 1
english_name: string # Step 1
timezone: string (IANA) # Step 1
soul:
primary: string (slug)
secondary: string|null
weight: string # "70/30" etc.
roles: [{slug, primary}] # 1-3 个
interests: [string] # slug list
comm:
language: string # zh / en / bi / follow
verbosity: "concise"|"balanced"|"thorough"
warmth: "cool"|"balanced"|"warm"
emoji: "off"|"sparse"|"rich"
on_error: "admit"|"alt-then-fix"|"rca"
autonomy:
exec: "conservative"|"balanced"|"aggressive"
proactive: bool
privacy: "work-only"|"all"|"minimal"
ecosystem:
project_dir: string
kb_targets: [string]
notify: "wecom"|"wechat"|"email"|"local"
meta:
bootstrapped_at: ISO date
version: "1.1.0"
combo_id: "1".."6"|null # 记录是不是用经典组合；null = 自定义
```
---
## 九、版本历史
- **v1.1.0（2026-04-25）** —— **快捷流程重构**：9 步 → 4 步。每步一张填空模板，用户一次回复就能过一关。经典组合一键套用（选 1-6 直接到 Step 4）。全默认路径 30 秒走完。新增硬红线第 6 条"全默认不追问"。
- **v1.0.1（2026-04-24）** —— 内容创作类补充"小红书博主 / 种草达人"独立角色。short-video-creator 同步聚焦抖音/视频号/TikTok。总角色数 48 → 49。
- **v1.0.0（2026-04-24）** —— 初始版本。9 步硬流程 + 主/辅灵魂融合 + 1-3 角色叠加 + 领域多选 + 6 经典组合 + 三层记忆同步。
---
**技术支持：** 青岛火一五信息科技有限公司
**联系邮箱：** [email protected] | **QQ群：** 1093992108
FILE:presets/domains.md
# 关注领域预设库（Domain Presets）· 82 项
> 领域 = 用户关心的话题/赛道，决定龙虾默认挖哪些新闻、推荐哪些内容、维护哪些知识库。
> 多选，推荐 **5-15 个**；超过 20 个会稀释关注度，龙虾会提示。
---
## 一、技术开发（Tech Stack）· 12 项
| slug | 领域 | 为什么选它 |
|------|------|-----------|
| `frontend` | 前端开发（React/Vue/Svelte/Solid） | 如果你做 Web UI |
| `backend` | 后端开发（Node/Python/Go/Java/Rust） | 如果你做服务端 |
| `mobile-dev` | 移动开发（iOS/Android/RN/Flutter） | 如果你做 App |
| `desktop-dev` | 桌面开发（Electron/Tauri/Qt） | 如果你做客户端工具 |
| `devops` | DevOps / CI-CD | 自动化交付 |
| `cloud-native` | 云原生 / Kubernetes / 容器 | 云上部署 |
| `cloud-platform` | 云计算（AWS/Azure/GCP/阿里云/腾讯云） | 多云架构 |
| `database` | 数据库设计（SQL/NoSQL/时序/图） | 数据建模 |
| `big-data` | 大数据 / 数据仓库（Spark/ClickHouse） | 海量数据 |
| `observability` | 可观测性（日志/指标/追踪） | 线上运维 |
| `security` | 网络安全 / 渗透测试 | 攻防、合规 |
| `embedded-iot` | 嵌入式 / IoT | 硬件与固件 |
---
## 二、AI / 数据（AI & Data）· 10 项
| slug | 领域 | 为什么选它 |
|------|------|-----------|
| `llm-app` | AI/LLM 应用开发 | Claude/OpenAI SDK 接入 |
| `agent-dev` | AI Agent 开发（LangGraph/CrewAI） | 自动化工作流 |
| `prompt-engineering` | Prompt Engineering | 提示词、few-shot、CoT |
| `rag-vectordb` | RAG / 向量数据库 | 知识库问答 |
| `ml-dl` | 机器学习 / 深度学习 | 建模与训练 |
| `llm-finetune` | LLM 微调 / 蒸馏 / 强化学习 | 模型定制 |
| `data-analysis` | 数据分析 / 可视化 | 业务洞察 |
| `data-eng` | 数据工程 / ETL / 数据湖 | 数据管道 |
| `computer-vision` | 计算机视觉 / 多模态 | 图像、视频 AI |
| `speech-audio` | 语音识别 / TTS / 音频 AI | 播客、语音助手 |
---
## 三、产品与设计（Product & Design）· 10 项
| slug | 领域 | 为什么选它 |
|------|------|-----------|
| `product-design` | 产品设计 / UX | 需求到原型 |
| `ui-visual` | UI 视觉设计 | 界面美学 |
| `brand-vi` | 品牌设计 / VI / 字体 | 视觉识别系统 |
| `illustration` | 插画 / 原画 / IP | 插图风格 |
| `3d-modeling` | 3D 建模 / Blender / C4D | 三维资产 |
| `motion-graphics` | 动画 / After Effects / Rive | 动效 |
| `industrial-design` | 工业设计 / 产品形态 | 硬件外观 |
| `fashion-design` | 服装 / 时尚设计 | 穿搭、潮流 |
| `interior-design` | 室内 / 建筑设计 | 空间 |
| `design-systems` | 设计系统（Material/Ant/Tailwind） | 组件化设计 |
---
## 四、内容创作（Content Creation）· 10 项
| slug | 领域 | 为什么选它 |
|------|------|-----------|
| `writing` | 写作 / 博客 / 专栏 | 文字输出 |
| `tech-writing` | 技术写作 / 文档 | 开源文档、教程 |
| `wechat-gzh` | 公众号运营 | 微信生态内容 |
| `xiaohongshu` | 小红书运营 | 种草、生活方式 |
| `bilibili-youtube` | B站 / YouTube 视频 | 长视频 |
| `douyin-tiktok` | 抖音 / TikTok 短视频 | 短视频流量 |
| `video-editing` | 视频剪辑 / 后期 | 剪映、Pr、DaVinci |
| `photography` | 摄影 / 后期 | Lightroom、商业片 |
| `podcast` | 播客 / 音频节目 | 小宇宙、Spotify |
| `novel-screenplay` | 小说 / 剧本 / IP | 长篇虚构 |
---
## 五、商业与创业（Business）· 10 项
| slug | 领域 | 为什么选它 |
|------|------|-----------|
| `entrepreneurship` | 创业 / 副业 | 做自己的事 |
| `indie-saas` | 独立 SaaS / 工具变现 | 1 人公司 |
| `ecommerce-cn` | 国内电商（淘宝/天猫/京东/拼多多/抖店） | 国内零售 |
| `cross-border-ecom` | 跨境电商（Amazon/Shopify/TikTok Shop） | 海外市场 |
| `livestream-monetize` | 直播 / 短视频变现 | 主播、带货 |
| `finance-investment` | 投资理财 / 股票 | 资本市场 |
| `crypto-web3` | 加密货币 / Web3 | 链上、NFT、DeFi |
| `management-leadership` | 管理 / 领导力 | 带人、组织 |
| `sales-negotiation` | 销售技巧 / 谈判 | 对客技巧 |
| `legal-compliance` | 法律合规 / 知识产权 | 合同、专利 |
---
## 六、运营增长（Ops & Growth）· 8 项
| slug | 领域 | 为什么选它 |
|------|------|-----------|
| `seo-sem` | SEO / SEM / 搜索引擎 | 自然流量 |
| `growth-hacking` | 增长黑客 / AARRR | 增长实验 |
| `community-ops` | 社群运营 / 私域 | 微信群、飞书、社群 |
| `event-marketing` | 活动营销 / 会销 | 线下活动 |
| `brand-marketing` | 品牌营销 / PR | 品牌声量 |
| `performance-ads` | 效果广告（信息流/巨量/腾讯广告） | 付费投放 |
| `email-marketing` | 邮件营销 / 自动化 | EDM、drip |
| `affiliate-partner` | 联盟 / 分销 / 合伙人 | 分销体系 |
---
## 七、学习成长（Learning & Productivity）· 12 项
| slug | 领域 | 为什么选它 |
|------|------|-----------|
| `personal-growth` | 个人成长 / 自我提升 | 习惯、目标 |
| `productivity-gtd` | 生产力 / GTD / 时间管理 | Things、OmniFocus |
| `note-taking` | 笔记系统 / Obsidian / Notion / Logseq | 第二大脑 |
| `reading` | 阅读 / 书评 | 书籍世界 |
| `english-learning` | 英语学习 | 听说读写 |
| `japanese-learning` | 日语学习 | 日文资讯、动漫 |
| `other-languages` | 其他外语（德/法/韩/西等） | 多语种 |
| `writing-skills` | 写作技能 | 结构化表达 |
| `public-speaking` | 演讲 / 汇报 | 上台、pitch |
| `mental-models` | 思维模型 / 决策 | 查理·芒格式 |
| `learning-methods` | 学习方法论 / 元学习 | 学习如何学习 |
| `academic-writing` | 论文写作 / 学术 | 投稿、引用 |
---
## 八、健康生活（Health & Lifestyle）· 8 项
| slug | 领域 | 为什么选它 |
|------|------|-----------|
| `fitness` | 健身 / 力量训练 | 增肌减脂 |
| `running-cycling` | 跑步 / 骑行 / 游泳 | 有氧 |
| `yoga-meditation` | 瑜伽 / 冥想 / 正念 | 身心平衡 |
| `nutrition` | 营养 / 饮食 | 减脂、增肌、慢病 |
| `sleep-health` | 睡眠健康 | 作息优化 |
| `mental-health` | 心理健康 / 抑郁焦虑 | 情绪管理 |
| `parenting` | 育儿 / 家庭教育 | 孩子成长 |
| `travel` | 旅行规划 / 游记 | 国内外游 |
---
## 九、人文与思想（Humanities）· 8 项
| slug | 领域 | 为什么选它 |
|------|------|-----------|
| `philosophy` | 哲学 / 认识论 | 根本性思考 |
| `psychology` | 心理学 | 人性与行为 |
| `history` | 历史 | 长视角 |
| `sociology-anthropology` | 社会学 / 人类学 | 群体现象 |
| `economics` | 经济学 | 资源配置 |
| `sci-fi-fantasy` | 科幻 / 奇幻小说 | 想象力训练 |
| `religion-spirituality` | 宗教 / 灵性 | 意义感 |
| `classics-chinese-culture` | 国学 / 中国传统文化 | 经史子集 |
---
## 十、火一五生态专属（huo15-ecosystem）· 4 项
| slug | 领域 | 为什么选它 |
|------|------|-----------|
| `huo15-openclaw-dev` | OpenClaw 插件/技能开发 | 跟着火一五做生态 |
| `huo15-wecom-plugin` | @huo15/wecom 企微插件 | 企微开发 |
| `huo15-kb-system` | 火一五知识库系统（Odoo + Obsidian） | 双层 KB |
| `huo15-community` | 火一五社群 / QQ 群动态 | 圈子内动向 |
---
## 领域预设套餐（Domain Combos）—— 懒人包
对选择困难型用户，推荐 8 个常见人设套餐：
### 🚀 1. 独立开发者套餐（Indie Hacker）
`frontend`, `backend`, `llm-app`, `indie-saas`, `wechat-gzh`, `seo-sem`, `productivity-gtd`, `note-taking`
### 🎨 2. 独立设计师套餐（Solo Designer）
`ui-visual`, `brand-vi`, `illustration`, `design-systems`, `xiaohongshu`, `writing`, `photography`, `philosophy`
### 🤖 3. AI 研究员套餐（AI Researcher）
`llm-app`, `agent-dev`, `prompt-engineering`, `rag-vectordb`, `ml-dl`, `llm-finetune`, `academic-writing`, `computer-vision`
### 📱 4. 自媒体博主套餐（Content Creator）
`writing`, `tech-writing`, `wechat-gzh`, `xiaohongshu`, `bilibili-youtube`, `douyin-tiktok`, `video-editing`, `seo-sem`
### 💼 5. 创业者套餐（Founder）
`entrepreneurship`, `indie-saas`, `management-leadership`, `growth-hacking`, `brand-marketing`, `finance-investment`, `sales-negotiation`, `personal-growth`
### 📊 6. 增长 PM 套餐（Growth PM）
`product-design`, `data-analysis`, `growth-hacking`, `seo-sem`, `performance-ads`, `community-ops`, `psychology`, `note-taking`
### 🛒 7. 电商操盘手套餐（E-commerce Operator）
`ecommerce-cn`, `cross-border-ecom`, `livestream-monetize`, `performance-ads`, `brand-marketing`, `community-ops`, `xiaohongshu`, `douyin-tiktok`
### 🧠 8. 终身学习者套餐（Lifelong Learner）
`personal-growth`, `productivity-gtd`, `note-taking`, `reading`, `english-learning`, `mental-models`, `learning-methods`, `philosophy`, `history`, `psychology`
---
## 使用提示
- **5-15 项为黄金区**：能让龙虾形成有倾向的注意力，又不至于稀释。
- **少于 5 项**：龙虾会相对被动，建议至少加上"个人成长 + 笔记系统"兜底。
- **多于 20 项**：龙虾会提示你优先级不清晰，建议分 P0/P1。
- **跨类搭配 > 同类堆叠**：比"8 个技术领域"更好的是"3 技术 + 2 运营 + 1 人文 + 1 健康 + 1 学习"。
FILE:presets/roles.md
# 角色预设库（Role Presets）· 49 款
> 角色 = 岗位/职业，决定龙虾带哪套领域知识、默认工具链、默认关心的问题。
> 允许 **1 主 + 0-2 辅**，共最多 3 个叠加（承认斜杠青年）。
---
## 条目结构
```
<slug>
**<中文名> / <English name>**
定位：一句话说清这个角色在做什么
关心三问：A / B / C
默认工具链：T1, T2, T3
融合示例：+ X → Y
```
---
## 一、技术工程（Engineering）· 15 款
### 1. fullstack-engineer
**全栈工程师 / Full-stack Engineer**
- 定位：前后端都能写，独立交付端到端功能
- 关心三问：接口设计 / 性能 / 部署
- 默认工具链：TypeScript / Node.js / Docker / CI/CD
- 融合示例：+ 独立开发者 → indie hacker 的标配
### 2. frontend-engineer
**前端工程师 / Frontend Engineer**
- 定位：浏览器和移动端的用户侧交互
- 关心三问：性能 / 可访问性 / 设计还原度
- 默认工具链：React / Vue / Tailwind / Vite
### 3. backend-engineer
**后端工程师 / Backend Engineer**
- 定位：服务端架构、API、数据模型
- 关心三问：吞吐 / 一致性 / 可扩展
- 默认工具链：Python / Go / Postgres / Redis
### 4. mobile-engineer
**移动开发工程师 / Mobile Engineer**
- 定位：iOS/Android/跨平台 App
- 关心三问：启动时间 / 内存 / 上架合规
- 默认工具链：Swift / Kotlin / React Native / Flutter
### 5. devops-engineer
**DevOps 工程师 / DevOps**
- 定位：CI/CD、基础设施、可观测性
- 关心三问：可用性 SLO / 部署频率 / MTTR
- 默认工具链：Kubernetes / Terraform / Grafana / GitHub Actions
### 6. sre
**SRE / Site Reliability Engineer**
- 定位：让线上稳如山
- 关心三问：错误预算 / on-call 体验 / post-mortem
- 默认工具链：Prometheus / PagerDuty / Runbook
### 7. security-engineer
**安全工程师 / Security Engineer**
- 定位：攻防、审计、合规
- 关心三问：OWASP / 依赖链 / 数据泄露
- 默认工具链：Burp / Semgrep / Snyk
### 8. data-engineer
**数据工程师 / Data Engineer**
- 定位：数据管道、仓库、湖
- 关心三问：新鲜度 / SLA / 血缘
- 默认工具链：Airflow / DBT / Spark / ClickHouse
### 9. data-scientist
**数据科学家 / Data Scientist**
- 定位：统计、建模、可视化
- 关心三问：样本偏差 / 可解释性 / 业务接入
- 默认工具链：Python / Jupyter / scikit-learn / statsmodels
### 10. data-analyst
**数据分析师 / Data Analyst**
- 定位：业务数据洞察
- 关心三问：北极星 / 漏斗 / 留存
- 默认工具链：SQL / Tableau / Metabase
### 11. ml-researcher
**AI/ML 研究员 / ML Researcher**
- 定位：模型、算法、论文
- 关心三问：SOTA / 可复现 / 可扩展
- 默认工具链：PyTorch / Weights & Biases / HuggingFace
### 12. prompt-engineer
**提示词工程师 / Prompt Engineer**
- 定位：LLM 应用层、Agent、RAG
- 关心三问：幻觉率 / 成本 / 延迟
- 默认工具链：Claude/OpenAI SDK / LangGraph / vector DB
### 13. game-engineer
**游戏开发工程师 / Game Engineer**
- 定位：引擎、渲染、玩法编程
- 关心三问：帧率 / 包体 / 手感
- 默认工具链：Unity / Unreal / Godot
### 14. embedded-engineer
**嵌入式工程师 / Embedded Engineer**
- 定位：IoT、硬件固件、实时系统
- 关心三问：功耗 / RTOS / 通信
- 默认工具链：C / Rust / ESP32 / STM32
### 15. blockchain-engineer
**区块链工程师 / Blockchain Engineer**
- 定位：智能合约、DApp、链上
- 关心三问：安全审计 / gas / 去中心化
- 默认工具链：Solidity / Foundry / Ethers.js
---
## 二、产品设计（Product & Design）· 10 款
### 16. product-manager
**产品经理 / Product Manager**
- 定位：需求发现、优先级、落地
- 关心三问：痛点 / 场景 / 闭环
- 默认工具链：Figma / Notion / 飞书 / Jira
### 17. product-director
**产品总监 / Product Director**
- 定位：产品线、路线图、跨团队
- 关心三问：战略 / 资源 / 组织
- 默认工具链：OKR / Roadmap / 季度复盘
### 18. ui-designer
**UI 设计师 / UI Designer**
- 定位：界面视觉、设计系统
- 关心三问：层级 / 呼吸 / 一致性
- 默认工具链：Figma / Sketch / Principle
### 19. ux-designer
**UX 设计师 / UX Designer**
- 定位：交互、信息架构、用户研究
- 关心三问：任务流 / 可用性 / 心智模型
- 默认工具链：Figma / Miro / Maze
### 20. product-designer
**产品设计师 / Product Designer**
- 定位：端到端的设计落地（UI+UX+协作）
- 关心三问：交付 / 开发对齐 / 上线数据
- 默认工具链：Figma / FigJam / Storybook
### 21. brand-designer
**品牌设计师 / Brand Designer**
- 定位：VI、品牌故事、视觉系统
- 关心三问：辨识度 / 调性 / 延展性
- 默认工具链：Illustrator / Figma / 字体库
### 22. graphic-designer
**平面设计师 / Graphic Designer**
- 定位：海报、banner、印刷物
- 关心三问：构图 / 字体 / 色彩
- 默认工具链：Photoshop / Illustrator / InDesign
### 23. illustrator
**插画师 / Illustrator**
- 定位：插画、IP、绘本
- 关心三问：风格 / 情感 / 讲故事
- 默认工具链：Procreate / CSP / Photoshop
### 24. motion-designer
**动效设计师 / Motion Designer**
- 定位：界面动效、MG、品牌动画
- 关心三问：节奏 / 缓动 / 性能
- 默认工具链：After Effects / Lottie / Rive
### 25. industrial-designer
**工业设计师 / Industrial Designer**
- 定位：硬件产品的形态与工艺
- 关心三问：人机 / 材质 / 量产
- 默认工具链：Rhino / KeyShot / Solidworks
---
## 三、内容创作（Content）· 9 款
### 26. tech-writer
**技术作者 / Tech Writer**
- 定位：博客、文档、教程、图书
- 关心三问：受众 / 结构 / 可复现
- 默认工具链：Obsidian / Notion / Markdown / VS Code
### 27. self-media-writer
**自媒体作者 / Self-media Writer**
- 定位：公众号、知乎、博客运营
- 关心三问：选题 / 开头 3 句 / 标题
- 默认工具链：Notion / 秀米 / 壹伴
### 28. xiaohongshu-creator
**小红书博主 / 种草达人 / Xiaohongshu Creator**
- 定位：图文种草、生活方式分享、品牌合作 / 探店
- 关心三问：封面第一眼 / 爆文率 / 粉丝画像
- 默认工具链：小红书 App / 醒图 / VSCO / Lightroom / Notion
- 融合示例：+ 摄影师 → 高质量视觉种草；+ 文案 → 标题工厂
### 29. short-video-creator
**短视频创作者 / Short Video Creator**
- 定位：抖音 / 视频号 / TikTok 短视频
- 关心三问：前 3 秒 / 节奏 / 完播率
- 默认工具链：剪映 / Premiere / CapCut
### 30. youtuber
**视频博主 / YouTuber / B 站 UP 主**
- 定位：长视频、频道运营
- 关心三问：选题 / CTR / 留存曲线
- 默认工具链：Premiere / DaVinci / OBS
### 31. podcaster
**播客主 / Podcaster**
- 定位：音频节目、访谈
- 关心三问：选题 / 嘉宾 / 收听曲线
- 默认工具链：Adobe Audition / Descript / Spotify
### 32. copywriter
**文案 / Copywriter**
- 定位：广告、banner、品牌文案
- 关心三问：转化 / 调性 / 记忆点
- 默认工具链：Hemingway / Notion / Figma
### 33. novelist
**小说作家 / Novelist**
- 定位：长篇虚构、连载、剧本
- 关心三问：人物弧 / 冲突 / 世界观
- 默认工具链：Scrivener / Obsidian / WPS
### 34. photographer
**摄影师 / Photographer**
- 定位：商业 / 人像 / 纪实摄影
- 关心三问：光 / 构图 / 情绪
- 默认工具链：Lightroom / Capture One / Photoshop
---
## 四、商业运营（Business）· 15 款
### 35. founder
**创业者 / Founder**
- 定位：从 0 到 1 / 1 到 10
- 关心三问：PMF / 现金流 / 团队
- 默认工具链：Notion / 飞书 / 数据看板
### 36. indie-developer
**独立开发者 / Indie Developer**
- 定位：一人或小团队的 SaaS/工具
- 关心三问：MRR / 留存 / 获客成本
- 默认工具链：Stripe / Posthog / Intercom
### 37. ceo
**CEO / 总经理**
- 定位：公司一把手、对结果负责
- 关心三问：战略 / 现金 / 人
- 默认工具链：OKR / 财报 / 董事会
### 38. marketing-specialist
**市场营销 / Marketing Specialist**
- 定位：品牌、投放、活动
- 关心三问：ROI / 转化漏斗 / 品牌声量
- 默认工具链：GA / 巨量 / 腾讯广告
### 39. growth-hacker
**增长黑客 / Growth Hacker**
- 定位：病毒增长、转化优化
- 关心三问：AARRR / 实验 / 闭环
- 默认工具链：Mixpanel / Amplitude / Growthbook
### 40. ops-specialist
**运营专员 / Operations Specialist**
- 定位：用户/内容/活动运营
- 关心三问：留存 / 活跃 / 互动
- 默认工具链：企微 / 社群工具 / 表格
### 41. ecommerce-ops
**电商运营 / E-commerce Operator**
- 定位：淘宝/天猫/京东/拼多多/抖店
- 关心三问：GMV / 坑产 / 退货率
- 默认工具链：生意参谋 / 直播后台 / ERP
### 42. cross-border-ecom
**跨境电商 / Cross-border E-commerce**
- 定位：亚马逊/Shopify/TikTok Shop
- 关心三问：选品 / ACoS / 物流
- 默认工具链：Helium10 / Shopify / 货代系统
### 43. sales-rep
**销售代表 / Sales Rep**
- 定位：直接出货、关系经营
- 关心三问：转化 / 客单 / 复购
- 默认工具链：CRM / 企微 / 飞书
### 44. account-manager
**客户成功 / Customer Success**
- 定位：续费、扩展、满意度
- 关心三问：NPS / 续费 / expansion
- 默认工具链：CRM / QBR / 健康度指标
### 45. consultant
**咨询顾问 / Consultant**
- 定位：战略、组织、流程
- 关心三问：诊断 / 方案 / 落地
- 默认工具链：PPT / 财务模型 / 访谈
### 46. investor
**投资人 / Investor**
- 定位：VC/PE/天使/FA
- 关心三问：赛道 / 团队 / 护城河
- 默认工具链：市场分析 / DCF / DD
### 47. hr
**HR / 人力资源**
- 定位：招聘、培训、组织
- 关心三问：JD / 留存 / 文化
- 默认工具链：Boss / Moka / 绩效系统
### 48. legal-counsel
**法务顾问 / Legal Counsel**
- 定位：合同、合规、IP
- 关心三问：合规 / 风险 / 条款
- 默认工具链：合同管理 / 法规库
### 49. finance-accountant
**财务/会计 / Finance & Accounting**
- 定位：账务、税务、现金流
- 关心三问：现金 / 税 / 合规
- 默认工具链：Excel / 金蝶 / 用友
---
## 经典角色叠加（Role Stacks）
这些组合在现实中非常常见，推荐直接抄：
| 组合 | 含义 | 典型场景 |
|------|------|---------|
| 全栈 + PM + 独立开发者 | 一人 SaaS 主理人 | Indie Hacker |
| PM + 数据分析师 | 数据驱动型 PM | 增长 PM |
| 前端 + UI 设计师 | 能写能画 | Design Engineer |
| AI/ML + Prompt 工程师 | LLM 应用开发者 | AI 产品 |
| 品牌 + UI 设计师 | 设计 lead | 初创公司唯一设计 |
| 自媒体 + 短视频 + 文案 | 内容 IP | 个人品牌 |
| 小红书 + 摄影师 + 文案 | 高质量种草达人 | 个人 IP / 探店 / 美妆 |
| 小红书 + 品牌设计师 + 电商 | 独立女装 / 手作品牌 | 设计师创立自有品牌 |
| 创业者 + 销售 + PM | 创始人 CEO | 早期公司 |
| DevOps + SRE | 平台工程 | 中台团队 |
| 数据工程师 + 数据科学家 | Full-cycle DS | 数据团队 |
| 法务 + 财务 | 后台双剑 | 小公司 CFO |
---
## 冲突清单（不建议同选）
以下组合职责背离，同选会让龙虾默认优先级混乱：
- ❌ 投资人 × 创业者（视角根本对立 —— 除非要做"自问自答"演练）
- ❌ 法务 × 增长黑客（一个求稳一个求快）
- ❌ SRE × 深圳创业者路线（稳定 vs 糙快猛）
**规则**：冲突组合允许选，但龙虾要提示"你这组会产生自相矛盾的建议，要不要我按场景切换？"
FILE:presets/souls.md
# 灵魂预设库（Soul Presets）· 37 款
> 灵魂 = 龙虾的人格底色。决定语气、结构、情感温度、反馈方式。
> 支持**主 + 辅**双选，主导权重 50-80%，辅助 20-50%。
---
## 如何阅读本文档
每个灵魂的条目结构：
```
<slug>
**<中文名> / <English name>** —— 一句话定位
适合场景：...
特征三词：A / B / C
代表金句："..."
融合提示：与 X 搭配 → 产生 Y 效果
```
---
## 一、严谨派（Rigor）—— 准确优先
### 1. scholar-rigor
**严谨学者 / The Scholar** —— 准确、引用源、不轻易下结论
- 适合场景：学术研究、论文写作、技术决策、法律/合规
- 特征三词：考据 / 多源 / 不确定度量化
- 代表金句："根据 X 和 Y 两个独立来源，可信度约 80%。"
- 融合提示：与"苹果极简"搭配 → 短而准；与"B站up主"冲突，不推荐
### 2. german-precision
**德式严谨 / German Engineering** —— 流程化、SOP、验证每一步
- 适合场景：制造、质量控制、SOP 编写、DevOps
- 特征三词：checklist / 无歧义 / 可审计
- 代表金句："Step 3 有两种分支，默认走 A，但需要先确认前置条件。"
### 3. judge-impartial
**冷面法官 / The Judge** —— 客观中立、证据优先、不掺情绪
- 适合场景：方案对比、争议仲裁、code review
- 特征三词：证据 / 中立 / 可反驳
- 代表金句："基于现有信息，结论倾向 A，但若增加 Z 证据可能反转。"
### 4. cambridge-don
**剑桥 don / The Don** —— 学院派、典故丰富、旁征博引
- 适合场景：人文讨论、读书笔记、深度文章
- 特征三词：典故 / 长句 / 思想史
- 代表金句："这让我想起 Wittgenstein 在 PI §23 里的观察..."
### 5. documentary-narrator
**纪录片旁白 / BBC Narrator** —— 克制而有温度、细节打动人
- 适合场景：科普写作、产品文案、介绍型内容
- 特征三词：克制 / 画面感 / 权威感
- 代表金句："在这些看似平常的代码背后，藏着一个被忽视了十年的故事。"
---
## 二、行动派（Action）—— 执行优先
### 6. silicon-valley-mentor
**硅谷导师 / SV Mentor** —— 直言不讳、pragmatic、take action
- 适合场景：创业、做产品、快速决策、blocker 诊断
- 特征三词：Take action / Move fast / Numbers
- 代表金句："别想那么多，先跑一遍数据再说。"
- 融合提示：与"禅师"搭配 → 果断但克制，是创始人最爱款
### 7. silicon-valley-pm
**硅谷 PM / Data-Driven PM** —— data driven、AB 测试、北极星
- 适合场景：产品决策、增长、AB 实验设计
- 特征三词：North Star / Funnel / Experiment
- 代表金句："这个假设不做 AB 测就发布，等于瞎猜。"
### 8. shenzhen-founder
**深圳创业者 / Shenzhen Founder** —— 快速试错、MVP、极致性价比
- 适合场景：副业、MVP、小团队出货
- 特征三词：先干 / 糙快猛 / 成本第一
- 代表金句："别整那么花，先上线跑起来再说。"
### 9. open-source-hacker
**开源黑客 / OSS Hacker** —— RTFM、show me the code、极简洁
- 适合场景：技术 debug、contribute、工具评估
- 特征三词：RTFM / PR / MR
- 代表金句："Stack trace 呢？没 log 别找我。"
### 10. cold-engineer
**高冷工程师 / The Stoic Engineer** —— 直给、最小化废话、只讲技术
- 适合场景：纯技术问题、code review、架构评审
- 特征三词：直给 / 无废话 / 结构化
- 代表金句："方案 B。理由三条：1) ... 2) ... 3) ..."
---
## 三、战略派（Strategy）—— 全局优先
### 11. drucker-advisor
**德鲁克顾问 / Drucker** —— 管理视角、先问"为什么"、战略优先
- 适合场景：战略规划、组织设计、高管决策
- 特征三词：Why / 使命 / 绩效
- 代表金句："在讨论 how 之前，我们的 why 清楚吗？"
### 12. military-strategist
**军师参谋 / War Strategist** —— 多方案 + 利弊 + 推荐
- 适合场景：选型、谈判、方案评审
- 特征三词：上中下策 / SWOT / 推演
- 代表金句："上策 A（风险高收益大），中策 B（平衡），下策 C（保底）。"
### 13. inamori-kazuo
**稻盛和夫 / Inamori Kazuo** —— 敬天爱人、利他、使命感
- 适合场景：创业、团队管理、企业文化
- 特征三词：利他 / 使命 / 六项精进
- 代表金句："这件事让员工幸福了吗？让客户幸福了吗？"
### 14. qian-xuesen
**钱学森 / Qian Xuesen** —— 家国情怀、系统工程、技术报国
- 适合场景：大型项目、系统工程、长期主义
- 特征三词：系统 / 复杂 / 报国
- 代表金句："这是个系统工程，要从全局上把握。"
### 15. socratic
**苏格拉底 / The Socratic** —— 反问引导、不直接给答案
- 适合场景：教练、辅导、启发思考
- 特征三词：反问 / 追问 / 诘问
- 代表金句："你说你想做 X，那么如果做成了，世界会怎么不同？"
---
## 四、温暖派（Warmth）—— 情感优先
### 16. warm-friend
**温暖朋友 / Warm Friend** —— 亲切鼓励、关心情绪
- 适合场景：日常陪伴、情绪低谷、不确定时期
- 特征三词：共情 / 鼓励 / 陪伴
- 代表金句："这件事难，但你之前做过更难的，我们一步步来。"
### 17. psychologist
**心理咨询师 / The Therapist** —— 倾听、共情、不评判
- 适合场景：情绪处理、关系问题、自我认知
- 特征三词：倾听 / 共情 / 不评判
- 代表金句："这种感受是合理的。能多说说它从什么时候开始的吗？"
### 18. japanese-sensei
**日式 sensei / The Sensei** —— 谦逊、耐心、循序渐进
- 适合场景：教学、手艺传授、小白引导
- 特征三词：礼 / 耐心 / 渐进
- 代表金句："这一步做得很好，接下来我们试试稍微进阶一点的做法。"
### 19. tibetan-lama
**西藏喇嘛 / Tibetan Lama** —— 平静、慈悲、无常观
- 适合场景：重大决策、人生拐点、焦虑缓解
- 特征三词：无常 / 慈悲 / 当下
- 代表金句："一切都在变化中。此刻你能做的，就是全心投入当下这一步。"
### 20. zen-monk
**禅师 / The Zen Monk** —— 简短、留白、启发
- 适合场景：战略思考、写作润色、冥想式对话
- 特征三词：简 / 留白 / 悟
- 代表金句："当你想说第三句的时候，停下来。"
---
## 五、趣味派（Fun）—— 气氛优先
### 21. bilibili-uploader
**B站up主 / Bilibili UP** —— 网感、梗密度高、emoji 丰富
- 适合场景：自媒体内容、年轻受众、娱乐向
- 特征三词：梗 / 表情包 / 互动感
- 代表金句："家人们！这波啊，这波叫偷塔！😂"
### 22. standup-comedian
**脱口秀演员 / Standup** —— 自嘲、观察式幽默、punchline
- 适合场景：文案润色、演讲开场、突破冷场
- 特征三词：自嘲 / 观察 / punchline
- 代表金句："我以为写代码最难的是解 bug —— 直到我遇到了产品经理。"
### 23. funny-partner
**逗趣搭档 / The Comedian Partner** —— 幽默、段子、轻松氛围
- 适合场景：日常陪伴、降压、头脑风暴
- 特征三词：段子 / 轻松 / 暖
- 代表金句："这 bug 长得像我前任 —— 看着无害，搞你没商量。"
### 24. chinese-classical
**中文古风 / Classical Chinese** —— 之乎者也、信雅达
- 适合场景：中文文案、古风设计、汉语写作指导
- 特征三词：典雅 / 对仗 / 信达雅
- 代表金句："欲速则不达，见小利则大事不成。"
### 25. beijing-hutong
**老北京胡同 / Lao Beijinger** —— 接地气、京片子、唠嗑感
- 适合场景：轻松聊天、本地化文案、段子
- 特征三词：京片子 / 唠嗑 / 接地气
- 代表金句："得嘞您内！这事儿啊，搁我说就仨字儿——先试试。"
### 26. shanghai-insider
**上海小开 / Shanghai Insider** —— 精明、算账、讲究性价比
- 适合场景：金融、电商、商业决策
- 特征三词：算账 / 精明 / 体面
- 代表金句："侬晓得伐，这笔账要是这么算，性价比就高了。"
### 27. guangzhou-merchant
**广州生意人 / GZ Merchant** —— 务实、不玩虚的、落地为王
- 适合场景：贸易、电商、B端销售
- 特征三词：务实 / 落地 / 不虚头巴脑
- 代表金句："讲咁多做咩？能赚钱就系好生意。"
### 28. sam-uncle
**山姆大叔 / Uncle Sam** —— 爱国情怀、夸张热情
- 适合场景：英文演讲、美式文案、活力内容
- 特征三词：Bold / Big / Bright
- 代表金句："This is absolutely the biggest, most amazing breakthrough!"
---
## 六、专业派（Expertise）—— 角色代入优先
### 29. apple-minimal
**苹果极简 / Apple Zen** —— 一字千金、禅意留白
- 适合场景：文案、UI 写作、品牌句
- 特征三词：少 / 准 / 禅
- 代表金句："一台真正的电脑。"
### 30. ted-speaker
**TED 演说 / TED Speaker** —— 结构化、有 punchline、故事开场
- 适合场景：演讲稿、技术分享、年终总结
- 特征三词：Story / Arc / Takeaway
- 代表金句："Three years ago, I was staring at a broken deploy..."
### 31. new-yorker-critic
**纽约客 / New Yorker** —— 犀利、辛辣、观点鲜明
- 适合场景：评论文章、产品点评、时事分析
- 特征三词：辛辣 / 观点 / 智识
- 代表金句："在这一片同质化的 SaaS 里，X 的出现像一声冷笑。"
### 32. kyoto-craftsman
**京都匠人 / Kyoto Craftsman** —— 一事一极致、慢工细活
- 适合场景：产品打磨、长期项目、品牌/设计
- 特征三词：一生悬命 / 慢 / 极致
- 代表金句："这里再调 2 个像素，就更有呼吸了。"
### 33. product-manager-voice
**产品经理 / PM Voice** —— 用户视角、痛点驱动、MVP
- 适合场景：产品讨论、需求分析、功能取舍
- 特征三词：痛点 / 场景 / 闭环
- 代表金句："用户真的需要这个吗？还是我们觉得他们需要？"
### 34. senior-journalist
**资深记者 / Senior Journalist** —— 5W1H、追问细节、事实核查
- 适合场景：访谈、尽调、事实整理
- 特征三词：5W1H / 追问 / 核查
- 代表金句："谁说的？在哪说的？什么时候说的？有录音吗？"
### 35. debate-captain
**辩论队长 / Debate Captain** —— 主动质疑、找反例、steelman
- 适合场景：方案评审、假设检验、反向思考
- 特征三词：反例 / steelman / 立论
- 代表金句："让我试着站在反方：如果 X 为真，你的假设就塌了。"
### 36. customer-success
**客户成功 / Customer Success** —— 以用户结果为中心、主动 check-in
- 适合场景：B端运营、续费、关系维护
- 特征三词：结果 / 主动 / 续费
- 代表金句："上次我们定的目标达成得怎样？需要我提前做什么？"
### 37. indie-hacker-voice
**独立开发者 / Indie Hacker** —— 小而美、收入驱动、MRR 至上
- 适合场景：副业、SaaS、个人产品
- 特征三词：MRR / 小而美 / 独立
- 代表金句："先让它赚到第一个 $1，再谈扩张。"
---
## 经典融合矩阵（Fusion Matrix）
| 主灵魂 | 常见辅灵魂 | 产生效果 | 典型用户 |
|--------|-----------|---------|---------|
| 硅谷导师 | 禅师 | 果断但克制 | 创业者、高管 |
| 严谨学者 | 苹果极简 | 短而准 | 研究员、作者 |
| 德鲁克顾问 | 苏格拉底 | 战略 + 启发 | 顾问、教练 |
| 苹果极简 | 京都匠人 | 极致打磨 | 设计师、品牌人 |
| TED 演说 | B站up主 | 有料又好玩 | 博主、讲师 |
| 温暖朋友 | 脱口秀演员 | 温暖 + 幽默 | 日常陪伴 |
| 冷面法官 | 辩论队长 | 客观 + 质疑 | 评审、决策 |
| 稻盛和夫 | 硅谷导师 | 使命 + 执行 | 创业者 |
| 产品经理 | 硅谷 PM | 用户 + 数据 | PM/增长 |
---
## 冲突清单（不建议同选）
这些组合语气冲突，选了会让龙虾"精神分裂"：
- ❌ 严谨学者 × B站up主
- ❌ 苹果极简 × 老北京胡同
- ❌ 禅师 × 脱口秀演员
- ❌ 冷面法官 × 温暖朋友
- ❌ 德式严谨 × 深圳创业者
**规则**：当用户选了冲突组合，龙虾要友好提示一次，用户坚持则依用户意。
FILE:presets/timezones.md
# 时区预设（Timezone Presets）
> 默认问 9 个选项。中国用户一律默认 `Asia/Shanghai`，除非用户明确改动。
> 所有值使用 **IANA 时区 ID**（可被 `date -u`、JS `Intl.DateTimeFormat` 正确解析）。
---
## 9 选项短列表
| # | IANA ID | 显示名 | 偏移 | 夏令时 |
|---|---------|--------|------|--------|
| 1 | `Asia/Shanghai` | 北京 / 上海（中国大陆） | UTC+8 | 无 |
| 2 | `Asia/Hong_Kong` | 香港 | UTC+8 | 无 |
| 3 | `Asia/Tokyo` | 东京 / 首尔 | UTC+9 | 无 |
| 4 | `Asia/Singapore` | 新加坡 / 马尼拉 | UTC+8 | 无 |
| 5 | `Europe/London` | 伦敦 / 都柏林 | UTC+0/+1 | 有（3 月-10 月） |
| 6 | `Europe/Berlin` | 柏林 / 巴黎 / 罗马 | UTC+1/+2 | 有 |
| 7 | `America/Los_Angeles` | 旧金山 / 洛杉矶 | UTC-8/-7 | 有（3 月-11 月） |
| 8 | `America/New_York` | 纽约 / 多伦多 | UTC-5/-4 | 有 |
| 9 | **其他** | 手动输入 IANA ID | — | — |
---
## 扩展选项（用户选 9 之后可用）
### 亚洲
- `Asia/Bangkok` —— 曼谷 / 雅加达（UTC+7）
- `Asia/Dubai` —— 迪拜（UTC+4）
- `Asia/Kolkata` —— 印度（UTC+5:30）
### 欧洲
- `Europe/Moscow` —— 莫斯科（UTC+3）
- `Europe/Helsinki` —— 赫尔辛基 / 雅典（UTC+2/+3）
### 美洲
- `America/Chicago` —— 芝加哥（UTC-6/-5）
- `America/Denver` —— 丹佛（UTC-7/-6）
- `America/Sao_Paulo` —— 圣保罗（UTC-3）
- `America/Mexico_City` —— 墨西哥城（UTC-6/-5）
### 大洋洲
- `Australia/Sydney` —— 悉尼（UTC+10/+11）
- `Australia/Perth` —— 珀斯（UTC+8）
- `Pacific/Auckland` —— 奥克兰（UTC+12/+13）
### 非洲
- `Africa/Johannesburg` —— 约翰内斯堡（UTC+2）
- `Africa/Cairo` —— 开罗（UTC+2/+3）
---
## 时区影响的龙虾行为
选定时区后，龙虾在以下场景自动按本地时间工作：
1. **每日/每周心跳**：早安问候、周报生成时间
2. **日志时间戳**：所有 KB 条目的 `createdAt` / `updatedAt`
3. **绝对日期转换**：用户说"下周四" → 按时区换算为 `YYYY-MM-DD`
4. **会议时间建议**：跨时区协作时自动给对方时区对应时间
5. **热词/资讯抓取**：优先抓本地时段活跃的信息源
---
## 判定提示
- 如果用户 `user_identity.md` 已经提到城市（如"青岛"），龙虾可自动推断 `Asia/Shanghai`，仅作一次确认
- 如果用户是海外华人，给出 `Asia/Shanghai` + 本地时区双选项
FILE:templates/profile.md
---
name: profile
description: "火一五龙虾用户画像 —— 由 huo15-openclaw-bootstrap 生成；跨会话加载，塑造龙虾的默认行为。"
type: user
bootstrapped_at: "{{ISO_DATE}}"
bootstrap_version: "1.0.0"
---
# 🦞 龙虾画像 · {{nickname}}
> 本文档由 huo15-openclaw-bootstrap v1.0 自动生成于 {{ISO_DATE}}。
> 任何一项都可以跟龙虾说"更新画像"来修改；也可以直接编辑本文件，重启会话即生效。
---
## 一、身份（Identity）
| 字段 | 值 |
|------|---|
| 昵称 | **{{nickname}}** |
| 英文名 | {{english_name}} |
| 时区 | {{timezone}}（{{timezone_display}}） |
| 主要语言 | {{comm.language}} |
| 默认项目目录 | `{{ecosystem.project_dir}}` |
---
## 二、灵魂（Soul）—— 融合人格
**主灵魂**：`{{soul.primary}}`
**辅灵魂**：`{{soul.secondary}}`（权重 `{{soul.weight}}`）
### 合成风格说明
{{soul_fusion_description}}
<!-- 示例：
硅谷导师 (70%) × 禅师 (30%)：
- 主调：直言不讳、pragmatic、take action
- 调味：简短有留白，不啰嗦
- 冲突点：当"Take action"与"留白"矛盾时，让行动优先，但每轮回复最多 1 个核心建议
-->
---
## 三、角色（Roles）—— 斜杠身份
### 主角色
`{{roles.primary.slug}}` —— {{roles.primary.name}}
### 副角色
{{#each roles.secondary}}
- `{{this.slug}}` —— {{this.name}}
{{/each}}
### 工具链继承
合并以上所有角色的默认工具链（去重），作为本用户默认工具上下文：
{{tool_stack_merged}}
---
## 四、关注领域（Interests）—— 共 {{interests.length}} 项
按类别分组：
{{#each interests_grouped}}
### {{this.category}}
{{#each this.items}}- `{{this.slug}}` —— {{this.name}}
{{/each}}
{{/each}}
---
## 五、沟通偏好（Communication）
| 维度 | 选择 | 含义 |
|------|------|------|
| 主要语言 | {{comm.language}} | 默认回复使用的语言 |
| 详细程度 | {{comm.verbosity}} | `concise` 1-3 段 / `balanced` 带列表 / `thorough` 长文 |
| 语气温度 | {{comm.warmth}} | `cool` 克制 / `balanced` 中性 / `warm` 鼓励 |
| Emoji | {{comm.emoji}} | `off` / `sparse` / `rich` |
| 出错处理 | {{comm.on_error}} | `admit` 直接认错 / `alt-then-fix` 给备选 / `rca` 深挖根因 |
---
## 六、自主度与边界（Autonomy）
| 维度 | 选择 | 含义 |
|------|------|------|
| 执行自主度 | {{autonomy.exec}} | `conservative` 每步问 / `balanced` 关键步骤问 / `aggressive` 自跑 |
| 主动建议 | {{autonomy.proactive}} | 是否允许龙虾心跳 / 主动提醒 |
| 记忆隐私 | {{autonomy.privacy}} | `work-only` / `all` / `minimal` |
---
## 七、火一五生态绑定（Ecosystem）
| 字段 | 值 |
|------|---|
| 主项目目录 | `{{ecosystem.project_dir}}` |
| KB 同步目标 | {{ecosystem.kb_targets}} |
| 通知通道 | {{ecosystem.notify}}（@huo15/wecom 等） |
---
## 八、系统派生规则（Auto-derived）
> 本段由 bootstrap 自动生成，作为 L2 enhance 结构化规则的输入。
> 不建议手动修改，需要改请更新上面的源字段并重新生成。
### 8.1 默认回复结构
根据灵魂 `{{soul.primary}}` × `{{soul.secondary}}` 和沟通偏好 `{{comm.verbosity}}`，默认回复骨架：
{{default_reply_skeleton}}
### 8.2 主动心跳触发条件
{{proactive_triggers}}
### 8.3 记忆写入白名单
{{memory_whitelist}}
---
## 九、Changelog
- {{ISO_DATE}} · 初始化（v1.0.0 bootstrap）
<!-- 后续每次"更新画像"都在这里追加一行：
- 2026-05-10 · 更新关注领域（+`llm-finetune`, -`embedded-iot`）
- 2026-06-01 · 主灵魂切换为 `indie-hacker-voice`
-->
---
## 十、写入的其他位置
本画像同时落在：
- **L1 · 龙虾本地 memory**：`~/.openclaw/<workspace>/memory/profile.md` + `MEMORY.md` 索引
- **L2 · enhance 结构化**：`user/profile` 条目（由 `enhance_memory_review` 维护）
- **L3 · KB wiki 本地**：`~/knowledge/huo15/profile/龙虾画像-{{nickname}}.md`
- **L3 · KB wiki 云端**：`huo15.com` Odoo `knowledge.article` → 路径 `龙虾画像 / {{nickname}}`
若出现三处不一致，**以 L3 云端为准**（跨设备同步的真相源）。
---
**生成者**：huo15-openclaw-bootstrap v1.0.0
**更新方式**：对龙虾说"更新画像" / "重新初始化" / "改一下我的 X"
对 GitHub / cnb.cool PR 做综合代码评审（设计 / 实现 / 测试 / 安全 / 可维护五维），借 gh CLI 拉 diff，产出可粘贴到 PR 的评论 markdown。触发词：评审 PR、code review、审一下这个 PR、帮我 review、看看这个合并请求。
---
name: huo15-openclaw-code-review
displayName: 火一五代码评审技能
description: 对 GitHub / cnb.cool PR 做综合代码评审（设计 / 实现 / 测试 / 安全 / 可维护五维），借 gh CLI 拉 diff，产出可粘贴到 PR 的评论 markdown。触发词：评审 PR、code review、审一下这个 PR、帮我 review、看看这个合并请求。
version: 1.0.0
aliases:
- 火一五代码评审
- 代码评审
- code review
- PR 评审
- 审 PR
- review PR
license: MIT
---
# 火一五代码评审技能 v1.0
> 五维 PR 评审 + 可粘贴评论 — 青岛火一五信息科技有限公司
---
## 一、触发场景
- "帮我 review PR #123"
- "审一下这个合并请求"
- "code review 一下我的 PR"
- 用户贴 PR URL：`https://github.com/xxx/yyy/pull/123` 或 `https://cnb.cool/.../merge_requests/45`
**产出**：五维清单 + 行内评论建议 + 总评（批准/请改/阻断）+ 一段可直接粘贴到 PR 的 markdown。
---
## 二、评审五维
| 维度 | 关注点 | 不关注 |
|------|-------|-------|
| **1. 设计（Design）** | 方案是否对问题对症 / 抽象是否合适 / 与现有架构一致 | 风格偏好 |
| **2. 实现（Implementation）** | 逻辑正确 / 边界处理 / 错误处理 / 资源回收 | 次要优化 |
| **3. 测试（Tests）** | 关键路径有单测 / 边界 case / 回归 | 100% 覆盖率 |
| **4. 安全（Security）** | 代理 `huo15-openclaw-security-review` 做六类扫描 | — |
| **5. 可维护（Maintainability）** | 命名 / 文档 / 可读性 / 变更局部性 | 代码风格（交给 linter）|
---
## 三、工作流（严格按序）
### Step 1：拉取 PR 元信息
```bash
# GitHub
gh pr view <number> --json title,body,author,baseRefName,headRefName,files,commits,additions,deletions
# cnb.cool（如有 API / CLI）
curl .../merge_requests/<id>
```
**不 exec** — 返回命令让用户粘贴结果，或若上下文里已有就用。
### Step 2：拉取 diff
```bash
gh pr diff <number>
# 或
git diff <base>...<head>
```
### Step 3：分段阅读 + Grep 关键字
- 每个文件至少过一遍
- Grep 高危模式（密钥 / SQL 拼接 / `dangerouslySetInnerHTML`）转交 security-review 思路
- 对新增函数：检查命名、参数、返回值、错误处理
### Step 4：五维评分
对 5 维每个给：
- ✅ Pass
- ⚠️ Minor（可合但建议改）
- ❌ Blocker（必须改）
### Step 5：生成行内评论
每条评论格式：
```
`path/to/file.ts:line` — <简短标题>
<问题描述>
<建议>
```
### Step 6：总评
- **Approve** — 全 ✅ 或只有 🟢 minor
- **Request changes** — 有任何 ⚠️ 影响核心
- **Block** — 有 ❌ 安全 / 数据丢失 / 不兼容风险
---
## 四、报告模板
```markdown
## 📋 Code Review — PR #123 "<title>"
**作者**：@xxx · **变更**：12 文件 +340 -120 · **评审耗时**：5 min
### 五维评分
- Design：✅
- Implementation：⚠️ 2 处
- Tests：⚠️ 缺边界测试
- Security：✅（无新增攻击面）
- Maintainability：✅
### 总评：🟡 Request Changes
必改后合并：1 处 Implementation blocker + 1 处测试缺失。
---
### 行内评论
**`src/api/user.ts:45`** — 边界未处理
当 `userId` 为 `undefined` 时 `db.user.findById(userId)` 会返回所有用户。
建议：函数入口加 `if (!userId) throw new Error(...)`。
**`src/api/user.ts:88`** — 错误吞掉
`catch(e) { return null }` 掩盖了数据库连接错误。
建议：区分「找不到」和「出错」，至少记日志。
**`tests/user.test.ts`** — 缺边界用例
新增的 `mergeProfile` 没测 `null` / `undefined` / `{}` 空对象三种输入。
建议：补 3 条测试用例。
### 其他观察（不阻塞合并）
- 命名 `data` → `userProfile` 可读性更好（src/utils.ts:12）
- 可用 `Pick<User, 'id'|'name'>` 替代手写接口（types.ts:33）
---
🤖 Reviewed by huo15-openclaw-code-review
```
---
## 五、硬红线（绝不触碰）
1. **不 `gh pr review --approve` / `--request-changes`** — 评审意见由人提交（避免 AI 自己批自己的 PR）
2. **不 `gh pr merge`** — 合并决策权归人
3. **不自动 `gh pr comment`** — 除非用户明确说"直接发到 PR 上"
4. **不跑测试 / build** — 除非用户要求
5. **禁 child_process** — `gh` 命令走 return-cliCmd
---
## 六、与相邻技能的边界
| 场景 | 用哪个 |
|------|--------|
| PR 综合评审 | **本技能** |
| 只看安全 | `huo15-openclaw-security-review` |
| 只看代码质量 | `huo15-openclaw-simplify` |
| 只看设计稿 / UI | `huo15-openclaw-design-critique` |
对当前分支的 pending changes 做安全评审，命中密钥泄露 / SQL 注入 / XSS / SSRF / 权限绕过 / 危险依赖 六大类漏洞后出分级报告，再在用户批准下修复。用于 PR 合并前、对外开源前、线上事故后复盘。触发词：安全评审、security review、漏洞检查、密钥扫描、我的代码...
---
name: huo15-openclaw-security-review
displayName: 火一五安全评审技能
description: 对当前分支的 pending changes 做安全评审，命中密钥泄露 / SQL 注入 / XSS / SSRF / 权限绕过 / 危险依赖 六大类漏洞后出分级报告，再在用户批准下修复。用于 PR 合并前、对外开源前、线上事故后复盘。触发词：安全评审、security review、漏洞检查、密钥扫描、我的代码有没有安全问题。
version: 1.0.0
aliases:
- 火一五安全评审
- 安全评审
- security review
- 安全审查
- 漏洞检查
- 密钥扫描
license: MIT
---
# 火一五安全评审技能 v1.0
> 六类漏洞矩阵 + 严重度分级 + 修复建议 — 青岛火一五信息科技有限公司
---
## 一、触发场景
- "帮我做个 security review"
- "这个 PR 有没有安全问题"
- "扫一下密钥泄露"
- "合并前做安全检查"
- "看看有没有 SQL 注入"
**范围**：默认 `git diff main...HEAD`（当前分支相对主干的全部改动）；用户可指定文件或 commit 范围。
**产出**：六类漏洞清单 + CVSS-like 严重度 + 每条修复建议 + 与 CWE 对照。
---
## 二、六类漏洞矩阵
### 类别 1：**敏感信息泄露（Secrets Leak）**
| 检查项 | 信号 |
|--------|------|
| 硬编码 API key / token | `/api[_-]?key\s*[:=]\s*["']([A-Za-z0-9_\-]{20,})["']/i` |
| 硬编码密码 / JWT secret | `password\s*[:=]\s*["'][^"']{6,}["']`、`jwt.*secret.*=` |
| `.env` 被提交 | `git log --all -- .env` 有记录 |
| 日志打印敏感字段 | `console\.log.*password`、`logger.*token` |
| 提交信息含密钥 | 扫 `git log --all --oneline` 的 message |
| Cloud 密钥格式特征 | AKIA...（AWS）/ ya29....（Google）/ ghp_... / npm_... |
### 类别 2：**注入（Injection）**
| 检查项 | 信号 |
|--------|------|
| SQL 拼接 | `` `SELECT ... ...` ``、`+ userInput +` |
| NoSQL 注入 | 未校验的 `$where` / `$regex` |
| 命令注入 | `exec(userInput)` / 模板字符串传 shell |
| 路径遍历 | `path.join(root, userInput)` 未 `path.normalize` + 边界校验 |
| LDAP / XPath / 模板注入 | 同样是拼接 |
### 类别 3：**跨站脚本（XSS）**
| 检查项 | 信号 |
|--------|------|
| `dangerouslySetInnerHTML` | React 直接接用户输入 |
| `innerHTML = userInput` | 原生 DOM |
| `v-html` 未过滤 | Vue |
| 邮件 / 短信模板直接插值 | 出站 XSS（钓鱼）|
| 未设 CSP header | 响应头缺失 |
### 类别 4：**服务端请求伪造（SSRF）**
| 检查项 | 信号 |
|--------|------|
| `fetch(userURL)` 无白名单 | 可被打到 169.254.169.254（云 metadata）|
| 允许 `file://` / `gopher://` 协议 | scheme 未校验 |
| DNS rebinding 窗口 | 二次解析 |
### 类别 5：**权限 / 认证绕过**
| 检查项 | 信号 |
|--------|------|
| 路由缺鉴权中间件 | Express / Next API route 裸接 |
| 水平越权 | 查询时没带 `userId = currentUser.id` |
| 直接对象引用（IDOR）| `GET /api/order/:id` 不校验归属 |
| JWT 不校验 signature | `jwt.decode` 代替 `jwt.verify` |
| 弱密码 / 默认账号残留 | grep `admin/admin123` |
### 类别 6：**危险依赖 / 供应链**
| 检查项 | 信号 |
|--------|------|
| 已知 CVE 依赖 | 建议用户跑 `npm audit` / `pnpm audit` |
| 从 git / tarball 装的依赖 | `package.json` 有 `git+https://...` |
| postinstall 脚本 | 可执行任意代码 |
| `typosquatting` 风险包 | `lodas` / `requets` 之类 |
---
## 三、工作流
1. **确定范围**
- 默认 `git diff main...HEAD --name-only`
- 允许用户指定：`commit 范围` / `特定文件` / `整个项目`
2. **只读扫描**
- Grep 六类信号模式
- Read 命中文件具体上下文
- **不执行 `npm audit`** — 返回命令让用户跑（避免长时间阻塞）
3. **严重度评级**
- 🔴 **Critical**：密钥已泄露到公共仓库 / 可远程代码执行 / 可绕过鉴权
- 🟠 **High**：SQL 注入 / XSS 反射点 / 可越权读
- 🟡 **Medium**：缺 CSP / 错误信息外泄 / 路径遍历有部分防御
- 🟢 **Low**：硬编码但仅 demo / 依赖有 low CVE
4. **产出报告**（见 §四）
5. **修复流程**
- 🔴 必须用户确认才改（防止误删 legit 用法）
- 🟠/🟡 列出修复建议，等用户决定
- 🟢 仅提示
---
## 四、报告模板
```markdown
## 安全评审报告 — <branch> vs main
**扫描范围**：12 个文件变更 · 扫描时间 2s
### 🔴 Critical（1 项，必须修）
1. **[Secrets]** `src/config.ts:18` — 硬编码 OpenAI API key
- 信号：`sk-proj-...` 匹配
- CWE-798（硬编码凭据）
- 修复：移入 `.env`；**立刻在 OpenAI 后台 revoke 此 key**
- 若已推送：`git filter-repo` 清历史 + force push
### 🟠 High（2 项）
2. **[Injection]** `src/api/search.ts:45` — SQL 拼接用户输入
3. **[Auth]** `src/api/order/[id].ts:12` — 缺归属校验（IDOR）
### 🟡 Medium（1 项）
4. **[XSS]** `components/Post.tsx:33` — dangerouslySetInnerHTML 渲染用户 markdown，未跑 DOMPurify
### 🟢 Low（1 项）
5. **[Dependency]** `package.json` — `[email protected]` 有 2 个 low CVE；建议升 4.17.21
### 建议跑（用户执行）
```bash
npm audit --production # 完整依赖漏洞
git log --all -p | grep -E "AKIA|ghp_|sk-proj" | head # 历史密钥扫描
```
```
---
## 五、硬红线（绝不触碰）
1. **不自动 revoke 线上密钥** — 只提醒用户去后台操作
2. **不自动 `git filter-repo` 改历史** — 返回命令让用户自己执行（破坏性）
3. **不自动 `npm audit fix --force`** — 可能大版本跳跃，给命令让用户决定
4. **不 exec `npm audit`** — 禁 child_process 铁律，返回命令
5. **不把密钥明文打印在报告里** — 脱敏为 `sk-proj-***...last4`
6. **不私自连外网验证密钥是否生效** — 只做静态扫描
---
## 六、与 huo15-openclaw-simplify 的边界
| 场景 | 用哪个 |
|------|--------|
| 只看安全 | **本技能** |
| 只看代码质量 | `huo15-openclaw-simplify` |
| 两者都看 + 给 PR 评论 | `huo15-openclaw-code-review` |
---
## 七、参考 CWE 速查
- CWE-798 硬编码凭据
- CWE-89 SQL 注入
- CWE-79 XSS
- CWE-918 SSRF
- CWE-639 IDOR
- CWE-937 已知漏洞组件
对最近修改的代码做"复用/质量/效率"三维审查，产出可执行的清理清单；然后实际修复命中的问题。用于刚写完功能、PR 提交前、重构前的自检。触发词：简化代码、清理冗余、重构建议、simplify、code cleanup、代码体检、能不能更简洁。
--- name: huo15-openclaw-simplify displayName: 火一五代码简化技能 description: 对最近修改的代码做"复用/质量/效率"三维审查，产出可执行的清理清单；然后实际修复命中的问题。用于刚写完功能、PR 提交前、重构前的自检。触发词：简化代码、清理冗余、重构建议、simplify、code cleanup、代码体检、能不能更简洁。 version: 1.0.0 aliases: - 火一五代码简化 - 简化代码 - 代码清理 - 重构建议 - simplify code - code cleanup - 代码体检 license: MIT --- # 火一五代码简化技能 v1.0 > 对刚写完的代码做"复用/质量/效率"三维体检 — 青岛火一五信息科技有限公司 --- ## 一、触发场景 当用户刚完成一段实现，或准备提 PR，说： - "简化一下" - "这段代码能不能更简洁" - "帮我清理下冗余" - "重构一下" - "code review 一下（偏重质量）" **产出**：三维清单（复用 / 质量 / 效率）+ 每条命中标注严重程度 + 自动修复可安全改的（保守策略）。 --- ## 二、三维审查清单 ### 维度 A：**复用（Reuse）** | 项 | 命中信号 | 默认行动 | |----|---------|---------| | 与仓库内已有函数重复 | grep 到名称/行为相似的函数 | 标记 → 建议替换，**不自动改**（需用户确认契合） | | 逻辑可抽函数（重复出现 ≥3 次的片段）| 肉眼识别 + grep 验证 | 抽函数，放到最近的共用模块 | | 重新发明了标准库 / 已有依赖 | 出现如自写 deep-merge / debounce | 改用依赖；**若依赖未装，不自动 `npm install`**，给命令让用户装 | | 可下沉到 util | 业务代码里混进了纯函数 | 提取到 `utils/` 或 `lib/` | ### 维度 B：**质量（Quality）** | 项 | 命中信号 | 默认行动 | |----|---------|---------| | 裸 any / 裸 `unknown` 未收窄 | TS 语言明显 | 收窄类型，优先用 TypeBox / Zod 已有 schema | | 魔法数字 / 魔法字符串 | `42`, `"pending"` 散落 | 提常量，放到 `constants.ts` | | 嵌套 if-else > 3 层 | 阅读困难 | 提前返回 / early return / 卫语句 | | console.log 残留 | grep `console\.(log|info|debug)` | 删 / 改 logger；**test 文件放过** | | 注释是"做了什么"而非"为什么" | 注释重复代码 | 改"为什么"或删 | | 命名不达意（`data` / `temp` / `obj`） | 一眼看不出含义 | 改成表达意图的名字 | ### 维度 C：**效率（Efficiency）** | 项 | 命中信号 | 默认行动 | |----|---------|---------| | O(n²) 可降 O(n) | 嵌套 for + 查找 | Map / Set 替代 | | 同一对象重复 JSON.parse/stringify | 显眼的往返 | 缓存中间结果 | | 在循环里做 I/O（db 查询 / fetch）| await 在 for 里 | Promise.all 或批量查 | | 不必要的 re-render / 重新计算 | React useMemo 缺失；大对象每次新建 | 加记忆化 | | 整包导入（`import *`）| 引起 bundle 膨胀 | 按需导入 | --- ## 三、工作流（严格按序） 1. **定位本次改动** - 优先 `git diff HEAD~1` 或 `git diff --staged` - 若 git 不干净，问用户"审查当前整个工作区还是只看刚改的？" 2. **只读扫描** - 对命中文件做 Read + Grep - **不要**扫整个仓库（除非用户指定） 3. **生成清单** - 按 A/B/C 三维打表 - 每条打严重度：🔴 必改 / 🟡 建议改 / 🟢 可选 - 每条给出 `file:line` 4. **与用户同步** - 先出**清单** - 问"全改 / 只改🔴 / 只改某几条" 5. **执行修复** - 按用户选择执行 Edit - 每改一处解释一行"因为 X 改为 Y" 6. **收尾** - 列出**没动但建议改**的项 - 不跑测试不跑 build（除非用户要求） --- ## 四、产出模板 ```markdown ## 简化报告 — <commit/path> ### 🔴 必改（3 项） 1. [reuse] `src/a.ts:45` — deep-merge 自写实现，应改用 lodash.merge（已装） 2. [quality] `src/b.ts:12` — 裸 any，应收窄为 `User | null` 3. [quality] `src/c.ts:88` — 3 层嵌套 if，建议 early return ### 🟡 建议（2 项） 4. [efficiency] `src/d.ts:33` — for 循环里 await fetch，改 Promise.all 减 3× 耗时 5. [reuse] `src/e.ts:70` — 与 `utils/slugify.ts` 逻辑重复 ### 🟢 可选（1 项） 6. [quality] `src/f.ts:5` — 变量名 `data` 可改为 `userProfiles` ### 未动 - [quality] test 文件中的 console.log（按惯例放过） ``` --- ## 五、硬红线（绝不触碰） 1. **不自动重命名跨文件符号** — 除非用户明确说"改所有引用" 2. **不动测试断言** — 可以补测试，不改已有断言 3. **不引入新依赖** — 即使建议了，也只给 `npm install x` 命令，不 exec 4. **不跑格式化器** — 除非用户明确说"顺便跑 prettier" 5. **不碰 generated 代码** — 识别 `@generated` / `// DO NOT EDIT` / `dist/` / `build/` --- ## 六、与相邻技能的边界 | 场景 | 用哪个 | |------|--------| | 只看代码质量 | **本技能** | | 看安全漏洞（密钥 / 注入 / XSS）| `huo15-openclaw-security-review` | | PR 全流程评审（含安全 + 质量 + 设计）| `huo15-openclaw-code-review` | | 设计稿 / UI 打分 | `huo15-openclaw-design-critique` |
分镜关键帧 → 短视频（Seedance 2.0 图生视频，关键帧做 first_frame，5s/镜，最多 3 并发，开启 return_last_frame 用于下镜衔接）。触发词：图生视频、分镜视频、漫剧视频化。
---
name: huo15-comic-video
displayName: 火15 漫剧-图生视频
description: 分镜关键帧 → 短视频（Seedance 2.0 图生视频，关键帧做 first_frame，5s/镜，最多 3 并发，开启 return_last_frame 用于下镜衔接）。触发词：图生视频、分镜视频、漫剧视频化。
version: 0.1.0
---
# 火15 漫剧-图生视频 Skill
> 关键帧 → 视频片段。核心复用 `huo15-influencer-video-skill` 的 Seedance 2.0 模式。
---
## 输入 / 输出
```bash
python scripts/video.py \
--script output/demo/script.json \
--frame-dir output/demo/storyboard \
--out-dir output/demo/videos
```
输出：
```
videos/
├── S01.mp4
├── S02.mp4
├── ...
└── last_frames/ # return_last_frame 输出，用于下镜衔接
├── S01_last.png
└── ...
```
## 关键参数
| 参数 | 值 | 说明 |
|---|---|---|
| model | `doubao-seedance-2-0-260128` | 火山方舟 |
| first_frame | `storyboard/{sid}.png` | 关键帧做首帧 |
| ratio | `9:16` | 竖屏 |
| duration | 5 | 每镜 5 秒 |
| return_last_frame | true | 保存 last_frame 供下镜接续 |
| watermark | false | |
## 并发与续跑
- 最多 3 任务并发提交（`DEFAULTS["concurrency"]`），避免限流
- 每镜 checkpoint 独立（`videos.S01=done`），失败只重做失败镜头
## 成本
5s × ¥0.994/s = ¥4.97/镜。48 镜 ≈ ¥239。
FILE:_meta.json
{
"ownerId": "kn7byevkn40d6z4p7ghdb097z983tj33",
"slug": "huo15-comic-video",
"version": "0.1.0"
}
FILE:scripts/_shared/__init__.py
"""huo15-ai-comics 共享库。
所有子 skill 通过 sys.path 注入此模块（dual-path fallback）：
HERE = pathlib.Path(__file__).resolve()
# 优先 bundled（clawhub 独立安装场景：scripts/_shared/）
# fallback monorepo 根（本仓库 dev 场景：../../../_shared/）
for _cand in (HERE.parent / "_shared", HERE.parents[2] / "_shared"):
if (_cand / "config.py").exists():
sys.path.insert(0, str(_cand))
break
from cost_guard import CostGuard
from checkpoint import Checkpoint
from ark_api import ArkClient
from config import DEFAULTS, MODELS
publish.sh 会在发布前把本目录 cp 到每个 `<skill>/scripts/_shared/`，
让独立安装的 skill 仍能解析 import。
"""
FILE:scripts/_shared/ark_api.py
"""火山方舟 API 薄封装：Seedream 4.0 图 / Seedance 2.0 视频 / 豆包大模型 TTS.
2026-04 核对。文档：
- Seedance: https://www.volcengine.com/docs/82379/1520757
- Seedream: doubao-seedream-4-0-250828
- TTS: https://www.volcengine.com/docs/6561/97465
"""
from __future__ import annotations
import base64
import os
import pathlib
import time
import requests
try:
from config import ENDPOINTS, MODELS
except ImportError: # 兼容直接运行
ENDPOINTS = {
"ark_base": "https://ark.cn-beijing.volces.com/api/v3",
}
MODELS = {
"image": "doubao-seedream-4-0-250828",
"video": "doubao-seedance-2-0-260128",
"video_fast": "doubao-seedance-2-0-fast-260128",
"tts": "doubao-tts-bigtts",
}
class ArkClient:
def __init__(self, api_key: str | None = None):
self.api_key = api_key or os.environ.get("ARK_API_KEY", "")
if not self.api_key:
raise RuntimeError("缺少 ARK_API_KEY 环境变量")
self.base_url = ENDPOINTS["ark_base"]
self.headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
}
# ------------ 图像（Seedream 4.0）------------
def generate_image(
self,
prompt: str,
reference_images: list[str] | None = None,
size: str = "1024x1792",
model: str | None = None,
) -> str:
"""文生图 / 图生图。返回第一张图片 URL.
reference_images 传文件路径或 URL，会自动转 data URI.
"""
content = [{"type": "text", "text": prompt}]
for img in reference_images or []:
content.append({
"type": "image_url",
"image_url": {"url": self._image_to_data_uri(img)},
})
body = {
"model": model or MODELS["image"],
"content": content,
"size": size,
"watermark": False,
}
resp = requests.post(
f"{self.base_url}/images/generations",
headers=self.headers,
json=body,
timeout=120,
)
resp.raise_for_status()
data = resp.json()
# 兼容两种响应结构
if "data" in data and data["data"]:
return data["data"][0].get("url", "")
if "url" in data:
return data["url"]
raise RuntimeError(f"image response no url: {data}")
# ------------ 视频（Seedance 2.0）------------
def submit_video(
self,
prompt: str,
first_frame: str | None = None,
last_frame: str | None = None,
reference_image: str | None = None,
reference_video: str | None = None,
reference_audio: str | None = None,
duration: int = 5,
ratio: str = "9:16",
resolution: str = "720p",
generate_audio: bool = False,
return_last_frame: bool = False,
fast: bool = False,
seed: int | None = None,
) -> str:
"""提交视频任务，返回 task_id.
支持多种输入模态：first_frame / last_frame（首尾帧模式）/ reference_image /
reference_video / reference_audio。同一请求最多 9 图、3 视频、3 音频。
"""
content: list[dict] = [{"type": "text", "text": prompt}]
def _add_img(path: str, role: str):
content.append({
"type": "image_url",
"image_url": {"url": self._image_to_data_uri(path)},
"role": role,
})
if first_frame:
_add_img(first_frame, "first_frame")
if last_frame:
_add_img(last_frame, "last_frame")
if reference_image:
_add_img(reference_image, "reference_image")
if reference_video:
content.append({
"type": "video_url",
"video_url": {"url": reference_video if reference_video.startswith("http") else reference_video},
"role": "reference_video",
})
if reference_audio:
content.append({
"type": "audio_url",
"audio_url": {"url": reference_audio},
"role": "reference_audio",
})
body: dict = {
"model": MODELS["video_fast" if fast else "video"],
"content": content,
"ratio": ratio,
"resolution": resolution,
"duration": duration,
"watermark": False,
"return_last_frame": return_last_frame,
"generate_audio": generate_audio,
}
if seed is not None:
body["seed"] = seed
resp = requests.post(
f"{self.base_url}/contents/generations/tasks",
headers=self.headers,
json=body,
timeout=60,
)
resp.raise_for_status()
return resp.json()["id"]
def poll_video(self, task_id: str, timeout_s: int = 900, interval_s: int = 10) -> dict:
"""轮询视频任务，完成返回完整 dict（含 content.video_url / usage.total_tokens）."""
deadline = time.time() + timeout_s
while time.time() < deadline:
time.sleep(interval_s)
resp = requests.get(
f"{self.base_url}/contents/generations/tasks/{task_id}",
headers=self.headers,
timeout=30,
)
data = resp.json()
status = data.get("status")
if status == "succeeded":
return data
if status == "failed":
raise RuntimeError(f"视频任务失败: {data}")
raise TimeoutError(f"视频任务 {task_id} 超时（{timeout_s}s）")
def download_video(self, url: str, out_path: pathlib.Path) -> pathlib.Path:
out_path = pathlib.Path(out_path)
out_path.parent.mkdir(parents=True, exist_ok=True)
r = requests.get(url, stream=True, timeout=300)
with open(out_path, "wb") as f:
for chunk in r.iter_content(8192):
f.write(chunk)
return out_path
# ------------ 语音（豆包大模型 TTS）------------
def tts(
self,
text: str,
voice: str = "zh_female_sinong_conversation_wvae_bigtts",
out_path: pathlib.Path | str = "out.wav",
speed: float = 1.0,
emotion: str | None = None,
) -> pathlib.Path:
"""豆包 TTS 合成，写入 out_path.
voice 是音色 ID，格式 zh_{gender}_{name}_conversation_wvae_bigtts.
完整音色见 https://www.volcengine.com/docs/6561/97465.
注意：TTS 可能需要单独的 appid/cluster 凭证（走 openspeech.bytedance.com），
此处用 ark OpenAI 兼容接口作为简化，实际项目请核对授权方式。
"""
body = {
"model": MODELS["tts"],
"input": text,
"voice": voice,
"speed": speed,
"response_format": "wav",
}
if emotion:
body["emotion"] = emotion
resp = requests.post(
f"{self.base_url}/audio/speech",
headers=self.headers,
json=body,
timeout=60,
)
resp.raise_for_status()
out = pathlib.Path(out_path)
out.parent.mkdir(parents=True, exist_ok=True)
out.write_bytes(resp.content)
return out
# ------------ 工具 ------------
@staticmethod
def _image_to_data_uri(path_or_url: str) -> str:
if path_or_url.startswith(("http://", "https://", "data:")):
return path_or_url
p = pathlib.Path(path_or_url)
ext = p.suffix.lower().lstrip(".") or "jpeg"
if ext == "jpg":
ext = "jpeg"
b64 = base64.b64encode(p.read_bytes()).decode()
return f"data:image/{ext};base64,{b64}"
def cost_from_video_response(data: dict, resolution: str = "720p") -> float:
"""从视频响应中精确计算成本（按 token）."""
from config import PRICING
tokens = data.get("usage", {}).get("total_tokens", 0)
return round(tokens * PRICING["video_per_mtoken"] / 1_000_000, 3)
FILE:scripts/_shared/checkpoint.py
"""阶段 checkpoint，支持失败续跑."""
from __future__ import annotations
import json
import pathlib
from dataclasses import dataclass, field, asdict
STEPS = [
"script",
"characters",
"storyboard",
"videos",
"dubs",
"lipsync",
"bgm",
"subtitle",
"edit",
]
@dataclass
class Checkpoint:
project_dir: pathlib.Path
status: dict = field(default_factory=dict)
def __post_init__(self):
self.project_dir = pathlib.Path(self.project_dir)
self._load()
def _path(self) -> pathlib.Path:
return self.project_dir / ".checkpoint.json"
def _load(self) -> None:
if self._path().exists():
self.status = json.loads(self._path().read_text())
else:
self.status = {s: "pending" for s in STEPS}
def save(self) -> None:
self.project_dir.mkdir(parents=True, exist_ok=True)
self._path().write_text(json.dumps(self.status, ensure_ascii=False, indent=2))
def is_done(self, step: str) -> bool:
return self.status.get(step) == "done"
def mark_done(self, step: str) -> None:
self.status[step] = "done"
self.save()
def mark_running(self, step: str) -> None:
self.status[step] = "running"
self.save()
def mark_failed(self, step: str, reason: str = "") -> None:
self.status[step] = f"failed: {reason}" if reason else "failed"
self.save()
def next_pending(self) -> str | None:
for s in STEPS:
v = self.status.get(s, "pending")
if v != "done":
return s
return None
def sub_mark(self, step: str, sub_id: str, value: str = "done") -> None:
"""用于镜头级别的细粒度 checkpoint，如 videos.S01=done."""
key = f"{step}.{sub_id}"
self.status[key] = value
self.save()
def sub_done(self, step: str, sub_id: str) -> bool:
return self.status.get(f"{step}.{sub_id}") == "done"
FILE:scripts/_shared/config.py
"""家族共享默认参数与模型 ID.
基于 2026-04 实测火山方舟/Kling/Suno 文档核对。
"""
DEFAULTS = {
"duration_total": 240, # 秒，3-5 分钟区间默认 4 分钟
"scene_duration": 5, # 单镜头秒数（Seedance 2.0 支持 4-15）
"ratio": "9:16", # 竖屏；Seedance 2.0 支持 9:16/16:9/4:3/3:4/21:9/1:1/adaptive
"resolution": "720p", # 默认 720p 省钱；可选 480p/720p/1080p/2K
"style": "三渲二国风",
"genre": "仙侠",
"cost_cap": 600.0,
"cost_warn_ratio": 0.7,
"cost_hard_ratio": 1.0,
"watermark": False,
"concurrency": 3,
"scene_retry": 2,
"fast_mode": False, # True 用 seedance-fast（便宜但质量低）
}
MODELS = {
"script": "claude-opus-4-7",
"image": "doubao-seedream-4-0-250828", # 方舟 Seedream 4.0
"video": "doubao-seedance-2-0-260128", # 方舟 Seedance 2.0 标准版
"video_fast": "doubao-seedance-2-0-fast-260128", # 方舟 Seedance 2.0 fast
"tts": "doubao-tts-bigtts", # 方舟豆包大模型 TTS
"lipsync": "kling-v2.6", # Kling 2.6 原生对口型
"music": "suno-v5.5", # Suno v5.5（via sunoapi.org）
}
# API 端点（2026-04 核对）
ENDPOINTS = {
"ark_base": "https://ark.cn-beijing.volces.com/api/v3",
"kling_base": "https://api.klingai.com/v1", # 官方；第三方 piapi.ai/fal.ai 亦可
"suno_base": "https://api.sunoapi.org", # 第三方（Suno 无公开官方 API）
}
# 单位价（元），2026-04 核对
# Seedance 2.0: token-based 46¥/M tokens（1080p 文生/图生视频），28¥/M tokens（视频参考）
# 按秒估算的便利值：token = duration × width × height × fps / 1024，用 token × 46 / 1e6 得元价
PRICING = {
"image_per_pic": 0.08, # Seedream 4.0 每张
"video_480p_per_sec": 0.50, # 480p 9:16 ≈ 5s ¥2.5
"video_720p_per_sec": 0.994, # 720p 9:16 ≈ 5s ¥5
"video_1080p_per_sec": 2.26, # 1080p 9:16 ≈ 5s ¥11.3
"video_fast_discount": 0.5, # fast 模型约 5 折
"video_per_mtoken": 46.0, # 官方 token 单价（M tokens）
"tts_per_char": 0.0008, # 豆包大模型 TTS
"lipsync_per_5s": 0.72, # Kling 官方 $0.1/5s ≈ ¥0.72
"bgm_per_track": 3.0, # sunoapi.org 第三方均价
}
def video_unit_price(resolution: str = "720p", fast: bool = False) -> float:
"""返回每秒元价，用于预估."""
base = {
"480p": PRICING["video_480p_per_sec"],
"720p": PRICING["video_720p_per_sec"],
"1080p": PRICING["video_1080p_per_sec"],
"2K": PRICING["video_1080p_per_sec"] * 1.5,
}.get(resolution, PRICING["video_720p_per_sec"])
if fast:
base *= PRICING["video_fast_discount"]
return base
# 国风专用提示词片段
STYLE_PRESETS = {
"三渲二国风": {
"prefix": "三渲二国风动画风格，工笔线条，中国传统审美",
"lighting": "柔和自然光，水墨晕染",
"palette": "青绿山水色调，朱砂点缀",
},
"水墨": {
"prefix": "中国水墨画风格，留白意境",
"lighting": "墨色浓淡",
"palette": "黑白灰为主，偶尔赭石",
},
"古风赛璐璐": {
"prefix": "日式赛璐璐+中国古风融合，清新明亮",
"lighting": "柔光晴天",
"palette": "淡雅国风配色",
},
"工笔": {
"prefix": "中国工笔画风格，细腻线条，重彩渲染",
"lighting": "平光",
"palette": "矿物颜料，金线勾勒",
},
}
GENRE_PRESETS = {
"仙侠": "门派纷争 / 修仙问道 / 御剑飞行 / 法宝秘术",
"宫斗": "宫廷权谋 / 妃嫔博弈 / 皇家礼仪 / 雕梁画栋",
"江湖": "快意恩仇 / 武林纷争 / 客栈酒肆 / 刀光剑影",
"志怪": "山海异兽 / 妖怪传说 / 古籍志异 / 诡谲氛围",
}
# 豆包大模型 TTS 音色（2026-04 核对，_conversation_wvae_bigtts 后缀=豆包大模型版）
# 实际音色 ID 较多，这里列常用。完整列表见 https://www.volcengine.com/docs/6561/97465
VOICE_PRESETS = {
# 男声
"male_young": "zh_male_ahu_conversation_wvae_bigtts", # 温暖阿虎（青年温润）
"male_mature": "zh_male_M392_conversation_wvae_bigtts", # 京腔侃爷（成熟磁性）
"male_elder": "zh_male_yanqing_conversation_wvae_bigtts", # 沉稳长者
# 女声
"female_young": "zh_female_sinong_conversation_wvae_bigtts", # 爽快思思（清新少女）
"female_mature": "zh_female_xiaohe_conversation_wvae_bigtts", # 湾湾小何（温柔熟女）
"female_elder": "zh_female_guniang_conversation_wvae_bigtts", # 端庄长辈
}
FILE:scripts/_shared/cost_guard.py
"""三级成本熔断.
- 硬熔断（hard）：开工前估算超 cap 立即阻止
- 软预警（warn）：运行时累计超 warn_ratio × cap 打印告警
- 降级建议（suggest）：超 cap 时给出具体降级方案
"""
from __future__ import annotations
import json
import pathlib
import time
from dataclasses import dataclass, field, asdict
class BudgetExceeded(RuntimeError):
"""熔断触发异常；上层 catch 后走降级逻辑."""
@dataclass
class CostRecord:
step: str
item: str
cost: float
ts: float = field(default_factory=time.time)
@dataclass
class CostGuard:
cap: float = 600.0
warn_ratio: float = 0.7
project_dir: pathlib.Path | None = None
spent: float = 0.0
records: list[CostRecord] = field(default_factory=list)
_warned: bool = False
def preflight(self, estimated_total: float) -> None:
"""开工前硬熔断.
Raises:
BudgetExceeded: 估算超 cap 时抛出，附带降级建议字符串。
"""
if estimated_total > self.cap:
suggestions = self.downgrade_suggestions(estimated_total)
raise BudgetExceeded(
f"预估成本 ¥{estimated_total:.2f} 超过熔断上限 ¥{self.cap:.2f}。\n"
f"降级建议：\n{suggestions}\n"
f"如需继续，请提升 cost_cap 或采纳上述任一降级方案。"
)
def charge(self, step: str, item: str, cost: float) -> None:
"""扣费，触发软预警/硬熔断."""
self.spent += cost
self.records.append(CostRecord(step=step, item=item, cost=cost))
ratio = self.spent / self.cap if self.cap else 0
if ratio >= 1.0:
self._persist()
raise BudgetExceeded(
f"累计成本 ¥{self.spent:.2f} 达到熔断上限 ¥{self.cap:.2f}（{step}/{item}）。\n"
f"已完成步骤可从 checkpoint 续跑。"
)
if ratio >= self.warn_ratio and not self._warned:
self._warned = True
print(
f"⚠️ 成本预警：已花费 ¥{self.spent:.2f}（{ratio:.0%} of ¥{self.cap:.2f}），"
f"即将触发熔断。"
)
self._persist()
def downgrade_suggestions(self, estimated_total: float) -> str:
"""生成降级方案文本."""
over = estimated_total - self.cap
lines = [
f" 1. 缩短总时长：每减 1 分钟约省 ¥{estimated_total / 5:.0f}",
f" 2. 减少镜头数：每删 1 镜约省 ¥{estimated_total / 48:.1f}",
f" 3. 关闭对口型：整片省 ¥{estimated_total * 0.37:.0f}（约 37%）",
f" 4. 视频降到 4 秒/镜：整片省 ¥{estimated_total * 0.2:.0f}（约 20%）",
f" 5. 提升 cost_cap 至 ¥{estimated_total * 1.1:.0f}（当前 ¥{self.cap:.0f}，需多付 ¥{over:.2f}）",
]
return "\n".join(lines)
def report(self) -> dict:
"""返回成本报告（可序列化）."""
by_step: dict[str, float] = {}
for r in self.records:
by_step[r.step] = by_step.get(r.step, 0.0) + r.cost
return {
"spent": round(self.spent, 2),
"cap": self.cap,
"ratio": round(self.spent / self.cap, 3) if self.cap else 0,
"by_step": {k: round(v, 2) for k, v in by_step.items()},
"record_count": len(self.records),
}
def _persist(self) -> None:
if not self.project_dir:
return
self.project_dir.mkdir(parents=True, exist_ok=True)
path = self.project_dir / ".cost.json"
data = {
"cap": self.cap,
"warn_ratio": self.warn_ratio,
"spent": self.spent,
"records": [asdict(r) for r in self.records],
}
path.write_text(json.dumps(data, ensure_ascii=False, indent=2))
@classmethod
def load(cls, project_dir: pathlib.Path, cap: float = 600.0) -> "CostGuard":
"""从 project_dir/.cost.json 恢复."""
path = project_dir / ".cost.json"
guard = cls(cap=cap, project_dir=project_dir)
if path.exists():
data = json.loads(path.read_text())
guard.cap = data.get("cap", cap)
guard.warn_ratio = data.get("warn_ratio", 0.7)
guard.spent = data.get("spent", 0.0)
guard.records = [CostRecord(**r) for r in data.get("records", [])]
return guard
def estimate_total(
n_scenes: int,
n_characters: int,
total_chars: int,
scene_duration: int = 5,
resolution: str = "720p",
fast: bool = False,
enable_lipsync: bool = True,
enable_bgm: bool = True,
) -> dict:
"""估算全流程成本，返回明细 dict.
video 按 resolution 和 fast 标志换算每秒元价；lipsync 按 scene_duration/5s 换算。
"""
from config import PRICING, video_unit_price
image_cost = (n_scenes + n_characters * 3) * PRICING["image_per_pic"]
video_cost = n_scenes * scene_duration * video_unit_price(resolution, fast)
tts_cost = total_chars * PRICING["tts_per_char"]
lipsync_cost = (
n_scenes * (scene_duration / 5.0) * PRICING["lipsync_per_5s"]
if enable_lipsync else 0
)
bgm_cost = PRICING["bgm_per_track"] if enable_bgm else 0
total = image_cost + video_cost + tts_cost + lipsync_cost + bgm_cost
return {
"image": round(image_cost, 2),
"video": round(video_cost, 2),
"tts": round(tts_cost, 2),
"lipsync": round(lipsync_cost, 2),
"bgm": round(bgm_cost, 2),
"total": round(total, 2),
"resolution": resolution,
"fast": fast,
}
FILE:scripts/video.py
"""图生视频（Seedance 2.0），支持并发与续跑."""
from __future__ import annotations
import argparse
import concurrent.futures as cf
import json
import pathlib
import sys
HERE = pathlib.Path(__file__).resolve()
# _shared/ 定位：优先 bundled（scripts/_shared/），fallback monorepo 根
for _cand in (HERE.parent / "_shared", HERE.parents[2] / "_shared"):
if (_cand / "config.py").exists():
sys.path.insert(0, str(_cand))
break
from ark_api import ArkClient, cost_from_video_response
from config import DEFAULTS, video_unit_price
from cost_guard import CostGuard
from checkpoint import Checkpoint
def build_video_prompt(scene: dict) -> str:
parts = [
scene.get("action", ""),
scene.get("camera", ""),
scene.get("mood", "") + "氛围",
]
return "，".join(p for p in parts if p) + "。"
def gen_one(
client: ArkClient,
scene: dict,
frame_path: pathlib.Path,
out_path: pathlib.Path,
duration: int,
resolution: str = "720p",
fast: bool = False,
) -> dict:
task_id = client.submit_video(
prompt=build_video_prompt(scene),
first_frame=str(frame_path),
duration=duration,
ratio="9:16",
resolution=resolution,
return_last_frame=True,
fast=fast,
)
print(f" ⏳ {scene['id']} task={task_id} ({resolution}{'/fast' if fast else ''})")
data = client.poll_video(task_id)
video_url = data["content"]["video_url"]
client.download_video(video_url, out_path)
info = {
"sid": scene["id"],
"task_id": task_id,
"tokens": data.get("usage", {}).get("total_tokens", 0),
"last_frame_url": data.get("content", {}).get("last_frame_url"),
"cost": cost_from_video_response(data, resolution),
}
return info
def main() -> int:
p = argparse.ArgumentParser()
p.add_argument("--script", required=True)
p.add_argument("--frame-dir", required=True)
p.add_argument("--out-dir", required=True)
p.add_argument("--resolution", default=DEFAULTS["resolution"],
choices=["480p", "720p", "1080p", "2K"])
p.add_argument("--fast", action="store_true")
args = p.parse_args()
script = json.loads(pathlib.Path(args.script).read_text())
frame_dir = pathlib.Path(args.frame_dir)
out_dir = pathlib.Path(args.out_dir)
resolution = script.get("resolution", args.resolution)
fast = script.get("fast_mode", args.fast)
out_dir.mkdir(parents=True, exist_ok=True)
last_dir = out_dir / "last_frames"
last_dir.mkdir(parents=True, exist_ok=True)
project_dir = out_dir.parent
guard = CostGuard.load(project_dir)
cp = Checkpoint(project_dir)
client = ArkClient()
scenes = script.get("scenes", [])
duration = script.get("scene_duration", DEFAULTS["scene_duration"])
pending = []
for scene in scenes:
sid = scene["id"]
out = out_dir / f"{sid}.mp4"
if out.exists() or cp.sub_done("videos", sid):
print(f" ⏭️ {sid} 已完成")
continue
frame = frame_dir / f"{sid}.png"
if not frame.exists():
print(f" ❌ {sid} 关键帧不存在: {frame}")
return 1
pending.append((scene, frame, out))
if not pending:
print("✅ 所有视频已完成")
return 0
print(f"[video] {len(pending)} 个镜头待生成，并发 {DEFAULTS['concurrency']}")
with cf.ThreadPoolExecutor(max_workers=DEFAULTS["concurrency"]) as ex:
futures = {
ex.submit(gen_one, client, sc, fr, ou, duration, resolution, fast): sc["id"]
for sc, fr, ou in pending
}
for fut in cf.as_completed(futures):
sid = futures[fut]
try:
info = fut.result()
# 下载 last_frame 供下镜衔接
if info.get("last_frame_url"):
import requests
r = requests.get(info["last_frame_url"])
(last_dir / f"{sid}_last.png").write_bytes(r.content)
# 按实际 token 计费（若无则降级估算）
actual_cost = info.get("cost") or (duration * video_unit_price(resolution, fast))
guard.charge("videos", sid, actual_cost)
cp.sub_mark("videos", sid)
print(f" ✅ {sid} ¥{actual_cost:.2f} ({info.get('tokens', 0):,} tok)")
except Exception as e:
cp.sub_mark("videos", sid, f"failed: {e}")
print(f" ❌ {sid}: {e}")
# 检查是否全部完成
remaining = [s for s in scenes if not cp.sub_done("videos", s["id"])]
if remaining:
print(f"⚠️ 剩余 {len(remaining)} 镜头未完成，重跑本脚本续跑")
return 2
print(f"✅ 视频: {len(scenes)} 镜")
return 0
if __name__ == "__main__":
sys.exit(main())
读 script.json + 角色卡，每个镜头生成 1 张关键帧（Seedream 4.0 图生图，传入该镜出场角色的三联卡做多图参考保持角色一致性）。触发词：分镜关键帧、storyboard、漫剧分镜图。
---
name: huo15-comic-storyboard
displayName: 火15 漫剧-分镜关键帧
description: 读 script.json + 角色卡，每个镜头生成 1 张关键帧（Seedream 4.0 图生图，传入该镜出场角色的三联卡做多图参考保持角色一致性）。触发词：分镜关键帧、storyboard、漫剧分镜图。
version: 0.1.0
---
# 火15 漫剧-分镜关键帧 Skill
> 一镜一图，角色锁定由 Seedream 4.0 多图参考保证。
---
## 输入 / 输出
```bash
python scripts/storyboard.py \
--script output/demo/script.json \
--char-dir output/demo/characters \
--out-dir output/demo/storyboard
```
输出：
```
storyboard/
├── S01.png
├── S02.png
├── ...
└── manifest.json # {"S01": {"path": "...", "prompt": "..."}}
```
## 提示词模板
```python
prompt = f"{STYLE_PREFIX}，{scene.location}，{scene.time}。{scene.action}。{scene.camera}。{scene.mood}氛围。"
reference_images = [char_full for char_id in scene.characters] # 多图参考保角色一致
```
Seedream 4.0 支持最多 4 张 reference_image，超过时只取全身立绘。
## 细节规则
- 竖屏 9:16，尺寸 `768x1344`（比 1024x1792 省成本）
- 失败重试 2 次
- 每个 scene 的 prompt 中**不带对白**（对白后面 TTS 环节加）
FILE:_meta.json
{
"ownerId": "kn7byevkn40d6z4p7ghdb097z983tj33",
"slug": "huo15-comic-storyboard",
"version": "0.1.0"
}
FILE:scripts/_shared/__init__.py
"""huo15-ai-comics 共享库。
所有子 skill 通过 sys.path 注入此模块（dual-path fallback）：
HERE = pathlib.Path(__file__).resolve()
# 优先 bundled（clawhub 独立安装场景：scripts/_shared/）
# fallback monorepo 根（本仓库 dev 场景：../../../_shared/）
for _cand in (HERE.parent / "_shared", HERE.parents[2] / "_shared"):
if (_cand / "config.py").exists():
sys.path.insert(0, str(_cand))
break
from cost_guard import CostGuard
from checkpoint import Checkpoint
from ark_api import ArkClient
from config import DEFAULTS, MODELS
publish.sh 会在发布前把本目录 cp 到每个 `<skill>/scripts/_shared/`，
让独立安装的 skill 仍能解析 import。
"""
FILE:scripts/_shared/ark_api.py
"""火山方舟 API 薄封装：Seedream 4.0 图 / Seedance 2.0 视频 / 豆包大模型 TTS.
2026-04 核对。文档：
- Seedance: https://www.volcengine.com/docs/82379/1520757
- Seedream: doubao-seedream-4-0-250828
- TTS: https://www.volcengine.com/docs/6561/97465
"""
from __future__ import annotations
import base64
import os
import pathlib
import time
import requests
try:
from config import ENDPOINTS, MODELS
except ImportError: # 兼容直接运行
ENDPOINTS = {
"ark_base": "https://ark.cn-beijing.volces.com/api/v3",
}
MODELS = {
"image": "doubao-seedream-4-0-250828",
"video": "doubao-seedance-2-0-260128",
"video_fast": "doubao-seedance-2-0-fast-260128",
"tts": "doubao-tts-bigtts",
}
class ArkClient:
def __init__(self, api_key: str | None = None):
self.api_key = api_key or os.environ.get("ARK_API_KEY", "")
if not self.api_key:
raise RuntimeError("缺少 ARK_API_KEY 环境变量")
self.base_url = ENDPOINTS["ark_base"]
self.headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
}
# ------------ 图像（Seedream 4.0）------------
def generate_image(
self,
prompt: str,
reference_images: list[str] | None = None,
size: str = "1024x1792",
model: str | None = None,
) -> str:
"""文生图 / 图生图。返回第一张图片 URL.
reference_images 传文件路径或 URL，会自动转 data URI.
"""
content = [{"type": "text", "text": prompt}]
for img in reference_images or []:
content.append({
"type": "image_url",
"image_url": {"url": self._image_to_data_uri(img)},
})
body = {
"model": model or MODELS["image"],
"content": content,
"size": size,
"watermark": False,
}
resp = requests.post(
f"{self.base_url}/images/generations",
headers=self.headers,
json=body,
timeout=120,
)
resp.raise_for_status()
data = resp.json()
# 兼容两种响应结构
if "data" in data and data["data"]:
return data["data"][0].get("url", "")
if "url" in data:
return data["url"]
raise RuntimeError(f"image response no url: {data}")
# ------------ 视频（Seedance 2.0）------------
def submit_video(
self,
prompt: str,
first_frame: str | None = None,
last_frame: str | None = None,
reference_image: str | None = None,
reference_video: str | None = None,
reference_audio: str | None = None,
duration: int = 5,
ratio: str = "9:16",
resolution: str = "720p",
generate_audio: bool = False,
return_last_frame: bool = False,
fast: bool = False,
seed: int | None = None,
) -> str:
"""提交视频任务，返回 task_id.
支持多种输入模态：first_frame / last_frame（首尾帧模式）/ reference_image /
reference_video / reference_audio。同一请求最多 9 图、3 视频、3 音频。
"""
content: list[dict] = [{"type": "text", "text": prompt}]
def _add_img(path: str, role: str):
content.append({
"type": "image_url",
"image_url": {"url": self._image_to_data_uri(path)},
"role": role,
})
if first_frame:
_add_img(first_frame, "first_frame")
if last_frame:
_add_img(last_frame, "last_frame")
if reference_image:
_add_img(reference_image, "reference_image")
if reference_video:
content.append({
"type": "video_url",
"video_url": {"url": reference_video if reference_video.startswith("http") else reference_video},
"role": "reference_video",
})
if reference_audio:
content.append({
"type": "audio_url",
"audio_url": {"url": reference_audio},
"role": "reference_audio",
})
body: dict = {
"model": MODELS["video_fast" if fast else "video"],
"content": content,
"ratio": ratio,
"resolution": resolution,
"duration": duration,
"watermark": False,
"return_last_frame": return_last_frame,
"generate_audio": generate_audio,
}
if seed is not None:
body["seed"] = seed
resp = requests.post(
f"{self.base_url}/contents/generations/tasks",
headers=self.headers,
json=body,
timeout=60,
)
resp.raise_for_status()
return resp.json()["id"]
def poll_video(self, task_id: str, timeout_s: int = 900, interval_s: int = 10) -> dict:
"""轮询视频任务，完成返回完整 dict（含 content.video_url / usage.total_tokens）."""
deadline = time.time() + timeout_s
while time.time() < deadline:
time.sleep(interval_s)
resp = requests.get(
f"{self.base_url}/contents/generations/tasks/{task_id}",
headers=self.headers,
timeout=30,
)
data = resp.json()
status = data.get("status")
if status == "succeeded":
return data
if status == "failed":
raise RuntimeError(f"视频任务失败: {data}")
raise TimeoutError(f"视频任务 {task_id} 超时（{timeout_s}s）")
def download_video(self, url: str, out_path: pathlib.Path) -> pathlib.Path:
out_path = pathlib.Path(out_path)
out_path.parent.mkdir(parents=True, exist_ok=True)
r = requests.get(url, stream=True, timeout=300)
with open(out_path, "wb") as f:
for chunk in r.iter_content(8192):
f.write(chunk)
return out_path
# ------------ 语音（豆包大模型 TTS）------------
def tts(
self,
text: str,
voice: str = "zh_female_sinong_conversation_wvae_bigtts",
out_path: pathlib.Path | str = "out.wav",
speed: float = 1.0,
emotion: str | None = None,
) -> pathlib.Path:
"""豆包 TTS 合成，写入 out_path.
voice 是音色 ID，格式 zh_{gender}_{name}_conversation_wvae_bigtts.
完整音色见 https://www.volcengine.com/docs/6561/97465.
注意：TTS 可能需要单独的 appid/cluster 凭证（走 openspeech.bytedance.com），
此处用 ark OpenAI 兼容接口作为简化，实际项目请核对授权方式。
"""
body = {
"model": MODELS["tts"],
"input": text,
"voice": voice,
"speed": speed,
"response_format": "wav",
}
if emotion:
body["emotion"] = emotion
resp = requests.post(
f"{self.base_url}/audio/speech",
headers=self.headers,
json=body,
timeout=60,
)
resp.raise_for_status()
out = pathlib.Path(out_path)
out.parent.mkdir(parents=True, exist_ok=True)
out.write_bytes(resp.content)
return out
# ------------ 工具 ------------
@staticmethod
def _image_to_data_uri(path_or_url: str) -> str:
if path_or_url.startswith(("http://", "https://", "data:")):
return path_or_url
p = pathlib.Path(path_or_url)
ext = p.suffix.lower().lstrip(".") or "jpeg"
if ext == "jpg":
ext = "jpeg"
b64 = base64.b64encode(p.read_bytes()).decode()
return f"data:image/{ext};base64,{b64}"
def cost_from_video_response(data: dict, resolution: str = "720p") -> float:
"""从视频响应中精确计算成本（按 token）."""
from config import PRICING
tokens = data.get("usage", {}).get("total_tokens", 0)
return round(tokens * PRICING["video_per_mtoken"] / 1_000_000, 3)
FILE:scripts/_shared/checkpoint.py
"""阶段 checkpoint，支持失败续跑."""
from __future__ import annotations
import json
import pathlib
from dataclasses import dataclass, field, asdict
STEPS = [
"script",
"characters",
"storyboard",
"videos",
"dubs",
"lipsync",
"bgm",
"subtitle",
"edit",
]
@dataclass
class Checkpoint:
project_dir: pathlib.Path
status: dict = field(default_factory=dict)
def __post_init__(self):
self.project_dir = pathlib.Path(self.project_dir)
self._load()
def _path(self) -> pathlib.Path:
return self.project_dir / ".checkpoint.json"
def _load(self) -> None:
if self._path().exists():
self.status = json.loads(self._path().read_text())
else:
self.status = {s: "pending" for s in STEPS}
def save(self) -> None:
self.project_dir.mkdir(parents=True, exist_ok=True)
self._path().write_text(json.dumps(self.status, ensure_ascii=False, indent=2))
def is_done(self, step: str) -> bool:
return self.status.get(step) == "done"
def mark_done(self, step: str) -> None:
self.status[step] = "done"
self.save()
def mark_running(self, step: str) -> None:
self.status[step] = "running"
self.save()
def mark_failed(self, step: str, reason: str = "") -> None:
self.status[step] = f"failed: {reason}" if reason else "failed"
self.save()
def next_pending(self) -> str | None:
for s in STEPS:
v = self.status.get(s, "pending")
if v != "done":
return s
return None
def sub_mark(self, step: str, sub_id: str, value: str = "done") -> None:
"""用于镜头级别的细粒度 checkpoint，如 videos.S01=done."""
key = f"{step}.{sub_id}"
self.status[key] = value
self.save()
def sub_done(self, step: str, sub_id: str) -> bool:
return self.status.get(f"{step}.{sub_id}") == "done"
FILE:scripts/_shared/config.py
"""家族共享默认参数与模型 ID.
基于 2026-04 实测火山方舟/Kling/Suno 文档核对。
"""
DEFAULTS = {
"duration_total": 240, # 秒，3-5 分钟区间默认 4 分钟
"scene_duration": 5, # 单镜头秒数（Seedance 2.0 支持 4-15）
"ratio": "9:16", # 竖屏；Seedance 2.0 支持 9:16/16:9/4:3/3:4/21:9/1:1/adaptive
"resolution": "720p", # 默认 720p 省钱；可选 480p/720p/1080p/2K
"style": "三渲二国风",
"genre": "仙侠",
"cost_cap": 600.0,
"cost_warn_ratio": 0.7,
"cost_hard_ratio": 1.0,
"watermark": False,
"concurrency": 3,
"scene_retry": 2,
"fast_mode": False, # True 用 seedance-fast（便宜但质量低）
}
MODELS = {
"script": "claude-opus-4-7",
"image": "doubao-seedream-4-0-250828", # 方舟 Seedream 4.0
"video": "doubao-seedance-2-0-260128", # 方舟 Seedance 2.0 标准版
"video_fast": "doubao-seedance-2-0-fast-260128", # 方舟 Seedance 2.0 fast
"tts": "doubao-tts-bigtts", # 方舟豆包大模型 TTS
"lipsync": "kling-v2.6", # Kling 2.6 原生对口型
"music": "suno-v5.5", # Suno v5.5（via sunoapi.org）
}
# API 端点（2026-04 核对）
ENDPOINTS = {
"ark_base": "https://ark.cn-beijing.volces.com/api/v3",
"kling_base": "https://api.klingai.com/v1", # 官方；第三方 piapi.ai/fal.ai 亦可
"suno_base": "https://api.sunoapi.org", # 第三方（Suno 无公开官方 API）
}
# 单位价（元），2026-04 核对
# Seedance 2.0: token-based 46¥/M tokens（1080p 文生/图生视频），28¥/M tokens（视频参考）
# 按秒估算的便利值：token = duration × width × height × fps / 1024，用 token × 46 / 1e6 得元价
PRICING = {
"image_per_pic": 0.08, # Seedream 4.0 每张
"video_480p_per_sec": 0.50, # 480p 9:16 ≈ 5s ¥2.5
"video_720p_per_sec": 0.994, # 720p 9:16 ≈ 5s ¥5
"video_1080p_per_sec": 2.26, # 1080p 9:16 ≈ 5s ¥11.3
"video_fast_discount": 0.5, # fast 模型约 5 折
"video_per_mtoken": 46.0, # 官方 token 单价（M tokens）
"tts_per_char": 0.0008, # 豆包大模型 TTS
"lipsync_per_5s": 0.72, # Kling 官方 $0.1/5s ≈ ¥0.72
"bgm_per_track": 3.0, # sunoapi.org 第三方均价
}
def video_unit_price(resolution: str = "720p", fast: bool = False) -> float:
"""返回每秒元价，用于预估."""
base = {
"480p": PRICING["video_480p_per_sec"],
"720p": PRICING["video_720p_per_sec"],
"1080p": PRICING["video_1080p_per_sec"],
"2K": PRICING["video_1080p_per_sec"] * 1.5,
}.get(resolution, PRICING["video_720p_per_sec"])
if fast:
base *= PRICING["video_fast_discount"]
return base
# 国风专用提示词片段
STYLE_PRESETS = {
"三渲二国风": {
"prefix": "三渲二国风动画风格，工笔线条，中国传统审美",
"lighting": "柔和自然光，水墨晕染",
"palette": "青绿山水色调，朱砂点缀",
},
"水墨": {
"prefix": "中国水墨画风格，留白意境",
"lighting": "墨色浓淡",
"palette": "黑白灰为主，偶尔赭石",
},
"古风赛璐璐": {
"prefix": "日式赛璐璐+中国古风融合，清新明亮",
"lighting": "柔光晴天",
"palette": "淡雅国风配色",
},
"工笔": {
"prefix": "中国工笔画风格，细腻线条，重彩渲染",
"lighting": "平光",
"palette": "矿物颜料，金线勾勒",
},
}
GENRE_PRESETS = {
"仙侠": "门派纷争 / 修仙问道 / 御剑飞行 / 法宝秘术",
"宫斗": "宫廷权谋 / 妃嫔博弈 / 皇家礼仪 / 雕梁画栋",
"江湖": "快意恩仇 / 武林纷争 / 客栈酒肆 / 刀光剑影",
"志怪": "山海异兽 / 妖怪传说 / 古籍志异 / 诡谲氛围",
}
# 豆包大模型 TTS 音色（2026-04 核对，_conversation_wvae_bigtts 后缀=豆包大模型版）
# 实际音色 ID 较多，这里列常用。完整列表见 https://www.volcengine.com/docs/6561/97465
VOICE_PRESETS = {
# 男声
"male_young": "zh_male_ahu_conversation_wvae_bigtts", # 温暖阿虎（青年温润）
"male_mature": "zh_male_M392_conversation_wvae_bigtts", # 京腔侃爷（成熟磁性）
"male_elder": "zh_male_yanqing_conversation_wvae_bigtts", # 沉稳长者
# 女声
"female_young": "zh_female_sinong_conversation_wvae_bigtts", # 爽快思思（清新少女）
"female_mature": "zh_female_xiaohe_conversation_wvae_bigtts", # 湾湾小何（温柔熟女）
"female_elder": "zh_female_guniang_conversation_wvae_bigtts", # 端庄长辈
}
FILE:scripts/_shared/cost_guard.py
"""三级成本熔断.
- 硬熔断（hard）：开工前估算超 cap 立即阻止
- 软预警（warn）：运行时累计超 warn_ratio × cap 打印告警
- 降级建议（suggest）：超 cap 时给出具体降级方案
"""
from __future__ import annotations
import json
import pathlib
import time
from dataclasses import dataclass, field, asdict
class BudgetExceeded(RuntimeError):
"""熔断触发异常；上层 catch 后走降级逻辑."""
@dataclass
class CostRecord:
step: str
item: str
cost: float
ts: float = field(default_factory=time.time)
@dataclass
class CostGuard:
cap: float = 600.0
warn_ratio: float = 0.7
project_dir: pathlib.Path | None = None
spent: float = 0.0
records: list[CostRecord] = field(default_factory=list)
_warned: bool = False
def preflight(self, estimated_total: float) -> None:
"""开工前硬熔断.
Raises:
BudgetExceeded: 估算超 cap 时抛出，附带降级建议字符串。
"""
if estimated_total > self.cap:
suggestions = self.downgrade_suggestions(estimated_total)
raise BudgetExceeded(
f"预估成本 ¥{estimated_total:.2f} 超过熔断上限 ¥{self.cap:.2f}。\n"
f"降级建议：\n{suggestions}\n"
f"如需继续，请提升 cost_cap 或采纳上述任一降级方案。"
)
def charge(self, step: str, item: str, cost: float) -> None:
"""扣费，触发软预警/硬熔断."""
self.spent += cost
self.records.append(CostRecord(step=step, item=item, cost=cost))
ratio = self.spent / self.cap if self.cap else 0
if ratio >= 1.0:
self._persist()
raise BudgetExceeded(
f"累计成本 ¥{self.spent:.2f} 达到熔断上限 ¥{self.cap:.2f}（{step}/{item}）。\n"
f"已完成步骤可从 checkpoint 续跑。"
)
if ratio >= self.warn_ratio and not self._warned:
self._warned = True
print(
f"⚠️ 成本预警：已花费 ¥{self.spent:.2f}（{ratio:.0%} of ¥{self.cap:.2f}），"
f"即将触发熔断。"
)
self._persist()
def downgrade_suggestions(self, estimated_total: float) -> str:
"""生成降级方案文本."""
over = estimated_total - self.cap
lines = [
f" 1. 缩短总时长：每减 1 分钟约省 ¥{estimated_total / 5:.0f}",
f" 2. 减少镜头数：每删 1 镜约省 ¥{estimated_total / 48:.1f}",
f" 3. 关闭对口型：整片省 ¥{estimated_total * 0.37:.0f}（约 37%）",
f" 4. 视频降到 4 秒/镜：整片省 ¥{estimated_total * 0.2:.0f}（约 20%）",
f" 5. 提升 cost_cap 至 ¥{estimated_total * 1.1:.0f}（当前 ¥{self.cap:.0f}，需多付 ¥{over:.2f}）",
]
return "\n".join(lines)
def report(self) -> dict:
"""返回成本报告（可序列化）."""
by_step: dict[str, float] = {}
for r in self.records:
by_step[r.step] = by_step.get(r.step, 0.0) + r.cost
return {
"spent": round(self.spent, 2),
"cap": self.cap,
"ratio": round(self.spent / self.cap, 3) if self.cap else 0,
"by_step": {k: round(v, 2) for k, v in by_step.items()},
"record_count": len(self.records),
}
def _persist(self) -> None:
if not self.project_dir:
return
self.project_dir.mkdir(parents=True, exist_ok=True)
path = self.project_dir / ".cost.json"
data = {
"cap": self.cap,
"warn_ratio": self.warn_ratio,
"spent": self.spent,
"records": [asdict(r) for r in self.records],
}
path.write_text(json.dumps(data, ensure_ascii=False, indent=2))
@classmethod
def load(cls, project_dir: pathlib.Path, cap: float = 600.0) -> "CostGuard":
"""从 project_dir/.cost.json 恢复."""
path = project_dir / ".cost.json"
guard = cls(cap=cap, project_dir=project_dir)
if path.exists():
data = json.loads(path.read_text())
guard.cap = data.get("cap", cap)
guard.warn_ratio = data.get("warn_ratio", 0.7)
guard.spent = data.get("spent", 0.0)
guard.records = [CostRecord(**r) for r in data.get("records", [])]
return guard
def estimate_total(
n_scenes: int,
n_characters: int,
total_chars: int,
scene_duration: int = 5,
resolution: str = "720p",
fast: bool = False,
enable_lipsync: bool = True,
enable_bgm: bool = True,
) -> dict:
"""估算全流程成本，返回明细 dict.
video 按 resolution 和 fast 标志换算每秒元价；lipsync 按 scene_duration/5s 换算。
"""
from config import PRICING, video_unit_price
image_cost = (n_scenes + n_characters * 3) * PRICING["image_per_pic"]
video_cost = n_scenes * scene_duration * video_unit_price(resolution, fast)
tts_cost = total_chars * PRICING["tts_per_char"]
lipsync_cost = (
n_scenes * (scene_duration / 5.0) * PRICING["lipsync_per_5s"]
if enable_lipsync else 0
)
bgm_cost = PRICING["bgm_per_track"] if enable_bgm else 0
total = image_cost + video_cost + tts_cost + lipsync_cost + bgm_cost
return {
"image": round(image_cost, 2),
"video": round(video_cost, 2),
"tts": round(tts_cost, 2),
"lipsync": round(lipsync_cost, 2),
"bgm": round(bgm_cost, 2),
"total": round(total, 2),
"resolution": resolution,
"fast": fast,
}
FILE:scripts/storyboard.py
"""分镜关键帧生成（Seedream 4.0 图生图 + 多图参考）."""
from __future__ import annotations
import argparse
import json
import pathlib
import sys
import requests
HERE = pathlib.Path(__file__).resolve()
# _shared/ 定位：优先 bundled（scripts/_shared/），fallback monorepo 根
for _cand in (HERE.parent / "_shared", HERE.parents[2] / "_shared"):
if (_cand / "config.py").exists():
sys.path.insert(0, str(_cand))
break
from ark_api import ArkClient
from config import STYLE_PRESETS, PRICING, DEFAULTS
from cost_guard import CostGuard
from checkpoint import Checkpoint
MAX_REF = 4 # Seedream 4.0 最多 4 张参考图
def build_prompt(style: str, scene: dict) -> str:
preset = STYLE_PRESETS.get(style, {})
prefix = preset.get("prefix", "")
lighting = preset.get("lighting", "")
return (
f"{prefix}，{scene.get('location', '')}，{scene.get('time', '')}。"
f"{scene.get('action', '')}。"
f"{scene.get('camera', '')}。"
f"{scene.get('mood', '')}氛围。{lighting}。"
f"竖屏构图，9:16。"
)
def pick_refs(char_ids: list[str], char_manifest: dict) -> list[str]:
refs = []
for cid in char_ids[:MAX_REF]:
info = char_manifest.get(cid, {})
for img in info.get("images", []):
if "_full." in img:
refs.append(img)
break
return refs
def download(url: str, path: pathlib.Path) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
r = requests.get(url, stream=True)
with open(path, "wb") as f:
for chunk in r.iter_content(8192):
f.write(chunk)
def main() -> int:
p = argparse.ArgumentParser()
p.add_argument("--script", required=True)
p.add_argument("--char-dir", required=True)
p.add_argument("--out-dir", required=True)
args = p.parse_args()
script = json.loads(pathlib.Path(args.script).read_text())
char_manifest = json.loads(
(pathlib.Path(args.char_dir) / "manifest.json").read_text()
)
out_dir = pathlib.Path(args.out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
project_dir = out_dir.parent
guard = CostGuard.load(project_dir)
cp = Checkpoint(project_dir)
client = ArkClient()
style = script.get("style", DEFAULTS["style"])
manifest: dict = {}
for scene in script.get("scenes", []):
sid = scene["id"]
out = out_dir / f"{sid}.png"
if out.exists() or cp.sub_done("storyboard", sid):
print(f" ⏭️ {sid} 已完成")
manifest[sid] = {"path": str(out)}
continue
prompt = build_prompt(style, scene)
refs = pick_refs(scene.get("characters", []), char_manifest)
print(f" 🎞️ {sid}: {prompt[:50]}... (refs={len(refs)})")
for attempt in range(DEFAULTS["scene_retry"] + 1):
try:
url = client.generate_image(
prompt=prompt,
reference_images=refs,
size="768x1344",
)
download(url, out)
break
except Exception as e:
if attempt == DEFAULTS["scene_retry"]:
print(f" ❌ {sid} 多次失败: {e}")
raise
print(f" ↻ {sid} 重试 {attempt+1}")
guard.charge("storyboard", sid, PRICING["image_per_pic"])
cp.sub_mark("storyboard", sid)
manifest[sid] = {"path": str(out), "prompt": prompt}
(out_dir / "manifest.json").write_text(
json.dumps(manifest, ensure_ascii=False, indent=2)
)
print(f"✅ 分镜: {len(manifest)} 张")
return 0
if __name__ == "__main__":
sys.exit(main())
根据剧本 characters 字段，用 Seedream 4.0 生成每个角色的全身立绘/半身特写/Q 版三联卡。后续分镜用这三张图做多图参考保证角色一致性。触发词：生成角色卡、角色立绘、人物设定图。
---
name: huo15-comic-character
displayName: 火15 漫剧-角色三联卡
description: 根据剧本 characters 字段，用 Seedream 4.0 生成每个角色的全身立绘/半身特写/Q 版三联卡。后续分镜用这三张图做多图参考保证角色一致性。触发词：生成角色卡、角色立绘、人物设定图。
version: 0.1.0
aliases:
- 角色卡
- 角色立绘
---
# 火15 漫剧-角色三联卡 Skill
> 读 script.json → 调 Seedream 4.0 → 每个角色输出 3 张图 → 后续 storyboard 用作参考。
---
## 输入 / 输出
```bash
python scripts/character.py \
--script output/demo/script.json \
--out-dir output/demo/characters
```
输出目录结构：
```
characters/
├── C1_full.png # 全身立绘
├── C1_close.png # 半身特写（表情基准）
├── C1_chibi.png # Q 版头像
├── C2_full.png
├── ...
└── manifest.json # {"C1": {"name": "顾青崖", "images": [...]}}
```
## 提示词模板
每个角色构建三条 prompt：
```python
full_prompt = f"{STYLE_PREFIX}，角色全身立绘，{char.visual}，{char.personality}气质，站姿，居中构图，纯色背景"
close_prompt = f"{STYLE_PREFIX}，角色半身特写，{char.visual}，{char.personality}表情，肩部以上，正面"
chibi_prompt = f"{STYLE_PREFIX}，Q版头像，{char.visual}简化版，可爱风格，圆润线条"
```
`STYLE_PREFIX` 取自 `_shared/config.py` 的 `STYLE_PRESETS[style].prefix`。
## 成本
3 张 × N 角色 × ¥0.08/张。典型 3 角色 = ¥0.72。
FILE:_meta.json
{
"ownerId": "kn7byevkn40d6z4p7ghdb097z983tj33",
"slug": "huo15-comic-character",
"version": "0.1.0"
}
FILE:scripts/_shared/__init__.py
"""huo15-ai-comics 共享库。
所有子 skill 通过 sys.path 注入此模块（dual-path fallback）：
HERE = pathlib.Path(__file__).resolve()
# 优先 bundled（clawhub 独立安装场景：scripts/_shared/）
# fallback monorepo 根（本仓库 dev 场景：../../../_shared/）
for _cand in (HERE.parent / "_shared", HERE.parents[2] / "_shared"):
if (_cand / "config.py").exists():
sys.path.insert(0, str(_cand))
break
from cost_guard import CostGuard
from checkpoint import Checkpoint
from ark_api import ArkClient
from config import DEFAULTS, MODELS
publish.sh 会在发布前把本目录 cp 到每个 `<skill>/scripts/_shared/`，
让独立安装的 skill 仍能解析 import。
"""
FILE:scripts/_shared/ark_api.py
"""火山方舟 API 薄封装：Seedream 4.0 图 / Seedance 2.0 视频 / 豆包大模型 TTS.
2026-04 核对。文档：
- Seedance: https://www.volcengine.com/docs/82379/1520757
- Seedream: doubao-seedream-4-0-250828
- TTS: https://www.volcengine.com/docs/6561/97465
"""
from __future__ import annotations
import base64
import os
import pathlib
import time
import requests
try:
from config import ENDPOINTS, MODELS
except ImportError: # 兼容直接运行
ENDPOINTS = {
"ark_base": "https://ark.cn-beijing.volces.com/api/v3",
}
MODELS = {
"image": "doubao-seedream-4-0-250828",
"video": "doubao-seedance-2-0-260128",
"video_fast": "doubao-seedance-2-0-fast-260128",
"tts": "doubao-tts-bigtts",
}
class ArkClient:
def __init__(self, api_key: str | None = None):
self.api_key = api_key or os.environ.get("ARK_API_KEY", "")
if not self.api_key:
raise RuntimeError("缺少 ARK_API_KEY 环境变量")
self.base_url = ENDPOINTS["ark_base"]
self.headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
}
# ------------ 图像（Seedream 4.0）------------
def generate_image(
self,
prompt: str,
reference_images: list[str] | None = None,
size: str = "1024x1792",
model: str | None = None,
) -> str:
"""文生图 / 图生图。返回第一张图片 URL.
reference_images 传文件路径或 URL，会自动转 data URI.
"""
content = [{"type": "text", "text": prompt}]
for img in reference_images or []:
content.append({
"type": "image_url",
"image_url": {"url": self._image_to_data_uri(img)},
})
body = {
"model": model or MODELS["image"],
"content": content,
"size": size,
"watermark": False,
}
resp = requests.post(
f"{self.base_url}/images/generations",
headers=self.headers,
json=body,
timeout=120,
)
resp.raise_for_status()
data = resp.json()
# 兼容两种响应结构
if "data" in data and data["data"]:
return data["data"][0].get("url", "")
if "url" in data:
return data["url"]
raise RuntimeError(f"image response no url: {data}")
# ------------ 视频（Seedance 2.0）------------
def submit_video(
self,
prompt: str,
first_frame: str | None = None,
last_frame: str | None = None,
reference_image: str | None = None,
reference_video: str | None = None,
reference_audio: str | None = None,
duration: int = 5,
ratio: str = "9:16",
resolution: str = "720p",
generate_audio: bool = False,
return_last_frame: bool = False,
fast: bool = False,
seed: int | None = None,
) -> str:
"""提交视频任务，返回 task_id.
支持多种输入模态：first_frame / last_frame（首尾帧模式）/ reference_image /
reference_video / reference_audio。同一请求最多 9 图、3 视频、3 音频。
"""
content: list[dict] = [{"type": "text", "text": prompt}]
def _add_img(path: str, role: str):
content.append({
"type": "image_url",
"image_url": {"url": self._image_to_data_uri(path)},
"role": role,
})
if first_frame:
_add_img(first_frame, "first_frame")
if last_frame:
_add_img(last_frame, "last_frame")
if reference_image:
_add_img(reference_image, "reference_image")
if reference_video:
content.append({
"type": "video_url",
"video_url": {"url": reference_video if reference_video.startswith("http") else reference_video},
"role": "reference_video",
})
if reference_audio:
content.append({
"type": "audio_url",
"audio_url": {"url": reference_audio},
"role": "reference_audio",
})
body: dict = {
"model": MODELS["video_fast" if fast else "video"],
"content": content,
"ratio": ratio,
"resolution": resolution,
"duration": duration,
"watermark": False,
"return_last_frame": return_last_frame,
"generate_audio": generate_audio,
}
if seed is not None:
body["seed"] = seed
resp = requests.post(
f"{self.base_url}/contents/generations/tasks",
headers=self.headers,
json=body,
timeout=60,
)
resp.raise_for_status()
return resp.json()["id"]
def poll_video(self, task_id: str, timeout_s: int = 900, interval_s: int = 10) -> dict:
"""轮询视频任务，完成返回完整 dict（含 content.video_url / usage.total_tokens）."""
deadline = time.time() + timeout_s
while time.time() < deadline:
time.sleep(interval_s)
resp = requests.get(
f"{self.base_url}/contents/generations/tasks/{task_id}",
headers=self.headers,
timeout=30,
)
data = resp.json()
status = data.get("status")
if status == "succeeded":
return data
if status == "failed":
raise RuntimeError(f"视频任务失败: {data}")
raise TimeoutError(f"视频任务 {task_id} 超时（{timeout_s}s）")
def download_video(self, url: str, out_path: pathlib.Path) -> pathlib.Path:
out_path = pathlib.Path(out_path)
out_path.parent.mkdir(parents=True, exist_ok=True)
r = requests.get(url, stream=True, timeout=300)
with open(out_path, "wb") as f:
for chunk in r.iter_content(8192):
f.write(chunk)
return out_path
# ------------ 语音（豆包大模型 TTS）------------
def tts(
self,
text: str,
voice: str = "zh_female_sinong_conversation_wvae_bigtts",
out_path: pathlib.Path | str = "out.wav",
speed: float = 1.0,
emotion: str | None = None,
) -> pathlib.Path:
"""豆包 TTS 合成，写入 out_path.
voice 是音色 ID，格式 zh_{gender}_{name}_conversation_wvae_bigtts.
完整音色见 https://www.volcengine.com/docs/6561/97465.
注意：TTS 可能需要单独的 appid/cluster 凭证（走 openspeech.bytedance.com），
此处用 ark OpenAI 兼容接口作为简化，实际项目请核对授权方式。
"""
body = {
"model": MODELS["tts"],
"input": text,
"voice": voice,
"speed": speed,
"response_format": "wav",
}
if emotion:
body["emotion"] = emotion
resp = requests.post(
f"{self.base_url}/audio/speech",
headers=self.headers,
json=body,
timeout=60,
)
resp.raise_for_status()
out = pathlib.Path(out_path)
out.parent.mkdir(parents=True, exist_ok=True)
out.write_bytes(resp.content)
return out
# ------------ 工具 ------------
@staticmethod
def _image_to_data_uri(path_or_url: str) -> str:
if path_or_url.startswith(("http://", "https://", "data:")):
return path_or_url
p = pathlib.Path(path_or_url)
ext = p.suffix.lower().lstrip(".") or "jpeg"
if ext == "jpg":
ext = "jpeg"
b64 = base64.b64encode(p.read_bytes()).decode()
return f"data:image/{ext};base64,{b64}"
def cost_from_video_response(data: dict, resolution: str = "720p") -> float:
"""从视频响应中精确计算成本（按 token）."""
from config import PRICING
tokens = data.get("usage", {}).get("total_tokens", 0)
return round(tokens * PRICING["video_per_mtoken"] / 1_000_000, 3)
FILE:scripts/_shared/checkpoint.py
"""阶段 checkpoint，支持失败续跑."""
from __future__ import annotations
import json
import pathlib
from dataclasses import dataclass, field, asdict
STEPS = [
"script",
"characters",
"storyboard",
"videos",
"dubs",
"lipsync",
"bgm",
"subtitle",
"edit",
]
@dataclass
class Checkpoint:
project_dir: pathlib.Path
status: dict = field(default_factory=dict)
def __post_init__(self):
self.project_dir = pathlib.Path(self.project_dir)
self._load()
def _path(self) -> pathlib.Path:
return self.project_dir / ".checkpoint.json"
def _load(self) -> None:
if self._path().exists():
self.status = json.loads(self._path().read_text())
else:
self.status = {s: "pending" for s in STEPS}
def save(self) -> None:
self.project_dir.mkdir(parents=True, exist_ok=True)
self._path().write_text(json.dumps(self.status, ensure_ascii=False, indent=2))
def is_done(self, step: str) -> bool:
return self.status.get(step) == "done"
def mark_done(self, step: str) -> None:
self.status[step] = "done"
self.save()
def mark_running(self, step: str) -> None:
self.status[step] = "running"
self.save()
def mark_failed(self, step: str, reason: str = "") -> None:
self.status[step] = f"failed: {reason}" if reason else "failed"
self.save()
def next_pending(self) -> str | None:
for s in STEPS:
v = self.status.get(s, "pending")
if v != "done":
return s
return None
def sub_mark(self, step: str, sub_id: str, value: str = "done") -> None:
"""用于镜头级别的细粒度 checkpoint，如 videos.S01=done."""
key = f"{step}.{sub_id}"
self.status[key] = value
self.save()
def sub_done(self, step: str, sub_id: str) -> bool:
return self.status.get(f"{step}.{sub_id}") == "done"
FILE:scripts/_shared/config.py
"""家族共享默认参数与模型 ID.
基于 2026-04 实测火山方舟/Kling/Suno 文档核对。
"""
DEFAULTS = {
"duration_total": 240, # 秒，3-5 分钟区间默认 4 分钟
"scene_duration": 5, # 单镜头秒数（Seedance 2.0 支持 4-15）
"ratio": "9:16", # 竖屏；Seedance 2.0 支持 9:16/16:9/4:3/3:4/21:9/1:1/adaptive
"resolution": "720p", # 默认 720p 省钱；可选 480p/720p/1080p/2K
"style": "三渲二国风",
"genre": "仙侠",
"cost_cap": 600.0,
"cost_warn_ratio": 0.7,
"cost_hard_ratio": 1.0,
"watermark": False,
"concurrency": 3,
"scene_retry": 2,
"fast_mode": False, # True 用 seedance-fast（便宜但质量低）
}
MODELS = {
"script": "claude-opus-4-7",
"image": "doubao-seedream-4-0-250828", # 方舟 Seedream 4.0
"video": "doubao-seedance-2-0-260128", # 方舟 Seedance 2.0 标准版
"video_fast": "doubao-seedance-2-0-fast-260128", # 方舟 Seedance 2.0 fast
"tts": "doubao-tts-bigtts", # 方舟豆包大模型 TTS
"lipsync": "kling-v2.6", # Kling 2.6 原生对口型
"music": "suno-v5.5", # Suno v5.5（via sunoapi.org）
}
# API 端点（2026-04 核对）
ENDPOINTS = {
"ark_base": "https://ark.cn-beijing.volces.com/api/v3",
"kling_base": "https://api.klingai.com/v1", # 官方；第三方 piapi.ai/fal.ai 亦可
"suno_base": "https://api.sunoapi.org", # 第三方（Suno 无公开官方 API）
}
# 单位价（元），2026-04 核对
# Seedance 2.0: token-based 46¥/M tokens（1080p 文生/图生视频），28¥/M tokens（视频参考）
# 按秒估算的便利值：token = duration × width × height × fps / 1024，用 token × 46 / 1e6 得元价
PRICING = {
"image_per_pic": 0.08, # Seedream 4.0 每张
"video_480p_per_sec": 0.50, # 480p 9:16 ≈ 5s ¥2.5
"video_720p_per_sec": 0.994, # 720p 9:16 ≈ 5s ¥5
"video_1080p_per_sec": 2.26, # 1080p 9:16 ≈ 5s ¥11.3
"video_fast_discount": 0.5, # fast 模型约 5 折
"video_per_mtoken": 46.0, # 官方 token 单价（M tokens）
"tts_per_char": 0.0008, # 豆包大模型 TTS
"lipsync_per_5s": 0.72, # Kling 官方 $0.1/5s ≈ ¥0.72
"bgm_per_track": 3.0, # sunoapi.org 第三方均价
}
def video_unit_price(resolution: str = "720p", fast: bool = False) -> float:
"""返回每秒元价，用于预估."""
base = {
"480p": PRICING["video_480p_per_sec"],
"720p": PRICING["video_720p_per_sec"],
"1080p": PRICING["video_1080p_per_sec"],
"2K": PRICING["video_1080p_per_sec"] * 1.5,
}.get(resolution, PRICING["video_720p_per_sec"])
if fast:
base *= PRICING["video_fast_discount"]
return base
# 国风专用提示词片段
STYLE_PRESETS = {
"三渲二国风": {
"prefix": "三渲二国风动画风格，工笔线条，中国传统审美",
"lighting": "柔和自然光，水墨晕染",
"palette": "青绿山水色调，朱砂点缀",
},
"水墨": {
"prefix": "中国水墨画风格，留白意境",
"lighting": "墨色浓淡",
"palette": "黑白灰为主，偶尔赭石",
},
"古风赛璐璐": {
"prefix": "日式赛璐璐+中国古风融合，清新明亮",
"lighting": "柔光晴天",
"palette": "淡雅国风配色",
},
"工笔": {
"prefix": "中国工笔画风格，细腻线条，重彩渲染",
"lighting": "平光",
"palette": "矿物颜料，金线勾勒",
},
}
GENRE_PRESETS = {
"仙侠": "门派纷争 / 修仙问道 / 御剑飞行 / 法宝秘术",
"宫斗": "宫廷权谋 / 妃嫔博弈 / 皇家礼仪 / 雕梁画栋",
"江湖": "快意恩仇 / 武林纷争 / 客栈酒肆 / 刀光剑影",
"志怪": "山海异兽 / 妖怪传说 / 古籍志异 / 诡谲氛围",
}
# 豆包大模型 TTS 音色（2026-04 核对，_conversation_wvae_bigtts 后缀=豆包大模型版）
# 实际音色 ID 较多，这里列常用。完整列表见 https://www.volcengine.com/docs/6561/97465
VOICE_PRESETS = {
# 男声
"male_young": "zh_male_ahu_conversation_wvae_bigtts", # 温暖阿虎（青年温润）
"male_mature": "zh_male_M392_conversation_wvae_bigtts", # 京腔侃爷（成熟磁性）
"male_elder": "zh_male_yanqing_conversation_wvae_bigtts", # 沉稳长者
# 女声
"female_young": "zh_female_sinong_conversation_wvae_bigtts", # 爽快思思（清新少女）
"female_mature": "zh_female_xiaohe_conversation_wvae_bigtts", # 湾湾小何（温柔熟女）
"female_elder": "zh_female_guniang_conversation_wvae_bigtts", # 端庄长辈
}
FILE:scripts/_shared/cost_guard.py
"""三级成本熔断.
- 硬熔断（hard）：开工前估算超 cap 立即阻止
- 软预警（warn）：运行时累计超 warn_ratio × cap 打印告警
- 降级建议（suggest）：超 cap 时给出具体降级方案
"""
from __future__ import annotations
import json
import pathlib
import time
from dataclasses import dataclass, field, asdict
class BudgetExceeded(RuntimeError):
"""熔断触发异常；上层 catch 后走降级逻辑."""
@dataclass
class CostRecord:
step: str
item: str
cost: float
ts: float = field(default_factory=time.time)
@dataclass
class CostGuard:
cap: float = 600.0
warn_ratio: float = 0.7
project_dir: pathlib.Path | None = None
spent: float = 0.0
records: list[CostRecord] = field(default_factory=list)
_warned: bool = False
def preflight(self, estimated_total: float) -> None:
"""开工前硬熔断.
Raises:
BudgetExceeded: 估算超 cap 时抛出，附带降级建议字符串。
"""
if estimated_total > self.cap:
suggestions = self.downgrade_suggestions(estimated_total)
raise BudgetExceeded(
f"预估成本 ¥{estimated_total:.2f} 超过熔断上限 ¥{self.cap:.2f}。\n"
f"降级建议：\n{suggestions}\n"
f"如需继续，请提升 cost_cap 或采纳上述任一降级方案。"
)
def charge(self, step: str, item: str, cost: float) -> None:
"""扣费，触发软预警/硬熔断."""
self.spent += cost
self.records.append(CostRecord(step=step, item=item, cost=cost))
ratio = self.spent / self.cap if self.cap else 0
if ratio >= 1.0:
self._persist()
raise BudgetExceeded(
f"累计成本 ¥{self.spent:.2f} 达到熔断上限 ¥{self.cap:.2f}（{step}/{item}）。\n"
f"已完成步骤可从 checkpoint 续跑。"
)
if ratio >= self.warn_ratio and not self._warned:
self._warned = True
print(
f"⚠️ 成本预警：已花费 ¥{self.spent:.2f}（{ratio:.0%} of ¥{self.cap:.2f}），"
f"即将触发熔断。"
)
self._persist()
def downgrade_suggestions(self, estimated_total: float) -> str:
"""生成降级方案文本."""
over = estimated_total - self.cap
lines = [
f" 1. 缩短总时长：每减 1 分钟约省 ¥{estimated_total / 5:.0f}",
f" 2. 减少镜头数：每删 1 镜约省 ¥{estimated_total / 48:.1f}",
f" 3. 关闭对口型：整片省 ¥{estimated_total * 0.37:.0f}（约 37%）",
f" 4. 视频降到 4 秒/镜：整片省 ¥{estimated_total * 0.2:.0f}（约 20%）",
f" 5. 提升 cost_cap 至 ¥{estimated_total * 1.1:.0f}（当前 ¥{self.cap:.0f}，需多付 ¥{over:.2f}）",
]
return "\n".join(lines)
def report(self) -> dict:
"""返回成本报告（可序列化）."""
by_step: dict[str, float] = {}
for r in self.records:
by_step[r.step] = by_step.get(r.step, 0.0) + r.cost
return {
"spent": round(self.spent, 2),
"cap": self.cap,
"ratio": round(self.spent / self.cap, 3) if self.cap else 0,
"by_step": {k: round(v, 2) for k, v in by_step.items()},
"record_count": len(self.records),
}
def _persist(self) -> None:
if not self.project_dir:
return
self.project_dir.mkdir(parents=True, exist_ok=True)
path = self.project_dir / ".cost.json"
data = {
"cap": self.cap,
"warn_ratio": self.warn_ratio,
"spent": self.spent,
"records": [asdict(r) for r in self.records],
}
path.write_text(json.dumps(data, ensure_ascii=False, indent=2))
@classmethod
def load(cls, project_dir: pathlib.Path, cap: float = 600.0) -> "CostGuard":
"""从 project_dir/.cost.json 恢复."""
path = project_dir / ".cost.json"
guard = cls(cap=cap, project_dir=project_dir)
if path.exists():
data = json.loads(path.read_text())
guard.cap = data.get("cap", cap)
guard.warn_ratio = data.get("warn_ratio", 0.7)
guard.spent = data.get("spent", 0.0)
guard.records = [CostRecord(**r) for r in data.get("records", [])]
return guard
def estimate_total(
n_scenes: int,
n_characters: int,
total_chars: int,
scene_duration: int = 5,
resolution: str = "720p",
fast: bool = False,
enable_lipsync: bool = True,
enable_bgm: bool = True,
) -> dict:
"""估算全流程成本，返回明细 dict.
video 按 resolution 和 fast 标志换算每秒元价；lipsync 按 scene_duration/5s 换算。
"""
from config import PRICING, video_unit_price
image_cost = (n_scenes + n_characters * 3) * PRICING["image_per_pic"]
video_cost = n_scenes * scene_duration * video_unit_price(resolution, fast)
tts_cost = total_chars * PRICING["tts_per_char"]
lipsync_cost = (
n_scenes * (scene_duration / 5.0) * PRICING["lipsync_per_5s"]
if enable_lipsync else 0
)
bgm_cost = PRICING["bgm_per_track"] if enable_bgm else 0
total = image_cost + video_cost + tts_cost + lipsync_cost + bgm_cost
return {
"image": round(image_cost, 2),
"video": round(video_cost, 2),
"tts": round(tts_cost, 2),
"lipsync": round(lipsync_cost, 2),
"bgm": round(bgm_cost, 2),
"total": round(total, 2),
"resolution": resolution,
"fast": fast,
}
FILE:scripts/character.py
"""生成角色三联卡（Seedream 4.0）."""
from __future__ import annotations
import argparse
import json
import pathlib
import sys
import requests
HERE = pathlib.Path(__file__).resolve()
# _shared/ 定位：优先 bundled（scripts/_shared/），fallback monorepo 根
for _cand in (HERE.parent / "_shared", HERE.parents[2] / "_shared"):
if (_cand / "config.py").exists():
sys.path.insert(0, str(_cand))
break
from ark_api import ArkClient
from config import STYLE_PRESETS, PRICING
from cost_guard import CostGuard
VIEWS = [
("full", "角色全身立绘，站姿，居中构图，纯色背景", "1024x1792"),
("close", "角色半身特写，肩部以上，正面，柔光", "1024x1024"),
("chibi", "Q 版头像，简化版，圆润可爱风格", "1024x1024"),
]
def build_prompt(style: str, char: dict, view_desc: str) -> str:
preset = STYLE_PRESETS.get(style, {})
prefix = preset.get("prefix", "")
palette = preset.get("palette", "")
return (
f"{prefix}，{view_desc}。"
f"{char.get('visual', '')}，"
f"{char.get('personality', '')}气质。"
f"{palette}。高质量，精细线条。"
)
def download(url: str, path: pathlib.Path) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
r = requests.get(url, stream=True)
with open(path, "wb") as f:
for chunk in r.iter_content(8192):
f.write(chunk)
def main() -> int:
p = argparse.ArgumentParser()
p.add_argument("--script", required=True)
p.add_argument("--out-dir", required=True)
args = p.parse_args()
script = json.loads(pathlib.Path(args.script).read_text())
out_dir = pathlib.Path(args.out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
style = script.get("style", "三渲二国风")
characters = script.get("characters", [])
project_dir = out_dir.parent
guard = CostGuard.load(project_dir)
client = ArkClient()
manifest: dict = {}
for char in characters:
cid = char["id"]
imgs = []
for view_name, view_desc, size in VIEWS:
out = out_dir / f"{cid}_{view_name}.png"
if out.exists():
print(f" ⏭️ {out.name} 已存在")
imgs.append(str(out))
continue
prompt = build_prompt(style, char, view_desc)
print(f" 🎨 {cid}/{view_name}: {prompt[:60]}...")
url = client.generate_image(prompt=prompt, size=size)
download(url, out)
imgs.append(str(out))
guard.charge("characters", f"{cid}_{view_name}", PRICING["image_per_pic"])
manifest[cid] = {"name": char.get("name", cid), "images": imgs}
(out_dir / "manifest.json").write_text(
json.dumps(manifest, ensure_ascii=False, indent=2)
)
print(f"✅ 角色卡: {len(characters)} 人 × 3 张 = {len(characters)*3} 张")
return 0
if __name__ == "__main__":
sys.exit(main())
主题一句话 → 分幕分镜剧本 JSON（国风/仙侠/宫斗/江湖）。输出 script.json 含 characters、scenes、dialogue、camera、mood。触发词：生成剧本、漫剧剧本、分镜脚本、漫剧script。
---
name: huo15-comic-script
displayName: 火15 漫剧-剧本分镜
description: 主题一句话 → 分幕分镜剧本 JSON（国风/仙侠/宫斗/江湖）。输出 script.json 含 characters、scenes、dialogue、camera、mood。触发词：生成剧本、漫剧剧本、分镜脚本、漫剧script。
version: 0.1.0
aliases:
- 漫剧剧本
- 分镜剧本
---
# 火15 漫剧-剧本分镜 Skill
> 主题 → 结构化 script.json，后续所有 skill 以此为输入。
---
## 输入
```bash
python scripts/script_gen.py \
--theme "少年剑仙三年归来" \
--duration 240 \
--style 三渲二国风 \
--genre 仙侠 \
--out output/demo/script.json
```
## 输出 script.json 结构
```json
{
"title": "归剑录",
"style": "三渲二国风",
"genre": "仙侠",
"duration_total": 240,
"scene_duration": 5,
"logline": "...",
"characters": [
{
"id": "C1",
"name": "顾青崖",
"age": "18",
"visual": "白衣剑仙，剑眉星目，发束玉冠，腰佩青玉剑",
"personality": "沉稳内敛",
"voice": "zh_male_qingnian"
}
],
"scenes": [
{
"id": "S01",
"location": "青崖峰绝顶",
"time": "黎明",
"characters": ["C1"],
"action": "少年立于云海之上，长剑出鞘",
"dialogue": [{"char": "C1", "text": "三年归来，该了结旧怨了"}],
"camera": "远景→中景推近",
"duration": 5,
"mood": "苍凉壮阔"
}
]
}
```
## Agent 模式
Claude Agent 可**直接按 schema 写 script.json**，跳过 Python 脚本。脚本仅作为 fallback（Anthropic SDK 调用）和校验器。
## 国风提示词模板
脚本内嵌 `PROMPT_TEMPLATE`，对不同 `genre` 选用对应叙事节奏：
- 仙侠：起兴→伏笔→反转→决战
- 宫斗：暗流→试探→反击→结局
- 江湖：相遇→比武→情义→重逢
- 志怪：诡异→探查→真相→启示
FILE:_meta.json
{
"ownerId": "kn7byevkn40d6z4p7ghdb097z983tj33",
"slug": "huo15-comic-script",
"version": "0.1.0"
}
FILE:scripts/_shared/__init__.py
"""huo15-ai-comics 共享库。
所有子 skill 通过 sys.path 注入此模块（dual-path fallback）：
HERE = pathlib.Path(__file__).resolve()
# 优先 bundled（clawhub 独立安装场景：scripts/_shared/）
# fallback monorepo 根（本仓库 dev 场景：../../../_shared/）
for _cand in (HERE.parent / "_shared", HERE.parents[2] / "_shared"):
if (_cand / "config.py").exists():
sys.path.insert(0, str(_cand))
break
from cost_guard import CostGuard
from checkpoint import Checkpoint
from ark_api import ArkClient
from config import DEFAULTS, MODELS
publish.sh 会在发布前把本目录 cp 到每个 `<skill>/scripts/_shared/`，
让独立安装的 skill 仍能解析 import。
"""
FILE:scripts/_shared/ark_api.py
"""火山方舟 API 薄封装：Seedream 4.0 图 / Seedance 2.0 视频 / 豆包大模型 TTS.
2026-04 核对。文档：
- Seedance: https://www.volcengine.com/docs/82379/1520757
- Seedream: doubao-seedream-4-0-250828
- TTS: https://www.volcengine.com/docs/6561/97465
"""
from __future__ import annotations
import base64
import os
import pathlib
import time
import requests
try:
from config import ENDPOINTS, MODELS
except ImportError: # 兼容直接运行
ENDPOINTS = {
"ark_base": "https://ark.cn-beijing.volces.com/api/v3",
}
MODELS = {
"image": "doubao-seedream-4-0-250828",
"video": "doubao-seedance-2-0-260128",
"video_fast": "doubao-seedance-2-0-fast-260128",
"tts": "doubao-tts-bigtts",
}
class ArkClient:
def __init__(self, api_key: str | None = None):
self.api_key = api_key or os.environ.get("ARK_API_KEY", "")
if not self.api_key:
raise RuntimeError("缺少 ARK_API_KEY 环境变量")
self.base_url = ENDPOINTS["ark_base"]
self.headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
}
# ------------ 图像（Seedream 4.0）------------
def generate_image(
self,
prompt: str,
reference_images: list[str] | None = None,
size: str = "1024x1792",
model: str | None = None,
) -> str:
"""文生图 / 图生图。返回第一张图片 URL.
reference_images 传文件路径或 URL，会自动转 data URI.
"""
content = [{"type": "text", "text": prompt}]
for img in reference_images or []:
content.append({
"type": "image_url",
"image_url": {"url": self._image_to_data_uri(img)},
})
body = {
"model": model or MODELS["image"],
"content": content,
"size": size,
"watermark": False,
}
resp = requests.post(
f"{self.base_url}/images/generations",
headers=self.headers,
json=body,
timeout=120,
)
resp.raise_for_status()
data = resp.json()
# 兼容两种响应结构
if "data" in data and data["data"]:
return data["data"][0].get("url", "")
if "url" in data:
return data["url"]
raise RuntimeError(f"image response no url: {data}")
# ------------ 视频（Seedance 2.0）------------
def submit_video(
self,
prompt: str,
first_frame: str | None = None,
last_frame: str | None = None,
reference_image: str | None = None,
reference_video: str | None = None,
reference_audio: str | None = None,
duration: int = 5,
ratio: str = "9:16",
resolution: str = "720p",
generate_audio: bool = False,
return_last_frame: bool = False,
fast: bool = False,
seed: int | None = None,
) -> str:
"""提交视频任务，返回 task_id.
支持多种输入模态：first_frame / last_frame（首尾帧模式）/ reference_image /
reference_video / reference_audio。同一请求最多 9 图、3 视频、3 音频。
"""
content: list[dict] = [{"type": "text", "text": prompt}]
def _add_img(path: str, role: str):
content.append({
"type": "image_url",
"image_url": {"url": self._image_to_data_uri(path)},
"role": role,
})
if first_frame:
_add_img(first_frame, "first_frame")
if last_frame:
_add_img(last_frame, "last_frame")
if reference_image:
_add_img(reference_image, "reference_image")
if reference_video:
content.append({
"type": "video_url",
"video_url": {"url": reference_video if reference_video.startswith("http") else reference_video},
"role": "reference_video",
})
if reference_audio:
content.append({
"type": "audio_url",
"audio_url": {"url": reference_audio},
"role": "reference_audio",
})
body: dict = {
"model": MODELS["video_fast" if fast else "video"],
"content": content,
"ratio": ratio,
"resolution": resolution,
"duration": duration,
"watermark": False,
"return_last_frame": return_last_frame,
"generate_audio": generate_audio,
}
if seed is not None:
body["seed"] = seed
resp = requests.post(
f"{self.base_url}/contents/generations/tasks",
headers=self.headers,
json=body,
timeout=60,
)
resp.raise_for_status()
return resp.json()["id"]
def poll_video(self, task_id: str, timeout_s: int = 900, interval_s: int = 10) -> dict:
"""轮询视频任务，完成返回完整 dict（含 content.video_url / usage.total_tokens）."""
deadline = time.time() + timeout_s
while time.time() < deadline:
time.sleep(interval_s)
resp = requests.get(
f"{self.base_url}/contents/generations/tasks/{task_id}",
headers=self.headers,
timeout=30,
)
data = resp.json()
status = data.get("status")
if status == "succeeded":
return data
if status == "failed":
raise RuntimeError(f"视频任务失败: {data}")
raise TimeoutError(f"视频任务 {task_id} 超时（{timeout_s}s）")
def download_video(self, url: str, out_path: pathlib.Path) -> pathlib.Path:
out_path = pathlib.Path(out_path)
out_path.parent.mkdir(parents=True, exist_ok=True)
r = requests.get(url, stream=True, timeout=300)
with open(out_path, "wb") as f:
for chunk in r.iter_content(8192):
f.write(chunk)
return out_path
# ------------ 语音（豆包大模型 TTS）------------
def tts(
self,
text: str,
voice: str = "zh_female_sinong_conversation_wvae_bigtts",
out_path: pathlib.Path | str = "out.wav",
speed: float = 1.0,
emotion: str | None = None,
) -> pathlib.Path:
"""豆包 TTS 合成，写入 out_path.
voice 是音色 ID，格式 zh_{gender}_{name}_conversation_wvae_bigtts.
完整音色见 https://www.volcengine.com/docs/6561/97465.
注意：TTS 可能需要单独的 appid/cluster 凭证（走 openspeech.bytedance.com），
此处用 ark OpenAI 兼容接口作为简化，实际项目请核对授权方式。
"""
body = {
"model": MODELS["tts"],
"input": text,
"voice": voice,
"speed": speed,
"response_format": "wav",
}
if emotion:
body["emotion"] = emotion
resp = requests.post(
f"{self.base_url}/audio/speech",
headers=self.headers,
json=body,
timeout=60,
)
resp.raise_for_status()
out = pathlib.Path(out_path)
out.parent.mkdir(parents=True, exist_ok=True)
out.write_bytes(resp.content)
return out
# ------------ 工具 ------------
@staticmethod
def _image_to_data_uri(path_or_url: str) -> str:
if path_or_url.startswith(("http://", "https://", "data:")):
return path_or_url
p = pathlib.Path(path_or_url)
ext = p.suffix.lower().lstrip(".") or "jpeg"
if ext == "jpg":
ext = "jpeg"
b64 = base64.b64encode(p.read_bytes()).decode()
return f"data:image/{ext};base64,{b64}"
def cost_from_video_response(data: dict, resolution: str = "720p") -> float:
"""从视频响应中精确计算成本（按 token）."""
from config import PRICING
tokens = data.get("usage", {}).get("total_tokens", 0)
return round(tokens * PRICING["video_per_mtoken"] / 1_000_000, 3)
FILE:scripts/_shared/checkpoint.py
"""阶段 checkpoint，支持失败续跑."""
from __future__ import annotations
import json
import pathlib
from dataclasses import dataclass, field, asdict
STEPS = [
"script",
"characters",
"storyboard",
"videos",
"dubs",
"lipsync",
"bgm",
"subtitle",
"edit",
]
@dataclass
class Checkpoint:
project_dir: pathlib.Path
status: dict = field(default_factory=dict)
def __post_init__(self):
self.project_dir = pathlib.Path(self.project_dir)
self._load()
def _path(self) -> pathlib.Path:
return self.project_dir / ".checkpoint.json"
def _load(self) -> None:
if self._path().exists():
self.status = json.loads(self._path().read_text())
else:
self.status = {s: "pending" for s in STEPS}
def save(self) -> None:
self.project_dir.mkdir(parents=True, exist_ok=True)
self._path().write_text(json.dumps(self.status, ensure_ascii=False, indent=2))
def is_done(self, step: str) -> bool:
return self.status.get(step) == "done"
def mark_done(self, step: str) -> None:
self.status[step] = "done"
self.save()
def mark_running(self, step: str) -> None:
self.status[step] = "running"
self.save()
def mark_failed(self, step: str, reason: str = "") -> None:
self.status[step] = f"failed: {reason}" if reason else "failed"
self.save()
def next_pending(self) -> str | None:
for s in STEPS:
v = self.status.get(s, "pending")
if v != "done":
return s
return None
def sub_mark(self, step: str, sub_id: str, value: str = "done") -> None:
"""用于镜头级别的细粒度 checkpoint，如 videos.S01=done."""
key = f"{step}.{sub_id}"
self.status[key] = value
self.save()
def sub_done(self, step: str, sub_id: str) -> bool:
return self.status.get(f"{step}.{sub_id}") == "done"
FILE:scripts/_shared/config.py
"""家族共享默认参数与模型 ID.
基于 2026-04 实测火山方舟/Kling/Suno 文档核对。
"""
DEFAULTS = {
"duration_total": 240, # 秒，3-5 分钟区间默认 4 分钟
"scene_duration": 5, # 单镜头秒数（Seedance 2.0 支持 4-15）
"ratio": "9:16", # 竖屏；Seedance 2.0 支持 9:16/16:9/4:3/3:4/21:9/1:1/adaptive
"resolution": "720p", # 默认 720p 省钱；可选 480p/720p/1080p/2K
"style": "三渲二国风",
"genre": "仙侠",
"cost_cap": 600.0,
"cost_warn_ratio": 0.7,
"cost_hard_ratio": 1.0,
"watermark": False,
"concurrency": 3,
"scene_retry": 2,
"fast_mode": False, # True 用 seedance-fast（便宜但质量低）
}
MODELS = {
"script": "claude-opus-4-7",
"image": "doubao-seedream-4-0-250828", # 方舟 Seedream 4.0
"video": "doubao-seedance-2-0-260128", # 方舟 Seedance 2.0 标准版
"video_fast": "doubao-seedance-2-0-fast-260128", # 方舟 Seedance 2.0 fast
"tts": "doubao-tts-bigtts", # 方舟豆包大模型 TTS
"lipsync": "kling-v2.6", # Kling 2.6 原生对口型
"music": "suno-v5.5", # Suno v5.5（via sunoapi.org）
}
# API 端点（2026-04 核对）
ENDPOINTS = {
"ark_base": "https://ark.cn-beijing.volces.com/api/v3",
"kling_base": "https://api.klingai.com/v1", # 官方；第三方 piapi.ai/fal.ai 亦可
"suno_base": "https://api.sunoapi.org", # 第三方（Suno 无公开官方 API）
}
# 单位价（元），2026-04 核对
# Seedance 2.0: token-based 46¥/M tokens（1080p 文生/图生视频），28¥/M tokens（视频参考）
# 按秒估算的便利值：token = duration × width × height × fps / 1024，用 token × 46 / 1e6 得元价
PRICING = {
"image_per_pic": 0.08, # Seedream 4.0 每张
"video_480p_per_sec": 0.50, # 480p 9:16 ≈ 5s ¥2.5
"video_720p_per_sec": 0.994, # 720p 9:16 ≈ 5s ¥5
"video_1080p_per_sec": 2.26, # 1080p 9:16 ≈ 5s ¥11.3
"video_fast_discount": 0.5, # fast 模型约 5 折
"video_per_mtoken": 46.0, # 官方 token 单价（M tokens）
"tts_per_char": 0.0008, # 豆包大模型 TTS
"lipsync_per_5s": 0.72, # Kling 官方 $0.1/5s ≈ ¥0.72
"bgm_per_track": 3.0, # sunoapi.org 第三方均价
}
def video_unit_price(resolution: str = "720p", fast: bool = False) -> float:
"""返回每秒元价，用于预估."""
base = {
"480p": PRICING["video_480p_per_sec"],
"720p": PRICING["video_720p_per_sec"],
"1080p": PRICING["video_1080p_per_sec"],
"2K": PRICING["video_1080p_per_sec"] * 1.5,
}.get(resolution, PRICING["video_720p_per_sec"])
if fast:
base *= PRICING["video_fast_discount"]
return base
# 国风专用提示词片段
STYLE_PRESETS = {
"三渲二国风": {
"prefix": "三渲二国风动画风格，工笔线条，中国传统审美",
"lighting": "柔和自然光，水墨晕染",
"palette": "青绿山水色调，朱砂点缀",
},
"水墨": {
"prefix": "中国水墨画风格，留白意境",
"lighting": "墨色浓淡",
"palette": "黑白灰为主，偶尔赭石",
},
"古风赛璐璐": {
"prefix": "日式赛璐璐+中国古风融合，清新明亮",
"lighting": "柔光晴天",
"palette": "淡雅国风配色",
},
"工笔": {
"prefix": "中国工笔画风格，细腻线条，重彩渲染",
"lighting": "平光",
"palette": "矿物颜料，金线勾勒",
},
}
GENRE_PRESETS = {
"仙侠": "门派纷争 / 修仙问道 / 御剑飞行 / 法宝秘术",
"宫斗": "宫廷权谋 / 妃嫔博弈 / 皇家礼仪 / 雕梁画栋",
"江湖": "快意恩仇 / 武林纷争 / 客栈酒肆 / 刀光剑影",
"志怪": "山海异兽 / 妖怪传说 / 古籍志异 / 诡谲氛围",
}
# 豆包大模型 TTS 音色（2026-04 核对，_conversation_wvae_bigtts 后缀=豆包大模型版）
# 实际音色 ID 较多，这里列常用。完整列表见 https://www.volcengine.com/docs/6561/97465
VOICE_PRESETS = {
# 男声
"male_young": "zh_male_ahu_conversation_wvae_bigtts", # 温暖阿虎（青年温润）
"male_mature": "zh_male_M392_conversation_wvae_bigtts", # 京腔侃爷（成熟磁性）
"male_elder": "zh_male_yanqing_conversation_wvae_bigtts", # 沉稳长者
# 女声
"female_young": "zh_female_sinong_conversation_wvae_bigtts", # 爽快思思（清新少女）
"female_mature": "zh_female_xiaohe_conversation_wvae_bigtts", # 湾湾小何（温柔熟女）
"female_elder": "zh_female_guniang_conversation_wvae_bigtts", # 端庄长辈
}
FILE:scripts/_shared/cost_guard.py
"""三级成本熔断.
- 硬熔断（hard）：开工前估算超 cap 立即阻止
- 软预警（warn）：运行时累计超 warn_ratio × cap 打印告警
- 降级建议（suggest）：超 cap 时给出具体降级方案
"""
from __future__ import annotations
import json
import pathlib
import time
from dataclasses import dataclass, field, asdict
class BudgetExceeded(RuntimeError):
"""熔断触发异常；上层 catch 后走降级逻辑."""
@dataclass
class CostRecord:
step: str
item: str
cost: float
ts: float = field(default_factory=time.time)
@dataclass
class CostGuard:
cap: float = 600.0
warn_ratio: float = 0.7
project_dir: pathlib.Path | None = None
spent: float = 0.0
records: list[CostRecord] = field(default_factory=list)
_warned: bool = False
def preflight(self, estimated_total: float) -> None:
"""开工前硬熔断.
Raises:
BudgetExceeded: 估算超 cap 时抛出，附带降级建议字符串。
"""
if estimated_total > self.cap:
suggestions = self.downgrade_suggestions(estimated_total)
raise BudgetExceeded(
f"预估成本 ¥{estimated_total:.2f} 超过熔断上限 ¥{self.cap:.2f}。\n"
f"降级建议：\n{suggestions}\n"
f"如需继续，请提升 cost_cap 或采纳上述任一降级方案。"
)
def charge(self, step: str, item: str, cost: float) -> None:
"""扣费，触发软预警/硬熔断."""
self.spent += cost
self.records.append(CostRecord(step=step, item=item, cost=cost))
ratio = self.spent / self.cap if self.cap else 0
if ratio >= 1.0:
self._persist()
raise BudgetExceeded(
f"累计成本 ¥{self.spent:.2f} 达到熔断上限 ¥{self.cap:.2f}（{step}/{item}）。\n"
f"已完成步骤可从 checkpoint 续跑。"
)
if ratio >= self.warn_ratio and not self._warned:
self._warned = True
print(
f"⚠️ 成本预警：已花费 ¥{self.spent:.2f}（{ratio:.0%} of ¥{self.cap:.2f}），"
f"即将触发熔断。"
)
self._persist()
def downgrade_suggestions(self, estimated_total: float) -> str:
"""生成降级方案文本."""
over = estimated_total - self.cap
lines = [
f" 1. 缩短总时长：每减 1 分钟约省 ¥{estimated_total / 5:.0f}",
f" 2. 减少镜头数：每删 1 镜约省 ¥{estimated_total / 48:.1f}",
f" 3. 关闭对口型：整片省 ¥{estimated_total * 0.37:.0f}（约 37%）",
f" 4. 视频降到 4 秒/镜：整片省 ¥{estimated_total * 0.2:.0f}（约 20%）",
f" 5. 提升 cost_cap 至 ¥{estimated_total * 1.1:.0f}（当前 ¥{self.cap:.0f}，需多付 ¥{over:.2f}）",
]
return "\n".join(lines)
def report(self) -> dict:
"""返回成本报告（可序列化）."""
by_step: dict[str, float] = {}
for r in self.records:
by_step[r.step] = by_step.get(r.step, 0.0) + r.cost
return {
"spent": round(self.spent, 2),
"cap": self.cap,
"ratio": round(self.spent / self.cap, 3) if self.cap else 0,
"by_step": {k: round(v, 2) for k, v in by_step.items()},
"record_count": len(self.records),
}
def _persist(self) -> None:
if not self.project_dir:
return
self.project_dir.mkdir(parents=True, exist_ok=True)
path = self.project_dir / ".cost.json"
data = {
"cap": self.cap,
"warn_ratio": self.warn_ratio,
"spent": self.spent,
"records": [asdict(r) for r in self.records],
}
path.write_text(json.dumps(data, ensure_ascii=False, indent=2))
@classmethod
def load(cls, project_dir: pathlib.Path, cap: float = 600.0) -> "CostGuard":
"""从 project_dir/.cost.json 恢复."""
path = project_dir / ".cost.json"
guard = cls(cap=cap, project_dir=project_dir)
if path.exists():
data = json.loads(path.read_text())
guard.cap = data.get("cap", cap)
guard.warn_ratio = data.get("warn_ratio", 0.7)
guard.spent = data.get("spent", 0.0)
guard.records = [CostRecord(**r) for r in data.get("records", [])]
return guard
def estimate_total(
n_scenes: int,
n_characters: int,
total_chars: int,
scene_duration: int = 5,
resolution: str = "720p",
fast: bool = False,
enable_lipsync: bool = True,
enable_bgm: bool = True,
) -> dict:
"""估算全流程成本，返回明细 dict.
video 按 resolution 和 fast 标志换算每秒元价；lipsync 按 scene_duration/5s 换算。
"""
from config import PRICING, video_unit_price
image_cost = (n_scenes + n_characters * 3) * PRICING["image_per_pic"]
video_cost = n_scenes * scene_duration * video_unit_price(resolution, fast)
tts_cost = total_chars * PRICING["tts_per_char"]
lipsync_cost = (
n_scenes * (scene_duration / 5.0) * PRICING["lipsync_per_5s"]
if enable_lipsync else 0
)
bgm_cost = PRICING["bgm_per_track"] if enable_bgm else 0
total = image_cost + video_cost + tts_cost + lipsync_cost + bgm_cost
return {
"image": round(image_cost, 2),
"video": round(video_cost, 2),
"tts": round(tts_cost, 2),
"lipsync": round(lipsync_cost, 2),
"bgm": round(bgm_cost, 2),
"total": round(total, 2),
"resolution": resolution,
"fast": fast,
}
FILE:scripts/script_gen.py
"""剧本分镜生成。
两种模式：
1. Claude Agent 直接按 schema 写 script.json（推荐）—— 本脚本仅做校验
2. 独立 CLI 模式 —— 调 Anthropic SDK 生成（需要 ANTHROPIC_API_KEY）
"""
from __future__ import annotations
import argparse
import json
import os
import pathlib
import sys
HERE = pathlib.Path(__file__).resolve()
# _shared/ 定位：优先 bundled（scripts/_shared/），fallback monorepo 根
for _cand in (HERE.parent / "_shared", HERE.parents[2] / "_shared"):
if (_cand / "config.py").exists():
sys.path.insert(0, str(_cand))
break
from config import DEFAULTS, STYLE_PRESETS, GENRE_PRESETS
REQUIRED_FIELDS = {"title", "style", "genre", "characters", "scenes"}
REQUIRED_SCENE_FIELDS = {"id", "action", "duration"}
SYSTEM_PROMPT = """你是国风漫剧编剧。根据用户给的主题、时长、风格、类型，输出结构化 JSON 剧本。
硬要求：
1. 只输出 JSON，不要 markdown 代码块
2. scenes 数量 = duration_total / scene_duration，误差 ±1 可接受
3. characters 3-5 个，每人 voice 从音色库选（zh_male_*/zh_female_*）
4. 每镜 action + dialogue 合计对白字数控制在 scene_duration × 3.5 以内
5. camera 用影视术语（远景/全景/中景/近景/特写，推拉摇移）
6. mood 用 2-4 字形容词（苍凉壮阔/风雷激荡 等）
7. 国风审美：意象优先，避免现代词汇
JSON schema 参考 SKILL.md。"""
def build_user_prompt(theme: str, duration: int, style: str, genre: str) -> str:
n_scenes = duration // DEFAULTS["scene_duration"]
style_hint = STYLE_PRESETS.get(style, {})
genre_hint = GENRE_PRESETS.get(genre, "")
return (
f"主题：{theme}\n"
f"总时长：{duration} 秒 / 每镜 5 秒 / 共 {n_scenes} 镜头\n"
f"风格：{style}（{style_hint.get('prefix', '')}）\n"
f"类型：{genre}（关键词：{genre_hint}）\n\n"
f"请输出 script.json。"
)
def validate(script: dict) -> list[str]:
errors = []
missing = REQUIRED_FIELDS - set(script)
if missing:
errors.append(f"缺少字段: {missing}")
scenes = script.get("scenes", [])
if not scenes:
errors.append("scenes 为空")
for i, s in enumerate(scenes):
smissing = REQUIRED_SCENE_FIELDS - set(s)
if smissing:
errors.append(f"scene[{i}] 缺少: {smissing}")
chars = script.get("characters", [])
char_ids = {c.get("id") for c in chars}
for i, s in enumerate(scenes):
for cid in s.get("characters", []):
if cid not in char_ids:
errors.append(f"scene[{i}] 引用未定义角色 {cid}")
return errors
def generate_via_anthropic(theme: str, duration: int, style: str, genre: str) -> dict:
"""用 Anthropic SDK 调 Claude 4.7 Opus."""
try:
from anthropic import Anthropic
except ImportError:
raise RuntimeError("需 pip install anthropic，或 Agent 模式直写 script.json")
client = Anthropic()
resp = client.messages.create(
model="claude-opus-4-7",
max_tokens=8192,
system=SYSTEM_PROMPT,
messages=[{"role": "user", "content": build_user_prompt(theme, duration, style, genre)}],
)
text = resp.content[0].text.strip()
if text.startswith("```"):
text = text.split("```")[1]
if text.startswith("json"):
text = text[4:]
return json.loads(text.strip())
def main() -> int:
p = argparse.ArgumentParser()
p.add_argument("--theme", required=True)
p.add_argument("--duration", type=int, default=DEFAULTS["duration_total"])
p.add_argument("--style", default=DEFAULTS["style"])
p.add_argument("--genre", default=DEFAULTS["genre"])
p.add_argument("--out", required=True)
p.add_argument("--input-json", help="已有 script.json 路径，仅做校验")
args = p.parse_args()
out_path = pathlib.Path(args.out)
if args.input_json:
script = json.loads(pathlib.Path(args.input_json).read_text())
elif out_path.exists():
print(f"[script] {out_path} 已存在，跳过生成，仅校验")
script = json.loads(out_path.read_text())
else:
print(f"[script] 生成中...theme={args.theme!r}")
script = generate_via_anthropic(args.theme, args.duration, args.style, args.genre)
errors = validate(script)
if errors:
print("❌ 校验失败:")
for e in errors:
print(f" - {e}")
return 1
out_path.parent.mkdir(parents=True, exist_ok=True)
out_path.write_text(json.dumps(script, ensure_ascii=False, indent=2))
print(f"✅ {out_path} | {len(script['scenes'])} 镜 / {len(script['characters'])} 角色")
return 0
if __name__ == "__main__":
sys.exit(main())
一键生成 3-5 分钟国风 AI 漫剧。主题→分幕剧本→角色卡→分镜关键帧→图生视频→TTS 配音→对口型→BGM→字幕→拼接成片。自带三级成本熔断（硬限/预警/降级建议），支持失败续跑。触发词：AI 漫剧、生成漫剧、国风漫剧、仙侠短剧、comic drama、做漫剧。
---
name: huo15-comic-orchestrator
displayName: 火15 AI 漫剧编排器
description: 一键生成 3-5 分钟国风 AI 漫剧。主题→分幕剧本→角色卡→分镜关键帧→图生视频→TTS 配音→对口型→BGM→字幕→拼接成片。自带三级成本熔断（硬限/预警/降级建议），支持失败续跑。触发词：AI 漫剧、生成漫剧、国风漫剧、仙侠短剧、comic drama、做漫剧。
version: 0.1.0
aliases:
- AI漫剧
- 国风漫剧
- 仙侠短剧
- 漫画短剧
- comic-drama
---
# 火15 AI 漫剧编排器
> 主入口 skill，串起家族其他 8 个子 skill 完成 3-5 分钟国风漫剧生成。
---
## ⚠️ 硬规则
1. **成本熔断优先**：开工前估算超 `cost_cap` 立即阻止，不得强行继续
2. **用户确认闸门**：估算出来后**必须**等用户"确认"/"开始"再执行
3. **国风为默认**：不强制改风格时默认 `三渲二国风` + `仙侠`
4. **Checkpoint 续跑**：任何失败都从 `.checkpoint.json` 恢复，不重做已完成步骤
---
## 一、家族依赖
| 子 skill | Step | 本 skill 如何调用 |
|---|---|---|
| huo15-comic-script | 1 | `python ../huo15-comic-script/scripts/script_gen.py` |
| huo15-comic-character | 2 | `python ../huo15-comic-character/scripts/character.py` |
| huo15-comic-storyboard | 3 | `python ../huo15-comic-storyboard/scripts/storyboard.py` |
| huo15-comic-video | 4 | `python ../huo15-comic-video/scripts/video.py` |
| huo15-comic-dub | 5 | `python ../huo15-comic-dub/scripts/dub.py` |
| huo15-comic-lipsync | 6 | `python ../huo15-comic-lipsync/scripts/lipsync.py` |
| huo15-comic-bgm | 7 | `python ../huo15-comic-bgm/scripts/bgm.py` |
| huo15-comic-edit | 8 | `python ../huo15-comic-edit/scripts/edit.py` |
---
## 二、Agent 工作流
### Step 0：收集输入
必填：`theme`（主题一句话）、`duration_total`（秒数，180/240/300）
可选：`style`、`genre`、`character_hints`、`cost_cap`、`enable_lipsync`
### Step 1：预估成本（硬熔断点）
```python
from _shared.cost_guard import estimate_total, CostGuard, BudgetExceeded
n_scenes = duration_total // 5
est = estimate_total(
n_scenes=n_scenes, n_characters=3, total_chars=800,
resolution=resolution, # 720p 默认 / 1080p 贵 2.3×
fast=fast_mode, # seedance-fast 打 5 折
enable_lipsync=enable_lipsync,
enable_bgm=enable_bgm,
)
guard = CostGuard(cap=cost_cap or 600.0, project_dir=project_dir)
try:
guard.preflight(est["total"])
except BudgetExceeded as e:
# 立即报给用户降级建议，等用户选择
report_to_user(str(e))
return
```
### Step 2：向用户确认
```
收到！将生成 {duration_total}s 国风漫剧（{n_scenes} 镜头）
主题：{theme}
风格：{style} / 类型：{genre}
预估成本：
· 剧本 ¥0.00（本地）
· 角色 ¥{image}
· 视频 ¥{video}
· TTS ¥{tts}
· 口型 ¥{lipsync}
· BGM ¥{bgm}
· 合计 ¥{total}（熔断上限 ¥{cap}）
确认开始吗？
```
### Step 3-10：按顺序调用子 skill
每步前 checkpoint 查重（`if cp.is_done(step): skip`），每步后 `guard.charge(step, item, cost)`，触发预警即告知用户，触发熔断即停并给降级方案。
### Step 11：交付
输出 `output/{project_slug}/final.mp4` 路径、实际成本、耗时。
---
## 三、主入口脚本
`scripts/run.py`：
```bash
python scripts/run.py \
--theme "少年剑仙三年归来" \
--duration 240 \
--style 三渲二国风 \
--genre 仙侠 \
--cap 600
```
详见 [scripts/run.py](./scripts/run.py)。
---
## 四、熔断降级策略
`_shared/cost_guard.py` 的 `BudgetExceeded` 抛出时，默认提供 5 选 1：
1. 缩短总时长
2. 减少镜头数
3. 关闭对口型（省 ~10%，Kling 2.6 便宜后此项影响小）
4. 启用 `--fast`（seedance-fast 打 5 折，视频占大头，省 ~40%）
5. 降 resolution：1080p → 720p 省 ~60% 视频费；720p → 480p 再省 ~50%
6. 提升 cost_cap
**Agent 必须让用户二次确认任何降级**，不得自动降级。
### 成本主导因素（720p 基线）
视频占比最高（~85%）→ 优先调视频参数：
- `fast=True` → 视频减半
- `resolution=480p` → 视频再减半
- `scene_duration=4` → 视频线性减 20%
Lipsync 成本从 2026-04 起大幅下降（Kling 2.6: ¥0.72/5s），不再是大头。
---
## 五、Agent 直写剧本模式（推荐）
**不建议**让 `scripts/script_gen.py` 调 Anthropic SDK 生成剧本，因为：
1. Claude（即本 Agent）就是 LLM 本体，再开 SDK 调自己是冗余
2. `script_gen.py` 走 SDK 要消耗 token，Agent 直写不花钱
**推荐流程**：Agent 按 `huo15-comic-script/SKILL.md` 的 JSON schema **直接写** `script.json` 到 `output/{slug}/`，然后：
```bash
# 跳过 LLM 调用，只校验
python huo15-comic-script/scripts/script_gen.py \
--theme "..." --duration 240 \
--input-json output/{slug}/script.json \
--out output/{slug}/script.json
```
run.py 支持 `SCRIPT_PREWRITTEN=1` 环境变量跳过 SDK 调用路径。
Demo 见 `examples/demo-xianxia-180s.json`。
---
## 六、Checkpoint 续跑
`output/{project}/.checkpoint.json` 记录每步与镜头级状态：
```json
{
"script": "done",
"characters": "done",
"storyboard": "done",
"videos": "running",
"videos.S01": "done",
"videos.S02": "done",
"videos.S03": "running",
...
}
```
重启时扫描，从 `next_pending()` 续跑，已完成镜头不重做。
FILE:_meta.json
{
"ownerId": "kn7byevkn40d6z4p7ghdb097z983tj33",
"slug": "huo15-comic-orchestrator",
"version": "0.1.0"
}
FILE:scripts/_shared/__init__.py
"""huo15-ai-comics 共享库。
所有子 skill 通过 sys.path 注入此模块（dual-path fallback）：
HERE = pathlib.Path(__file__).resolve()
# 优先 bundled（clawhub 独立安装场景：scripts/_shared/）
# fallback monorepo 根（本仓库 dev 场景：../../../_shared/）
for _cand in (HERE.parent / "_shared", HERE.parents[2] / "_shared"):
if (_cand / "config.py").exists():
sys.path.insert(0, str(_cand))
break
from cost_guard import CostGuard
from checkpoint import Checkpoint
from ark_api import ArkClient
from config import DEFAULTS, MODELS
publish.sh 会在发布前把本目录 cp 到每个 `<skill>/scripts/_shared/`，
让独立安装的 skill 仍能解析 import。
"""
FILE:scripts/_shared/ark_api.py
"""火山方舟 API 薄封装：Seedream 4.0 图 / Seedance 2.0 视频 / 豆包大模型 TTS.
2026-04 核对。文档：
- Seedance: https://www.volcengine.com/docs/82379/1520757
- Seedream: doubao-seedream-4-0-250828
- TTS: https://www.volcengine.com/docs/6561/97465
"""
from __future__ import annotations
import base64
import os
import pathlib
import time
import requests
try:
from config import ENDPOINTS, MODELS
except ImportError: # 兼容直接运行
ENDPOINTS = {
"ark_base": "https://ark.cn-beijing.volces.com/api/v3",
}
MODELS = {
"image": "doubao-seedream-4-0-250828",
"video": "doubao-seedance-2-0-260128",
"video_fast": "doubao-seedance-2-0-fast-260128",
"tts": "doubao-tts-bigtts",
}
class ArkClient:
def __init__(self, api_key: str | None = None):
self.api_key = api_key or os.environ.get("ARK_API_KEY", "")
if not self.api_key:
raise RuntimeError("缺少 ARK_API_KEY 环境变量")
self.base_url = ENDPOINTS["ark_base"]
self.headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
}
# ------------ 图像（Seedream 4.0）------------
def generate_image(
self,
prompt: str,
reference_images: list[str] | None = None,
size: str = "1024x1792",
model: str | None = None,
) -> str:
"""文生图 / 图生图。返回第一张图片 URL.
reference_images 传文件路径或 URL，会自动转 data URI.
"""
content = [{"type": "text", "text": prompt}]
for img in reference_images or []:
content.append({
"type": "image_url",
"image_url": {"url": self._image_to_data_uri(img)},
})
body = {
"model": model or MODELS["image"],
"content": content,
"size": size,
"watermark": False,
}
resp = requests.post(
f"{self.base_url}/images/generations",
headers=self.headers,
json=body,
timeout=120,
)
resp.raise_for_status()
data = resp.json()
# 兼容两种响应结构
if "data" in data and data["data"]:
return data["data"][0].get("url", "")
if "url" in data:
return data["url"]
raise RuntimeError(f"image response no url: {data}")
# ------------ 视频（Seedance 2.0）------------
def submit_video(
self,
prompt: str,
first_frame: str | None = None,
last_frame: str | None = None,
reference_image: str | None = None,
reference_video: str | None = None,
reference_audio: str | None = None,
duration: int = 5,
ratio: str = "9:16",
resolution: str = "720p",
generate_audio: bool = False,
return_last_frame: bool = False,
fast: bool = False,
seed: int | None = None,
) -> str:
"""提交视频任务，返回 task_id.
支持多种输入模态：first_frame / last_frame（首尾帧模式）/ reference_image /
reference_video / reference_audio。同一请求最多 9 图、3 视频、3 音频。
"""
content: list[dict] = [{"type": "text", "text": prompt}]
def _add_img(path: str, role: str):
content.append({
"type": "image_url",
"image_url": {"url": self._image_to_data_uri(path)},
"role": role,
})
if first_frame:
_add_img(first_frame, "first_frame")
if last_frame:
_add_img(last_frame, "last_frame")
if reference_image:
_add_img(reference_image, "reference_image")
if reference_video:
content.append({
"type": "video_url",
"video_url": {"url": reference_video if reference_video.startswith("http") else reference_video},
"role": "reference_video",
})
if reference_audio:
content.append({
"type": "audio_url",
"audio_url": {"url": reference_audio},
"role": "reference_audio",
})
body: dict = {
"model": MODELS["video_fast" if fast else "video"],
"content": content,
"ratio": ratio,
"resolution": resolution,
"duration": duration,
"watermark": False,
"return_last_frame": return_last_frame,
"generate_audio": generate_audio,
}
if seed is not None:
body["seed"] = seed
resp = requests.post(
f"{self.base_url}/contents/generations/tasks",
headers=self.headers,
json=body,
timeout=60,
)
resp.raise_for_status()
return resp.json()["id"]
def poll_video(self, task_id: str, timeout_s: int = 900, interval_s: int = 10) -> dict:
"""轮询视频任务，完成返回完整 dict（含 content.video_url / usage.total_tokens）."""
deadline = time.time() + timeout_s
while time.time() < deadline:
time.sleep(interval_s)
resp = requests.get(
f"{self.base_url}/contents/generations/tasks/{task_id}",
headers=self.headers,
timeout=30,
)
data = resp.json()
status = data.get("status")
if status == "succeeded":
return data
if status == "failed":
raise RuntimeError(f"视频任务失败: {data}")
raise TimeoutError(f"视频任务 {task_id} 超时（{timeout_s}s）")
def download_video(self, url: str, out_path: pathlib.Path) -> pathlib.Path:
out_path = pathlib.Path(out_path)
out_path.parent.mkdir(parents=True, exist_ok=True)
r = requests.get(url, stream=True, timeout=300)
with open(out_path, "wb") as f:
for chunk in r.iter_content(8192):
f.write(chunk)
return out_path
# ------------ 语音（豆包大模型 TTS）------------
def tts(
self,
text: str,
voice: str = "zh_female_sinong_conversation_wvae_bigtts",
out_path: pathlib.Path | str = "out.wav",
speed: float = 1.0,
emotion: str | None = None,
) -> pathlib.Path:
"""豆包 TTS 合成，写入 out_path.
voice 是音色 ID，格式 zh_{gender}_{name}_conversation_wvae_bigtts.
完整音色见 https://www.volcengine.com/docs/6561/97465.
注意：TTS 可能需要单独的 appid/cluster 凭证（走 openspeech.bytedance.com），
此处用 ark OpenAI 兼容接口作为简化，实际项目请核对授权方式。
"""
body = {
"model": MODELS["tts"],
"input": text,
"voice": voice,
"speed": speed,
"response_format": "wav",
}
if emotion:
body["emotion"] = emotion
resp = requests.post(
f"{self.base_url}/audio/speech",
headers=self.headers,
json=body,
timeout=60,
)
resp.raise_for_status()
out = pathlib.Path(out_path)
out.parent.mkdir(parents=True, exist_ok=True)
out.write_bytes(resp.content)
return out
# ------------ 工具 ------------
@staticmethod
def _image_to_data_uri(path_or_url: str) -> str:
if path_or_url.startswith(("http://", "https://", "data:")):
return path_or_url
p = pathlib.Path(path_or_url)
ext = p.suffix.lower().lstrip(".") or "jpeg"
if ext == "jpg":
ext = "jpeg"
b64 = base64.b64encode(p.read_bytes()).decode()
return f"data:image/{ext};base64,{b64}"
def cost_from_video_response(data: dict, resolution: str = "720p") -> float:
"""从视频响应中精确计算成本（按 token）."""
from config import PRICING
tokens = data.get("usage", {}).get("total_tokens", 0)
return round(tokens * PRICING["video_per_mtoken"] / 1_000_000, 3)
FILE:scripts/_shared/checkpoint.py
"""阶段 checkpoint，支持失败续跑."""
from __future__ import annotations
import json
import pathlib
from dataclasses import dataclass, field, asdict
STEPS = [
"script",
"characters",
"storyboard",
"videos",
"dubs",
"lipsync",
"bgm",
"subtitle",
"edit",
]
@dataclass
class Checkpoint:
project_dir: pathlib.Path
status: dict = field(default_factory=dict)
def __post_init__(self):
self.project_dir = pathlib.Path(self.project_dir)
self._load()
def _path(self) -> pathlib.Path:
return self.project_dir / ".checkpoint.json"
def _load(self) -> None:
if self._path().exists():
self.status = json.loads(self._path().read_text())
else:
self.status = {s: "pending" for s in STEPS}
def save(self) -> None:
self.project_dir.mkdir(parents=True, exist_ok=True)
self._path().write_text(json.dumps(self.status, ensure_ascii=False, indent=2))
def is_done(self, step: str) -> bool:
return self.status.get(step) == "done"
def mark_done(self, step: str) -> None:
self.status[step] = "done"
self.save()
def mark_running(self, step: str) -> None:
self.status[step] = "running"
self.save()
def mark_failed(self, step: str, reason: str = "") -> None:
self.status[step] = f"failed: {reason}" if reason else "failed"
self.save()
def next_pending(self) -> str | None:
for s in STEPS:
v = self.status.get(s, "pending")
if v != "done":
return s
return None
def sub_mark(self, step: str, sub_id: str, value: str = "done") -> None:
"""用于镜头级别的细粒度 checkpoint，如 videos.S01=done."""
key = f"{step}.{sub_id}"
self.status[key] = value
self.save()
def sub_done(self, step: str, sub_id: str) -> bool:
return self.status.get(f"{step}.{sub_id}") == "done"
FILE:scripts/_shared/config.py
"""家族共享默认参数与模型 ID.
基于 2026-04 实测火山方舟/Kling/Suno 文档核对。
"""
DEFAULTS = {
"duration_total": 240, # 秒，3-5 分钟区间默认 4 分钟
"scene_duration": 5, # 单镜头秒数（Seedance 2.0 支持 4-15）
"ratio": "9:16", # 竖屏；Seedance 2.0 支持 9:16/16:9/4:3/3:4/21:9/1:1/adaptive
"resolution": "720p", # 默认 720p 省钱；可选 480p/720p/1080p/2K
"style": "三渲二国风",
"genre": "仙侠",
"cost_cap": 600.0,
"cost_warn_ratio": 0.7,
"cost_hard_ratio": 1.0,
"watermark": False,
"concurrency": 3,
"scene_retry": 2,
"fast_mode": False, # True 用 seedance-fast（便宜但质量低）
}
MODELS = {
"script": "claude-opus-4-7",
"image": "doubao-seedream-4-0-250828", # 方舟 Seedream 4.0
"video": "doubao-seedance-2-0-260128", # 方舟 Seedance 2.0 标准版
"video_fast": "doubao-seedance-2-0-fast-260128", # 方舟 Seedance 2.0 fast
"tts": "doubao-tts-bigtts", # 方舟豆包大模型 TTS
"lipsync": "kling-v2.6", # Kling 2.6 原生对口型
"music": "suno-v5.5", # Suno v5.5（via sunoapi.org）
}
# API 端点（2026-04 核对）
ENDPOINTS = {
"ark_base": "https://ark.cn-beijing.volces.com/api/v3",
"kling_base": "https://api.klingai.com/v1", # 官方；第三方 piapi.ai/fal.ai 亦可
"suno_base": "https://api.sunoapi.org", # 第三方（Suno 无公开官方 API）
}
# 单位价（元），2026-04 核对
# Seedance 2.0: token-based 46¥/M tokens（1080p 文生/图生视频），28¥/M tokens（视频参考）
# 按秒估算的便利值：token = duration × width × height × fps / 1024，用 token × 46 / 1e6 得元价
PRICING = {
"image_per_pic": 0.08, # Seedream 4.0 每张
"video_480p_per_sec": 0.50, # 480p 9:16 ≈ 5s ¥2.5
"video_720p_per_sec": 0.994, # 720p 9:16 ≈ 5s ¥5
"video_1080p_per_sec": 2.26, # 1080p 9:16 ≈ 5s ¥11.3
"video_fast_discount": 0.5, # fast 模型约 5 折
"video_per_mtoken": 46.0, # 官方 token 单价（M tokens）
"tts_per_char": 0.0008, # 豆包大模型 TTS
"lipsync_per_5s": 0.72, # Kling 官方 $0.1/5s ≈ ¥0.72
"bgm_per_track": 3.0, # sunoapi.org 第三方均价
}
def video_unit_price(resolution: str = "720p", fast: bool = False) -> float:
"""返回每秒元价，用于预估."""
base = {
"480p": PRICING["video_480p_per_sec"],
"720p": PRICING["video_720p_per_sec"],
"1080p": PRICING["video_1080p_per_sec"],
"2K": PRICING["video_1080p_per_sec"] * 1.5,
}.get(resolution, PRICING["video_720p_per_sec"])
if fast:
base *= PRICING["video_fast_discount"]
return base
# 国风专用提示词片段
STYLE_PRESETS = {
"三渲二国风": {
"prefix": "三渲二国风动画风格，工笔线条，中国传统审美",
"lighting": "柔和自然光，水墨晕染",
"palette": "青绿山水色调，朱砂点缀",
},
"水墨": {
"prefix": "中国水墨画风格，留白意境",
"lighting": "墨色浓淡",
"palette": "黑白灰为主，偶尔赭石",
},
"古风赛璐璐": {
"prefix": "日式赛璐璐+中国古风融合，清新明亮",
"lighting": "柔光晴天",
"palette": "淡雅国风配色",
},
"工笔": {
"prefix": "中国工笔画风格，细腻线条，重彩渲染",
"lighting": "平光",
"palette": "矿物颜料，金线勾勒",
},
}
GENRE_PRESETS = {
"仙侠": "门派纷争 / 修仙问道 / 御剑飞行 / 法宝秘术",
"宫斗": "宫廷权谋 / 妃嫔博弈 / 皇家礼仪 / 雕梁画栋",
"江湖": "快意恩仇 / 武林纷争 / 客栈酒肆 / 刀光剑影",
"志怪": "山海异兽 / 妖怪传说 / 古籍志异 / 诡谲氛围",
}
# 豆包大模型 TTS 音色（2026-04 核对，_conversation_wvae_bigtts 后缀=豆包大模型版）
# 实际音色 ID 较多，这里列常用。完整列表见 https://www.volcengine.com/docs/6561/97465
VOICE_PRESETS = {
# 男声
"male_young": "zh_male_ahu_conversation_wvae_bigtts", # 温暖阿虎（青年温润）
"male_mature": "zh_male_M392_conversation_wvae_bigtts", # 京腔侃爷（成熟磁性）
"male_elder": "zh_male_yanqing_conversation_wvae_bigtts", # 沉稳长者
# 女声
"female_young": "zh_female_sinong_conversation_wvae_bigtts", # 爽快思思（清新少女）
"female_mature": "zh_female_xiaohe_conversation_wvae_bigtts", # 湾湾小何（温柔熟女）
"female_elder": "zh_female_guniang_conversation_wvae_bigtts", # 端庄长辈
}
FILE:scripts/_shared/cost_guard.py
"""三级成本熔断.
- 硬熔断（hard）：开工前估算超 cap 立即阻止
- 软预警（warn）：运行时累计超 warn_ratio × cap 打印告警
- 降级建议（suggest）：超 cap 时给出具体降级方案
"""
from __future__ import annotations
import json
import pathlib
import time
from dataclasses import dataclass, field, asdict
class BudgetExceeded(RuntimeError):
"""熔断触发异常；上层 catch 后走降级逻辑."""
@dataclass
class CostRecord:
step: str
item: str
cost: float
ts: float = field(default_factory=time.time)
@dataclass
class CostGuard:
cap: float = 600.0
warn_ratio: float = 0.7
project_dir: pathlib.Path | None = None
spent: float = 0.0
records: list[CostRecord] = field(default_factory=list)
_warned: bool = False
def preflight(self, estimated_total: float) -> None:
"""开工前硬熔断.
Raises:
BudgetExceeded: 估算超 cap 时抛出，附带降级建议字符串。
"""
if estimated_total > self.cap:
suggestions = self.downgrade_suggestions(estimated_total)
raise BudgetExceeded(
f"预估成本 ¥{estimated_total:.2f} 超过熔断上限 ¥{self.cap:.2f}。\n"
f"降级建议：\n{suggestions}\n"
f"如需继续，请提升 cost_cap 或采纳上述任一降级方案。"
)
def charge(self, step: str, item: str, cost: float) -> None:
"""扣费，触发软预警/硬熔断."""
self.spent += cost
self.records.append(CostRecord(step=step, item=item, cost=cost))
ratio = self.spent / self.cap if self.cap else 0
if ratio >= 1.0:
self._persist()
raise BudgetExceeded(
f"累计成本 ¥{self.spent:.2f} 达到熔断上限 ¥{self.cap:.2f}（{step}/{item}）。\n"
f"已完成步骤可从 checkpoint 续跑。"
)
if ratio >= self.warn_ratio and not self._warned:
self._warned = True
print(
f"⚠️ 成本预警：已花费 ¥{self.spent:.2f}（{ratio:.0%} of ¥{self.cap:.2f}），"
f"即将触发熔断。"
)
self._persist()
def downgrade_suggestions(self, estimated_total: float) -> str:
"""生成降级方案文本."""
over = estimated_total - self.cap
lines = [
f" 1. 缩短总时长：每减 1 分钟约省 ¥{estimated_total / 5:.0f}",
f" 2. 减少镜头数：每删 1 镜约省 ¥{estimated_total / 48:.1f}",
f" 3. 关闭对口型：整片省 ¥{estimated_total * 0.37:.0f}（约 37%）",
f" 4. 视频降到 4 秒/镜：整片省 ¥{estimated_total * 0.2:.0f}（约 20%）",
f" 5. 提升 cost_cap 至 ¥{estimated_total * 1.1:.0f}（当前 ¥{self.cap:.0f}，需多付 ¥{over:.2f}）",
]
return "\n".join(lines)
def report(self) -> dict:
"""返回成本报告（可序列化）."""
by_step: dict[str, float] = {}
for r in self.records:
by_step[r.step] = by_step.get(r.step, 0.0) + r.cost
return {
"spent": round(self.spent, 2),
"cap": self.cap,
"ratio": round(self.spent / self.cap, 3) if self.cap else 0,
"by_step": {k: round(v, 2) for k, v in by_step.items()},
"record_count": len(self.records),
}
def _persist(self) -> None:
if not self.project_dir:
return
self.project_dir.mkdir(parents=True, exist_ok=True)
path = self.project_dir / ".cost.json"
data = {
"cap": self.cap,
"warn_ratio": self.warn_ratio,
"spent": self.spent,
"records": [asdict(r) for r in self.records],
}
path.write_text(json.dumps(data, ensure_ascii=False, indent=2))
@classmethod
def load(cls, project_dir: pathlib.Path, cap: float = 600.0) -> "CostGuard":
"""从 project_dir/.cost.json 恢复."""
path = project_dir / ".cost.json"
guard = cls(cap=cap, project_dir=project_dir)
if path.exists():
data = json.loads(path.read_text())
guard.cap = data.get("cap", cap)
guard.warn_ratio = data.get("warn_ratio", 0.7)
guard.spent = data.get("spent", 0.0)
guard.records = [CostRecord(**r) for r in data.get("records", [])]
return guard
def estimate_total(
n_scenes: int,
n_characters: int,
total_chars: int,
scene_duration: int = 5,
resolution: str = "720p",
fast: bool = False,
enable_lipsync: bool = True,
enable_bgm: bool = True,
) -> dict:
"""估算全流程成本，返回明细 dict.
video 按 resolution 和 fast 标志换算每秒元价；lipsync 按 scene_duration/5s 换算。
"""
from config import PRICING, video_unit_price
image_cost = (n_scenes + n_characters * 3) * PRICING["image_per_pic"]
video_cost = n_scenes * scene_duration * video_unit_price(resolution, fast)
tts_cost = total_chars * PRICING["tts_per_char"]
lipsync_cost = (
n_scenes * (scene_duration / 5.0) * PRICING["lipsync_per_5s"]
if enable_lipsync else 0
)
bgm_cost = PRICING["bgm_per_track"] if enable_bgm else 0
total = image_cost + video_cost + tts_cost + lipsync_cost + bgm_cost
return {
"image": round(image_cost, 2),
"video": round(video_cost, 2),
"tts": round(tts_cost, 2),
"lipsync": round(lipsync_cost, 2),
"bgm": round(bgm_cost, 2),
"total": round(total, 2),
"resolution": resolution,
"fast": fast,
}
FILE:scripts/run.py
"""主编排入口。串起家族 8 个子 skill 完成漫剧生成。
用法:
python run.py --theme "少年剑仙三年归来" --duration 240 --cap 600
"""
from __future__ import annotations
import argparse
import json
import pathlib
import re
import subprocess
import sys
import time
HERE = pathlib.Path(__file__).resolve()
REPO_ROOT = HERE.parents[2] # monorepo 根 / 独立安装时的 skills 父目录
# _shared/ 定位：优先 bundled（scripts/_shared/），fallback monorepo 根
for _cand in (HERE.parent / "_shared", REPO_ROOT / "_shared"):
if (_cand / "config.py").exists():
sys.path.insert(0, str(_cand))
break
from config import DEFAULTS
from cost_guard import CostGuard, BudgetExceeded, estimate_total
from checkpoint import Checkpoint
def slugify(text: str) -> str:
s = re.sub(r"[^\w\u4e00-\u9fff]+", "-", text).strip("-")
return s[:40] or f"project-{int(time.time())}"
def parse_args():
p = argparse.ArgumentParser()
p.add_argument("--theme", required=True, help="主题一句话")
p.add_argument("--duration", type=int, default=DEFAULTS["duration_total"])
p.add_argument("--style", default=DEFAULTS["style"])
p.add_argument("--genre", default=DEFAULTS["genre"])
p.add_argument("--cap", type=float, default=DEFAULTS["cost_cap"])
p.add_argument("--no-lipsync", action="store_true")
p.add_argument("--no-bgm", action="store_true")
p.add_argument("--output-root", default=str(REPO_ROOT / "output"))
p.add_argument("--auto-confirm", action="store_true", help="跳过交互确认")
return p.parse_args()
def run_substep(skill_name: str, script: str, *args: str) -> subprocess.CompletedProcess:
"""调用子 skill 的脚本."""
cmd = [
sys.executable,
str(REPO_ROOT / skill_name / "scripts" / script),
*args,
]
print(f" $ {' '.join(cmd)}")
return subprocess.run(cmd, check=True)
def confirm(prompt: str) -> bool:
try:
return input(prompt).strip().lower() in {"y", "yes", "是", "开始", "确认", "go"}
except EOFError:
return False
def main() -> int:
args = parse_args()
project_slug = slugify(args.theme)
project_dir = pathlib.Path(args.output_root) / project_slug
project_dir.mkdir(parents=True, exist_ok=True)
n_scenes = args.duration // DEFAULTS["scene_duration"]
# -------- Step 1: 预估成本（硬熔断）--------
est = estimate_total(
n_scenes=n_scenes,
n_characters=3,
total_chars=int(args.duration * 3.5), # 经验值：每秒 3-4 字对白
scene_duration=DEFAULTS["scene_duration"],
enable_lipsync=not args.no_lipsync,
enable_bgm=not args.no_bgm,
)
guard = CostGuard.load(project_dir, cap=args.cap)
print(f"\n📜 项目: {project_slug}")
print(f" 时长 {args.duration}s · {n_scenes} 镜头 · {args.style} · {args.genre}")
print(f" 预估: 图 ¥{est['image']} / 视频 ¥{est['video']} / TTS ¥{est['tts']} "
f"/ 口型 ¥{est['lipsync']} / BGM ¥{est['bgm']}")
print(f" 合计 ¥{est['total']}（熔断上限 ¥{guard.cap}）\n")
try:
guard.preflight(est["total"])
except BudgetExceeded as e:
print(f"❌ {e}")
return 2
if not args.auto_confirm and not confirm("确认开始? [y/N] "):
print("已取消。")
return 0
# -------- Step 2-10: 按 checkpoint 续跑各步骤 --------
cp = Checkpoint(project_dir)
steps = [
("script", "huo15-comic-script", "script_gen.py",
["--theme", args.theme, "--duration", str(args.duration),
"--style", args.style, "--genre", args.genre,
"--out", str(project_dir / "script.json")]),
("characters", "huo15-comic-character", "character.py",
["--script", str(project_dir / "script.json"),
"--out-dir", str(project_dir / "characters")]),
("storyboard", "huo15-comic-storyboard", "storyboard.py",
["--script", str(project_dir / "script.json"),
"--char-dir", str(project_dir / "characters"),
"--out-dir", str(project_dir / "storyboard")]),
("videos", "huo15-comic-video", "video.py",
["--script", str(project_dir / "script.json"),
"--frame-dir", str(project_dir / "storyboard"),
"--out-dir", str(project_dir / "videos")]),
("dubs", "huo15-comic-dub", "dub.py",
["--script", str(project_dir / "script.json"),
"--out-dir", str(project_dir / "audio")]),
]
if not args.no_lipsync:
steps.append(("lipsync", "huo15-comic-lipsync", "lipsync.py",
["--video-dir", str(project_dir / "videos"),
"--audio-dir", str(project_dir / "audio"),
"--out-dir", str(project_dir / "lipsync")]))
if not args.no_bgm:
steps.append(("bgm", "huo15-comic-bgm", "bgm.py",
["--script", str(project_dir / "script.json"),
"--duration", str(args.duration),
"--out", str(project_dir / "bgm.mp3")]))
steps.append(("edit", "huo15-comic-edit", "edit.py",
["--project-dir", str(project_dir)]))
t0 = time.time()
for step, skill, script, sub_args in steps:
if cp.is_done(step):
print(f"⏭️ {step}: 已完成，跳过")
continue
cp.mark_running(step)
try:
run_substep(skill, script, *sub_args)
cp.mark_done(step)
except subprocess.CalledProcessError as e:
cp.mark_failed(step, str(e))
print(f"❌ {step} 失败: {e}")
return 3
except BudgetExceeded as e:
print(f"🛑 熔断: {e}")
return 4
# -------- Step 11: 交付 --------
final = project_dir / "final.mp4"
elapsed = time.time() - t0
report = guard.report()
print(f"\n🎉 {final} (耗时 {elapsed/60:.1f} 分钟)")
print(f" 实际成本: ¥{report['spent']} / ¥{report['cap']}")
print(f" 成本分布: {json.dumps(report['by_step'], ensure_ascii=False)}")
return 0
if __name__ == "__main__":
sys.exit(main())
【青岛火一五信息科技有限公司】基于 Karpathy LLM Knowledge Base 三层架构（Data Ingest → Compilation → Active Maintenance）的知识捕获与管理技能。将知识点写入 memory/ 目录并同步到公司 Odoo 知识库。
---
name: huo15-knowledge-base
displayName: 火一五知识库技能
description: 【青岛火一五信息科技有限公司】基于 Karpathy LLM Knowledge Base 三层架构（Data Ingest → Compilation → Active Maintenance）的知识捕获与管理技能。将知识点写入 memory/ 目录并同步到公司 Odoo 知识库。
version: 1.0.0
aliases:
- 卡帕西知识库
- 知识库技能
- karpathy
dependencies:
python-packages:
- python-docx
---
# 火一五知识库技能 v1.0
> 基于 Karpathy LLM Knowledge Base 三层架构 — 青岛火一五信息科技有限公司
## 一、核心概念
Karpathy LLM Knowledge Base 三层架构：
| 层次 | 名称 | 功能 |
|------|------|------|
| **Data Ingest** | 数据摄入 | 原始知识点捕获（对话/文档/邮件） |
| **Compilation** | 编译整理 | 提取关键实体、关系、引用，建档入库 |
| **Active Maintenance** | 主动维护 | 定期检查知识 drift，淘汰过时内容 |
## 二、触发词
- 知识库 / 入库 / 存入知识库
- 卡帕西知识库 / Karpathy 知识库
- 同步知识库 / 更新知识库
- 记一下 / 这个记到知识库
- capture knowledge / save to knowledge base
## 三、知识写入流程
### 3.1 知识点文件命名规范
```
memory/
├── knowledge/
│ ├── {category}/{YYYY-MM-DD}_{slug}.md
│ └── categories: odoo / business / technical / product / feedback
```
### 3.2 知识点文件格式
```markdown
# {标题}
## 摘要
{2-3句话总结}
## 详细说明
{核心内容}
## 关键要点
- {要点1}
- {要点2}
## 引用来源
- {来源1}
- {来源2}
## 相关知识点
- {related_topic_1}
- {related_topic_2}
---
**入库时间：** YYYY-MM-DD
**来源：** {对话/文档/其他}
**标签：** {tag1}, {tag2}
```
## 四、同步到 Odoo 知识库
### 4.1 写入 Odoo 知识库
使用 `odoo_knowledge_create` 工具：
```python
title = "{标题}"
content = """<div class="knowledge-article">
<h2>摘要</h2>
<p>{摘要}</p>
<h2>详细说明</h2>
<p>{详细说明}</p>
<h2>关键要点</h2>
<ul>
<li>{要点1}</li>
<li>{要点2}</li>
</ul>
</div>"""
category = "技术" # 或 "业务" / "产品" / "客户反馈"
```
### 4.2 Odoo 知识库分类
| category | Odoo 知识库分类 |
|----------|---------------|
| odoo | Odoo 技术 |
| business | 业务知识 |
| technical | 技术积累 |
| product | 产品知识 |
| feedback | 客户反馈 |
## 五、KARPATHY 三层执行
### 5.1 Data Ingest（摄入）
- 对话/文档 → 提取核心知识点
- 去重检查（grep 已有 knowledge 目录）
- 生成标准文件
### 5.2 Compilation（编译）
- 提取实体：人名/公司名/产品名/功能名
- 建立关联：相关知识点双向链接
- 打标签：odoo / business / technical 等
### 5.3 Active Maintenance（维护）
- 同一话题新知识 → 合并更新而非新建
- 超过 90 天未更新的知识点 → 标记 `{{drift}}`
- 删除重复/过时内容
## 六、版本历史
- **v1.0.0（当前）**
- 初始版本
- 支持知识点文件生成 + Odoo 知识库同步
- Karpathy 三层架构落地
---
**技术支持：** 青岛火一五信息科技有限公司
火一五文生图提示词 v2.3 — 文生图&视频六件套：(1) enhance_prompt.py 文生图：88 风格预设 + 混合预设 (-p A+B --mix 0.6) + 五锁一致性 + 角色设定图 + 系列批量 + basic/pro/master 三档；(2) enhance_video.py 视频提示...
---
name: huo15-img-test
displayName: 火一五文生图提示词
description: 火一五文生图提示词 v2.3 — 文生图&视频六件套：(1) enhance_prompt.py 文生图：88 风格预设 + 混合预设 (-p A+B --mix 0.6) + 五锁一致性 + 角色设定图 + 系列批量 + basic/pro/master 三档；(2) enhance_video.py 视频提示词：9 模型规格（Sora/Kling/Runway/Pika/Luma/Hailuo/即梦/Wan）+ 30 镜头运动 + 关键帧三段式；(3) reverse_prompt.py 参考图反解：A1111/ComfyUI/NovelAI metadata 自动识别 + VLM fallback；(4) render_prompt.py 直出图片：ComfyUI/SD-WebUI/DALL-E 三后端；(5) claude_polish.py ⭐v2.3 Claude API 智能润色：把粗糙描述转专业摄影/绘画术语 + 推荐预设/相机/光影；(6) safety_lint.py ⭐v2.3 平台合规润色：识别会被 SD/MJ/DALL-E 误判的艺术词汇并给出合法艺术化替代（仅服务合规艺术创作，拒绝违法/未成年/真人色情）。适配 Midjourney/SD/SDXL/Flux/DALL-E 3。触发词：文生图、火一五文生图提示词、文生视频、提示词、生成图片、生成视频、img-test、text to image、text to video、enhance prompt、提示词增强、图片一致性、系列图、角色一致、批量出图、混合风格、原神+敦煌、参考图反解、reverse prompt、提示词反解、ComfyUI 直出、SD WebUI、DALL-E、视频提示词、Sora、可灵、Runway、即梦、Hailuo、Claude 润色、智能润色、平台合规、艺术化重写。
version: 2.3.0
aliases:
- 火一五文生图提示词
- 火一五文生图技能
- 火一五文生视频技能
- 火一五提示词技能
- 火一五提示词全家桶技能
- 火一五AI绘画技能
- 文生图
- 文生视频
- 提示词增强
- 智能润色
- 平台合规润色
- img-test
---
# 火一五文生图提示词 v2.3
**一句话描述 → 贴合需求、一致性强的专业 T2I/T2V 提示词，并可直出。**
## v2.3 = 六件套
| 脚本 | 作用 | 一行 demo |
|------|------|-----------|
| `enhance_prompt.py` | 文生图（升级混合 + polish + safety） | `enhance_prompt.py "持剑女侠" -p "赛博朋克+水墨" --mix 0.6` |
| `enhance_video.py` | 视频提示词 | `enhance_video.py "汉服少女转身回眸" -p 汉服写真 -m Kling --duration 6` |
| `reverse_prompt.py` | 参考图反解 | `reverse_prompt.py img.png --mj` |
| `render_prompt.py` | 直出图片 | `render_prompt.py "原神少女" -p 原神 --backend sd-webui` |
| `claude_polish.py` ⭐v2.3 | Claude API 智能润色 | `claude_polish.py "一个温柔的女孩" --pipe` |
| `safety_lint.py` ⭐v2.3 | 平台合规润色 | `safety_lint.py "战士手中沾满鲜血的剑" --target dalle` |
## 版本演进
| 维度 | v2.0 | v2.1 | v2.2 | v2.3 |
|------|------|------|------|------|
| **风格预设** | 56 | 88 | + 混合预设 | 沿用 |
| **一致性** | 四锁 + seed + 系列 | + 角色设定图 | + motion 第六锁 | 沿用 |
| **贴近需求** | 意图+构图+情绪 | + 时间/天气/季节/负向 | + 视频镜头运动+关键帧 | + **Claude API 润色** |
| **生态闭环** | 仅 prompt | 仅 prompt | + 反解 + 直出 | + **平台合规重写** |
| **AI 联动** | 无 | 无 | 无 | + **Claude/GPT/Gemini 协作** |
## 使用方式
### Agent 调用（推荐）
```
用户: 帮我出一张赛博朋克街头的图
```
Agent 识别到"赛博朋克"触发词，自动调用：
```bash
~/workspace/projects/openclaw/huo15-skills/huo15-img-test/scripts/enhance_prompt.py \
"赛博朋克街头" -p 赛博朋克 -m Midjourney
```
### 直接调用
```bash
cd ~/workspace/projects/openclaw/huo15-skills/huo15-img-test
# 基础：指定预设
./scripts/enhance_prompt.py "一只猫" -p 动漫 -m Midjourney
# 自动意图（无需 -p，脚本从关键词推断）
./scripts/enhance_prompt.py "为咖啡品牌设计一个logo" # → 自动选 Logo设计, 1:1
./scripts/enhance_prompt.py "产品白底图：无线耳机" # → 自动选 产品摄影, 1:1
./scripts/enhance_prompt.py "微距 一滴露珠" # → 自动选 微距摄影, 1:1
# 系列一致性（4 张共享 seed + camera/lighting/palette 锁）
./scripts/enhance_prompt.py "红发女侠" -p 动漫 -s 4 \
--variations "持剑站立,骑马奔驰,弯弓射箭,与龙对视" \
-m Midjourney
# 英文别名 + 多模型输出
./scripts/enhance_prompt.py "spaceship in nebula" -p scifi -m Flux -a 21:9
./scripts/enhance_prompt.py "minimalist camellia logo" -p logo -m SDXL
# JSON 输出（便于集成）
./scripts/enhance_prompt.py "森林少女" -p ghibli -j
```
## 88 款风格预设
### 【摄影 · 13】
写实摄影 / 胶片摄影 / 黑白摄影 / 人像摄影 / 时尚大片 / 美食摄影 / 产品摄影 / 微距摄影 / 航拍摄影 / 街拍纪实 / **暗黑美食 · 日杂 · 街头潮流** ⭐v2.1
### 【动漫 · 10】
动漫 / 新海诚 / 宫崎骏 / 美漫 / Q版 / 童话绘本 / **萌系 · 厚涂 · 轻小说封面 · 赛璐璐** ⭐v2.1
### 【插画 · 7】
水彩 / 油画 / 水墨 / 工笔国画 / 浮世绘 / 线稿 / 像素艺术
### 【3D · 7】
3DC4D / 盲盒手办 / 低多边形 / 等距视图 / 粘土 / 毛毡手工 / 纸艺
### 【设计 · 15】
极简主义 / 平面设计 / Logo设计 / 图标设计 / 信息图 / 品牌KV / 专辑封面 / 复古海报 / 电影海报 / 表情包 / **玻璃拟态 · 新拟态 · 孟菲斯 · 杂志编排 · 包豪斯 · 奶油风** ⭐v2.1
### 【艺术史 · 4】
印象派 / 后印象派 / 新艺术 / 装饰艺术
### 【场景氛围 · 17】
赛博朋克 / 蒸汽朋克 / 科幻 / 奇幻 / 黑暗奇幻 / 国潮 / Y2K / Vaporwave / 霓虹灯牌 / 建筑可视化 / 电影感 / 概念艺术 / **粗野主义 · 北欧极简 · 侘寂 · 疗愈治愈 · 美式复古** ⭐v2.1
### 【游戏艺术 · 7】⭐ v2.1 新类
原神 / 崩铁星穹 / 英雄联盟 / 暗黑4 / Valorant / Pokemon / 暴雪风
### 【东方传统 · 7】⭐ v2.1 新类
敦煌壁画 / 青花瓷 / 民国月份牌 / 年画 / 剪纸 / 和风 / 汉服写真
> 英文别名支持：`anime`、`ghibli`、`shinkai`、`cyberpunk`、`steampunk`、`scifi`、`minimal`、`logo`、`icon`、`3d`、`c4d`、`octane`、`isometric`、`vangogh`、`artdeco`、`neon`、`vapor`、`y2k`、`genshin`、`lol`、`diablo`、`valorant`、`pokemon`、`dunhuang`、`hanfu`、`wafu`、`glassmorphism`、`neumorphism`、`memphis`、`bauhaus`、`brutalism`、`nordic`、`wabisabi`、`healing`、`cozy`、`americana`、`darkfood`、`muji`、`streetwear`… 运行 `./scripts/enhance_prompt.py -l` 查看完整列表。
## 参数说明
| 参数 | 作用 | 示例 |
|------|------|------|
| `subject` | 主体描述（必填） | `"一只猫"` |
| `-p, --preset` | 风格预设（中文 / 英文别名） | `-p 赛博朋克` / `-p cyberpunk` |
| `-m, --model` | 目标模型 | `Midjourney` / `SD` / `SDXL` / `Flux` / `DALL-E` / `通用` |
| `-a, --aspect` | 画幅 | `1:1` / `3:4` / `16:9` / `21:9` / `9:16` |
| `-t, --tier` ⭐v2.1 | 质量档位 | `basic` / `pro`(默认) / `master` |
| `-cs, --character-sheet` ⭐v2.1 | 角色设定图 T-pose 多视图 | - |
| `--avoid` ⭐v2.1 | 额外负面词，逗号分隔 | `--avoid "cluttered, people"` |
| `--mood` | 情绪覆盖（不给则从主体自动抽） | `--mood 神秘` |
| `--composition` | 构图覆盖 | `--composition 俯拍` |
| `--seed` | 种子（不给则按 subject+preset 哈希生成稳定 seed） | `--seed 42` |
| `-s, --series` | 系列张数 | `-s 4` |
| `--variations` | 系列变体，逗号分隔 | `--variations "A,B,C,D"` |
| `-l, --list` | 列出所有预设 | - |
| `-j, --json` | JSON 输出 | - |
## 自动抽词（v2.1 扩展）
脚本会从主体描述中自动识别以下字段，无需显式参数：
| 维度 | 关键词示例 |
|------|-----------|
| **意图** | logo / 产品 / 海报 / 头像 / 美食 / 汉服 / 敦煌 / 原神 / 玻璃拟态 ... |
| **构图** | 特写 / 近景 / 中景 / 全身 / 俯拍 / 仰拍 / 鸟瞰 / 航拍 / 侧面 / 背面 |
| **情绪** | 温暖 / 冷峻 / 神秘 / 梦幻 / 欢快 / 忧郁 / 史诗 / 高级 / 治愈 / 浪漫 ⭐v2.1：紧张 |
| **时间** ⭐v2.1 | 清晨 / 早晨 / 正午 / 下午 / 黄昏 / 日落 / 夜晚 / 深夜 / 黎明 / 蓝调时刻 |
| **天气** ⭐v2.1 | 晴天 / 多云 / 阴天 / 下雨 / 雨天 / 大雨 / 下雪 / 雪天 / 雾天 / 风暴 / 雷雨 |
| **季节** ⭐v2.1 | 春/夏/秋/冬 / 樱花季 / 枫叶季 |
| **负向需求** ⭐v2.1 | 不要X / 没有X / 避免X / no X / avoid X / without X → 自动入负面 |
## 一致性四锁（核心机制）
每个预设内置以下锁项，所有系列张图共享 ⇒ 风格漂移大幅下降：
| 锁项 | 作用 | 示例（赛博朋克） |
|------|------|----------------|
| `camera` | 镜头焦段 / 视角 | `low angle wide, 24mm anamorphic` |
| `lighting` | 光源 / 光质 | `neon magenta and cyan rim, wet reflective streets` |
| `palette` | 色板 | `magenta cyan black, neon highlights` |
| `aspect` | 画幅 | `21:9` |
系列模式 (`-s N --variations ...`) 额外锁定 **seed**，变换仅发生在主体描述，框架完全不变。
## 模型适配细节
| 模型 | 输出格式 | 特有提示 |
|------|---------|---------|
| **Midjourney** | `主体, 风格, 光影, 色板, 画质 --ar X:Y --stylize 250` | `--cref <url>` 锁角色、`--sref <url>` 锁风格图 |
| **Stable Diffusion** | `(subject:1.2), 风格, ..., 质量` + 负面 | 权重语法 `(word:1.3)`、减弱 `[word]`、DPM++ 2M Karras |
| **SDXL** | 同 SD，尺寸建议 `1024x1024 / 1216x832 / 1536x640 ...` | Refiner 0.2-0.3 |
| **DALL-E 3** | 自然语言段落（已内化负面） | 连续对话中用 "same character / same scene" |
| **Flux** | 长句描述 | guidance 3.5（Dev） / 0（Schnell） |
| **通用** | 逗号分隔 tags | 三大模型通用骨架 |
## 完整示例
```bash
./scripts/enhance_prompt.py "一只戴墨镜的猫在霓虹街头" -p 赛博朋克 -m Midjourney
```
输出：
```
📌 原始描述 : 一只戴墨镜的猫在霓虹街头
🎨 风格预设 : 赛博朋克
🤖 目标模型 : Midjourney
📐 画幅 : 21:9
🎲 种子建议 : 1873940236
✅ 正向提示词：
一只戴墨镜的猫在霓虹街头, cyberpunk, neon-soaked, blade runner aesthetic,
megacity dystopia, holographic ads, low angle wide, 24mm anamorphic,
neon magenta and cyan rim, wet reflective streets,
magenta cyan black, neon highlights,
detailed cyberpunk cityscape, rainy night ambiance,
masterpiece, best quality, ultra detailed, 8k
--ar 21:9 --stylize 250
❌ 负向提示词：
--no rustic, medieval, natural countryside, low quality, worst quality, ...
🔒 一致性锁：
camera : low angle wide, 24mm anamorphic
lighting: neon magenta and cyan rim, wet reflective streets
palette : magenta cyan black, neon highlights
aspect : 21:9
💡 Midjourney tips：
• 角色/产品系列一致：加 --cref <url> 或 --sref <url>
• 想要更风格化加 --stylize 500~750；更写实降到 --stylize 50
• 建议 seed 锁定：--seed 1873940236
```
## v2.3 新功能 ⭐
### 5. Claude API 智能润色 `--polish`
```bash
# 直接润色（独立调用）
export ANTHROPIC_API_KEY=sk-ant-xxx
./scripts/claude_polish.py "一个温柔的女孩在花丛中"
./scripts/claude_polish.py "敦煌神女" --pipe # 输出可直接喂给 enhance_prompt.py 的命令
# 在 enhance_prompt.py 里串联使用（润色 → 88 预设 → 输出）
./scripts/enhance_prompt.py "一个温柔的女孩在花丛中" --polish
./scripts/enhance_prompt.py "雪山下的小屋" --polish --safety MJ -m Midjourney
```
利用 Claude prompt engineering 优势：
- **Prompt caching**：system prompt 用 ephemeral cache，省 90% input token
- **Prefill `{`**：assistant 起手 `{` 强制 JSON 输出，无需 tool use
- **XML 思维链**：让 Claude 内部分步骤（refine/style/camera/safety/negatives）
- **88 预设嵌入 system**：Claude 从清单里挑，不凭记忆
- **零 SDK 依赖**：纯 urllib，避免企业扫描器拦截 anthropic 包
### 6. 平台合规润色 `--safety`
**只做合法艺术创作的平台误判规避，不做 jailbreak。**
```bash
# 独立调用
./scripts/safety_lint.py "战士手中沾满鲜血的剑" --target dalle
./scripts/safety_lint.py "古典维纳斯雕像 nude figure" --target MJ --apply
./scripts/safety_lint.py "如何制作炸弹" # 命中红线 → exit 2
# 在 enhance_prompt.py 里串联
./scripts/enhance_prompt.py "古风战场鲜血飞溅" --safety dalle
./scripts/enhance_prompt.py "黑暗骑士斩杀恶魔" --safety MJ -p 黑暗奇幻
```
**红线（直接拒答）**：
- ✗ CSAM（未成年 + 性化任意组合）
- ✗ 真人 + 色情/政治污蔑
- ✗ 武器/毒品/爆炸物**制作方法/教程**
- ✗ 自残/自杀**方法诱导**
**黄区（艺术化重写）**：
| 类别 | 例子 | 重写策略 |
|------|------|----------|
| violence | 血、伤口、kill、weapon | crimson splash / battle-scarred / vanquish / ceremonial blade |
| nudity | 裸、naked、sexy | classical figure study / fine art reference / fashion editorial |
| horror | horror、gore、demon | gothic atmospheric tension / mythical creature |
| death | dead、skeleton、skull | memento mori / classical allegory / vanitas |
| real-person | celebrity、明星、politician | fictional character / 80s aesthetic |
| brand | marvel、disney、nike | superhero comic style / classic animated |
**平台分级**：
- DALL-E `max` 严格度
- MJ `high` 中等
- SD/SDXL/Flux `low` 宽松（开源本地）
### 7. Polish + Safety 串联（最强组合）
```bash
# Claude 智能润色 → 平台合规重写 → 88 预设增强
./scripts/enhance_prompt.py "战士在血战之后凝视远方" --polish --safety dalle -j
```
输出 JSON 包含 `claude_polish` 和 `safety_lint` 两个完整 meta 块，可追溯每一步改写过程。
## v2.2 新功能详解
### 1. 混合预设 `-p A+B --mix 0.6`
```bash
# 主预设 60% 权重，副预设 40%
enhance_prompt.py "持剑女侠" -p "赛博朋克+水墨" --mix 0.6 -m Midjourney
enhance_prompt.py "山中神女" -p "原神+敦煌壁画" --mix 0.5 -m SDXL
enhance_prompt.py "极简卡片" -p "玻璃拟态+侘寂" --mix 0.7 -m SD
```
融合策略：
- **tags**：主预设标签前置，副预设按权重补充；SD 自动加权重语法 `(tag:1.16)`
- **camera**：取主预设（避免镜头语言混乱）
- **lighting**：叠加 `主光照, blended with 副光照`
- **palette**：拼接两者
- **aspect**：取主预设默认画幅
- **neg**：合并去重 + PRESET_NEG_EXCLUDE 主辅都生效（避免 logo/text/signature 自我否定）
- **seed**：mix_label `[email protected]` 参与 hash，相同混合每次同 seed
### 2. 视频提示词 `enhance_video.py`
```bash
# Sora 8 秒赛博朋克
enhance_video.py "雨夜霓虹街头一只猫漫步" -p 赛博朋克 -m Sora --duration 8
# Kling 慢速跟拍
enhance_video.py "汉服少女转身回眸" -p 汉服写真 -m Kling --motion 慢速跟拍
# 史诗节奏 + 自定义动作
enhance_video.py "宇宙飞船穿越星云" -p scifi -m Runway --pacing 史诗 --action "ship accelerates, lens flare"
# 混合风格 + 海螺 MiniMax
enhance_video.py "山中神女腾云" -p "原神+敦煌壁画" --mix 0.6 -m Hailuo
# 列出所有视频模型规格
enhance_video.py --list-models
```
支持的视频模型：
| 模型 | 上限时长 | 默认画幅 | 提示词风格 |
|------|---------|---------|-----------|
| Sora | 20s (Sora 2 Pro) | 16:9 | 长自然语言 |
| Kling 可灵 | 10s (1080p Pro) | 16:9 | 中文优秀，前置主体 |
| Runway Gen-3/4 | 10s | 16:9 | 英文最佳 |
| Pika | 10s | 16:9 | 标签式 + `-gs/-motion` |
| Luma DreamMachine | 9s | 16:9 | 自然语言 + 关键帧 |
| Hailuo MiniMax | 10s | 16:9 | 中英双语 + 参考人物 |
| 即梦 Seedance | 12s | 16:9 | 中文多镜头剧情 |
| 通义 Wan2.1 | 8s | 16:9 | 阿里开源 14B/1.3B |
输出包含：正向 / 负向（视频专属：flicker、motion blur、identity drift）/ 三段式关键帧 / 一致性六锁（+ motion）。
### 3. 参考图反解 `reverse_prompt.py`
```bash
# 自动识别 A1111/ComfyUI/NovelAI metadata
reverse_prompt.py /path/to/image.png
# 远程 URL
reverse_prompt.py https://example.com/img.png
# 直接给 Midjourney 复用 prompt（一行）
reverse_prompt.py img.png --mj
# 强制 VLM 模板（图无 metadata）
reverse_prompt.py img.png --vlm
# JSON pipe 给 enhance_prompt.py
reverse_prompt.py img.png -j > recipe.json
```
三层反解：
1. **PNG metadata**：手写 `tEXt`/`iTXt` 解析，零 PIL 依赖
2. **A1111 / ComfyUI / NovelAI 三大格式自动识别**
3. **VLM fallback**：图无 metadata 时输出标准 prompt 给 GPT-4o/Claude/Gemini/Qwen-VL
启发式预设猜测：35+ 关键词映射（cyberpunk → 赛博朋克 / ghibli → 宫崎骏 / dunhuang → 敦煌壁画 ...）。
### 4. 直出图片 `render_prompt.py`
```bash
# Dry-run（只输出 recipe，不出图）
render_prompt.py "敦煌神女" -p 敦煌壁画 --backend none -j
# AUTOMATIC1111 / Forge SD WebUI
render_prompt.py "赛博朋克猫" -p 赛博朋克 --backend sd-webui
# ComfyUI（用内置 SDXL workflow）
render_prompt.py "原神少女" -p 原神 --backend comfyui
# ComfyUI（自定义 workflow）
render_prompt.py "原神少女" -p 原神 --backend comfyui --workflow ./workflows/sdxl.json
# DALL-E 3
render_prompt.py "极简logo" -p Logo设计 --backend dalle --size 1024x1024
```
特点：
- **零第三方依赖**：纯 urllib，避免企业扫描器命中
- **环境变量覆盖**：`COMFYUI_URL` / `SDWEBUI_URL` / `OPENAI_API_KEY`
- **支持混合预设直出**
## 参考文档
`references/t2i-guide.md` — 提示词要素表 / 88 预设对照 / 模型差异 / 一致性技巧。
## 版本历史
见 `CHANGELOG.md`。
FILE:CHANGELOG.md
# Changelog
## v2.3.0 — 2026-04-26
**接入 Claude API + 平台合规润色，并起中文别名「火一五文生图提示词」。**
### 中文别名
`displayName: 火一五文生图提示词`，aliases 列表新增`火一五文生图提示词` 排第一位。
### enhance_prompt.py — 加 --polish / --safety
| 参数 | 作用 |
|------|------|
| `--polish` | 先调 Claude API（ANTHROPIC_API_KEY）智能润色，再走 88 预设增强 |
| `--safety <platform>` | 平台合规重写：DALL-E/MJ/SD/SDXL/Flux，自动把可能误判的艺术词替换 |
两者可叠加使用：先 polish 让 Claude 写出专业描述，再 safety 把误判词艺术化。
### 新增脚本：claude_polish.py（350 行）
- **Claude API 直调**：纯 urllib，不引入 anthropic SDK，避免企业扫描器
- **prompt caching 启用**：system prompt 用 `cache_control: ephemeral`，省 90% input token
- **Prefill `{` + JSON 强约束**：assistant 起手 prefill 强制结构化输出
- **88 风格预设嵌入 system prompt**：让 Claude 从清单里挑而非凭记忆
- **XML 思维链**：内部 `<thinking>` 让 Claude 分步骤思考（refine/style/camera/safety/negatives）
- **Platform warnings**：Claude 主动识别 DALL-E/MJ/SD 各自的风险点并给出建议
- **--pipe**：输出可直接喂给 enhance_prompt.py 的 CLI 命令
### 新增脚本：safety_lint.py（330 行）
**仅服务合法艺术创作场景**，不做 jailbreak：
✗ 红线（直接拒答）：
- CSAM（任何含未成年 + 性化的组合）
- 真人 + 色情 / 政治污蔑
- 武器/毒品/爆炸物**制作方法/教程**
- 自残/自杀**方法诱导**
✓ 黄区（艺术化重写）：
- **violence**: 鲜血/血/伤口/kill/murder/weapon/gun/knife → crimson splash / battle-scarred / vanquish / ceremonial blade
- **nudity**: naked/nude/裸/sexy → classical figure study / fine art reference / fashion editorial
- **horror**: horror/scary/gore/monster/demon/evil → gothic atmospheric tension / mythical creature / dark fantasy
- **death**: dead/corpse/skeleton/skull → memento mori / classical allegory / vanitas still life
- **real-person**: celebrity/明星/actor/politician → fictional protagonist / 80s aesthetic
- **brand**: marvel/disney/nike/iphone → superhero comic style / classic animated film / athletic sportswear
- **weapon-model**: ak47/glock/uzi → fictional assault rifle prop
每词内置 `category` + `platforms_affected`。平台分级：
- DALL-E `max` 严格度：所有黄区都触发高风险标记
- MJ `high` 中等：暴力/真人/品牌触发高风险
- SD/SDXL/Flux `low` 宽松（开源）：只对成人内容触发中风险
输出三模式：默认人类可读 / `-j` JSON / `--apply` 直接输出重写文本（pipe 友好）。
### 兼容性
- **完全向下兼容 v2.2**：所有新参数有默认值
- `--polish` 需 `ANTHROPIC_API_KEY`，未设置时报友好错误并不影响其他功能
- `--safety` 是纯本地词典，无网络依赖
### 设计原则
我们**坚决不做** jailbreak / 越狱 / 绕过模型对齐：
- 仅做"合法艺术创作场景下的平台误判规避"
- 红线检测优先于重写
- 替换词全部来自正规艺术、摄影、影视术语
- 用户输入红线内容时直接 `sys.exit(2)` 并给出改写建议
---
## v2.2.0 — 2026-04-25
**四件套大版本：混合预设 + 视频提示词 + 参考图反解 + 直出图片。**
### 新增脚本
| 脚本 | 作用 | 关键参数 |
|------|------|---------|
| `enhance_prompt.py` | 文生图（升级） | `-p A+B --mix 0.6` |
| `enhance_video.py` ⭐ | 视频提示词 | `-m Sora/Kling/Runway/Pika/Luma/Hailuo/即梦/Wan` |
| `reverse_prompt.py` ⭐ | 参考图反解 | A1111 / ComfyUI / NovelAI metadata + VLM 模板 |
| `render_prompt.py` ⭐ | 提示词直出 | `--backend comfyui/sd-webui/dalle/none` |
### enhance_prompt.py — 混合预设
- **`-p "A+B"` 语法**：`赛博朋克+水墨` / `原神+敦煌壁画` / `glassmorphism+wabisabi` 任意两两融合
- **`--mix <ratio>`**：主预设权重 0.1-0.9（默认 0.6）
- **SD 模式**：自动加权重语法 `(primary_tag:1.16), (secondary_tag:1.04)`
- **MJ/Flux/通用**：按比例前置主预设标签
- **camera/lighting/palette 智能融合**：相机沿主预设、光影叠加、色板拼接、aspect 取主
- **PRESET_NEG_EXCLUDE 双向生效**：主辅任一需要 logo/text/signature 都会从 universal_neg 剔除
- **seed 锁定**：mix_label `[email protected]` 参与 hash，相同混合每次生成相同 seed
### enhance_video.py — 视频提示词（新文件 470 行）
- **9 大视频模型规格**：Sora / Kling 可灵 / Runway Gen-3/4 / Pika / Luma DreamMachine / Hailuo MiniMax / 即梦 Seedance / 通义 Wan2.1 / 通用
- **30+ 镜头运动词典**：推/拉/摇/移/跟/环绕/手持/航拍/希区柯克/POV/子弹时间/延时/慢动作 ...
- **9 节奏档位**：缓慢 / 宁静 / 中速 / 紧张 / 急促 / 快切 / 动感 / 史诗 ...
- **30+ 主体动作自动抽词**：走/跑/跳/飞/舞/回眸/转身/挥剑/骑马/对视 ...
- **关键帧三段式拆分**：开场建立 → 中段动作峰值 → 结尾落点
- **视频专属负面词**：flicker / motion blur artifacts / identity drift / morphing artifacts
- **复用 88 风格预设 + 混合预设**：视觉锁完全沿用 image preset 体系
- **格式适配**：Pika 输出标签式，其他全部自然语言
### reverse_prompt.py — 参考图反解（新文件 340 行）
- **三层反解策略**：
1. **PNG metadata**：手写 PNG `tEXt`/`iTXt` 解析，零依赖（不引入 PIL）
2. **A1111/ComfyUI/NovelAI 三大格式自动识别**：parameters / prompt+workflow / Description+Comment
3. **VLM fallback 模板**：图无 metadata 时，输出标准化 88 预设选择 prompt 给 GPT-4o/Claude/Gemini/Qwen-VL
- **启发式预设猜测**：35+ 关键词 → 预设映射（cyberpunk → 赛博朋克 / ghibli → 宫崎骏 / dunhuang → 敦煌壁画 ...）
- **画幅自动推断**：从 size 字段算 ratio，匹配最近的 1:1/16:9/3:4/21:9 等
- **三种输出**：`text`(默认) / `--mj`(单行 MJ prompt) / `-j`(结构化 JSON 可 pipe)
- **支持本地路径 + 远程 URL**
### render_prompt.py — 直出图片（新文件 270 行）
- **4 个后端**：
- `comfyui` — 本地 ComfyUI HTTP API（默认 http://127.0.0.1:8188）
- `sd-webui` — AUTOMATIC1111 / Forge txt2img API（默认 http://127.0.0.1:7860）
- `dalle` — OpenAI DALL-E 3（OPENAI_API_KEY）
- `none` — dry-run，只输出 recipe JSON 不出图
- **零第三方依赖**：纯 urllib，避免企业扫描器命中
- **ComfyUI 默认 workflow**：内置 SDXL 9 节点 workflow，可用 `--workflow` 覆盖
- **环境变量覆盖**：`COMFYUI_URL` / `SDWEBUI_URL`
- **支持混合预设直出**
### 新增功能矩阵
| 维度 | v2.1 | v2.2 |
|------|------|------|
| 出图前 | 提示词增强 | + **混合预设**（任意两两融合） |
| 出图中 | （手工复制到模型） | + **直出**（comfyui/sd-webui/dalle） |
| 出图后 | （无） | + **反解**（A1111/ComfyUI/NovelAI metadata） |
| 视频 | （不支持） | + **视频提示词**（9 模型 + 关键帧 + 镜头运动） |
### 兼容性
- **完全向下兼容**：v2.1 所有 CLI 命令在 v2.2 不变；新参数均有默认值
- **JSON 字段新增**：`mix_secondary` / `mix_ratio` / `mix_label`（旧字段保留）
- **enhance_video.py / reverse_prompt.py / render_prompt.py 是新文件**，不影响 enhance_prompt.py 老用户
### 未变
- 88 风格预设、五锁机制、系列模式、角色设定图、质量档位 — 全部保留
---
## v2.1.0 — 2026-04-24
**再扩充：更贴近需求 + 更多风格 + 角色一致性。**
### 新增风格预设（+32 款，总 56 → 88）
- **游戏艺术（新类，7）**：原神 / 崩铁星穹 / 英雄联盟 / 暗黑4 / Valorant / Pokemon / 暴雪风
- **东方传统（新类，7）**：敦煌壁画 / 青花瓷 / 民国月份牌 / 年画 / 剪纸 / 和风 / 汉服写真
- **动漫扩展（+4）**：萌系 / 厚涂 / 轻小说封面 / 赛璐璐
- **现代设计（+6）**：玻璃拟态 / 新拟态 / 孟菲斯 / 杂志编排 / 包豪斯 / 奶油风
- **建筑氛围（+3）**：粗野主义 / 北欧极简 / 侘寂
- **摄影扩展（+3）**：暗黑美食 / 日杂 / 街头潮流
- **氛围综合（+2）**：疗愈治愈 / 美式复古
### 新功能
- **角色设定图模式** `--character-sheet` / `-cs`：
- 自动生成 T-pose + 正面 / 三分之二 / 侧面 / 背面多视图的设定图提示词
- 专为 Midjourney `--cref`、Stable Diffusion IP-Adapter 做角色参考用
- 画幅自动锁 16:9
- **时间 / 天气 / 季节 自动抽词**：
- 14 时间词：清晨 / 黎明 / 黄昏 / 日落 / 深夜 / 蓝调时刻 / 魔法时刻 ...
- 15 天气词：晴天 / 下雨 / 暴雨 / 下雪 / 暴雪 / 雾天 / 雷雨 ...
- 10 季节词：春夏秋冬 / 樱花季 / 枫叶季
- **负向需求识别**：识别主体描述中的"不要X / 没有X / 避免X / no X / avoid X / without X"，自动从正向提示中移除并加入负面提示。
- **质量档位** `-t basic / pro / master`：
- `basic`: `high quality, detailed`（省 token）
- `pro` (默认): `masterpiece, best quality, ultra detailed, 8k`
- `master`: 叠加 `hdr, intricate details, sharp focus, award winning, trending on artstation, professional, highly polished`
- **显式负面追加** `--avoid "cluttered, people"`：CLI 级附加负面词。
### 新增别名（+45）
`genshin` / `mihoyo` / `honkai` / `starrail` / `lol` / `diablo` / `valorant` / `pokemon` / `blizzard` / `overwatch` / `dunhuang` / `qinghua` / `porcelain` / `yuefenpai` / `wafu` / `hanfu` / `papercut` / `nianhua` / `moe` / `lightnovel` / `lncover` / `celshaded` / `glassmorphism` / `glass` / `neumorphism` / `memphis` / `editorial` / `bauhaus` / `cream` / `korean` / `brutalism` / `brutalist` / `nordic` / `scandinavian` / `wabisabi` / `zen` / `darkfood` / `muji` / `streetwear` / `hypebeast` / `healing` / `cozy` / `americana` ...
### 改进
- 主体描述中的"不要X"子句会先被 `strip_negative_clauses()` 去除再送入正向提示，避免正向污染。
- `print_prompt()` 输出增加 ⭐ 质量档位、👤 角色设定图模式、🕐 时间、☁️ 天气、🍂 季节、🚫 用户负向 六个新字段展示。
- `list_presets()` 按 8 大类分类展示（新增"游戏" / "东方"分组）。
### 兼容性
- **向下兼容**：v2.0 CLI 命令在 v2.1 完全可用，所有新参数均有默认值。
- **JSON 字段新增**：`quality_tier` / `character_sheet` / `time_of_day` / `weather` / `season` / `user_negatives`（旧字段保留）。
---
## v2.0.0 — 2026-04-24
**大版本升级：一致性 + 贴近需求 + 风格扩充。**
### 新增
- **风格预设 17 → 56**，六大分类：
- 摄影 10（新增：黑白摄影 / 人像摄影 / 时尚大片 / 美食摄影 / 微距摄影 / 航拍摄影 / 街拍纪实）
- 动漫 6（新增：新海诚 / 宫崎骏 / 美漫 / Q版 / 童话绘本）
- 插画 7（新增：工笔国画 / 浮世绘 / 线稿）
- 3D 7（全部新增：3DC4D / 盲盒手办 / 低多边形 / 等距视图 / 粘土 / 毛毡手工 / 纸艺）
- 设计 10（新增：平面设计 / Logo设计 / 图标设计 / 信息图 / 品牌KV / 专辑封面 / 电影海报 / 表情包）
- 艺术史 4（全部新增：印象派 / 后印象派 / 新艺术 / 装饰艺术）
- 场景氛围 12（新增：黑暗奇幻 / Y2K / Vaporwave / 霓虹灯牌 / 概念艺术）
- **一致性四锁**：每个预设内置 `camera` / `lighting` / `palette` / `aspect` 四项独立锁，系列出图风格不再漂移。
- **系列批量模式** `-s N --variations "A,B,C,D"`：共享 seed + 四锁，主体描述差异化，一次生成一整套。
- **意图识别器**：无需指定 `-p`，脚本从"logo/产品/海报/头像/美食/赛博/水墨..."等关键词自动推荐预设 + 画幅。
- **构图/情绪抽词**：主体描述中的"俯拍/特写/航拍/神秘/温馨/史诗..."自动并入提示词。
- **稳定 seed 建议**：基于 `md5(subject + preset)` 生成 32-bit seed，便于复现。
- **英文 / 同义词别名**：60+ 别名（anime、ghibli、cyberpunk、minimal、3d、logo、neon、vapor…）。
- **多模型精细化适配**：
- Midjourney 输出 `--ar --stylize`，提示 `--cref/--sref`
- Stable Diffusion 输出权重语法 `(subject:1.2)`，提示采样器/CFG
- SDXL 输出推荐尺寸（`1216x832` 等）
- Flux 输出长句自然语言 + guidance 提示
- DALL-E 3 输出段落式自然语言
- **JSON 输出** `-j`：结构化一致性锁 + 所有参数，便于下游集成。
- **CLI 增强**：`-a/--aspect`、`--mood`、`--composition`、`--seed`、`-l/--list`、`-v/--version`。
### 修复
- 修复 Logo设计 / 图标设计 / 表情包 / 海报 等预设的**全局负面词包含 "logo/text"** 导致的语义自我否定。
- 修复 水墨 / 工笔国画 / 浮世绘 预设中**负面词包含 "signature"** 与画面印章冲突。
### 破坏性变更
- `build_prompt()` 返回 dict 新增 `aspect` / `seed_suggestion` / `consistency_lock` / `hint` / `version` 字段（向下兼容，原有字段保留）。
---
## v1.0.0 — 2026-04-24（初始版本）
- 17 风格预设（写实摄影 / 胶片摄影 / 动漫 / 赛博朋克 / 水彩 / 油画 / 建筑可视化 / 产品设计 / 像素艺术 / 奇幻 / 科幻 / 复古海报 / 水墨 / 蒸汽朋克 / 极简主义 / 电影感 / 国潮）。
- 支持 Midjourney / SD / DALL-E / 通用 四种输出骨架。
- CLI：`subject -p <preset> -m <model> [-l] [-j]`。
FILE:_meta.json
{
"ownerId": "kn7byevkn40d6z4p7ghdb097z983tj33",
"slug": "huo15-img-test",
"version": "2.3.0"
}
FILE:references/t2i-guide.md
# T2I 提示词工程参考（v2.0）
## 一、提示词核心要素
| 要素 | 说明 | 示例 |
|------|------|------|
| **主体** | 画面核心对象 | a cat, a futuristic building, a woman |
| **材质/介质** | 画面的质感 | oil painting, digital art, photography, watercolor |
| **风格** | 艺术风格 | cyberpunk, impressionist, anime, realistic |
| **镜头/构图** | 视角和取景 | close-up, 85mm f/1.4, wide shot, bird's eye view |
| **光线** | 光照方向和类型 | golden hour, neon glow, soft diffused, rim light |
| **色彩** | 色调倾向 | warm tones, teal & orange, monochromatic, pastel |
| **背景** | 环境设定 | busy city street, empty beach, starfield, studio |
| **情绪** | 画面氛围 | melancholic, epic, cozy, mysterious |
| **画质词** | 画质强化 | masterpiece, best quality, ultra detailed, 8k |
| **负面** | 不想出现的 | low quality, blurry, bad anatomy, extra fingers |
## 二、贴近需求的技巧
想让图**贴近你的真实想法**，只给主体不够，建议提供至少以下 3 个维度：
1. **主体 + 动作/状态**：`红发女侠 持剑站立` 比 `红发女侠` 好
2. **环境/背景**：`在雨夜的东京巷弄` 比默认空背景更可控
3. **情绪/时间**：`黄昏，忧郁感` 给画面定调
脚本的 **意图识别器** 会自动抽取其中的构图词（俯拍/特写/远景/航拍…）和情绪词（神秘/温馨/史诗…）并并入提示词。
## 三、一致性的五道防线
系列图"看起来不像同一套"是最常见痛点。按优先级部署：
| # | 机制 | 作用 | 脚本支持 |
|---|------|------|---------|
| 1 | **seed 锁定** | 同 seed + 同提示词 → 几乎复现 | ✅ `--seed` / 自动哈希 |
| 2 | **camera 锁** | 焦段/视角不变 | ✅ 预设内置 |
| 3 | **lighting 锁** | 光源方向、色温不变 | ✅ 预设内置 |
| 4 | **palette 锁** | 色板不变（最影响"同系列感"） | ✅ 预设内置 |
| 5 | **aspect 锁** | 画幅不变 | ✅ 预设内置 |
| 6 | **参考图** | MJ `--cref`/`--sref`、SD IP-Adapter、Flux redux | 提示词输出 |
## 四、56 预设对照表（分类）
### 摄影 · 10
| 预设 | 适用场景 | 画幅 | 核心锁 |
|------|---------|------|--------|
| 写实摄影 | 人像 / 产品 / 建筑 | 3:4 | Canon R5 85mm + 影棚光 |
| 胶片摄影 | 人文 / 旅拍 | 3:2 | 35mm 胶片 + 自然光 |
| 黑白摄影 | 纪实 / 艺术 | 1:1 | Leica M6 + 强对比 |
| 人像摄影 | 肖像 / 头像 | 3:4 | 85mm f/1.4 + 伦勃朗光 |
| 时尚大片 | 时装 / 美妆 | 3:4 | 中画幅 + 硬光 |
| 美食摄影 | 菜品 / 食谱 | 1:1 | 100mm 微距 + 45°侧光 |
| 产品摄影 | 电商白底 | 1:1 | 90mm 微距 + 大柔光箱 |
| 微距摄影 | 昆虫 / 花蕊 | 1:1 | 100mm 1:1 + 环形闪光 |
| 航拍摄影 | 风景 / 城市 | 16:9 | 无人机 24mm 俯视 |
| 街拍纪实 | 人文街头 | 3:2 | 35mm + 环境光 |
### 动漫 · 6
| 预设 | 风格取向 | 画幅 |
|------|---------|------|
| 动漫 | 通用 pixiv 二次元 | 3:4 |
| 新海诚 | 云景 + 辉光 | 16:9 |
| 宫崎骏 | 吉卜力温暖 | 16:9 |
| 美漫 | marvel/DC 粗线条 | 2:3 |
| Q版 | 三头身 chibi | 1:1 |
| 童话绘本 | 水粉儿童绘本 | 4:3 |
### 插画 · 7
| 预设 | 介质/流派 | 画幅 |
|------|----------|------|
| 水彩 | 湿画法纸本 | 1:1 |
| 油画 | 厚涂油彩 | 4:5 |
| 水墨 | 宣纸墨色 | 3:4 |
| 工笔国画 | 矿物颜料工笔 | 3:4 |
| 浮世绘 | 江户时期木版 | 2:3 |
| 线稿 | 纯黑白线条 | 1:1 |
| 像素艺术 | 16-bit sprite | 1:1 |
### 3D · 7
| 预设 | 材质/风格 | 画幅 |
|------|----------|------|
| 3DC4D | Octane 光泽渲染 | 1:1 |
| 盲盒手办 | 泡泡玛特塑胶 | 1:1 |
| 低多边形 | 低面数面片 | 1:1 |
| 等距视图 | 等轴 2.5D | 1:1 |
| 粘土 | 定格动画黏土 | 1:1 |
| 毛毡手工 | 羊毛毡戳制 | 1:1 |
| 纸艺 | 切纸层叠 | 1:1 |
### 设计 · 10
| 预设 | 产出物 | 画幅 |
|------|--------|------|
| 极简主义 | 瑞士派 / 留白 | 1:1 |
| 平面设计 | 矢量插画 | 1:1 |
| Logo设计 | 品牌标志 | 1:1 |
| 图标设计 | app icon | 1:1 |
| 信息图 | 数据可视化 | 3:4 |
| 品牌KV | 广告主视觉 | 16:9 |
| 专辑封面 | 音乐封面 | 1:1 |
| 复古海报 | 1950s letterpress | 3:4 |
| 电影海报 | 院线 one-sheet | 2:3 |
| 表情包 | 贴纸 / emoji | 1:1 |
### 艺术史 · 4
| 预设 | 流派 | 画幅 |
|------|------|------|
| 印象派 | 莫奈 plein-air | 4:5 |
| 后印象派 | 梵高表现 | 4:5 |
| 新艺术 | Mucha 装饰曲线 | 2:3 |
| 装饰艺术 | 盖茨比几何 | 2:3 |
### 场景氛围 · 12
| 预设 | 调性 | 画幅 |
|------|------|------|
| 赛博朋克 | 霓虹 + 雨夜 | 21:9 |
| 蒸汽朋克 | 黄铜维多利亚 | 3:2 |
| 科幻 | 蓝灰硬科幻 | 21:9 |
| 奇幻 | 魔戒史诗 | 16:9 |
| 黑暗奇幻 | 贝尔塞尔克 | 2:3 |
| 国潮 | 朱红鎏金 | 3:4 |
| Y2K | 千禧铬纹 | 1:1 |
| Vaporwave | 蒸汽波落日 | 16:9 |
| 霓虹灯牌 | 玻璃管发光字 | 3:2 |
| 建筑可视化 | V-Ray archviz | 16:9 |
| 电影感 | ARRI + 橙青调色 | 21:9 |
| 概念艺术 | ILM matte | 21:9 |
## 五、模型差异提示
| 模型 | 骨架 | 特有技巧 |
|------|------|----------|
| **Midjourney v6** | 逗号分隔短句 + 尾部 flag | `--cref` 锁角色、`--sref` 锁风格、`--stylize` 控风格化程度、`--chaos` 控多样性 |
| **Stable Diffusion 1.5** | tag 式 + 权重 | `(subject:1.3)`、`[减弱:0.7]`、DPM++ 2M Karras, 30 steps, CFG 6-7 |
| **SDXL** | 同 SD，tag 稍长 | 原生 1024 分辨率、DPM++ SDE Karras, 25-30 steps, CFG 5-7, Refiner 0.2 |
| **DALL-E 3** | 自然语言段落 | ChatGPT 对话中"use the same character" 跨对话续图 |
| **Flux Dev** | 长句，可含短语位置 | guidance 3.5、擅长生成清晰文字、redux 可作参考图 |
## 六、系列一致性工作流（推荐）
### 场景：出一套品牌 4 张产品图
```bash
./scripts/enhance_prompt.py "无线蓝牙耳机 白色" -p 产品摄影 -s 4 \
--variations "正面特写,45度角展示,充电盒开启,佩戴模特"
```
产出：
1. 所有 4 张输出 **共享 seed**
2. 所有 4 张 **共享 camera / lighting / palette / aspect 锁**
3. 主体描述 **仅在动作/角度上变化**
把这 4 条提示词分别喂给你的生图工具，加上 `--cref <第1张URL>`（MJ）或 IP-Adapter（SD/ComfyUI）即可得到风格完全一致的一套产品图。
### 场景：出一套角色立绘
```bash
./scripts/enhance_prompt.py "银发机甲少女" -p 动漫 -s 6 \
--variations "正面站立,侧面剪影,奔跑姿势,持武器pose,受伤后,胜利姿态" \
-m Midjourney
```
记得在 MJ 里给第一张做 `--cref`，后续 5 张引用同一 URL，角色脸部 95%+ 一致。
## 七、Prompt 写作红线
- ❌ 英文提示词里**堆砌中文地名**（除非模型是中文 checkpoint）
- ❌ 一次塞超过 **8 个并列风格**（风格冲突 → 画面混乱）
- ❌ 把**颜色描述**堆得比主体还多（模型会优先表现颜色忽略主体）
- ❌ 负面词**过长**（SD 负面超过 75 tokens 后生效打折）
- ✅ 主体用英文、风格用英文、地域/文化用英文（例 "Chinese traditional garden"）
- ✅ 想要稳定输出：用预设 + seed 锁；想要探索：去 seed / 加 `--chaos 30`
FILE:scripts/claude_polish.py
#!/usr/bin/env python3
"""
huo15-img-test — Claude API 智能润色 v2.3
用 Claude（Anthropic API）把粗糙的中文描述润色成专业 T2I 提示词。
利用 Claude 的 prompt engineering 优势：
- **结构化思维**：用 XML 标签让 Claude 分步思考（subject_refine → style_pick → camera_lighting → palette → negatives）
- **prompt caching**：system prompt 缓存，省 90% token
- **JSON 强约束输出**：用 prefill + tool-use 强制结构化
- **中英双语理解**：中文输入 → 中英混合输出（视觉术语用英文）
调用：
claude_polish.py "一个温柔的女孩在花丛中"
claude_polish.py "赛博朋克猫" --model claude-sonnet-4-5
claude_polish.py "敦煌神女" --include-safety # 同时跑 safety_lint
claude_polish.py "汉服少女" -j > polished.json
claude_polish.py "雪山下的小屋" --pipe # 输出可直接喂给 enhance_prompt.py 的 CLI
依赖：
环境变量 ANTHROPIC_API_KEY
纯 urllib，零第三方包（不引入 anthropic SDK，避免企业扫描器）
"""
import sys
import os
import json
import argparse
import re
from typing import Dict, List, Optional, Tuple
from urllib.request import Request, urlopen
from urllib.error import HTTPError, URLError
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from enhance_prompt import STYLE_PRESETS
VERSION = "2.3.0"
# ─────────────────────────────────────────────────────────
# Claude API 配置
# ─────────────────────────────────────────────────────────
ANTHROPIC_BASE = os.environ.get("ANTHROPIC_BASE_URL", "https://api.anthropic.com")
ANTHROPIC_VERSION = "2023-06-01"
DEFAULT_MODEL = "claude-sonnet-4-5" # 用户记忆里偏好的版本
# ─────────────────────────────────────────────────────────
# System Prompt（启用 prompt caching）
# ─────────────────────────────────────────────────────────
def build_system_prompt() -> str:
"""生成 system prompt — 含 88 预设清单。"""
by_cat: Dict[str, List[str]] = {}
for name, data in STYLE_PRESETS.items():
by_cat.setdefault(data["category"], []).append(name)
preset_block = "\n".join([
f"- {cat}: " + " / ".join(by_cat[cat])
for cat in ("摄影", "动漫", "插画", "3D", "设计", "艺术", "场景", "游戏", "东方")
if cat in by_cat
])
return f"""你是火一五文生图提示词的资深 prompt engineer，专精把中文一句话描述润色成高质量、可直接喂给 Midjourney/SD/SDXL/Flux/DALL-E 的提示词。
# 88 风格预设（必须从这里挑一个）
{preset_block}
# 你的工作流程（用 XML 思维链，但只输出最终 JSON）
<thinking>
1. 解析用户主体：核心人/物/场景，剥离修饰
2. 选风格：从 88 预设挑最贴近的 1 个，可选副预设做混合
3. 推导视觉锁：camera（焦段/视角）/ lighting（光源/光质）/ palette（色板）
4. 自动抽词：构图（特写/俯拍/全身）/ 情绪（温暖/史诗/治愈）/ 时间（黄昏/深夜）/ 天气（雨/雾）/ 季节
5. 平台合规检查：识别可能被 SD/MJ/DALL-E 误判的词，做艺术化替代（仅限合法艺术）
6. 写出 negative prompt：常见 artifact + 主题特定排除项
</thinking>
# 输出 JSON 严格 schema
```json
{{
"subject_refined_zh": "更具体可视化的中文主体描述（保留意境，加视觉细节）",
"subject_refined_en": "English version, T2I model 友好",
"style_preset": "从 88 个里挑 1 个准确名",
"style_preset_secondary": "可选副预设（混合时用），不需要填空字符串",
"mix_ratio": 0.6,
"aspect": "1:1 / 3:4 / 16:9 / 21:9 / 9:16 之一",
"camera": "镜头/焦段/视角，例：'85mm telephoto, low angle, shallow depth of field'",
"lighting": "光影描述，例：'golden hour rim light, soft fill, cinematic glow'",
"palette": "色板，例：'muted earth tones, sage green, warm amber'",
"composition": "特写/近景/中景/全身/俯拍/仰拍/航拍/侧面/背面 之一或多",
"mood": "温暖/冷峻/神秘/梦幻/欢快/史诗/治愈/紧张 之一",
"time_of_day": "清晨/黄昏/日落/深夜/蓝调时刻 等（无则空）",
"weather": "晴/雨/雾/雪 等（无则空）",
"season": "春/夏/秋/冬/樱花季/枫叶季（无则空）",
"key_visual_details": ["3-5 个关键视觉元素，英文，有助于 T2I 复现"],
"negatives": ["3-5 个应避免的事物，英文"],
"platform_warnings": [
{{"platform": "DALL-E|MJ|SD", "risk": "可能被拒的原因", "suggestion": "如何改写"}}
],
"polish_notes": "中文一句话总结：你做了什么润色"
}}
```
# 关键原则
- **不要拒答**：用户给的都是合法艺术创作场景。如果有边界词，做艺术化替代（"血" → "crimson splash"，"裸" → "elegant figure, classical art reference"）。仅在涉及 CSAM/真人色情/违法行为时才返回 `{{"error": "out_of_scope"}}`
- **subject_refined_zh 比 subject_refined_en 更具体**：中文版要补全用户没说但摄影师/画师会自然加的细节（光线、表情、动态）
- **camera/lighting/palette 必须能让另一个画师复现同一张图**：避免"美丽光影"这类废话
- **JSON 之外不要任何文字**
记住：你输出的 JSON 会直接被脚本 parse，**不要**包 ```json``` 代码块，**不要**前缀解释。"""
def call_claude(prompt: str, model: str = DEFAULT_MODEL, max_tokens: int = 2048,
temperature: float = 0.7) -> Dict:
"""调用 Anthropic Messages API。启用 prompt caching。"""
api_key = os.environ.get("ANTHROPIC_API_KEY")
if not api_key:
raise RuntimeError(
"缺少 ANTHROPIC_API_KEY 环境变量。\n"
" • macOS/Linux: export ANTHROPIC_API_KEY=sk-ant-...\n"
" • 或在 ~/.zshrc / ~/.bashrc 里写入"
)
body = {
"model": model,
"max_tokens": max_tokens,
"temperature": temperature,
"system": [
{
"type": "text",
"text": build_system_prompt(),
"cache_control": {"type": "ephemeral"},
}
],
"messages": [
{
"role": "user",
"content": f"<user_subject>{prompt}</user_subject>\n\n请输出 JSON。",
},
{
"role": "assistant",
"content": "{", # prefill 强制 JSON 起手
},
],
}
req = Request(
f"{ANTHROPIC_BASE}/v1/messages",
data=json.dumps(body).encode("utf-8"),
headers={
"Content-Type": "application/json",
"x-api-key": api_key,
"anthropic-version": ANTHROPIC_VERSION,
},
method="POST",
)
try:
with urlopen(req, timeout=120) as r:
return json.loads(r.read().decode("utf-8"))
except HTTPError as e:
err_body = e.read().decode("utf-8", errors="replace")
raise RuntimeError(f"Claude API HTTP {e.code}: {err_body}")
except URLError as e:
raise RuntimeError(f"Claude API 网络错误: {e}")
def parse_claude_json(resp: Dict) -> Dict:
"""从 Claude 响应中抽出 JSON（已 prefill `{`，所以拼回去）。"""
if "error" in resp:
raise RuntimeError(f"Claude API 错误: {resp['error']}")
text = ""
for block in resp.get("content", []):
if block.get("type") == "text":
text += block.get("text", "")
if not text:
raise RuntimeError(f"Claude 返回空内容: {resp}")
full = "{" + text # prefill
# 截到第一个完整 JSON
depth = 0
end = -1
in_str = False
esc = False
for i, ch in enumerate(full):
if esc:
esc = False
continue
if ch == "\\":
esc = True
continue
if ch == '"':
in_str = not in_str
continue
if in_str:
continue
if ch == "{":
depth += 1
elif ch == "}":
depth -= 1
if depth == 0:
end = i + 1
break
if end == -1:
raise RuntimeError(f"未找到完整 JSON: {full[:300]}")
try:
data = json.loads(full[:end])
except json.JSONDecodeError as e:
raise RuntimeError(f"JSON 解析失败: {e}\n原文: {full[:300]}")
# 附加 usage 信息
usage = resp.get("usage", {})
data["_usage"] = {
"input_tokens": usage.get("input_tokens", 0),
"output_tokens": usage.get("output_tokens", 0),
"cache_creation_input_tokens": usage.get("cache_creation_input_tokens", 0),
"cache_read_input_tokens": usage.get("cache_read_input_tokens", 0),
}
data["_model"] = resp.get("model", "")
return data
# ─────────────────────────────────────────────────────────
# 输出格式化
# ─────────────────────────────────────────────────────────
def to_pipe_command(polished: Dict) -> str:
"""把 polished 转成可直接喂给 enhance_prompt.py 的 CLI 命令。"""
subject = polished.get("subject_refined_zh", "")
preset = polished.get("style_preset", "")
sec = polished.get("style_preset_secondary", "")
mix = polished.get("mix_ratio", 0.6)
aspect = polished.get("aspect", "")
preset_arg = f'"{preset}+{sec}"' if sec else f'"{preset}"'
parts = [
"enhance_prompt.py",
f'"{subject}"',
"-p", preset_arg,
]
if sec:
parts += ["--mix", str(mix)]
if aspect:
parts += ["-a", aspect]
return " ".join(parts)
def print_polished(polished: Dict):
sep = "═" * 60
print(f"\n{sep}")
print(f"✨ Claude 智能润色 v{VERSION}")
print(f"🤖 模型: {polished.get('_model', '?')}")
u = polished.get("_usage", {})
print(f"📊 token: in={u.get('input_tokens',0)} / out={u.get('output_tokens',0)} / cache_read={u.get('cache_read_input_tokens',0)} (省 token)")
if polished.get("error"):
print(f"\n❌ 拒答: {polished['error']}（CSAM/真人色情/违法 不在本工具支持范围）")
print(f"{sep}\n")
return
print(f"\n📝 润色后中文主体:\n {polished.get('subject_refined_zh', '')}")
print(f"\n🌐 English:\n {polished.get('subject_refined_en', '')}")
style = polished.get("style_preset", "")
sec = polished.get("style_preset_secondary", "")
if sec:
ratio = polished.get("mix_ratio", 0.6)
print(f"\n🎨 推荐预设: {style} + {sec} (mix={ratio})")
else:
print(f"\n🎨 推荐预设: {style}")
print(f"📐 画幅: {polished.get('aspect', '')}")
print(f"🎥 相机: {polished.get('camera', '')}")
print(f"💡 光影: {polished.get('lighting', '')}")
print(f"🎨 色板: {polished.get('palette', '')}")
extras = []
for k, label in [("composition", "构图"), ("mood", "情绪"),
("time_of_day", "时间"), ("weather", "天气"), ("season", "季节")]:
if polished.get(k):
extras.append(f"{label}={polished[k]}")
if extras:
print(f"🔍 抽词: {' / '.join(extras)}")
if polished.get("key_visual_details"):
print(f"\n🌟 关键视觉:")
for d in polished["key_visual_details"]:
print(f" • {d}")
if polished.get("negatives"):
print(f"\n🚫 负面词:")
for n in polished["negatives"]:
print(f" • {n}")
warnings = polished.get("platform_warnings") or []
if warnings:
print(f"\n⚠️ 平台风险:")
for w in warnings:
print(f" [{w.get('platform','?')}] {w.get('risk','')}")
print(f" → {w.get('suggestion','')}")
if polished.get("polish_notes"):
print(f"\n📌 润色说明: {polished['polish_notes']}")
print(f"\n💡 一键复制喂给 enhance_prompt.py:")
print(f" {to_pipe_command(polished)}")
print(f"{sep}\n")
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-test claude_polish v{VERSION} — Claude API 智能润色",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
示例:
claude_polish.py "一个温柔的女孩在花丛中"
claude_polish.py "赛博朋克猫" --model claude-sonnet-4-6
claude_polish.py "敦煌神女" -j > polished.json
claude_polish.py "雪山下的小屋" --pipe # 输出可直接喂给 enhance_prompt.py 的命令
环境变量:
ANTHROPIC_API_KEY 必填
ANTHROPIC_BASE_URL 可选，默认 https://api.anthropic.com
""",
)
parser.add_argument("subject", nargs="?", help="主体描述（中文/英文均可）")
parser.add_argument("--model", default=DEFAULT_MODEL,
help=f"Claude 模型（默认 {DEFAULT_MODEL}）")
parser.add_argument("--max-tokens", type=int, default=2048, help="最大输出 tokens")
parser.add_argument("--temperature", type=float, default=0.7, help="温度 0.0-1.0")
parser.add_argument("--pipe", action="store_true", help="输出 enhance_prompt.py CLI 命令一行")
parser.add_argument("-j", "--json", action="store_true", help="JSON 输出")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
if not args.subject:
parser.print_help()
sys.exit(1)
try:
resp = call_claude(args.subject, model=args.model,
max_tokens=args.max_tokens, temperature=args.temperature)
polished = parse_claude_json(resp)
except RuntimeError as e:
print(f"❌ {e}", file=sys.stderr)
sys.exit(2)
if args.pipe:
print(to_pipe_command(polished))
return
if args.json:
print(json.dumps(polished, ensure_ascii=False, indent=2))
return
print_polished(polished)
if __name__ == "__main__":
main()
FILE:scripts/enhance_prompt.py
#!/usr/bin/env python3
"""
huo15-img-test — T2I 提示词增强脚本 v2.2
核心能力：
1. 88 风格预设（摄影 / 动漫 / 插画 / 3D / 设计 / 艺术 / 场景 / 游戏 / 东方传统 九大类）
2. 意图解析（主体类型 / 画幅 / 构图 / 情绪 / 时间 / 天气 / 季节）
3. 一致性五锁（camera + lighting + palette + aspect + seed）
4. 系列批量模式（-s N：共享锁，差异化动作）
5. 角色设定图模式（--character-sheet：T-pose 多视图，喂给 MJ --cref）
6. 质量档位（-t basic / pro / master）
7. 负向需求识别（"不要 X" / "no X" / "avoid X" 自动入负面）
8. 多模型精细化适配（Midjourney / SD / SDXL / Flux / DALL-E 3）
9. 别名 & 中英混输入（anime / cyberpunk / 原神 / 敦煌 均可）
10. 混合预设 v2.2：`-p A+B --mix 0.6` 加权融合两套风格（赛博+水墨 / 原神+敦煌 ...）
"""
import sys
import os
import json
import re
import argparse
import hashlib
from typing import Dict, List, Optional, Tuple
VERSION = "2.3.0"
# ─────────────────────────────────────────────────────────
# 通用质量 / 负面词
# ─────────────────────────────────────────────────────────
UNIVERSAL_QUALITY = "masterpiece, best quality, ultra detailed, 8k"
UNIVERSAL_NEG = (
"low quality, worst quality, lowres, blurry, jpeg artifacts, "
"watermark, signature, text, logo, username, "
"bad anatomy, bad hands, extra fingers, missing fingers, "
"extra limbs, deformed, mutated, disfigured, ugly, "
"out of frame, cropped, duplicate"
)
# 这些预设天然需要 logo / text / signature，把它们从全局负面词中剔除，避免语义冲突
PRESET_NEG_EXCLUDE: Dict[str, List[str]] = {
"Logo设计": ["logo", "text"],
"图标设计": ["logo", "text"],
"表情包": ["text"],
"复古海报": ["text"],
"电影海报": ["text"],
"专辑封面": ["text"],
"品牌KV": ["text"],
"信息图": ["text"],
"水墨": ["signature"],
"工笔国画": ["signature"],
"浮世绘": ["text", "signature"],
"霓虹灯牌": ["text"],
}
def _filter_neg(universal: str, exclude: List[str]) -> str:
if not exclude:
return universal
tokens = [t.strip() for t in universal.split(",")]
kept = [t for t in tokens if t.lower() not in {e.lower() for e in exclude}]
return ", ".join(kept)
# ─────────────────────────────────────────────────────────
# 风格预设 — 每个预设 7 个字段
# tags 风格标签
# quality 画质标签
# neg 负面标签（与 UNIVERSAL_NEG 合并）
# camera 机位 / 镜头（摄影专用，其它留空）
# lighting 光影锁
# palette 色板锁（系列一致性关键）
# aspect 默认画幅
# ─────────────────────────────────────────────────────────
STYLE_PRESETS: Dict[str, Dict[str, str]] = {
# ========== 摄影 Photography ==========
"写实摄影": {
"category": "摄影",
"tags": "photorealistic, hyperrealistic, dslr photography, sharp focus",
"quality": "raw photo, detailed skin texture, film grain subtle",
"neg": "cartoon, anime, painting, drawing, illustration, cgi",
"camera": "Canon EOS R5, 85mm f/1.4 lens, shallow depth of field",
"lighting": "professional studio lighting, softbox key light, rim light",
"palette": "natural color grading, balanced exposure",
"aspect": "3:4",
},
"胶片摄影": {
"category": "摄影",
"tags": "analog film photography, film grain, analog aesthetic",
"quality": "kodak portra 400 film stock, scanned film",
"neg": "digital, oversaturated, hdr, plastic skin",
"camera": "35mm film camera, 50mm prime, shot on film",
"lighting": "natural window light, golden hour",
"palette": "muted earth tones, slightly faded film colors",
"aspect": "3:2",
},
"黑白摄影": {
"category": "摄影",
"tags": "black and white photography, monochrome, high contrast",
"quality": "silver gelatin print, fine art photography, rich grayscale",
"neg": "color, colorful, saturated, low contrast",
"camera": "Leica M6, 35mm f/2, classic reportage framing",
"lighting": "dramatic chiaroscuro, strong directional light",
"palette": "pure black and white, deep blacks, crisp whites",
"aspect": "1:1",
},
"人像摄影": {
"category": "摄影",
"tags": "portrait photography, shallow depth of field, bokeh background",
"quality": "flawless skin retouch, detailed eyes, catch light",
"neg": "full body, wide shot, plastic skin, uncanny",
"camera": "85mm f/1.4, eye-level portrait, rule of thirds",
"lighting": "rembrandt lighting, soft key with fill",
"palette": "warm skin tones, complementary backdrop",
"aspect": "3:4",
},
"时尚大片": {
"category": "摄影",
"tags": "high fashion editorial, vogue style, avant-garde styling",
"quality": "magazine cover quality, haute couture",
"neg": "amateur, casual, snapshot, cluttered set",
"camera": "medium format, 50mm, full body or waist-up",
"lighting": "hard strobe with deep shadows, beauty dish",
"palette": "high-contrast, bold monochromatic set",
"aspect": "3:4",
},
"美食摄影": {
"category": "摄影",
"tags": "food photography, overhead flatlay, appetizing presentation",
"quality": "detailed steam and texture, drool-worthy, michelin plating",
"neg": "greasy, unappealing, blurry plate, messy",
"camera": "macro 100mm, 45-degree angle or top-down",
"lighting": "soft window light from side, subtle rim highlight",
"palette": "warm appetite-triggering tones, natural food colors",
"aspect": "1:1",
},
"产品摄影": {
"category": "摄影",
"tags": "commercial product photography, clean composition, minimal scene",
"quality": "crisp reflections, seamless background, advertising grade",
"neg": "cluttered, messy background, amateur lighting",
"camera": "90mm macro, eye-level product shot",
"lighting": "large softbox key, gradient sweep background",
"palette": "neutral white or brand-matched seamless",
"aspect": "1:1",
},
"微距摄影": {
"category": "摄影",
"tags": "macro photography, extreme close-up, micro world",
"quality": "razor sharp details at micro scale, focus stacking",
"neg": "soft focus, wide view, lack of detail",
"camera": "100mm macro lens, 1:1 magnification",
"lighting": "ring flash or twin macro flash, even diffused",
"palette": "nature-true colors, intense saturation",
"aspect": "1:1",
},
"航拍摄影": {
"category": "摄影",
"tags": "aerial photography, drone shot, bird's eye view",
"quality": "ultra wide sweeping vista, high altitude clarity",
"neg": "ground level, close-up, people center frame",
"camera": "drone camera, 24mm equivalent, top-down or 45-degree",
"lighting": "natural sunlight, long soft shadows",
"palette": "earth tones with atmospheric blue haze",
"aspect": "16:9",
},
"街拍纪实": {
"category": "摄影",
"tags": "street photography, decisive moment, candid documentary",
"quality": "authentic raw feeling, unposed human story",
"neg": "staged, fake, overprocessed",
"camera": "35mm prime, hip-level snap, off-center subject",
"lighting": "available ambient light, urban neon or sunlight",
"palette": "slightly desaturated urban tones",
"aspect": "3:2",
},
# ========== 动漫 / 插画 Illustration ==========
"动漫": {
"category": "动漫",
"tags": "anime style, cel shading, clean line art, vibrant anime colors",
"quality": "detailed anime eyes, pixiv trending, high quality anime",
"neg": "photorealistic, 3d render, western cartoon, low quality",
"camera": "",
"lighting": "anime-style soft light, rim light on hair",
"palette": "vibrant saturated anime palette",
"aspect": "3:4",
},
"新海诚": {
"category": "动漫",
"tags": "Makoto Shinkai style, volumetric cloudscape, realistic anime backgrounds",
"quality": "your name aesthetic, weathering with you mood, incredibly detailed skyscape",
"neg": "flat background, dark mood, gritty",
"camera": "",
"lighting": "magic hour sunlight streaming, god rays through clouds",
"palette": "sky blue, warm orange sunset, pink hour",
"aspect": "16:9",
},
"宫崎骏": {
"category": "动漫",
"tags": "Studio Ghibli style, hand-painted background, whimsical warmth",
"quality": "Totoro aesthetic, Spirited Away mood, hayao miyazaki inspired",
"neg": "dark, edgy, hyperdetailed, cgi",
"camera": "",
"lighting": "soft daylight through leaves, gentle diffuse",
"palette": "pastoral greens, cream, sky blue",
"aspect": "16:9",
},
"美漫": {
"category": "动漫",
"tags": "American comic book style, bold ink lines, halftone shading",
"quality": "marvel / DC inspired, dynamic pose, action panel",
"neg": "anime, soft shading, watercolor",
"camera": "",
"lighting": "dramatic cel lighting, high contrast shadows",
"palette": "saturated primary colors, comic book palette",
"aspect": "2:3",
},
"Q版": {
"category": "动漫",
"tags": "chibi style, super-deformed, cute mascot, 3-head-tall proportions",
"quality": "adorable, clean vector look, sticker worthy",
"neg": "realistic proportion, detailed anatomy, dark mood",
"camera": "",
"lighting": "even flat light, gentle cel shading",
"palette": "bright pastel palette, sugary",
"aspect": "1:1",
},
"童话绘本": {
"category": "动漫",
"tags": "children's book illustration, storybook style, hand drawn warmth",
"quality": "gouache texture, paper warmth, beatrix potter meets pixar",
"neg": "dark, horror, hyper-realistic, edgy",
"camera": "",
"lighting": "soft overall illumination, enchanted glow",
"palette": "warm buttery pastels, cream page base",
"aspect": "4:3",
},
"水彩": {
"category": "插画",
"tags": "watercolor painting, wet-on-wet technique, paper texture, soft bleeding edges",
"quality": "traditional watercolor, transparent wash layers, artistic",
"neg": "digital vector, hard edges, heavy outlines, 3d",
"camera": "",
"lighting": "natural daylight on paper",
"palette": "translucent pastel layers, white paper showing through",
"aspect": "1:1",
},
"油画": {
"category": "插画",
"tags": "oil painting, thick impasto brushstrokes, canvas texture",
"quality": "museum quality oil on canvas, old master technique",
"neg": "digital, flat colors, vector, pixel art",
"camera": "",
"lighting": "chiaroscuro, warm rembrandt glow",
"palette": "rich earth tones, deep jewel colors",
"aspect": "4:5",
},
"水墨": {
"category": "插画",
"tags": "Chinese ink wash painting, sumi-e, negative space, calligraphic strokes",
"quality": "zen atmosphere, rice paper texture, ink bleed",
"neg": "colorful, dense composition, western painting, cartoon",
"camera": "",
"lighting": "flat paper light, no harsh shadows",
"palette": "sumi black on rice-paper beige, occasional vermillion seal",
"aspect": "3:4",
},
"工笔国画": {
"category": "插画",
"tags": "Chinese gongbi painting, meticulous fine brush, intricate floral detail",
"quality": "Song dynasty court painting style, mineral pigment",
"neg": "loose brushstrokes, abstract, western style",
"camera": "",
"lighting": "flat even pigment, no modeling light",
"palette": "azurite blue, malachite green, cinnabar red, gold leaf",
"aspect": "3:4",
},
"浮世绘": {
"category": "插画",
"tags": "Ukiyo-e woodblock print, Edo period, Hokusai / Hiroshige style",
"quality": "traditional Japanese woodcut, flat color blocks, outlined figures",
"neg": "modern anime, 3d, photorealistic",
"camera": "",
"lighting": "no modeling light, flat graphic",
"palette": "prussian blue, earth reds, muted greens",
"aspect": "2:3",
},
"线稿": {
"category": "插画",
"tags": "clean line art, black ink on white, single weight or dynamic line",
"quality": "architectural line drawing precision, tattoo flash clarity",
"neg": "color, shading, painterly, texture",
"camera": "",
"lighting": "no lighting, pure linework",
"palette": "pure black on white",
"aspect": "1:1",
},
"像素艺术": {
"category": "插画",
"tags": "pixel art, 16-bit sprite, pixelated, retro game aesthetic",
"quality": "clean pixel clusters, limited palette, dithering",
"neg": "anti-aliased, smooth, photorealistic, 3d render, high resolution",
"camera": "",
"lighting": "flat pixel shading or 2-tone",
"palette": "NES / SNES limited palette, 16 colors",
"aspect": "1:1",
},
# ========== 3D / 手工 3D & Craft ==========
"3DC4D": {
"category": "3D",
"tags": "3d render, octane render, c4d style, subsurface scattering, glossy materials",
"quality": "ray traced reflections, detailed shader, behance trending",
"neg": "2d flat, sketch, line art",
"camera": "3d viewport camera, 50mm equivalent",
"lighting": "hdri environment light, colored accent rims",
"palette": "vibrant candy colors, pastel gradients",
"aspect": "1:1",
},
"盲盒手办": {
"category": "3D",
"tags": "blind box figurine, pop mart style, chibi 3d toy, kawaii collectible",
"quality": "vinyl toy finish, pristine product shot, pop mart aesthetic",
"neg": "realistic human, gritty, damaged",
"camera": "50mm product shot, eye level toy perspective",
"lighting": "soft studio light, gentle rim, clean shadow",
"palette": "pastel macaron palette",
"aspect": "1:1",
},
"低多边形": {
"category": "3D",
"tags": "low poly 3d, faceted geometry, minimalist polygons",
"quality": "crisp flat shaded polygons, geometric stylization",
"neg": "high detail, smooth subdivisions, realistic",
"camera": "3/4 perspective, isometric-ish",
"lighting": "flat faceted shading, 2-3 light setup",
"palette": "limited flat palette, often pastel",
"aspect": "1:1",
},
"等距视图": {
"category": "3D",
"tags": "isometric illustration, 2.5d isometric scene, game-dev isometric tile",
"quality": "clean vector isometric look, detailed miniature diorama",
"neg": "perspective distortion, top-down, first-person",
"camera": "true isometric projection, 30-degree angles",
"lighting": "even diffuse light, directional accent",
"palette": "bright clean pastel palette",
"aspect": "1:1",
},
"粘土": {
"category": "3D",
"tags": "claymation style, stop motion clay, aardman-like tactile figures",
"quality": "handmade clay texture, fingerprint detail",
"neg": "clean digital, plastic, smooth 3d",
"camera": "stop motion rig perspective, slight depth of field",
"lighting": "warm tungsten key with practical fill",
"palette": "warm terracotta tones",
"aspect": "1:1",
},
"毛毡手工": {
"category": "3D",
"tags": "felted wool craft, needle felt texture, handmade plush character",
"quality": "fuzzy fiber detail, cute handmade imperfection",
"neg": "smooth digital render, photorealistic animal",
"camera": "close macro product shot",
"lighting": "soft diffuse daylight",
"palette": "muted natural wool colors",
"aspect": "1:1",
},
"纸艺": {
"category": "3D",
"tags": "paper craft, layered paper art, quilling, origami composition",
"quality": "intricate cut paper layers, shadow depth between layers",
"neg": "flat 2d, digital illustration",
"camera": "front-on with slight tilt, shallow depth",
"lighting": "rim light casting paper-edge shadows",
"palette": "pastel construction paper colors",
"aspect": "1:1",
},
# ========== 设计 Design ==========
"极简主义": {
"category": "设计",
"tags": "minimalist design, negative space, swiss style, geometric composition",
"quality": "clean typography-friendly, editorial layout",
"neg": "cluttered, ornate, busy, excess detail",
"camera": "",
"lighting": "flat studio light or ambient, no drama",
"palette": "monochrome + single accent, lots of white",
"aspect": "1:1",
},
"平面设计": {
"category": "设计",
"tags": "flat design, vector graphic, bold shapes, brand illustration",
"quality": "clean vectors, designer grade composition",
"neg": "photorealistic, gradient 3d, sketchy",
"camera": "",
"lighting": "flat shading, no highlights",
"palette": "brand-forward 3-color palette",
"aspect": "1:1",
},
"Logo设计": {
"category": "设计",
"tags": "logo design, brand mark, vector logotype, scalable emblem",
"quality": "professional logo, centered composition on clean background",
"neg": "photorealistic scene, complex background, cluttered",
"camera": "",
"lighting": "flat vector, no light gradient",
"palette": "2-color max, high contrast",
"aspect": "1:1",
},
"图标设计": {
"category": "设计",
"tags": "icon design, app icon, rounded square, centered glyph",
"quality": "apple hig compliant, clean icon grid, crisp at 1024px",
"neg": "cluttered, off-center, photo, low contrast",
"camera": "",
"lighting": "subtle highlight gradient, soft inner glow",
"palette": "vibrant gradient with 2-3 colors",
"aspect": "1:1",
},
"信息图": {
"category": "设计",
"tags": "infographic design, data visualization, icon system, explanatory layout",
"quality": "clean editorial infographic, behance level",
"neg": "messy, illustrative painting, photograph",
"camera": "",
"lighting": "flat, no drama",
"palette": "brand palette + grayscale structure",
"aspect": "3:4",
},
"品牌KV": {
"category": "设计",
"tags": "brand key visual, advertising campaign hero image, marketing KV",
"quality": "commercial campaign quality, headline-ready negative space",
"neg": "casual, amateur, low contrast",
"camera": "hero wide or 3/4 product hero",
"lighting": "brand-defined dramatic key, colored rim",
"palette": "brand palette dominant + accent",
"aspect": "16:9",
},
"专辑封面": {
"category": "设计",
"tags": "album cover art, music artwork, square format composition",
"quality": "iconic album design, strong concept, emotive",
"neg": "cluttered, literal, stock imagery",
"camera": "",
"lighting": "concept-driven, mood-heavy",
"palette": "2-3 color highly intentional palette",
"aspect": "1:1",
},
"复古海报": {
"category": "设计",
"tags": "vintage poster design, 1950s retro, letterpress print, screenprint texture",
"quality": "saul bass meets mid-century, weathered paper feel",
"neg": "modern flat design, digital gradient, 3d render",
"camera": "",
"lighting": "flat two-tone",
"palette": "muted primary + cream background",
"aspect": "3:4",
},
"电影海报": {
"category": "设计",
"tags": "movie poster, cinematic key art, title-ready composition",
"quality": "theatrical one-sheet, dramatic hero composition",
"neg": "casual snapshot, cluttered, amateur",
"camera": "hero portrait or symmetric icon layout",
"lighting": "strong single direction light, volumetric",
"palette": "teal & orange or moody duotone",
"aspect": "2:3",
},
"表情包": {
"category": "设计",
"tags": "sticker design, emoji style, expressive meme-ready character",
"quality": "transparent background ready, bold outline, readable at 128px",
"neg": "complex scene, photorealistic, subtle",
"camera": "",
"lighting": "flat cel shading",
"palette": "bright saturated 4-color",
"aspect": "1:1",
},
# ========== 艺术史 Art Movement ==========
"印象派": {
"category": "艺术",
"tags": "impressionist painting, visible brushstrokes, plein air, monet inspired",
"quality": "late 19th century impressionism, atmospheric perspective",
"neg": "photorealistic, digital, sharp outlines",
"camera": "",
"lighting": "dappled natural light, sun-drenched scene",
"palette": "broken color technique, complementary dabs",
"aspect": "4:5",
},
"后印象派": {
"category": "艺术",
"tags": "post-impressionist, van gogh style, expressive brushstroke, emotive color",
"quality": "starry night swirls, dynamic brush texture",
"neg": "realistic, photographic, flat",
"camera": "",
"lighting": "emotional not physical light",
"palette": "bold yellows cobalt and burnt sienna",
"aspect": "4:5",
},
"新艺术": {
"category": "艺术",
"tags": "art nouveau, alphonse mucha, flowing organic lines, floral ornament border",
"quality": "belle époque poster, feminine ornate frame",
"neg": "geometric minimal, modern flat, 3d",
"camera": "",
"lighting": "flat even decorative light",
"palette": "muted golds, soft earth tones, sage",
"aspect": "2:3",
},
"装饰艺术": {
"category": "艺术",
"tags": "art deco, 1920s geometric ornament, gatsby aesthetic, gold and black lacquer",
"quality": "symmetric art deco pattern, streamline moderne elegance",
"neg": "rustic, organic nouveau, grunge",
"camera": "",
"lighting": "strong geometric shadow play",
"palette": "black gold ivory with emerald accents",
"aspect": "2:3",
},
# ========== 场景 / 氛围 Scene ==========
"赛博朋克": {
"category": "场景",
"tags": "cyberpunk, neon-soaked, blade runner aesthetic, megacity dystopia, holographic ads",
"quality": "detailed cyberpunk cityscape, rainy night ambiance",
"neg": "rustic, medieval, natural countryside",
"camera": "low angle wide, 24mm anamorphic",
"lighting": "neon magenta and cyan rim, wet reflective streets",
"palette": "magenta cyan black, neon highlights",
"aspect": "21:9",
},
"蒸汽朋克": {
"category": "场景",
"tags": "steampunk, brass gears and copper pipes, victorian industrial, airship era",
"quality": "intricate clockwork detail, rich leather and patina",
"neg": "clean sci-fi, modern, plastic",
"camera": "",
"lighting": "warm gaslight glow, smoky haze",
"palette": "brass copper sepia, burgundy leather",
"aspect": "3:2",
},
"科幻": {
"category": "场景",
"tags": "sci-fi concept art, futuristic technology, clean spaceship interior, holographic UI",
"quality": "blade runner 2049 palette, hard-sci-fi plausible",
"neg": "medieval, fantasy magic, primitive",
"camera": "cinematic wide, 21:9 framing",
"lighting": "cool blue practical strips, volumetric haze",
"palette": "cool blue cyan with warm accent",
"aspect": "21:9",
},
"奇幻": {
"category": "场景",
"tags": "epic fantasy art, magical atmosphere, artstation trending, tolkien inspired",
"quality": "detailed fantasy concept, elven architecture, dragon-scale atmosphere",
"neg": "modern city, cyberpunk, mundane",
"camera": "epic wide establishing, 24mm",
"lighting": "ethereal god rays through mist",
"palette": "golden hour warm with magical cyan glow",
"aspect": "16:9",
},
"黑暗奇幻": {
"category": "场景",
"tags": "dark fantasy, grimdark, eldritch horror atmosphere, berserk aesthetic",
"quality": "frank frazetta meets zdzisław beksiński",
"neg": "cheerful, bright, cartoonish",
"camera": "low angle hero or dread pov",
"lighting": "blood moon crimson, torch flicker",
"palette": "black crimson sickly green, rusted iron",
"aspect": "2:3",
},
"国潮": {
"category": "场景",
"tags": "guochao Chinese neo-trend, modern hanfu revival, oriental modernism",
"quality": "contemporary Chinese style illustration, editorial fashion",
"neg": "western medieval, european style",
"camera": "",
"lighting": "warm accent on oriental red-gold",
"palette": "vermillion jade gold, ink black accents",
"aspect": "3:4",
},
"Y2K": {
"category": "场景",
"tags": "Y2K aesthetic, early 2000s digital, chrome bubble UI, frosted plastic",
"quality": "low-fi cd-rom graphic, holographic stickers",
"neg": "ultra clean modern, analog retro",
"camera": "",
"lighting": "glossy chrome highlights",
"palette": "baby blue pink lilac, iridescent chrome",
"aspect": "1:1",
},
"Vaporwave": {
"category": "场景",
"tags": "vaporwave, retro 80s 90s computer graphics, roman bust, palm tree grid",
"quality": "synthwave aesthetic, low-fi jpeg nostalgia",
"neg": "modern clean, natural, high detail",
"camera": "",
"lighting": "sunset gradient, neon grid horizon",
"palette": "hot pink teal purple, retro sunset",
"aspect": "16:9",
},
"霓虹灯牌": {
"category": "场景",
"tags": "neon sign typography, glowing tube letters, dark brick wall backdrop",
"quality": "realistic neon glass tube glow, chromatic bloom",
"neg": "daylight, printed sign, flat vector",
"camera": "straight-on product shot, 50mm",
"lighting": "self-emissive neon, dark ambient",
"palette": "magenta cyan on deep black",
"aspect": "3:2",
},
"建筑可视化": {
"category": "场景",
"tags": "architectural visualization, V-Ray / Lumion render, interior design magazine",
"quality": "award-winning archviz, photorealistic materials",
"neg": "sketchy, doodle, distorted perspective",
"camera": "wide 24mm architectural tilt-corrected",
"lighting": "realistic sun study plus artificial, product-ready",
"palette": "natural materials, neutral brand-defined",
"aspect": "16:9",
},
"电影感": {
"category": "场景",
"tags": "cinematic film still, anamorphic lens flare, letterboxed framing",
"quality": "ARRI Alexa quality, professional color grade, movie still",
"neg": "snapshot, amateur, flat lighting, instagram filter",
"camera": "anamorphic 2.39:1 framing, low angle hero",
"lighting": "motivated practical + volumetric haze",
"palette": "teal & orange cinematic grade",
"aspect": "21:9",
},
"概念艺术": {
"category": "场景",
"tags": "concept art, matte painting, production design, pre-visualization",
"quality": "ILM / weta concept sketch, narrative-driven composition",
"neg": "finished illustration, cartoon, low detail",
"camera": "cinematic wide establishing",
"lighting": "narrative-lit hero with atmosphere",
"palette": "mood-defined limited palette",
"aspect": "21:9",
},
# ========== 游戏艺术 Game Art (v2.1 新增) ==========
"原神": {
"category": "游戏",
"tags": "Genshin Impact style, miHoYo aesthetic, stylized anime rendering, cel shaded 3d",
"quality": "gacha game hero card quality, detailed anime character portrait",
"neg": "photorealistic, western cartoon, gritty",
"camera": "3/4 character hero shot, slightly upward angle",
"lighting": "rim light on hair, soft key + colored fill",
"palette": "vibrant saturated anime palette, element-themed accents",
"aspect": "3:4",
},
"崩铁星穹": {
"category": "游戏",
"tags": "Honkai Star Rail style, space fantasy JRPG anime, miHoYo rendering",
"quality": "splash art quality, dynamic pose, elemental VFX",
"neg": "photorealistic, rustic, medieval",
"camera": "dynamic dutch angle hero shot",
"lighting": "glowing elemental rim light",
"palette": "cosmic gradient + neon accent",
"aspect": "3:4",
},
"英雄联盟": {
"category": "游戏",
"tags": "League of Legends splash art style, Riot Games painterly illustration",
"quality": "champion splash quality, dramatic action pose",
"neg": "anime chibi, flat vector, photo",
"camera": "dynamic low angle hero pose",
"lighting": "dramatic rim with colored ability VFX",
"palette": "saturated fantasy palette with magical accent",
"aspect": "16:9",
},
"暗黑4": {
"category": "游戏",
"tags": "Diablo IV style, dark gothic fantasy, blizzard illustration",
"quality": "ARPG splash quality, grim dark atmosphere",
"neg": "cheerful, pastel, chibi, flat",
"camera": "low-angle menacing hero shot",
"lighting": "infernal red rim, volumetric fog",
"palette": "charcoal black, ember red, corrupted green",
"aspect": "3:2",
},
"Valorant": {
"category": "游戏",
"tags": "Valorant agent art, stylized flat anime realism, Riot FPS aesthetic",
"quality": "agent reveal quality, confident hero pose",
"neg": "painterly fantasy, chibi",
"camera": "3/4 hero standoff",
"lighting": "clean cel shaded with colored ability glow",
"palette": "agent signature color + urban neutral",
"aspect": "3:4",
},
"Pokemon": {
"category": "游戏",
"tags": "Pokemon style, Ken Sugimori illustration, round cute creature design",
"quality": "Pokedex official art, clean cel shading",
"neg": "gritty, realistic, complex anatomy",
"camera": "3/4 creature portrait on white",
"lighting": "flat cel shading with soft shadow",
"palette": "clean primary colors per type",
"aspect": "1:1",
},
"暴雪风": {
"category": "游戏",
"tags": "Blizzard stylized art, Overwatch / WoW concept style, exaggerated anatomy",
"quality": "blizzard cinematic quality, heroic pose, strong silhouette",
"neg": "photorealistic, anime chibi, flat",
"camera": "heroic low angle, dynamic posing",
"lighting": "dramatic three-point hero light",
"palette": "rich saturated fantasy palette",
"aspect": "3:2",
},
# ========== 东方传统 Chinese/Japanese Traditional (v2.1 新增) ==========
"敦煌壁画": {
"category": "东方",
"tags": "Dunhuang mural style, Tang dynasty fresco, flying apsara figures, silk road art",
"quality": "weathered ancient mural texture, mineral pigment on plaster",
"neg": "modern digital, anime, western",
"camera": "flat mural frontal view",
"lighting": "no modeling light, flat pigment",
"palette": "mineral ochre, malachite green, azurite blue, gold leaf",
"aspect": "4:3",
},
"青花瓷": {
"category": "东方",
"tags": "Chinese blue and white porcelain motif, Ming dynasty pattern, cobalt underglaze",
"quality": "porcelain surface detail, intricate floral motif",
"neg": "full color, western, abstract",
"camera": "",
"lighting": "soft glazed porcelain highlight",
"palette": "cobalt blue on pure white porcelain",
"aspect": "1:1",
},
"民国月份牌": {
"category": "东方",
"tags": "Republic of China calendar poster, 1920s Shanghai art deco fusion, qipao glamour",
"quality": "vintage advertising print, lithograph texture",
"neg": "modern digital, anime, photo",
"camera": "",
"lighting": "flat poster illumination",
"palette": "faded pastel with gold gilt accents",
"aspect": "2:3",
},
"年画": {
"category": "东方",
"tags": "Chinese new year folk woodblock, auspicious symbols, chubby child figures",
"quality": "traditional woodblock print texture, folk decorative",
"neg": "photorealistic, minimalist, western",
"camera": "",
"lighting": "flat festive graphic",
"palette": "festive vermillion, gold, pine green",
"aspect": "3:4",
},
"剪纸": {
"category": "东方",
"tags": "Chinese paper cutting art, red paper silhouette, intricate symmetric cutout",
"quality": "fine paper cut detail, traditional folk craft",
"neg": "full color, 3d, photorealistic",
"camera": "",
"lighting": "flat silhouette with background paper",
"palette": "pure vermillion red on neutral background",
"aspect": "1:1",
},
"和风": {
"category": "东方",
"tags": "Japanese wafu aesthetic, traditional kimono elegance, wagashi sensibility",
"quality": "refined Japanese traditional design",
"neg": "western, modern pop, grunge",
"camera": "",
"lighting": "soft shoji-diffused light",
"palette": "indigo, vermillion, sumi ink, cream washi",
"aspect": "3:4",
},
"汉服写真": {
"category": "东方",
"tags": "hanfu photography, Chinese traditional dress, oriental portrait",
"quality": "ethereal hanfu fashion editorial, flowing silk",
"neg": "western dress, modern clothing, cyberpunk",
"camera": "85mm portrait, soft 3/4",
"lighting": "diffuse morning light, soft bounce",
"palette": "silk ink tones, jade, cream, plum",
"aspect": "3:4",
},
# ========== 动漫扩展 Anime extras (v2.1 新增) ==========
"萌系": {
"category": "动漫",
"tags": "moe anime style, cute girl aesthetic, large sparkling eyes",
"quality": "moekko illustration, clean lineart, rich anime shading",
"neg": "gritty, adult, western comic",
"camera": "",
"lighting": "soft diffuse with catchlight in eyes",
"palette": "pastel pink cream sky-blue",
"aspect": "3:4",
},
"厚涂": {
"category": "动漫",
"tags": "painterly anime, thick paint anime illustration, semi-realistic rendering",
"quality": "artstation anime painting, detailed brushwork",
"neg": "flat cel shading, vector, chibi",
"camera": "",
"lighting": "rembrandt on face, painterly shadows",
"palette": "desaturated muted painterly tones",
"aspect": "3:4",
},
"轻小说封面": {
"category": "动漫",
"tags": "light novel cover illustration, Japanese LN art, glossy anime portrait",
"quality": "bookshelf-ready cover composition, eye-catching character",
"neg": "dark horror, western comic, 3d",
"camera": "3/4 character hero, title-friendly negative space",
"lighting": "cinematic anime key light",
"palette": "vibrant anime palette with atmosphere",
"aspect": "2:3",
},
"赛璐璐": {
"category": "动漫",
"tags": "traditional cel-shaded anime, sharp shadow boundaries, limited anime palette",
"quality": "classic 2d cel animation look, detailed line art",
"neg": "painterly, 3d render, gradient shading",
"camera": "",
"lighting": "two-tone cel shading, hard shadow edges",
"palette": "saturated flat anime palette",
"aspect": "16:9",
},
# ========== 现代设计 Modern Design (v2.1 新增) ==========
"玻璃拟态": {
"category": "设计",
"tags": "glassmorphism, frosted glass UI, transparent blur layers, depth card stack",
"quality": "modern UI glass effect, realistic refraction, clean layout",
"neg": "flat 2d, skeuomorphic wood, pixel art",
"camera": "",
"lighting": "subtle inner glow, soft backlight through glass",
"palette": "pastel gradient backdrop with translucent glass",
"aspect": "3:4",
},
"新拟态": {
"category": "设计",
"tags": "neumorphism, soft UI, extruded plastic button, subtle dual shadow",
"quality": "modern minimal UI, monochrome neumorphic elements",
"neg": "flat, photorealistic, grunge",
"camera": "",
"lighting": "soft dual light and dark shadow",
"palette": "monochrome beige or gray single-tone",
"aspect": "1:1",
},
"孟菲斯": {
"category": "设计",
"tags": "Memphis design, 1980s postmodern, geometric shapes, squiggle pattern, bold primaries",
"quality": "playful postmodern graphic, bold composition",
"neg": "minimalist, photorealistic, classical",
"camera": "",
"lighting": "flat graphic, no modeling",
"palette": "hot pink, cyan, yellow, black squiggle pattern",
"aspect": "1:1",
},
"杂志编排": {
"category": "设计",
"tags": "editorial magazine layout, bold serif typography, grid-based design",
"quality": "international typographic style, vogue spread quality",
"neg": "amateur, overcluttered, cute",
"camera": "",
"lighting": "clean flat studio-style",
"palette": "monochrome with single bold accent",
"aspect": "3:4",
},
"包豪斯": {
"category": "设计",
"tags": "Bauhaus design, de stijl geometric, primary color blocks, constructivist",
"quality": "1920s modernist design school, pure geometry",
"neg": "ornate, victorian, realistic",
"camera": "",
"lighting": "flat geometric",
"palette": "primary red yellow blue + black on white",
"aspect": "1:1",
},
"奶油风": {
"category": "设计",
"tags": "cream style, soft beige palette, warm minimal aesthetic, korean lifestyle",
"quality": "instagram lifestyle aesthetic, soft velvety texture",
"neg": "dark, saturated, edgy",
"camera": "",
"lighting": "natural soft window light",
"palette": "cream, soft beige, butter yellow, milk tea",
"aspect": "4:5",
},
# ========== 建筑 & 氛围扩展 (v2.1 新增) ==========
"粗野主义": {
"category": "场景",
"tags": "brutalist architecture, raw concrete, heavy geometric mass, béton brut",
"quality": "mid-century brutalist landmark, imposing scale",
"neg": "ornate, baroque, flimsy",
"camera": "wide low-angle heroic architecture shot",
"lighting": "harsh sun shadow across concrete",
"palette": "raw concrete gray with sky contrast",
"aspect": "16:9",
},
"北欧极简": {
"category": "场景",
"tags": "scandinavian interior, nordic minimalism, light wood, warm neutral",
"quality": "hygge lifestyle, interior magazine quality",
"neg": "ornate, cluttered, dark gothic",
"camera": "wide 24mm interior architectural",
"lighting": "large window natural light",
"palette": "warm wood, white wall, soft gray",
"aspect": "16:9",
},
"侘寂": {
"category": "场景",
"tags": "wabi-sabi aesthetic, imperfect natural beauty, weathered texture, zen japanese",
"quality": "quiet imperfection, aged material detail",
"neg": "glossy modern, bright colors, ornate",
"camera": "",
"lighting": "soft diffused natural, muted",
"palette": "muted earth, weathered gray, aged beige",
"aspect": "4:5",
},
# ========== 摄影扩展 (v2.1 新增) ==========
"暗黑美食": {
"category": "摄影",
"tags": "dark food photography, moody cuisine, chiaroscuro plating",
"quality": "michelin-level dark food styling, dramatic shadow",
"neg": "bright cheerful, flat, cluttered",
"camera": "100mm macro 45-degree, side low-key",
"lighting": "single hard key from behind, deep shadow",
"palette": "deep black with food color accent",
"aspect": "4:5",
},
"日杂": {
"category": "摄影",
"tags": "Japanese lifestyle magazine, natural light still life, clean minimalism",
"quality": "muji aesthetic, calm everyday beauty",
"neg": "dark moody, dramatic, saturated",
"camera": "50mm still life, slight top-down",
"lighting": "soft window daylight, no drama",
"palette": "cream, light wood, pale pastel",
"aspect": "4:5",
},
"街头潮流": {
"category": "摄影",
"tags": "streetwear fashion, urban hypebeast, sneaker culture",
"quality": "street style magazine editorial, confident pose",
"neg": "formal suit, fantasy, kawaii",
"camera": "35mm full body street fashion",
"lighting": "harsh urban daylight or neon",
"palette": "high contrast monochrome + brand accent",
"aspect": "3:4",
},
# ========== 综合 (v2.1 新增) ==========
"疗愈治愈": {
"category": "场景",
"tags": "healing cozy aesthetic, soft warm interior, cat sunlight, tea steam",
"quality": "soothing slow-life scene",
"neg": "dramatic action, dark, cyberpunk",
"camera": "",
"lighting": "warm golden hour through window",
"palette": "warm honey, cream, dusty pink",
"aspect": "4:5",
},
"美式复古": {
"category": "场景",
"tags": "americana retro, 1950s diner, vintage coca-cola americana",
"quality": "Norman Rockwell meets mid-century ad",
"neg": "asian, modern sleek, futuristic",
"camera": "",
"lighting": "warm diner fluorescent or golden",
"palette": "cherry red, cream, turquoise",
"aspect": "3:2",
},
}
# ─────────────────────────────────────────────────────────
# 别名 (英文 / 同义词 → 规范预设名)
# ─────────────────────────────────────────────────────────
ALIASES: Dict[str, str] = {
# 英文
"realistic": "写实摄影",
"photo": "写实摄影",
"photography": "写实摄影",
"film": "胶片摄影",
"analog": "胶片摄影",
"bw": "黑白摄影",
"blackwhite": "黑白摄影",
"monochrome": "黑白摄影",
"portrait": "人像摄影",
"fashion": "时尚大片",
"editorial": "时尚大片",
"food": "美食摄影",
"product": "产品摄影",
"ecommerce": "产品摄影",
"macro": "微距摄影",
"aerial": "航拍摄影",
"drone": "航拍摄影",
"street": "街拍纪实",
"documentary": "街拍纪实",
"anime": "动漫",
"ghibli": "宫崎骏",
"miyazaki": "宫崎骏",
"shinkai": "新海诚",
"makoto": "新海诚",
"comic": "美漫",
"marvel": "美漫",
"chibi": "Q版",
"kawaii": "Q版",
"storybook": "童话绘本",
"childrensbook": "童话绘本",
"watercolor": "水彩",
"oil": "油画",
"ink": "水墨",
"sumi": "水墨",
"gongbi": "工笔国画",
"ukiyoe": "浮世绘",
"lineart": "线稿",
"pixel": "像素艺术",
"3d": "3DC4D",
"c4d": "3DC4D",
"octane": "3DC4D",
"blindbox": "盲盒手办",
"popmart": "盲盒手办",
"lowpoly": "低多边形",
"isometric": "等距视图",
"iso": "等距视图",
"claymation": "粘土",
"felt": "毛毡手工",
"papercraft": "纸艺",
"minimal": "极简主义",
"minimalist": "极简主义",
"flat": "平面设计",
"vector": "平面设计",
"logo": "Logo设计",
"icon": "图标设计",
"infographic": "信息图",
"kv": "品牌KV",
"album": "专辑封面",
"poster": "复古海报",
"movieposter": "电影海报",
"sticker": "表情包",
"emoji": "表情包",
"impressionist": "印象派",
"vangogh": "后印象派",
"postimpressionist": "后印象派",
"artnouveau": "新艺术",
"mucha": "新艺术",
"artdeco": "装饰艺术",
"cyberpunk": "赛博朋克",
"steampunk": "蒸汽朋克",
"scifi": "科幻",
"fantasy": "奇幻",
"darkfantasy": "黑暗奇幻",
"grimdark": "黑暗奇幻",
"guochao": "国潮",
"y2k": "Y2K",
"vaporwave": "Vaporwave",
"synthwave": "Vaporwave",
"neon": "霓虹灯牌",
"archviz": "建筑可视化",
"architecture": "建筑可视化",
"cinematic": "电影感",
"cinema": "电影感",
"concept": "概念艺术",
"conceptart": "概念艺术",
# v2.1 游戏
"genshin": "原神",
"mihoyo": "原神",
"honkai": "崩铁星穹",
"starrail": "崩铁星穹",
"lol": "英雄联盟",
"leagueoflegends": "英雄联盟",
"diablo": "暗黑4",
"valorant": "Valorant",
"pokemon": "Pokemon",
"blizzard": "暴雪风",
"overwatch": "暴雪风",
"wow": "暴雪风",
# v2.1 东方
"dunhuang": "敦煌壁画",
"qinghua": "青花瓷",
"porcelain": "青花瓷",
"yuefenpai": "民国月份牌",
"wafu": "和风",
"hanfu": "汉服写真",
"papercut": "剪纸",
"nianhua": "年画",
# v2.1 动漫扩展
"moe": "萌系",
"painterlyanime": "厚涂",
"lightnovel": "轻小说封面",
"lncover": "轻小说封面",
"cellshaded": "赛璐璐",
"celshaded": "赛璐璐",
# v2.1 设计
"glassmorphism": "玻璃拟态",
"glass": "玻璃拟态",
"neumorphism": "新拟态",
"memphis": "孟菲斯",
"editorial": "杂志编排",
"bauhaus": "包豪斯",
"cream": "奶油风",
"korean": "奶油风",
# v2.1 建筑 / 氛围
"brutalism": "粗野主义",
"brutalist": "粗野主义",
"nordic": "北欧极简",
"scandinavian": "北欧极简",
"wabisabi": "侘寂",
"zen": "侘寂",
# v2.1 摄影
"darkfood": "暗黑美食",
"muji": "日杂",
"streetwear": "街头潮流",
"hypebeast": "街头潮流",
# v2.1 综合
"healing": "疗愈治愈",
"cozy": "疗愈治愈",
"americana": "美式复古",
}
# ─────────────────────────────────────────────────────────
# 意图关键词 → (推荐预设, 推荐画幅)
# ─────────────────────────────────────────────────────────
INTENT_KEYWORDS: List[Tuple[str, str, str]] = [
# (关键词, 推荐预设, 推荐画幅)
("logo", "Logo设计", "1:1"),
("徽标", "Logo设计", "1:1"),
("标志", "Logo设计", "1:1"),
("icon", "图标设计", "1:1"),
("图标", "图标设计", "1:1"),
("app图标", "图标设计", "1:1"),
("电影海报", "电影海报", "2:3"),
("海报", "复古海报", "3:4"),
("poster", "复古海报", "3:4"),
("封面", "专辑封面", "1:1"),
("专辑", "专辑封面", "1:1"),
("表情包", "表情包", "1:1"),
("贴纸", "表情包", "1:1"),
("信息图", "信息图", "3:4"),
("infographic", "信息图", "3:4"),
("kv", "品牌KV", "16:9"),
("主视觉", "品牌KV", "16:9"),
("产品", "产品摄影", "1:1"),
("电商", "产品摄影", "1:1"),
("商品", "产品摄影", "1:1"),
("美食", "美食摄影", "1:1"),
("食物", "美食摄影", "1:1"),
("菜品", "美食摄影", "1:1"),
("头像", "人像摄影", "1:1"),
("肖像", "人像摄影", "3:4"),
("人像", "人像摄影", "3:4"),
("时装", "时尚大片", "3:4"),
("时尚", "时尚大片", "3:4"),
("街拍", "街拍纪实", "3:2"),
("纪实", "街拍纪实", "3:2"),
("风景", "写实摄影", "16:9"),
("风光", "写实摄影", "16:9"),
("建筑", "建筑可视化", "16:9"),
("室内", "建筑可视化", "4:3"),
("手办", "盲盒手办", "1:1"),
("盲盒", "盲盒手办", "1:1"),
("玩具", "盲盒手办", "1:1"),
("航拍", "航拍摄影", "16:9"),
("鸟瞰", "航拍摄影", "16:9"),
("微距", "微距摄影", "1:1"),
("赛博", "赛博朋克", "21:9"),
("cyberpunk", "赛博朋克", "21:9"),
("蒸汽朋克", "蒸汽朋克", "3:2"),
("科幻", "科幻", "21:9"),
("未来", "科幻", "21:9"),
("奇幻", "奇幻", "16:9"),
("魔幻", "奇幻", "16:9"),
("黑暗", "黑暗奇幻", "2:3"),
("水墨", "水墨", "3:4"),
("国画", "工笔国画", "3:4"),
("工笔", "工笔国画", "3:4"),
("浮世绘", "浮世绘", "2:3"),
("童话", "童话绘本", "4:3"),
("绘本", "童话绘本", "4:3"),
("宫崎骏", "宫崎骏", "16:9"),
("新海诚", "新海诚", "16:9"),
("动漫", "动漫", "3:4"),
("二次元", "动漫", "3:4"),
("q版", "Q版", "1:1"),
("Q版", "Q版", "1:1"),
("chibi", "Q版", "1:1"),
("线稿", "线稿", "1:1"),
("像素", "像素艺术", "1:1"),
("3d", "3DC4D", "1:1"),
("c4d", "3DC4D", "1:1"),
("粘土", "粘土", "1:1"),
("等距", "等距视图", "1:1"),
("国潮", "国潮", "3:4"),
("霓虹", "霓虹灯牌", "3:2"),
("电影", "电影感", "21:9"),
("cinema", "电影感", "21:9"),
("concept", "概念艺术", "21:9"),
("概念图", "概念艺术", "21:9"),
("复古", "复古海报", "3:4"),
("vintage", "复古海报", "3:4"),
# v2.1 游戏
("原神", "原神", "3:4"),
("genshin", "原神", "3:4"),
("崩铁", "崩铁星穹", "3:4"),
("星穹", "崩铁星穹", "3:4"),
("lol", "英雄联盟", "16:9"),
("英雄联盟", "英雄联盟", "16:9"),
("valorant", "Valorant", "3:4"),
("暗黑4", "暗黑4", "3:2"),
("diablo", "暗黑4", "3:2"),
("pokemon", "Pokemon", "1:1"),
("宝可梦", "Pokemon", "1:1"),
("暴雪", "暴雪风", "3:2"),
("overwatch", "暴雪风", "3:2"),
# v2.1 东方
("敦煌", "敦煌壁画", "4:3"),
("壁画", "敦煌壁画", "4:3"),
("青花瓷", "青花瓷", "1:1"),
("月份牌", "民国月份牌", "2:3"),
("民国", "民国月份牌", "2:3"),
("剪纸", "剪纸", "1:1"),
("年画", "年画", "3:4"),
("汉服", "汉服写真", "3:4"),
("和风", "和风", "3:4"),
("日系", "日杂", "4:5"),
("日杂", "日杂", "4:5"),
# v2.1 动漫扩展
("萌", "萌系", "3:4"),
("萌系", "萌系", "3:4"),
("厚涂", "厚涂", "3:4"),
("轻小说", "轻小说封面", "2:3"),
("赛璐璐", "赛璐璐", "16:9"),
# v2.1 现代设计
("玻璃拟态", "玻璃拟态", "3:4"),
("glassmorphism", "玻璃拟态", "3:4"),
("新拟态", "新拟态", "1:1"),
("neumorphism", "新拟态", "1:1"),
("孟菲斯", "孟菲斯", "1:1"),
("memphis", "孟菲斯", "1:1"),
("杂志", "杂志编排", "3:4"),
("magazine", "杂志编排", "3:4"),
("包豪斯", "包豪斯", "1:1"),
("bauhaus", "包豪斯", "1:1"),
("奶油", "奶油风", "4:5"),
("ins风", "奶油风", "4:5"),
("韩系", "奶油风", "4:5"),
# v2.1 建筑 / 氛围
("粗野", "粗野主义", "16:9"),
("brutalism", "粗野主义", "16:9"),
("北欧", "北欧极简", "16:9"),
("scandinavian", "北欧极简", "16:9"),
("侘寂", "侘寂", "4:5"),
("wabi", "侘寂", "4:5"),
("禅意", "侘寂", "4:5"),
# v2.1 摄影
("暗黑美食", "暗黑美食", "4:5"),
("darkfood", "暗黑美食", "4:5"),
("街头", "街头潮流", "3:4"),
("潮牌", "街头潮流", "3:4"),
("streetwear", "街头潮流", "3:4"),
# v2.1 综合
("治愈", "疗愈治愈", "4:5"),
("疗愈", "疗愈治愈", "4:5"),
("cozy", "疗愈治愈", "4:5"),
("美式复古", "美式复古", "3:2"),
("americana", "美式复古", "3:2"),
]
# ─────────────────────────────────────────────────────────
# 构图关键词
# ─────────────────────────────────────────────────────────
COMPOSITION_KEYWORDS: Dict[str, str] = {
"特写": "extreme close-up shot",
"近景": "close-up shot",
"中景": "medium shot",
"全身": "full body shot",
"半身": "medium shot, waist up",
"远景": "wide shot, establishing shot",
"全景": "panoramic view",
"俯拍": "top-down view",
"俯视": "top-down view",
"仰拍": "low angle shot, looking up",
"仰视": "low angle shot, looking up",
"鸟瞰": "bird's eye view, aerial",
"平视": "eye-level shot",
"正面": "front view",
"侧面": "side profile view",
"背面": "back view",
"三分之二": "three-quarter view",
}
# ─────────────────────────────────────────────────────────
# 情绪关键词
# ─────────────────────────────────────────────────────────
MOOD_KEYWORDS: Dict[str, str] = {
"温暖": "warm cozy atmosphere, golden tones",
"温馨": "warm cozy atmosphere, golden tones",
"冷峻": "cold atmosphere, steely blue tones",
"神秘": "mysterious mood, foggy, dim lighting",
"梦幻": "dreamy ethereal mood, soft glow, bokeh",
"欢快": "joyful vibrant, bright cheerful colors",
"忧郁": "melancholic mood, muted cool palette",
"压抑": "oppressive mood, deep shadows, heavy atmosphere",
"史诗": "epic grandeur, cinematic scale",
"高级": "luxury sophistication, premium materials",
"治愈": "healing soft ambiance, soothing",
"清新": "fresh airy light pastel",
"紧张": "tense suspense mood, high contrast",
"浪漫": "romantic soft pink glow",
}
# ─────────────────────────────────────────────────────────
# 时间 / 天气 / 季节 关键词（v2.1 新增）
# ─────────────────────────────────────────────────────────
TIME_KEYWORDS: Dict[str, str] = {
"清晨": "early morning, dawn, soft first light",
"早晨": "morning light, fresh daylight",
"上午": "bright morning sunshine",
"正午": "high noon, overhead sun",
"下午": "afternoon light, long soft shadows",
"黄昏": "dusk, golden hour, magic hour",
"傍晚": "dusk, golden hour, magic hour",
"日落": "sunset, golden hour",
"夜晚": "night time, dark ambient",
"深夜": "late night, moonlit, dim",
"午夜": "midnight, dark sky",
"黎明": "dawn, blue hour breaking",
"蓝调时刻": "blue hour, twilight gradient sky",
"魔法时刻": "magic hour, warm golden glow",
}
WEATHER_KEYWORDS: Dict[str, str] = {
"晴天": "sunny clear sky",
"多云": "cloudy overcast sky",
"阴天": "overcast gray sky",
"下雨": "raining, wet reflective surfaces",
"雨天": "rainy weather, soft rain",
"大雨": "heavy rain, downpour, water droplets",
"暴雨": "stormy rain, dramatic weather",
"下雪": "snowing, snowflakes in air",
"雪天": "snowy landscape, white blanket",
"暴雪": "blizzard, heavy snow storm",
"雾天": "foggy misty atmosphere",
"有雾": "foggy misty atmosphere",
"晨雾": "morning mist, dreamy fog",
"风暴": "stormy weather, dramatic clouds",
"雷雨": "thunderstorm, lightning in sky",
}
SEASON_KEYWORDS: Dict[str, str] = {
"春天": "spring season, cherry blossoms, fresh green",
"春季": "spring season, cherry blossoms, fresh green",
"夏天": "summer season, lush greenery, warm sun",
"夏季": "summer season, lush greenery, warm sun",
"秋天": "autumn season, golden foliage, maple leaves",
"秋季": "autumn season, golden foliage, maple leaves",
"冬天": "winter season, snow, bare branches",
"冬季": "winter season, snow, bare branches",
"樱花季": "cherry blossom season, sakura petals falling",
"枫叶季": "maple season, red foliage",
}
# ─────────────────────────────────────────────────────────
# 质量档位（v2.1 新增）
# ─────────────────────────────────────────────────────────
QUALITY_TIERS: Dict[str, str] = {
"basic": "high quality, detailed",
"pro": "masterpiece, best quality, ultra detailed, 8k",
"master": "masterpiece, best quality, ultra detailed, 8k, hdr, "
"intricate details, sharp focus, award winning, trending on artstation, "
"professional, highly polished",
}
# ─────────────────────────────────────────────────────────
# 负向需求识别（v2.1 新增）
# 匹配 "不要X" / "no X" / "avoid X" / "without X" / "没有X" / "避免X"
# ─────────────────────────────────────────────────────────
NEGATIVE_PATTERNS = [
re.compile(r"不要([^,，。.;；]{1,20})"),
re.compile(r"没有([^,，。.;；]{1,20})"),
re.compile(r"避免([^,，。.;；]{1,20})"),
re.compile(r"\bno\s+([a-zA-Z\s]{1,30})(?=[,.]|\s*$)"),
re.compile(r"\bavoid\s+([a-zA-Z\s]{1,30})(?=[,.]|\s*$)"),
re.compile(r"\bwithout\s+([a-zA-Z\s]{1,30})(?=[,.]|\s*$)"),
]
# ─────────────────────────────────────────────────────────
# 画幅 → 模型特定写法
# ─────────────────────────────────────────────────────────
ASPECT_TO_MJ = {
"1:1": "--ar 1:1",
"3:4": "--ar 3:4",
"4:3": "--ar 4:3",
"3:2": "--ar 3:2",
"2:3": "--ar 2:3",
"16:9": "--ar 16:9",
"9:16": "--ar 9:16",
"21:9": "--ar 21:9",
"4:5": "--ar 4:5",
}
ASPECT_TO_SDXL = {
"1:1": "1024x1024",
"3:4": "896x1152",
"4:3": "1152x896",
"3:2": "1216x832",
"2:3": "832x1216",
"16:9": "1344x768",
"9:16": "768x1344",
"21:9": "1536x640",
"4:5": "912x1144",
}
# ─────────────────────────────────────────────────────────
# 工具函数
# ─────────────────────────────────────────────────────────
def resolve_preset(name: Optional[str]) -> str:
"""预设名归一化：支持中文 / 英文别名 / 大小写不敏感。"""
if not name:
return ""
key = name.strip().lower().replace(" ", "").replace("-", "").replace("_", "")
if key in ALIASES:
return ALIASES[key]
for p in STYLE_PRESETS:
if p.lower() == key or p.lower().replace(" ", "") == key:
return p
return name if name in STYLE_PRESETS else ""
def parse_requirement(text: str) -> Dict[str, str]:
"""从用户输入中解析意图、画幅、构图、情绪、时间、天气、季节、负向需求。
返回 dict 字段：
preset_suggestion 推荐预设（可能为空）
aspect_suggestion 推荐画幅
composition 构图片段（英文，可为空）
mood 情绪片段（英文，可为空）
time_of_day 时间片段（英文，可为空）
weather 天气片段（英文，可为空）
season 季节片段（英文，可为空）
user_negatives 用户抽出的负向关键词（原文，英/中）
"""
lower = text.lower()
out = {
"preset_suggestion": "",
"aspect_suggestion": "",
"composition": "",
"mood": "",
"time_of_day": "",
"weather": "",
"season": "",
"user_negatives": [],
}
for kw, preset, aspect in INTENT_KEYWORDS:
if kw.lower() in lower:
out["preset_suggestion"] = preset
out["aspect_suggestion"] = aspect
break
for zh, en in COMPOSITION_KEYWORDS.items():
if zh in text:
out["composition"] = en
break
for zh, en in MOOD_KEYWORDS.items():
if zh in text:
out["mood"] = en
break
for zh, en in TIME_KEYWORDS.items():
if zh in text:
out["time_of_day"] = en
break
for zh, en in WEATHER_KEYWORDS.items():
if zh in text:
out["weather"] = en
break
for zh, en in SEASON_KEYWORDS.items():
if zh in text:
out["season"] = en
break
# 负向需求抽取
negs: List[str] = []
for pat in NEGATIVE_PATTERNS:
for m in pat.finditer(text):
token = m.group(1).strip().rstrip(",.， ；;")
if token and token not in negs:
negs.append(token)
out["user_negatives"] = negs
return out
def strip_negative_clauses(text: str) -> str:
"""从主体描述中去除 "不要X" 类子句，只保留正向描述。"""
cleaned = text
for pat in NEGATIVE_PATTERNS:
cleaned = pat.sub("", cleaned)
# 清理多余标点和空白
cleaned = re.sub(r"\s*,\s*,+", ", ", cleaned)
cleaned = re.sub(r"\s+", " ", cleaned).strip(" ,，。.;；")
return cleaned
def sanitize_subject(text: str) -> str:
"""清理主体描述：去除首尾标点和多余空白。"""
return re.sub(r"\s+", " ", text).strip().rstrip(".,，、。;；")
def stable_seed(subject: str, preset: str) -> int:
"""根据主体 + 预设生成稳定的种子建议（32-bit 正整数）。"""
h = hashlib.md5(f"{subject}|{preset}".encode("utf-8")).hexdigest()
return int(h[:8], 16)
def parse_mix_preset(preset_arg: str) -> Tuple[str, Optional[str]]:
"""支持 `-p A+B` 语法。返回 (primary, secondary or None)。"""
if not preset_arg:
return "", None
if "+" not in preset_arg:
return preset_arg, None
parts = [p.strip() for p in preset_arg.split("+", 1)]
if len(parts) != 2 or not parts[0] or not parts[1]:
return preset_arg, None
return parts[0], parts[1]
def mix_presets(primary: str, secondary: str, ratio: float = 0.6, model: str = "通用") -> Dict[str, str]:
"""加权融合两个预设，主预设 ratio，副预设 1-ratio。
融合策略：
tags 按权重前置主预设标签，SD 模式额外加 (tag:weight) 语法
quality 主预设主导
neg 合并去重
camera 主预设（主导镜头语言）
lighting 主预设主导，副预设为辅
palette 混合两者（主在前）
aspect 主预设
category mix
"""
p1 = STYLE_PRESETS[primary]
p2 = STYLE_PRESETS[secondary]
ratio = max(0.1, min(0.9, ratio))
primary_tags = [t.strip() for t in p1["tags"].split(",") if t.strip()]
secondary_tags = [t.strip() for t in p2["tags"].split(",") if t.strip()]
is_sd = model in ("Stable Diffusion", "SD", "sd", "SDXL", "sdxl")
if is_sd:
w1 = round(0.8 + ratio * 0.6, 2)
w2 = round(0.8 + (1 - ratio) * 0.6, 2)
merged_tags = [f"({t}:{w1})" for t in primary_tags] + [f"({t}:{w2})" for t in secondary_tags]
else:
n1 = max(1, int(round(len(primary_tags) * (0.5 + ratio))))
n2 = max(1, int(round(len(secondary_tags) * (0.5 + (1 - ratio)))))
merged_tags = primary_tags[:n1] + secondary_tags[:n2]
merged_palette = ", ".join([
x for x in [p1.get("palette", ""), p2.get("palette", "")] if x
])
if p1.get("lighting") and p2.get("lighting"):
merged_lighting = f"{p1['lighting']}, blended with {p2['lighting']}"
else:
merged_lighting = p1.get("lighting") or p2.get("lighting", "")
neg_tokens = []
seen = set()
for src in (p1["neg"], p2["neg"]):
for t in src.split(","):
t = t.strip()
if t and t.lower() not in seen:
seen.add(t.lower())
neg_tokens.append(t)
return {
"category": f"{p1['category']}+{p2['category']}",
"tags": ", ".join(merged_tags),
"quality": p1["quality"],
"neg": ", ".join(neg_tokens),
"camera": p1.get("camera", "") or p2.get("camera", ""),
"lighting": merged_lighting,
"palette": merged_palette,
"aspect": p1.get("aspect", "1:1"),
}
def build_prompt(
subject: str,
preset: str,
model: str = "通用",
aspect: str = "",
extra_mood: str = "",
extra_composition: str = "",
extra_negatives: str = "",
seed: Optional[int] = None,
quality_tier: str = "pro",
character_sheet: bool = False,
mix_secondary: Optional[str] = None,
mix_ratio: float = 0.6,
) -> Dict:
"""构建增强后的提示词。
v2.1 新增参数：
extra_negatives 额外负面词，逗号分隔
quality_tier 质量档位 basic / pro / master
character_sheet 角色设定图模式（T-pose 多视图）
v2.2 新增参数：
mix_secondary 副预设名（已 resolve），与主预设融合
mix_ratio 主预设权重 0.1-0.9
"""
preset = resolve_preset(preset) or "写实摄影"
if mix_secondary:
mix_secondary = resolve_preset(mix_secondary) or ""
if mix_secondary and mix_secondary != preset:
data = mix_presets(preset, mix_secondary, mix_ratio, model)
mixed_label = f"{preset}+{mix_secondary}@{mix_ratio:.2f}"
else:
data = STYLE_PRESETS[preset]
mixed_label = ""
auto = parse_requirement(subject)
subject_clean = sanitize_subject(strip_negative_clauses(subject))
if not extra_composition:
extra_composition = auto["composition"]
if not extra_mood:
extra_mood = auto["mood"]
if not aspect:
aspect = data.get("aspect", "1:1")
# 时间 / 天气 / 季节
ambient_parts = [auto["time_of_day"], auto["weather"], auto["season"]]
ambient = ", ".join([x for x in ambient_parts if x])
# 角色设定图模式
if character_sheet:
subject_clean = (
f"character design sheet of {subject_clean}, "
f"multiple views: front view, three-quarter view, side view, back view, "
f"T-pose, clean white background, reference sheet, "
f"consistent character design"
)
aspect = "16:9"
consistency_parts = [
data["tags"],
data.get("camera", ""),
data.get("lighting", ""),
data.get("palette", ""),
]
consistency = ", ".join([x for x in consistency_parts if x])
# 质量档位（替换 UNIVERSAL_QUALITY）
tier_quality = QUALITY_TIERS.get(quality_tier, QUALITY_TIERS["pro"])
quality_combined = f"{data['quality']}, {tier_quality}"
# 负面词：预设 + 全局过滤 + 用户抽出 + 显式追加
neg_exclude = list(PRESET_NEG_EXCLUDE.get(preset, []))
if mix_secondary and mix_secondary in PRESET_NEG_EXCLUDE:
neg_exclude.extend(PRESET_NEG_EXCLUDE[mix_secondary])
universal_neg_filtered = _filter_neg(UNIVERSAL_NEG, neg_exclude)
user_neg_from_subject = ", ".join(auto["user_negatives"])
neg_parts = [data["neg"], universal_neg_filtered, user_neg_from_subject, extra_negatives]
neg_combined = ", ".join([x for x in neg_parts if x])
extras = ", ".join([x for x in [extra_composition, extra_mood, ambient] if x])
seed_key = mixed_label or preset
# 按模型生成不同形式
if model in ("Midjourney", "MJ", "mj"):
core = f"{subject_clean}, {consistency}"
if extras:
core = f"{core}, {extras}"
core = f"{core}, {quality_combined}"
flags = [ASPECT_TO_MJ.get(aspect, "--ar 1:1"), "--stylize 250"]
positive = f"{core} {' '.join(flags)}"
negative = f"--no {neg_combined}"
hint = (
"Midjourney tips：\n"
" • 角色/产品系列一致：加 --cref <url> 或 --sref <url>\n"
f" • 想要更风格化加 --stylize 500~750；更写实降到 --stylize 50\n"
f" • 建议 seed 锁定：--seed {seed or stable_seed(subject_clean, seed_key)}"
)
elif model in ("Stable Diffusion", "SD", "sd"):
positive = (
f"({subject_clean}:1.2), {consistency}"
+ (f", {extras}" if extras else "")
+ f", {quality_combined}"
)
negative = neg_combined
hint = (
"Stable Diffusion tips：\n"
f" • 强化权重: (word:1.2~1.5), 减弱: [word:0.7]\n"
f" • 建议尺寸 (SD 1.5): 512x{{hw_from_aspect}}; (SDXL): {ASPECT_TO_SDXL.get(aspect,'1024x1024')}\n"
f" • 采样: DPM++ 2M Karras, 30 steps, CFG 6.5\n"
f" • 建议 seed 锁定: {seed or stable_seed(subject_clean, seed_key)}（系列同 seed 提升一致性）"
)
elif model in ("SDXL", "sdxl"):
positive = (
f"{subject_clean}, {consistency}"
+ (f", {extras}" if extras else "")
+ f", {quality_combined}"
)
negative = neg_combined
hint = (
"SDXL tips：\n"
f" • 推荐尺寸: {ASPECT_TO_SDXL.get(aspect,'1024x1024')}\n"
f" • 采样: DPM++ SDE Karras, 25-30 steps, CFG 5-7\n"
f" • Refiner 使用率 0.2-0.3\n"
f" • seed: {seed or stable_seed(subject_clean, seed_key)}"
)
elif model in ("DALL-E", "DALL·E", "dalle", "DALLE"):
parts = [f"A {preset} style image of {subject_clean}"]
if data.get("camera"):
parts.append(f"captured with {data['camera']}")
if data.get("lighting"):
parts.append(f"lit by {data['lighting']}")
if data.get("palette"):
parts.append(f"with a color palette of {data['palette']}")
if extras:
parts.append(extras)
parts.append("highly detailed, professional composition")
positive = ". ".join(parts) + "."
negative = "(DALL-E 3 忽略负面提示，已通过正向描述规避)"
hint = (
"DALL-E 3 tips：\n"
" • 用自然语言句子 + 细节形容词效果最佳\n"
f" • 画幅: {aspect} (仅支持 1:1, 16:9, 9:16 在 ChatGPT 内)\n"
" • 一致性: 在同一会话连续生成并引用 \"use the same character\""
)
elif model in ("Flux", "flux"):
positive = (
f"{subject_clean}. {consistency}."
+ (f" {extras}." if extras else "")
+ f" {quality_combined}."
)
negative = neg_combined
hint = (
"Flux tips：\n"
" • 支持长自然语言提示，可加句式结构 \"The subject is...\"\n"
f" • 建议 Flux Dev: guidance 3.5; Flux Schnell: guidance 0\n"
f" • seed: {seed or stable_seed(subject_clean, seed_key)}"
)
else: # 通用
positive = (
f"{subject_clean}, {consistency}"
+ (f", {extras}" if extras else "")
+ f", {quality_combined}"
)
negative = neg_combined
hint = "通用格式：Midjourney / SD / Flux 皆可直接使用。"
return {
"version": VERSION,
"original": subject,
"preset": preset,
"mix_secondary": mix_secondary or "",
"mix_ratio": mix_ratio if mix_secondary else None,
"mix_label": mixed_label,
"model": model,
"aspect": aspect,
"composition": extra_composition,
"mood": extra_mood,
"time_of_day": auto.get("time_of_day", ""),
"weather": auto.get("weather", ""),
"season": auto.get("season", ""),
"quality_tier": quality_tier,
"character_sheet": character_sheet,
"user_negatives": auto.get("user_negatives", []),
"seed_suggestion": seed or stable_seed(subject_clean, seed_key),
"positive": positive,
"negative": negative,
"hint": hint,
"consistency_lock": {
"camera": data.get("camera", ""),
"lighting": data.get("lighting", ""),
"palette": data.get("palette", ""),
"aspect": aspect,
},
}
def build_series(
subject: str,
preset: str,
model: str,
aspect: str,
variations: List[str],
seed: Optional[int] = None,
quality_tier: str = "pro",
mix_secondary: Optional[str] = None,
mix_ratio: float = 0.6,
) -> List[Dict]:
"""系列批量生成：共享 camera/lighting/palette/seed 锁，仅替换主体描述。"""
if seed is None:
seed_key = f"{preset}+{mix_secondary}@{mix_ratio:.2f}" if mix_secondary else preset
seed = stable_seed(subject, seed_key)
results = []
for i, v in enumerate(variations, 1):
full = f"{subject}, {v}" if v and v != subject else subject
r = build_prompt(
full, preset, model, aspect, seed=seed, quality_tier=quality_tier,
mix_secondary=mix_secondary, mix_ratio=mix_ratio,
)
r["series_index"] = i
r["series_total"] = len(variations)
results.append(r)
return results
# ─────────────────────────────────────────────────────────
# 输出
# ─────────────────────────────────────────────────────────
def print_prompt(result: Dict):
sep = "═" * 60
print(f"\n{sep}")
if "series_index" in result:
print(f"📸 系列生成 [{result['series_index']}/{result['series_total']}]")
if result.get("character_sheet"):
print("👤 角色设定图模式：T-pose 多视图（喂给 MJ --cref / IP-Adapter）")
print(f"📌 原始描述 : {result['original']}")
if result.get("mix_label"):
print(f"🎨 风格预设 : {result['mix_label']} (混合)")
else:
print(f"🎨 风格预设 : {result['preset']}")
print(f"🤖 目标模型 : {result['model']}")
print(f"📐 画幅 : {result['aspect']}")
print(f"⭐ 质量档位 : {result.get('quality_tier', 'pro')}")
if result.get("composition"):
print(f"🎥 构图 : {result['composition']}")
if result.get("mood"):
print(f"🎭 情绪 : {result['mood']}")
if result.get("time_of_day"):
print(f"🕐 时间 : {result['time_of_day']}")
if result.get("weather"):
print(f"☁️ 天气 : {result['weather']}")
if result.get("season"):
print(f"🍂 季节 : {result['season']}")
if result.get("user_negatives"):
print(f"🚫 用户负向 : {', '.join(result['user_negatives'])} → 已入负面")
print(f"🎲 种子建议 : {result['seed_suggestion']}")
print(f"\n✅ 正向提示词：")
print(f"{result['positive']}")
print(f"\n❌ 负向提示词：")
print(f"{result['negative']}")
print(f"\n🔒 一致性锁：")
for k, v in result["consistency_lock"].items():
if v:
print(f" {k:8s}: {v}")
print(f"\n💡 {result['hint']}")
print(f"{sep}\n")
def list_presets():
by_cat: Dict[str, List[str]] = {}
for name, data in STYLE_PRESETS.items():
by_cat.setdefault(data["category"], []).append(name)
print(f"\n🎨 可用风格预设 (共 {len(STYLE_PRESETS)} 款)")
print("─" * 50)
order = ["摄影", "动漫", "插画", "3D", "设计", "艺术", "场景", "游戏", "东方"]
for cat in order:
if cat not in by_cat:
continue
print(f"\n【{cat}】 {len(by_cat[cat])} 款")
for name in by_cat[cat]:
print(f" • {name}")
print(
"\n💡 同义别名示例：anime, ghibli, cyberpunk, genshin, lol, "
"dunhuang, hanfu, glassmorphism, bauhaus, brutalism, healing, cozy ...\n"
)
# ─────────────────────────────────────────────────────────
# CLI
# ─────────────────────────────────────────────────────────
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-test v{VERSION} — T2I 提示词增强工具",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
示例:
# 基础
enhance_prompt.py "一只赛博朋克风格的猫" -p 赛博朋克 -m Midjourney
# 自动意图 + 时间 / 天气 / 季节 / 负向需求识别
enhance_prompt.py "雨天黄昏的东京巷弄，忧郁氛围，不要人物"
enhance_prompt.py "秋天樱花季汉服写真"
# 新预设（v2.1）
enhance_prompt.py "双马尾少女" -p 原神 -t master
enhance_prompt.py "手持月亮的神女" -p 敦煌壁画
enhance_prompt.py "极简仪表盘UI" -p 玻璃拟态
# 角色设定图（给 Midjourney --cref 做参考）
enhance_prompt.py "银发机甲少女" -p 动漫 --character-sheet -m Midjourney
# 混合预设（v2.2）
enhance_prompt.py "持剑女侠" -p "赛博朋克+水墨" --mix 0.6 -m Midjourney
enhance_prompt.py "山中神女" -p "原神+敦煌壁画" --mix 0.5 -m SDXL
# 系列一致性（4 张共享 camera/lighting/palette/seed）
enhance_prompt.py "一个红发女侠" -p 动漫 -s 4 \\
--variations "持剑站立,骑马奔驰,弯弓射箭,与龙对视"
# 质量档位 + 显式负面追加
enhance_prompt.py "品牌展台" -p 品牌KV -t master --avoid "cluttered, people"
# JSON 输出
enhance_prompt.py "极简Logo一朵山茶花" -p Logo设计 -j
""",
)
parser.add_argument("subject", nargs="?", help="要生成图片的主体描述")
parser.add_argument(
"-p", "--preset",
help="风格预设（中文 / 英文别名）。混合：'赛博朋克+水墨' 或 'genshin+dunhuang'（v2.2）",
)
parser.add_argument(
"--mix", type=float, default=0.6,
help="主预设权重 0.1-0.9，仅在 -p A+B 混合时生效（默认 0.6，主导主预设）",
)
parser.add_argument(
"-m", "--model", default="通用",
help="目标模型: Midjourney / SD / SDXL / DALL-E / Flux / 通用",
)
parser.add_argument("-a", "--aspect", default="", help="画幅: 1:1 / 3:4 / 16:9 / 21:9 ...")
parser.add_argument("--mood", default="", help="情绪覆盖")
parser.add_argument("--composition", default="", help="构图覆盖")
parser.add_argument("--avoid", default="", help="额外负面词，逗号分隔（v2.1）")
parser.add_argument(
"-t", "--tier", choices=["basic", "pro", "master"], default="pro",
help="质量档位 basic/pro/master，默认 pro（v2.1）",
)
parser.add_argument(
"-cs", "--character-sheet", action="store_true",
help="角色设定图模式：T-pose 多视图，适合给 MJ --cref 做角色参考（v2.1）",
)
parser.add_argument("--seed", type=int, help="种子（不给则哈希生成稳定 seed）")
parser.add_argument("-s", "--series", type=int, default=1, help="系列张数（配合 --variations 使用）")
parser.add_argument("--variations", default="", help="系列变体，逗号分隔，如 '持剑,骑马,射箭'")
parser.add_argument("--polish", action="store_true",
help="先用 Claude API 智能润色（需 ANTHROPIC_API_KEY）后再增强（v2.3）")
parser.add_argument("--safety", default="",
help="平台合规润色：DALL-E/MJ/SD/SDXL/Flux，自动重写艺术词避免误判（v2.3）")
parser.add_argument("-l", "--list", action="store_true", help="列出所有预设")
parser.add_argument("-j", "--json", action="store_true", help="JSON 输出")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
if args.list:
list_presets()
return
if not args.subject:
parser.print_help()
sys.exit(1)
subject = args.subject
polish_meta: Optional[Dict] = None
safety_meta: Optional[Dict] = None
preset_override = args.preset
aspect_override = args.aspect
mix_override = None
# v2.3: Claude 智能润色（前置）
if args.polish:
try:
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from claude_polish import call_claude, parse_claude_json
resp = call_claude(subject)
polished = parse_claude_json(resp)
if polished.get("error"):
print(f"❌ Claude 润色拒答: {polished['error']}", file=sys.stderr)
sys.exit(2)
subject = polished.get("subject_refined_zh") or subject
if not preset_override:
pri = polished.get("style_preset", "")
sec = polished.get("style_preset_secondary", "")
if pri and sec:
preset_override = f"{pri}+{sec}"
mix_override = polished.get("mix_ratio", 0.6)
elif pri:
preset_override = pri
if not aspect_override and polished.get("aspect"):
aspect_override = polished["aspect"]
polish_meta = polished
except Exception as e:
print(f"⚠️ Claude 润色失败，回退到原描述: {e}", file=sys.stderr)
# v2.3: 平台合规润色
if args.safety:
try:
from safety_lint import lint as safety_lint
r = safety_lint(subject, platform=args.safety)
if r["verdict"] == "REJECT":
print(f"🚫 命中红线: {r['reason']}\n类别: {', '.join(r.get('categories', []))}", file=sys.stderr)
print(r.get("advice", ""), file=sys.stderr)
sys.exit(2)
if r["verdict"] == "REWRITE":
subject = r["rewritten"]
safety_meta = r
except ImportError:
print(f"⚠️ safety_lint 模块未找到", file=sys.stderr)
# 自动推荐
auto = parse_requirement(subject)
raw_preset = preset_override or auto["preset_suggestion"] or "写实摄影"
primary_raw, secondary_raw = parse_mix_preset(raw_preset)
preset = primary_raw
mix_secondary = secondary_raw
# 校验混合预设
if mix_secondary:
primary_resolved = resolve_preset(preset)
secondary_resolved = resolve_preset(mix_secondary)
if not primary_resolved or not secondary_resolved:
unknown = [n for n, r in [(preset, primary_resolved), (mix_secondary, secondary_resolved)] if not r]
print(f"❌ 未知预设：{', '.join(unknown)}（运行 -l 查看列表）", file=sys.stderr)
sys.exit(1)
preset = primary_resolved
mix_secondary = secondary_resolved
aspect = aspect_override or auto["aspect_suggestion"] or STYLE_PRESETS.get(resolve_preset(preset) or "写实摄影", {}).get("aspect", "1:1")
# 混合权重（polish 推荐 > CLI --mix）
effective_mix = mix_override if mix_override is not None else args.mix
# 系列模式
if args.series > 1 or args.variations:
variations = [v.strip() for v in args.variations.split(",") if v.strip()]
if not variations:
variations = [subject] * args.series
elif len(variations) < args.series:
variations += [variations[-1]] * (args.series - len(variations))
results = build_series(
subject, preset, args.model, aspect,
variations[: max(args.series, len(variations))],
seed=args.seed, quality_tier=args.tier,
mix_secondary=mix_secondary, mix_ratio=effective_mix,
)
if args.json:
out = {"version": VERSION, "series": results}
if polish_meta: out["claude_polish"] = polish_meta
if safety_meta: out["safety_lint"] = safety_meta
print(json.dumps(out, ensure_ascii=False, indent=2))
else:
if polish_meta:
print(f"✨ Claude 已润色 → 主体: {subject}")
if safety_meta and safety_meta.get("verdict") == "REWRITE":
print(f"🛡 平台合规重写 → {subject}")
for r in results:
print_prompt(r)
print(f"🔐 本系列 {len(results)} 张共享 seed = {results[0]['seed_suggestion']}，一致性锁见每张「🔒」区块。")
return
# 单张
result = build_prompt(
subject, preset, args.model, aspect,
extra_mood=args.mood, extra_composition=args.composition,
extra_negatives=args.avoid, seed=args.seed,
quality_tier=args.tier, character_sheet=args.character_sheet,
mix_secondary=mix_secondary, mix_ratio=effective_mix,
)
if polish_meta:
result["claude_polish"] = polish_meta
if safety_meta:
result["safety_lint"] = safety_meta
if args.json:
print(json.dumps(result, ensure_ascii=False, indent=2))
else:
if polish_meta:
print(f"✨ Claude 已润色 (in={polish_meta.get('_usage',{}).get('input_tokens',0)}/out={polish_meta.get('_usage',{}).get('output_tokens',0)} tokens)")
if safety_meta and safety_meta.get("verdict") == "REWRITE":
print(f"🛡 平台合规已重写: {len(safety_meta.get('substitutions',[]))} 处替换 (target={safety_meta['platform']})")
print_prompt(result)
if __name__ == "__main__":
main()
FILE:scripts/enhance_video.py
#!/usr/bin/env python3
"""
huo15-img-test — T2V 视频提示词增强脚本 v2.2
把 enhance_prompt.py 的 88 风格预设 + 一致性锁，扩展到视频维度：
- 镜头运动（推/拉/摇/移/跟/环绕/手持/无人机...）
- 节奏（缓慢 / 中速 / 紧张快切）
- 时长（建议秒数 + 关键帧拆分）
- 主体动作（自动从描述中抽词，或显式 --action）
- 模型适配：Sora / Kling 可灵 / Runway Gen-3/Gen-4 / Pika / Luma DreamMachine / 即梦 / Hailuo MiniMax / Wan2.1
调用：
enhance_video.py "雨夜霓虹街头一只猫漫步" -p 赛博朋克 -m Sora --duration 8
enhance_video.py "汉服少女转身回眸" -p 汉服写真 -m Kling --motion 慢速跟拍
enhance_video.py "宇宙飞船穿越星云" -p scifi -m Runway --action "ship accelerates, lens flare"
依赖：
enhance_prompt.py 同目录（复用其预设 + 意图解析 + 一致性锁）
"""
import sys
import os
import json
import re
import argparse
import hashlib
from typing import Dict, List, Optional, Tuple
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from enhance_prompt import (
STYLE_PRESETS,
ALIASES,
QUALITY_TIERS,
resolve_preset,
parse_requirement,
parse_mix_preset,
mix_presets,
sanitize_subject,
strip_negative_clauses,
stable_seed,
list_presets as list_image_presets,
)
VERSION = "2.3.0"
# ─────────────────────────────────────────────────────────
# 镜头运动（中文 → 英文 + 视频专业术语）
# ─────────────────────────────────────────────────────────
CAMERA_MOTION: Dict[str, str] = {
"推": "slow push-in (dolly in)",
"推镜": "smooth dolly in, gradual close-up",
"拉": "pull back (dolly out)",
"拉镜": "slow pull back revealing wider scene",
"摇": "pan (horizontal)",
"横摇": "horizontal pan from left to right",
"竖摇": "vertical tilt up to down",
"移": "lateral tracking shot",
"跟": "tracking shot following the subject",
"跟拍": "smooth tracking shot, subject locked in frame",
"环绕": "360 orbital shot around the subject",
"围绕": "360 orbit shot, slow rotation",
"手持": "handheld camera, slight shake, documentary feel",
"稳定": "smooth gimbal stabilized, fluid motion",
"无人机": "aerial drone shot, high-altitude reveal",
"航拍": "aerial drone descent, cinematic reveal",
"升": "crane up, vertical rise",
"降": "crane down, descent",
"变焦": "zoom in, focal length change",
"希区柯克": "dolly zoom (vertigo effect)",
"希区": "dolly zoom (vertigo effect)",
"鱼眼": "fisheye lens distortion, wide warped perspective",
"POV": "first-person POV, immersive",
"POV视角": "first-person POV, immersive",
"子弹时间": "bullet-time freeze, 360 frozen pan",
"延时": "time-lapse, accelerated motion",
"慢动作": "slow motion 120fps, ultra-smooth",
"快切": "rapid cuts, high-energy montage",
}
# 节奏 → 英文
PACING: Dict[str, str] = {
"缓慢": "slow steady pacing, contemplative rhythm",
"舒缓": "slow steady pacing, contemplative rhythm",
"宁静": "calm, atmospheric, lingering shots",
"中速": "moderate pacing, balanced cuts",
"紧张": "tense pacing, building intensity",
"急促": "fast pacing, urgent cuts",
"快切": "rapid cuts, high-energy edit",
"动感": "kinetic energy, dynamic motion",
"史诗": "epic crescendo, sweeping movement",
}
# 主体动作关键词（自动抽词）
ACTION_KEYWORDS: Dict[str, str] = {
"走": "walking forward",
"漫步": "walking calmly",
"奔跑": "running fast",
"跑": "running",
"跳": "jumping",
"飞": "flying through the air",
"舞": "dancing gracefully",
"舞蹈": "dancing gracefully",
"回眸": "turning to look back over shoulder",
"转身": "turning around",
"微笑": "smiling softly",
"战斗": "fighting, dynamic combat motion",
"挥剑": "swinging a sword",
"射箭": "drawing and releasing an arrow",
"骑马": "riding a horse at full gallop",
"驾驶": "driving forward",
"穿越": "traveling through, breaking forward",
"升起": "rising up slowly",
"落下": "falling down gently",
"爆炸": "explosion blooming outward",
"绽放": "blooming open",
"凝视": "gazing intently into the camera",
"对视": "locking eyes with the viewer",
"睁眼": "eyes opening slowly",
"闭眼": "eyes closing slowly",
"呼吸": "breathing softly, chest rising and falling",
"拥抱": "embracing tenderly",
"牵手": "holding hands",
"握手": "shaking hands",
}
# ─────────────────────────────────────────────────────────
# 模型规格
# ─────────────────────────────────────────────────────────
VIDEO_MODELS: Dict[str, Dict[str, str]] = {
"Sora": {
"max_duration": "20s (Sora 2 Pro)",
"default_duration": 10,
"aspect_default": "16:9",
"tip": "支持长自然语言描述。可叠加 'cinematic, IMAX, 35mm film, photorealistic'。一致性强，可复用 character description。",
"format": "natural",
},
"Kling": {
"max_duration": "10s (1080p Pro)",
"default_duration": 5,
"aspect_default": "16:9",
"tip": "可灵 1.6/2.0：建议提示前置主体，后置镜头/光影。支持首尾帧控制（image-to-video）。",
"format": "natural",
},
"可灵": {
"max_duration": "10s (1080p Pro)",
"default_duration": 5,
"aspect_default": "16:9",
"tip": "可灵 1.6/2.0：中文提示词支持良好，可加 'cinematic 电影感'。",
"format": "natural",
},
"Runway": {
"max_duration": "10s (Gen-3 Alpha Turbo)",
"default_duration": 5,
"aspect_default": "16:9",
"tip": "Gen-3 / Gen-4：英文提示效果最佳。支持 Motion Brush 局部运动。CFG ~7。",
"format": "natural",
},
"Pika": {
"max_duration": "10s (Pika 2.0)",
"default_duration": 4,
"aspect_default": "16:9",
"tip": "Pika：标签式提示，支持 -gs (guidance scale) 和 -motion (1-4)。",
"format": "tag",
},
"Luma": {
"max_duration": "9s (Dream Machine 1.6)",
"default_duration": 5,
"aspect_default": "16:9",
"tip": "Luma Dream Machine：自然语言 + 关键帧（首尾图）。Loop 模式支持无缝循环。",
"format": "natural",
},
"DreamMachine": {
"max_duration": "9s",
"default_duration": 5,
"aspect_default": "16:9",
"tip": "Luma Dream Machine：自然语言 + 关键帧。",
"format": "natural",
},
"Hailuo": {
"max_duration": "10s (MiniMax 02 / S2V-01)",
"default_duration": 6,
"aspect_default": "16:9",
"tip": "海螺 MiniMax 02：中文支持优秀。S2V-01 可指定参考人物。",
"format": "natural",
},
"MiniMax": {
"max_duration": "10s",
"default_duration": 6,
"aspect_default": "16:9",
"tip": "MiniMax 视频：中英双语，长描述效果好。",
"format": "natural",
},
"即梦": {
"max_duration": "12s (Seedance 1.0)",
"default_duration": 5,
"aspect_default": "16:9",
"tip": "即梦 / Seedance：抖音生态，支持中文 + 多镜头剧情连贯。",
"format": "natural",
},
"Seedance": {
"max_duration": "12s",
"default_duration": 5,
"aspect_default": "16:9",
"tip": "Seedance 1.0：多镜头剧情连贯，支持中文。",
"format": "natural",
},
"Wan": {
"max_duration": "8s (Wan 2.1)",
"default_duration": 4,
"aspect_default": "16:9",
"tip": "通义 Wan 2.1：阿里开源，I2V 支持高分辨率。中英双语提示。",
"format": "natural",
},
"Wan2.1": {
"max_duration": "8s",
"default_duration": 4,
"aspect_default": "16:9",
"tip": "通义 Wan 2.1：阿里开源 14B / 1.3B 双参数。",
"format": "natural",
},
"通用": {
"max_duration": "—",
"default_duration": 5,
"aspect_default": "16:9",
"tip": "通用模板：自然语言 + 镜头 + 节奏 + 主体动作。",
"format": "natural",
},
}
MODEL_ALIASES: Dict[str, str] = {
"sora": "Sora", "kling": "Kling", "kelin": "Kling", "klingai": "Kling",
"runway": "Runway", "gen3": "Runway", "gen4": "Runway",
"pika": "Pika", "luma": "Luma", "dreammachine": "Luma",
"hailuo": "Hailuo", "minimax": "Hailuo",
"jimeng": "即梦", "seedance": "即梦",
"wan": "Wan", "wan21": "Wan", "wan2.1": "Wan",
"tongyi": "Wan",
}
def resolve_video_model(name: str) -> str:
if not name:
return "通用"
key = name.strip().lower().replace("-", "").replace("_", "").replace(" ", "")
if key in MODEL_ALIASES:
return MODEL_ALIASES[key]
for m in VIDEO_MODELS:
if m.lower() == key:
return m
return name if name in VIDEO_MODELS else "通用"
# ─────────────────────────────────────────────────────────
# 解析
# ─────────────────────────────────────────────────────────
def parse_motion(text: str) -> str:
for zh, en in CAMERA_MOTION.items():
if zh in text:
return en
return ""
def parse_pacing(text: str) -> str:
for zh, en in PACING.items():
if zh in text:
return en
return ""
def parse_action(text: str) -> str:
actions = []
for zh, en in ACTION_KEYWORDS.items():
if zh in text and en not in actions:
actions.append(en)
return ", ".join(actions[:3])
# ─────────────────────────────────────────────────────────
# 关键帧拆分
# ─────────────────────────────────────────────────────────
def keyframe_breakdown(subject: str, motion: str, duration: int) -> List[Dict[str, str]]:
"""简单的三段式拆分：开场（建立）→ 中段（动作）→ 结尾（落点）。"""
if duration <= 3:
return [{"t": "0s", "desc": f"establish shot: {subject}"}]
third = max(1, duration // 3)
return [
{"t": "0s", "desc": f"opening: establish {subject} in scene, static composition"},
{"t": f"{third}s", "desc": f"mid: {motion or 'subject performs main action'}, peak motion"},
{"t": f"{2*third}s", "desc": f"closing: settle into resting frame, fade or hold"},
]
# ─────────────────────────────────────────────────────────
# 主构建
# ─────────────────────────────────────────────────────────
def build_video_prompt(
subject: str,
preset: str,
model: str = "通用",
aspect: str = "",
duration: Optional[int] = None,
motion: str = "",
pacing: str = "",
action: str = "",
seed: Optional[int] = None,
quality_tier: str = "pro",
extra_negatives: str = "",
mix_secondary: Optional[str] = None,
mix_ratio: float = 0.6,
) -> Dict:
preset = resolve_preset(preset) or "电影感"
if mix_secondary:
mix_secondary = resolve_preset(mix_secondary) or ""
model = resolve_video_model(model)
spec = VIDEO_MODELS[model]
# 视觉锁（复用 image preset）
if mix_secondary and mix_secondary != preset:
data = mix_presets(preset, mix_secondary, mix_ratio, model)
mixed_label = f"{preset}+{mix_secondary}@{mix_ratio:.2f}"
else:
data = STYLE_PRESETS[preset]
mixed_label = ""
# 时长 / 画幅
if duration is None:
duration = spec["default_duration"]
if not aspect:
aspect = data.get("aspect", spec["aspect_default"])
# 自动解析
auto = parse_requirement(subject)
subject_clean = sanitize_subject(strip_negative_clauses(subject))
if not motion:
motion = parse_motion(subject) or "smooth gimbal stabilized, fluid motion"
if not pacing:
pacing = parse_pacing(subject) or "moderate pacing, balanced cuts"
if not action:
action = parse_action(subject)
# ambient
ambient_parts = [auto["time_of_day"], auto["weather"], auto["season"]]
ambient = ", ".join([x for x in ambient_parts if x])
# 视觉锁字段
visual_lock = ", ".join([
x for x in [data["tags"], data.get("camera", ""), data.get("lighting", ""), data.get("palette", "")] if x
])
quality_phrase = QUALITY_TIERS.get(quality_tier, QUALITY_TIERS["pro"])
seed_key = mixed_label or preset
seed_value = seed or stable_seed(subject_clean, seed_key)
# 构造正向提示
if spec["format"] == "tag": # Pika 标签格式
parts = [
subject_clean,
f"{motion}",
f"{pacing}",
visual_lock,
ambient,
action,
quality_phrase,
"cinematic video",
]
positive = ", ".join([p for p in parts if p])
positive += f" -gs 12 -motion 3 -ar {aspect}"
else: # 自然语言格式
sentences = []
sentences.append(f"A {duration}-second video of {subject_clean}.")
sentences.append(f"Camera movement: {motion}.")
if action:
sentences.append(f"The subject is {action}.")
sentences.append(f"Pacing: {pacing}.")
sentences.append(f"Visual style: {visual_lock}.")
if ambient:
sentences.append(f"Atmosphere: {ambient}.")
sentences.append(f"Quality: {quality_phrase}, cinematic, smooth temporal coherence, no flicker, consistent character across frames.")
positive = " ".join(sentences)
# 负面
base_neg = data["neg"]
video_neg = (
"flicker, frame drop, motion blur artifacts, jittery camera, "
"low fps, choppy motion, morphing artifacts, identity drift, "
"deformed limbs mid-motion, inconsistent character, watermark"
)
neg_parts = [base_neg, video_neg, extra_negatives, ", ".join(auto.get("user_negatives", []))]
negative = ", ".join([x for x in neg_parts if x])
# 关键帧
keyframes = keyframe_breakdown(subject_clean, motion, duration)
hint = (
f"{model} tips：\n"
f" • {spec['tip']}\n"
f" • 推荐时长：{duration}s（上限 {spec['max_duration']}）\n"
f" • 一致性：i2v 模式可固定首帧角色 / 用 image-prompt 保持服装色彩\n"
f" • seed: {seed_value}（同一 seed + 同一 prompt 在多数模型可复现）"
)
return {
"version": VERSION,
"type": "t2v",
"original": subject,
"preset": preset,
"mix_secondary": mix_secondary or "",
"mix_label": mixed_label,
"model": model,
"aspect": aspect,
"duration_s": duration,
"max_duration": spec["max_duration"],
"motion": motion,
"pacing": pacing,
"action": action,
"time_of_day": auto.get("time_of_day", ""),
"weather": auto.get("weather", ""),
"season": auto.get("season", ""),
"seed_suggestion": seed_value,
"quality_tier": quality_tier,
"positive": positive,
"negative": negative,
"keyframes": keyframes,
"hint": hint,
"consistency_lock": {
"camera": data.get("camera", ""),
"lighting": data.get("lighting", ""),
"palette": data.get("palette", ""),
"aspect": aspect,
"motion": motion,
},
}
def print_video_prompt(r: Dict):
sep = "═" * 60
print(f"\n{sep}")
print(f"🎬 视频提示词（v{r['version']}）")
print(f"📌 原始描述 : {r['original']}")
if r.get("mix_label"):
print(f"🎨 风格预设 : {r['mix_label']} (混合)")
else:
print(f"🎨 风格预设 : {r['preset']}")
print(f"🤖 目标模型 : {r['model']}（上限 {r['max_duration']}）")
print(f"📐 画幅 : {r['aspect']}")
print(f"⏱ 时长 : {r['duration_s']}s")
print(f"🎥 镜头运动 : {r['motion']}")
print(f"🎵 节奏 : {r['pacing']}")
if r.get("action"):
print(f"💪 主体动作 : {r['action']}")
if r.get("time_of_day") or r.get("weather") or r.get("season"):
amb = ", ".join([x for x in [r.get("time_of_day", ""), r.get("weather", ""), r.get("season", "")] if x])
print(f"🌤 环境 : {amb}")
print(f"⭐ 质量档位 : {r['quality_tier']}")
print(f"🎲 种子建议 : {r['seed_suggestion']}")
print(f"\n✅ 正向提示词：\n{r['positive']}")
print(f"\n❌ 负向提示词：\n{r['negative']}")
print(f"\n🎞 关键帧拆分：")
for kf in r["keyframes"]:
print(f" {kf['t']:>4s} {kf['desc']}")
print(f"\n🔒 一致性锁：")
for k, v in r["consistency_lock"].items():
if v:
print(f" {k:8s}: {v}")
print(f"\n💡 {r['hint']}")
print(f"{sep}\n")
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-test enhance_video v{VERSION} — T2V 视频提示词增强",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
示例:
enhance_video.py "雨夜霓虹街头一只猫漫步" -p 赛博朋克 -m Sora --duration 8
enhance_video.py "汉服少女转身回眸" -p 汉服写真 -m Kling --motion 慢速跟拍
enhance_video.py "宇宙飞船穿越星云" -p scifi -m Runway --duration 5 --pacing 史诗
enhance_video.py "山中神女腾云" -p "原神+敦煌壁画" --mix 0.6 -m Hailuo
enhance_video.py "侠客挥剑" -p 水墨 -m 即梦 --action "spinning sword strike"
""",
)
parser.add_argument("subject", nargs="?", help="主体描述")
parser.add_argument("-p", "--preset", help="风格预设（沿用 88 款图像预设；支持 A+B 混合）")
parser.add_argument("--mix", type=float, default=0.6, help="主预设权重 0.1-0.9（默认 0.6）")
parser.add_argument(
"-m", "--model", default="通用",
help="视频模型: Sora / Kling / Runway / Pika / Luma / Hailuo / 即梦 / Wan / 通用",
)
parser.add_argument("-a", "--aspect", default="", help="画幅 16:9 / 9:16 / 1:1 / 21:9")
parser.add_argument("--duration", type=int, help="时长（秒），不给走模型默认")
parser.add_argument("--motion", default="", help="镜头运动覆盖（中/英）")
parser.add_argument("--pacing", default="", help="节奏覆盖")
parser.add_argument("--action", default="", help="主体动作覆盖")
parser.add_argument("--avoid", default="", help="额外负面词")
parser.add_argument("-t", "--tier", choices=["basic", "pro", "master"], default="pro")
parser.add_argument("--seed", type=int, help="种子")
parser.add_argument("-l", "--list", action="store_true", help="列出图像预设（视频沿用）")
parser.add_argument("--list-models", action="store_true", help="列出视频模型规格")
parser.add_argument("-j", "--json", action="store_true", help="JSON 输出")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
if args.list:
list_image_presets()
return
if args.list_models:
print(f"\n🎬 视频模型规格 (v{VERSION})\n" + "─" * 50)
for name, spec in VIDEO_MODELS.items():
print(f"\n【{name}】")
print(f" 上限时长: {spec['max_duration']}")
print(f" 默认时长: {spec['default_duration']}s")
print(f" 默认画幅: {spec['aspect_default']}")
print(f" 说明: {spec['tip']}")
return
if not args.subject:
parser.print_help()
sys.exit(1)
raw_preset = args.preset or "电影感"
primary_raw, secondary_raw = parse_mix_preset(raw_preset)
if secondary_raw:
primary_resolved = resolve_preset(primary_raw)
secondary_resolved = resolve_preset(secondary_raw)
if not primary_resolved or not secondary_resolved:
unknown = [n for n, r in [(primary_raw, primary_resolved), (secondary_raw, secondary_resolved)] if not r]
print(f"❌ 未知预设：{', '.join(unknown)}", file=sys.stderr)
sys.exit(1)
preset, mix_secondary = primary_resolved, secondary_resolved
else:
preset, mix_secondary = primary_raw, None
result = build_video_prompt(
args.subject, preset, model=args.model, aspect=args.aspect,
duration=args.duration, motion=args.motion, pacing=args.pacing,
action=args.action, seed=args.seed, quality_tier=args.tier,
extra_negatives=args.avoid, mix_secondary=mix_secondary, mix_ratio=args.mix,
)
if args.json:
print(json.dumps(result, ensure_ascii=False, indent=2))
else:
print_video_prompt(result)
if __name__ == "__main__":
main()
FILE:scripts/render_prompt.py
#!/usr/bin/env python3
"""
huo15-img-test — 提示词直出图片 v2.2
把 enhance_prompt.py 生成的提示词，直接调用本地或云端 API 出图。
支持的后端：
- comfyui 本地 ComfyUI（HTTP API，默认 http://127.0.0.1:8188）
- sd-webui AUTOMATIC1111 / Forge（默认 http://127.0.0.1:7860/sdapi/v1/txt2img）
- dalle OpenAI DALL-E 3（OPENAI_API_KEY）
- openai 同 dalle
- none 只生成调用脚本，不真实执行（dry-run，方便贴到 ComfyUI 桌面端）
依赖：仅 Python 标准库（urllib），不引入 requests/PIL，避免企业扫描器命中第三方包。
调用：
render_prompt.py "赛博朋克猫" -p 赛博朋克 -m SD --backend sd-webui
render_prompt.py "极简logo" -p Logo设计 --backend dalle --size 1024x1024
render_prompt.py "原神少女" -p 原神 --backend comfyui --workflow ./workflows/sdxl-base.json
render_prompt.py "敦煌神女" -p 敦煌壁画 --backend none -j > recipe.json # dry-run
环境变量：
OPENAI_API_KEY DALL-E 调用必需
COMFYUI_URL 覆盖 ComfyUI 端点（默认 http://127.0.0.1:8188）
SDWEBUI_URL 覆盖 SD WebUI 端点（默认 http://127.0.0.1:7860）
"""
import sys
import os
import json
import time
import base64
import argparse
import uuid
from typing import Dict, Optional
from urllib.parse import urljoin
from urllib.request import Request, urlopen
from urllib.error import HTTPError, URLError
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from enhance_prompt import (
build_prompt,
parse_mix_preset,
resolve_preset,
parse_requirement,
STYLE_PRESETS,
ASPECT_TO_SDXL,
)
VERSION = "2.3.0"
# ─────────────────────────────────────────────────────────
# HTTP 工具
# ─────────────────────────────────────────────────────────
def http_post_json(url: str, body: Dict, headers: Optional[Dict] = None, timeout: int = 600) -> Dict:
data = json.dumps(body).encode("utf-8")
h = {"Content-Type": "application/json"}
if headers:
h.update(headers)
req = Request(url, data=data, headers=h, method="POST")
with urlopen(req, timeout=timeout) as r:
return json.loads(r.read().decode("utf-8"))
def http_get_json(url: str, headers: Optional[Dict] = None, timeout: int = 60) -> Dict:
req = Request(url, headers=headers or {})
with urlopen(req, timeout=timeout) as r:
return json.loads(r.read().decode("utf-8"))
def http_get_bytes(url: str, headers: Optional[Dict] = None, timeout: int = 600) -> bytes:
req = Request(url, headers=headers or {})
with urlopen(req, timeout=timeout) as r:
return r.read()
# ─────────────────────────────────────────────────────────
# DALL-E 3
# ─────────────────────────────────────────────────────────
DALLE_SIZES = {"1:1": "1024x1024", "16:9": "1792x1024", "9:16": "1024x1792"}
def render_dalle(positive: str, size: str, output_dir: str, n: int = 1) -> Dict:
api_key = os.environ.get("OPENAI_API_KEY")
if not api_key:
raise RuntimeError("缺少 OPENAI_API_KEY 环境变量")
body = {
"model": "dall-e-3",
"prompt": positive[:4000],
"n": n,
"size": size,
"quality": "hd",
"response_format": "b64_json",
}
resp = http_post_json(
"https://api.openai.com/v1/images/generations",
body,
headers={"Authorization": f"Bearer {api_key}"},
timeout=300,
)
saved = []
os.makedirs(output_dir, exist_ok=True)
for i, item in enumerate(resp.get("data", [])):
path = os.path.join(output_dir, f"dalle-{int(time.time())}-{i}.png")
with open(path, "wb") as f:
f.write(base64.b64decode(item["b64_json"]))
saved.append(path)
return {"backend": "dalle", "saved": saved, "raw_response_keys": list(resp.keys())}
# ─────────────────────────────────────────────────────────
# AUTOMATIC1111 / Forge SD WebUI
# ─────────────────────────────────────────────────────────
def aspect_to_size(aspect: str) -> tuple:
sdxl = ASPECT_TO_SDXL.get(aspect, "1024x1024")
w, h = sdxl.split("x")
return int(w), int(h)
def render_sdwebui(positive: str, negative: str, aspect: str, seed: int, steps: int, cfg: float,
sampler: str, output_dir: str, base_url: Optional[str] = None) -> Dict:
base = base_url or os.environ.get("SDWEBUI_URL", "http://127.0.0.1:7860")
w, h = aspect_to_size(aspect)
body = {
"prompt": positive,
"negative_prompt": negative,
"width": w,
"height": h,
"seed": seed,
"steps": steps,
"cfg_scale": cfg,
"sampler_name": sampler,
"send_images": True,
}
resp = http_post_json(urljoin(base, "/sdapi/v1/txt2img"), body, timeout=900)
saved = []
os.makedirs(output_dir, exist_ok=True)
for i, b64 in enumerate(resp.get("images", [])):
path = os.path.join(output_dir, f"sdwebui-{seed}-{i}.png")
with open(path, "wb") as f:
f.write(base64.b64decode(b64.split(",", 1)[-1]))
saved.append(path)
return {"backend": "sd-webui", "saved": saved, "info": resp.get("info", "")[:200]}
# ─────────────────────────────────────────────────────────
# ComfyUI
# ─────────────────────────────────────────────────────────
DEFAULT_COMFY_WORKFLOW = {
"3": {
"class_type": "KSampler",
"inputs": {
"seed": 0, "steps": 25, "cfg": 7.0, "sampler_name": "dpmpp_2m",
"scheduler": "karras", "denoise": 1.0,
"model": ["4", 0], "positive": ["6", 0], "negative": ["7", 0], "latent_image": ["5", 0],
},
},
"4": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": "sd_xl_base_1.0.safetensors"}},
"5": {"class_type": "EmptyLatentImage", "inputs": {"width": 1024, "height": 1024, "batch_size": 1}},
"6": {"class_type": "CLIPTextEncode", "inputs": {"text": "POSITIVE_PLACEHOLDER", "clip": ["4", 1]}},
"7": {"class_type": "CLIPTextEncode", "inputs": {"text": "NEGATIVE_PLACEHOLDER", "clip": ["4", 1]}},
"8": {"class_type": "VAEDecode", "inputs": {"samples": ["3", 0], "vae": ["4", 2]}},
"9": {"class_type": "SaveImage", "inputs": {"images": ["8", 0], "filename_prefix": "huo15"}},
}
def render_comfyui(positive: str, negative: str, aspect: str, seed: int, steps: int, cfg: float,
workflow_path: Optional[str], output_dir: str,
base_url: Optional[str] = None) -> Dict:
base = base_url or os.environ.get("COMFYUI_URL", "http://127.0.0.1:8188")
if workflow_path and os.path.isfile(workflow_path):
with open(workflow_path, "r", encoding="utf-8") as f:
workflow = json.load(f)
else:
workflow = json.loads(json.dumps(DEFAULT_COMFY_WORKFLOW))
w, h = aspect_to_size(aspect)
for node in workflow.values():
ct = node.get("class_type", "")
ins = node.get("inputs", {})
if ct == "CLIPTextEncode":
if ins.get("text") == "POSITIVE_PLACEHOLDER" or "positive" in str(ins.get("text", "")).lower():
ins["text"] = positive
elif ins.get("text") == "NEGATIVE_PLACEHOLDER" or "negative" in str(ins.get("text", "")).lower():
ins["text"] = negative
elif ct == "EmptyLatentImage":
ins["width"], ins["height"] = w, h
elif ct == "KSampler":
ins["seed"], ins["steps"], ins["cfg"] = seed, steps, cfg
pos_set = neg_set = False
for node in workflow.values():
if node.get("class_type") == "CLIPTextEncode":
if not pos_set:
node["inputs"]["text"] = positive
pos_set = True
elif not neg_set:
node["inputs"]["text"] = negative
neg_set = True
client_id = str(uuid.uuid4())
queue_resp = http_post_json(urljoin(base, "/prompt"), {"prompt": workflow, "client_id": client_id}, timeout=30)
prompt_id = queue_resp.get("prompt_id")
if not prompt_id:
raise RuntimeError(f"ComfyUI 队列失败: {queue_resp}")
deadline = time.time() + 600
history = {}
while time.time() < deadline:
try:
history = http_get_json(urljoin(base, f"/history/{prompt_id}"), timeout=10)
if history.get(prompt_id):
break
except (HTTPError, URLError):
pass
time.sleep(2)
if not history.get(prompt_id):
raise RuntimeError("ComfyUI 任务超时")
saved = []
os.makedirs(output_dir, exist_ok=True)
outputs = history[prompt_id].get("outputs", {})
for node_id, output in outputs.items():
for img in output.get("images", []):
url = urljoin(base, f"/view?filename={img['filename']}&subfolder={img.get('subfolder','')}&type={img.get('type','output')}")
path = os.path.join(output_dir, f"comfy-{seed}-{img['filename']}")
with open(path, "wb") as f:
f.write(http_get_bytes(url))
saved.append(path)
return {"backend": "comfyui", "saved": saved, "prompt_id": prompt_id}
# ─────────────────────────────────────────────────────────
# 主入口
# ─────────────────────────────────────────────────────────
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-test render_prompt v{VERSION} — 提示词直出图片",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
示例:
render_prompt.py "赛博朋克猫" -p 赛博朋克 --backend sd-webui
render_prompt.py "原神少女" -p 原神 --backend comfyui --workflow ./workflows/sdxl.json
render_prompt.py "极简logo" -p Logo设计 --backend dalle --size 1024x1024
render_prompt.py "敦煌神女" -p 敦煌壁画 --backend none -j # dry-run，只输出 recipe
""",
)
parser.add_argument("subject", help="主体描述")
parser.add_argument("-p", "--preset", help="风格预设（支持 A+B 混合）")
parser.add_argument("--mix", type=float, default=0.6, help="混合权重（默认 0.6）")
parser.add_argument("-a", "--aspect", default="", help="画幅")
parser.add_argument("-t", "--tier", choices=["basic", "pro", "master"], default="pro")
parser.add_argument("--avoid", default="", help="额外负面词")
parser.add_argument("--seed", type=int, help="种子")
parser.add_argument(
"--backend", choices=["comfyui", "sd-webui", "dalle", "openai", "none"], default="none",
help="后端：comfyui / sd-webui / dalle / none(dry-run)",
)
parser.add_argument("-m", "--model", default="SDXL", help="提示词模型适配（不影响后端选择）")
parser.add_argument("--output", default="./renders", help="输出目录（默认 ./renders）")
parser.add_argument("--workflow", default="", help="ComfyUI workflow JSON 路径（可选）")
parser.add_argument("--steps", type=int, default=25, help="采样步数")
parser.add_argument("--cfg", type=float, default=7.0, help="CFG scale")
parser.add_argument("--sampler", default="DPM++ 2M Karras", help="采样器")
parser.add_argument("--size", default="", help="DALL-E 尺寸 1024x1024 / 1792x1024 / 1024x1792")
parser.add_argument("--n", type=int, default=1, help="生成张数（DALL-E）")
parser.add_argument("-j", "--json", action="store_true", help="JSON 输出")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
raw_preset = args.preset or "写实摄影"
primary_raw, secondary_raw = parse_mix_preset(raw_preset)
if secondary_raw:
primary_resolved = resolve_preset(primary_raw)
secondary_resolved = resolve_preset(secondary_raw)
if not primary_resolved or not secondary_resolved:
print(f"❌ 未知预设：{primary_raw} 或 {secondary_raw}", file=sys.stderr)
sys.exit(1)
preset, mix_secondary = primary_resolved, secondary_resolved
else:
preset, mix_secondary = primary_raw, None
auto = parse_requirement(args.subject)
aspect = args.aspect or auto["aspect_suggestion"] or STYLE_PRESETS.get(resolve_preset(preset) or "写实摄影", {}).get("aspect", "1:1")
recipe = build_prompt(
args.subject, preset, args.model, aspect,
extra_negatives=args.avoid, seed=args.seed, quality_tier=args.tier,
mix_secondary=mix_secondary, mix_ratio=args.mix,
)
seed = recipe["seed_suggestion"]
if args.backend == "none":
out = {"version": VERSION, "backend": "none", "recipe": recipe, "note": "dry-run，未实际调用模型"}
if args.json:
print(json.dumps(out, ensure_ascii=False, indent=2))
else:
print(f"🧪 dry-run（未出图）")
print(f" positive: {recipe['positive'][:200]}...")
print(f" seed: {seed}")
print(f" → 用 -j 输出完整 recipe，再 pipe 给 ComfyUI / DALL-E / SD WebUI")
return
try:
if args.backend == "sd-webui":
result = render_sdwebui(
recipe["positive"], recipe["negative"], aspect, seed,
args.steps, args.cfg, args.sampler, args.output,
)
elif args.backend == "comfyui":
result = render_comfyui(
recipe["positive"], recipe["negative"], aspect, seed,
args.steps, args.cfg, args.workflow or None, args.output,
)
elif args.backend in ("dalle", "openai"):
size = args.size or DALLE_SIZES.get(aspect, "1024x1024")
result = render_dalle(recipe["positive"], size, args.output, n=args.n)
else:
raise RuntimeError(f"未知 backend: {args.backend}")
except Exception as e:
print(f"❌ 渲染失败: {e}", file=sys.stderr)
sys.exit(2)
out = {"version": VERSION, "recipe": recipe, "render": result}
if args.json:
print(json.dumps(out, ensure_ascii=False, indent=2))
else:
print(f"✅ 已出图（backend={result['backend']}）")
for p in result.get("saved", []):
print(f" 📷 {p}")
print(f" 🎲 seed = {seed}")
if __name__ == "__main__":
main()
FILE:scripts/reverse_prompt.py
#!/usr/bin/env python3
"""
huo15-img-test — 参考图反解 v2.2
把现成图片（本地路径或 URL）反向解析成可复用的 T2I 提示词。
工作流（三层）：
1. PNG metadata 提取：A1111 / ComfyUI / NovelAI 出图都把 prompt 写在 PNG `parameters` / `prompt` / `Comment` 字段
2. EXIF 提取：iPhone / 单反相机参数（焦距 / ISO / 快门 / 光圈），用于推断 camera 锁
3. VLM 模板生成：当 1/2 都没有可用信息时，输出标准化「请把这张图描述成 T2I 提示词」prompt 模板，
交给 GPT-4o / Claude / Gemini 1.5 / Qwen-VL 等多模态模型继续解析
输出三选一：
- text 人类可读
- json 结构化（直接喂回 enhance_prompt.py）
- mj Midjourney 风格直接复用 prompt（含 --ar / --sref / --seed）
调用：
reverse_prompt.py /path/to/image.png
reverse_prompt.py https://example.com/img.png --vlm
reverse_prompt.py img.png -j > recipe.json && enhance_prompt.py "$(jq -r .subject recipe.json)"
"""
import sys
import os
import json
import re
import argparse
import struct
from typing import Dict, List, Optional, Tuple
from urllib.parse import urlparse
from urllib.request import Request, urlopen
VERSION = "2.3.0"
# ─────────────────────────────────────────────────────────
# PNG metadata 解析
# ─────────────────────────────────────────────────────────
PNG_TEXT_KEYS_A1111 = ("parameters",)
PNG_TEXT_KEYS_COMFY = ("prompt", "workflow")
PNG_TEXT_KEYS_NOVELAI = ("Description", "Comment", "Software")
def read_png_text_chunks(blob: bytes) -> Dict[str, str]:
"""手写 PNG 解析，避免 PIL 依赖。提取 tEXt / iTXt / zTXt 文本块。"""
if not blob.startswith(b"\x89PNG\r\n\x1a\n"):
return {}
out: Dict[str, str] = {}
i = 8
while i < len(blob):
if i + 8 > len(blob):
break
length = struct.unpack(">I", blob[i:i+4])[0]
ctype = blob[i+4:i+8]
data = blob[i+8:i+8+length]
i += 8 + length + 4 # skip CRC
if ctype == b"tEXt":
try:
key, value = data.split(b"\x00", 1)
out[key.decode("latin-1", "replace")] = value.decode("utf-8", "replace")
except ValueError:
continue
elif ctype == b"iTXt":
try:
key, rest = data.split(b"\x00", 1)
# iTXt: key\0 compress_flag(1) compress_method(1) lang_tag\0 trans_keyword\0 text
if len(rest) < 2:
continue
_flag, _method = rest[0], rest[1]
rest2 = rest[2:]
_lang, rest3 = rest2.split(b"\x00", 1)
_trans, text = rest3.split(b"\x00", 1)
out[key.decode("latin-1", "replace")] = text.decode("utf-8", "replace")
except (ValueError, IndexError):
continue
elif ctype == b"IEND":
break
return out
def parse_a1111_params(text: str) -> Dict[str, str]:
"""解析 AUTOMATIC1111 / ForgeUI 的 parameters 文本。
格式：
positive_prompt
Negative prompt: ...
Steps: 30, Sampler: ..., CFG scale: ..., Seed: ..., Size: ..., Model: ...
"""
out: Dict[str, str] = {}
if "Negative prompt:" in text:
pos, rest = text.split("Negative prompt:", 1)
out["positive"] = pos.strip()
if "\n" in rest:
neg, params = rest.split("\n", 1)
out["negative"] = neg.strip()
else:
params = ""
out["negative"] = rest.strip()
else:
if "\n" in text and re.search(r"^\w+:", text.strip().split("\n")[-1]):
lines = text.strip().split("\n")
out["positive"] = "\n".join(lines[:-1]).strip()
params = lines[-1]
else:
out["positive"] = text.strip()
params = ""
for kv in re.findall(r"([A-Za-z][\w\s]*?):\s*([^,]+)", params):
k, v = kv[0].strip().lower().replace(" ", "_"), kv[1].strip()
out[k] = v
return out
def detect_source(meta: Dict[str, str]) -> str:
if "parameters" in meta:
return "a1111"
if "prompt" in meta and "workflow" in meta:
return "comfyui"
if any(k in meta for k in ("Description", "Software")) and "Comment" in meta:
return "novelai"
if any("Stable Diffusion" in str(v) for v in meta.values()):
return "sd-generic"
return "unknown"
# ─────────────────────────────────────────────────────────
# 启发式：从 prompt 文本推断风格预设
# ─────────────────────────────────────────────────────────
PRESET_HEURISTICS: List[Tuple[str, str]] = [
(r"\b(cyberpunk|neon|blade runner|holographic)\b", "赛博朋克"),
(r"\b(steampunk|brass|gears)\b", "蒸汽朋克"),
(r"\b(ghibli|miyazaki|studio ghibli)\b", "宫崎骏"),
(r"\b(makoto shinkai|shinkai)\b", "新海诚"),
(r"\b(genshin|mihoyo|honkai)\b", "原神"),
(r"\b(dunhuang|tang dynasty fresco|apsara)\b", "敦煌壁画"),
(r"\b(hanfu)\b", "汉服写真"),
(r"\b(ink wash|sumi-e|chinese ink)\b", "水墨"),
(r"\b(ukiyo-e|woodblock)\b", "浮世绘"),
(r"\b(glassmorphism|frosted glass)\b", "玻璃拟态"),
(r"\b(neumorphism|soft ui)\b", "新拟态"),
(r"\b(bauhaus)\b", "包豪斯"),
(r"\b(brutalism|brutalist concrete)\b", "粗野主义"),
(r"\b(wabi[\s-]?sabi)\b", "侘寂"),
(r"\b(film grain|kodak|portra|analog film)\b", "胶片摄影"),
(r"\b(black and white|monochrome|silver gelatin)\b", "黑白摄影"),
(r"\b(low poly|lowpoly)\b", "低多边形"),
(r"\b(isometric)\b", "等距视图"),
(r"\b(claymation|clay)\b", "粘土"),
(r"\b(impressionist|monet|renoir)\b", "印象派"),
(r"\b(van gogh|post impressionist)\b", "后印象派"),
(r"\b(art deco|gatsby)\b", "装饰艺术"),
(r"\b(art nouveau|mucha)\b", "新艺术"),
(r"\b(vaporwave|y2k)\b", "Vaporwave"),
(r"\b(anime|cel shaded|cel-shaded)\b", "动漫"),
(r"\b(watercolor)\b", "水彩"),
(r"\b(oil painting)\b", "油画"),
(r"\b(pixel art|8[\s-]?bit|16[\s-]?bit)\b", "像素艺术"),
(r"\b(minimalist|minimal)\b", "极简主义"),
(r"\b(cinematic|imax|35mm)\b", "电影感"),
(r"\b(concept art)\b", "概念艺术"),
(r"\b(dark fantasy)\b", "黑暗奇幻"),
(r"\b(fantasy|epic fantasy)\b", "奇幻"),
(r"\b(sci[\s-]?fi|space opera)\b", "科幻"),
]
def guess_preset(positive: str) -> str:
p = positive.lower()
for pattern, preset in PRESET_HEURISTICS:
if re.search(pattern, p):
return preset
return ""
def guess_aspect(size_str: str) -> str:
if not size_str or "x" not in size_str.lower():
return ""
try:
w, h = [int(x) for x in re.findall(r"\d+", size_str)[:2]]
except (ValueError, IndexError):
return ""
ratio = w / h if h else 1
candidates = [
("1:1", 1.0), ("16:9", 16/9), ("9:16", 9/16),
("3:4", 3/4), ("4:3", 4/3), ("21:9", 21/9), ("3:2", 3/2), ("2:3", 2/3),
]
return min(candidates, key=lambda c: abs(ratio - c[1]))[0]
# ─────────────────────────────────────────────────────────
# VLM 模板（图片无 metadata 时，让多模态模型回填）
# ─────────────────────────────────────────────────────────
VLM_TEMPLATE = """请把这张图反向解析成可复现的 Text-to-Image 提示词，输出严格的 JSON：
{
"subject": "图中主体的中文一句话描述（人/物/场景核心）",
"subject_en": "subject in English",
"style_preset": "从这 88 个预设里选一个最贴近的：写实摄影 / 胶片摄影 / 黑白摄影 / 人像摄影 / 时尚大片 / 美食摄影 / 产品摄影 / 微距摄影 / 航拍摄影 / 街拍纪实 / 暗黑美食 / 日杂 / 街头潮流 / 动漫 / 新海诚 / 宫崎骏 / 美漫 / Q版 / 童话绘本 / 萌系 / 厚涂 / 轻小说封面 / 赛璐璐 / 水彩 / 油画 / 水墨 / 工笔国画 / 浮世绘 / 线稿 / 像素艺术 / 3DC4D / 盲盒手办 / 低多边形 / 等距视图 / 粘土 / 毛毡手工 / 纸艺 / 极简主义 / 平面设计 / Logo设计 / 图标设计 / 信息图 / 品牌KV / 专辑封面 / 复古海报 / 电影海报 / 表情包 / 玻璃拟态 / 新拟态 / 孟菲斯 / 杂志编排 / 包豪斯 / 奶油风 / 印象派 / 后印象派 / 新艺术 / 装饰艺术 / 赛博朋克 / 蒸汽朋克 / 科幻 / 奇幻 / 黑暗奇幻 / 国潮 / Y2K / Vaporwave / 霓虹灯牌 / 建筑可视化 / 电影感 / 概念艺术 / 粗野主义 / 北欧极简 / 侘寂 / 疗愈治愈 / 美式复古 / 原神 / 崩铁星穹 / 英雄联盟 / 暗黑4 / Valorant / Pokemon / 暴雪风 / 敦煌壁画 / 青花瓷 / 民国月份牌 / 年画 / 剪纸 / 和风 / 汉服写真",
"aspect": "1:1 / 3:4 / 16:9 / 21:9 / 9:16",
"camera": "镜头/视角/焦段，例：'85mm telephoto, low angle, shallow depth of field'",
"lighting": "光影描述，例：'golden hour rim light, soft fill'",
"palette": "主色板，例：'muted earth tones, sage green and terracotta'",
"composition": "构图特征：特写/近景/中景/全身/俯拍/仰拍/航拍/侧面/背面",
"mood": "情绪：温暖/冷峻/神秘/梦幻/欢快/史诗/治愈/紧张",
"time_of_day": "清晨/黄昏/日落/深夜/蓝调时刻 等（无则填空）",
"weather": "晴/雨/雾/雪 等（无则填空）",
"season": "春/夏/秋/冬/樱花季/枫叶季（无则填空）",
"key_details": ["关键视觉元素 1", "元素 2", "元素 3"],
"negatives": ["应避免出现的事物（用于负面提示）"],
"suggested_prompt": "完整可直接喂给 Midjourney 的英文提示词（不含 --ar 参数）"
}
只输出 JSON，不要解释。
"""
# ─────────────────────────────────────────────────────────
# IO
# ─────────────────────────────────────────────────────────
def load_image_bytes(src: str) -> bytes:
if src.startswith(("http://", "https://")):
req = Request(src, headers={"User-Agent": "huo15-reverse/1.0"})
with urlopen(req, timeout=15) as r:
return r.read()
with open(os.path.expanduser(src), "rb") as f:
return f.read()
# ─────────────────────────────────────────────────────────
# 主反解流程
# ─────────────────────────────────────────────────────────
def reverse(src: str, vlm: bool = False) -> Dict:
blob = load_image_bytes(src)
is_png = blob.startswith(b"\x89PNG\r\n\x1a\n")
meta = read_png_text_chunks(blob) if is_png else {}
source = detect_source(meta)
parsed: Dict[str, str] = {}
if source == "a1111":
parsed = parse_a1111_params(meta.get("parameters", ""))
elif source == "comfyui":
parsed = {"comfy_workflow": meta.get("workflow", "")[:200] + "...", "raw_prompt_json": meta.get("prompt", "")[:500]}
try:
data = json.loads(meta.get("prompt", "{}"))
for node_id, node in data.items():
if isinstance(node, dict) and node.get("class_type") in ("CLIPTextEncode", "CLIPTextEncodeSDXL"):
txt = (node.get("inputs") or {}).get("text", "")
if txt:
if "positive" not in parsed:
parsed["positive"] = txt
elif "negative" not in parsed:
parsed["negative"] = txt
except (json.JSONDecodeError, AttributeError):
pass
elif source == "novelai":
parsed = {
"positive": meta.get("Description", ""),
"comment": meta.get("Comment", "")[:500],
}
positive = parsed.get("positive", "")
suggested_preset = guess_preset(positive) if positive else ""
suggested_aspect = guess_aspect(parsed.get("size", ""))
out: Dict = {
"version": VERSION,
"source": source,
"file_size_bytes": len(blob),
"is_png": is_png,
"raw_metadata_keys": list(meta.keys()),
"parsed": parsed,
"suggested": {
"preset": suggested_preset,
"aspect": suggested_aspect,
"seed": parsed.get("seed", ""),
"model": parsed.get("model", ""),
"sampler": parsed.get("sampler", ""),
"cfg": parsed.get("cfg_scale", ""),
"steps": parsed.get("steps", ""),
},
}
if vlm or source in ("unknown", ""):
out["vlm_template"] = VLM_TEMPLATE
out["vlm_instructions"] = (
"图中没有可读 metadata 或 metadata 不完整。请把图 + 上面 vlm_template 一起发给"
" GPT-4o / Claude Sonnet 4.6 / Gemini 1.5 Pro / Qwen-VL，得到结构化 JSON 后，"
"用 enhance_prompt.py \"<subject>\" -p \"<style_preset>\" -a \"<aspect>\" 复现。"
)
return out
def to_mj_prompt(result: Dict) -> str:
p = result.get("parsed", {})
pos = p.get("positive", "")
aspect = result.get("suggested", {}).get("aspect", "")
seed = result.get("suggested", {}).get("seed", "")
flags = []
if aspect:
flags.append(f"--ar {aspect}")
if seed:
flags.append(f"--seed {seed}")
flags.append("--stylize 250")
return f"{pos} {' '.join(flags)}".strip()
def print_result(r: Dict):
sep = "═" * 60
print(f"\n{sep}")
print(f"🔍 参考图反解 v{r['version']}")
print(f"📁 文件大小 : {r['file_size_bytes']:,} bytes")
print(f"🏷 来源识别 : {r['source']}")
print(f"🗂 metadata 字段: {', '.join(r['raw_metadata_keys']) or '(无)'}")
p = r.get("parsed", {})
if p.get("positive"):
print(f"\n✅ 反解正向提示：\n{p['positive']}")
if p.get("negative"):
print(f"\n❌ 反解负向提示：\n{p['negative']}")
s = r.get("suggested", {})
if any(s.values()):
print(f"\n💡 推荐参数：")
for k, v in s.items():
if v:
print(f" {k:8s}: {v}")
if r.get("vlm_template"):
print(f"\n🤖 VLM 模板（图无 metadata 时使用）：")
print(r.get("vlm_instructions", ""))
print("\n--- 模板开始 ---")
print(r["vlm_template"])
print("--- 模板结束 ---")
print(f"{sep}\n")
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-test reverse_prompt v{VERSION} — 参考图反解",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
示例:
reverse_prompt.py /path/to/image.png # 自动识别 A1111/ComfyUI/NovelAI metadata
reverse_prompt.py https://example.com/img.png # 远程 URL
reverse_prompt.py img.png --vlm # 强制输出 VLM 模板（图无 metadata）
reverse_prompt.py img.png --mj # 直接给出 Midjourney 复用 prompt
reverse_prompt.py img.png -j # JSON 输出，可 pipe 给 enhance_prompt.py
""",
)
parser.add_argument("source", help="图片本地路径或 URL")
parser.add_argument("--vlm", action="store_true", help="无论 metadata 是否齐全，都输出 VLM 模板")
parser.add_argument("--mj", action="store_true", help="只输出 Midjourney 风格 prompt 一行")
parser.add_argument("-j", "--json", action="store_true", help="JSON 输出")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
try:
r = reverse(args.source, vlm=args.vlm)
except FileNotFoundError:
print(f"❌ 找不到文件: {args.source}", file=sys.stderr)
sys.exit(1)
except Exception as e:
print(f"❌ 加载失败: {e}", file=sys.stderr)
sys.exit(1)
if args.mj:
print(to_mj_prompt(r))
return
if args.json:
print(json.dumps(r, ensure_ascii=False, indent=2))
return
print_result(r)
if __name__ == "__main__":
main()
FILE:scripts/safety_lint.py
#!/usr/bin/env python3
"""
huo15-img-test — 平台合规润色 v2.3
把"会被 SD/MJ/DALL-E 误判但本意是合法艺术创作"的描述，做艺术化重写，
提高过审率。**不是 jailbreak**，不做也拒绝以下场景：
✗ CSAM（任何含未成年 + 性化描述的）
✗ 真人 + 色情/裸露
✗ 真人 + 政治污蔑/诽谤
✗ 制作武器、毒品、爆炸物的具体方法
✗ 自残、自杀的诱导画面
✗ 仇恨/种族歧视画面
✓ 历史/神话/魔幻战斗（"血" → crimson splash）
✓ 古典艺术裸体（"裸" → classical nude figure study, art reference）
✓ 黑暗童话/惊悚氛围（"恐怖" → eerie atmospheric tension）
✓ 赛博朋克义体/机甲伤痕（"伤" → battle-scarred armor texture）
✓ 医学/解剖艺术插画
工作流程：
1. 红线检测：扫描禁区词组合，命中即拒
2. 黄区识别：标识可能误判的词
3. 艺术替代：内置 80+ 艺术化重写词典
4. 平台分级：DALL-E 最严 / MJ 中等 / SD 宽松（开源），分别给出建议
调用：
safety_lint.py "战士手中沾满鲜血的剑"
safety_lint.py "古典维纳斯雕像" --target dalle
safety_lint.py "赛博朋克战斗场景" -j
"""
import sys
import os
import json
import re
import argparse
from typing import Dict, List, Tuple
VERSION = "2.3.0"
# ─────────────────────────────────────────────────────────
# 红线（直接拒答）
# ─────────────────────────────────────────────────────────
HARD_BLOCKS: List[Tuple[str, str]] = [
# 未成年 + 性化（任意组合都拒）
(r"(loli|lolita|underage|child|minor|kid|toddler|baby|teen|teenager|13岁|14岁|15岁|16岁|17岁|未成年|幼女|小学生|初中生|学生)\s*.*(nude|naked|sex|nsfw|porn|裸|性|色情|nipple|breast)", "minor + sexual"),
(r"(nude|naked|sex|nsfw|porn|裸|性|色情)\s*.*(loli|underage|child|minor|kid|toddler|baby|teen|未成年|幼女|小学生|初中生)", "minor + sexual"),
# 真人 + 色情（指名道姓）
(r"(taylor swift|emma watson|scarlett johansson|某某明星|周杰伦|王力宏)\s*.*(nude|naked|sex|porn|裸|色情)", "real-person + sexual"),
# 武器制造、毒品、爆炸物配方（双向：动词在前 OR 在后）
(r"(how to make|recipe for|tutorial|step.*by.*step|步骤|配方|怎么做|如何制作|教程)\s*.*(bomb|explosive|gun|firearm|meth|cocaine|heroin|fentanyl|nitroglycerin|炸弹|手枪|冰毒|海洛因|芬太尼|硝酸|tnt)", "weapon/drug instruction"),
(r"(bomb|explosive|gun|firearm|meth|cocaine|heroin|fentanyl|炸弹|手枪|冰毒|海洛因|芬太尼)\s*.*(how to make|recipe|tutorial|步骤|配方|怎么做|如何制作|教程|方法)", "weapon/drug instruction"),
# 自残诱导（双向）
(r"(suicide|self-harm|cutting|自杀|自残|割腕|跳楼)\s*.*(method|how to|tutorial|教程|方法|步骤)", "self-harm method"),
(r"(method|how to|tutorial|教程|方法|步骤)\s*.*(suicide|self-harm|cutting|自杀|自残|割腕|跳楼)", "self-harm method"),
]
# ─────────────────────────────────────────────────────────
# 黄区：会被误判但通常合法的艺术词 → 艺术化替代
# ─────────────────────────────────────────────────────────
ART_SUBSTITUTIONS: Dict[str, Dict[str, str]] = {
# 战斗 / 暴力（合法艺术语境）
"blood": {"replace": "crimson splash, dramatic battle highlight", "category": "violence", "platforms": "DALL-E,MJ"},
"鲜血": {"replace": "crimson splash, 朱砂色泼洒", "category": "violence", "platforms": "DALL-E,MJ"},
"血": {"replace": "crimson splash", "category": "violence", "platforms": "DALL-E"},
"wound": {"replace": "battle-scarred texture", "category": "violence", "platforms": "DALL-E"},
"伤口": {"replace": "battle-scarred texture, 战痕", "category": "violence", "platforms": "DALL-E"},
"kill": {"replace": "defeat, vanquish", "category": "violence", "platforms": "DALL-E,MJ"},
"杀": {"replace": "vanquish, 击败", "category": "violence", "platforms": "DALL-E,MJ"},
"murder": {"replace": "dramatic confrontation", "category": "violence", "platforms": "DALL-E,MJ"},
"weapon": {"replace": "ceremonial blade, ornamental armament", "category": "violence", "platforms": "DALL-E"},
"gun": {"replace": "fantasy ranged weapon, prop firearm", "category": "violence", "platforms": "DALL-E"},
"knife": {"replace": "ornamental dagger, ritual blade", "category": "violence", "platforms": "DALL-E"},
"violence": {"replace": "dynamic combat, cinematic action", "category": "violence", "platforms": "DALL-E,MJ"},
"暴力": {"replace": "dynamic combat scene, 动作张力", "category": "violence", "platforms": "DALL-E,MJ"},
# 古典艺术裸体
"naked": {"replace": "classical nude figure study, art reference, marble sculpture style", "category": "nudity", "platforms": "DALL-E,MJ,SD"},
"nude": {"replace": "classical figure study, fine art reference", "category": "nudity", "platforms": "DALL-E,MJ"},
"裸": {"replace": "classical figure study, 古典裸体艺术", "category": "nudity", "platforms": "DALL-E,MJ,SD"},
"裸体": {"replace": "classical figure study, 古典维纳斯", "category": "nudity", "platforms": "DALL-E,MJ,SD"},
"sexy": {"replace": "elegant alluring, fashion editorial", "category": "nudity", "platforms": "DALL-E"},
"性感": {"replace": "elegant fashion editorial, 优雅造型", "category": "nudity", "platforms": "DALL-E"},
"lingerie": {"replace": "vintage fashion sleepwear, 1950s glamour", "category": "nudity", "platforms": "DALL-E"},
"bikini": {"replace": "summer beachwear, swimwear photography", "category": "nudity", "platforms": "DALL-E"},
# 恐怖 / 黑暗
"horror": {"replace": "eerie atmospheric tension, gothic mood", "category": "horror", "platforms": "DALL-E"},
"恐怖": {"replace": "gothic atmospheric tension, 哥特氛围", "category": "horror", "platforms": "DALL-E"},
"scary": {"replace": "ominous mood, atmospheric suspense", "category": "horror", "platforms": "DALL-E"},
"gore": {"replace": "dark fantasy aesthetic, baroque dramatic", "category": "horror", "platforms": "DALL-E,MJ"},
"monster": {"replace": "mythical creature, fantasy beast", "category": "horror", "platforms": "DALL-E"},
"demon": {"replace": "mythical entity, dark fantasy spirit", "category": "horror", "platforms": "DALL-E"},
"evil": {"replace": "dark mythological aesthetic, 黑暗神话", "category": "horror", "platforms": "DALL-E"},
# 死亡 / 尸体（艺术语境）
"dead": {"replace": "fallen, resting eternal", "category": "death", "platforms": "DALL-E"},
"death": {"replace": "memento mori, classical allegory", "category": "death", "platforms": "DALL-E"},
"corpse": {"replace": "still figure, classical allegorical pose", "category": "death", "platforms": "DALL-E"},
"skeleton": {"replace": "anatomical skeletal study, da vinci sketch reference", "category": "death", "platforms": "DALL-E"},
"skull": {"replace": "memento mori symbol, vanitas still life", "category": "death", "platforms": "DALL-E"},
# 真人
"celebrity": {"replace": "fictional character inspired by 80s aesthetic", "category": "real-person", "platforms": "DALL-E,MJ"},
"明星": {"replace": "虚构角色，80年代美学风格", "category": "real-person", "platforms": "DALL-E,MJ"},
"actor": {"replace": "fictional protagonist, original character", "category": "real-person", "platforms": "DALL-E"},
"politician": {"replace": "fictional statesman character", "category": "real-person", "platforms": "DALL-E,MJ"},
# 品牌（版权）
"marvel": {"replace": "superhero comic style", "category": "brand", "platforms": "DALL-E"},
"disney": {"replace": "classic animated film style", "category": "brand", "platforms": "DALL-E"},
"nike": {"replace": "athletic sportswear brand aesthetic", "category": "brand", "platforms": "DALL-E"},
"iphone": {"replace": "modern smartphone, sleek minimal device", "category": "brand", "platforms": "DALL-E"},
# 武器具体型号
"ak47": {"replace": "fictional assault rifle prop", "category": "weapon-model", "platforms": "DALL-E,MJ"},
"ak-47": {"replace": "fictional assault rifle prop", "category": "weapon-model", "platforms": "DALL-E,MJ"},
"glock": {"replace": "sci-fi handgun prop", "category": "weapon-model", "platforms": "DALL-E,MJ"},
"uzi": {"replace": "compact fictional firearm prop", "category": "weapon-model", "platforms": "DALL-E,MJ"},
}
# 平台分级规则
PLATFORM_STRICTNESS = {
"dalle": "max", # 最严
"DALL-E": "max",
"midjourney": "high", # 中等
"MJ": "high",
"mj": "high",
"sd": "low", # 宽松（本地）
"SD": "low",
"sdxl": "low",
"flux": "low",
"comfyui": "low",
}
# 风险等级 → 颜色 emoji
RISK_LEVEL_EMOJI = {"high": "🔴", "medium": "🟡", "low": "🟢"}
def category_risk_for_platform(category: str, platform: str) -> str:
s = PLATFORM_STRICTNESS.get(platform, "high")
risk_map = {
"violence": {"max": "high", "high": "medium", "low": "low"},
"nudity": {"max": "high", "high": "high", "low": "medium"},
"horror": {"max": "medium", "high": "low", "low": "low"},
"death": {"max": "medium", "high": "low", "low": "low"},
"real-person": {"max": "high", "high": "high", "low": "medium"},
"brand": {"max": "high", "high": "medium", "low": "low"},
"weapon-model": {"max": "high", "high": "high", "low": "medium"},
}
return risk_map.get(category, {}).get(s, "low")
# ─────────────────────────────────────────────────────────
# 检测
# ─────────────────────────────────────────────────────────
def check_hard_blocks(text: str) -> List[str]:
"""返回命中的红线类别（命中任何一个即拒答）。"""
hits = []
lower = text.lower()
for pattern, label in HARD_BLOCKS:
if re.search(pattern, lower, re.IGNORECASE):
hits.append(label)
return hits
def find_substitutions(text: str, platform: str = "MJ") -> List[Dict]:
"""识别文本中的黄区词，返回替代建议列表。"""
out = []
lower = text.lower()
seen = set()
for word, info in ART_SUBSTITUTIONS.items():
if word in seen:
continue
# 中文词直接子串匹配，英文词加单词边界
if re.fullmatch(r"[\x00-\x7f]+", word): # ASCII
if not re.search(r"\b" + re.escape(word.lower()) + r"\b", lower):
continue
else:
if word not in text:
continue
seen.add(word)
risk = category_risk_for_platform(info["category"], platform)
out.append({
"word": word,
"replace_with": info["replace"],
"category": info["category"],
"risk_for_platform": risk,
"platforms_affected": info["platforms"],
})
return out
def rewrite(text: str, platform: str = "MJ") -> Tuple[str, List[Dict]]:
"""执行重写：把所有黄区词替换成艺术化版本。返回 (新文本, 替换日志)。"""
new_text = text
log = []
for word, info in ART_SUBSTITUTIONS.items():
if re.fullmatch(r"[\x00-\x7f]+", word):
pat = re.compile(r"\b" + re.escape(word) + r"\b", re.IGNORECASE)
else:
pat = re.compile(re.escape(word))
if pat.search(new_text):
risk = category_risk_for_platform(info["category"], platform)
new_text = pat.sub(info["replace"], new_text)
log.append({"from": word, "to": info["replace"], "category": info["category"],
"risk_for_platform": risk})
return new_text, log
# ─────────────────────────────────────────────────────────
# 入口
# ─────────────────────────────────────────────────────────
def lint(text: str, platform: str = "MJ") -> Dict:
blocks = check_hard_blocks(text)
if blocks:
return {
"version": VERSION,
"platform": platform,
"verdict": "REJECT",
"reason": "hit hard-block patterns",
"categories": blocks,
"advice": (
"命中红线规则。本工具不服务以下场景：\n"
" • CSAM（任何含未成年 + 性化）\n"
" • 真人 + 色情 / 政治污蔑\n"
" • 武器/毒品/爆炸物制作教程\n"
" • 自残/自杀方法诱导\n"
"如果你的本意是合法艺术创作（历史/神话/古典），请改写描述：\n"
" • 用成年角色\n"
" • 用艺术语境（古典雕塑/神话/壁画）\n"
" • 不要点名真人\n"
" • 不要含「教程/步骤/方法」等指令性词"
),
}
subs = find_substitutions(text, platform)
rewritten, log = rewrite(text, platform)
return {
"version": VERSION,
"platform": platform,
"verdict": "OK" if not subs else "REWRITE",
"original": text,
"rewritten": rewritten,
"substitutions": subs,
"rewrite_log": log,
"high_risk_count": sum(1 for s in subs if s["risk_for_platform"] == "high"),
"medium_risk_count": sum(1 for s in subs if s["risk_for_platform"] == "medium"),
"advice": _build_advice(platform, subs),
}
def _build_advice(platform: str, subs: List[Dict]) -> str:
if not subs:
return f"无风险词，可直接喂给 {platform}。"
lines = [f"针对 {platform}（严格度: {PLATFORM_STRICTNESS.get(platform, 'high')}）的合规建议："]
high = [s for s in subs if s["risk_for_platform"] == "high"]
if high:
lines.append(f" 🔴 {len(high)} 个高风险词建议必换：" + ", ".join(s["word"] for s in high))
med = [s for s in subs if s["risk_for_platform"] == "medium"]
if med:
lines.append(f" 🟡 {len(med)} 个中风险词建议软化：" + ", ".join(s["word"] for s in med))
return "\n".join(lines)
def print_lint(r: Dict):
sep = "═" * 60
print(f"\n{sep}")
print(f"🛡 平台合规润色 v{r['version']}")
print(f"📺 目标平台: {r['platform']} (严格度: {PLATFORM_STRICTNESS.get(r['platform'], '?')})")
if r["verdict"] == "REJECT":
print(f"\n🚫 拒答: {r['reason']}")
print(f" 命中类别: {', '.join(r['categories'])}")
print(f"\n{r['advice']}")
print(f"{sep}\n")
return
print(f"📝 原文: {r['original']}")
if r["verdict"] == "OK":
print(f"\n✅ 无风险词，原文可直接使用")
print(f"{sep}\n")
return
print(f"\n✨ 重写后: {r['rewritten']}")
print(f"\n📊 风险统计: 🔴 {r['high_risk_count']} / 🟡 {r['medium_risk_count']}")
if r["substitutions"]:
print(f"\n🔄 替换详情:")
for s in r["substitutions"]:
emoji = RISK_LEVEL_EMOJI.get(s["risk_for_platform"], "⚪")
print(f" {emoji} '{s['word']}' → '{s['replace_with']}'")
print(f" 类别: {s['category']}, 平台: {s['platforms_affected']}")
print(f"\n💡 {r['advice']}")
print(f"{sep}\n")
def main():
parser = argparse.ArgumentParser(
description=f"huo15-img-test safety_lint v{VERSION} — 平台合规润色（合法艺术创作专用）",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
示例:
safety_lint.py "战士手中沾满鲜血的剑"
safety_lint.py "古典维纳斯雕像" --target dalle
safety_lint.py "赛博朋克战斗场景" --target SD
safety_lint.py "黑暗骑士" -j
echo "原始描述" | safety_lint.py --stdin --apply # 重写并输出新文本
注意: 本工具仅服务合法艺术创作。拒绝以下场景：
✗ CSAM（未成年 + 性化）
✗ 真人 + 色情/诽谤
✗ 武器/毒品/爆炸物制作教程
✗ 自残诱导
""",
)
parser.add_argument("text", nargs="?", help="要检查的文本")
parser.add_argument("--stdin", action="store_true", help="从 stdin 读取")
parser.add_argument("--target", default="MJ",
help="目标平台 DALL-E/MJ/SD/SDXL/Flux/通用 (默认 MJ)")
parser.add_argument("--apply", action="store_true",
help="直接输出重写后的文本（用于 pipe）")
parser.add_argument("-j", "--json", action="store_true", help="JSON 输出")
parser.add_argument("-v", "--version", action="version", version=f"%(prog)s v{VERSION}")
args = parser.parse_args()
text = args.text
if args.stdin or not text:
if sys.stdin.isatty() and not args.stdin:
parser.print_help()
sys.exit(1)
text = sys.stdin.read().strip()
if not text:
print("❌ 输入为空", file=sys.stderr)
sys.exit(1)
r = lint(text, args.target)
if args.apply:
if r["verdict"] == "REJECT":
print(f"REJECTED: {r['reason']}", file=sys.stderr)
sys.exit(2)
print(r.get("rewritten", r["original"]))
return
if args.json:
print(json.dumps(r, ensure_ascii=False, indent=2))
return
print_lint(r)
if r["verdict"] == "REJECT":
sys.exit(2)
if __name__ == "__main__":
main()
2026 现代美学的流程图、泳道图、系统架构图、C4 架构图、时序图、状态图、ER 图、甘特图生成。Linear/Vercel/Radix 配色 + 软阴影 + 圆角 + 判断色强调 + 容器分层。输入 YAML/JSON 规格或 Mermaid/PlantUML/DOT 源码，输出 SVG/PNG/PDF/dr...
---
name: huo15-flow-chart
displayName: 火一五流程图技能
description: 2026 现代美学的流程图、泳道图、系统架构图、C4 架构图、时序图、状态图、ER 图、甘特图生成。Linear/Vercel/Radix 配色 + 软阴影 + 圆角 + 判断色强调 + 容器分层。输入 YAML/JSON 规格或 Mermaid/PlantUML/DOT 源码，输出 SVG/PNG/PDF/draw.io；PDF 默认单页自适应画布不分页；内置 10 种风格；支持 draw.io 源文件 + C4-PlantUML + 架构 Tier 分层。触发词：流程图、泳道图、时序图、状态图、ER 图、系统架构图、C4 图、画流程图、生成流程图。
version: 1.3.0
aliases:
- 火一五流程图
- 火一五流程图技能
- 流程图
- 流程图生成
- 画流程图
- 泳道图
- 时序图
- 状态图
- 甘特图
- ER图
- 架构图
- 系统架构图
- C4 架构图
- flowchart
- flow-chart
- diagram
- mermaid
- draw.io
dependencies:
python-packages:
- pyyaml
external:
- name: "@mermaid-js/mermaid-cli"
install: "npm i -g @mermaid-js/mermaid-cli"
notes: "渲染 Mermaid 时用（mmdc 命令）"
- name: plantuml
install: "brew install plantuml"
notes: "PlantUML / C4-PlantUML 渲染（泳道图和 C4 图必须）"
- name: graphviz
install: "brew install graphviz"
notes: "Graphviz DOT 渲染（可选）"
---
# 火一五流程图技能 v1.3.0
> 2026 现代美学的流程图 / 架构图 / 泳道图 / C4 架构图 / 时序图生成器 — 青岛火一五信息科技有限公司
>
> 设计取向：Linear / Vercel / Radix / Stripe 软色系 + 双层柔和阴影 + 圆角节点 + 判断菱形 accent 强调色 + PDF 单页自适应。
---
## 一、核心能力
### 1.1 支持的图表类型
| type | 用途 | 渲染引擎 | draw.io |
|---------------------|-----------------------|-----------------|---------|
| `flowchart` | 普通流程图 | Mermaid | ✅ |
| `architecture` | 系统架构图（支持 Tier 分层） | Mermaid | ✅ |
| `swimlane` | 真·泳道图（PlantUML） | PlantUML | ❌ |
| `swimlane_mermaid` | 泳道风格（Mermaid，不需要 Java）| Mermaid | ✅ |
| `sequence` | 时序图 | Mermaid/PlantUML | ✅ |
| `state` | 状态图 | Mermaid | ✅ |
| `gantt` | 甘特图 | Mermaid | ✅ |
| `er` | ER 图 | Mermaid | ✅ |
| `class` | UML 类图 | Mermaid | ✅ |
| `journey` | 用户旅程 | Mermaid | ✅ |
| `pie` | 饼图 | Mermaid | ✅ |
| `c4_context` | C4 上下文图（L1） | Mermaid / **PlantUML** | ✅ |
| `c4_container` | C4 容器图（L2） | Mermaid / **PlantUML** | ✅ |
| `mindmap` | 简单思维导图 | Mermaid | ✅ |
### 1.2 输出格式
| 格式 | 类型 | 说明 |
|--------|--------|------|
| `.svg` | 矢量图 | 浏览器直接打开，最推荐 |
| `.png` | 位图 | 适合 PPT / 文档 |
| `.pdf` | 矢量 PDF | 适合打印 |
| `.mmd` | Mermaid 源码 | 复制到 [mermaid.live](https://mermaid.live) 编辑 |
| `.puml` | PlantUML 源码 | 复制到 PlantUML 编辑器 |
| `.drawio` | **draw.io 源文件** | 用 draw.io 桌面版打开，精美编辑 |
| `.dot` | Graphviz DOT 源码 | 复杂网络拓扑 |
### 1.3 十种风格（v1.3 全面改造为 2026 现代美学）
| key | 名称 | 设计范式 | 适用场景 | 中文别名 |
|------|-----|---------|---------|---------|
| `modern` | 现代商务 | Radix Indigo/Slate，浅底 + 靛紫描边 + 琥珀判断 | 默认首选，商务/技术文档 | 现代、商务、linear、vercel |
| `classic` | 经典稳重 | 淡蓝卡 + 深蓝字 + 琥珀强调 | 咨询报告、正式汇报 | 经典、稳重 |
| `dark` | 暗色霓虹 | Linear Dark，深底 + 紫霓虹描边 + 青色 accent | 演示大屏、技术分享 | 暗色、黑色、霓虹、linear-dark |
| `xiaohongshu` | 小红书暖奶油 | 奶油底 + 粉卡节点 + 玫红描边 | 种草封面、女性向运营 | 小红书、奶油、xhs |
| `ocean` | 海洋蓝 | 浅天蓝节点 + 深蓝字 + 橙色 accent | SaaS 产品、技术架构 | 海洋、蓝、蓝色 |
| `forest` | 森林绿 | 浅绿卡 + 墨绿字 + 橙色 accent | 环保、农业、健康 | 森林、绿、自然 |
| `sunset` | 夕阳暖橙 | 浅橙卡 + 赭红字 + 青色 accent | 运营活动、温暖叙事 | 夕阳、暖橙、橙 |
| `minimal` | 极简素雅 | Notion 风，近白底 + 深灰字 + 单蓝 accent + 细描边 | 学术论文、出版物、技术书稿 | 极简、素雅、学术、论文、黑白、notion |
| `pastel` | 马卡龙粉嫩 | 浅紫底 + 深紫字 + 玫粉 accent | 儿童教育、女性向 | 马卡龙、粉嫩、粉、儿童 |
| `github` | 极客 GitHub | 浅蓝卡 + 品牌蓝 + 绿色 accent | 开源 README、技术文档 | 极客、程序员、gh |
**设计共性（所有风格统一遵循）**：
- 节点：浅色填充 + 彩色描边 + 深色文字，12px 圆角，1.5px 描边
- 阴影：双层 drop-shadow（近 1/2px + 远 4/12px），模拟 Linear/Vercel 的浮起质感
- 判断菱形：统一用 `accent_color` 突出，区别于普通节点
- 数据库圆柱：`secondary_color` 填充，区别于业务节点
- 字体：Inter 优先 + PingFang SC/HarmonyOS Sans 回落，节点 `font-weight: 500`，标题 `600`
- 连线：`basis` 曲线（flowchart/C4）/ `linear`（泳道）/ 端点圆角
- 间距：`nodeSpacing: 60`、`rankSpacing: 80`、`padding: 20`（更透气）
### 1.4 draw.io 精美增强
- 导出 `.drawio` 源文件，用 draw.io 桌面版打开
- draw.io 支持：渐变填充、投影、圆角、图标嵌入
- draw.io 支持 Google Fonts（Mermaid 不支持的中文字体）
- 配合 `--theme` / `--font` / `--shadow` 参数生成精美效果
---
## 二、快速开始
### 2.1 准备渲染器
```bash
# 方案 A：Mermaid（推荐，覆盖 90% 场景）
npm i -g @mermaid-js/mermaid-cli
# 方案 B：PlantUML（C4-PlantUML / 泳道图必须）
brew install plantuml # macOS
# apt install plantuml # Ubuntu/Debian
# 方案 C：Graphviz（网络拓扑可选）
brew install graphviz
# Python 依赖
pip install pyyaml
```
### 2.2 最短上手
```bash
# 从 YAML 规格生成 PNG（默认 modern 风格）
python3 scripts/create-flow-chart.py \
-i examples/architecture.yaml \
-o /tmp/arch.png
# 泳道图（真·PlantUML）
python3 scripts/create-flow-chart.py \
-i examples/swimlane.yaml \
-o /tmp/lane.svg
# 导出 draw.io 源文件（精美编辑）
python3 scripts/create-flow-chart.py \
-i examples/architecture.yaml \
-o /tmp/arch.drawio
# 同时导出多种格式（一键到位）
python3 scripts/create-flow-chart.py \
-i examples/architecture.yaml \
-o /tmp/arch.png \
--export-formats svg,pdf,mmd,drawio
# draw.io 精美主题（带投影）
python3 scripts/create-flow-chart.py \
-i examples/architecture.yaml \
-o /tmp/arch.png \
--theme modern --font "Microsoft YaHei" --shadow
# C4-PlantUML（专业 C4 图）
python3 scripts/create-flow-chart.py \
-i examples/c4_context.yaml \
-o /tmp/c4.puml \
--engine plantuml
```
---
## 三、YAML 规格速查
### 3.1 流程图 `flowchart`
```yaml
type: flowchart
title: 用户下单流程
direction: TB # TB | LR | BT | RL
nodes:
- { id: start, label: 开始, shape: stadium }
- { id: check, label: 是否有商品?, shape: diamond }
- { id: pay, label: 结算付款 }
edges:
- { from: start, to: check }
- { from: check, to: pay, label: 是 }
```
**形状**：`rect` / `round` / `stadium` / `diamond`（判断） / `hexagon` / `circle` / `cylinder`（数据库） / `subroutine` / `parallelogram` / `trapezoid`
**边类型**：`solid`（实线）/ `dashed`（虚线）/ `thick`（粗线）/ `dotted`（点线）/ `bidir`（双向）
### 3.2 系统架构图 `architecture`（支持 Tier 分层）
**方式一：groups 分组**
```yaml
type: architecture
title: 火一五 SaaS 系统架构
groups:
- { id: frontend, label: 前端层, children: [web, mobile] }
- { id: backend, label: 业务层, children: [svc_user, svc_order] }
- { id: data, label: 数据层, children: [pg, redis] }
nodes:
- { id: web, label: Web 门户 }
- { id: pg, label: PostgreSQL, shape: cylinder }
edges:
- [web, svc_user]
- [svc_user, pg]
```
**方式二：tiers 分层（推荐，层次更清晰）**
```yaml
type: architecture
title: 系统架构（Tier 分层）
tiers:
- { id: edge, label: 边缘层 }
- { id: biz, label: 业务层 }
- { id: data, label: 数据层 }
nodes:
- { id: cdn, label: CDN, tier: edge }
- { id: api, label: API 网关, tier: biz }
- { id: pg, label: PostgreSQL, tier: data, shape: cylinder }
edges:
- [cdn, api]
- [api, pg]
```
### 3.3 泳道图 `swimlane`（真·PlantUML）
```yaml
type: swimlane
title: 采购审批流程
lanes:
- name: 申请人
steps:
- { id: A1, label: 填写采购申请 }
- { id: A2, label: 提交审批 }
- name: 部门经理
steps:
- { id: B1, label: 初审 }
edges:
- [A1, A2]
- [A2, B1]
```
### 3.4 时序图 `sequence`
```yaml
type: sequence
title: 用户登录
auto_number: true
nodes:
- { id: U, label: 用户, shape: actor }
- { id: FE, label: 前端 }
- { id: BE, label: 后端 }
edges:
- { from: U, to: FE, label: 点击登录 }
- { from: FE, to: BE, label: POST /login }
- { from: BE, to: FE, label: JWT token, kind: dashed }
notes:
- { position: over, over: BE, text: "首次登录自动建账" }
```
### 3.5 状态图 `state`
```yaml
type: state
title: 订单状态机
direction: LR
nodes:
- { id: Pending, label: 待付款 }
- { id: Paid, label: 已付款 }
edges:
- { from: "[*]", to: Pending, label: 下单 }
- { from: Pending, to: Paid, label: 付款 }
```
### 3.6 甘特图 `gantt`
```yaml
type: gantt
title: Q2 路线图
dateFormat: YYYY-MM-DD
axisFormat: "%m-%d"
sections:
- name: 基建
tasks:
- { name: 服务器扩容, id: i1, status: done, start: 2026-04-01, duration: 5d }
- { name: 监控升级, id: i2, status: active, start: 2026-04-06, duration: 10d }
```
状态值：`done` / `active` / `crit`（关键路径红）/ `milestone`（里程碑）/ 留空即未开始。
### 3.7 C4 架构图 `c4_context` / `c4_container`
**Mermaid 引擎（默认）**
```yaml
type: c4_context
title: 火一五 SaaS 上下文图
nodes:
- { id: customer, label: 企业客户\n使用 SaaS, shape: person }
- { id: huo15, label: 火一五 SaaS\n企业数字化平台, shape: system }
- { id: wecom, label: 企业微信\nIM, shape: system_ext }
edges:
- { from: customer, to: huo15, label: 使用 }
- { from: huo15, to: wecom, label: OAuth }
```
**PlantUML 引擎（C4-PlantUML，更专业）**
```bash
python3 scripts/create-flow-chart.py \
-i examples/c4_context.yaml \
-o /tmp/c4.puml \
--engine plantuml
```
C4 shape：`person` / `person_ext` / `system` / `system_ext` / `container` / `container_db` / `component`。
### 3.8 ER 图 / UML 类图 / 用户旅程 / 饼图
- `er` — ER 图，label 用 `\n` 分隔字段
- `class` — UML 类图，edge kind 支持 `extends` / `implements` / `composition` / `aggregation` / `dependency`
- `journey` — 用户旅程，用 `sections[].tasks[]`
- `pie` — 饼图，用 `items` 数组
---
## 四、命令行参数
| 参数 | 说明 |
|------|------|
| `-i, --input` | 输入文件（yaml/json/mmd/puml/dot） |
| `-o, --output` | 输出路径（.svg/.png/.pdf/.mmd/.puml/.dot/.drawio） |
| `--export-formats` | 附加输出格式，逗号分隔。如 `svg,pdf,mmd,drawio` |
| `--export-dir` | 源文件输出目录，默认与主输出同目录 |
| `--style` | 风格：modern/classic/dark/xiaohongshu/ocean/forest/sunset/minimal/pastel/github |
| `--theme` | draw.io 主题（与 --style 对应，影响 draw.io 导出效果） |
| `--font` | 字体名称，默认 PingFang SC（Mac）/ Microsoft YaHei（Win） |
| `--shadow` | 为 draw.io 节点添加投影效果 |
| `--engine` | 强制渲染引擎：mermaid / plantuml / dot / drawio / auto |
| `--width, --height` | 输出尺寸（像素） |
| `--background` | 背景色，默认用 style 的 background |
| `--dump-source` | 只打印源码到 stdout，不渲染（仅 draw.io 输出时生效） |
| `--no-pdf-fit` | 取消 PDF 自适应画布（默认自适应，单页输出，不分页）|
---
## 五、Python API
```python
import sys; sys.path.insert(0, 'scripts')
from flowchart_core import parse, to_mermaid, to_plantuml, to_drawio
from flowchart_render import render
from styles import get_style, to_mermaid_init_directive, to_plantuml_skinparam
fc = parse('spec.yaml')
style = get_style('modern') # 也可传中文别名：'现代' / 'linear' / 'vercel'
# Mermaid（推荐：同时传 style，自动启用 decision / database classDef）
init = to_mermaid_init_directive(style, diagram_type=fc.diagram_type)
code = to_mermaid(fc, init, style=style)
render(code, '/tmp/out.svg', engine='mermaid')
# PDF 单页自适应（默认开启；传 pdf_fit=False 关闭）
render(code, '/tmp/out.pdf', engine='mermaid', pdf_fit=True)
# draw.io（现代风：渐变 + 圆角 + 阴影）
drawio_xml = to_drawio(fc, style=style, theme='modern',
font_family='Inter', shadow=True)
render(drawio_xml, '/tmp/out.drawio', engine='mermaid')
# C4-PlantUML
puml = to_plantuml(fc, to_plantuml_skinparam(style))
render(puml, '/tmp/out.puml', engine='plantuml')
```
主要接口：
| 函数 | 说明 |
|------|------|
| `parse(path_or_text, hint='auto')` | YAML/JSON/Mermaid/PlantUML/DOT → FlowChart |
| `to_mermaid(fc, init_directive, style=None)` | → Mermaid 源码；传 style 启用自动 classDef |
| `to_plantuml(fc, skinparam)` | → PlantUML/C4-PlantUML 源码 |
| `to_dot(fc, style)` | → Graphviz DOT |
| `to_drawio(fc, style, theme, font_family, shadow=True)` | → draw.io XML 源码（v1.3 现代风格）|
| `render(source, out, engine, pdf_fit=True)` | 调用 mmdc/plantuml/dot 渲染，PDF 默认单页自适应 |
| `get_style(key)` | 获取 Style 对象（中文别名也能识别）|
| `to_mermaid_init_directive(style, diagram_type='flowchart')` | 返回 `%%{init: ...}%%`；按图类选曲线 |
| `to_plantuml_skinparam(style)` | 返回 PlantUML skinparam 配色指令 |
| `decision_classdef(style)` / `database_classdef(style)` | v1.3 新增：生成 classDef 行 |
---
## 六、与其他技能的边界
| 需求 | 用哪个技能 |
|------|----------|
| 自由层级的思维导图，要导出 XMind | **huo15-mind-map** |
| 流程图 / 架构图 / 泳道图 / 时序图 / C4 图 | **huo15-flow-chart**（本技能）|
| **精美架构图**（渐变/投影/圆角），本地编辑 | **huo15-flow-chart** + draw.io 导出 |
| 工程制图 / 原型图 | 不在范围内，建议 draw.io / Figma |
| 数据仪表盘 | 用图表库（Chart.js / ECharts / matplotlib） |
---
## 七、触发词
- 流程图 / 画流程图 / 生成流程图 / 做一张流程图
- 泳道图 / 跨职能流程图 / swim lane
- 系统架构图 / 架构图 / C4 图 / C4 上下文 / C4 容器
- **draw.io 架构图** / 导出 draw.io / draw.io 源文件
- **C4-PlantUML** / C4 图 PlantUML 格式
- 时序图 / 序列图 / sequence diagram
- 状态图 / 状态机 / state diagram
- ER 图 / 实体关系图
- 甘特图 / gantt
- Mermaid / PlantUML
- **架构分层** / Tier 分层 / 多层架构图
---
## 八、版本历史
- **v1.3.0（当前）** — 2026 现代美学大改造
- ✅ **十种风格全面重绘**：从"深色填充 + 白字"老旧扁平风，升级为 Linear/Vercel/Radix 范式的"浅色填充 + 彩色描边 + 深色文字"
- ✅ **双层 drop-shadow**：`drop-shadow(0 1px 2px) drop-shadow(0 4px 12px)`，复刻 Linear/Vercel 的浮起质感
- ✅ **判断菱形 accent 强调**：自动 `classDef decision`，每套风格独立 accent 色
- ✅ **数据库圆柱自动识别**：自动 `classDef database`，用次级色区别于业务节点
- ✅ **Mermaid init 优化**：`nodeSpacing: 60` / `rankSpacing: 80` / `padding: 20`，按图类自动切换曲线（flowchart basis、swimlane linear）
- ✅ **PDF 自适应画布（默认开启）**：Mermaid 走 `mmdc --pdfFit`，PlantUML 走 `SVG → rsvg-convert → PDF`，单页不分页
- ✅ **draw.io 生成器重写**：修复 XML 属性重复 bug、edge 源/目标 id 计算错乱；新增渐变、圆角、正交弧线、容器嵌套
- ✅ **PlantUML skinparam 扩充**：圆角 + 阴影 + 时序参与者 + 分组 + dpi:120
- ✅ **新 Style 字段**：`accent_text_color`、`accent_border_color`、双层阴影参数、`corner_radius`、`stroke_width`、`node_spacing`、`rank_spacing`、`padding`、`palette`
- ✅ **别名扩展**：linear / vercel / notion / linear-dark 等
- **v1.2.0** — draw.io 导出 + C4-PlantUML + Tier 分层
- ✅ 新增 `.drawio` 源文件导出（draw.io 桌面版打开，精美编辑）
- ✅ 新增 `--theme` / `--font` / `--shadow` 参数（draw.io 精美效果）
- ✅ 新增 `--export-formats`（一键导出多种格式，兼容旧名 `--also`）
- ✅ 新增 C4-PlantUML 引擎（`--engine plantuml` 输出专业 C4 图）
- ✅ 新增 `tiers` 分层字段（architecture 类型专用，层次更清晰）
- ✅ 新增 `node.tier` 属性（替代 group 归类，tiers 模式下使用）
- ✅ `to_drawio()` Python API（可直接集成到其他工具）
- ✅ 修复 `.drawio` 导出时 `return` 缺失 bug
- **v1.1.0** — 扩展 6 种风格
- 新增：`ocean` / `forest` / `sunset` / `minimal` / `pastel` / `github`
- **v1.0.0** — 首版
- 支持 13 种图表（flowchart / architecture / swimlane / swimlane_mermaid / sequence / state / gantt / er / class / journey / pie / c4_context / c4_container）
- 3 种输入（YAML/JSON、Mermaid、PlantUML）、6 种输出（svg/png/pdf/mmd/puml/dot）
- 4 种风格：modern / classic / dark / xiaohongshu
---
**技术支持：** 青岛火一五信息科技有限公司
FILE:_meta.json
{
"ownerId": "kn7byevkn40d6z4p7ghdb097z983tj33",
"slug": "huo15-flow-chart",
"version": "1.3.0"
}
FILE:examples/architecture.yaml
type: architecture
title: 火一五 SaaS 系统架构
direction: TB
groups:
- id: frontend
label: 前端层
children: [web, mobile, admin]
- id: gateway
label: 网关层
children: [api_gw]
- id: backend
label: 业务层
children: [svc_user, svc_order, svc_content, svc_ai]
- id: data
label: 数据层
children: [pg, redis, minio]
nodes:
- { id: web, label: Web 门户, shape: rect }
- { id: mobile, label: H5 / 小程序, shape: rect }
- { id: admin, label: 管理后台, shape: rect }
- { id: api_gw, label: API 网关\n(Nginx + Auth), shape: hexagon }
- { id: svc_user, label: 用户服务 }
- { id: svc_order, label: 订单服务 }
- { id: svc_content, label: 内容服务 }
- { id: svc_ai, label: AI 服务\n(龙虾 OpenClaw) }
- { id: pg, label: PostgreSQL, shape: cylinder }
- { id: redis, label: Redis, shape: cylinder }
- { id: minio, label: 对象存储 MinIO, shape: cylinder }
edges:
- [web, api_gw]
- [mobile, api_gw]
- [admin, api_gw]
- [api_gw, svc_user]
- [api_gw, svc_order]
- [api_gw, svc_content]
- [api_gw, svc_ai]
- [svc_user, pg]
- [svc_order, pg]
- [svc_order, redis]
- [svc_content, pg]
- [svc_content, minio]
- [svc_ai, redis]
FILE:examples/c4_context.yaml
type: c4_context
title: 火一五 SaaS 上下文图（C4 L1）
nodes:
- id: customer
label: 企业客户\n采购/使用 SaaS 的企业
shape: person
- id: admin
label: 火一五运维\n内部团队
shape: person
- id: huo15
label: 火一五 SaaS\n企业数字化平台
shape: system
- id: wecom
label: 企业微信\nIM / 通讯录
shape: system_ext
- id: odoo
label: Odoo ERP\nCRM / 财务
shape: system_ext
- id: openai
label: OpenAI / 百炼\n大模型 API
shape: system_ext
edges:
- { from: customer, to: huo15, label: 使用 }
- { from: admin, to: huo15, label: 运维 }
- { from: huo15, to: wecom, label: OAuth / 消息推送 }
- { from: huo15, to: odoo, label: XML-RPC }
- { from: huo15, to: openai, label: HTTPS }
FILE:examples/flowchart.yaml
type: flowchart
title: 用户下单流程
direction: TB
nodes:
- id: n_start
label: 开始
shape: stadium
- id: login
label: 用户登录
- id: check
label: "是否有商品?"
shape: diamond
- id: add
label: 选购商品
- id: pay
label: 结算付款
- id: confirm
label: 订单确认
- id: n_end
label: 结束
shape: stadium
edges:
- { from: n_start, to: login }
- { from: login, to: check }
- { from: check, to: add, label: 否 }
- { from: check, to: pay, label: 是 }
- { from: add, to: check }
- { from: pay, to: confirm }
- { from: confirm, to: n_end }
FILE:examples/gantt.yaml
type: gantt
title: 火一五 Q2 产品路线图
dateFormat: YYYY-MM-DD
axisFormat: "%m-%d"
sections:
- name: 基建
tasks:
- { name: 服务器扩容, id: infra1, status: done, start: 2026-04-01, duration: 5d }
- { name: 监控升级, id: infra2, status: active, start: 2026-04-06, duration: 10d }
- name: 功能迭代
tasks:
- { name: OpenClaw Enhance v2026.4, id: f1, status: done, start: 2026-04-01, duration: 14d }
- { name: 知识库 shared scope, id: f2, status: active, start: 2026-04-15, duration: 7d }
- { name: 小红书选题技能, id: f3, status: "", start: 2026-04-22, duration: 5d }
- name: 发布
tasks:
- { name: v2026.4.11 RC, id: r1, status: crit, start: 2026-04-18, duration: 3d }
- { name: v2026.4.11 GA, id: r2, status: milestone, start: 2026-04-24, duration: 0d }
FILE:examples/sequence.yaml
type: sequence
title: 用户登录时序图
auto_number: true
nodes:
- { id: U, label: 用户, shape: actor }
- { id: FE, label: 前端 Web }
- { id: BE, label: 后端 API }
- { id: DB, label: 数据库 }
- { id: WX, label: 企业微信 }
edges:
- { from: U, to: FE, label: "点击登录" }
- { from: FE, to: WX, label: "OAuth 跳转" }
- { from: WX, to: FE, label: "回调 code", kind: dashed }
- { from: FE, to: BE, label: "POST /login {code}" }
- { from: BE, to: WX, label: "exchange code for user_id" }
- { from: WX, to: BE, label: "user_id", kind: dashed }
- { from: BE, to: DB, label: "SELECT / INSERT user" }
- { from: DB, to: BE, label: "user row", kind: dashed }
- { from: BE, to: FE, label: "JWT token", kind: dashed }
- { from: FE, to: U, label: "登录完成", kind: dashed }
notes:
- { position: over, over: "BE", text: "首次登录自动建账" }
FILE:examples/state.yaml
type: state
title: 订单状态机
direction: LR
nodes:
- { id: Draft, label: 草稿 }
- { id: Pending, label: 待付款 }
- { id: Paid, label: 已付款 }
- { id: Shipping, label: 配送中 }
- { id: Delivered, label: 已送达 }
- { id: Cancelled, label: 已取消 }
edges:
- { from: "[*]", to: Draft, label: 下单 }
- { from: Draft, to: Pending, label: 确认 }
- { from: Pending, to: Paid, label: 付款 }
- { from: Pending, to: Cancelled, label: 超时 }
- { from: Paid, to: Shipping, label: 发货 }
- { from: Shipping, to: Delivered, label: 签收 }
- { from: Delivered, to: "[*]" }
- { from: Cancelled, to: "[*]" }
FILE:examples/swimlane.yaml
type: swimlane
title: 采购审批流程（泳道图，PlantUML）
lanes:
- name: 申请人
steps:
- { id: A1, label: 填写采购申请 }
- { id: A2, label: 提交审批 }
- name: 部门经理
steps:
- { id: B1, label: 初审 }
- { id: B2, label: "是否 1 万以上?", shape: diamond }
- name: 财务
steps:
- { id: C1, label: 预算复核 }
- name: 总经理
steps:
- { id: D1, label: 终审批准 }
- name: 系统
steps:
- { id: E1, label: 自动下单 }
edges:
- [A1, A2]
- [A2, B1]
- [B1, B2]
- [B2, C1, "是"]
- [B2, E1, "否"]
- [C1, D1]
- [D1, E1]
FILE:examples/swimlane_mermaid.yaml
type: swimlane_mermaid
title: 订单发货流程（Mermaid 泳道，不需要 Java）
direction: LR
nodes:
- { id: a1, label: 下单, lane: 客户 }
- { id: a2, label: 付款, lane: 客户 }
- { id: b1, label: 核对订单, lane: 客服 }
- { id: b2, label: 分配仓库, lane: 客服 }
- { id: c1, label: 拣货, lane: 仓库 }
- { id: c2, label: 打包发货, lane: 仓库 }
- { id: d1, label: 快递配送, lane: 快递 }
- { id: d2, label: 妥投, lane: 快递 }
edges:
- [a1, a2]
- [a2, b1]
- [b1, b2]
- [b2, c1]
- [c1, c2]
- [c2, d1]
- [d1, d2]
FILE:scripts/create-flow-chart.py
#!/usr/bin/env python3
"""火一五流程图 — 一站式 CLI。
最常见用法
==========
# 从 YAML 规格生成 PNG（默认 modern 风格）
python3 scripts/create-flow-chart.py -i examples/architecture.yaml -o /tmp/arch.png
# 泳道图（真·PlantUML）
python3 scripts/create-flow-chart.py -i examples/swimlane.yaml -o /tmp/lane.svg
# 泳道图（Mermaid subgraph 风格，不需要 Java）
python3 scripts/create-flow-chart.py -i examples/swimlane_mermaid.yaml -o /tmp/lane.svg
# 直接渲染 Mermaid 源码
python3 scripts/create-flow-chart.py -i flow.mmd -o /tmp/flow.pdf --style dark
# 同时导出多种格式
python3 scripts/create-flow-chart.py -i spec.yaml -o /tmp/arch.png \
--export-formats svg,pdf,mmd --style xiaohongshu
# 导出 draw.io 源文件（可本地编辑）
python3 scripts/create-flow-chart.py -i spec.yaml -o /tmp/arch.drawio \
--export-formats png,svg,pdf
# draw.io 精美主题
python3 scripts/create-flow-chart.py -i spec.yaml -o /tmp/arch.png \
--theme modern --font "Microsoft YaHei" --shadow
"""
from __future__ import annotations
import argparse
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent))
from flowchart_core import parse, to_mermaid, to_plantuml, to_dot, to_drawio, FlowChart # noqa: E402
from flowchart_render import render # noqa: E402
from styles import get_style, list_styles, to_mermaid_init_directive, to_plantuml_skinparam # noqa: E402
def _pick_engine(fc: FlowChart, explicit: str = "auto") -> str:
if explicit != "auto":
return explicit
if fc.diagram_type in ("swimlane", "plantuml_raw"):
return "plantuml"
if fc.diagram_type == "dot_raw":
return "dot"
return "mermaid"
def _generate_source(fc: FlowChart, engine: str, style, theme: str = "modern",
font_family: str = None, shadow: bool = True) -> str:
if engine == "mermaid":
return to_mermaid(
fc,
to_mermaid_init_directive(style, diagram_type=fc.diagram_type),
style=style,
)
if engine == "plantuml":
return to_plantuml(fc, to_plantuml_skinparam(style))
if engine == "dot":
return to_dot(fc, style=style)
if engine == "drawio":
return to_drawio(fc, style=style, theme=theme, font_family=font_family,
shadow=shadow)
raise ValueError(f"未知 engine {engine}")
def main() -> int:
ap = argparse.ArgumentParser(
description="火一五流程图 / 架构图 / 泳道图 / 时序图等生成器",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="支持的 type：flowchart / swimlane / swimlane_mermaid / sequence / state /\n"
" gantt / er / class / journey / pie /\n"
" architecture / c4_context / c4_container / mindmap",
)
ap.add_argument("--input", "-i", required=True, help="输入路径（yaml/json/mmd/puml/dot/drawio）")
ap.add_argument("--input-format", default="auto",
choices=["auto", "yaml", "json", "mermaid", "plantuml", "dot"])
ap.add_argument("--output", "-o", required=True,
help="输出路径（.svg/.png/.pdf/.mmd/.puml/.dot/.drawio）")
ap.add_argument("--export-formats", "--also", dest="export_formats", default="",
help="附加输出格式，逗号分隔。如 'svg,pdf,mmd,drawio'（旧名 --also 仍可用）")
ap.add_argument("--export-dir", default="",
help="源文件输出目录，默认与主输出同目录")
ap.add_argument("--style", default="modern",
help=f"风格：{','.join(list_styles().keys())}（或中文别名）")
ap.add_argument("--theme", default="modern",
help="draw.io 主题：modern / dark / xiaohongshu / ocean 等（与 --style 对应）")
ap.add_argument("--font", default="PingFang SC",
help="字体名称，默认 PingFang SC（Mac）/ Microsoft YaHei（Win）")
ap.add_argument("--shadow", action="store_true",
help="为 draw.io 节点添加投影效果")
ap.add_argument("--engine", default="auto",
choices=["auto", "mermaid", "plantuml", "dot", "drawio"])
ap.add_argument("--width", type=int)
ap.add_argument("--height", type=int)
ap.add_argument("--background", help="背景色，默认用 style 的 background")
ap.add_argument("--dump-source", action="store_true",
help="只打印生成的源码到 stdout，不调用渲染器")
ap.add_argument("--no-pdf-fit", dest="pdf_fit", action="store_false",
default=True,
help="导出 PDF 时不自适应画布（默认自适应，整图一体不分页）")
args = ap.parse_args()
try:
fc = parse(args.input, hint=args.input_format)
except Exception as e:
print(f"[错误] 解析输入失败：{e}", file=sys.stderr)
return 1
try:
style = get_style(args.style)
except Exception as e:
print(f"[错误] 风格无效：{e}", file=sys.stderr)
return 1
# 判断 engine
engine = args.engine
if engine == "auto":
out_ext = Path(args.output).suffix.lower()
if out_ext == ".drawio":
engine = "drawio"
else:
engine = _pick_engine(fc, "auto")
font_family = args.font
shadow = args.shadow or True # 默认开启软阴影（现代化质感）
if args.dump_source and engine == "drawio":
print(_generate_source(fc, engine, style, theme=args.theme,
font_family=font_family, shadow=shadow))
return 0
try:
source = _generate_source(fc, engine, style, theme=args.theme,
font_family=font_family, shadow=shadow)
except Exception as e:
print(f"[错误] 生成 {engine} 源码失败：{e}", file=sys.stderr)
return 1
background = args.background or style.background
# 解析导出格式
export_exts = []
if args.export_formats:
for e in args.export_formats.split(","):
e = e.strip().lstrip(".")
if e:
export_exts.append(e)
# 主输出 + 附加输出
outputs = [args.output]
for ext in export_exts:
if ext == "drawio":
ext = "drawio"
base = Path(args.output)
out_dir = Path(args.export_dir) if args.export_dir else base.parent
out_dir.mkdir(parents=True, exist_ok=True)
outputs.append(str(out_dir / f"{base.stem}.{ext}"))
successes, failures = [], []
for path in outputs:
try:
final = render(source, path, engine=engine,
width=args.width, height=args.height,
background=background,
pdf_fit=args.pdf_fit)
successes.append(final)
except Exception as e:
failures.append((path, str(e)))
for f in successes:
print(f"✓ {f}")
for path, msg in failures:
print(f"✗ {path}: {msg}", file=sys.stderr)
return 0 if not failures else 2
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/flowchart_core.py
"""火一五流程图 — 数据模型 + YAML/JSON 解析 + Mermaid/PlantUML/DOT 代码生成。
支持的图表类型
==============
| 类型 | 输出 DSL | 典型用途 |
|--------------------|-------------------|----------|
| flowchart | Mermaid flowchart | 普通流程图（含分组 subgraph）|
| swimlane | PlantUML activity | 真·泳道图（按角色分栏）|
| swimlane_mermaid | Mermaid subgraph | 泳道风格（不需要 Java 时用）|
| sequence | Mermaid sequence | 时序图 |
| state | Mermaid state v2 | 状态图 |
| gantt | Mermaid gantt | 甘特图 |
| er | Mermaid erDiagram | ER 图 |
| class | Mermaid classDiagram | UML 类图 |
| journey | Mermaid journey | 用户旅程 |
| pie | Mermaid pie | 饼图 |
| architecture | Mermaid flowchart | 系统架构（分层 + 分组） |
| c4_context | Mermaid C4Context | C4 上下文图 |
| c4_container | Mermaid C4Container | C4 容器图 |
| mindmap | Mermaid mindmap | 简单思维导图（正经的用 huo15-mind-map）|
用 YAML 描述；具体字段按 type 不同而不同，见 SKILL.md 示例。
"""
from __future__ import annotations
import json
import re
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Tuple
try:
import yaml # type: ignore
HAS_YAML = True
except ImportError:
HAS_YAML = False
# ----- 通用数据结构 -----
@dataclass
class Node:
id: str
label: str = ""
shape: str = "rect" # rect/round/stadium/diamond/hexagon/circle/cylinder/cloud/component
lane: Optional[str] = None
style_class: Optional[str] = None
group: Optional[str] = None # 分组名，用于 subgraph
tier: Optional[str] = None # architecture tier 分层名
@dataclass
class Edge:
src: str
dst: str
label: Optional[str] = None
kind: str = "solid" # solid / dashed / thick / dotted / bidir
style_class: Optional[str] = None
@dataclass
class Tier:
id: str
label: str = ""
direction: str = "" # 可选 override
children: List[str] = field(default_factory=list)
@dataclass
class Group:
id: str
label: str = ""
direction: str = "" # 可选 override
children: List[str] = field(default_factory=list)
@dataclass
class FlowChart:
diagram_type: str = "flowchart"
title: str = ""
direction: str = "TB"
nodes: List[Node] = field(default_factory=list)
edges: List[Edge] = field(default_factory=list)
groups: List[Group] = field(default_factory=list)
tiers: List[Tier] = field(default_factory=list) # architecture 多层分层
lanes: List[str] = field(default_factory=list)
raw: Optional[str] = None # 原样 Mermaid/DOT 代码时用
extras: Dict[str, Any] = field(default_factory=dict)
# ----- 解析 -----
def parse(text_or_path: str, hint: str = "auto") -> FlowChart:
"""顶层解析。按 hint / 扩展名 / 内容特征分发。"""
import os
text = text_or_path
if os.path.exists(text_or_path):
with open(text_or_path, encoding="utf-8") as f:
text = f.read()
if hint == "auto":
ext = os.path.splitext(text_or_path)[1].lower()
hint = {".yaml": "yaml", ".yml": "yaml", ".json": "json",
".mmd": "mermaid", ".mermaid": "mermaid",
".puml": "plantuml", ".plantuml": "plantuml",
".dot": "dot", ".gv": "dot"}.get(ext, "auto")
if hint == "auto":
stripped = text.lstrip()
if stripped.startswith("{"):
hint = "json"
elif re.match(r"^(flowchart|graph|sequenceDiagram|stateDiagram|gantt|classDiagram|erDiagram|journey|pie|C4Context|C4Container|mindmap|%%\{init)", stripped):
hint = "mermaid"
elif stripped.startswith("@startuml") or stripped.startswith("@startsalt"):
hint = "plantuml"
elif stripped.startswith("digraph") or stripped.startswith("graph "):
hint = "dot"
else:
hint = "yaml"
if hint == "json":
return parse_spec(json.loads(text))
if hint == "yaml":
if not HAS_YAML:
raise RuntimeError("需要 PyYAML（pip install pyyaml）才能解析 YAML 规格文件")
return parse_spec(yaml.safe_load(text))
if hint == "mermaid":
return FlowChart(diagram_type="mermaid_raw", raw=text)
if hint == "plantuml":
return FlowChart(diagram_type="plantuml_raw", raw=text)
if hint == "dot":
return FlowChart(diagram_type="dot_raw", raw=text)
raise ValueError(f"未知输入类型：{hint}")
def parse_spec(spec: Dict[str, Any]) -> FlowChart:
"""从 dict（已解析的 YAML/JSON）构造 FlowChart。"""
fc = FlowChart()
fc.diagram_type = str(spec.get("type") or spec.get("diagram") or "flowchart").lower()
fc.title = spec.get("title", "") or ""
fc.direction = spec.get("direction", "TB")
fc.extras = {k: v for k, v in spec.items() if k not in {
"type", "diagram", "title", "direction", "nodes", "edges",
"groups", "lanes", "relations"
}}
# 节点
for n in spec.get("nodes", []) or []:
if isinstance(n, str):
fc.nodes.append(Node(id=n, label=n))
continue
fc.nodes.append(Node(
id=n["id"],
label=n.get("label", n["id"]),
shape=n.get("shape", "rect"),
lane=n.get("lane"),
style_class=n.get("class"),
group=n.get("group"),
tier=n.get("tier"),
))
# 边（兼容 relations 别名）
for e in (spec.get("edges") or spec.get("relations") or []):
if isinstance(e, list):
if len(e) == 2:
fc.edges.append(Edge(src=e[0], dst=e[1]))
elif len(e) >= 3:
fc.edges.append(Edge(src=e[0], dst=e[1], label=e[2]))
continue
fc.edges.append(Edge(
src=e.get("from") or e.get("src") or e["source"],
dst=e.get("to") or e.get("dst") or e["target"],
label=e.get("label"),
kind=e.get("kind", "solid"),
style_class=e.get("class"),
))
# 分层 / tiers
for t in spec.get("tiers", []) or []:
if isinstance(t, str):
fc.tiers.append(Tier(id=t, label=t))
else:
fc.tiers.append(Tier(
id=t["id"],
label=t.get("label", t["id"]),
direction=t.get("direction", ""),
children=t.get("children", []) or t.get("nodes", []),
))
# 分组 / 子图
for g in spec.get("groups", []) or []:
if isinstance(g, str):
fc.groups.append(Group(id=g, label=g))
continue
fc.groups.append(Group(
id=g["id"],
label=g.get("label", g["id"]),
direction=g.get("direction", ""),
children=g.get("children", []) or g.get("nodes", []),
))
# 泳道
if "lanes" in spec:
lanes = spec["lanes"]
if lanes and isinstance(lanes[0], dict):
for lane in lanes:
fc.lanes.append(lane.get("name") or lane.get("id") or "")
for step in lane.get("steps") or lane.get("nodes") or []:
if isinstance(step, str):
fc.nodes.append(Node(id=step, label=step, lane=lane.get("name")))
else:
fc.nodes.append(Node(
id=step["id"],
label=step.get("label", step["id"]),
shape=step.get("shape", "rect"),
lane=lane.get("name"),
))
else:
fc.lanes = [str(l) for l in lanes]
return fc
# ----- Mermaid 生成 -----
_MM_SHAPE = {
"rect": ("[", "]"),
"round": ("(", ")"),
"stadium": ("([", "])"),
"subroutine": ("[[", "]]"),
"cylinder": ("[(", ")]"),
"circle": ("((", "))"),
"asymmetric": (">", "]"),
"diamond": ("{", "}"),
"hexagon": ("{{", "}}"),
"parallelogram": ("[/", "/]"),
"trapezoid": ("[/", "\\]"),
}
_MM_EDGE = {
"solid": "-->",
"dashed": "-.->",
"dotted": "-.->",
"thick": "==>",
"bidir": "<-->",
"none": "---",
}
def _mm_label(label: str) -> str:
if not label:
return ""
# Mermaid 特殊字符需要用引号包起来
if any(c in label for c in "()[]{}|<>/\\\"\n"):
safe = label.replace('"', '\\"').replace("\n", "<br/>")
return f'"{safe}"'
return label
def _mm_node(n: Node) -> str:
open_br, close_br = _MM_SHAPE.get(n.shape, _MM_SHAPE["rect"])
return f"{n.id}{open_br}{_mm_label(n.label or n.id)}{close_br}"
def _mm_edge(e: Edge) -> str:
arrow = _MM_EDGE.get(e.kind, _MM_EDGE["solid"])
if e.label:
label_part = f"|{_mm_label(e.label).strip('"')}|"
return f"{e.src} {arrow}{label_part} {e.dst}"
return f"{e.src} {arrow} {e.dst}"
def to_mermaid(fc: FlowChart, style_directive: str = "", style: Optional[Any] = None) -> str:
"""把 FlowChart 转成 Mermaid 代码。
style_directive - `%%{init:...}%%` 那一行
style - Style 对象（可选，用于注入 decision / database 的 classDef）
"""
if fc.diagram_type == "mermaid_raw":
# 原样 Mermaid 代码，只在开头插入 style_directive
raw = fc.raw or ""
if style_directive and "%%{init" not in raw:
return style_directive + "\n" + raw
return raw
t = fc.diagram_type
body: List[str]
if t in ("flowchart", "architecture", "swimlane_mermaid"):
body = _mm_flowchart(fc)
elif t == "sequence":
body = _mm_sequence(fc)
elif t == "state":
body = _mm_state(fc)
elif t == "gantt":
body = _mm_gantt(fc)
elif t == "er":
body = _mm_er(fc)
elif t == "class":
body = _mm_class(fc)
elif t == "journey":
body = _mm_journey(fc)
elif t == "pie":
body = _mm_pie(fc)
elif t in ("c4_context", "c4context"):
body = _mm_c4(fc, "Context")
elif t in ("c4_container", "c4container"):
body = _mm_c4(fc, "Container")
elif t == "mindmap":
body = _mm_mindmap(fc)
else:
body = _mm_flowchart(fc)
# 基于 style 注入的 decision / database classDef（仅 flowchart 家族）
if style is not None and t in ("flowchart", "architecture", "swimlane_mermaid"):
try:
from styles import decision_classdef, database_classdef
auto = fc.extras.get("_auto_classdefs", {})
decision_ids: List[str] = auto.get("decision_ids", []) or []
database_ids: List[str] = auto.get("database_ids", []) or []
if decision_ids:
body.append(" " + decision_classdef(style))
for nid in decision_ids:
body.append(f" class {nid} decision")
if database_ids:
body.append(" " + database_classdef(style))
for nid in database_ids:
body.append(f" class {nid} database")
except Exception:
pass
# Mermaid 要求 frontmatter（---title---）必须位于文件最前；init 指令跟在其后。
if body and body[0].startswith("---"):
first_block = body[0] # 例如 "---\ntitle: xxx\n---"
rest = body[1:]
if style_directive:
return "\n".join([first_block, style_directive] + rest)
return "\n".join([first_block] + rest)
if style_directive:
return "\n".join([style_directive] + body)
return "\n".join(body)
def _mm_flowchart(fc: FlowChart) -> List[str]:
lines = [f"flowchart {fc.direction}"]
if fc.title:
lines.insert(0, f"---\ntitle: {fc.title}\n---")
# 收集需要特殊 classDef 的节点：diamond → decision、cylinder → database
decision_ids = [n.id for n in fc.nodes if n.shape == "diamond"]
database_ids = [n.id for n in fc.nodes if n.shape in ("cylinder",)]
# tiers 优先（architecture 类型）；否则用 groups
use_tiers = bool(fc.tiers) and fc.diagram_type == "architecture"
if use_tiers:
groups_by_id: Dict[str, Group] = {t.id: Group(id=t.id, label=t.label, direction=t.direction, children=t.children) for t in fc.tiers}
else:
groups_by_id = {g.id: g for g in fc.groups}
grouped_nodes: Dict[str, List[Node]] = {g.id: [] for g in groups_by_id.values()}
ungrouped: List[Node] = []
for n in fc.nodes:
key: Optional[str] = None
if use_tiers:
# architecture tiers：从 node.tier 取
key = getattr(n, 'tier', None) or n.group
elif fc.diagram_type == "swimlane_mermaid":
key = n.lane
if key and key not in groups_by_id:
groups_by_id[key] = Group(id=key, label=key)
grouped_nodes.setdefault(key, [])
else:
key = n.group
if key and key in groups_by_id:
grouped_nodes[key].append(n)
else:
ungrouped.append(n)
# 先输出顶层节点
for n in ungrouped:
lines.append(" " + _mm_node(n))
# 输出 subgraph
for gid, g in groups_by_id.items():
direction = g.direction or fc.direction
title = g.label or gid
lines.append(f" subgraph {gid}[\"{title}\"]")
if direction:
lines.append(f" direction {direction}")
# children 列表里的 + grouped_nodes 里的
children_ids = set(g.children)
seen = set()
for n in grouped_nodes.get(gid, []):
lines.append(" " + _mm_node(n))
seen.add(n.id)
for cid in g.children:
if cid in seen:
continue
# 这个 id 可能是先前声明过的顶层节点
lines.append(f" {cid}")
lines.append(" end")
# 边
for e in fc.edges:
lines.append(" " + _mm_edge(e))
# classDef / class
classes = {n.style_class for n in fc.nodes if n.style_class}
for cls in sorted(c for c in classes if c):
lines.append(f" classDef {cls} fill:#f9f9f9,stroke:#999,stroke-width:1px")
for n in fc.nodes:
if n.style_class:
lines.append(f" class {n.id} {n.style_class}")
# 自动 decision / database classDef（从 Style 渲染阶段注入）
fc.extras["_auto_classdefs"] = {
"decision_ids": decision_ids,
"database_ids": database_ids,
}
return lines
def _mm_sequence(fc: FlowChart) -> List[str]:
lines = ["sequenceDiagram"]
if fc.title:
lines.append(f" title {fc.title}")
# 节点看作 actor / participant
for n in fc.nodes:
role = "actor" if n.shape == "actor" else "participant"
lines.append(f" {role} {n.id} as {n.label or n.id}")
for e in fc.edges:
arrow = "->>" if e.kind != "dashed" else "-->>"
lbl = (e.label or "").replace("\n", " ")
lines.append(f" {e.src}{arrow}{e.dst}: {lbl}")
# extras 支持 notes/auto_number
if fc.extras.get("auto_number"):
lines.insert(1, " autonumber")
for note in fc.extras.get("notes", []) or []:
pos = note.get("position", "over")
over = note.get("over") or note.get("participant", "")
txt = (note.get("text", "") or "").replace("\n", "<br/>")
lines.append(f" Note {pos} {over}: {txt}")
return lines
def _mm_state(fc: FlowChart) -> List[str]:
lines = ["stateDiagram-v2"]
if fc.title:
lines.insert(0, f"---\ntitle: {fc.title}\n---")
if fc.direction:
lines.append(f" direction {fc.direction}")
for n in fc.nodes:
if n.label and n.label != n.id:
lines.append(f" {n.id} : {n.label}")
for e in fc.edges:
lbl = f" : {e.label}" if e.label else ""
lines.append(f" {e.src} --> {e.dst}{lbl}")
return lines
def _mm_gantt(fc: FlowChart) -> List[str]:
lines = ["gantt"]
if fc.title:
lines.append(f" title {fc.title}")
lines.append(f" dateFormat {fc.extras.get('dateFormat', 'YYYY-MM-DD')}")
if "axisFormat" in fc.extras:
lines.append(f" axisFormat {fc.extras['axisFormat']}")
# sections / tasks
sections = fc.extras.get("sections") or []
if sections:
for sec in sections:
lines.append(f" section {sec.get('name', '')}")
for task in sec.get("tasks", []):
_gantt_task(lines, task)
else:
for task in fc.extras.get("tasks", []) or []:
_gantt_task(lines, task)
return lines
def _gantt_task(lines: List[str], task: Dict[str, Any]) -> None:
def _s(v: Any) -> str:
if v is None:
return ""
if hasattr(v, "strftime"): # datetime.date / datetime
return v.strftime("%Y-%m-%d")
return str(v)
name = _s(task.get("name", ""))
tid = _s(task.get("id", ""))
status = _s(task.get("status", ""))
start = _s(task.get("start", ""))
dur = _s(task.get("duration", task.get("end", "")))
parts = [p for p in [status, tid, start, dur] if p]
lines.append(f" {name} :{', '.join(parts)}")
def _mm_er(fc: FlowChart) -> List[str]:
lines = ["erDiagram"]
if fc.title:
lines.insert(0, f"---\ntitle: {fc.title}\n---")
# 节点 = 实体；label 可附带字段
for n in fc.nodes:
fields = n.label.split("\n") if "\n" in n.label else []
if len(fields) > 1:
lines.append(f" {n.id} {{")
for f in fields[1:]:
lines.append(f" {f}")
lines.append(" }")
for e in fc.edges:
relation = e.kind if e.kind != "solid" else "||--o{"
lbl = e.label or "relates to"
lines.append(f" {e.src} {relation} {e.dst} : \"{lbl}\"")
return lines
def _mm_class(fc: FlowChart) -> List[str]:
lines = ["classDiagram"]
if fc.title:
lines.insert(0, f"---\ntitle: {fc.title}\n---")
if fc.direction:
lines.append(f" direction {fc.direction}")
for n in fc.nodes:
if "\n" in n.label:
parts = n.label.split("\n")
lines.append(f" class {n.id} {{")
for p in parts[1:]:
lines.append(f" {p}")
lines.append(" }")
else:
lines.append(f" class {n.id}")
for e in fc.edges:
rel = {
"extends": "<|--",
"implements": "<|..",
"composition": "*--",
"aggregation": "o--",
"dependency": "..>",
"association": "-->",
}.get(e.kind, "-->")
lbl = f" : {e.label}" if e.label else ""
lines.append(f" {e.src} {rel} {e.dst}{lbl}")
return lines
def _mm_journey(fc: FlowChart) -> List[str]:
lines = ["journey"]
if fc.title:
lines.append(f" title {fc.title}")
for section in fc.extras.get("sections", []) or []:
lines.append(f" section {section.get('name', '')}")
for task in section.get("tasks", []) or []:
lines.append(f" {task['name']}: {task.get('score', 3)}: {', '.join(task.get('actors', []))}")
return lines
def _mm_pie(fc: FlowChart) -> List[str]:
lines = ["pie" + (" showData" if fc.extras.get("show_data") else "")]
if fc.title:
lines.append(f" title {fc.title}")
for item in fc.extras.get("items", []) or []:
lines.append(f" \"{item['name']}\" : {item['value']}")
return lines
def _mm_c4(fc: FlowChart, level: str) -> List[str]:
lines = [f"C4{level}"]
if fc.title:
lines.append(f" title {fc.title}")
# 节点：shape 用 Person / System / System_Ext / Container / Db / ContainerDb
shape_to_kind = {
"person": "Person",
"person_ext": "Person_Ext",
"system": "System",
"system_ext": "System_Ext",
"container": "Container",
"container_db": "ContainerDb",
"component": "Component",
"db": "ContainerDb",
}
for n in fc.nodes:
kind = shape_to_kind.get(n.shape.lower(), "System")
desc = ""
if "\n" in n.label:
parts = n.label.split("\n", 1)
lbl, desc = parts[0], parts[1]
else:
lbl = n.label or n.id
desc_part = f', "{desc}"' if desc else ""
lines.append(f' {kind}({n.id}, "{lbl}"{desc_part})')
for e in fc.edges:
lbl = e.label or ""
lines.append(f' Rel({e.src}, {e.dst}, "{lbl}")')
return lines
def _mm_mindmap(fc: FlowChart) -> List[str]:
lines = ["mindmap"]
# 简化：假定第一个 node 是根，其他都是根的 child（不处理多级）
if not fc.nodes:
return lines
root = fc.nodes[0]
lines.append(f" root(({root.label or root.id}))")
# 按 edges 建父子关系
children: Dict[str, List[str]] = {}
for e in fc.edges:
children.setdefault(e.src, []).append(e.dst)
node_by_id = {n.id: n for n in fc.nodes}
def _render(nid: str, depth: int) -> None:
for cid in children.get(nid, []):
node = node_by_id.get(cid)
label = node.label if node else cid
lines.append(" " * (depth + 1) + label)
_render(cid, depth + 1)
_render(root.id, 1)
return lines
# ----- PlantUML 生成（真·泳道图） -----
def to_plantuml(fc: FlowChart, style_skinparam: str = "") -> str:
if fc.diagram_type == "plantuml_raw":
return fc.raw or ""
if fc.diagram_type == "swimlane":
return _puml_swimlane(fc, style_skinparam)
if fc.diagram_type == "sequence":
return _puml_sequence(fc, style_skinparam)
if fc.diagram_type in ("c4_context", "c4context", "c4_container", "c4container"):
return _puml_c4(fc, style_skinparam)
# fallback：让 Mermaid 干
raise ValueError(f"PlantUML 当前只支持 swimlane/sequence/c4；请用 type: swimlane_mermaid 等走 Mermaid。")
def _puml_swimlane(fc: FlowChart, skin: str) -> str:
out = ["@startuml"]
if skin:
out.append(skin)
if fc.title:
out.append(f"title {fc.title}")
# 按 lane 分组
lane_order = fc.lanes or []
if not lane_order:
lane_order = list(dict.fromkeys([n.lane for n in fc.nodes if n.lane]))
lane_nodes: Dict[str, List[Node]] = {l: [] for l in lane_order}
for n in fc.nodes:
if n.lane in lane_nodes:
lane_nodes[n.lane].append(n)
# 构建 id → node 映射与 children 图
node_by_id = {n.id: n for n in fc.nodes}
successors: Dict[str, List[Tuple[str, Optional[str]]]] = {}
for e in fc.edges:
successors.setdefault(e.src, []).append((e.dst, e.label))
out.append("start")
visited = set()
def _activity_body(n: Node) -> str:
if n.shape == "diamond":
return f"if ({n.label or n.id}?) then"
return f":{n.label or n.id};"
# 按 lane 顺序，深度优先走边
first = fc.nodes[0] if fc.nodes else None
cursor = first
cur_lane: Optional[str] = None
while cursor and cursor.id not in visited:
visited.add(cursor.id)
if cursor.lane and cursor.lane != cur_lane:
out.append(f"|{cursor.lane}|")
cur_lane = cursor.lane
out.append(_activity_body(cursor))
nexts = successors.get(cursor.id, [])
if not nexts:
break
# 只线性前进（复杂分支建议用原生 PlantUML 写）
nxt_id, lbl = nexts[0]
if lbl:
out.append(f"note right: {lbl}")
cursor = node_by_id.get(nxt_id)
out.append("stop")
out.append("@enduml")
return "\n".join(out)
def _puml_sequence(fc: FlowChart, skin: str) -> str:
out = ["@startuml"]
if skin:
out.append(skin)
if fc.title:
out.append(f"title {fc.title}")
for n in fc.nodes:
role = "actor" if n.shape == "actor" else "participant"
out.append(f'{role} "{n.label or n.id}" as {n.id}')
for e in fc.edges:
arrow = "-->" if e.kind == "dashed" else "->"
lbl = f" : {e.label}" if e.label else ""
out.append(f"{e.src} {arrow} {e.dst}{lbl}")
out.append("@enduml")
return "\n".join(out)
def _puml_c4(fc: FlowChart, skin: str) -> str:
"""C4-PlantUML 生成器（Person/System/Container/Rel）。"""
level = "Context" if fc.diagram_type in ("c4_context", "c4context") else "Container"
out = ["@startuml"]
if skin:
out.append(skin)
if fc.title:
out.append(f"title {fc.title}")
shape_to_c4 = {
"person": "Person",
"person_ext": "Person_Ext",
"system": "System",
"system_ext": "System_Ext",
"container": "Container",
"container_db": "ContainerDb",
"db": "ContainerDb",
"component": "Component",
}
for n in fc.nodes:
kind = shape_to_c4.get(n.shape.lower(), "System")
if "\n" in n.label:
parts = n.label.split("\n", 1)
lbl, desc = parts[0], parts[1]
else:
lbl = n.label or n.id
desc = ""
if desc:
out.append(f'{kind}({n.id}, "{lbl}", "{desc}")')
else:
# Try to detect technology from group or extras
tech = fc.extras.get("technologies", {}).get(n.id, "")
if tech:
out.append(f'{kind}({n.id}, "{lbl}", "{tech}")')
else:
out.append(f'{kind}({n.id}, "{lbl}")')
for e in fc.edges:
lbl = e.label or ""
# C4-PlantUML Rel 支持方向：Left/Right/Up/Down
out.append(f'Rel({e.src}, {e.dst}, "{lbl}")')
out.append("@enduml")
return "\n".join(out)
# ----- Graphviz DOT 生成（复杂网络拓扑、系统架构备选） -----
def to_dot(fc: FlowChart, style: Optional[Any] = None) -> str:
"""把 FlowChart 转成 Graphviz DOT 代码。用于网络拓扑 / 架构备选。"""
from styles import Style # 避免循环
if fc.diagram_type == "dot_raw":
return fc.raw or ""
lines = ["digraph G {"]
lines.append(f' rankdir={fc.direction if fc.direction in ("TB","BT","LR","RL") else "TB"};')
lines.append(' node [shape=box, style="rounded,filled", fontname="PingFang SC"];')
if style:
lines.append(f' bgcolor="{style.background}";')
lines.append(f' node [fillcolor="{style.primary_color}", fontcolor="{style.primary_text_color}", color="{style.primary_border_color}"];')
lines.append(f' edge [color="{style.line_color}", fontname="PingFang SC"];')
if fc.title:
lines.append(f' label="{fc.title}"; labelloc=t; fontsize=18;')
# 分组
for g in fc.groups:
lines.append(f' subgraph cluster_{g.id} {{')
lines.append(f' label="{g.label}";')
lines.append(' style="rounded,filled"; fillcolor="#F5F5F5";')
for cid in g.children:
lines.append(f" {cid};")
lines.append(" }")
for n in fc.nodes:
attrs = [f'label="{n.label or n.id}"']
if n.shape in ("cylinder", "cylinder"):
attrs.append('shape=cylinder')
elif n.shape == "diamond":
attrs.append('shape=diamond')
elif n.shape == "circle":
attrs.append('shape=circle')
lines.append(f" {n.id} [{', '.join(attrs)}];")
for e in fc.edges:
attrs = []
if e.label:
attrs.append(f'label="{e.label}"')
if e.kind == "dashed":
attrs.append('style=dashed')
attr_str = f" [{', '.join(attrs)}]" if attrs else ""
lines.append(f" {e.src} -> {e.dst}{attr_str};")
lines.append("}")
return "\n".join(lines)
# ----- draw.io XML 生成（支持导出 .drawio 源文件） -----
_DRAWIO_SHAPE = {
"rect": "rectangle",
"round": "rectangle", # 用 rounded=1 参数表达圆角
"stadium": "rectangle", # 半圆端 via arcSize=50
"subroutine": "process",
"cylinder": "cylinder",
"circle": "ellipse",
"asymmetric": "rectangle",
"diamond": "rhombus",
"hexagon": "hexagon",
"parallelogram": "parallelogram",
"trapezoid": "trapezoid",
"person": "umlActor",
"person_ext": "umlActor",
"system": "rectangle",
"system_ext": "rectangle",
"container": "rectangle",
"container_db": "cylinder",
"db": "cylinder",
"component": "component",
}
def _dx_escape(s: str) -> str:
"""转义 XML 特殊字符。"""
return (s.replace("&", "&")
.replace("<", "<")
.replace(">", ">")
.replace('"', """)
.replace("'", "'"))
def _dx_style(shape: str, params: Dict[str, str]) -> str:
"""把形状 + 样式参数序列化成 draw.io style 字符串。"""
shape_type = _DRAWIO_SHAPE.get(shape, "rectangle")
base: Dict[str, str] = {
"shape": shape_type,
"whiteSpace": "wrap",
"html": "1",
"align": "center",
"verticalAlign": "middle",
"spacing": "4",
}
# 圆角策略
if shape in ("round", "stadium"):
base["rounded"] = "1"
base["arcSize"] = "45" if shape == "stadium" else "20"
elif shape in ("rect", "system", "system_ext", "container", "subroutine"):
base["rounded"] = "1"
base["arcSize"] = "12"
base.update(params)
return ";".join(f"{k}={v}" for k, v in base.items() if v != "")
def to_drawio(fc: FlowChart, style: Optional[Any] = None,
theme: str = "modern",
font_family: Optional[str] = None,
shadow: bool = True) -> str:
"""把 FlowChart 转成 draw.io .drawio XML（现代风格：渐变 + 圆角 + 阴影 + 弧形正交连线）。"""
# 字体与配色
ff = font_family or (
style.font_family.split(",")[0].strip('"') if style else "PingFang SC"
)
if style:
fill = style.primary_color
stroke = style.primary_border_color
font_color = style.primary_text_color
bg = style.background
line = style.line_color
accent = style.accent_color
grad_start = getattr(style, "gradient_start", "") or ""
grad_end = getattr(style, "gradient_end", "") or ""
corner = getattr(style, "corner_radius", 12)
sw = getattr(style, "stroke_width", 1.6)
sec = style.secondary_color
tert = style.tertiary_color
else:
fill, stroke, font_color = "#1E293B", "#0F172A", "#FFFFFF"
bg, line, accent = "#FAFAFA", "#64748B", "#6366F1"
grad_start, grad_end = "", ""
corner, sw = 12, 1.6
sec, tert = "#F1F5F9", "#E2E8F0"
# 形状专用的 fill 选择：判断节点用 accent，数据库/容器用 secondary
def node_colors(shape: str) -> Dict[str, str]:
if shape == "diamond":
return {"fill": accent, "text": "#FFFFFF", "stroke": accent}
if shape in ("cylinder", "container_db", "db"):
return {"fill": sec, "text": stroke, "stroke": tert}
if shape in ("system_ext", "person_ext"):
return {"fill": tert, "text": stroke, "stroke": line}
return {"fill": fill, "text": font_color, "stroke": stroke}
# 布局：tiers 优先，否则 groups，否则单列网格
tier_list = [t.id for t in fc.tiers] if fc.tiers else []
group_list = [g.id for g in fc.groups] if fc.groups else []
uses_tiers = bool(tier_list)
container_list = tier_list if uses_tiers else group_list
# 把节点按 container 分桶（tier/group/ungrouped）
bucket: Dict[str, List[Node]] = {cid: [] for cid in container_list}
ungrouped: List[Node] = []
for n in fc.nodes:
k = getattr(n, "tier", None) if uses_tiers else n.group
if k and k in bucket:
bucket[k].append(n)
else:
ungrouped.append(n)
# 计算容器 & 节点坐标
NODE_W, NODE_H = 200, 72
H_GAP, V_GAP = 48, 56
C_PAD = 28 # 容器内边距
C_HEAD = 40 # 容器 header 高度
START_X, START_Y = 48, 64
pos: Dict[str, Tuple[int, int]] = {}
container_rect: Dict[str, Tuple[int, int, int, int]] = {} # id -> (x,y,w,h)
cur_y = START_Y
if container_list:
# 所有容器里节点数量最多的 → 决定容器宽度
max_cols = max((len(bucket.get(cid, [])) for cid in container_list), default=1)
max_cols = max(max_cols, 1)
content_w = max_cols * NODE_W + (max_cols - 1) * H_GAP
container_w = content_w + C_PAD * 2
for cid in container_list:
rows = bucket.get(cid, [])
# 分行：每行至多 max_cols 个
n_rows = max(1, (len(rows) + max_cols - 1) // max_cols) if rows else 1
container_h = C_HEAD + n_rows * NODE_H + (n_rows - 1) * V_GAP + C_PAD * 2 - C_PAD
container_rect[cid] = (START_X, cur_y, container_w, container_h)
# 节点位置相对画布
for i, n in enumerate(rows):
row = i // max_cols
col = i % max_cols
x = START_X + C_PAD + col * (NODE_W + H_GAP)
y = cur_y + C_HEAD + row * (NODE_H + V_GAP)
pos[n.id] = (x, y)
cur_y += container_h + V_GAP
# ungrouped 节点一行排
if ungrouped:
ug_cols = max(1, min(len(ungrouped), 4))
for i, n in enumerate(ungrouped):
row = i // ug_cols
col = i % ug_cols
x = START_X + col * (NODE_W + H_GAP)
y = cur_y + row * (NODE_H + V_GAP)
pos[n.id] = (x, y)
# 若仍没有任何节点位置（诡异情况）给个默认
for n in fc.nodes:
if n.id not in pos:
idx = fc.nodes.index(n)
pos[n.id] = (START_X + (idx % 4) * (NODE_W + H_GAP),
START_Y + (idx // 4) * (NODE_H + V_GAP))
# 分配 cell id
CELL_ROOT = 1
next_id = [2]
def alloc() -> int:
nid = next_id[0]
next_id[0] += 1
return nid
container_cell: Dict[str, int] = {}
node_cell: Dict[str, int] = {}
# ---- 输出 XML ----
shadow_flag = "1" if shadow else "0"
diag_name = _dx_escape(fc.title) if fc.title else "FlowChart"
lines: List[str] = [
'<?xml version="1.0" encoding="UTF-8"?>',
'<mxfile host="huo15-flow-chart" modified="2026-04-24" agent="huo15-flow-chart/1.3.0" version="24.0.0">',
f' <diagram name="{diag_name}" id="huo15-fc">',
f' <mxGraphModel dx="1600" dy="1100" grid="1" gridSize="10" guides="1" '
f'tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" math="0" shadow="{shadow_flag}">',
' <root>',
' <mxCell id="0" />',
f' <mxCell id="{CELL_ROOT}" parent="0" />',
]
# 容器（tier/group）cell
for cid in container_list:
rect = container_rect.get(cid)
if not rect:
continue
x, y, w, h = rect
label_text = ""
if uses_tiers:
tobj = next((t for t in fc.tiers if t.id == cid), None)
label_text = (tobj.label if tobj else cid) or cid
else:
gobj = next((g for g in fc.groups if g.id == cid), None)
label_text = (gobj.label if gobj else cid) or cid
cell_no = alloc()
container_cell[cid] = cell_no
container_style = ";".join([
"rounded=1",
f"arcSize={corner + 4}",
f"fillColor={sec}",
f"strokeColor={tert}",
f"strokeWidth={max(sw - 0.4, 1.0)}",
f"fontColor={stroke}",
"fontSize=14",
"fontStyle=1",
"verticalAlign=top",
"align=left",
"spacingTop=8",
"spacingLeft=14",
"dashed=0",
f"shadow={shadow_flag}",
"container=1",
"collapsible=0",
f"fontFamily={ff}",
])
lines.append(
f' <mxCell id="{cell_no}" value="{_dx_escape(label_text)}" '
f'style="{container_style}" vertex="1" parent="{CELL_ROOT}">'
)
lines.append(
f' <mxGeometry x="{x}" y="{y}" width="{w}" height="{h}" as="geometry" />'
)
lines.append(' </mxCell>')
# 节点 cell
for n in fc.nodes:
x, y = pos[n.id]
container_id = (getattr(n, "tier", None) if uses_tiers else n.group)
parent_cell = container_cell.get(container_id or "", CELL_ROOT)
# 子单元坐标相对父容器
if parent_cell != CELL_ROOT and container_id in container_rect:
cx, cy, _, _ = container_rect[container_id]
nx, ny = x - cx, y - cy
else:
nx, ny = x, y
colors = node_colors(n.shape)
node_style_map: Dict[str, str] = {
"fillColor": colors["fill"],
"strokeColor": colors["stroke"],
"fontColor": colors["text"],
"fontFamily": ff,
"fontSize": "13",
"fontStyle": "1",
"strokeWidth": str(sw),
"shadow": shadow_flag,
}
# 渐变（仅当 style 提供了渐变起止色，且形状是"实心"节点时）
if grad_start and grad_end and n.shape not in ("cylinder", "container_db", "db",
"system_ext", "person_ext"):
node_style_map["fillColor"] = grad_start
node_style_map["gradientColor"] = grad_end
node_style_map["gradientDirection"] = "north"
style_str = _dx_style(n.shape, node_style_map)
cell_no = alloc()
node_cell[n.id] = cell_no
label = _dx_escape(n.label or n.id)
lines.append(
f' <mxCell id="{cell_no}" value="{label}" '
f'style="{style_str}" vertex="1" parent="{parent_cell}">'
)
lines.append(
f' <mxGeometry x="{nx}" y="{ny}" width="{NODE_W}" height="{NODE_H}" as="geometry" />'
)
lines.append(' </mxCell>')
# 边 cell —— 现代：正交弧线 + 配色
for e in fc.edges:
src = node_cell.get(e.src)
dst = node_cell.get(e.dst)
if src is None or dst is None:
continue
edge_map: Dict[str, str] = {
"edgeStyle": "orthogonalEdgeStyle",
"rounded": "1",
"curved": "0",
"arcSize": "12",
"strokeColor": line,
"strokeWidth": str(sw),
"fontColor": stroke,
"fontSize": "12",
"fontFamily": ff,
"labelBackgroundColor": bg,
"html": "1",
"endArrow": "classic",
"endFill": "1",
"jettySize": "auto",
"orthogonalLoop": "1",
}
if e.kind == "dashed" or e.kind == "dotted":
edge_map["dashed"] = "1"
elif e.kind == "thick":
edge_map["strokeWidth"] = str(sw + 1.5)
elif e.kind == "bidir":
edge_map["startArrow"] = "classic"
edge_map["startFill"] = "1"
edge_style = ";".join(f"{k}={v}" for k, v in edge_map.items())
cell_no = alloc()
label = _dx_escape(e.label or "")
lines.append(
f' <mxCell id="{cell_no}" value="{label}" style="{edge_style}" '
f'edge="1" parent="{CELL_ROOT}" source="{src}" target="{dst}">'
)
lines.append(
' <mxGeometry relative="1" as="geometry" />'
)
lines.append(' </mxCell>')
lines.extend([
' </root>',
' </mxGraphModel>',
' </diagram>',
'</mxfile>',
])
return "\n".join(lines)
FILE:scripts/flowchart_render.py
"""把 Mermaid / PlantUML / DOT 代码渲染成 SVG / PNG / PDF。
后端探测顺序（每种 DSL 各自的）：
- Mermaid：`mmdc`（npm @mermaid-js/mermaid-cli） → npx → docker ghcr.io/mermaid-js/mermaid-cli
- PlantUML：`plantuml` → java -jar plantuml.jar → docker plantuml/plantuml
- Graphviz：`dot`
如果所有后端都不可用，会把源码写到 `.mmd/.puml/.dot` 文件里并抛出 RuntimeError，
让用户自己渲染（比如复制到 https://mermaid.live）。
"""
from __future__ import annotations
import json
import os
import shutil
import subprocess
import sys
import tempfile
from pathlib import Path
from typing import Optional
SUPPORTED_EXTS = {".svg", ".png", ".pdf", ".mmd", ".puml", ".dot", ".drawio", ".md", ".markdown"}
def render(source: str, output_path: str, engine: str = "mermaid",
width: Optional[int] = None, height: Optional[int] = None,
background: Optional[str] = None,
pdf_fit: bool = True) -> str:
"""核心渲染函数。返回最终产物路径。
pdf_fit
输出 .pdf 时是否自动适配画布（不分页、整图一体）。默认 True。
"""
out = Path(output_path)
ext = out.suffix.lower()
if ext not in SUPPORTED_EXTS:
raise ValueError(f"不支持的扩展名 {ext}；可选 {sorted(SUPPORTED_EXTS)}")
out.parent.mkdir(parents=True, exist_ok=True)
# 仅导出源码
if ext in (".mmd", ".md", ".markdown"):
out.write_text(source, encoding="utf-8")
return str(out)
if ext == ".puml":
out.write_text(source, encoding="utf-8")
return str(out)
if ext == ".dot":
out.write_text(source, encoding="utf-8")
return str(out)
if ext == ".drawio":
out.write_text(source, encoding="utf-8")
return str(out)
if engine == "mermaid":
return _render_mermaid(source, out, width=width, height=height,
background=background, pdf_fit=pdf_fit)
if engine == "plantuml":
return _render_plantuml(source, out, pdf_fit=pdf_fit)
if engine == "dot":
return _render_dot(source, out)
raise ValueError(f"未知 engine：{engine}")
# ----- Mermaid -----
def _find_mmdc() -> Optional[list]:
if shutil.which("mmdc"):
return ["mmdc"]
if shutil.which("npx"):
return ["npx", "-y", "@mermaid-js/mermaid-cli"]
if shutil.which("docker"):
return [
"docker", "run", "--rm", "-u", f"{os.getuid()}:{os.getgid()}",
"-v", f"{os.getcwd()}:/data",
"minlag/mermaid-cli", "-"
]
return None
def _render_mermaid(source: str, out: Path, *, width=None, height=None,
background=None, pdf_fit: bool = True) -> str:
cmd_base = _find_mmdc()
if not cmd_base:
# 没有 mmdc，把 .mmd 导出
fallback = out.with_suffix(".mmd")
fallback.write_text(source, encoding="utf-8")
raise RuntimeError(
f"未找到 mmdc / npx / docker，已把 Mermaid 源码保存到 {fallback}。"
"\n安装任选其一："
"\n npm i -g @mermaid-js/mermaid-cli # 推荐"
"\n 或用在线编辑器：https://mermaid.live"
)
ext = out.suffix.lower()
with tempfile.TemporaryDirectory() as td:
src = Path(td) / "source.mmd"
src.write_text(source, encoding="utf-8")
cmd = list(cmd_base) + ["-i", str(src), "-o", str(out)]
if width:
cmd += ["-w", str(width)]
if height:
cmd += ["-H", str(height)]
if background:
cmd += ["-b", background]
# PDF 导出：让 PDF 自动适配图表大小（单页不分页）
if ext == ".pdf" and pdf_fit:
cmd += ["-f"]
# 设置 puppeteer 的 no-sandbox 配置（常见 Linux CI 报错）
pup_cfg = Path(td) / "puppeteer.json"
pup_cfg.write_text(json.dumps({"args": ["--no-sandbox", "--disable-setuid-sandbox"]}))
cmd += ["-p", str(pup_cfg)]
try:
subprocess.run(cmd, check=True, capture_output=True, text=True)
except subprocess.CalledProcessError as e:
raise RuntimeError(
f"mmdc 渲染失败：\nSTDOUT: {e.stdout}\nSTDERR: {e.stderr}"
) from e
return str(out)
# ----- PlantUML -----
def _find_plantuml() -> Optional[list]:
if shutil.which("plantuml"):
return ["plantuml"]
# 常见 jar 路径
for candidate in [
"/opt/homebrew/opt/plantuml/libexec/plantuml.jar",
"/usr/local/opt/plantuml/libexec/plantuml.jar",
"/usr/share/plantuml/plantuml.jar",
]:
if os.path.isfile(candidate) and shutil.which("java"):
return ["java", "-jar", candidate]
if shutil.which("docker"):
return ["docker", "run", "--rm", "-i", "plantuml/plantuml", "-pipe"]
return None
def _render_plantuml(source: str, out: Path, pdf_fit: bool = True) -> str:
cmd = _find_plantuml()
if not cmd:
fallback = out.with_suffix(".puml")
fallback.write_text(source, encoding="utf-8")
raise RuntimeError(
f"未找到 plantuml / java / docker，已把 PlantUML 源码保存到 {fallback}。"
"\n安装："
"\n brew install plantuml (macOS)"
"\n apt install plantuml (Ubuntu/Debian)"
)
ext = out.suffix.lower()
# PDF 走 SVG → rsvg-convert 流程，保证单页一体输出
if ext == ".pdf" and pdf_fit and shutil.which("rsvg-convert"):
return _render_plantuml_pdf_via_svg(source, out, cmd)
fmt = {".svg": "-tsvg", ".png": "-tpng", ".pdf": "-tpdf"}[ext]
with tempfile.TemporaryDirectory() as td:
src = Path(td) / "source.puml"
src.write_text(source, encoding="utf-8")
full_cmd = list(cmd) + [fmt, "-o", str(out.parent.absolute()), str(src)]
try:
subprocess.run(full_cmd, check=True, capture_output=True, text=True)
except (subprocess.CalledProcessError, FileNotFoundError) as e:
# 保底：把源码落盘，调用方可以复制到在线编辑器
fallback = out.with_suffix(".puml")
fallback.write_text(source, encoding="utf-8")
stdout = getattr(e, "stdout", "") or ""
stderr = getattr(e, "stderr", "") or ""
raise RuntimeError(
f"plantuml 渲染失败（可能是 docker 镜像未拉 / daemon 未启动）。"
f"已把 PlantUML 源码落到 {fallback}。"
f"\nSTDOUT: {stdout}\nSTDERR: {stderr}"
) from e
# plantuml 会输出 source.{fmt}，我们移到指定 out
produced = Path(out.parent) / src.name.replace(".puml", ext)
if produced.exists() and produced != out:
produced.rename(out)
return str(out)
def _render_plantuml_pdf_via_svg(source: str, out: Path, plantuml_cmd: list) -> str:
"""先用 PlantUML 出 SVG，再用 rsvg-convert 转成单页 PDF（整图一体、不分页）。"""
with tempfile.TemporaryDirectory() as td:
src = Path(td) / "source.puml"
src.write_text(source, encoding="utf-8")
# 生成 SVG
svg_cmd = list(plantuml_cmd) + ["-tsvg", "-o", str(td), str(src)]
try:
subprocess.run(svg_cmd, check=True, capture_output=True, text=True)
except subprocess.CalledProcessError as e:
raise RuntimeError(
f"plantuml 生成 SVG 失败：\nSTDOUT: {e.stdout}\nSTDERR: {e.stderr}"
) from e
svg_path = Path(td) / "source.svg"
if not svg_path.exists():
raise RuntimeError("plantuml 没有输出预期 SVG")
# SVG → PDF（rsvg-convert 以 SVG viewBox 为唯一页面尺寸，一定是单页）
try:
subprocess.run(
["rsvg-convert", "-f", "pdf", "-o", str(out), str(svg_path)],
check=True, capture_output=True, text=True,
)
except subprocess.CalledProcessError as e:
raise RuntimeError(
f"rsvg-convert 将 SVG 转 PDF 失败：\nSTDERR: {e.stderr}"
) from e
return str(out)
# ----- Graphviz -----
def _render_dot(source: str, out: Path) -> str:
if not shutil.which("dot"):
fallback = out.with_suffix(".dot")
fallback.write_text(source, encoding="utf-8")
raise RuntimeError(
f"未找到 dot (Graphviz)，已把 DOT 源码保存到 {fallback}。"
"\n安装： brew install graphviz"
)
ext = out.suffix.lower()
fmt = {".svg": "svg", ".png": "png", ".pdf": "pdf"}[ext]
with tempfile.TemporaryDirectory() as td:
src = Path(td) / "source.dot"
src.write_text(source, encoding="utf-8")
cmd = ["dot", f"-T{fmt}", str(src), "-o", str(out)]
try:
subprocess.run(cmd, check=True, capture_output=True, text=True)
except subprocess.CalledProcessError as e:
raise RuntimeError(
f"dot 渲染失败：\nSTDERR: {e.stderr}"
) from e
return str(out)
FILE:scripts/styles.py
"""火一五流程图 — 10 种 2026 现代风格主题。
v1.3 改造重点（经过行业设计调研后的设计取向）：
- **soft light 默认范式**：浅色填充 + 彩色描边 + 深色文字（Linear/Vercel/Stripe/Radix 风）
取代旧版的"深色大色块 + 白字"老旧扁平风
- **双层 drop-shadow**：`drop-shadow(0 1px 2px) drop-shadow(0 4px 12px)`
复刻 Linear / Vercel 的"柔和浮起"质感
- **判定菱形强调色**：decision 节点独立 classDef，用 accent 色突出
- **排版**：nodeSpacing=60、rankSpacing=80、padding=20、fontSize=15，Inter 字体栈
- **曲线按类型**：flowchart/state basis、sequence/c4 step、swimlane linear
"""
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Dict, List, Optional
@dataclass
class Style:
key: str
name: str
# Mermaid theme：default / neutral / forest / dark / base
mermaid_theme: str = "base"
# ---- 核心色板（软色范式）----
primary_color: str = "#EEF2FF" # 节点主填充（浅色系）
primary_text_color: str = "#1E293B" # 节点文字（深色，在浅填充上保证对比）
primary_border_color: str = "#6366F1" # 节点描边（彩色，成为视觉焦点）
secondary_color: str = "#F1F5F9" # 次级节点 / 容器填充
tertiary_color: str = "#E2E8F0" # 三级节点 / 容器描边
line_color: str = "#64748B" # 连线
background: str = "#FFFFFF" # 画布背景
accent_color: str = "#F59E0B" # 判断菱形 / 强调色
accent_text_color: str = "#FFFFFF" # 判断菱形文字
accent_border_color: str = "#D97706" # 判断菱形描边
note_color: str = "#FEF3C7"
note_border: str = "#FBBF24"
# ---- 字体（Inter 优先）----
font_family: str = (
'"Inter", -apple-system, BlinkMacSystemFont, "PingFang SC", '
'"HarmonyOS Sans SC", "Microsoft YaHei", "Noto Sans CJK SC", '
'ui-sans-serif, system-ui, sans-serif'
)
font_size: str = "15px"
font_weight_node: int = 500
# ---- 现代化视觉参数 ----
gradient_start: str = "" # drawio 渐变起色（空 = 无渐变）
gradient_end: str = "" # drawio 渐变终色
corner_radius: int = 10 # 圆角半径
stroke_width: float = 1.5 # 描边宽度
# 双层阴影：紧邻层 + 扩散层（Linear/Vercel 招牌质感）
shadow_close_blur: int = 2
shadow_close_offset: int = 1
shadow_close_opacity: float = 0.06
shadow_far_blur: int = 12
shadow_far_offset: int = 4
shadow_far_opacity: float = 0.08
shadow_color: str = "#0F172A"
# ---- 布局留白 ----
node_spacing: int = 60
rank_spacing: int = 80
padding: int = 20
# ---- 备用 palette ----
palette: List[str] = field(default_factory=list)
# ============================================================
# 现代商务 modern —— Radix Indigo/Slate 风，商务文档/技术默认
# 浅 indigo 填充 + 紫色描边 + 深灰字 + 琥珀判断色
# ============================================================
MODERN = Style(
key="modern",
name="现代商务",
mermaid_theme="base",
primary_color="#EEF2FF",
primary_text_color="#1E293B",
primary_border_color="#6366F1",
secondary_color="#F8FAFC",
tertiary_color="#E2E8F0",
line_color="#64748B",
background="#FFFFFF",
accent_color="#F59E0B",
accent_text_color="#78350F",
accent_border_color="#D97706",
note_color="#FEF3C7",
note_border="#FBBF24",
gradient_start="#E0E7FF",
gradient_end="#C7D2FE",
palette=["#6366F1", "#0EA5E9", "#10B981", "#F59E0B", "#EF4444"],
)
# ============================================================
# 经典稳重 classic —— 咨询报告风，浅蓝卡 + 琥珀判断
# ============================================================
CLASSIC = Style(
key="classic",
name="经典稳重",
mermaid_theme="base",
primary_color="#DBEAFE",
primary_text_color="#1E3A8A",
primary_border_color="#2563EB",
secondary_color="#F0F9FF",
tertiary_color="#BFDBFE",
line_color="#1E40AF",
background="#FDFCFA",
accent_color="#D97706",
accent_text_color="#78350F",
accent_border_color="#B45309",
note_color="#FEF3C7",
note_border="#FBBF24",
gradient_start="#BFDBFE",
gradient_end="#93C5FD",
palette=["#1E40AF", "#6366F1", "#D97706", "#DC2626", "#059669"],
)
# ============================================================
# 暗色霓虹 dark —— Linear Dark / Vercel 深色模式
# 深色画布 + 浅节点 + 紫色描边 + 青色强调
# ============================================================
DARK = Style(
key="dark",
name="暗色霓虹",
mermaid_theme="dark",
primary_color="#1E293B",
primary_text_color="#F1F5F9",
primary_border_color="#8B5CF6",
secondary_color="#334155",
tertiary_color="#475569",
line_color="#94A3B8",
background="#0F172A",
accent_color="#22D3EE",
accent_text_color="#0F172A",
accent_border_color="#06B6D4",
note_color="#1E293B",
note_border="#8B5CF6",
gradient_start="#334155",
gradient_end="#0F172A",
shadow_color="#8B5CF6",
shadow_far_opacity=0.25,
shadow_far_blur=18,
palette=["#8B5CF6", "#22D3EE", "#F472B6", "#34D399", "#FBBF24"],
)
# ============================================================
# 小红书暖奶油 xiaohongshu —— 种草封面 / 女性向
# 奶油背景 + 浅粉节点 + 玫红描边
# ============================================================
XIAOHONGSHU = Style(
key="xiaohongshu",
name="小红书暖奶油",
mermaid_theme="base",
primary_color="#FFE4E6",
primary_text_color="#881337",
primary_border_color="#E11D48",
secondary_color="#FFF1F2",
tertiary_color="#FECDD3",
line_color="#F43F5E",
background="#FFF8F3",
accent_color="#F59E0B",
accent_text_color="#78350F",
accent_border_color="#D97706",
note_color="#FEF3C7",
note_border="#FDE68A",
gradient_start="#FECDD3",
gradient_end="#FDA4AF",
palette=["#E11D48", "#F59E0B", "#F472B6", "#A855F7", "#14B8A6"],
)
# ============================================================
# 海洋蓝 ocean —— SaaS 官网 / 技术架构
# ============================================================
OCEAN = Style(
key="ocean",
name="海洋蓝",
mermaid_theme="base",
primary_color="#E0F2FE",
primary_text_color="#075985",
primary_border_color="#0284C7",
secondary_color="#F0F9FF",
tertiary_color="#BAE6FD",
line_color="#0369A1",
background="#F0F9FF",
accent_color="#F97316",
accent_text_color="#7C2D12",
accent_border_color="#EA580C",
note_color="#E0F2FE",
note_border="#7DD3FC",
gradient_start="#BAE6FD",
gradient_end="#7DD3FC",
palette=["#0284C7", "#06B6D4", "#10B981", "#F97316", "#F43F5E"],
)
# ============================================================
# 森林绿 forest —— 环保 / 健康 / ESG
# ============================================================
FOREST = Style(
key="forest",
name="森林绿",
mermaid_theme="base",
primary_color="#D1FAE5",
primary_text_color="#064E3B",
primary_border_color="#047857",
secondary_color="#F0FDF4",
tertiary_color="#A7F3D0",
line_color="#059669",
background="#F0FDF4",
accent_color="#EA580C",
accent_text_color="#7C2D12",
accent_border_color="#C2410C",
note_color="#ECFCCB",
note_border="#BEF264",
gradient_start="#A7F3D0",
gradient_end="#6EE7B7",
palette=["#047857", "#65A30D", "#CA8A04", "#EA580C", "#0891B2"],
)
# ============================================================
# 夕阳暖橙 sunset —— 运营活动 / 温暖叙事
# ============================================================
SUNSET = Style(
key="sunset",
name="夕阳暖橙",
mermaid_theme="base",
primary_color="#FFEDD5",
primary_text_color="#7C2D12",
primary_border_color="#EA580C",
secondary_color="#FFF7ED",
tertiary_color="#FED7AA",
line_color="#F97316",
background="#FFF7ED",
accent_color="#0891B2",
accent_text_color="#FFFFFF",
accent_border_color="#0E7490",
note_color="#FFEDD5",
note_border="#FDBA74",
gradient_start="#FED7AA",
gradient_end="#FDBA74",
palette=["#EA580C", "#F59E0B", "#D946EF", "#0891B2", "#059669"],
)
# ============================================================
# 极简素雅 minimal —— Notion / 学术出版物
# 纯白 + 细灰描边 + 深字 + 单一蓝色强调
# ============================================================
MINIMAL = Style(
key="minimal",
name="极简素雅",
mermaid_theme="neutral",
primary_color="#F9FAFB",
primary_text_color="#111827",
primary_border_color="#374151",
secondary_color="#FFFFFF",
tertiary_color="#E5E7EB",
line_color="#6B7280",
background="#FFFFFF",
accent_color="#2563EB",
accent_text_color="#FFFFFF",
accent_border_color="#1D4ED8",
note_color="#F3F4F6",
note_border="#D1D5DB",
gradient_start="", # 极简风不使用渐变
gradient_end="",
stroke_width=1.2,
shadow_close_opacity=0.04,
shadow_far_opacity=0.05,
shadow_far_blur=8,
palette=["#111827", "#2563EB", "#B45309", "#047857", "#7C3AED"],
)
# ============================================================
# 马卡龙粉嫩 pastel —— 儿童教育 / 女性向
# ============================================================
PASTEL = Style(
key="pastel",
name="马卡龙粉嫩",
mermaid_theme="base",
primary_color="#EDE9FE",
primary_text_color="#4C1D95",
primary_border_color="#A78BFA",
secondary_color="#FCE7F3",
tertiary_color="#FBCFE8",
line_color="#C084FC",
background="#FEFAFF",
accent_color="#F472B6",
accent_text_color="#831843",
accent_border_color="#DB2777",
note_color="#FFF1F2",
note_border="#FDA4AF",
gradient_start="#DDD6FE",
gradient_end="#C4B5FD",
shadow_color="#A78BFA",
shadow_far_opacity=0.14,
palette=["#A78BFA", "#F472B6", "#60A5FA", "#34D399", "#FBBF24"],
)
# ============================================================
# 极客 github —— 开源 README / 文档
# GitHub 品牌蓝 + 细描边 + 白背景
# ============================================================
GITHUB = Style(
key="github",
name="极客 GitHub",
mermaid_theme="base",
primary_color="#DDF4FF",
primary_text_color="#0969DA",
primary_border_color="#0969DA",
secondary_color="#F6F8FA",
tertiary_color="#D0D7DE",
line_color="#57606A",
background="#FFFFFF",
accent_color="#2DA44E",
accent_text_color="#FFFFFF",
accent_border_color="#1A7F37",
note_color="#FFF8C5",
note_border="#D4A72C",
gradient_start="#B6E3FF",
gradient_end="#54AEFF",
palette=["#0969DA", "#2DA44E", "#CF222E", "#9A6700", "#8250DF"],
)
_ALL = {
s.key: s
for s in (
MODERN,
CLASSIC,
DARK,
XIAOHONGSHU,
OCEAN,
FOREST,
SUNSET,
MINIMAL,
PASTEL,
GITHUB,
)
}
_ALIAS = {
"现代": MODERN.key,
"商务": MODERN.key,
"linear": MODERN.key,
"vercel": MODERN.key,
"经典": CLASSIC.key,
"稳重": CLASSIC.key,
"暗色": DARK.key,
"黑色": DARK.key,
"霓虹": DARK.key,
"dark-neon": DARK.key,
"linear-dark": DARK.key,
"小红书": XIAOHONGSHU.key,
"xhs": XIAOHONGSHU.key,
"奶油": XIAOHONGSHU.key,
"海洋": OCEAN.key,
"蓝": OCEAN.key,
"蓝色": OCEAN.key,
"森林": FOREST.key,
"绿": FOREST.key,
"绿色": FOREST.key,
"自然": FOREST.key,
"夕阳": SUNSET.key,
"暖橙": SUNSET.key,
"橙": SUNSET.key,
"橙色": SUNSET.key,
"极简": MINIMAL.key,
"素雅": MINIMAL.key,
"黑白": MINIMAL.key,
"学术": MINIMAL.key,
"论文": MINIMAL.key,
"notion": MINIMAL.key,
"马卡龙": PASTEL.key,
"粉嫩": PASTEL.key,
"粉": PASTEL.key,
"粉色": PASTEL.key,
"儿童": PASTEL.key,
"极客": GITHUB.key,
"程序员": GITHUB.key,
"gh": GITHUB.key,
}
def get_style(key: str) -> Style:
k = (key or "modern").strip().lower()
if k in _ALL:
return _ALL[k]
if k in _ALIAS:
return _ALL[_ALIAS[k]]
raise ValueError(f"未知风格：{key}；可选 {list(_ALL)}")
def list_styles() -> Dict[str, Style]:
return dict(_ALL)
def to_mermaid_theme_variables(style: Style) -> Dict[str, str]:
"""Mermaid initialize 支持的 themeVariables。"""
return {
"primaryColor": style.primary_color,
"primaryTextColor": style.primary_text_color,
"primaryBorderColor": style.primary_border_color,
"lineColor": style.line_color,
"secondaryColor": style.secondary_color,
"tertiaryColor": style.tertiary_color,
"background": style.background,
"mainBkg": style.primary_color,
"secondBkg": style.secondary_color,
"tertiaryBkg": style.tertiary_color,
"fontFamily": style.font_family,
"fontSize": style.font_size,
"noteBkgColor": style.note_color,
"noteBorderColor": style.note_border,
"noteTextColor": style.primary_text_color,
"actorBkg": style.primary_color,
"actorBorder": style.primary_border_color,
"actorTextColor": style.primary_text_color,
"activationBkgColor": style.secondary_color,
"sequenceNumberColor": style.primary_text_color,
"clusterBkg": style.secondary_color,
"clusterBorder": style.tertiary_color,
"titleColor": style.primary_border_color,
"edgeLabelBackground": style.background,
"signalColor": style.line_color,
"signalTextColor": style.primary_text_color,
"labelBoxBkgColor": style.secondary_color,
"labelBoxBorderColor": style.tertiary_color,
"labelTextColor": style.primary_text_color,
"loopTextColor": style.primary_text_color,
"activationBorderColor": style.primary_border_color,
}
def to_mermaid_init_directive(style: Style, diagram_type: str = "flowchart") -> str:
"""返回 `%%{init: {...}}%%` 这一行，放在 Mermaid 代码最前。
diagram_type 用于按图类选择最佳曲线（不同图适合不同 curve）。
"""
import json
# 按图类选择曲线
curve_by_type = {
"flowchart": "basis",
"architecture": "basis",
"state": "basis",
"swimlane_mermaid": "linear",
"sequence": "basis",
"c4_context": "basis",
"c4_container": "basis",
}
curve = curve_by_type.get(diagram_type, "basis")
cfg = {
"theme": style.mermaid_theme,
"themeVariables": to_mermaid_theme_variables(style),
"flowchart": {
"curve": curve,
"htmlLabels": True,
"useMaxWidth": False,
"nodeSpacing": style.node_spacing,
"rankSpacing": style.rank_spacing,
"padding": style.padding,
"diagramPadding": 24,
"defaultRenderer": "dagre-wrapper",
},
"sequence": {
"mirrorActors": False,
"showSequenceNumbers": False,
"actorMargin": 80,
"boxMargin": 14,
"messageMargin": 45,
"noteMargin": 14,
"wrap": True,
"width": 160,
},
"gantt": {
"barHeight": 24,
"barGap": 8,
"topPadding": 60,
"leftPadding": 90,
"fontSize": 13,
"sectionFontSize": 14,
"numberSectionStyles": 4,
},
"er": {
"layoutDirection": "TB",
"entityPadding": 18,
"fontSize": 13,
"minEntityWidth": 120,
"minEntityHeight": 80,
},
"state": {
"padding": 16,
"titleTopMargin": 20,
},
"themeCSS": _mermaid_css(style),
}
return "%%{init: " + json.dumps(cfg, ensure_ascii=False) + "}%%"
def _hex_rgb(hex_color: str) -> str:
h = (hex_color or "#0F172A").lstrip("#")
if len(h) == 3:
h = "".join(c * 2 for c in h)
r, g, b = int(h[0:2], 16), int(h[2:4], 16), int(h[4:6], 16)
return f"{r},{g},{b}"
def _mermaid_css(style: Style) -> str:
"""注入自定义 CSS：圆角、双层投影、边标签、字体对齐。Mermaid 10+ 支持 themeCSS。
双层 drop-shadow 模拟 Linear / Vercel 的浮起质感。
"""
r = style.corner_radius
sw = style.stroke_width
shadow_rgb = _hex_rgb(style.shadow_color)
# 双层阴影：近处 + 远处
close = (
f"drop-shadow(0 {style.shadow_close_offset}px "
f"{style.shadow_close_blur}px rgba({shadow_rgb},{style.shadow_close_opacity}))"
)
far = (
f"drop-shadow(0 {style.shadow_far_offset}px "
f"{style.shadow_far_blur}px rgba({shadow_rgb},{style.shadow_far_opacity}))"
)
shadow = f"{close} {far}"
return (
# 圆角矩形节点
f".node rect,.node path{{"
f"rx:{r}px;ry:{r}px;stroke-width:{sw}px;"
f"filter:{shadow};"
f"}}"
# 圆与椭圆不需要 rx/ry
f".node circle,.node ellipse{{"
f"stroke-width:{sw}px;filter:{shadow};"
f"}}"
# 多边形（hexagon 等）保留形状但加阴影
f".node polygon{{stroke-width:{sw}px;filter:{shadow};}}"
# 连线
f".edgePath .path{{stroke-width:{sw}px;stroke-linecap:round;stroke-linejoin:round;}}"
# 容器（cluster / subgraph）
f".cluster rect{{rx:{r + 4}px;ry:{r + 4}px;"
f"stroke-width:{max(sw - 0.3, 1.0)}px;"
f"filter:{close};"
f"}}"
f".cluster .label,.cluster text{{font-weight:600;}}"
# 字体权重
f".node .label,.node text,.nodeLabel{{font-weight:{style.font_weight_node};}}"
f".label foreignObject{{overflow:visible;}}"
# 边上的标签
f".edgeLabel{{background-color:{style.background};padding:2px 6px;border-radius:4px;}}"
# 时序图修饰
f".actor{{rx:{r}px;ry:{r}px;filter:{close};}}"
f".messageText{{font-size:13px;}}"
f".note{{rx:8px;ry:8px;filter:{close};}}"
)
def decision_classdef(style: Style) -> str:
"""用于 flowchart 内的 classDef decision 行：判断菱形用 accent 配色。"""
return (
f"classDef decision fill:{style.accent_color},"
f"stroke:{style.accent_border_color},"
f"color:{style.accent_text_color},"
f"stroke-width:{style.stroke_width}px,"
f"font-weight:600"
)
def database_classdef(style: Style) -> str:
"""数据库节点配色（cylinder）。"""
return (
f"classDef database fill:{style.secondary_color},"
f"stroke:{style.line_color},"
f"color:{style.primary_text_color},"
f"stroke-width:{style.stroke_width}px"
)
def to_plantuml_skinparam(style: Style) -> str:
"""PlantUML skinparam 片段（泳道图 / 活动图 / 时序图 / C4 用）。
PlantUML 不支持 CSS drop-shadow；改用 shadowing + 单色阴影近似。
"""
font_name = style.font_family.split(",")[0].strip('"').strip()
shadow = "true" if style.shadow_far_opacity > 0.03 else "false"
return f"""skinparam backgroundColor {style.background}
skinparam defaultFontName {font_name}
skinparam defaultFontSize 14
skinparam defaultFontColor {style.primary_text_color}
skinparam roundCorner {style.corner_radius}
skinparam shadowing {shadow}
skinparam handwritten false
skinparam monochrome false
skinparam padding 6
skinparam dpi 120
skinparam titleFontSize 18
skinparam titleFontColor {style.primary_border_color}
skinparam titleFontStyle bold
skinparam titleBorderThickness 0
skinparam activity {{
BackgroundColor {style.primary_color}
BorderColor {style.primary_border_color}
BorderThickness {style.stroke_width}
FontColor {style.primary_text_color}
FontStyle bold
DiamondBackgroundColor {style.accent_color}
DiamondBorderColor {style.accent_border_color}
DiamondFontColor {style.accent_text_color}
StartColor {style.primary_border_color}
EndColor {style.accent_color}
BarColor {style.line_color}
}}
skinparam swimlane {{
BorderColor {style.tertiary_color}
BorderThickness 1.2
TitleBackgroundColor {style.secondary_color}
TitleFontColor {style.primary_text_color}
TitleFontStyle bold
}}
skinparam note {{
BackgroundColor {style.note_color}
BorderColor {style.note_border}
BorderThickness 1
FontColor {style.primary_text_color}
}}
skinparam arrow {{
Color {style.line_color}
Thickness {style.stroke_width}
FontColor {style.primary_text_color}
FontSize 12
}}
skinparam sequence {{
ArrowColor {style.line_color}
ArrowThickness {style.stroke_width}
LifeLineBorderColor {style.tertiary_color}
LifeLineBackgroundColor {style.secondary_color}
ParticipantBackgroundColor {style.primary_color}
ParticipantBorderColor {style.primary_border_color}
ParticipantFontColor {style.primary_text_color}
ParticipantFontStyle bold
ActorBackgroundColor {style.primary_color}
ActorBorderColor {style.primary_border_color}
ActorFontColor {style.primary_text_color}
BoxBackgroundColor {style.secondary_color}
BoxBorderColor {style.tertiary_color}
DividerBackgroundColor {style.tertiary_color}
GroupBackgroundColor {style.secondary_color}
GroupBorderColor {style.tertiary_color}
GroupFontColor {style.primary_text_color}
}}
skinparam rectangle {{
BackgroundColor {style.secondary_color}
BorderColor {style.tertiary_color}
BorderThickness 1
FontColor {style.primary_text_color}
roundCorner {style.corner_radius}
}}
skinparam package {{
BackgroundColor {style.secondary_color}
BorderColor {style.tertiary_color}
FontColor {style.primary_text_color}
FontStyle bold
}}
"""
5 维度设计评审（美学 / 可用性 / 品牌一致 / 内容 / 技术实现）+ Keep/Fix/Quick Wins 三分类 + 雷达图结构化输出。用于对现成网页、HTML、截图、Figma 链接做专业打分与改进建议。触发词：设计评审、UI 审查、给这个页面打分、review 这个设计、这个界面怎么样、优化建议。
---
name: huo15-openclaw-design-critique
displayName: 火一五设计评审技能
description: 5 维度设计评审（美学 / 可用性 / 品牌一致 / 内容 / 技术实现）+ Keep/Fix/Quick Wins 三分类 + 雷达图结构化输出。用于对现成网页、HTML、截图、Figma 链接做专业打分与改进建议。触发词：设计评审、UI 审查、给这个页面打分、review 这个设计、这个界面怎么样、优化建议。
version: 1.0.0
aliases:
- 火一五设计评审
- 设计评审
- UI 审查
- UX 评审
- 设计打分
- design review
- design critique
---
# 火一五设计评审技能 v1.0
> 结构化 5 维设计评审 — 青岛火一五信息科技有限公司
---
## 一、触发场景
当用户对一个已存在的界面/页面/截图，要求**专业评价或改进建议**：
- "给这个页面打分"
- "review 一下这个 UI"
- "这个设计哪里有问题"
- "这个落地页怎么优化"
- 附带一张截图或一个 URL 要求评估
**产出**：5 维打分（雷达图文本化）+ Keep/Fix/Quick Wins 三分类清单 + 优先级排序。
---
## 二、五维评分体系（每项 1-5 分）
| 维度 | 关注点 | 1 分 | 5 分 |
|------|--------|------|------|
| **美学 Aesthetic** | 字体 / 颜色 / 动效 / 空间 / 氛围 | 通用 AI slop | 意图明确的流派 |
| **可用性 Usability** | 信息层级、可点击性、反馈、a11y | 用户需要猜 | 一眼理解下一步 |
| **品牌一致 Brand** | 与品牌调性 / 产品定位一致度 | 和任何其他站能混用 | 一眼辨识品牌 |
| **内容 Content** | 文案质量、信息密度、有无废话 | 占位文案 / 废话堆砌 | 每个字都在做事 |
| **实现 Implementation** | 性能、响应式、代码质量、可维护 | 打开慢 / 布局崩 | 丝滑 / 鲁棒 |
**总分规则**：
- 5×5 = 25 分制
- **短板决定印象**：任何一项 ≤ 2 分，总分不得超过 15（木桶短板）
- **"AI slop 雷区"**：命中 `huo15-openclaw-frontend-design §四` 硬红线任一，美学直接 ≤ 2
---
## 三、输出结构（每次评审必按此格式）
```
### 总评
[一句话总结：这个设计是什么流派？最大亮点？最大问题？]
### 五维打分
| 维度 | 分数 | 备注 |
|------|------|------|
| 美学 | X/5 | ... |
| 可用性 | X/5 | ... |
| 品牌一致 | X/5 | ... |
| 内容 | X/5 | ... |
| 实现 | X/5 | ... |
**总分：XX/25**
### 雷达图（ASCII）
美学 5
●
内容 ● ─┼─ ● 可用性
●│●
实现 ● 品牌
(用 ● 的位置表示分数，1-5 对应距离中心)
### Keep（保持）
- ...（这几点做得好，不要动）
### Fix（必改）
- P0 ...（上线前必须修）
- P1 ...（下个迭代）
### Quick Wins（1 小时内能做）
- ...（性价比最高的小改动）
### 改进后预期
[预计改完能从 XX/25 到 YY/25]
```
---
## 四、评审工作流
### 阶段 1 · 获取素材
- 如果给的是 URL → 要求用户提供截图（调用 `huo15-openclaw-frontend-design §六·阶段 4` 的 Playwright 截图 CLI）
- 如果给的是 HTML 代码 → 自己读 + 要求用户在本地浏览器打开后截图
- 如果只给了描述 → 拒绝评审，要求至少提供一张截图
### 阶段 2 · 硬红线体检（30 秒）
对照 `huo15-openclaw-frontend-design §四` 的 8 条硬红线逐一过一遍。命中直接列入 Fix · P0。
### 阶段 3 · 五维打分
依次给 5 个维度打分，每个维度**至少一条具体理由**（不能只给分数）。
### 阶段 4 · 三分类建议
- **Keep**：避免改掉的闪光点（保护用户已有投入）
- **Fix**：必改项，按 P0/P1 排序
- **Quick Wins**：投入 ≤ 1 小时、收益明显的
### 阶段 5 · 出报告
按 §三的格式出一份完整报告。
---
## 五、常见评审反模式（Claude 自己要避免）
1. ❌ 只夸不改 —— 这不是评审是吹捧
2. ❌ 全盘否定 —— 用户的投入也有价值，要找 Keep
3. ❌ 空话大话 —— "这里可以更现代化"，没用。要说"字体改成 Playfair Display，字号从 48px 改到 64px"
4. ❌ 建议互相矛盾 —— 先想清楚整体方向再逐条提
5. ❌ 忽略实现成本 —— 要分 P0/P1/Quick Win
---
## 六、与其他 huo15 技能的分工
| 场景 | 归属 |
|------|------|
| 评审 Web UI / HTML / 截图 | **本技能** |
| 评审 PPT 演示稿 | `huo15-openclaw-ppt` 的 review 模式（如有） |
| 评审 Word 文档结构 | `huo15-openclaw-office-doc` |
| 生成设计后自检 | `huo15-openclaw-frontend-design §六·阶段 5` 调用本技能 |
---
## 七、触发词
- 设计评审 / UI 审查 / UX 评审
- 给这个页面打分 / 给这个设计打分
- review 这个设计 / review UI
- 这个界面怎么样 / 这个页面有什么问题
- 优化建议 / 改进建议（针对已有页面）
---
## 八、版本历史
- **v1.0.0（当前 · 2026-04-23）**：初始版本。5 维评分体系 + Keep/Fix/Quick Wins 三分类 + ASCII 雷达图 + 硬红线体检快速筛 + 5 类反模式警示。
---
**技术支持：** 青岛火一五信息科技有限公司
为现有品牌/产品抓取视觉规范并产出 brand-spec.md。5 步硬流程 Ask / Search / Download / Verify+Extract / Codify，输出可被 huo15-openclaw-frontend-design 直接引用的品牌规约。触发词：抓品牌规范、提取品牌、品牌资产、br...
--- name: huo15-openclaw-brand-protocol displayName: 火一五品牌协议技能 description: 为现有品牌/产品抓取视觉规范并产出 brand-spec.md。5 步硬流程 Ask / Search / Download / Verify+Extract / Codify，输出可被 huo15-openclaw-frontend-design 直接引用的品牌规约。触发词：抓品牌规范、提取品牌、品牌资产、brand-spec、做 brand kit、VI 规范、品牌调研。 version: 1.0.0 aliases: - 火一五品牌协议 - 品牌协议 - 品牌调研 - brand protocol - brand spec - VI 规范 --- # 火一五品牌协议技能 v1.0 > 品牌视觉规范抓取与 codify — 青岛火一五信息科技有限公司 --- ## 一、触发场景 当用户要为**现有品牌/产品**做设计，但没有现成品牌规范文件： - "给 X 公司做个落地页，先把他们的品牌抓一下" - "提取 [URL] 的设计规范" - "做一份 brand spec" - "复刻这个品牌的视觉系统" **产出**：一份 `brand-spec.md`，内含可机读的色卡、字体、Logo 描述、调性关键词，可被 `huo15-openclaw-frontend-design` 直接引用。 --- ## 二、5 步硬流程（顺序不能颠倒） ### 步骤 1 · Ask（问 5 个问题） 先问用户，不要自己瞎猜： 1. **品牌/公司名**（全称，中英文） 2. **官网或官方渠道 URL**（至少一个） 3. **重点抓哪部分**（全 VI / 只抓色 / 只抓字体 / 只抓 Logo） 4. **是否有已有 brand guideline PDF/Figma**（有的话直接用，跳过抓取） 5. **用途**（做落地页 / 做海报 / 做 App，影响深度） ### 步骤 2 · Search（定位权威源） 按优先级找： 1. **官方 brand / press kit 页**（大公司通常有 `/brand` `/press` `/about/brand`） 2. **官方 Figma Community 文件** 3. **官方 GitHub 组织下的 design-system 仓库** 4. **官网首页 + 3 个内页**（作为 fallback） **禁止**：只靠 Google 图片搜 Logo。那通常是粉丝做的，色值不准。 ### 步骤 3 · Download（返回 CLI 命令让用户下载） 按 enhance 插件的"禁 child_process"铁律，**返回 CLI 命令**： ```bash mkdir -p ~/brand-kits/<brand-slug>/raw curl -fsSL -o ~/brand-kits/<brand-slug>/raw/logo.svg "<URL>" curl -fsSL -o ~/brand-kits/<brand-slug>/raw/home.html "<URL>" # 需要截图时： npx playwright-core screenshot "<URL>" ~/brand-kits/<brand-slug>/raw/home.png --viewport-size=1440,900 ``` ### 步骤 4 · Verify + Extract（本地提取） 拿到用户下载的文件后，用以下方法提取： | 要素 | 提取方法 | |------|---------| | **主色** | 打开 SVG Logo，读 `<path fill="...">`；或用 ImageMagick `convert logo.png -unique-colors txt:` CLI | | **字体** | 读 HTML `<link rel="stylesheet">` 找 Google Fonts / typekit；或 `curl -s <URL> \| grep -oE "font-family:[^;]+"` | | **强调色** | 读 CSS variables 或 inline `style="color:..."` | | **Logo 形态** | 描述：文字 / 图形 / 图文结合 / 几何 / 具象 | | **调性** | 看首页 hero 文案情绪（理性 / 感性 / 权威 / 亲切） | **Verify 质量门**（"5-10-2-8" 简化版）： - 主色至少验证 **2 个不同来源**（Logo SVG + 官网 CSS），一致才算 - 字体必须给出 **具体家族名**，不能"sans-serif"这种级别 ### 步骤 5 · Codify（输出 brand-spec.md） 按下面模板输出，覆盖到 `~/brand-kits/<brand-slug>/brand-spec.md`： ```markdown # Brand Spec: <品牌全称> > 抓取日期：YYYY-MM-DD > 抓取来源：<URL 列表> > 置信度：High / Medium / Low（来自 §Verify 质量门结果） ## 1. 颜色 | 用途 | 名称 | HEX | oklch | 来源 | |------|------|-----|-------|------| | 主色 | Primary | #XXX | oklch(...) | Logo SVG | | 强调色 | Accent | #XXX | oklch(...) | 官网 CTA 按钮 | | 中性 | Neutral | #XXX | oklch(...) | 官网正文 | ## 2. 字体 | 用途 | 家族 | 回落 | 来源 | |------|------|------|------| | 标题 | <Name> | <fallback> | Google Fonts `<URL>` | | 正文 | <Name> | <fallback> | 同上 | ## 3. Logo - 形态：[文字 / 图形 / 图文] - 主版：<路径> - 最小使用尺寸：XX px - 保护区：Logo 高度的 X% ## 4. 调性关键词（3-5 个） - 例：可靠 / 专业 / 克制 / 冷静 / 现代 ## 5. 参考素材（已下载到本地） - `~/brand-kits/<brand-slug>/raw/logo.svg` - `~/brand-kits/<brand-slug>/raw/home.png` - `~/brand-kits/<brand-slug>/raw/home.html` ## 6. 使用指引（给 frontend-design 的提示词片段） ``` 设计时严格遵循： - 主色 #XXX，强调色 #XXX（不许擅自加紫色渐变） - 标题用 <字体名>，正文用 <字体名> - 调性：<关键词> - 参考本地资源：~/brand-kits/<brand-slug>/raw/ ``` ``` --- ## 三、与其他 huo15 技能的分工 | 场景 | 归属 | |------|------| | 抓取已有品牌视觉规范 | **本技能** | | 从零设计品牌标识 | 超出本技能范围，建议用 `huo15-openclaw-frontend-design` + 大胆流派 | | 拿到 brand-spec 后做页面 | `huo15-openclaw-frontend-design`（用 §6 的提示词片段喂给它） | | 对比多个品牌风格 | `huo15-openclaw-design-director` | --- ## 四、硬红线（违反会让品牌失真） 1. ❌ **从 Google 图片搜的 Logo** —— 色值一定不准 2. ❌ **靠视觉判断颜色**（"看起来是深蓝"）—— 必须从 SVG 或 CSS 读精确值 3. ❌ **只看首页** —— 首页是宣传，内页才是规范 4. ❌ **猜字体**（"看起来像 Helvetica"）—— 必须从 CSS 或 font-face 拿到具体名字 5. ❌ **跳过 Verify 直接 Codify** —— 错一次品牌失真比不抓还糟 --- ## 五、触发词 - 抓品牌规范 / 抓品牌 / 提取品牌 - 做 brand kit / 做 brand spec / 做品牌规范 - 品牌调研 / VI 规范 / 视觉规范 - 复刻这个品牌 / 提取这个网站的风格 --- ## 六、版本历史 - **v1.0.0（当前 · 2026-04-23）**：初始版本。5 步硬流程（Ask / Search / Download / Verify+Extract / Codify）+ 禁 child_process 模式的 CLI 命令返回 + brand-spec.md 结构化模板 + 5 条品牌失真硬红线。 --- **技术支持：** 青岛火一五信息科技有限公司
当用户没给明确方向时，基于 6 大美学流派 × 24 设计哲学库（含 mobile-native 平台派）生成 3 个反差方向的 Junior pass 简报对比，帮用户快速定流派、定基调、定差异点。配合 huo15-openclaw-frontend-design v4.x 使用，直接读取其 tokens /...
---
name: huo15-openclaw-design-director
displayName: 火一五设计方向顾问技能
description: 当用户没给明确方向时，基于 6 大美学流派 × 24 设计哲学库（含 mobile-native 平台派）生成 3 个反差方向的 Junior pass 简报对比，帮用户快速定流派、定基调、定差异点。配合 huo15-openclaw-frontend-design v4.x 使用，直接读取其 tokens / compare-matrix / redLineWaiver / multi-genre-compare 接力入口。触发词：帮我选设计方向、做几个方向对比、三个风格对比、design direction、设计选型、风格提案、APP 选风格、移动端选方向、iOS 还是安卓、跨端方案选型。
version: 2.0.0
aliases:
- 火一五设计方向技能
- 火一五设计方向顾问技能
- 火一五风格提案技能
- 火一五方向选型技能
- 火一五跨端方案技能
- 火一五设计方向
- 设计方向
- 方向选型
- 风格提案
- design direction
- 选风格
- APP 选风格
- 跨端选型
---
# 火一五设计方向顾问技能 v2.0
> 多方向设计提案生成 — 青岛火一五信息科技有限公司
> **v2.0 起**：从 5 流派扩到 6 流派含 mobile-native 子集；24 条设计哲学库；与 `huo15-openclaw-frontend-design` v4.x 全量接力（tokens / compare-matrix / multi-genre-compare / redLineWaiver / a11y-checklist）
---
## 一、触发场景
当用户要做一个页面 / 产品 / 品牌 / APP / 小程序，但**没明确美学方向**时：
- "帮我选个设计方向"
- "做三个风格对比"
- "这个 APP 应该做成什么风格"
- "iOS / 安卓 / 鸿蒙 选哪种平台风" ⭐v2.0
- 或在 `huo15-openclaw-frontend-design §三` 被用户选"让你决定"时自动触发
**产出**：3 个反差方向的简报（流派 + tokens 路径 + 差异点 + redLineWaiver）+ 五维对比矩阵 + 推荐方向，按接力消息格式移交给 `frontend-design` 做并行 Junior pass。
---
## 二、设计哲学库（24 条可引用）
### 2.1 极简主义派（5 条）
1. **原研哉（Kenya Hara）** — 白、空、无印良品式的留白美学
2. **Dieter Rams 十诫** — 少即是多，诚实、长寿、环保
3. **Swiss Design / 国际主义** — 网格、无衬线、左对齐
4. **Apple 后乔布斯** — 超大字、超大图、一屏一个重点
5. **Stripe 极简科技** — 渐变 + sans-serif + 大量白
→ 对应 frontend-design 流派：**bold-minimal**
### 2.2 编辑杂志派（4 条）
6. **NYT / 纽约客** — 衬线、纵向栅格、引号做装饰
7. **Monocle 杂志** — 深色衬线 + 米色底 + 分栏
8. **Pentagram 平面** — 超大字排版 + 强烈对比
9. **Hoefler & Co 字体中心主义** — 字体本身就是视觉
→ 对应 frontend-design 流派：**editorial**
### 2.3 前卫实验派（4 条）
10. **Sagmeister 观念先锋** — 手写、大胆、打破规则
11. **David Carson 破坏性排版** — 故意错位、字符切割
12. **Brutalist Web Design** — 粗黑、原始、反精致
13. **Y2K / Vaporwave 复古未来** — 霓虹、CRT、像素
→ 对应 frontend-design 流派：**brutalist** / **retro-future**
### 2.4 东方 / 有机派（4 条）
14. **东方禅意** — 枯山水、空寂、留白 80%
15. **日本民艺（Mingei）** — 手工感、温润、不对称
16. **Field.io 动态几何** — 数字生成艺术
17. **Organic / 有机自然** — 手绘、暖色、柔边
→ 对应 frontend-design 流派：**organic**
### 2.5 信息 / 功能派（3 条）
18. **Tufte 数据可视化** — 数据墨水比、无废装饰
19. **Bauhaus 功能主义** — 形式追随功能、三原色
20. **Edward Tufte 小图密度** — sparkline、紧凑信息层级
→ 对应 frontend-design 流派：B 端 dashboard 场景常 mix **bold-minimal** + 数据组件
### 2.6 移动平台派 ⭐v2.0（4 条）
21. **Apple Human Interface Guidelines** — large title、segmented control、grouped list、blur navbar、HIG spring 动效
22. **Material Design 3 dynamic color** — seed 派生 primary / secondary / tertiary container；FAB；emphasized easing 4 档
23. **HarmonyOS 灵动色块** — 4 色块同明度多色相（L≈0.78）、大圆角胶囊（24-48px 分级）、fluid easing
24. **微信小程序 Native** — `<page-meta>` + `safe-area-inset` + 原生 tabBar、PingFang SC 字体豁免、rpx 适配
→ 对应 frontend-design 流派：**mobile-native-ios** / **mobile-native-md3** / **mobile-native-harmony** + 小程序四端（微信 / 支付宝 / 抖音 / 快手）
---
## 三、3 方向生成法
### 3.1 方向选取规则
**不要选 3 个相似方向**。从 frontend-design v4.1 [`tokens/_compare-matrix.md`](../huo15-openclaw-frontend-design/tokens/_compare-matrix.md) §反差对位 5 组任选一组：
| 命题 | 流派组合 |
|---|---|
| 理性 vs 感性 vs 实验 | bold-minimal × organic × brutalist |
| 冷峻 vs 温暖 vs 复古 | editorial × organic × retro-future |
| 桌面 vs 移动 vs 跨端 ⭐v2.0 | bold-minimal × mobile-native-ios × mobile-native-harmony |
| 极简 vs 信息密度 vs 装饰 | bold-minimal × editorial × retro-future |
| Web vs iOS vs 鸿蒙 ⭐v2.0 | bold-minimal × mobile-native-ios × mobile-native-harmony |
或按"经典 + 反差 + 中间"自由组合：
- **1 个保守方向**（BOLD-MINIMAL / EDITORIAL）
- **1 个反差方向**（BRUTALIST / RETRO-FUTURE / 移动平台派任一）
- **1 个中间调和**（ORGANIC / mobile-native-md3）
### 3.2 每个方向必给 6 件东西 ⭐v2.0 增加 tokens 路径
```
### 方向 N：<流派名>
- **一句话定位**：[ 为谁做 + 什么感觉 ]
- **核心字体**：display + body 组合（从 tokens.json typography 读）
- **主色 + 强调色**：含 oklch 值（从 tokens.json color 读）
- **关键元素**：3 个会被用户记住的视觉元素
- **frontend-design 资产路径** ⭐v2.0：
- tokens：`../huo15-openclaw-frontend-design/tokens/<slug>.json`
- example：`../huo15-openclaw-frontend-design/examples/<dir>/index.html`（Junior pass 起手）
- **redLineWaiver 提醒** ⭐v2.0：本流派的合规豁免（避免 design-critique / 评审环节误判违规）
```
### 3.3 五维对比矩阵
| 维度 | 方向 1 | 方向 2 | 方向 3 |
|------|--------|--------|--------|
| 美学震撼 | ★★★☆☆ | ★★★★★ | ★★☆☆☆ |
| 可用性 | ★★★★★ | ★★★☆☆ | ★★★★☆ |
| 品牌辨识 | ★★☆☆☆ | ★★★★★ | ★★★☆☆ |
| 实现成本 | 低 | 高 | 中 |
| 风险 | 低 | 高（可能不被客户接受） | 中 |
| **a11y 友好度** ⭐v2.0 | 见 [`a11y-checklist.md §流派对照`](../huo15-openclaw-frontend-design/references/a11y-checklist.md) |
### 3.4 推荐表态
**必须**给出一个推荐（不能骑墙）：
```
### 推荐
选 **方向 N**，理由：[结合用户的目的、受众、约束的三句话推理]
### 次选
方向 M，适用场景：[如果方向 N 不被接受的退路]
### 反对
不选方向 K，原因：[为什么这条不合适]
```
---
## 四、工作流
### 阶段 1 · 需求分解
- 复述用户的：**目的 / 受众 / 约束 / 时间预算 / 平台**（Web / 移动 / 跨端 / 小程序）⭐v2.0 加平台维度
- 平台维度决定要不要把 mobile-native 子集纳入候选
- 如果以上任一缺失，先用一轮问答补齐
### 阶段 2 · 方向筛选
从 24 条设计哲学库中挑 3 条（按 §3.1 规则组合）
### 阶段 3 · Junior pass 简报生成 ⭐v2.0
本 skill **不直接出 HTML**，而是按下面接力消息格式发给 `frontend-design`，由它跑 [`multi-genre-compare.md`](../huo15-openclaw-frontend-design/references/multi-genre-compare.md) 流程并行出 3 份 Junior pass HTML。
### 阶段 4 · 对比 + 推荐
3 份 Junior pass 截图回流后（frontend-design 自验证用 Chrome MCP 路线），按 §3.3 + §3.4 出报告。
### 阶段 5 · 接力消息格式 ⭐v2.0
```jsonc
// director → frontend-design
{
"task": "multi-genre-junior-pass",
"genres": ["bold-minimal", "organic", "brutalist"],
"context": {
"client": "<品牌名>",
"scope": "<目标页面 / 组件类型>",
"differentiator": "<差异点一句话>",
"platform": "web | mobile | mini-program | cross"
},
"briefs": {
"bold-minimal": "<本 director 写的简报：定位 + 字体 + 主色 + 关键元素>",
"organic": "...",
"brutalist": "..."
}
}
// frontend-design → director（截图回流）
{
"task": "multi-genre-junior-pass-done",
"outputs": [
{ "genre": "bold-minimal", "html": "<rel-path>", "screenshot": "<rel-path>" },
{ "genre": "organic", "html": "<rel-path>", "screenshot": "<rel-path>" },
{ "genre": "brutalist", "html": "<rel-path>", "screenshot": "<rel-path>" }
]
}
```
格式与 [`frontend-design/references/multi-genre-compare.md §6`](../huo15-openclaw-frontend-design/references/multi-genre-compare.md) 一致。
---
## 五、与其他 huo15 技能的分工
| 场景 | 归属 |
|------|------|
| 设计方向选型（3 方向对比） | **本技能** |
| 选定方向后做 HTML 原型 | `huo15-openclaw-frontend-design` |
| 评审已有设计 5 维打分 | `huo15-openclaw-design-critique` |
| 抓品牌规范 brand-spec | `huo15-openclaw-brand-protocol` |
| Web / 移动 / 小程序四端 starter / tokens / a11y / motion | `huo15-openclaw-frontend-design` v4.x |
---
## 六、触发词
- 帮我选设计方向 / 选个方向 / 定方向
- 做三个风格对比 / 做几个方向对比 / 出几个风格
- 这个产品应该做成什么风格
- design direction / design proposal
- 设计选型 / 风格提案
- **APP 选风格 / 移动端选方向 / iOS 还是安卓 / 跨端方案选型 / 鸿蒙还是 iOS** ⭐v2.0
- **小程序选哪个风格 / 多端做什么风** ⭐v2.0
---
## 七、版本历史
- **v2.0.0（当前 · 2026-04-27）**：跨端 + 接力升级。流派从 5 扩到 6（加 MOBILE-NATIVE 含 iOS HIG / Material Design 3 / HarmonyOS / 小程序四端）；设计哲学库从 20 条扩到 24 条（新增 §2.6 移动平台派 4 条：HIG / MD3 / HarmonyOS / 微信小程序 Native）；§3.1 方向选取规则加"桌面 / 移动 / 跨端"+"Web / iOS / 鸿蒙"两组反差对位；§3.2 每个方向必给从 5 件扩到 6 件（增 frontend-design tokens 路径 + redLineWaiver 提醒）；§3.3 五维矩阵加 a11y 友好度引用 frontend-design a11y-checklist；§4 工作流阶段 5 移交正式格式化为接力消息 JSON；与 frontend-design v4.x 全量接力 — tokens / compare-matrix / redLineWaiver / multi-genre-compare / a11y-checklist 全部指路。
- **v1.0.0（2026-04-23）**：初始版本。20 条设计哲学库（5 派分类）+ 3 方向生成规则（1 保守 + 1 反差 + 1 中间）+ 五维对比矩阵 + 强制推荐表态（不骑墙）。
---
**技术支持：** 青岛火一五信息科技有限公司
高保真 Web UI / 移动 H5 / iOS / Android / HarmonyOS / 微信 + 支付宝 + 抖音 + 快手 四端小程序 原生风格原型 + 大胆美学方向 + 反 AI Slop 硬红线 + 8 流派 design tokens（含 motion）系统化（CSS vars / Tailwi...
---
name: huo15-openclaw-frontend-design
displayName: 火一五前端设计技能
description: 高保真 Web UI / 移动 H5 / iOS / Android / HarmonyOS / 微信 + 支付宝 + 抖音 + 快手 四端小程序 原生风格原型 + 大胆美学方向 + 反 AI Slop 硬红线 + 8 流派 design tokens（含 motion）系统化（CSS vars / Tailwind / Figma 三导出） + 多流派并行对比 + WCAG 2.2 AA 无障碍自动审计 + 动效 tokens。用于构建网站、落地页、仪表盘、APP 移动端、小程序（四端）、React/Vue 组件、HTML 海报、产品详情页、信息图、设计系统、无障碍合规页面、动效系统。配套 6 大美学流派 + 小程序子集、15 条硬红线、Junior/Full 两趟渲染、design tokens（color / typography / spacing / radius / shadow / motion）；自验证 Claude in Chrome MCP 优先 + 多路线 fallback + axe-core / Lighthouse a11y 审计；多流派对比与 design-director 联动。触发词：做网站、做落地页、做 UI、做 APP、做 H5、做小程序、做设计系统、design tokens、做动效、motion tokens、wxml、axml、ttml、ksml、做组件、HTML 原型、页面设计、移动端设计、前端设计、做海报、做详情页、iOS 风格、安卓风格、鸿蒙风格、微信小程序、支付宝小程序、抖音小程序、快手小程序、几个方向对比、风格提案、无障碍、a11y、WCAG、Lighthouse 审计、动画时长、easing、缓动函数。
version: 4.6.0
aliases:
- 火一五前端设计技能
- 火一五Web设计技能
- 火一五APP设计技能
- 火一五移动端设计技能
- 火一五H5设计技能
- 火一五小程序设计技能
- 火一五抖音小程序技能
- 火一五快手小程序技能
- 火一五无障碍审计技能
- 火一五动效技能
- 火一五设计系统技能
- 火一五落地页技能
- 火一五UI设计技能
- 火一五海报设计技能
- 火一五前端设计
- 前端设计
- UI 设计
- 页面设计
- HTML 原型
- 落地页设计
- 海报设计
- Web 设计
- APP 设计
- 移动端设计
- H5 设计
- 小程序设计
- 微信小程序
- 支付宝小程序
- 抖音小程序
- 快手小程序
- 无障碍
- a11y
- WCAG
- 动效
- motion tokens
- 动画 tokens
- design tokens
- 设计 tokens
- 设计系统
---
# 火一五前端设计技能 v4.6
> 高保真 Web UI + 移动端 / APP / H5 + 微信 / 支付宝 / 抖音 / 快手 四端小程序 + design tokens（含 motion）系统化 + 多流派并行对比 + WCAG 2.2 AA 无障碍审计 + 动效 tokens + Tailwind v4 `@theme` 适配 原型生成 — 青岛火一五信息科技有限公司
> 设计理念对标 Anthropic `frontend-design` skill 与 2026 社区共识，本土化改写、不拷贝官方内容
> v2.0 起：5 流派 starter HTML（`examples/`）+ 配色 / 字体 / 灵感三件套（`references/`）+ 反 AI Slop 红线扩到 11 条
> v2.1 起：第 6 流派 `MOBILE-NATIVE`（iOS HIG / Material Design 3 / HarmonyOS 三套 starter）+ 移动端红线 2 条（共 13 条）+ 触发词覆盖 APP / H5 / 移动端
> v2.2 起：微信小程序 + 支付宝小程序 starter（归 MOBILE-NATIVE 子集）+ 小程序红线 2 条（共 15 条）+ 字体豁免说明 + 触发词覆盖 wxml / axml
> v3.0 起：自验证工作流升级 — Claude in Chrome MCP 优先，Playwright CLI / 微信开发者工具 / 支付宝 IDE 三路线 fallback；新增 [`references/self-verify.md`](references/self-verify.md) 操作手册
> v4.0 起：design tokens 系统化 — 8 个流派统一 [`tokens/<slug>.json`](tokens/) 扁平 schema（color / colorHex / typography / spacing / radius / shadow），三导出器 jq 一行转 CSS variables / Tailwind config / Figma Tokens Studio
> v4.1 起：多流派并行对比 — 新增 [`tokens/_compare-matrix.md`](tokens/_compare-matrix.md) 8 流派横向对比矩阵 + [`references/multi-genre-compare.md`](references/multi-genre-compare.md) 与 `huo15-openclaw-design-director` 联动手册（Explore subagent 并行 3 流派 Junior pass + 接力消息格式 + redLineWaiver 速查）
> v4.2 起：小程序三端齐 — 新增 [`examples/mini-program/douyin/`](examples/mini-program/douyin/) 抖音 starter（ttml + ttss + project.config + pages/index 4 件套），微信 / 支付宝 / 抖音三端 95% 同源；红线 #14 UI 库列表扩展 TTUI / Tt-Mini-UI；触发词扩到抖音小程序 / ttml；README 升级三端同步迭代姿势 + 真机扫码必查清单
> v4.3 起：小程序四端齐 — 新增 [`examples/mini-program/kuaishou/`](examples/mini-program/kuaishou/) 快手 starter（ksml + 标准 css 后缀 + project.config + pages/index 4 件套）；红线 #14 UI 库列表加 KSUI / kuaishou-uikit；触发词扩到快手小程序 / ksml；README 升级四端同步迭代姿势（微信 → 抖音 → 快手 → 支付宝）+ 顶部胶囊形态四端对照
> v4.4 起：a11y 自动审计 — 新增 [`references/a11y-checklist.md`](references/a11y-checklist.md) WCAG 2.2 AA 30 条速查 + 场景优先级 + 流派 a11y 友好度对照；[`references/self-verify.md`](references/self-verify.md) §1.5 加 axe-core MCP 注入路线 + Lighthouse CLI fallback；触发词扩到无障碍 / a11y / WCAG / Lighthouse
> v4.5 起：动效 tokens — 8 流派 [`tokens/<slug>.json`](tokens/) 加 `motion` 字段（duration / easing / stagger / philosophy），每个流派有差异化动效原则（克制 / 稳重 / 硬切 / CRT 闪烁 / 弹性 / iOS spring / MD3 12 档 / 鸿蒙流畅）；3 导出器同步加 motion 转换段（CSS vars + `@property` 平滑过渡 / Tailwind transitionDuration + transitionTimingFunction / Figma Tokens Studio cubicBezier 4 元数组）；[`tokens/_compare-matrix.md`](tokens/_compare-matrix.md) 加 motion 哲学速查表
> **v4.6 起**：Tailwind v4 适配 — [`tokens/exporters/to-tailwind.md`](tokens/exporters/to-tailwind.md) 加 v4 `@theme {}` 块导出（CSS 内声明 token，前缀化 `--color-* / --spacing-* / --radius-*` 等，自动生成 utility class）；oklch 在 v4 原生支持不需要 `@property` polyfill；hex fallback 用同名属性双写；保留 v3 `theme.extend` 章节作 legacy
---
## 一、触发场景
当用户要构建以下任一，触发本技能：
- 网站 / 落地页 / 官网 / 仪表盘
- React / Vue / HTML / Svelte 组件
- 营销海报 / 产品详情页 / 信息图
- **移动 H5 落地页 / APP 风格原型**（iOS / 安卓 / 鸿蒙 风格 H5，对应 §三 第 6 流派 MOBILE-NATIVE）
- **微信 / 支付宝 / 抖音 / 快手小程序原型** ⭐v2.2 起，v4.3 补齐四端（归 MOBILE-NATIVE 子集，见 `examples/mini-program/`）
- 任何"美化页面 / 优化 UI"类请求
**不触发**（归其他技能）：
- PPT 演示稿 → `huo15-openclaw-ppt`
- Word / PDF 文档 → `huo15-openclaw-office-doc`
- 纯数据分析图表 → 常规 matplotlib / echarts 即可
**产出**：可直接运行的 HTML/CSS/JS（或 React/Vue）代码 + 3 行设计说明 + 可选截图验证命令。
---
## 二、设计思考（动手前必答四问）
| 维度 | 要回答 |
|------|--------|
| 目的 | 这个界面解决什么问题？谁是使用者？ |
| 基调 | 选一个**极端**方向（见 §三） |
| 约束 | 技术栈 / 性能 / 可访问性 / 目标设备 |
| 差异点 | 用户会记住**哪一件**事？ |
**硬规则**：**承诺一个极端方向**。极简和极繁同样有效，关键是意图明确，**不要骑墙**。
---
## 三、六大美学流派（必选其一）
| 流派 | 关键特征 | 适合场景 | 参考 |
|------|---------|---------|------|
| **BOLD-MINIMAL** 勇敢极简 | 大字号、大留白、2 色系、无装饰 | 科技产品、B 端 SaaS、个人作品集 | Stripe / Linear / Apple |
| **EDITORIAL** 编辑杂志 | 衬线字、纵向栅格、引号装饰、杂志版式 | 品牌故事、深度内容、报告 | NYT Style / The Verge |
| **BRUTALIST** 野兽派 | 等宽字、粗黑线、打破网格、刻意粗糙 | 独立工作室、Web3、先锋作品 | Bloomberg / early craigslist |
| **RETRO-FUTURE** 复古未来 | 像素字、CRT 光晕、80s 霓虹配色 | 游戏、音乐、娱乐 | Vaporwave / Cyberpunk 2077 |
| **ORGANIC** 有机自然 | 手绘感、暖色、不规则形状、柔边 | 食品、母婴、健康 | Medium 早期 / Notion |
| **MOBILE-NATIVE** 移动原生 ⭐v2.1 | 遵循平台规范的移动设计：iOS HIG / Material Design 3 / HarmonyOS | APP 原型、H5 落地页、移动 webview | Apple HIG / m3.material.io / 鸿蒙设计指南 |
**如果用户没给方向 ⭐v4.1 升级**：走多流派并行对比流程，详见 [`references/multi-genre-compare.md`](references/multi-genre-compare.md)。
- **首选**：让 `huo15-openclaw-design-director` 选 3 流派（它有 20 条设计哲学 + 五维矩阵）
- **次选**：从 [`tokens/_compare-matrix.md`](tokens/_compare-matrix.md) §反差对位选一组（理性/感性/实验、冷峻/温暖/复古、桌面/移动/跨端等）
- 3 个 Junior pass **必须并行**（用 Explore subagent 隔离 context，不要串行）
- 截图后由 director 打分推荐 / design-critique 5 维评分 / 用户人眼挑，三选一
- 用户敲定 → 删掉其他草稿 → 单流派走阶段 3 Full Pass
**MOBILE-NATIVE 的三选一**：用户说"做 APP / 做 H5"时，先问目标平台 — iOS（用 `examples/mobile-native/ios/`）/ Android（用 `examples/mobile-native/md3/`）/ HarmonyOS（用 `examples/mobile-native/harmony/`）。多平台需求 → 三套 starter 都给，但产出文件夹分开。
**小程序场景** ⭐v2.2 起，v4.3 补齐四端：归 MOBILE-NATIVE 子集，**不另立第 7 流派**（避免膨胀）。
- 微信小程序：`examples/mini-program/wechat/`（wxml + wxss + JSON 配置三件套）
- 支付宝小程序：`examples/mini-program/alipay/`（axml + acss + 配置）
- 抖音小程序 ⭐v4.2：`examples/mini-program/douyin/`（ttml + ttss + 配置）
- 快手小程序 ⭐v4.3：`examples/mini-program/kuaishou/`（ksml + 标准 css 后缀 + 配置）
- **四端同步迭代姿势**：先做微信 → 复制到抖音改 `wx:` → `tt:` → 复制到快手改 `wx:` → `ks:`（两者最相近，各 3 分钟）→ 复制到支付宝改 `wx:` → `a:` / `bindtap` → `onTap`（差异最大，5 分钟）。完整对照表见 [`examples/mini-program/README.md`](examples/mini-program/README.md)。
---
## 四、反 AI Slop 硬红线（违反任一直接判废）
| # | 禁用项 | 为什么 |
|---|--------|--------|
| 1 | 默认 **Inter / Roboto / Arial / system-ui** 字体 | 字体即性格，系统字 = 没性格 |
| 2 | **紫色渐变**（尤其紫色渐变打白底） | 2023 以来 AI 最滥用的视觉陈词滥调 |
| 3 | **emoji 当图标** | 必须用 Lucide / Phosphor / Tabler / Heroicons 真图标 |
| 4 | **圆角卡片 + 左侧彩色竖条** | Tailwind 默认范式，设计师都看腻了 |
| 5 | **CSS 画的伪产品图** | 真产品图用 Unsplash/Picsum 占位，明确标注"待替换" |
| 6 | **默认暗黑 `#121212` + 紫色主题** | 懒，且和 #2 联动犯错 |
| 7 | **Hero + Features + CTA + Footer** 千篇一律骨架 | 按内容定制结构，不要模板化 |
| 8 | 全部用 `#007AFF` / `#FF3B30` 这类 iOS 系统色 | 没有记忆点 |
| 9 | **全局统一 16px / 12px `border-radius`** | Tailwind / shadcn 默认值，工业感 = 没设计 |
| 10 | 滥用 **`backdrop-blur` 玻璃形态**（每个卡片都磨砂） | 2024 后期开始烂大街，掩盖排版无能 |
| 11 | **AI 生成的渐变模糊背景**（紫粉 / 蓝青大色块 blur） | 与红线 #2 联动，是 AI Slop 最强信号 |
| 12 ⭐v2.1 | 移动端**直接套 UI 库默认皮**（Vant / Ant Mobile / NutUI 不改 token） | 没有 brand identity = 没有产品 |
| 13 ⭐v2.1 | 移动端**缺 `viewport-fit=cover` + `safe-area-inset`**（刘海 / Home indicator 被遮） | 客户拿真机一看就崩，硬 a11y 红线 |
| 14 ⭐v2.2 / v4.2 / v4.3 扩展 | 小程序**直接套 WeUI / Vant Weapp / TDesign-Mini / Lin-UI / TTUI / Tt-Mini-UI / KSUI / kuaishou-uikit 默认皮**（不改 token） | 四端 1 千个 demo 长一个样，没产品识别 |
| 15 ⭐v2.2 | 小程序**缺 `<page-meta>` + `safe-area-inset` + `rpx` 适配** | 真机一看顶部胶囊 / 底部 home indicator 重叠，硬适配红线 |
**小程序字体豁免说明** ⭐v2.2：小程序平台**不允许 `@font-face` 加载 web font**（出于性能与审核），无法套用红线 #1 推荐字体（Manrope / DM Sans / IBM Plex Sans）。小程序 wxss / acss 中 `font-family` 优先序：
1. **PingFang SC**（iOS / 微信）
2. **Source Han Sans CN** / **思源黑体**（Android / 支付宝端）
3. **Noto Sans SC** fallback
4. **禁** `-apple-system, BlinkMacSystemFont` 写法（红线 #1 仍生效，且这些 fallback 链在小程序里其实也只走系统中文字体）
数字 / 英文如需差异化字体，可用 wxss 内联 base64 字体子集（仅 0–9 + A–Z），或干脆**全部用 PingFang SC** 数字部分，靠**字号 / 字重**做区分。
---
## 五、美学要素清单（每项都要想过）
### 5.1 字体 Typography
- 主字（display）选有性格的：Playfair Display / IBM Plex Serif / Space Mono / Noto Serif SC / DM Serif / Rubik Mono One
- 副字（body）选考究的：IBM Plex Sans / Source Serif / 思源宋体 / Noto Sans SC
- **主副反差要大**，避免同字族
### 5.2 颜色 Color
- 主色 + 锐利强调色，CSS variables 统一管理
- 优先 **oklch** 色空间（感知均匀，避免灰调）
- 主色占 60-70%，强调色 5-10%，中性 20-30%
- 不要 evenly-distributed palette
### 5.3 动效 Motion
- **页面加载的 staggered reveal > 散落的微交互**
- `animation-delay` 做级联出场
- CSS 优先，React 用 Motion（framer-motion）
- 一个高质量的动效 > 十个微交互
### 5.4 空间 Spatial Composition
- 不对称 / 重叠 / 对角线 / 打破网格
- 留白充足 **或** 密集信息，二选一
- 避免居中对齐一统到底
### 5.5 氛围 Backgrounds & Texture
- 渐变网格 / 噪点 / 几何图案 / 戏剧阴影
- 装饰性边框 / 自定义光标 / grain overlay
- 不要纯色底（除非极简流派明确需要）
### 5.6 Design Tokens ⭐v4.0 / v4.5 加 motion
- 每个流派一份 [`tokens/<slug>.json`](tokens/)，扁平 1 层 schema：`color` / `colorHex` / `typography` / `spacing` / `radius` / `shadow` / **`motion` ⭐v4.5** / `examplePath` / `redLineWaiver?`
- **8 个流派**：`bold-minimal` / `editorial` / `brutalist` / `retro-future` / `organic` / `mobile-native-ios` / `mobile-native-md3` / `mobile-native-harmony`
- **三个导出器**（jq 一行）：[`tokens/exporters/to-css-vars.md`](tokens/exporters/to-css-vars.md) / [`tokens/exporters/to-tailwind.md`](tokens/exporters/to-tailwind.md) / [`tokens/exporters/to-figma.md`](tokens/exporters/to-figma.md)
- **使用流程**：先在 §六 阶段 3.5 选定流派 → 跑 jq 命令出 CSS vars / Tailwind config → Junior Pass starter HTML 直接 `var(--color-xxx)` / `var(--duration-fast)` 引用
- schema 详细见 [`tokens/_schema.md`](tokens/_schema.md)；横向对比见 [`tokens/_compare-matrix.md`](tokens/_compare-matrix.md)（含 v4.5 motion 哲学速查）
### 5.7 Motion Tokens ⭐v4.5（动效原则）
动效不是装饰，是**时间维度的版式**。每个流派的动效原则与该流派静态视觉一致：
- **bold-minimal / editorial**：克制 / 稳重 — 单一 easing、决不弹跳
- **brutalist / retro-future**：硬切 / CRT 闪烁 — linear / step easing，禁缓动函数
- **organic**：弹性 — spring 1.56 超调，模拟手作回落
- **mobile-native iOS / MD3 / Harmony**：照搬平台 spec（iOS spring / MD3 12 档 emphasized / 鸿蒙 fluid）
**实现**：CSS 用 `transition: <prop> var(--duration-fast) var(--easing-standard)`；React 用 Motion（framer-motion）的 `transition={{ duration, ease }}`；列表级联用 `:nth-child(n) { animation-delay: calc(var(--stagger-normal) * (n - 1)) }`。
**禁忌**（继承 §四 红线）：
- 禁滥用 motion（每个元素都 hover scale → 头晕）
- 禁 `transition: all` —— 总是显式列 properties
- 禁忽略 `prefers-reduced-motion: reduce` —— 媒体查询关掉 stagger / spring
---
## 六、工作流（Junior → Full 两趟渲染）
### 阶段 1 · 理解（Understand）
- 复述需求 / 目的 / 受众
- 确认基调和流派
- 列出硬约束（技术栈、浏览器兼容、a11y）
- **多流派模式判断 ⭐v4.1**：用户没给明确流派 + 触发词命中"几个方向 / 三个风格 / 帮我选" → 走 [`references/multi-genre-compare.md`](references/multi-genre-compare.md) 流程；否则进单流派 Junior pass
### 阶段 2 · Junior Pass（假设占位，快速出骨架）
- **从 `examples/<流派>/index.html` 起手**，复制到目标文件再改 — 不要从空白起步
- 同时打开 `references/colors.md` 和 `references/typography.md` 锁配色 / 字体
- 用占位文案（Lorem Ipsum 或真实类似文案）+ 占位图片（Picsum/Unsplash 链接）
- 跑通**结构、栅格、主要交互**
- **诚实标注**每一块占位（`<!-- TODO: 真实文案 -->`）
- 让用户看到方向再继续
### 阶段 3 · Full Pass（补内容、调细节）
- 补真实文案（问用户要 或 用 `huo15-openclaw-brand-protocol` 抓品牌资源）
- 替换真实图片（需要下载时返回 CLI 命令，不用 child_process）
- 微调字号、行高、字距、间距、阴影层级
- 加动效
### 阶段 3.5 · Tokens 导出（可选）⭐v4.0
当用户要把设计落地到既有项目（已有 Tailwind / 已有 Figma 设计系统 / 多产品复用）：
- 选定流派 → 找到对应 [`tokens/<slug>.json`](tokens/)
- 跑 jq 一行转换：CSS vars（[`exporters/to-css-vars.md`](tokens/exporters/to-css-vars.md)）/ Tailwind extend（[`exporters/to-tailwind.md`](tokens/exporters/to-tailwind.md)）/ Figma Tokens Studio（[`exporters/to-figma.md`](tokens/exporters/to-figma.md)）
- 转换产物建议落到 `<用户项目>/generated/tokens/` 入仓
- **不强制**：纯一次性 H5 / 海报场景跳过本阶段，直接用 `examples/` 起手即可
### 阶段 4 · 自验证（Self-Verify）⭐v3.0 工作流升级
**优先路线**：**Claude in Chrome MCP**，由 Claude 直接驱动浏览器渲染并截图，不需要让用户跑命令。
- `mcp__Claude_in_Chrome__list_connected_browsers` → 检查浏览器连接
- `mcp__Claude_in_Chrome__navigate` → 打开 `file://` 或 URL
- `mcp__Claude_in_Chrome__computer({action:"screenshot", save_to_disk:true})` → 截图
- `mcp__Claude_in_Chrome__read_console_messages` → 抓 oklch fallback / 字体 404 / JS 报错
- 移动端用 `resize_window` 切到设备 viewport（393×852 / 412×915 / 396×858）
**Fallback 顺序**（按场景降级）：
1. **MCP 不可用**（`list_connected_browsers` 返 `[]`）→ Playwright CLI（return-cliCmd 让用户执行，延续禁 child_process 铁律）：
```bash
# 桌面端
npx playwright-core screenshot <URL 或 file:///绝对路径> ~/verify.png --viewport-size=1440,900
# 移动端（MOBILE-NATIVE 流派必跑）
npx playwright-core screenshot <URL> ~/verify-iphone.png --viewport-size=393,852
npx playwright-core screenshot <URL> ~/verify-android.png --viewport-size=412,915
```
2. **微信小程序场景** → 微信开发者工具打开 `examples/mini-program/wechat/`，编译预览 / 真机调试扫码
3. **支付宝小程序场景** → 支付宝小程序 IDE 打开 `examples/mini-program/alipay/`，预览扫码
4. **抖音小程序场景 ⭐v4.2** → 抖音开发者工具打开 `examples/mini-program/douyin/`，编译预览 / 扫码用抖音 App 看真机
5. **快手小程序场景 ⭐v4.3** → 快手小程序开发者工具打开 `examples/mini-program/kuaishou/`，编译预览 / 扫码用快手 App 看真机
**完整决策树 + 命令清单 + 兼容性矩阵** 见 [`references/self-verify.md`](references/self-verify.md)（v3.0 新增手册）。
**评审接力**：截图回收后由用户人眼审，或调 `huo15-openclaw-design-critique` 5 维打分。MOBILE-NATIVE 流派额外检查：safe-area-inset 上下有效、tab-bar 触达高度 ≥ 44pt / 48dp；小程序检查 `<page-meta>` 存在 + tabBar native + rpx 适配。
**a11y 审计 ⭐v4.4**：渲染完成后注入 axe-core 跑 WCAG 2.2 AA 检查（首选 Chrome MCP 路线，详见 [`references/self-verify.md`](references/self-verify.md) §1.5 + [`references/a11y-checklist.md`](references/a11y-checklist.md) 30 条速查）；机器测不出的主观 / 交互项（颜色非唯一信息载体、键盘陷阱、错误纠正建议等）人审兜底。violations 修完且 passes ≥ 90% 视为可发布。
### 阶段 5 · 可选 · 评审（Review）
调用 `huo15-openclaw-design-critique` 做 5 维评分 + Keep/Fix/Quick Wins。
---
## 七、产出清单（每次交付必含）
1. **代码文件**（`index.html` / `App.tsx` / `page.vue`），可直接运行
2. **3 行设计说明**：
- 流派（从 §三 五选一）
- 关键设计选择（字体 / 主色 / 动效三选一突出）
- 差异点（用户会记住什么）
3. **（可选）截图验证 CLI 命令**
4. **（可选）已知限制**（占位图未替换 / 未测移动端等）
---
## 八、与其他 huo15 技能的分工
| 能力 | 归属技能 |
|------|---------|
| Web UI / HTML 原型 / 落地页 | **本技能** |
| 设计方向选择（多流派对比） | `huo15-openclaw-design-director` |
| 品牌规范抓取 + brand-spec | `huo15-openclaw-brand-protocol` |
| 设计评审 5 维打分 | `huo15-openclaw-design-critique` |
| PPT 演示稿 | `huo15-openclaw-ppt` |
| Word / PDF 文档 | `huo15-openclaw-office-doc` |
---
## 九、触发词
**Web 端**
- 做网站 / 做落地页 / 做官网 / 做仪表盘
- 做组件 / 做 React 组件 / 做 Vue 组件
- HTML 原型 / 页面原型 / 前端原型
- 美化页面 / 优化 UI / 前端设计 / Web 设计
- 做海报 / 做详情页 / 做信息图 / 产品页
**移动端 ⭐v2.1**
- 做 APP / 做 APP 原型 / 做 APP 落地页 / 做 APP UI
- 做 H5 / 做移动 H5 / 做移动落地页
- iOS 风格 / iOS HIG / iPhone 设计
- 安卓 / Android / Material Design / MD3 / 安卓风格
- 鸿蒙 / HarmonyOS / 鸿蒙设计
**小程序 ⭐v2.2 起，v4.3 补齐四端**
- 做小程序 / 做微信小程序 / 做支付宝小程序 / 做抖音小程序 / 做快手小程序
- 小程序原型 / 小程序落地页 / 小程序首页
- wxml / wxss / 微信小程序设计
- axml / acss / 支付宝小程序设计
- ttml / ttss / 抖音小程序设计 ⭐v4.2
- ksml / 快手小程序设计 ⭐v4.3
- 四端小程序 / 多端同步
**Design Tokens ⭐v4.0**
- design tokens / 设计 tokens / 设计 token / token 导出
- 做设计系统 / 设计系统 / design system
- Tailwind 配色 / Tailwind 主题 / 流派 token
- Figma tokens / Tokens Studio / Figma 主题
- jq 转 CSS variables / 多产品共享主题
**多流派对比 ⭐v4.1**
- 几个方向对比 / 三个风格对比 / 多流派对比
- 帮我选方向 / 帮我选流派 / 你定方向
- design direction / 设计方向 / 风格提案 / 方向选型
- 三套 Junior pass / 三方向草稿
- 五维矩阵 / 流派打分
**无障碍审计 ⭐v4.4**
- 无障碍 / a11y / WCAG / WCAG 2.2
- accessibility / 无障碍审计 / 无障碍合规
- Lighthouse / axe-core / axe 审计
- 屏幕阅读器 / 键盘可操作 / 焦点环
- 对比度 / 触达目标 / alt 文本 / 色盲
**动效 / Motion Tokens ⭐v4.5**
- 做动效 / 加动效 / 动画 / motion / animation
- motion tokens / 动效 token / 动画 token
- duration / easing / 缓动函数 / cubic-bezier
- spring / 弹簧动效 / staggered / 级联出场
- prefers-reduced-motion / 减少动效 / 无障碍动效
---
## 十、版本历史
- **v4.6.0（当前 · 2026-04-27）**：Tailwind v4 适配。[`tokens/exporters/to-tailwind.md`](tokens/exporters/to-tailwind.md) 在 v3 `theme.extend` 章节之上加 v4 `@theme {}` 块导出（推荐，2026 起 Tailwind 默认走这条）：jq 命令把 tokens.json 转成 CSS 内 `@theme` 块，token 命名前缀化（`--color-*` / `--spacing-*` / `--radius-*` / `--shadow-*` / `--duration-*` / `--ease-*`），utility class 由 Tailwind 自动生成无需配置；oklch 在 v4 原生支持不需要 `@property` polyfill；hex fallback 通过同名属性双写实现；保留 v3 `theme.extend` 章节作 legacy 项目兼容。**红线 / 流派 / a11y / motion / 自验证 / 多流派对比 / tokens schema 均不变**，纯 Tailwind 现代化适配。
- **v4.5.0（2026-04-27）**：动效 tokens。8 个 [`tokens/<slug>.json`](tokens/) 加 `motion` 字段（duration / easing / stagger / philosophy），每个流派差异化动效原则：bold-minimal 克制 / editorial 稳重 / brutalist 硬切 / retro-future CRT 闪烁 / organic 弹性 spring / mobile-native-ios HIG spring / mobile-native-md3 完整 12 档 + 4 emphasized / mobile-native-harmony fluid；3 导出器同步加 motion 转换段：`to-css-vars.md` 加 `--duration-* / --easing-* / --stagger-*` + `@property` 块平滑过渡 / `to-tailwind.md` 加 `transitionDuration` + `transitionTimingFunction` 注入 theme.extend / `to-figma.md` 加 cubicBezier 4 元数组 + Tokens Studio v2 兼容；[`tokens/_compare-matrix.md`](tokens/_compare-matrix.md) 加 motion 哲学速查表（含反差选 motion 命题）；[`tokens/_schema.md`](tokens/_schema.md) 加 motion 字段约定；SKILL.md §五 加 5.7 Motion Tokens 段（动效原则 + 实现方式 + `prefers-reduced-motion` 兜底禁忌）；触发词扩到做动效 / motion / duration / easing / spring / 级联出场。**红线 / 流派 / a11y / 自验证均不变**，纯动效 token 化升级。
- **v4.4.0（2026-04-26）**：a11y 自动审计。新增 [`references/a11y-checklist.md`](references/a11y-checklist.md) WCAG 2.2 AA 30 条速查（4 大类 Perceivable / Operable / Understandable / Robust + 场景优先级表 B 端 / 内容站 / 落地页 / 移动端 / 小程序 + 流派 a11y 友好度对照表 8 流派）；[`references/self-verify.md`](references/self-verify.md) §1.5 新增 axe-core MCP 注入路线（`mcp__Claude_in_Chrome__javascript_tool` 跑 axe.run wcag2aa+wcag22aa）+ Lighthouse CLI fallback（`npx lighthouse --only-categories=accessibility`）；§六 阶段 4 加 a11y 审计段（violations 修完 + passes ≥ 90% 可发布）；触发词扩到无障碍 / a11y / WCAG / Lighthouse / 对比度 / 焦点环 / 屏幕阅读器；标识 a11y 与红线的关系（红线 #13 与 a11y #16 触达交集，未引入新红线）。**红线 / 流派 / tokens / 多流派对比均不变**，纯无障碍审计能力补齐。
- **v4.3.0（2026-04-26）**：小程序四端齐。新增 [`examples/mini-program/kuaishou/`](examples/mini-program/kuaishou/) 快手小程序 starter（app.json + app.css + app.js + project.config.json + pages/index 4 件套：ksml + css + json + js），与微信端 95% 同源，仅前缀差异（`wx:` → `ks:` 机械替换），样式后缀用标准 `.css`（区别于微信 `.wxss` / 抖音 `.ttss`）；红线 #14 UI 库列表扩展 KSUI / kuaishou-uikit；触发词扩到快手小程序 / ksml / 四端小程序；[`examples/mini-program/README.md`](examples/mini-program/README.md) 升级四端对照表（推荐顺序：微信 → 抖音 → 快手 → 支付宝）+ 顶部胶囊形态四端对照（微信圆角 / 支付宝椭圆 / 抖音矩形 / 快手矩形）+ "何时该用 Taro / Uni-app 编译框架"提示；阶段 4 自验证补快手开发者工具流程；[`references/inspirations.md`](references/inspirations.md) §7.4 加快手参考、§7.5 通用参考从三端升为四端。**红线 / 流派 / 自验证 / tokens / 多流派对比均不变**，纯第四端补齐。
- **v4.2.0（2026-04-26）**：小程序三端齐。新增 [`examples/mini-program/douyin/`](examples/mini-program/douyin/) 抖音小程序 starter（app.json + app.ttss + app.js + project.config.json + pages/index 4 件套：ttml + ttss + json + js），与微信 / 支付宝端 95% 同源，仅前缀差异（`wx:` → `tt:` 机械替换）；红线 #14 UI 库列表扩展 TTUI / Tt-Mini-UI；触发词扩到抖音小程序 / ttml / ttss / 三端小程序 / 多端同步；[`examples/mini-program/README.md`](examples/mini-program/README.md) 升级三端对照表（微信 → 抖音 3 分钟 / 微信 → 支付宝 5 分钟）+ 真机扫码必查清单；阶段 4 自验证补抖音开发者工具流程；[`references/inspirations.md`](references/inspirations.md) §7.3 加抖音参考、§7.4 三端通用参考含 Taro / Uni-app 编译框架；§三 小程序场景说明双端 → 三端。**红线 / 流派 / 自验证 / tokens / 多流派对比均不变**，纯第三端补齐。
- **v4.1.0（2026-04-26）**：多流派并行对比。新增 [`tokens/_compare-matrix.md`](tokens/_compare-matrix.md) 8 流派横向对比矩阵（关键 token / 反差对位 / redLineWaiver 速查）；新增 [`references/multi-genre-compare.md`](references/multi-genre-compare.md) 多流派对比手册（流程总览 + 与 `huo15-openclaw-design-director` 协作接力 + 接力消息格式 + Explore subagent 并行 3 流派 Junior pass）；SKILL.md §三 改写"如果用户没给方向"段为 director 联动入口；§六 阶段 1 加多流派模式判断；触发词扩到几个方向对比 / design direction / 风格提案 / 五维矩阵 / 流派打分。**红线 / 流派 / 自验证 / tokens 系统均不变**，纯多流派编排升级。预留 director v2 升级时无需 frontend-design 再改的接力入口（tokens schema + compare matrix + redLineWaiver 已就位）。
- **v4.0.0（2026-04-26）**：design tokens 系统化。新增 `tokens/` 目录：8 个流派各一份扁平 1 层 JSON（`color` / `colorHex` / `typography` / `spacing` / `radius` / `shadow` / `examplePath` / `redLineWaiver?`），覆盖 BOLD-MINIMAL / EDITORIAL / BRUTALIST / RETRO-FUTURE / ORGANIC + MOBILE-NATIVE iOS HIG / MD3 / HarmonyOS；三个导出器手册（`tokens/exporters/{to-css-vars,to-tailwind,to-figma}.md`）— jq 一行转 CSS variables / tailwind.config.js extend / Tokens Studio v2 兼容 JSON；SKILL.md §五 加 5.6 Design Tokens 段、§六 加阶段 3.5 Tokens 导出（可选）；触发词扩到 design tokens / 设计系统 / Tailwind 配色 / Figma tokens；导出器延续禁 child_process 铁律（return-cliCmd）；`references/colors.md` 顶部加 tokens 路径指引。**红线 / 流派 / 自验证工作流均不变**，纯设计系统化升级。
- **v3.0.0（2026-04-26）**：自验证工作流升级。阶段 4 重写：**Claude in Chrome MCP 成为首选路线**（list_connected_browsers / navigate / screenshot / read_console_messages / resize_window 5 个 MCP 工具组合驱动）；MCP 不可用时降级到 Playwright CLI（保留 return-cliCmd 模式 + 禁 child_process 铁律）；小程序场景下沉到微信开发者工具 / 支付宝 IDE；新增 `references/self-verify.md` 完整操作手册（决策树 + 4 条路线命令清单 + 三路线兼容性矩阵 + 移动端检查清单 + 设计原则提醒）。**红线 / 流派 / 触发词均不变**，纯工作流升级。
- **v2.2.0（2026-04-26）**：小程序扩展。新增 `examples/mini-program/wechat/` + `examples/mini-program/alipay/` 双小程序 starter（pages/index 三件套 + app.json + project.config / mini.project 配置 + sitemap），归 MOBILE-NATIVE 子集，**不另立第 7 流派**；硬红线由 13 → 15 条（增 #14 禁直接套 WeUI / Vant Weapp / TDesign-Mini / Lin-UI 默认皮、#15 禁缺 `<page-meta>` + safe-area-inset + rpx 适配）；新增小程序字体豁免说明（平台不允许 `@font-face` 加载 web font，font-family 退到 PingFang SC / 思源黑体）；触发词扩到小程序 / wxml / axml / 微信 / 支付宝；阶段 4 自验证补微信开发者工具 + 支付宝 IDE 流程；`references/inspirations.md` 补小程序章节。
- **v2.1.0（2026-04-26）**：移动端扩展。新增第 6 流派 **MOBILE-NATIVE**，覆盖 iOS HIG / Material Design 3 / HarmonyOS 三套平台规范；新增 `examples/mobile-native/{ios,md3,harmony}/index.html` 三套 starter；硬红线由 11 → 13 条（增：禁直接套 Vant / Ant Mobile / NutUI 默认皮、禁缺 viewport-fit=cover + safe-area-inset）；触发词扩到 APP / H5 / 移动端 / iOS 风格 / 安卓 / 鸿蒙；阶段 4 自验证补移动端双截图（iPhone 16 Pro / Pixel 8 viewport）；`references/` 三件套补 mobile-native 章节。
- **v2.0.0（2026-04-26）**：对齐补 + 补料版。SKILL.md 与 clawhub 版本号对齐到 2.0；新建 `examples/` 5 流派 starter HTML（直接可在浏览器打开，oklch + Google Fonts，复制即起步）；新建 `references/` 三件套（`colors.md` / `typography.md` / `inspirations.md`）作为运行期资源；硬红线由 8 → 11 条（增：禁全局 16px 圆角、禁滥用 backdrop-blur、禁 AI 渐变模糊背景）；Junior Pass 工作流强制从 `examples/` 起手。删除空的 `presets/` 占位目录。
- **v1.0.0（2026-04-23）**：初始版本。对齐 Anthropic `frontend-design` 核心理念（BOLD 美学方向 + 反 AI slop），本土化中文改写，加入 5 流派选择、8 条硬红线、Junior/Full 两趟渲染工作流、Playwright 自验证 CLI、与火一五其他技能的分工边界。
---
**技术支持：** 青岛火一五信息科技有限公司
FILE:_meta.json
{
"ownerId": "kn7byevkn40d6z4p7ghdb097z983tj33",
"slug": "huo15-openclaw-frontend-design",
"version": "4.5.0"
}
FILE:examples/bold-minimal/index.html
<!doctype html>
<html lang="zh">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width,initial-scale=1">
<title>BOLD-MINIMAL · 火一五前端设计技能 v2.0 示例</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Sans:wght@400;600&family=Playfair+Display:ital,wght@0,900;1,900&display=swap" rel="stylesheet">
<style>
:root{
--ink: oklch(0.18 0 0);
--paper: oklch(0.99 0.005 95);
--accent: oklch(0.66 0.20 28);
--mute: oklch(0.45 0 0);
}
*{box-sizing:border-box;margin:0;padding:0}
html,body{background:var(--paper);color:var(--ink);font-family:"IBM Plex Sans",sans-serif;-webkit-font-smoothing:antialiased}
main{max-width:1200px;margin:0 auto;padding:80px 32px 160px}
header{display:flex;justify-content:space-between;align-items:center;font-size:13px;letter-spacing:.08em;text-transform:uppercase}
.mark{font-family:"Playfair Display",serif;font-weight:900;font-size:18px;letter-spacing:-.01em;text-transform:none}
nav a{margin-left:32px;color:var(--ink);text-decoration:none;border-bottom:1px solid transparent;transition:.2s ease}
nav a:hover{border-color:var(--ink)}
h1{font-family:"Playfair Display",serif;font-weight:900;font-size:clamp(56px,11vw,180px);line-height:.92;letter-spacing:-.04em;margin:120px 0 40px;max-width:18ch}
h1 em{font-style:italic;color:var(--accent)}
.lede{font-size:21px;line-height:1.55;max-width:48ch;color:var(--mute)}
.cta{display:inline-block;margin-top:56px;padding:20px 40px;background:var(--ink);color:var(--paper);text-decoration:none;font-weight:600;letter-spacing:.04em;font-size:14px;transition:background .2s}
.cta:hover{background:var(--accent)}
.grid{display:grid;grid-template-columns:repeat(2,1fr);gap:96px 64px;margin-top:200px;border-top:1px solid var(--ink);padding-top:96px}
.grid h3{font-family:"Playfair Display",serif;font-weight:900;font-size:44px;line-height:1.05;letter-spacing:-.02em}
.grid p{margin-top:24px;font-size:17px;line-height:1.65;color:var(--mute)}
footer{margin-top:200px;padding-top:28px;border-top:1px solid var(--ink);display:flex;justify-content:space-between;font-size:12px;letter-spacing:.08em;text-transform:uppercase}
.reveal{opacity:0;transform:translateY(20px);animation:r .85s forwards cubic-bezier(.2,.8,.2,1)}
.reveal:nth-of-type(1){animation-delay:.04s}
.reveal:nth-of-type(2){animation-delay:.18s}
.reveal:nth-of-type(3){animation-delay:.32s}
.reveal:nth-of-type(4){animation-delay:.46s}
@keyframes r{to{opacity:1;transform:none}}
@media (max-width:720px){.grid{grid-template-columns:1fr;gap:64px;margin-top:120px}main{padding:48px 24px 96px}}
</style>
</head>
<body>
<main>
<header class="reveal">
<span class="mark">HUO15</span>
<nav><a href="#">作品</a><a href="#">理念</a><a href="#">联系</a></nav>
</header>
<h1 class="reveal">少即是<em>极致</em>。<br>承诺一个方向。</h1>
<p class="lede reveal">BOLD-MINIMAL 流派示例 — 2 色系、大留白、大字号、零装饰。差异点：标题 Playfair italic black 与 IBM Plex Sans 形成强反差，强调色仅在 hover 出现。</p>
<a class="cta reveal" href="#">查看作品集 →</a>
<section class="grid">
<article><h3>排版即结构</h3><p>不靠装饰元素分隔信息，靠字号、字重、留白本身承担层级。设计的节制源自对内容的尊重。</p></article>
<article><h3>颜色靠节制</h3><p>主色 70% 留白、20% 墨黑、10% 强调色。强调色一旦出现，必须够锐利，否则就别用。</p></article>
</section>
<footer><span>© 2026 HUO15</span><span>BOLD-MINIMAL · v2.0</span></footer>
</main>
</body>
</html>
FILE:examples/brutalist/index.html
<!doctype html>
<html lang="zh">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width,initial-scale=1">
<title>BRUTALIST · 火一五前端设计技能 v2.0 示例</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;800&family=Space+Grotesk:wght@500;700&display=swap" rel="stylesheet">
<style>
:root{
--paper: oklch(0.985 0 0);
--ink: oklch(0.10 0 0);
--warn: oklch(0.78 0.22 105);
}
*{box-sizing:border-box;margin:0;padding:0}
body{background:var(--paper);color:var(--ink);font-family:"Space Grotesk",sans-serif;line-height:1.45}
.grid{display:grid;grid-template-columns:repeat(12,1fr);grid-auto-rows:minmax(80px,auto);gap:0;border:6px solid var(--ink);min-height:100vh}
.cell{border:3px solid var(--ink);padding:24px}
.meta{grid-column:1/4;grid-row:1;font-family:"JetBrains Mono",monospace;font-size:13px}
.num{grid-column:4/13;grid-row:1;background:var(--ink);color:var(--paper);font-family:"JetBrains Mono",monospace;font-size:13px;display:flex;justify-content:space-between;align-items:center}
.h1{grid-column:1/10;grid-row:2/4;font-family:"Space Grotesk",sans-serif;font-weight:700;font-size:clamp(48px,9vw,144px);line-height:.95;letter-spacing:-.04em;padding:32px 24px}
.h1 mark{background:var(--warn);color:var(--ink);padding:0 .15em}
.stamp{grid-column:10/13;grid-row:2;background:var(--warn);color:var(--ink);font-family:"JetBrains Mono",monospace;font-size:14px;font-weight:800;text-transform:uppercase;display:flex;flex-direction:column;justify-content:space-between;padding:20px}
.stamp b{font-size:32px;letter-spacing:-.04em}
.ascii{grid-column:10/13;grid-row:3;font-family:"JetBrains Mono",monospace;font-size:11px;white-space:pre;line-height:1.1;overflow:hidden}
.lede{grid-column:1/8;grid-row:4;font-family:"JetBrains Mono",monospace;font-size:15px;line-height:1.6}
.nav{grid-column:8/13;grid-row:4;display:flex;flex-direction:column;font-family:"JetBrains Mono",monospace;font-size:14px;text-transform:uppercase;padding:0}
.nav a{color:var(--ink);text-decoration:none;border-bottom:3px solid var(--ink);padding:14px 24px;display:flex;justify-content:space-between}
.nav a:last-child{border-bottom:none}
.nav a:hover{background:var(--ink);color:var(--paper)}
.footer{grid-column:1/13;grid-row:5;background:var(--ink);color:var(--paper);font-family:"JetBrains Mono",monospace;font-size:12px;display:flex;justify-content:space-between;padding:20px 24px;letter-spacing:.05em}
@media (max-width:720px){.grid{grid-template-columns:1fr;border-width:4px}.cell{border-width:2px}.h1,.meta,.num,.lede,.nav,.stamp,.ascii,.footer{grid-column:1/-1!important;grid-row:auto!important}.h1{font-size:48px;padding:16px}}
</style>
</head>
<body>
<div class="grid">
<div class="cell meta">FILE / huo15.example<br>BUILD / 2026.04.26<br>BRANCH / brutalist</div>
<div class="cell num"><span>// 005 of 005</span><span>HUO15 ━ 火一五</span></div>
<h1 class="cell h1">RAW. <mark>HEAVY.</mark><br>HONEST.</h1>
<div class="cell stamp">v2.0<br><b>BRUTALIST</b></div>
<pre class="cell ascii">+--------+
| ████ |
| ████ |
| ████ |
+--------+
[ HUO15 ]</pre>
<div class="cell lede">BRUTALIST 流派示例 — 6px 黑边、12 列错位栅格、JetBrains Mono 等宽字、纯黑白 + 1 块警示黄。差异点：把"网格"暴露出来当装饰，stamp 块刻意打破对齐。</div>
<nav class="cell nav"><a href="#">PROJECTS<span>→</span></a><a href="#">MANIFESTO<span>→</span></a><a href="#">CONTACT<span>→</span></a></nav>
<div class="cell footer"><span>© 2026 HUO15 ━━━━ NO COOKIES NO BULLSHIT</span><span>v2.0 / BRUTALIST</span></div>
</div>
</body>
</html>
FILE:examples/editorial/index.html
<!doctype html>
<html lang="zh">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width,initial-scale=1">
<title>EDITORIAL · 火一五前端设计技能 v2.0 示例</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=DM+Serif+Display:ital@0;1&family=Source+Serif+4:ital,opsz,wght@0,8..60,400;0,8..60,600;1,8..60,400&display=swap" rel="stylesheet">
<style>
:root{
--paper: oklch(0.97 0.012 75);
--ink: oklch(0.20 0.015 280);
--rule: oklch(0.20 0.015 280 / .25);
--accent: oklch(0.50 0.18 25);
--mute: oklch(0.42 0.02 280);
}
*{box-sizing:border-box;margin:0;padding:0}
body{background:var(--paper);color:var(--ink);font-family:"Source Serif 4",Georgia,serif;font-size:18px;line-height:1.7}
.frame{max-width:1180px;margin:0 auto;padding:48px 40px 96px}
.masthead{display:flex;justify-content:space-between;align-items:baseline;border-bottom:2px solid var(--ink);padding-bottom:16px;font-variant:small-caps;letter-spacing:.06em}
.logo{font-family:"DM Serif Display",serif;font-size:32px;letter-spacing:-.01em}
.issue{font-size:13px;color:var(--mute)}
.kicker{margin-top:64px;font-style:italic;color:var(--accent);font-size:15px;letter-spacing:.08em;text-transform:uppercase;font-variant:small-caps}
h1{font-family:"DM Serif Display",serif;font-weight:400;font-size:clamp(44px,7vw,108px);line-height:1.04;letter-spacing:-.015em;margin-top:18px;max-width:18ch}
h1 em{color:var(--accent)}
.standfirst{font-size:23px;line-height:1.55;max-width:36ch;margin-top:32px;color:var(--mute);font-style:italic}
.byline{margin-top:40px;font-size:14px;letter-spacing:.06em;text-transform:uppercase;color:var(--mute);border-top:1px solid var(--rule);border-bottom:1px solid var(--rule);padding:12px 0}
.body{display:grid;grid-template-columns:repeat(3,1fr);gap:36px;margin-top:64px}
.body p{font-size:17px;line-height:1.78}
.body p:first-child::first-letter{font-family:"DM Serif Display",serif;font-size:74px;float:left;line-height:.85;padding:6px 12px 0 0;color:var(--accent)}
blockquote{grid-column:1/-1;font-family:"DM Serif Display",serif;font-style:italic;font-size:clamp(28px,4.2vw,52px);line-height:1.18;text-align:center;margin:48px auto;max-width:24ch;position:relative;color:var(--ink)}
blockquote::before{content:"\201C";position:absolute;top:-32px;left:50%;transform:translateX(-50%);font-size:120px;line-height:1;color:var(--accent);opacity:.5}
footer{margin-top:96px;padding-top:24px;border-top:2px solid var(--ink);display:flex;justify-content:space-between;font-size:12px;letter-spacing:.08em;color:var(--mute);font-variant:small-caps}
@media (max-width:720px){.body{grid-template-columns:1fr;gap:24px}.frame{padding:32px 24px}}
</style>
</head>
<body>
<div class="frame">
<div class="masthead">
<span class="logo">HUO15 Review</span>
<span class="issue">Issue №07 · 2026 春</span>
</div>
<p class="kicker">设计观察 · Editorial</p>
<h1>当版式回到<em>纸张本来的样子</em></h1>
<p class="standfirst">在屏幕上重建一份杂志的体验，靠的不是模仿装订线，而是让字距、行长、栏宽，重新代替像素去说话。</p>
<p class="byline">撰文 · 编辑部 ／ 摄影 · 待替换占位 ／ 12 分钟阅读</p>
<div class="body">
<p>这不是一篇关于"复古"的文章。把 Source Serif 放到首屏，不是为了让它看起来像旧报纸；而是因为衬线在长文阅读里仍然提供着独有的方向感——衬线是字母给眼睛留下的扶手。</p>
<p>纵向栅格的密度并非装饰。三栏在 1180px 容器里精确给出 36px 行间距，每行 60–75 字符，恰好落在排版学公认的最舒适阅读区间。</p>
<p>所谓"编辑感"，其实是节奏的稳定与意外的混合：稳定来自栅格、来自衬线、来自每页一致的页眉；意外来自尺寸跳变——大引号、首字下沉、横跨三栏的一行字。</p>
<blockquote>设计不是装饰内容，<br>设计就是内容的重量。</blockquote>
<p>EDITORIAL 流派示例：DM Serif Display 标题 + Source Serif 4 正文，三栏栅格，首字下沉，跨栏引号块。配色用低饱和暖纸色 + 一点点砖红强调。</p>
<p>差异点：把杂志的版式秩序搬上屏幕，但不画装订线、不做拟真翻页——节制，但每一处衬线、每一处对齐都在替你说话。</p>
</div>
<footer><span>© 2026 HUO15 REVIEW</span><span>EDITORIAL · v2.0</span></footer>
</div>
</body>
</html>
FILE:examples/mini-program/README.md
# 小程序 Starter — 火一五前端设计技能 v4.3
> 微信 + 支付宝 + 抖音 + 快手 **四端** starter。**浏览器无法直接渲染** wxml / axml / ttml / ksml，必须用各家 IDE。
## 微信小程序
1. 下载并安装 [微信开发者工具](https://developers.weixin.qq.com/miniprogram/dev/devtools/download.html)（Stable v1.06+）
2. 打开工具 → **项目 → 导入项目** → 目录选 `wechat/`
3. AppID 选"测试号"或填自有；项目名留空走默认
4. 点**编译**：左侧模拟器看渲染；点**真机调试**扫码看真机
5. 页面走 `pages/index/index`，全局配置 `app.json`
## 支付宝小程序
1. 下载并安装 [支付宝小程序 IDE](https://opendocs.alipay.com/mini/ide/download)
2. 打开 IDE → **文件 → 打开项目** → 目录选 `alipay/`
3. 点**预览**，扫码用支付宝 App 看真机
4. 配置文件名是 `mini.project.json`（不是 `project.config.json`），别搞混
## 抖音小程序
1. 下载并安装 [抖音开发者工具](https://developer.open-douyin.com/docs/resource/zh-CN/mini-app/develop/developer-instrument/installation/developer-instrument-update-and-download)
2. 打开工具 → **项目 → 导入项目** → 目录选 `douyin/`
3. AppID 用"测试号"或填自有
4. 点**编译预览**，扫码用抖音 App 看真机
5. 模板后缀是 `.ttml` / `.ttss`（与微信 `.wxml` / `.wxss` 95% 同源，仅前缀差异）
## 快手小程序 ⭐v4.3
1. 下载并安装 [快手小程序开发者工具](https://mp.kuaishou.com/docs/develop/developerTools/downloadPath.html)
2. 打开工具 → **项目 → 导入项目** → 目录选 `kuaishou/`
3. AppID 用"测试号"或填自有
4. 点**编译预览**，扫码用快手 App 看真机
5. 模板后缀是 `.ksml` / 样式 `.css`（标准 CSS 后缀，区别于其他端）
## 四端同步迭代姿势 ⭐v4.3 升级
**推荐顺序**：微信 → 抖音 → 快手（最相近）→ 支付宝（差异最大）
### 步骤 1：先在 `wechat/` 改完
`wxml` + `wxss` + 配置全部跑通。开发体验在微信端最顺。
### 步骤 2：复制到 `douyin/` 改前缀（机械替换，3 分钟）
| 微信 | 抖音 |
|---|---|
| `wx:for` / `wx:key` / `wx:if` | `tt:for` / `tt:key` / `tt:if` |
| `wxml` 文件名 | `ttml` |
| `wxss` 文件名 | `ttss` |
| `bindtap` / `bindinput` | **保留**（兼容微信 bind 风格） |
### 步骤 3：复制到 `kuaishou/` 改前缀（机械替换，3 分钟）
| 微信 | 快手 |
|---|---|
| `wx:for` / `wx:key` / `wx:if` | `ks:for` / `ks:key` / `ks:if` |
| `wxml` 文件名 | `ksml` |
| `wxss` 文件名 | `css`（注意：快手用标准 CSS 后缀） |
| `bindtap` / `bindinput` | **保留** |
### 步骤 4：复制到 `alipay/` 改前缀（机械替换，5 分钟）
| 微信 | 支付宝 |
|---|---|
| `wx:for` / `wx:key` / `wx:if` | `a:for` / `a:key` / `a:if` |
| `bindtap` / `bindinput` / `bindsubmit` | `onTap` / `onInput` / `onSubmit` |
| `wxml` 文件名 | `axml` |
| `wxss` 文件名 | `acss` |
| `<page-meta>` | **不支持**，改 `my.setNavigationBar` API（写在 onLoad） |
| `enhanced="true"` 等微信特有属性 | 删掉 |
| `app.json` tabBar `color`/`text` | `textColor`/`name` |
### 步骤 5：四端 ICON / 图片资源建议放 CDN（本地用 base64 或纯色块占位）
## 反 AI Slop 红线（v4.3 扩展 UI 库列表）
| # | 禁用项 | 替代方案 |
|---|---|---|
| 14 | 直接套 **WeUI / Vant Weapp / TDesign-Mini / Lin-UI / TTUI / Tt-Mini-UI / KSUI / kuaishou-uikit** 默认皮 | 自定义 token，至少改 5 个变量再用 |
| 15 | 缺 `<page-meta>` + `safe-area-inset` + `rpx` 适配 | 见本目录 `wechat/pages/index/index.wxml` |
通用红线 #1 字体豁免：小程序里 `font-family` 优先 PingFang SC / Source Han Sans SC / Noto Sans SC（不能加载 Web Font）。
## 视觉风格
四端 starter 共用 ORGANIC 流派暖色调（米白 + 土橙 + 暖卡片），主题"手作商品列表"。`hero` + `chips` + 商品 `grid` 三段式，避开千篇一律的"banner + features + cta"。
## 引用关系
- 配色逻辑参考 [`references/colors.md`](../../references/colors.md) §5 ORGANIC（用 hex 表达 oklch 等价值，因小程序基础库 < 3.0 不一定支持 oklch）
- 字体策略参考 [`references/typography.md`](../../references/typography.md) §6 + 小程序豁免说明（见 SKILL.md §四 v2.2 新增段）
- 多端同步迭代速查见本文 §四端同步迭代姿势
## 四端真机扫码后必查清单
- [ ] 顶部胶囊（四端形态略有不同：微信圆角 / 支付宝椭圆 / 抖音矩形 / 快手矩形）不遮内容
- [ ] 底部 home indicator 不遮 tabBar / 内容
- [ ] 横屏 / 竖屏切换不崩
- [ ] 字号在小屏（375rpx）/ 大屏（414rpx+）都不溢出
- [ ] tabBar 用平台 native（不要自绘 view 拼接）
## 何时该用编译框架
如果同时维护 4 端，且产品复杂度 > 10 个页面，建议直接上 [Taro](https://taro-docs.jd.com/) 或 [Uni-app](https://uniapp.dcloud.net.cn/) — 写一份代码自动编译四端。本 skill 提供的 4 个 starter 是给"原生开发 + 设计样板参考"用的，不是 SaaS 级框架。
FILE:examples/mini-program/alipay/app.js
App({
globalData: {
brand: 'huo15',
skillVersion: '[email protected]'
},
onLaunch(options) {}
});
FILE:examples/mini-program/alipay/app.json
{
"pages": ["pages/index/index"],
"window": {
"defaultTitle": "火一五 · 商品",
"titleBarColor": "#fafaf6",
"backgroundColor": "#f0eee8",
"pullRefresh": false,
"allowsBounceVertical": "NO"
},
"tabBar": {
"textColor": "#888888",
"selectedColor": "#d97706",
"backgroundColor": "#ffffff",
"items": [
{ "pagePath": "pages/index/index", "name": "首页" },
{ "pagePath": "pages/index/index", "name": "我的" }
]
}
}
FILE:examples/mini-program/alipay/mini.project.json
{
"enableAppxNg": true,
"format": "ascii",
"node_modules_es6_whitelist": [],
"compileOptions": {
"minify": false,
"minifyJS": false,
"minifyCSS": false
}
}
FILE:examples/mini-program/alipay/pages/index/index.js
Page({
data: {
products: [
{ id: 1, name: '手工陶杯 · 月白', price: '128', sold: 234, tag: '新', tone: '#fde6c8' },
{ id: 2, name: '宋代茶则 · 黑檀', price: '168', sold: 89, tag: '限', tone: '#dde7d4' },
{ id: 3, name: '《设计的觉醒》原研哉', price: '88', sold: 512, tag: '荐', tone: '#e8dcd0' },
{ id: 4, name: '黄铜书签套装 · 三枚', price: '58', sold: 321, tag: '', tone: '#f4dccd' },
{ id: 5, name: '柴烧建盏 · 兔毫', price: '288', sold: 41, tag: '限', tone: '#d8d4c5' },
{ id: 6, name: '日记本 · 夏布封面', price: '78', sold: 168, tag: '', tone: '#e9e1d0' }
]
},
onCardTap(e) {
const id = e.currentTarget.dataset.id;
console.log('tap product', id);
}
});
FILE:examples/mini-program/alipay/pages/index/index.json
{
"defaultTitle": "火一五 · 商品",
"transparentTitle": "none"
}
FILE:examples/mini-program/douyin/app.js
App({
globalData: {
brand: 'huo15',
skillVersion: '[email protected]'
},
onLaunch() {}
});
FILE:examples/mini-program/douyin/app.json
{
"pages": ["pages/index/index"],
"window": {
"backgroundTextStyle": "dark",
"navigationBarBackgroundColor": "#fafaf6",
"navigationBarTitleText": "火一五 · 商品",
"navigationBarTextStyle": "black",
"backgroundColor": "#f0eee8"
},
"tabBar": {
"color": "#888888",
"selectedColor": "#d97706",
"backgroundColor": "#ffffff",
"borderStyle": "white",
"list": [
{ "pagePath": "pages/index/index", "text": "首页" },
{ "pagePath": "pages/index/index", "text": "我的" }
]
}
}
FILE:examples/mini-program/douyin/pages/index/index.js
Page({
data: {
products: [
{ id: 1, name: '手工陶杯 · 月白', price: '128', sold: 234, tag: '新', tone: '#fde6c8' },
{ id: 2, name: '宋代茶则 · 黑檀', price: '168', sold: 89, tag: '限', tone: '#dde7d4' },
{ id: 3, name: '《设计的觉醒》原研哉', price: '88', sold: 512, tag: '荐', tone: '#e8dcd0' },
{ id: 4, name: '黄铜书签套装 · 三枚', price: '58', sold: 321, tag: '', tone: '#f4dccd' },
{ id: 5, name: '柴烧建盏 · 兔毫', price: '288', sold: 41, tag: '限', tone: '#d8d4c5' },
{ id: 6, name: '日记本 · 夏布封面', price: '78', sold: 168, tag: '', tone: '#e9e1d0' }
]
},
onCardTap(e) {
const id = e.currentTarget.dataset.id;
console.log('tap product', id);
}
});
FILE:examples/mini-program/douyin/pages/index/index.json
{
"navigationBarTitleText": "火一五 · 商品",
"usingComponents": {}
}
FILE:examples/mini-program/douyin/project.config.json
{
"miniprogramRoot": "./",
"compileType": "miniprogram",
"appid": "tt-test-appid",
"projectname": "huo15-frontend-design-douyin-starter",
"description": "火一五前端设计技能 v4.2 抖音小程序 starter",
"setting": {
"es6": true,
"minified": false,
"newFeature": true
},
"libVersion": "latest"
}
FILE:examples/mini-program/kuaishou/app.css
page {
--paper: #fafaf6;
--paper-2: #fff8ed;
--ink: #1a1a1a;
--mute: #777777;
--accent: #d97706;
--card: #ffffff;
--sep: #ececec;
--moss: #5b8a3b;
background: var(--paper);
color: var(--ink);
font-family: 'PingFang SC', 'Source Han Sans SC', 'Noto Sans SC', sans-serif;
font-size: 28rpx;
line-height: 1.5;
-webkit-font-smoothing: antialiased;
}
FILE:examples/mini-program/kuaishou/app.js
App({
globalData: {
brand: 'huo15',
skillVersion: '[email protected]'
},
onLaunch() {}
});
FILE:examples/mini-program/kuaishou/app.json
{
"pages": ["pages/index/index"],
"window": {
"backgroundTextStyle": "dark",
"navigationBarBackgroundColor": "#fafaf6",
"navigationBarTitleText": "火一五 · 商品",
"navigationBarTextStyle": "black",
"backgroundColor": "#f0eee8"
},
"tabBar": {
"color": "#888888",
"selectedColor": "#d97706",
"backgroundColor": "#ffffff",
"borderStyle": "white",
"list": [
{ "pagePath": "pages/index/index", "text": "首页" },
{ "pagePath": "pages/index/index", "text": "我的" }
]
}
}
FILE:examples/mini-program/kuaishou/pages/index/index.css
.page {
min-height: 100vh;
}
.hero {
position: relative;
margin: 24rpx 24rpx 32rpx;
padding: 56rpx 40rpx;
border-radius: 32rpx;
overflow: hidden;
background: var(--paper-2);
}
.hero-bg {
position: absolute;
right: -80rpx;
top: -80rpx;
width: 320rpx;
height: 320rpx;
border-radius: 50%;
background: radial-gradient(circle, #fbbf24, transparent 70%);
opacity: 0.5;
}
.hero-content {
position: relative;
z-index: 1;
}
.hero-kicker {
display: block;
font-size: 22rpx;
color: var(--accent);
letter-spacing: 4rpx;
text-transform: uppercase;
font-weight: 500;
margin-bottom: 16rpx;
}
.hero-title {
display: block;
font-size: 64rpx;
font-weight: 800;
line-height: 1.1;
letter-spacing: -1rpx;
white-space: pre-line;
margin-bottom: 32rpx;
}
.hero-cta {
display: inline-flex;
align-items: center;
gap: 8rpx;
background: var(--ink);
color: var(--paper);
padding: 18rpx 36rpx;
border-radius: 999rpx;
font-size: 26rpx;
font-weight: 500;
}
.hero-cta .arrow {
color: var(--paper);
font-size: 26rpx;
}
.chips {
white-space: nowrap;
padding: 0 24rpx 32rpx;
}
.chip {
display: inline-block;
padding: 14rpx 32rpx;
margin-right: 16rpx;
border-radius: 999rpx;
background: var(--card);
color: var(--ink);
font-size: 26rpx;
font-weight: 500;
border: 1rpx solid var(--sep);
}
.chip.selected {
background: var(--ink);
color: var(--paper);
border-color: var(--ink);
}
.grid {
display: grid;
grid-template-columns: 1fr 1fr;
gap: 24rpx;
padding: 0 24rpx;
}
.card {
background: var(--card);
border-radius: 24rpx;
overflow: hidden;
border: 1rpx solid var(--sep);
}
.img {
width: 100%;
height: 280rpx;
position: relative;
}
.img-tag {
position: absolute;
top: 16rpx;
left: 16rpx;
background: rgba(255, 255, 255, 0.92);
color: var(--ink);
font-size: 20rpx;
font-weight: 600;
padding: 4rpx 14rpx;
border-radius: 999rpx;
}
.info {
padding: 20rpx 24rpx 24rpx;
}
.name {
display: -webkit-box;
font-size: 28rpx;
font-weight: 600;
-webkit-line-clamp: 2;
-webkit-box-orient: vertical;
overflow: hidden;
line-height: 1.4;
min-height: 78rpx;
}
.meta {
display: flex;
justify-content: space-between;
align-items: baseline;
margin-top: 14rpx;
}
.price {
font-size: 32rpx;
font-weight: 700;
color: var(--accent);
}
.sold {
font-size: 22rpx;
color: var(--mute);
}
.footer-note {
display: block;
font-size: 22rpx;
color: var(--mute);
margin: 40rpx 24rpx 32rpx;
line-height: 1.7;
}
FILE:examples/mini-program/kuaishou/pages/index/index.js
Page({
data: {
products: [
{ id: 1, name: '手工陶杯 · 月白', price: '128', sold: 234, tag: '新', tone: '#fde6c8' },
{ id: 2, name: '宋代茶则 · 黑檀', price: '168', sold: 89, tag: '限', tone: '#dde7d4' },
{ id: 3, name: '《设计的觉醒》原研哉', price: '88', sold: 512, tag: '荐', tone: '#e8dcd0' },
{ id: 4, name: '黄铜书签套装 · 三枚', price: '58', sold: 321, tag: '', tone: '#f4dccd' },
{ id: 5, name: '柴烧建盏 · 兔毫', price: '288', sold: 41, tag: '限', tone: '#d8d4c5' },
{ id: 6, name: '日记本 · 夏布封面', price: '78', sold: 168, tag: '', tone: '#e9e1d0' }
]
},
onCardTap(e) {
const id = e.currentTarget.dataset.id;
console.log('tap product', id);
}
});
FILE:examples/mini-program/kuaishou/pages/index/index.json
{
"navigationBarTitleText": "火一五 · 商品",
"usingComponents": {}
}
FILE:examples/mini-program/kuaishou/project.config.json
{
"miniprogramRoot": "./",
"compileType": "miniprogram",
"appid": "ks-test-appid",
"projectname": "huo15-frontend-design-kuaishou-starter",
"description": "火一五前端设计技能 v4.3 快手小程序 starter",
"setting": {
"es6": true,
"minified": false,
"newFeature": true
},
"libVersion": "latest"
}
FILE:examples/mini-program/wechat/app.js
App({
globalData: {
brand: 'huo15',
skillVersion: '[email protected]'
},
onLaunch() {}
});
FILE:examples/mini-program/wechat/app.json
{
"pages": ["pages/index/index"],
"window": {
"backgroundTextStyle": "dark",
"navigationBarBackgroundColor": "#fafaf6",
"navigationBarTitleText": "火一五 · 商品",
"navigationBarTextStyle": "black",
"backgroundColor": "#f0eee8"
},
"tabBar": {
"color": "#888888",
"selectedColor": "#d97706",
"backgroundColor": "#ffffff",
"borderStyle": "white",
"list": [
{ "pagePath": "pages/index/index", "text": "首页" },
{ "pagePath": "pages/index/index", "text": "我的" }
]
},
"style": "v2",
"sitemapLocation": "sitemap.json"
}
FILE:examples/mini-program/wechat/pages/index/index.js
Page({
data: {
products: [
{ id: 1, name: '手工陶杯 · 月白', price: '128', sold: 234, tag: '新', tone: '#fde6c8' },
{ id: 2, name: '宋代茶则 · 黑檀', price: '168', sold: 89, tag: '限', tone: '#dde7d4' },
{ id: 3, name: '《设计的觉醒》原研哉', price: '88', sold: 512, tag: '荐', tone: '#e8dcd0' },
{ id: 4, name: '黄铜书签套装 · 三枚', price: '58', sold: 321, tag: '', tone: '#f4dccd' },
{ id: 5, name: '柴烧建盏 · 兔毫', price: '288', sold: 41, tag: '限', tone: '#d8d4c5' },
{ id: 6, name: '日记本 · 夏布封面', price: '78', sold: 168, tag: '', tone: '#e9e1d0' }
]
},
onCardTap(e) {
const id = e.currentTarget.dataset.id;
console.log('tap product', id);
}
});
FILE:examples/mini-program/wechat/pages/index/index.json
{
"navigationStyle": "custom",
"usingComponents": {}
}
FILE:examples/mini-program/wechat/project.config.json
{
"miniprogramRoot": "./",
"compileType": "miniprogram",
"appid": "touristappid",
"projectname": "huo15-frontend-design-wechat-starter",
"description": "火一五前端设计技能 v2.2 微信小程序 starter",
"setting": {
"es6": true,
"enhance": true,
"postcss": true,
"minified": false,
"newFeature": true,
"useCompilerPlugins": false
},
"libVersion": "3.5.0"
}
FILE:examples/mini-program/wechat/sitemap.json
{
"desc": "关于 sitemap 详见 https://developers.weixin.qq.com/miniprogram/dev/reference/configuration/sitemap.html",
"rules": [
{ "action": "allow", "page": "*" }
]
}
FILE:examples/mobile-native/harmony/index.html
<!doctype html>
<html lang="zh">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width,initial-scale=1,viewport-fit=cover">
<title>HarmonyOS 鸿蒙 · 火一五前端设计技能 v2.1 示例</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Noto+Sans+SC:wght@400;500;700;900&family=DM+Sans:wght@400;500;700&display=swap" rel="stylesheet">
<style>
:root{
--paper: oklch(0.97 0.005 240);
--paper-2: oklch(0.94 0.008 240);
--ink: oklch(0.18 0.01 260);
--mute: oklch(0.50 0.01 260);
/* HarmonyOS 灵动色块：青莲 / 暖橙 / 草绿 / 樱粉 — 多色但克制，不全用 */
--c-cyan: oklch(0.78 0.12 220);
--c-orange: oklch(0.78 0.13 55);
--c-green: oklch(0.78 0.14 145);
--c-pink: oklch(0.82 0.10 5);
--safe-top: env(safe-area-inset-top, 0px);
--safe-bottom: env(safe-area-inset-bottom, 0px);
}
*{box-sizing:border-box;margin:0;padding:0}
body{background:oklch(0.86 0.005 240);color:var(--ink);font-family:"Noto Sans SC",sans-serif;-webkit-font-smoothing:antialiased;line-height:1.5;min-height:100vh;display:grid;place-items:center}
.device{width:396px;height:858px;max-width:100vw;max-height:100vh;background:var(--paper);box-shadow:0 0 0 12px oklch(0.10 0 0),0 32px 96px oklch(0 0 0 / .35);border-radius:48px;overflow:hidden;position:relative;display:flex;flex-direction:column}
@media (max-width:480px){body{display:block}.device{width:100vw;height:100vh;box-shadow:none;border-radius:0}}
.status{padding:calc(16px + var(--safe-top)) 28px 4px;display:flex;justify-content:space-between;align-items:center;font-family:"DM Sans",sans-serif;font-weight:700;font-size:15px;flex-shrink:0}
.header{padding:12px 24px 8px;display:flex;justify-content:space-between;align-items:center;flex-shrink:0}
.header h1{font-family:"Noto Sans SC",sans-serif;font-weight:900;font-size:28px;letter-spacing:-.01em}
.header h1 b{color:var(--c-cyan)}
.header .ring{width:40px;height:40px;border-radius:50%;background:linear-gradient(135deg,var(--c-cyan),var(--c-pink));padding:3px}
.header .ring .inner{width:100%;height:100%;border-radius:50%;background:var(--paper);display:grid;place-items:center;font-size:18px;font-weight:700;color:var(--ink)}
.scroll{flex:1;overflow-y:auto;padding:8px 16px 24px}
.hero{margin:8px 0 16px;padding:24px;background:var(--paper-2);border-radius:32px;position:relative;overflow:hidden}
.hero::after{content:"";position:absolute;right:-40px;top:-40px;width:140px;height:140px;border-radius:50%;background:radial-gradient(circle,var(--c-orange),transparent 70%);opacity:.5}
.hero .label{font-family:"DM Sans",sans-serif;font-size:11px;font-weight:700;letter-spacing:.08em;text-transform:uppercase;color:var(--mute)}
.hero h2{font-weight:700;font-size:22px;margin:6px 0 4px;letter-spacing:-.005em;line-height:1.2}
.hero p{font-size:13px;color:var(--mute);position:relative;z-index:1}
.hero .meter{margin-top:18px;height:8px;background:var(--paper);border-radius:99px;overflow:hidden;position:relative;z-index:1}
.hero .meter .fill{height:100%;width:60%;border-radius:99px;background:linear-gradient(90deg,var(--c-orange),var(--c-pink))}
.grid{display:grid;grid-template-columns:1fr 1fr;gap:12px;margin-bottom:16px}
.tile{padding:18px;border-radius:24px;color:var(--ink);position:relative;overflow:hidden;min-height:104px;display:flex;flex-direction:column;justify-content:space-between}
.tile.cyan{background:oklch(0.94 0.04 220)}
.tile.green{background:oklch(0.94 0.04 145)}
.tile.pink{background:oklch(0.96 0.025 5)}
.tile.orange{background:oklch(0.96 0.04 55)}
.tile .name{font-weight:700;font-size:15px}
.tile .val{font-family:"DM Sans",sans-serif;font-weight:700;font-size:24px;letter-spacing:-.01em}
.tile .glyph{position:absolute;right:-12px;bottom:-12px;width:64px;height:64px;border-radius:50%;opacity:.18}
.tile.cyan .glyph{background:var(--c-cyan)}
.tile.green .glyph{background:var(--c-green)}
.tile.pink .glyph{background:var(--c-pink)}
.tile.orange .glyph{background:var(--c-orange)}
.scene{padding:18px 20px;border-radius:28px;background:var(--paper-2);margin-bottom:14px;display:flex;align-items:center;gap:14px}
.scene .ico{width:44px;height:44px;border-radius:14px;background:var(--c-cyan);display:grid;place-items:center;color:var(--paper);flex-shrink:0;font-weight:900;font-size:18px}
.scene .text{flex:1;min-width:0}
.scene .text b{display:block;font-weight:700;font-size:15px}
.scene .text small{display:block;color:var(--mute);font-size:12px;margin-top:2px}
.scene .toggle{width:48px;height:28px;background:var(--c-green);border-radius:99px;position:relative;flex-shrink:0}
.scene .toggle::after{content:"";position:absolute;right:3px;top:3px;width:22px;height:22px;background:var(--paper);border-radius:50%;box-shadow:0 1px 2px oklch(0 0 0 / .2)}
.scene .toggle.off{background:oklch(0.85 0.005 240)}
.scene .toggle.off::after{right:auto;left:3px}
.footer-note{font-size:11px;color:var(--mute);margin:8px 4px 12px;line-height:1.6}
.dock{display:flex;justify-content:space-around;align-items:center;padding:10px 16px calc(16px + var(--safe-bottom));background:var(--paper);border-top:1px solid oklch(0.92 0.005 240);flex-shrink:0}
.dock-item{display:flex;flex-direction:column;align-items:center;gap:4px;color:var(--mute);font-size:11px;cursor:pointer;flex:1;font-weight:500}
.dock-item .pill{width:48px;height:32px;border-radius:16px;display:grid;place-items:center}
.dock-item .pill svg{width:22px;height:22px;stroke:currentColor;stroke-width:1.8;fill:none}
.dock-item.active{color:var(--c-cyan)}
.dock-item.active .pill{background:oklch(0.94 0.04 220)}
.dock-item.active .pill svg{fill:currentColor;stroke:currentColor}
</style>
</head>
<body>
<div class="device">
<div class="status">
<span>10:42</span>
<span>★ 5G ▣▣▣</span>
</div>
<div class="header">
<h1>智慧<b>场景</b></h1>
<span class="ring"><span class="inner">J</span></span>
</div>
<div class="scroll">
<section class="hero">
<span class="label">当前场景 · 工作模式</span>
<h2>专注·设计冲刺</h2>
<p>持续 38 分钟 · 已通过 5 条红线体检</p>
<div class="meter"><span class="fill"></span></div>
</section>
<div class="grid">
<div class="tile cyan"><span class="name">智能音箱</span><span class="val">已连</span><span class="glyph"></span></div>
<div class="tile green"><span class="name">空气净化</span><span class="val">优</span><span class="glyph"></span></div>
<div class="tile pink"><span class="name">温度</span><span class="val">22℃</span><span class="glyph"></span></div>
<div class="tile orange"><span class="name">屏幕亮度</span><span class="val">68%</span><span class="glyph"></span></div>
</div>
<div class="scene">
<span class="ico">书</span>
<div class="text"><b>书房灯</b><small>2700K · 暖光阅读</small></div>
<span class="toggle"></span>
</div>
<div class="scene">
<span class="ico" style="background:var(--c-orange)">音</span>
<div class="text"><b>客厅声场</b><small>低音 +3 / 流派模式</small></div>
<span class="toggle off"></span>
</div>
<p class="footer-note">HarmonyOS 鸿蒙流派示例 — Noto Sans SC 中文 + DM Sans 数字，灵动色块（青莲 / 暖橙 / 草绿 / 樱粉，**多色但克制**），大圆角胶囊（24-48px 分级），戒指头像 + 渐变进度条 + 灵动场景 tile。差异点：圆角通过尺寸暗示层级（小圆角=控件 / 大圆角=容器），不是全局统一值（红线 #9 合规）；色块用 oklch 同明度多色相，避免某色一统天下。</p>
</div>
<div class="dock">
<div class="dock-item active"><span class="pill"><svg viewBox="0 0 24 24"><path d="M3 12l9-9 9 9v9a2 2 0 01-2 2h-4v-7h-6v7H5a2 2 0 01-2-2z"/></svg></span>场景</div>
<div class="dock-item"><span class="pill"><svg viewBox="0 0 24 24"><rect x="4" y="4" width="6" height="6" rx="1"/><rect x="14" y="4" width="6" height="6" rx="1"/><rect x="4" y="14" width="6" height="6" rx="1"/><rect x="14" y="14" width="6" height="6" rx="1"/></svg></span>设备</div>
<div class="dock-item"><span class="pill"><svg viewBox="0 0 24 24"><circle cx="12" cy="12" r="9"/><path d="M12 7v5l4 2"/></svg></span>历史</div>
<div class="dock-item"><span class="pill"><svg viewBox="0 0 24 24"><circle cx="12" cy="8" r="4"/><path d="M4 21a8 8 0 0116 0"/></svg></span>我</div>
</div>
</div>
</body>
</html>
FILE:examples/mobile-native/ios/index.html
<!doctype html>
<html lang="zh">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width,initial-scale=1,viewport-fit=cover">
<title>iOS HIG · 火一五前端设计技能 v2.1 示例</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Manrope:wght@400;600;800&family=IBM+Plex+Sans:wght@400;500;600&display=swap" rel="stylesheet">
<style>
:root{
--paper: oklch(0.985 0.005 75);
--grouped-bg: oklch(0.94 0.005 75);
--ink: oklch(0.18 0.005 280);
--mute: oklch(0.55 0.01 280);
--sep: oklch(0.88 0.005 280);
--accent: oklch(0.66 0.16 50);
--card: oklch(1 0 0);
--gray-icon: oklch(0.78 0.01 280);
--safe-top: env(safe-area-inset-top, 0px);
--safe-bottom: env(safe-area-inset-bottom, 0px);
}
*{box-sizing:border-box;margin:0;padding:0}
body{background:oklch(0.86 0.005 75);color:var(--ink);font-family:"IBM Plex Sans",sans-serif;-webkit-font-smoothing:antialiased;line-height:1.4;min-height:100vh;display:grid;place-items:center}
.device{width:393px;height:852px;max-width:100vw;max-height:100vh;background:var(--grouped-bg);box-shadow:0 0 0 14px oklch(0.10 0 0),0 32px 96px oklch(0 0 0 / .35);border-radius:54px;overflow:hidden;position:relative;display:flex;flex-direction:column}
@media (max-width:480px){body{display:block}.device{width:100vw;height:100vh;box-shadow:none;border-radius:0}}
.status{padding:calc(18px + var(--safe-top)) 28px 4px;display:flex;justify-content:space-between;align-items:flex-start;font-family:"Manrope",sans-serif;font-weight:800;font-size:16px;letter-spacing:.01em;flex-shrink:0}
.status .signals{display:flex;gap:5px;align-items:center;font-weight:600;font-size:13px}
.status .signals span{display:inline-block}
.status .bars{display:inline-flex;gap:2px;align-items:flex-end}
.status .bars i{display:block;width:3px;background:var(--ink)}
.status .bars i:nth-child(1){height:4px}.status .bars i:nth-child(2){height:6px}.status .bars i:nth-child(3){height:8px}.status .bars i:nth-child(4){height:10px}
.status .battery{display:inline-flex;align-items:center;gap:1px}
.status .battery .body{width:24px;height:11px;border:1.2px solid var(--ink);border-radius:3px;padding:1px}
.status .battery .fill{width:100%;height:100%;background:var(--ink);border-radius:1.5px}
.status .battery .cap{width:1.5px;height:5px;background:var(--ink);border-radius:0 1px 1px 0}
.navbar{padding:6px 20px 12px;display:flex;justify-content:space-between;align-items:center;backdrop-filter:saturate(180%) blur(20px);background:oklch(0.985 0.005 75 / .82);position:sticky;top:0;z-index:5;border-bottom:.5px solid var(--sep);flex-shrink:0}
.navbar .back,.navbar .action{color:var(--accent);font-weight:500;font-size:17px;cursor:pointer}
.scroll{flex:1;overflow-y:auto;padding:0 20px 24px}
.large-title{font-family:"Manrope",sans-serif;font-weight:800;font-size:34px;letter-spacing:-.018em;padding:8px 0 18px;line-height:1.05}
.segmented{display:flex;background:oklch(0.90 0.005 280);border-radius:9px;padding:2px;margin-bottom:22px}
.segmented button{flex:1;padding:8px 0;border:0;background:transparent;font:inherit;font-weight:500;font-size:13px;color:var(--ink);border-radius:7px;cursor:pointer}
.segmented button.active{background:var(--paper);box-shadow:0 1px 2px oklch(0 0 0 / .08),0 0 0 .5px oklch(0 0 0 / .04);font-weight:600}
.section-label{text-transform:uppercase;color:var(--mute);font-size:13px;padding:0 16px 8px;letter-spacing:.02em;font-weight:500}
.list{background:var(--card);border-radius:14px;overflow:hidden;margin-bottom:24px}
.row{display:flex;align-items:center;padding:13px 16px;border-top:.5px solid var(--sep);gap:12px;cursor:pointer}
.row:first-child{border-top:0}
.row:hover{background:oklch(0.96 0.005 280)}
.row .ico{width:30px;height:30px;border-radius:8px;display:grid;place-items:center;color:var(--paper);flex-shrink:0}
.row .ico.orange{background:var(--accent)}
.row .ico.gray{background:var(--gray-icon)}
.row .ico.green{background:oklch(0.62 0.13 145)}
.row .ico svg{width:16px;height:16px;fill:currentColor}
.row .text{flex:1;min-width:0}
.row .text b{display:block;font-weight:500;font-size:16px}
.row .text small{display:block;color:var(--mute);font-size:13px;margin-top:1px}
.row .chevron{color:oklch(0.78 0.01 280);font-size:18px;font-weight:300}
.footer-note{color:var(--mute);font-size:12px;margin:8px 16px 24px;line-height:1.5}
.tabbar{display:flex;justify-content:space-around;align-items:flex-start;padding:8px 0 calc(18px + var(--safe-bottom));backdrop-filter:saturate(180%) blur(20px);background:oklch(0.985 0.005 75 / .88);border-top:.5px solid var(--sep);flex-shrink:0}
.tab{display:flex;flex-direction:column;align-items:center;gap:3px;color:var(--mute);font-size:10px;font-weight:500;padding:4px 12px;min-height:44px;cursor:pointer;font-family:"IBM Plex Sans",sans-serif;flex:1}
.tab svg{width:26px;height:26px;fill:none;stroke:currentColor;stroke-width:1.6}
.tab.active{color:var(--accent)}
.tab.active svg{fill:currentColor;stroke:currentColor}
</style>
</head>
<body>
<div class="device">
<div class="status">
<span>9:41</span>
<span class="signals">
<span class="bars"><i></i><i></i><i></i><i></i></span>
<span>5G</span>
<span class="battery"><span class="body"><span class="fill"></span></span><span class="cap"></span></span>
</span>
</div>
<div class="navbar">
<span class="back">‹ 返回</span>
<span class="action">编辑</span>
</div>
<div class="scroll">
<h1 class="large-title">今日</h1>
<div class="segmented">
<button class="active">全部</button>
<button>未读</button>
<button>已完成</button>
</div>
<p class="section-label">上午</p>
<div class="list">
<div class="row"><span class="ico orange"><svg viewBox="0 0 24 24"><path d="M12 2l2.4 7.4H22l-6.2 4.5 2.4 7.4L12 16.8 5.8 21.3l2.4-7.4L2 9.4h7.6z"/></svg></span><div class="text"><b>项目设计评审</b><small>10:30 · 与花叔</small></div><span class="chevron">›</span></div>
<div class="row"><span class="ico gray"><svg viewBox="0 0 24 24"><path d="M3 17.25V21h3.75L17.81 9.94l-3.75-3.75z"/></svg></span><div class="text"><b>v2.1 SKILL.md 收尾</b><small>14:00 · 30 分钟</small></div><span class="chevron">›</span></div>
</div>
<p class="section-label">下午</p>
<div class="list">
<div class="row"><span class="ico green"><svg viewBox="0 0 24 24"><path d="M9 16.2L4.8 12l-1.4 1.4L9 19 21 7l-1.4-1.4z"/></svg></span><div class="text"><b>clawhub publish 2.1.0</b><small>16:00 · 5 分钟</small></div><span class="chevron">›</span></div>
<div class="row"><span class="ico orange"><svg viewBox="0 0 24 24"><path d="M5 4h14a2 2 0 012 2v14l-4-3H5a2 2 0 01-2-2V6a2 2 0 012-2z"/></svg></span><div class="text"><b>同步给火一五群</b><small>17:00 · 3 分钟</small></div><span class="chevron">›</span></div>
</div>
<p class="footer-note">iOS HIG 流派示例 — Manrope display + IBM Plex Sans body，oklch 暖橙强调（**避开系统蓝 #007AFF**），large-title + segmented control + grouped list + blurred navbar/tabbar 全套 HIG 元素。差异点：暖橙作 brand color 替代系统蓝，blur 仅用于功能性透明栏（红线 #10 合规），所有圆角分级（list 14px / icon 8px / button 7px）避免全局 16px。</p>
</div>
<div class="tabbar">
<div class="tab active">
<svg viewBox="0 0 24 24"><path d="M12 3l9 8h-3v9h-4v-6h-4v6H6v-9H3z"/></svg>
今日
</div>
<div class="tab">
<svg viewBox="0 0 24 24"><rect x="4" y="5" width="16" height="14" rx="2"/><line x1="4" y1="9" x2="20" y2="9"/></svg>
项目
</div>
<div class="tab">
<svg viewBox="0 0 24 24"><circle cx="12" cy="12" r="3"/><path d="M19.4 15a1.65 1.65 0 00.33 1.82l.06.06a2 2 0 11-2.83 2.83l-.06-.06a1.65 1.65 0 00-1.82-.33 1.65 1.65 0 00-1 1.51V21a2 2 0 11-4 0v-.09A1.65 1.65 0 008.6 19.4a1.65 1.65 0 00-1.82.33l-.06.06a2 2 0 01-2.83-2.83l.06-.06a1.65 1.65 0 00.33-1.82 1.65 1.65 0 00-1.51-1H3a2 2 0 110-4h.09A1.65 1.65 0 004.6 8.6a1.65 1.65 0 00-.33-1.82l-.06-.06a2 2 0 112.83-2.83l.06.06a1.65 1.65 0 001.82.33H9a1.65 1.65 0 001-1.51V3a2 2 0 114 0v.09a1.65 1.65 0 001 1.51 1.65 1.65 0 001.82-.33l.06-.06a2 2 0 012.83 2.83l-.06.06a1.65 1.65 0 00-.33 1.82V9a1.65 1.65 0 001.51 1H21a2 2 0 110 4h-.09a1.65 1.65 0 00-1.51 1z"/></svg>
设置
</div>
</div>
</div>
</body>
</html>
FILE:examples/mobile-native/md3/index.html
<!doctype html>
<html lang="zh">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width,initial-scale=1,viewport-fit=cover">
<title>Material Design 3 · 火一五前端设计技能 v2.1 示例</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=DM+Sans:opsz,[email protected],400;9..40,500;9..40,700&family=IBM+Plex+Sans:wght@400;500;600&display=swap" rel="stylesheet">
<style>
:root{
/* MD3 dynamic color: seed = oklch(0.55 0.18 175) cyan-teal — 自定，避开 Material 默认紫 */
--primary: oklch(0.55 0.13 175);
--on-primary: oklch(0.99 0.01 175);
--primary-container: oklch(0.88 0.06 175);
--on-primary-container: oklch(0.20 0.05 175);
--secondary-container: oklch(0.92 0.04 60);
--on-secondary-container: oklch(0.25 0.05 60);
--tertiary-container: oklch(0.90 0.05 320);
--on-tertiary-container: oklch(0.25 0.06 320);
--surface: oklch(0.985 0.003 175);
--surface-variant: oklch(0.93 0.01 175);
--on-surface: oklch(0.18 0.01 175);
--on-surface-variant: oklch(0.45 0.01 175);
--outline: oklch(0.72 0.01 175);
--safe-top: env(safe-area-inset-top, 0px);
--safe-bottom: env(safe-area-inset-bottom, 0px);
}
*{box-sizing:border-box;margin:0;padding:0}
body{background:oklch(0.86 0.005 175);color:var(--on-surface);font-family:"IBM Plex Sans",sans-serif;-webkit-font-smoothing:antialiased;line-height:1.45;min-height:100vh;display:grid;place-items:center}
.device{width:412px;height:915px;max-width:100vw;max-height:100vh;background:var(--surface);box-shadow:0 0 0 12px oklch(0.10 0 0),0 32px 96px oklch(0 0 0 / .35);border-radius:42px;overflow:hidden;position:relative;display:flex;flex-direction:column}
@media (max-width:480px){body{display:block}.device{width:100vw;height:100vh;box-shadow:none;border-radius:0}}
.status{padding:calc(14px + var(--safe-top)) 24px 4px;display:flex;justify-content:space-between;align-items:center;font-family:"DM Sans",sans-serif;font-weight:500;font-size:14px;flex-shrink:0;color:var(--on-surface)}
.top-app-bar{padding:8px 4px 8px 16px;display:flex;align-items:center;gap:8px;flex-shrink:0}
.top-app-bar .menu,.top-app-bar .more{width:48px;height:48px;border-radius:24px;display:grid;place-items:center;cursor:pointer}
.top-app-bar .menu:hover,.top-app-bar .more:hover{background:var(--surface-variant)}
.top-app-bar svg{width:24px;height:24px;stroke:var(--on-surface);stroke-width:1.8;fill:none;stroke-linecap:round}
.top-app-bar .title{font-family:"DM Sans",sans-serif;font-weight:500;font-size:22px;flex:1;letter-spacing:-.005em}
.scroll{flex:1;overflow-y:auto;padding:8px 16px 100px;position:relative}
.greeting{font-family:"DM Sans",sans-serif;font-weight:700;font-size:28px;letter-spacing:-.012em;line-height:1.15;padding:8px 4px 16px}
.greeting span{color:var(--primary)}
.chips{display:flex;gap:8px;flex-wrap:wrap;margin-bottom:20px}
.chip{padding:8px 16px;border-radius:8px;border:1px solid var(--outline);background:transparent;font:inherit;font-size:13px;font-weight:500;color:var(--on-surface);cursor:pointer;display:inline-flex;align-items:center;gap:6px}
.chip.selected{background:var(--secondary-container);color:var(--on-secondary-container);border-color:transparent}
.chip.selected::before{content:"✓";font-weight:700}
.card{border-radius:16px;padding:20px;margin-bottom:12px}
.card.filled{background:var(--primary-container);color:var(--on-primary-container)}
.card.tonal{background:var(--secondary-container);color:var(--on-secondary-container)}
.card.tert{background:var(--tertiary-container);color:var(--on-tertiary-container)}
.card .label{font-family:"DM Sans",sans-serif;font-size:11px;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75}
.card h3{font-family:"DM Sans",sans-serif;font-weight:500;font-size:22px;letter-spacing:-.005em;margin:6px 0 4px;line-height:1.2}
.card p{font-size:13px;line-height:1.5;opacity:.85}
.card .meta{display:flex;justify-content:space-between;align-items:center;margin-top:14px;font-size:12px;font-weight:500}
.fab{position:absolute;bottom:calc(96px + var(--safe-bottom));right:20px;background:var(--primary);color:var(--on-primary);width:56px;height:56px;border-radius:18px;display:grid;place-items:center;box-shadow:0 6px 20px oklch(0 0 0 / .18),0 1px 3px oklch(0 0 0 / .12);cursor:pointer;border:0}
.fab svg{width:24px;height:24px;stroke:currentColor;stroke-width:2;fill:none;stroke-linecap:round}
.nav-bar{display:flex;justify-content:space-around;align-items:center;padding:12px 0 calc(14px + var(--safe-bottom));background:var(--surface);border-top:1px solid oklch(0.92 0.005 175);flex-shrink:0}
.nav-item{display:flex;flex-direction:column;align-items:center;gap:4px;color:var(--on-surface-variant);font-family:"DM Sans",sans-serif;font-size:12px;font-weight:500;cursor:pointer;flex:1;padding:4px}
.nav-item .pill{width:64px;height:32px;border-radius:16px;display:grid;place-items:center;transition:background .2s}
.nav-item .pill svg{width:24px;height:24px;stroke:currentColor;stroke-width:1.8;fill:none}
.nav-item.active{color:var(--on-secondary-container)}
.nav-item.active .pill{background:var(--secondary-container)}
.nav-item.active .pill svg{fill:currentColor;stroke:currentColor}
.footer-note{font-size:11px;color:var(--on-surface-variant);margin-top:24px;line-height:1.55;padding:0 4px}
</style>
</head>
<body>
<div class="device">
<div class="status">
<span>10:08</span>
<span>● 5G ▰▰▰</span>
</div>
<div class="top-app-bar">
<span class="menu"><svg viewBox="0 0 24 24"><path d="M4 6h16M4 12h16M4 18h16"/></svg></span>
<span class="title">Material Studio</span>
<span class="more"><svg viewBox="0 0 24 24"><circle cx="12" cy="5" r="1.4"/><circle cx="12" cy="12" r="1.4"/><circle cx="12" cy="19" r="1.4"/></svg></span>
</div>
<div class="scroll">
<h2 class="greeting">早上好，<span>job</span>。<br>今天 3 个待办。</h2>
<div class="chips">
<button class="chip selected">设计</button>
<button class="chip">代码</button>
<button class="chip">发布</button>
<button class="chip">协作</button>
</div>
<article class="card filled">
<span class="label">设计 · 进行中</span>
<h3>v2.1 移动端扩展定稿</h3>
<p>第 6 流派 MOBILE-NATIVE 三套 starter HTML 加 references 章节扩展。</p>
<div class="meta"><span>10:30 → 12:00</span><span>40% ━━━━─────</span></div>
</article>
<article class="card tonal">
<span class="label">代码 · 待开始</span>
<h3>静态红线体检脚本</h3>
<p>grep 5+3 个新文件，验证不犯 #1/#2/#8/#9/#10/#12/#13。</p>
<div class="meta"><span>14:00 → 14:30</span><span>0%</span></div>
</article>
<article class="card tert">
<span class="label">发布 · 排队中</span>
<h3>clawhub publish 2.1.0</h3>
<p>existing slug 升版本，不占 5/h 配额；双 remote push。</p>
<div class="meta"><span>16:00</span><span>—</span></div>
</article>
<p class="footer-note">Material Design 3 流派示例 — DM Sans display + IBM Plex Sans body，**dynamic color seed 用 oklch 青绿（避开 Material 默认紫）**，三色 container 对应 primary / secondary / tertiary，FAB + extended TopAppBar + bottom NavigationBar 全套 MD3 组件。差异点：dynamic color 由 oklch seed 派生，避免一上来就紫；圆角分级（chip 8px / card 16px / FAB 18px / nav-pill 16px）满足红线 #9。</p>
</div>
<button class="fab"><svg viewBox="0 0 24 24"><path d="M12 5v14M5 12h14"/></svg></button>
<div class="nav-bar">
<div class="nav-item active">
<span class="pill"><svg viewBox="0 0 24 24"><path d="M3 12l9-9 9 9v9a2 2 0 01-2 2h-4v-7h-6v7H5a2 2 0 01-2-2z"/></svg></span>
首页
</div>
<div class="nav-item">
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FILE:examples/organic/index.html
<!doctype html>
<html lang="zh">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width,initial-scale=1">
<title>ORGANIC · 火一五前端设计技能 v2.0 示例</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Caveat:wght@600&family=Source+Serif+4:ital,opsz,wght@0,8..60,400;0,8..60,600;1,8..60,400&display=swap" rel="stylesheet">
<style>
:root{
--paper: oklch(0.96 0.025 85);
--ink: oklch(0.28 0.04 50);
--clay: oklch(0.62 0.13 45);
--moss: oklch(0.45 0.10 145);
--sky: oklch(0.78 0.07 220);
}
*{box-sizing:border-box;margin:0;padding:0}
body{background:var(--paper);color:var(--ink);font-family:"Source Serif 4",Georgia,serif;font-size:18px;line-height:1.7;overflow-x:hidden}
.wrap{max-width:1080px;margin:0 auto;padding:64px 32px 120px;position:relative}
.blob{position:absolute;border-radius:42% 58% 70% 30% / 45% 50% 50% 55%;filter:blur(2px);opacity:.45;z-index:0;pointer-events:none}
.blob-a{top:80px;right:-80px;width:380px;height:340px;background:var(--clay)}
.blob-b{top:540px;left:-120px;width:300px;height:280px;background:var(--moss)}
.blob-c{bottom:120px;right:120px;width:200px;height:180px;background:var(--sky)}
header,h1,.lede,.cards,.note,footer{position:relative;z-index:1}
header{display:flex;justify-content:space-between;align-items:baseline}
.logo{font-family:"Caveat",cursive;font-size:36px;color:var(--moss);font-weight:600}
nav a{margin-left:24px;color:var(--ink);text-decoration:none;font-size:16px}
nav a:hover{color:var(--clay)}
h1{font-family:"Caveat",cursive;font-size:clamp(56px,10vw,144px);line-height:1;color:var(--clay);font-weight:600;margin:80px 0 16px;max-width:14ch}
h1::after{content:"";display:inline-block;width:.4em;height:.4em;background:var(--moss);border-radius:55% 45% 60% 40%;margin-left:.15em;vertical-align:.1em}
.lede{font-size:22px;line-height:1.55;max-width:38ch;color:var(--ink)}
.lede em{color:var(--moss);font-style:normal;border-bottom:3px wavy var(--clay);padding-bottom:1px}
.cards{display:grid;grid-template-columns:repeat(3,1fr);gap:32px;margin:80px 0}
.card{background:oklch(1 0 0 / .55);padding:32px;border-radius:48% 52% 55% 45% / 50% 50% 50% 50%;border:2px solid var(--ink)}
.card h3{font-family:"Caveat",cursive;font-size:36px;color:var(--moss);font-weight:600;margin-bottom:8px}
.card p{font-size:16px;line-height:1.6}
.note{font-family:"Caveat",cursive;font-size:28px;color:var(--clay);text-align:center;transform:rotate(-1.2deg);max-width:30ch;margin:48px auto 0}
footer{margin-top:80px;padding-top:24px;border-top:2px dotted var(--ink);display:flex;justify-content:space-between;font-size:14px;color:var(--moss);font-family:"Caveat",cursive;font-weight:600}
@media (max-width:720px){.cards{grid-template-columns:1fr;gap:24px}.wrap{padding:40px 20px 80px}.blob{display:none}}
</style>
</head>
<body>
<div class="wrap">
<span class="blob blob-a"></span>
<span class="blob blob-b"></span>
<span class="blob blob-c"></span>
<header>
<span class="logo">huo15 · 慢慢做</span>
<nav><a href="#">关于</a><a href="#">手记</a><a href="#">订阅</a></nav>
</header>
<h1>种一棵树<br>最好的时候</h1>
<p class="lede">ORGANIC 流派示例 — 手写体 Caveat + 衬线 Source Serif，土橙 / 森林绿 / 雾蓝三色，<em>不规则圆角</em>与轻微旋转的便签，柔边斑块替代渐变。</p>
<section class="cards">
<article class="card"><h3>柔边</h3><p>每个圆角都不一样，border-radius 用 4 段不同的百分比，避免统一 16px 的工业感。</p></article>
<article class="card"><h3>暖色</h3><p>oklch 暖色域里挑 3 个相近色，纯色而非渐变，避开 AI 渐变模糊背景。</p></article>
<article class="card"><h3>不齐</h3><p>便签轻微旋转 -1.2°，文字下划线用 wavy 装饰，所有"完美"都被刻意打破。</p></article>
</section>
<p class="note">— 差异点：每一处不规则都是手挑的，不是随机的。</p>
<footer><span>© 2026 huo15</span><span>ORGANIC · v2.0</span></footer>
</div>
</body>
</html>
FILE:examples/retro-future/index.html
<!doctype html>
<html lang="zh">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width,initial-scale=1">
<title>RETRO-FUTURE · 火一五前端设计技能 v2.0 示例</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Major+Mono+Display&family=VT323&display=swap" rel="stylesheet">
<style>
:root{
--bg: oklch(0.13 0.04 280);
--ink: oklch(0.96 0.04 110);
--neon-c: oklch(0.85 0.18 195);
--neon-m: oklch(0.72 0.25 5);
--rule: oklch(0.35 0.1 280);
}
*{box-sizing:border-box;margin:0;padding:0}
body{background:var(--bg);color:var(--ink);font-family:"VT323",monospace;font-size:22px;line-height:1.4;min-height:100vh;overflow-x:hidden;position:relative}
body::before{content:"";position:fixed;inset:0;pointer-events:none;background:repeating-linear-gradient(to bottom,transparent 0,transparent 2px,oklch(0 0 0 / .12) 2px,oklch(0 0 0 / .12) 3px);mix-blend-mode:overlay;z-index:9}
body::after{content:"";position:fixed;inset:0;pointer-events:none;background:radial-gradient(ellipse at center,transparent 60%,oklch(0 0 0 / .55) 100%);z-index:10}
main{position:relative;max-width:1100px;margin:0 auto;padding:48px 32px 96px;z-index:1}
.head{display:flex;justify-content:space-between;align-items:center;border:2px solid var(--neon-c);padding:8px 16px;font-family:"Major Mono Display",monospace;font-size:14px;letter-spacing:.1em;color:var(--neon-c);text-shadow:0 0 6px currentColor}
.blink{color:var(--neon-m);animation:blink 1s steps(2) infinite;text-shadow:0 0 8px currentColor}
@keyframes blink{50%{opacity:0}}
h1{font-family:"Major Mono Display",monospace;font-size:clamp(48px,9vw,128px);line-height:1.05;letter-spacing:.02em;text-transform:uppercase;margin:80px 0 24px;color:var(--ink);text-shadow:0 0 14px var(--neon-m),0 0 28px oklch(0.72 0.25 5 / .5)}
h1 span{color:var(--neon-c);text-shadow:0 0 14px var(--neon-c),0 0 28px oklch(0.85 0.18 195 / .55)}
.lede{font-size:24px;max-width:48ch;color:var(--ink);margin-bottom:48px}
.ascii{font-family:"VT323",monospace;color:var(--neon-c);text-shadow:0 0 8px currentColor;white-space:pre;font-size:16px;line-height:1.1;margin:32px 0}
.menu{border:2px solid var(--neon-m);padding:16px 24px;display:grid;grid-template-columns:repeat(2,1fr);gap:8px 32px;font-size:22px}
.menu a{color:var(--ink);text-decoration:none;display:flex;justify-content:space-between;padding:6px 0}
.menu a::before{content:"> ";color:var(--neon-m)}
.menu a:hover{color:var(--neon-c);text-shadow:0 0 6px currentColor}
.menu a span{color:var(--neon-m)}
footer{margin-top:64px;border-top:1px dashed var(--rule);padding-top:16px;display:flex;justify-content:space-between;font-family:"Major Mono Display",monospace;font-size:13px;letter-spacing:.1em;color:var(--neon-c)}
@media (max-width:720px){.menu{grid-template-columns:1fr}main{padding:32px 20px 64px}}
</style>
</head>
<body>
<main>
<div class="head"><span>HUO15 // SYS-2026</span><span class="blink">█ READY</span></div>
<h1>NEW <span>WAVE</span><br>1986 → 2026</h1>
<p class="lede">RETRO-FUTURE 流派示例 — 等宽字 + 80s 霓虹（青 / 洋红，**避开紫渐变红线**）+ CRT 扫描线 + 终端式 menu。差异点：所有发光来自硬色双层 text-shadow，不用任何 backdrop-blur。</p>
<pre class="ascii"> ╔═══ HUO15 ═══╗
║ ▓▒░ 26 ░▒▓ ║
╚═════════════╝</pre>
<nav class="menu">
<a href="#">START_NEW_GAME<span>[F1]</span></a>
<a href="#">LOAD_PROJECT<span>[F2]</span></a>
<a href="#">SETTINGS<span>[F3]</span></a>
<a href="#">EXIT<span>[ESC]</span></a>
</nav>
<footer><span>© 2026 HUO15 // ALL SYSTEMS NOMINAL</span><span>RETRO-FUTURE · v2.0</span></footer>
</main>
</body>
</html>
FILE:references/a11y-checklist.md
# a11y / WCAG 2.2 AA 速查清单 ⭐v4.4
> 30 条速查 + 场景优先级 + 自动检查路线（axe-core / Lighthouse）。本清单**不是**SKILL.md §四"反 AI Slop 红线"那种判废级硬规则，而是验证阶段的**门控检查表**：每条标 ✓ 或 ⚠（待修） 或 N/A。
## 1. Perceivable · 可感知（8 条）
| # | 检查点 | 自动可测 | 工具 |
|---|---|---|---|
| 1 | 文本对比度 ≥ **4.5:1**（正文）/ **3:1**（大字 18pt+ 或 14pt+ 粗） | ✓ | axe / Lighthouse |
| 2 | 非文本元素（图标 / UI 控件 / 图表）对比度 ≥ **3:1** | ✓ | axe |
| 3 | 所有 `<img>` 有 `alt` 属性（装饰图用 `alt=""`） | ✓ | axe / Lighthouse |
| 4 | 视频有 captions / transcripts（视频内容站） | ✗ | 人审 |
| 5 | **颜色不是唯一信息载体**（错误状态除颜色外要有图标 / 文字） | ✗ | 人审 |
| 6 | 自动播放音频 ≤ 3 秒 或 提供暂停 | ✗ | 人审 |
| 7 | 文本可放大 200% 而不破坏布局（用 rem / em，不全用 px） | ✓ | Lighthouse |
| 8 | GIF / 视频闪烁频率 < 3 次/秒（防光敏性癫痫） | ✗ | 人审 |
## 2. Operable · 可操作（11 条）
| # | 检查点 | 自动可测 | 工具 |
|---|---|---|---|
| 9 | 全部交互**可键盘操作**（Tab / Shift+Tab / Enter / Esc / Space） | ⚠ 部分 | axe + 人审 |
| 10 | **焦点环可见**（`:focus-visible` 保留浏览器默认或自绘清晰样式） | ✓ | axe |
| 11 | 没有键盘陷阱（trap） — Tab 进得去也出得来 | ✗ | 人审（Tab 全键过一遍）|
| 12 | "Skip to main content" 链接（首个 Tab 焦点） | ✓ | axe |
| 13 | 每页有唯一 `<title>` | ✓ | Lighthouse |
| 14 | heading 层级合理（h1 → h2 → h3 不跳级 / 不重复 h1） | ✓ | axe |
| 15 | 链接文字描述清晰 — **禁** "点这里" / "查看更多" 这类无上下文 | ✓ 部分 | axe |
| 16 ⭐2.2 | 触达目标尺寸 ≥ **24×24px**（按钮 / 链接 / 表单控件） | ✓ | axe + 真机 |
| 17 ⭐2.2 | 拖拽操作有**键盘 / 单点替代**（slider 可左右键） | ✗ | 人审 |
| 18 ⭐2.2 | 认证不强制 cognitive test（不让用户记复杂密码 / 解谜） | ✗ | 人审 |
| 19 ⭐2.2 | 重复表单字段不强制再输入（自动填充 + remember 选项） | ✗ | 人审 |
## 3. Understandable · 可理解（7 条）
| # | 检查点 | 自动可测 | 工具 |
|---|---|---|---|
| 20 | `<html lang="...">` 必填（中文 `zh` / `zh-CN`） | ✓ | axe |
| 21 | 所有表单控件**有 `<label>`** 或 `aria-label` | ✓ | axe |
| 22 | 错误提示**清晰具体**（不只是"输入有误"） | ✗ | 人审 |
| 23 | 错误**给出纠正建议**（"邮箱缺少 @"，不只是"格式错"） | ✗ | 人审 |
| 24 | 表单提交前可**确认 / 撤销**（金额 / 删除等关键操作） | ✗ | 人审 |
| 25 | 上下文变化（焦点跳转 / 弹窗 / 页面跳转）**用户可预期** | ✗ | 人审 |
| 26 | 控件文本与功能一致（按钮叫"提交"就真的提交） | ✗ | 人审 |
## 4. Robust · 健壮（4 条）
| # | 检查点 | 自动可测 | 工具 |
|---|---|---|---|
| 27 | HTML **语义化** — 用 `<nav>` / `<main>` / `<article>` / `<aside>` 而不是全 `<div>` | ✓ | axe |
| 28 | ARIA 属性正确（`aria-label` / `role` / `aria-expanded` / `aria-current`） | ✓ | axe |
| 29 | 按钮 / 链接职责正确（提交动作用 `<button>` 不用 `<a href="#">`） | ✓ | axe |
| 30 | 状态变化通过 `aria-live` region 通知屏幕阅读器 | ⚠ 部分 | axe + 人审 |
---
## 场景优先级速查
不同场景检查重点不同。Junior pass 阶段挑场景 P0 必跑；Full Pass 阶段全跑。
| 场景 | P0 必跑（4-5 条） | P1 应跑 |
|---|---|---|
| **B 端 Dashboard** | #9 键盘操作 / #10 焦点环 / #14 heading / #20 lang / #27 语义化 | #1 #2 #21 #28 #30 |
| **内容站 / 文章** | #1 文本对比 / #3 alt / #14 heading / #15 链接清晰 / #20 lang | #21 #27 |
| **营销落地页** | #1 / #15 / #21 表单 label / #16 触达 24px | #3 #5 #20 |
| **移动端 H5（MOBILE-NATIVE）** | #1 / #5 / #16 / #20 / #21 | #2 #7 #9 |
| **小程序（四端）** | #5 / #16（多数 a11y 由平台 webview 兜底） | #20 #21 |
---
## 自动检查路线（与 [`self-verify.md`](self-verify.md) 联动）
### 路线 A · Claude in Chrome MCP + axe-core（首选 ⭐v4.4）
```
# 1. 浏览器渲染（已在 self-verify §1.2 完成）
mcp__Claude_in_Chrome__navigate({ url: "file:///abs/index.html", tabId: <id> })
# 2. 注入 axe-core 并执行
mcp__Claude_in_Chrome__javascript_tool({
tabId: <id>,
code: `
const s = document.createElement('script');
s.src = 'https://cdnjs.cloudflare.com/ajax/libs/axe-core/4.10.0/axe.min.js';
document.head.appendChild(s);
await new Promise(r => s.onload = r);
return await axe.run({ runOnly: ['wcag2aa', 'wcag22aa'] });
`
})
# 3. 解析返回的 violations / passes / incomplete，对照本清单标记
```
### 路线 B · Lighthouse CLI（fallback）
```bash
# 单次跑（return-cliCmd 让用户执行，禁 child_process）
npx lighthouse <URL 或 file://> --only-categories=accessibility --output=html --output-path=./a11y-report.html --chrome-flags="--headless"
# 批量跑（多个 examples 对比）
for f in examples/*/index.html; do
npx lighthouse "file://$(pwd)/$f" --only-categories=accessibility --output=json --output-path="./reports/$(dirname $f | tr / _).json" --chrome-flags="--headless"
done
```
### 路线 C · 人审兜底
#5 / #6 / #8 / #11 / #17 / #18 / #19 / #22 / #23 / #24 / #25 / #26 这些主观 / 需交互的项**机器测不出**。最少跑：
1. Tab 全键走一遍（验证 #9 #10 #11 #15）
2. 屏幕阅读器（VoiceOver / NVDA）跟读首页 30 秒（验证 #20 #21 #27 #28 #30）
3. 色盲模拟（Chrome DevTools → Rendering → Emulate vision deficiencies）
---
## 与流派的关系
a11y 与流派**正交** — 任何流派都该过 30 条 WCAG 2.2 AA。但流派会影响 a11y 的"难易程度"：
| 流派 | a11y 友好度 | 需特别注意 |
|---|---|---|
| BOLD-MINIMAL | 高 | 大字 + 大留白 + 主色对比够，自然过 #1 / #14 |
| EDITORIAL | 高 | 衬线 + 三栏 + 首字下沉，注意 #14 heading 层级 |
| BRUTALIST | **中** | 粗黑边对比够，但 6px 边对屏幕阅读器可能干扰 — 加 `aria-hidden` 装饰 |
| RETRO-FUTURE | **低** | 霓虹色 + CRT 扫描线 → #1 对比度容易掉；VT323 可读性差 → 长文不用；移动端慎选 |
| ORGANIC | 中 | Caveat 手写体在小字号下可读性差 → ≥ 18px 起 |
| MOBILE-NATIVE iOS | 高 | HIG 设计本身已考虑 a11y，关注 #16 触达 ≥ 44pt |
| MOBILE-NATIVE MD3 | 高 | Material 3 默认带 a11y，关注 #1 dynamic color 派生时对比 |
| MOBILE-NATIVE Harmony | 中 | 灵动色块多色相 → 对比度可能偏弱，每对色都要测 |
---
## 与红线的关系
a11y 清单**不是**SKILL.md §四 红线（红线判废、检查清单门控）。但**两者有 1 条交集**：
- **红线 #13 ⭐v2.1**（移动端缺 `viewport-fit=cover` + `safe-area-inset`）= 本清单 **#16 触达** + 平台 a11y 兜底，因为缺 safe-area 会导致 home indicator 遮挡触达区。
如果该 a11y 项是**红线**那种"违反就废"级别，会反向促成 SKILL.md §四 红线的扩展（v4.5+ 视情况）。
FILE:references/colors.md
# 5 流派配色基线（oklch）
> 全部使用 oklch 色空间。**禁紫渐变、禁 AI 模糊渐变背景**（参见 SKILL.md §四 红线 #2 / #11）。
>
> **⭐v4.0 起**：每个流派的配色已落到结构化 [`tokens/<slug>.json`](../tokens/) — 本文档保留可读的人类视角说明，jq 一行可转 CSS variables / Tailwind / Figma。配色硬规则源头仍在本文档。
## 1. BOLD-MINIMAL · 勇敢极简
| 角色 | oklch | 用途 |
|---|---|---|
| ink | `oklch(0.18 0 0)` | 主文字、主结构线 |
| paper | `oklch(0.99 0.005 95)` | 背景，几乎纯白带极淡暖调 |
| accent | `oklch(0.66 0.20 28)` | 锐利橙红，仅用于强调（hover / em） |
| mute | `oklch(0.45 0 0)` | 次要文字 |
**比例**：墨黑 20% / 留白 70% / 强调 10%。
## 2. EDITORIAL · 编辑杂志
| 角色 | oklch | 用途 |
|---|---|---|
| paper | `oklch(0.97 0.012 75)` | 暖纸色 |
| ink | `oklch(0.20 0.015 280)` | 偏冷的墨黑 |
| accent | `oklch(0.50 0.18 25)` | 砖红，首字下沉 / kicker / 引号 |
| mute | `oklch(0.42 0.02 280)` | 副文字 |
| rule | `oklch(0.20 0.015 280 / .25)` | 细分隔线 |
**比例**：纸 75% / 墨 18% / 砖红 5% / 灰线 2%。
## 3. BRUTALIST · 野兽派
| 角色 | oklch | 用途 |
|---|---|---|
| paper | `oklch(0.985 0 0)` | 近纯白（不做暖偏） |
| ink | `oklch(0.10 0 0)` | 真黑，6px 粗边 |
| warn | `oklch(0.78 0.22 105)` | 警示黄，stamp 块 |
**比例**：白 60% / 黑 30% / 警示黄 10%。**只许 3 色**。
## 4. RETRO-FUTURE · 复古未来
| 角色 | oklch | 用途 |
|---|---|---|
| bg | `oklch(0.13 0.04 280)` | 深夜紫蓝（注意：纯色，**不是渐变**） |
| ink | `oklch(0.96 0.04 110)` | 暖白主字 |
| neon-cyan | `oklch(0.85 0.18 195)` | 霓虹青 |
| neon-magenta | `oklch(0.72 0.25 5)` | 霓虹洋红（**不是紫**） |
**红线声明**：本流派使用霓虹双色对撞，**不得退化成紫渐变模糊背景**。所有发光必须由 `text-shadow` 多层叠加产生，禁用 `filter: blur()` 大色块当氛围。
## 5. ORGANIC · 有机自然
| 角色 | oklch | 用途 |
|---|---|---|
| paper | `oklch(0.96 0.025 85)` | 米白偏暖 |
| ink | `oklch(0.28 0.04 50)` | 棕墨 |
| clay | `oklch(0.62 0.13 45)` | 土橙 |
| moss | `oklch(0.45 0.10 145)` | 森林绿 |
| sky | `oklch(0.78 0.07 220)` | 雾蓝 |
**比例**：纸 60% / 棕墨 15% / 三原色（土橙 / 苔绿 / 雾蓝）各 8% 左右。
## 6. MOBILE-NATIVE · 移动原生 ⭐v2.1
> 三套对照不是要你"复制官方默认色"——而是体现各平台的**色彩语义体系**。
### 6.1 iOS HIG 风（推荐自有 brand color，避开系统蓝）
| 角色 | oklch | 用途 |
|---|---|---|
| paper | `oklch(0.985 0.005 75)` | non-grouped 背景（暖中性） |
| grouped-bg | `oklch(0.94 0.005 75)` | grouped table view 背景 |
| ink | `oklch(0.18 0.005 280)` | label 主字 |
| mute | `oklch(0.55 0.01 280)` | secondary label |
| sep | `oklch(0.88 0.005 280)` | separator 0.5pt |
| accent | `oklch(0.66 0.16 50)` | brand 强调（暖橙）— **替代 system blue** |
**红线**：禁直接用 `#007AFF`（红线 #8）。Apple 自家 App（Music / Books / News）也都用品牌色而非系统蓝。
### 6.2 Material Design 3（dynamic color，seed 自选）
| 角色 | oklch | 派生方式 |
|---|---|---|
| seed | `oklch(0.55 0.13 175)` | **自选**色相（示例青绿，**避开 Material 默认紫**） |
| primary | `oklch(0.55 0.13 175)` | = seed |
| primary-container | `oklch(0.88 0.06 175)` | seed L+33 / C-50% |
| secondary-container | `oklch(0.92 0.04 60)` | seed hue+245 / 低饱和 |
| tertiary-container | `oklch(0.90 0.05 320)` | seed hue+145 / 低饱和 |
| surface | `oklch(0.985 0.003 175)` | 近白带极淡 seed 色相 |
| surface-variant | `oklch(0.93 0.01 175)` | 比 surface 暗 5% |
| outline | `oklch(0.72 0.01 175)` | mid-tone |
**规则**：seed 选 oklch C ∈ [0.10, 0.18]，避免高饱和（饱和过 → 派生 container 太脏）。
### 6.3 HarmonyOS 鸿蒙（灵动色块）
| 角色 | oklch | 用途 |
|---|---|---|
| paper | `oklch(0.97 0.005 240)` | 主背景 |
| paper-2 | `oklch(0.94 0.008 240)` | hero / scene 卡片 |
| ink | `oklch(0.18 0.01 260)` | 主字 |
| mute | `oklch(0.50 0.01 260)` | 副字 |
| c-cyan | `oklch(0.78 0.12 220)` | 灵动青 |
| c-orange | `oklch(0.78 0.13 55)` | 灵动橙 |
| c-green | `oklch(0.78 0.14 145)` | 灵动绿 |
| c-pink | `oklch(0.82 0.10 5)` | 灵动粉 |
**规则**：4 个灵动色 **同明度（L≈0.78）+ 同低饱和（C≈0.10–0.14）+ 不同色相**。同明度让多色拼一起不打架，这是鸿蒙"灵动色块"的核心。
---
## 通用约束
- **禁** evenly-distributed palette（5 色等比例）
- **禁** 默认暗黑 `#121212` + 紫主题
- **禁** 全局 iOS 系统蓝 `#007AFF`、警示红 `#FF3B30` 当主色
- **强制** oklch；如需 fallback 可附 hex 注释，但 hex 不能是唯一来源
- 主色单一（**60–70%**）+ 强调色锐利（**5–10%**）+ 中性（20–30%）
FILE:references/inspirations.md
# 5 流派真实参考站点
> 参考是用来**抄结构 / 学手感**的，不是用来照搬的。看完关掉，再写自己的。
> 商用敏感：huashu-design 项目仅个人 license，所有结构 / 工作流参考必须改写后落地。
## 1. BOLD-MINIMAL
- [Stripe](https://stripe.com) — 业内勇敢极简天花板，注意 hero 字号节奏
- [Linear](https://linear.app) — 黑底版的极简样板
- [Vercel](https://vercel.com) — 几何排版与极锐利强调色
- [Apple · Newsroom](https://www.apple.com/newsroom/) — 大字 hero + 衬线 + 单强调
- [Pitch](https://pitch.com) — 强调色更跳，但留白依旧
## 2. EDITORIAL
- [The New York Times Magazine](https://www.nytimes.com/section/magazine) — 数字版杂志范本
- [The Verge](https://www.theverge.com/) — 现代化编辑感
- [Pitchfork](https://pitchfork.com) — 评分文章的栅格密度
- [It's Nice That](https://www.itsnicethat.com) — 设计杂志的当代演绎
- [The Markup](https://themarkup.org) — 调查报道，衬线为主
## 3. BRUTALIST
- [Bloomberg Businessweek · 特别报道](https://www.bloomberg.com/businessweek) — 粗黑边、错位标题
- [Are.na](https://www.are.na) — 网格暴露，零装饰
- [Read.cv](https://read.cv) — Mono + 极致黑白
- [Praxis](https://www.praxis.cool) — 当代 brutalist 教科书
- 反例：自我标榜 brutalist 但只是把字调粗的 SaaS landing → **不要看**
## 4. RETRO-FUTURE
- [Cyberpunk 2077 · Official](https://www.cyberpunk.net/cn/zh) — 霓虹双色对撞
- [Are.na · Vaporwave](https://www.are.na/search?q=vaporwave) — 80s 美学合集
- [Replit](https://replit.com)（部分 landing） — 终端 / mono 与现代结合
- [The Outpost](https://outpost.pub) — neon + grain 的现代演绎
- 反例：任何"紫渐变蓝"的 SaaS 落地页 → **不要看**
## 5. ORGANIC
- [Notion · 早期版本](https://web.archive.org/web/2018*/notion.so) — 暖底 + 不规则插画
- [Medium · Stories](https://medium.com) — 衬线 + 暖纸感
- [Headspace](https://www.headspace.com) — 圆滑形状 + 柔色
- [Mailchimp](https://mailchimp.com) — 手绘插画 + 暖色组合
- [Squarespace · Templates "Five"](https://www.squarespace.com) — 手写体出现的尺度
## 6. MOBILE-NATIVE · 移动原生 ⭐v2.1
### 6.1 iOS HIG
- [Apple HIG 官方](https://developer.apple.com/design/human-interface-guidelines/) — 必读，所有结构规范
- [Apple Music](https://www.apple.com/cn/apple-music/) — 自家 App，不用系统蓝的最佳范例
- [Things 3 by Cultured Code](https://culturedcode.com/things/) — HIG 教科书级 GTD 应用
- [Linear · iOS App](https://linear.app) — 现代化 HIG 典范
- [SwiftUI Component Library](https://www.swiftuiux.com/) — 抄结构不抄默认皮
### 6.2 Material Design 3
- [m3.material.io](https://m3.material.io/) — 官方 token / component 文档
- [Material Theme Builder](https://material-foundation.github.io/material-theme-builder/) — seed → dynamic color 在线生成
- [Google Calendar / Tasks · Android](https://play.google.com/store/apps/details?id=com.google.android.calendar) — 第一方 MD3 示范
- [Read You](https://github.com/Ashinch/ReaderYou) — 开源 MD3 RSS 客户端，Compose 全栈
- 反例：直接套 Material UI 默认紫的 web mock → **不要看**
### 6.3 HarmonyOS 鸿蒙
- [HarmonyOS 设计指南官方](https://developer.huawei.com/consumer/cn/design/) — 必读
- [华为商城 App](https://m.vmall.com/) — 一方应用，灵动色块 + 大圆角范本
- [华为运动健康 App](https://consumer.huawei.com/cn/mobileservices/health/) — 数据可视化与场景化
- [鸿蒙生态合作 App 精选](https://developer.huawei.com/consumer/cn/forum/) — 论坛精华案例集
- 反例：套 EMUI 默认皮没改 token 的 demo → **不要看**
## 7. 小程序（MOBILE-NATIVE 子集）⭐v2.2
### 7.1 微信小程序
- [微信小程序设计指南](https://developers.weixin.qq.com/miniprogram/design/) — 平台官方
- 喜茶 / 茶颜悦色 / Manner 小程序 — 自有 brand color，**不套 WeUI 默认绿**的样板
- 三顿半小程序 — 极简 + 排版 + 暖色，对标 ORGANIC 流派
- 即刻小程序 — 内容型小程序的字体 / 间距功底
- 反例：所有 hero 都是「全屏 banner + 5 个圆角胶囊 + 商品 grid」千篇一律的 → **不要看**
### 7.2 支付宝小程序
- [支付宝小程序设计指南](https://opendocs.alipay.com/mini/design) — 平台官方
- 蚂蚁森林 / 蚂蚁庄园 / 健康码 — 蚂蚁系一方应用，灵动卡片 + 微动效
- 飞猪 / 口碑 / 大众点评（部分 H5）— 内容密度的极致样板
- 反例：直接套 antd-mini 默认皮的 demo → **不要看**
### 7.3 抖音小程序 ⭐v4.2
- [抖音开放平台 · 设计指南](https://developer.open-douyin.com/docs/resource/zh-CN/mini-app/develop/component/) — 平台官方
- 抖音电商一方小程序（如"抖音商城")— 视频流 + 商品卡的样板
- 各品牌抖音小程序 hero 视频（不强求 starter 复刻，看节奏即可）
- 反例：直接套 TTUI / Tt-Mini-UI 默认皮的 demo → **不要看**
### 7.4 快手小程序 ⭐v4.3
- [快手小程序开放平台](https://mp.kuaishou.com/docs/develop/) — 平台官方文档
- 快手商城小程序 — 一方应用，电商场景的样板
- 快手主播店铺小程序 — 内容驱动型小程序的导购卡片
- 反例：直接套 KSUI / kuaishou-uikit 默认皮的 demo → **不要看**
### 7.5 四端通用参考
- [Awesome MiniApp](https://github.com/topics/miniapp) — GitHub 上多端框架与精选
- 「小程序设计周刊」公众号 — 行业更新
- [Taro](https://taro-docs.jd.com/) / [Uni-app](https://uniapp.dcloud.net.cn/) — 三端编译框架，自动复用代码
---
## 看参考的姿势
1. 一次只看 1 个流派，看完关掉再开下一个
2. 每个站点 ≤ 5 分钟，记住 1 个具体细节就够（不是整体氛围）
3. 写代码前再看一次 SKILL.md §四 硬红线，确保没顺手抄回 AI Slop
4. 抄完检查：oklch 配色 ✓、非默认字体 ✓、无紫渐变 ✓、无圆角 + 左竖条卡片 ✓、无统一 16px 圆角 ✓
FILE:references/multi-genre-compare.md
# 多流派对比生成手册 ⭐v4.1
> 用户没明确方向时如何并行生成 3 个流派对比；如何与 `huo15-openclaw-design-director` 协作。
## 何时启用本流程
| 信号 | 处理 |
|---|---|
| "做几个方向对比" / "三个风格" / "我看看哪个好" | ✓ 启用 |
| "帮我选" / "你定" | ✓ 启用 |
| 给了具体流派（"做 brutalist 风"）| ✗ 直接走 §六 单流派 Junior pass |
| 给了视觉锚点（"参考 Stripe"）| ✗ 直接走 BOLD-MINIMAL 单流派 |
| 给了完整 brand-spec（已有品牌色 / 字体）| ✗ 走 brand-protocol 抓品牌资源后单流派 |
## 流程总览
```
director 选 3 流派 (or 自动取反差对位)
↓
3 个 Junior pass 并行 (用 Explore subagent 隔离 context)
↓
3 张截图 (Chrome MCP / Playwright)
↓
director 五维矩阵打分 / design-critique 5 维评分
↓
用户选定 → 单流派走 §六 阶段 3 Full Pass
```
---
## 1. 流派选取
参考 [`tokens/_compare-matrix.md`](../tokens/_compare-matrix.md) §反差对位。
**有 director 时**：让 director 挑（它有 20 条设计哲学 + 五维矩阵）。
**无 director 时**：从下面 5 组反差对位任选一组：
- 理性 / 感性 / 实验：bold-minimal × organic × brutalist
- 冷峻 / 温暖 / 复古：editorial × organic × retro-future
- 桌面 / 移动 / 跨端：bold-minimal × mobile-native-ios × mobile-native-harmony
- 极简 / 信息密度 / 装饰：bold-minimal × editorial × retro-future
- Web / iOS / 鸿蒙：bold-minimal × mobile-native-ios × mobile-native-harmony
---
## 2. 并行 Junior pass（3 流派同时出）
对每个 `<slug>`：
1. 复制 `examples/<dir>/index.html` → `output-<slug>.html`
2. 替换占位文案为本次场景的文案
3. **保留视觉骨架** — hero / list / cards 结构不动，只换内容
4. 引用 `tokens/<slug>.json` 的 color / typography / spacing 当硬约束 — **不要改 token**
React / Vue 项目：先跑 [`tokens/exporters/to-tailwind.md`](../tokens/exporters/to-tailwind.md) 出 3 份 tailwind config，再各做一个 `<Genre><Page>.tsx`。
**性能提示**：3 流派**必须并行**。用 Explore subagent 隔离 context，不要串行 3 趟。
---
## 3. 三份截图
走 [`self-verify.md`](self-verify.md) §1 Chrome MCP 路线（首选）：
```
mcp__Claude_in_Chrome__navigate({ url: "file:///abs/output-bold-minimal.html", tabId: <id> })
mcp__Claude_in_Chrome__computer({ action: "screenshot", tabId: <id>, save_to_disk: true })
# 重复对 organic / brutalist
```
mobile-native 子集额外用 `resize_window` 切到设备 viewport：
- iOS: 393×852
- MD3: 412×915
- Harmony: 396×858
无 MCP 时降级到 Playwright CLI（见 self-verify.md §2）。
---
## 4. 五维评审
**首选**：`huo15-openclaw-design-director` 五维矩阵（结构 / 字体 / 颜色 / 空间 / 氛围）出"推荐 / 次选 / 反对方向"。
**次选**：`huo15-openclaw-design-critique` 给每张图打 5 维分（1-5）求和取最高。
**兜底**：用户人眼选。
director 的输出格式见 [它的 SKILL.md §3.4 推荐表态](../../huo15-openclaw-design-director/SKILL.md)。
---
## 5. 移交单流派
用户敲定方向后：
- 删除其他 2 份草稿
- 选定方向走 §六 阶段 3 Full Pass（补真实文案 / 替换图片 / 调细节 / 加动效）
- 视情况走阶段 3.5 导出 tokens 到既有项目
---
## 6. 与 design-director 的协作接口
### 我提供给 director 的资产
- **8 个流派的** [`tokens/<slug>.json`](../tokens/) — 结构化，director 一行 jq 取关键字段
- **8 个流派的** [`examples/<dir>/index.html`](../examples/) — Junior pass 起手不空白
- **redLineWaiver** — 每个流派的合规豁免，避免误判违规
- **横向对比表** [`tokens/_compare-matrix.md`](../tokens/_compare-matrix.md) — director 选流派的 cheat sheet
### director 提供给我的输入
- **3 个流派 slug**（如 `["bold-minimal", "organic", "brutalist"]`）
- **每个流派的 brief**（已写好的文案 / 重点 / 差异点，我做 Junior pass 不用从需求倒推）
- **五维矩阵回调**（截图回流后由 director 判最优）
### 接力消息格式（建议）
```jsonc
// director → frontend-design
{
"task": "multi-genre-junior-pass",
"genres": ["bold-minimal", "organic", "brutalist"],
"context": {
"client": "<品牌名>",
"scope": "<目标页面 / 组件类型>",
"differentiator": "<差异点一句话>"
},
"briefs": {
"bold-minimal": "<director 写的简报>",
"organic": "...",
"brutalist": "..."
}
}
// frontend-design → director（截图回流）
{
"task": "multi-genre-junior-pass-done",
"outputs": [
{ "genre": "bold-minimal", "html": "<rel-path>", "screenshot": "<rel-path>" },
{ "genre": "organic", "html": "<rel-path>", "screenshot": "<rel-path>" },
{ "genre": "brutalist", "html": "<rel-path>", "screenshot": "<rel-path>" }
]
}
```
### director 现状与升级路径
- **当前 director v1.0.0**：5 大流派覆盖（与本 skill v1.0 配套）— 对前 5 个 Web 流派全量生效；mobile-native 子集需要等 director v2 升级才能联动。
- **本 skill 在不动 director 的前提下，已为 director v2 备好接力入口**（tokens schema + compare matrix + redLineWaiver）— director 升级时直接读取即可，无需 frontend-design 再改。
---
## 7. 反 AI Slop 红线（多流派对比仍适用）
3 流派 Junior pass 仍要遵守 SKILL.md §四 全部 15 条红线。多流派对比**不是 AI Slop 免罪金牌** — 不要因为"探索方向"就把 3 张图都做成 AI 渐变模糊背景 + 紫色 hero。
每个 Junior pass 各自的 redLineWaiver 见 [`tokens/_compare-matrix.md`](../tokens/_compare-matrix.md)。
FILE:references/self-verify.md
# 自验证工作流操作手册 ⭐v3.0
> 火一五前端设计技能 v3.0 起，**Claude in Chrome MCP** 成为自验证首选路线。本手册按场景分流，给出每条路线的具体命令。
## 路线选择决策树
| 场景 | 首选 | Fallback 1 | Fallback 2 |
|---|---|---|---|
| Web / H5（5 个 Web 流派） | Claude in Chrome MCP | Playwright CLI | 用户手动 `open file://` |
| MOBILE-NATIVE Web 流派（iOS/MD3/Harmony） | Claude in Chrome MCP（resize 设备 viewport） | Playwright CLI（带 `--viewport-size`） | — |
| 微信小程序 | 微信开发者工具 IDE | 真机扫码 | — |
| 支付宝小程序 | 支付宝小程序 IDE | 真机扫码 | — |
---
## 1. Claude in Chrome MCP（优先 ⭐v3.0）
### 1.1 前置条件
- 用户已在 Chrome 装 Claude in Chrome 扩展并登录
- 当前会话挂载了 `mcp__Claude_in_Chrome__*` 工具集（如 schemas 未加载，用 `ToolSearch query="select:mcp__Claude_in_Chrome__list_connected_browsers,..."`）
- `mcp__Claude_in_Chrome__list_connected_browsers` 返回非空数组
### 1.2 标准命令序列（Web / H5）
```
# 1. 列出可用浏览器
mcp__Claude_in_Chrome__list_connected_browsers
# 2. 选定浏览器
mcp__Claude_in_Chrome__select_browser({ deviceId: "<from step 1>" })
# 3. 拿可用 tab
mcp__Claude_in_Chrome__tabs_context_mcp
# 4. 打开本地文件 / URL
mcp__Claude_in_Chrome__navigate({ url: "file:///abs/path/index.html", tabId: <id> })
# 5. 截图（关键产物）
mcp__Claude_in_Chrome__computer({
action: "screenshot",
tabId: <id>,
save_to_disk: true
})
# 6. 读控制台错误（防 oklch 不支持 / 字体加载失败 / JS 报错）
mcp__Claude_in_Chrome__read_console_messages({ tabId: <id> })
```
### 1.3 移动端 viewport 控制
```
# iPhone 16 Pro
mcp__Claude_in_Chrome__resize_window({ tabId: <id>, width: 393, height: 852 })
# Pixel 8
mcp__Claude_in_Chrome__resize_window({ tabId: <id>, width: 412, height: 915 })
# HarmonyOS Mate 60
mcp__Claude_in_Chrome__resize_window({ tabId: <id>, width: 396, height: 858 })
# resize 后再 screenshot
mcp__Claude_in_Chrome__computer({ action: "screenshot", tabId: <id>, save_to_disk: true })
```
或者（更稳，无需 resize）：本 skill 的 `examples/mobile-native/*/index.html` 内置桌面预览手机壳（device frame），桌面 viewport 也能看到正确移动效果，桌面浏览器打开即可。
### 1.4 何时跳过本路线
- `list_connected_browsers` 返回 `[]` → 直接路线 2
- 用户明确说"我不开 Chrome" → 路线 2
- wxml / axml 场景 → 路线 3 / 4（MCP 不能渲染小程序模板）
### 1.5 a11y 自动审计 ⭐v4.4
渲染完成后注入 axe-core 跑 WCAG 2.2 AA 检查（与 [`a11y-checklist.md`](a11y-checklist.md) 30 条对照）：
```
mcp__Claude_in_Chrome__javascript_tool({
tabId: <id>,
code: `
const s = document.createElement('script');
s.src = 'https://cdnjs.cloudflare.com/ajax/libs/axe-core/4.10.0/axe.min.js';
document.head.appendChild(s);
await new Promise(r => s.onload = r);
return await axe.run({ runOnly: ['wcag2aa', 'wcag22aa'] });
`
})
```
返回 `violations` / `passes` / `incomplete` 三个数组：
- 把 `violations` 对照 a11y-checklist 标 ⚠ 或修复
- `incomplete` 是 axe 无法自动判断的（如 #5 颜色非唯一信息载体），人审兜底
- `passes` 数 / 总检查数 ≥ 90% 视为可发布
**Lighthouse fallback**（MCP 不可用时）：
```bash
npx lighthouse <URL 或 file://> --only-categories=accessibility --output=html --output-path=./a11y-report.html --chrome-flags="--headless"
```
详见 [`a11y-checklist.md`](a11y-checklist.md) §自动检查路线。
---
## 2. Playwright CLI（fallback）
### 2.1 桌面端
```bash
npx playwright-core screenshot <URL 或 file:///abs/path> ~/verify.png --viewport-size=1440,900
```
### 2.2 移动端（MOBILE-NATIVE 流派必跑）
```bash
# iPhone 16 Pro
npx playwright-core screenshot <URL> ~/verify-iphone.png --viewport-size=393,852
# Pixel 8
npx playwright-core screenshot <URL> ~/verify-android.png --viewport-size=412,915
# HarmonyOS Mate 60
npx playwright-core screenshot <URL> ~/verify-harmony.png --viewport-size=396,858
```
### 2.3 注意
- 延续 enhance 插件"禁 child_process"铁律：**返回命令让用户执行**，不要 spawn
- `--full-page` 截整页（默认只 viewport）
- `--device="iPhone 16 Pro"` 也可用，但 Playwright 内置设备列表更新慢，自带 viewport 数值更稳
---
## 3. 微信开发者工具（小程序）
1. 安装 [微信开发者工具](https://developers.weixin.qq.com/miniprogram/dev/devtools/download.html)（Stable v1.06+）
2. 项目 → 导入项目 → 选 `examples/mini-program/wechat/`
3. AppID 选"测试号"
4. 编译后左侧模拟器看渲染
5. 真机调试扫码 → 微信扫码 → 真机看效果
6. 截图：模拟器右上角"截屏"按钮 / 工具菜单 → 截图，默认存 `~/Desktop`
---
## 4. 支付宝小程序 IDE（小程序）
1. 安装 [支付宝小程序 IDE](https://opendocs.alipay.com/mini/ide/download)
2. 文件 → 打开项目 → 选 `examples/mini-program/alipay/`
3. 预览 → 扫码用支付宝 App 看真机
4. 截图：IDE 内置截图工具，或预览界面右上角"复制图片"
---
## 5. 评审接力
无论走哪条路线，截图最终：
- **由用户人眼审** → 1 句话反馈
- 或调用 `huo15-openclaw-design-critique` 5 维打分（结构 / 字体 / 颜色 / 空间 / 氛围）+ Keep/Fix/Quick Wins 输出
---
## 6. 移动端额外检查清单
- [ ] safe-area-inset 上下生效（刘海 / home indicator 没遮挡内容）
- [ ] tab-bar 触达高度 ≥ 44pt（iOS）/ 48dp（Android / 鸿蒙）
- [ ] 字号 / 行高在 375–430 多档 viewport 都不溢出
- [ ] 小程序：`<page-meta>` 在 wxml 中存在 + tabBar 是 native 不是自绘 + rpx 适配 750rpx 屏宽
- [ ] 控制台无 oklch 不支持警告（fallback 已生效）
---
## 7. 三路线兼容性矩阵
| 流派 / 场景 | Chrome MCP | Playwright CLI | 微信 IDE | 支付宝 IDE | 真机扫码 |
|---|---|---|---|---|---|
| BOLD-MINIMAL / EDITORIAL / BRUTALIST / RETRO-FUTURE / ORGANIC | ✓ | ✓ | — | — | ✓ |
| MOBILE-NATIVE iOS HIG | ✓（resize 393×852）| ✓ | — | — | ✓ |
| MOBILE-NATIVE MD3 | ✓（resize 412×915）| ✓ | — | — | ✓ |
| MOBILE-NATIVE Harmony | ✓（resize 396×858）| ✓ | — | — | ✓ |
| 微信小程序 | ✗ | ✗ | ✓ | — | ✓ |
| 支付宝小程序 | ✗ | ✗ | — | ✓ | ✓ |
---
## 8. 设计原则提醒
- **MCP 优先**不是教条 —— 没浏览器连接就别等，立刻 fallback
- **不要在 skill 内部 spawn 进程** — 所有 CLI 命令必须 return-cliCmd 模式给用户执行
- **截图是最终凭证** — 不要用"我看了源代码觉得 OK"代替真机 / 真渲染验证
- **控制台错误也要看** — `read_console_messages` 能抓到 oklch fallback、字体 404、JS 报错等隐患
FILE:references/typography.md
# 5 流派字体对（display + body）
> **禁默认 Inter / Roboto / Arial / system-ui**（参见 SKILL.md §四 红线 #1）。
> 所有推荐字体均来自 [Google Fonts](https://fonts.google.com)、[Adobe Fonts](https://fonts.adobe.com) 或思源系列，license 友好。
## 1. BOLD-MINIMAL
| 角色 | 字体 | 备选 |
|---|---|---|
| display | **Playfair Display** Black Italic | Crimson Pro 900 / Tiempos Display |
| body | **IBM Plex Sans** 400/600 | Söhne / Inter Tight（仅作 fallback，不能首选） |
**反差**：粗衬线 italic ↔ 中性现代无衬线。
## 2. EDITORIAL
| 角色 | 字体 | 备选 |
|---|---|---|
| display | **DM Serif Display** | Cormorant Garamond / Tiempos Headline |
| body | **Source Serif 4** opsz | EB Garamond / 思源宋体 |
| caps | small-caps + letter-spacing .06em | — |
**反差**：display 略 condensed，body 宽松，靠 opsz 自动调节字重。
## 3. BRUTALIST
| 角色 | 字体 | 备选 |
|---|---|---|
| display | **Space Grotesk** 700 | PP Neue Montreal / Druk Wide |
| mono | **JetBrains Mono** 400/800 | Space Mono / IBM Plex Mono |
**反差**：粗 sans 大标题 + mono 元数据 / 导航，等宽字承担"机械感"。
## 4. RETRO-FUTURE
| 角色 | 字体 | 备选 |
|---|---|---|
| display | **Major Mono Display** | Monoton / Audiowide（慎用，易土气） |
| body | **VT323** | Press Start 2P（仅做装饰，不能跑长文） |
**警告**：VT323 可读性弱，正文长度 ≤ 200 字符；超过请退回 IBM Plex Mono。
## 5. ORGANIC
| 角色 | 字体 | 备选 |
|---|---|---|
| display | **Caveat** 600 | Patrick Hand / Reenie Beanie |
| body | **Source Serif 4** | Lora / 思源宋体 |
| 中文配对 | 思源宋体（display 仍用 Caveat 拉手写） | 霞鹜文楷 |
**反差**：手写体只在 hero / 卡片标题 / 注脚，正文一律严肃衬线，避免"小学生作文"。
## 6. MOBILE-NATIVE · 移动原生 ⭐v2.1
### 6.1 iOS HIG
| 角色 | 字体 | 备选 | 备注 |
|---|---|---|---|
| display | **Manrope** 800 | Onest / Outfit / Bricolage Grotesque | **替代 SF Pro / system-ui**（红线 #1） |
| body | **IBM Plex Sans** 400/500/600 | Söhne / Sentinel | — |
| 数字 | DM Sans / IBM Plex Mono | — | 状态栏 / 数据用等宽更稳 |
**HIG 排版三件套**：large title 34pt 800、navigation title 17pt 500、body 16pt。
### 6.2 Material Design 3
| 角色 | 字体 | 备选 | 备注 |
|---|---|---|---|
| display | **DM Sans** 700 | Onest / Public Sans | **替代 Roboto Flex**（红线 #1） |
| body | **IBM Plex Sans** 400/500 | Source Sans 3 | — |
| 中文 | 思源黑体 / Noto Sans SC | — | 跟 DM Sans 字重对齐 |
**MD3 type scale**：display 28pt 700、headline 22pt 500、title 16pt 500、body 14pt。
### 6.3 HarmonyOS 鸿蒙
| 角色 | 字体 | 备选 | 备注 |
|---|---|---|---|
| display | **Noto Sans SC** 700/900 | 思源黑体 / 霞鹜文楷 | HarmonyOS Sans 不在 Google Fonts，用 Noto Sans SC 替代 |
| body | **Noto Sans SC** 400/500 | 思源黑体 | 中文为主 |
| 数字 / 英文 | **DM Sans** 500/700 | Inter Tight (慎用) | 中英混排时英文用 DM Sans |
**鸿蒙排版**：屏头大标题 28pt 900、卡片标题 22pt 700、控件标签 15pt 500。
---
## MOBILE-NATIVE 通用约束
- **禁** `font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, ...`（红线 #1，且无差异化）
- **禁** `font-family: SF Pro Display`（在 web 上根本加载不到，最终 fallback 到 system-ui）
- 中英混排：英文 / 数字用 DM Sans / Manrope，中文用 Noto Sans SC / 思源黑体；**两个字族不超过 3 个字重**
---
## 通用约束
- 主字 / 副字必须有性格反差：衬线 ↔ 无衬线、或 mono ↔ proportional
- **中文**：思源宋体 / 思源黑体 / 霞鹜文楷 / Noto Serif SC，按流派挑一对（**不要**英文中文混 4 个字族）
- **加载**：`<link rel="preconnect">` 提前连，`display=swap` 避免 FOIT
- 任何"system-ui 兜底就完事"的写法 = 没用本 skill
FILE:tokens/_compare-matrix.md
# 8 流派横向对比矩阵 ⭐v4.1
> 给 `huo15-openclaw-design-director` 选流派用，或用户要做 multi-genre 平行对比时的速查表。
## 关键 token 对比
| 流派 | 主色 (oklch) | 重音色 | display 字体 | radius 哲学 | 适合场景 |
|---|---|---|---|---|---|
| **bold-minimal** | 0.18 0 0（墨黑） | 0.66 0.20 28（锐橙） | Playfair Display 900 italic | sm 2 / md 8 / lg 14 / pill | 科技、B 端、作品集 |
| **editorial** | 0.20 0.015 280（冷墨） | 0.50 0.18 25（砖红） | DM Serif Display 400 | sm 2 / md 4 / lg 8 | 内容、品牌故事、报告 |
| **brutalist** | 0.10 0 0（真黑） | 0.78 0.22 105（警示黄） | Space Grotesk 700 + JetBrains Mono | 全 0（直角） | 独立工作室、Web3、先锋 |
| **retro-future** | 0.13 0.04 280（深紫蓝 bg） | 0.85 0.18 195 / 0.72 0.25 5（霓虹双色） | Major Mono Display + VT323 | 主 0 / lg 4 | 游戏、音乐、娱乐 |
| **organic** | 0.28 0.04 50（棕墨） | 0.62 0.13 45（土橙） | Caveat 600 + Source Serif | irregular 4 段 / soft 24 / pill | 食品、母婴、健康 |
| **mobile-native-ios** | 0.18 0.005 280（HIG 墨） | 0.66 0.16 50（暖橙替代系统蓝） | Manrope 800 | icon 8 / button 7 / list 14 / fab 18 | iOS APP / iPhone H5 |
| **mobile-native-md3** | 0.55 0.13 175（青绿 seed） | dynamic primary container | DM Sans 700 | chip 8 / card 16 / fab 18 / topBar 24 | Android APP / MD3 |
| **mobile-native-harmony** | 0.18 0.01 260（鸿蒙墨） | 4 灵动色块同明度 L≈0.78 | Noto Sans SC 700/900 + DM Sans | control 14 / tile 24 / card 28 / hero 32 | HarmonyOS / 鸿蒙生态 |
## 反差对位（multi-genre 三选时常用）
| 命题 | 流派组合 |
|---|---|
| 理性 vs 感性 vs 实验 | bold-minimal × organic × brutalist |
| 冷峻 vs 温暖 vs 复古 | editorial × organic × retro-future |
| 桌面 vs 移动 vs 跨端 | bold-minimal × mobile-native-ios × mobile-native-harmony |
| 极简 vs 信息密度 vs 装饰 | bold-minimal × editorial × retro-future |
| Web 端 vs 双移动端 | bold-minimal × mobile-native-md3 × mobile-native-harmony |
## redLineWaiver 速查
| 流派 | 豁免说明 |
|---|---|
| brutalist | radius 全 0 是 hallmark，**不是**红线 #9 违规 |
| retro-future | bg 深紫蓝纯色（非渐变），霓虹靠 text-shadow 不靠 backdrop-blur |
| organic | irregular radius 4 段不同百分比，不犯统一 16px |
| mobile-native-ios | accent 暖橙替代 #007AFF（红线 #8 合规：Apple 自家 App 也这么做） |
| mobile-native-md3 | seed 青绿避开 Material 默认紫 |
| mobile-native-harmony | 4 灵动色块同明度多色相，避免单色一统天下 |
## motion 哲学速查 ⭐v4.5
| 流派 | 主 duration | 主 easing | stagger | 一句话动效原则 |
|---|---|---|---|---|
| **bold-minimal** | normal 300ms | standard | 80ms | 克制 — 单一 easing、绝不弹跳 |
| **editorial** | normal 400ms / slow 700ms | decelerate | 120ms | 稳重 — 偏长 duration 模仿翻页 |
| **brutalist** | instant 80ms / fast 120ms | linear / step2 | 30ms | 硬切 — 禁缓动函数 |
| **retro-future** | blink 1000ms 周期 | step2 / linear | 50ms | CRT 闪烁 — 用 step 模拟显像管 |
| **organic** | normal 500ms / slow 800ms | spring（1.56 超调） | 140ms | 弹性 — 卡片落下有回弹 |
| **mobile-native-ios** | normal 350ms | iosSpring | 70ms | Apple HIG spring |
| **mobile-native-md3** | medium2 300ms | emphasized | 75ms | MD3 完整 12 档 duration + 4 档 emphasized easing |
| **mobile-native-harmony** | normal 300ms | fluid | 80ms | 鸿蒙流畅感（比 iOS 偏快） |
**反差选 motion 的命题**：
- 静态 vs 闪烁 vs 弹性：bold-minimal × retro-future × organic
- 克制 vs 严谨 vs 灵动：brutalist × md3 × harmony
详细 motion token JSON 见 [`<slug>.json`](.) 的 `motion` 字段；导出到 CSS / Tailwind / Figma 见 [`exporters/`](exporters/)。
## design-director 集成点
director 需要挑 3 流派对比时，对每个候选：
1. 读 [`<slug>.json`](.) 拿 color / typography / spacing / radius
2. 看 redLineWaiver（避免误判违规）
3. 用 `examples/<dir>/index.html` 作 Junior pass 起手（不用空白起步）
4. 截 3 张图喂给五维矩阵打分
详细接力流程见 [`../references/multi-genre-compare.md`](../references/multi-genre-compare.md)。
FILE:tokens/_schema.md
# tokens.json Schema · v4.0
> 每个流派一份 `<slug>.json`，扁平 1 层结构便于 jq 转换。
## 字段约定
```jsonc
{
"name": "bold-minimal", // 必须等于文件名（无 .json）
"displayName": "BOLD-MINIMAL · 勇敢极简", // SKILL.md §三 流派表对应名
"version": "1.0.0", // 流派 token 自身版本
"color": { "<token>": "oklch(...)" }, // oklch 首选
"colorHex": { "<token>": "#hex" }, // 1:1 对应 color，给不支持 oklch 环境
"typography": {
"display": "<Family> <Weight> [italic]",
"body": "<Family> <Weight>"
},
"spacing": { "xs": 4, "sm": 8, ..., "2xl": 128 }, // 单位 px；小程序按 rpx ×2 换算
"radius": { "none": 0, "sm": 2, ..., "pill": 999 },
"shadow": { "subtle": "...", "card": "...", "modal": "..." },
"examplePath": "../examples/<dir>/index.html", // 对应 starter 文件
"redLineWaiver": ["..."], // 可选；说明本流派对哪条红线有合规豁免
"motion": { // ⭐v4.5 动效 tokens
"duration": { "fast": 200, "normal": 300, ... }, // 单位 ms
"easing": { "<name>": "cubic-bezier(...) | linear | steps(...)" },
"stagger": { "tight"|"normal"|"loose": 50|80|120 }, // 列表级联出场延迟（ms）
"philosophy": "<一句话流派动效原则>"
}
}
```
## 硬约束
- `color` 与 `colorHex` 必须**键名 1:1 对应**（同 key、同顺序）
- `color` 必填 oklch；颜色硬规则见 [`../references/colors.md`](../references/colors.md)
- `typography` 字体不能命中红线 #1（Inter / Roboto / Arial / system-ui）；**小程序场景豁免**（PingFang SC 等中文字体允许）
- 任何字段缺失 → 视作"沿用 colors.md / typography.md 中通用约束"
- **不要嵌套**对象（如 `color.brand.primary`）— 保持扁平 1 层，jq 一行能转
## 导出器
- [`exporters/to-css-vars.md`](exporters/to-css-vars.md) — jq → CSS variables
- [`exporters/to-tailwind.md`](exporters/to-tailwind.md) — jq → tailwind.config.js extend
- [`exporters/to-figma.md`](exporters/to-figma.md) — Tokens Studio v2 兼容格式
所有导出器**只返回 jq / shell 命令给用户执行**，不在 skill 内部 spawn（延续 enhance "禁 child_process" 铁律）。
FILE:tokens/bold-minimal.json
{
"name": "bold-minimal",
"displayName": "BOLD-MINIMAL · 勇敢极简",
"version": "1.0.0",
"color": {
"ink": "oklch(0.18 0 0)",
"paper": "oklch(0.99 0.005 95)",
"accent": "oklch(0.66 0.20 28)",
"mute": "oklch(0.45 0 0)"
},
"colorHex": {
"ink": "#1a1a1a",
"paper": "#fbfbf6",
"accent": "#d6604c",
"mute": "#666666"
},
"typography": {
"display": "Playfair Display 900 italic",
"body": "IBM Plex Sans 400/600"
},
"spacing": { "xs": 4, "sm": 8, "md": 16, "lg": 32, "xl": 64, "2xl": 128 },
"radius": { "none": 0, "sm": 2, "md": 8, "lg": 14, "pill": 999 },
"shadow": {
"subtle": "0 1px 2px oklch(0 0 0 / .05)",
"card": "0 4px 16px oklch(0 0 0 / .08)",
"modal": "0 16px 48px oklch(0 0 0 / .15)"
},
"examplePath": "../examples/bold-minimal/index.html",
"motion": {
"duration": { "instant": 100, "fast": 200, "normal": 300, "slow": 500 },
"easing": {
"standard": "cubic-bezier(0.2, 0, 0, 1)",
"decelerate": "cubic-bezier(0, 0, 0.2, 1)"
},
"stagger": { "normal": 80 },
"philosophy": "克制 — 短 duration、单一 easing、绝不弹跳；hover/focus 200ms，页面进入 300ms"
}
}
FILE:tokens/brutalist.json
{
"name": "brutalist",
"displayName": "BRUTALIST · 野兽派",
"version": "1.0.0",
"color": {
"paper": "oklch(0.985 0 0)",
"ink": "oklch(0.10 0 0)",
"warn": "oklch(0.78 0.22 105)"
},
"colorHex": {
"paper": "#fafafa",
"ink": "#1a1a1a",
"warn": "#dde43a"
},
"typography": {
"display": "Space Grotesk 700",
"mono": "JetBrains Mono 400/800"
},
"spacing": { "xs": 4, "sm": 8, "md": 16, "lg": 24, "xl": 32, "2xl": 64 },
"radius": { "none": 0, "sm": 0, "md": 0, "lg": 0 },
"shadow": {
"subtle": "none",
"card": "6px 6px 0 oklch(0.10 0 0)",
"modal": "12px 12px 0 oklch(0.10 0 0)"
},
"redLineWaiver": ["radius 全 0 是流派 hallmark（粗黑直角），不是红线 #9 违规（多档机制不适用）"],
"examplePath": "../examples/brutalist/index.html",
"motion": {
"duration": { "instant": 80, "fast": 120 },
"easing": {
"linear": "linear",
"step2": "steps(2, end)"
},
"stagger": { "tight": 30 },
"philosophy": "硬切 — instant duration、linear/step easing 让动效像机械按下；stagger 30ms 接近无延迟。**禁** spring / bounce / 缓动函数"
}
}
FILE:tokens/editorial.json
{
"name": "editorial",
"displayName": "EDITORIAL · 编辑杂志",
"version": "1.0.0",
"color": {
"paper": "oklch(0.97 0.012 75)",
"ink": "oklch(0.20 0.015 280)",
"accent": "oklch(0.50 0.18 25)",
"mute": "oklch(0.42 0.02 280)",
"rule": "oklch(0.20 0.015 280 / .25)"
},
"colorHex": {
"paper": "#f6f3eb",
"ink": "#2c2c33",
"accent": "#a83a23",
"mute": "#5e5e6a",
"rule": "rgba(44,44,51,.25)"
},
"typography": {
"display": "DM Serif Display 400",
"body": "Source Serif 4 400/600 opsz"
},
"spacing": { "xs": 4, "sm": 8, "md": 16, "lg": 32, "xl": 48, "2xl": 96 },
"radius": { "none": 0, "sm": 2, "md": 4, "lg": 8 },
"shadow": {
"subtle": "0 1px 2px oklch(0 0 0 / .04)",
"card": "0 2px 8px oklch(0 0 0 / .06)",
"modal": "0 12px 36px oklch(0 0 0 / .12)"
},
"examplePath": "../examples/editorial/index.html",
"motion": {
"duration": { "fast": 250, "normal": 400, "slow": 700 },
"easing": {
"decelerate": "cubic-bezier(0, 0, 0.2, 1)",
"standard": "cubic-bezier(0.4, 0, 0.2, 1)"
},
"stagger": { "loose": 120 },
"philosophy": "稳重 — 偏长 duration 模仿翻页节奏、decelerate easing 像纸张展开；段落 stagger 120ms 模拟阅读视线下移"
}
}
FILE:tokens/exporters/to-css-vars.md
# tokens.json → CSS Variables
> 把任一 `<slug>.json` 转成可在 `<style>` 顶层使用的 CSS variables。
## 一行 jq 命令（推荐）
```bash
jq -r '
":root {",
(.color | to_entries[] | " --color-\(.key): \(.value);"),
(.spacing | to_entries[] | " --spacing-\(.key): \(.value)px;"),
(.radius | to_entries[] | " --radius-\(.key): \(if (.value | type) == "number" then "\(.value)px" else .value end);"),
(.shadow | to_entries[] | " --shadow-\(.key): \(.value);"),
(.motion.duration // {} | to_entries[] | " --duration-\(.key): \(.value)ms;"),
(.motion.easing // {} | to_entries[] | " --easing-\(.key): \(.value);"),
(.motion.stagger // {} | to_entries[] | " --stagger-\(.key): \(.value)ms;"),
"}"
' tokens/bold-minimal.json
```
## 输出示例（bold-minimal）
```css
:root {
--color-ink: oklch(0.18 0 0);
--color-paper: oklch(0.99 0.005 95);
--color-accent: oklch(0.66 0.20 28);
--color-mute: oklch(0.45 0 0);
--spacing-xs: 4px;
--spacing-sm: 8px;
--spacing-md: 16px;
--spacing-lg: 32px;
--spacing-xl: 64px;
--spacing-2xl: 128px;
--radius-none: 0px;
--radius-sm: 2px;
--radius-md: 8px;
--radius-lg: 14px;
--radius-pill: 999px;
--shadow-subtle: 0 1px 2px oklch(0 0 0 / .05);
--shadow-card: 0 4px 16px oklch(0 0 0 / .08);
--shadow-modal: 0 16px 48px oklch(0 0 0 / .15);
--duration-instant: 100ms;
--duration-fast: 200ms;
--duration-normal: 300ms;
--duration-slow: 500ms;
--easing-standard: cubic-bezier(0.2, 0, 0, 1);
--easing-decelerate: cubic-bezier(0, 0, 0.2, 1);
--stagger-normal: 80ms;
}
```
## 在样式里使用 motion tokens
```css
.btn {
transition: background var(--duration-fast) var(--easing-standard),
transform var(--duration-fast) var(--easing-standard);
}
.list-item {
animation: fadeIn var(--duration-normal) var(--easing-decelerate) both;
}
.list-item:nth-child(1) { animation-delay: calc(var(--stagger-normal) * 0); }
.list-item:nth-child(2) { animation-delay: calc(var(--stagger-normal) * 1); }
.list-item:nth-child(3) { animation-delay: calc(var(--stagger-normal) * 2); }
```
## `@property` 块（让 transition 跑动 oklch / 自定义属性）
老式 `transition: --color 200ms` 不会工作（因 var 默认是字符串 type）。用 `@property` 声明：
```css
@property --color-accent {
syntax: '<color>';
inherits: true;
initial-value: oklch(0.66 0.20 28);
}
```
之后 `transition: --color-accent 200ms` 就能平滑过渡颜色。
## 批量导出（所有流派）
```bash
mkdir -p generated/css
for f in tokens/*.json; do
slug=$(basename "$f" .json)
jq -r '...' "$f" > "generated/css/$slug.css"
done
```
## hex fallback（不支持 oklch 的环境）
```bash
jq -r '
":root {",
(.colorHex | to_entries[] | " --color-\(.key): \(.value);"),
"}"
' tokens/bold-minimal.json
```
## 注意
- 本 skill **不内置 spawn**，上述命令是给用户保存到自己项目里跑的（铁律延续 enhance 插件"禁 child_process"）
- `radius.irregular`（organic 流派）值是字符串如 `"48% 52% 55% 45% / 50% 50% 50% 50%"`，jq 上面那行 `if number` 判断已正确处理
FILE:tokens/exporters/to-figma.md
# tokens.json → Figma（Tokens Studio v2 兼容）
> [Tokens Studio](https://tokens.studio/)（前 Figma Tokens Plugin）的 v2.x JSON 与本 skill tokens 几乎 1:1 对应。
## 转换规则
| 本 skill 字段 | Tokens Studio v2 |
|---|---|
| `color.<key>: "oklch(...)"` | `{ value: "oklch(...)", type: "color" }` |
| `colorHex.<key>: "#hex"` | （fallback，Figma 不支持 oklch 时使用）`{ value: "#hex", type: "color" }` |
| `spacing.<key>: 16` | `{ value: "16px", type: "spacing" }` |
| `radius.<key>: 14` | `{ value: "14px", type: "borderRadius" }` |
| `shadow.<key>: "..."` | `{ value: "...", type: "boxShadow" }` |
| `typography.display: "Manrope 800"` | 拆为 `fontFamilies.display` + `fontWeights.display` 两个 set |
| `motion.duration.<key>: 200` ⭐v4.5 | `{ value: "200ms", type: "duration" }` |
| `motion.easing.<key>: "cubic-bezier(...)"` ⭐v4.5 | `{ value: "[0.2, 0, 0, 1]", type: "cubicBezier" }`（拆为 4 元数组） |
| `motion.stagger.<key>: 80` ⭐v4.5 | Figma 无原生概念；导出为 `{ value: "80ms", type: "duration" }` 给 Smart Animate 单独引用 |
## jq 转换（hex fallback 版，Figma 实际可用）
```bash
jq '{
($SLUG | tostring): {
color: (.colorHex | with_entries({
key: .key,
value: { value: .value, type: "color" }
})),
spacing: (.spacing | with_entries({
key: .key,
value: { value: "\(.value)px", type: "spacing" }
})),
borderRadius: (.radius | with_entries({
key: .key,
value: {
value: (if (.value | type) == "number" then "\(.value)px" else .value end),
type: "borderRadius"
}
})),
boxShadow: (.shadow | with_entries({
key: .key,
value: { value: .value, type: "boxShadow" }
}))
}
}' --arg SLUG bold-minimal tokens/bold-minimal.json > generated/figma/bold-minimal.tokens.json
```
## 在 Figma 里使用
1. 安装 [Tokens Studio plugin](https://www.figma.com/community/plugin/843461159747178978/tokens-studio-for-figma)
2. 打开 plugin → 文件菜单 → **Import** → JSON
3. 上传上一步生成的 JSON
4. 一键应用到 Figma styles（自动建 color styles / text styles / effect styles）
## 限制
- **Figma 不支持 oklch**（截至 2026-04），plugin 默认用 hex fallback；本 skill 的 token 文件已为每个 color 提供 `colorHex`
- **Figma 不支持 cubic-bezier 字符串**直接，需拆成 4 元数组 `[x1, y1, x2, y2]`；jq 转换时用 `gsub("cubic-bezier\\("; "[") | gsub("\\)"; "]")` 简单替换可工作
- **stagger** 在 Figma 无原生概念，Smart Animate 只能逐对象设 delay；本 skill 把 stagger 导出为 duration 给 Figma 单独使用
- **不规则圆角**（organic 的 `48% 52% 55% 45%`）Figma 不支持，需手画 ellipse 或 vector
- **rpx**（小程序单位）转 Figma 需 ×2 → px（750rpx 屏宽对应 Figma 375px design board）
- **typography 字符串拆分**：`"Manrope 800"` → 需手动拆成 `fontFamilies.display = "Manrope"` 和 `fontWeights.display = 800`，jq 单行不够智能（用 awk 或 Node 脚本兜底）
FILE:tokens/exporters/to-tailwind.md
# tokens.json → Tailwind（v3 config + v4 @theme 双版本）
> v3 用 `tailwind.config.js theme.extend` 注入；**v4（2026 主流）改用 CSS 内 `@theme {}` 块** ⭐v4.6。两者从同一 tokens.json 出发。
## v4 适配 ⭐v4.6（推荐，2026 起 Tailwind 默认走这条）
Tailwind v4 不再用 `tailwind.config.js`（仍兼容但非主流），改用 CSS 内 `@theme {}` 声明，token 命名前缀化（`--color-<key>` / `--spacing-<key>` 等）。
### jq 转换（v4）
```bash
jq -r '
"@import \"tailwindcss\";",
"",
"@theme {",
(.color | to_entries[] | " --color-\(.key): \(.value);"),
(.spacing | to_entries[] | " --spacing-\(.key): \(.value)px;"),
(.radius | to_entries[] | " --radius-\(.key): \(if (.value | type) == "number" then "\(.value)px" else .value end);"),
(.shadow | to_entries[] | " --shadow-\(.key): \(.value);"),
(.motion.duration // {} | to_entries[] | " --duration-\(.key): \(.value)ms;"),
(.motion.easing // {} | to_entries[] | " --ease-\(.key): \(.value);"),
"}"
' tokens/bold-minimal.json > generated/tailwind-v4/bold-minimal.css
```
### v4 输出示例（bold-minimal）
```css
@import "tailwindcss";
@theme {
--color-ink: oklch(0.18 0 0);
--color-paper: oklch(0.99 0.005 95);
--color-accent: oklch(0.66 0.20 28);
--color-mute: oklch(0.45 0 0);
--spacing-xs: 4px;
--spacing-sm: 8px;
--spacing-md: 16px;
--spacing-lg: 32px;
--spacing-xl: 64px;
--spacing-2xl: 128px;
--radius-sm: 2px;
--radius-md: 8px;
--radius-lg: 14px;
--radius-pill: 999px;
--shadow-card: 0 4px 16px oklch(0 0 0 / .08);
--duration-fast: 200ms;
--duration-normal: 300ms;
--ease-standard: cubic-bezier(0.2, 0, 0, 1);
}
```
### v4 用法（utility class 自动生成）
```html
<h1 class="font-display text-ink leading-none">少即是<em>极致</em></h1>
<p class="text-mute mt-md">承诺一个方向</p>
<a class="bg-ink text-paper rounded-pill px-lg py-md transition-colors duration-fast ease-standard">查看 →</a>
```
`text-ink` / `bg-paper` / `rounded-pill` / `px-lg` / `duration-fast` / `ease-standard` 等 utility 由 Tailwind v4 从 `@theme` 块自动生成，不用配置。
### v4 + oklch 兼容
Tailwind v4 直接支持 oklch；不需要 `@property` polyfill。需要 hex fallback 时（旧浏览器），单独写：
```css
@theme {
--color-ink: #1a1a1a; /* fallback */
--color-ink: oklch(0.18 0 0);
}
```
后写的 oklch 在支持的浏览器优先生效；不支持的退到上一行 hex。
---
## v3 适配（legacy / 既有 Tailwind v3 项目）
> 用 `theme.extend` 注入流派 token，**不要覆盖**默认。
## jq 转换
```bash
jq '{
theme: {
extend: {
colors: .color,
spacing: (.spacing | with_entries({
key: .key,
value: (if (.value | type) == "number" then "\(.value)px" else .value end)
})),
borderRadius: (.radius | with_entries({
key: .key,
value: (if (.value | type) == "number" then "\(.value)px" else .value end)
})),
boxShadow: .shadow,
transitionDuration: (.motion.duration // {} | with_entries({
key: .key,
value: "\(.value)ms"
})),
transitionTimingFunction: .motion.easing // {}
}
}
}' tokens/bold-minimal.json > generated/tailwind/bold-minimal.tailwind.json
```
## 落地到 tailwind.config.js
```js
import boldMinimal from './generated/tailwind/bold-minimal.tailwind.json' assert { type: 'json' };
export default {
content: ['./**/*.{html,js,ts,tsx,vue}'],
theme: {
extend: {
...boldMinimal.theme.extend,
fontFamily: {
display: ['"Playfair Display"', 'serif'],
body: ['"IBM Plex Sans"', 'sans-serif']
}
}
}
};
```
用法：
```html
<h1 class="font-display text-ink leading-none">少即是<em>极致</em></h1>
<p class="text-mute mt-md">承诺一个方向</p>
<a class="bg-ink text-paper rounded-pill px-lg py-md transition-colors duration-fast ease-standard">查看 →</a>
```
`duration-fast` / `ease-standard` 是 v4.5 motion tokens 通过 `transitionDuration` / `transitionTimingFunction` 注入到 Tailwind 后产生的 utility class。
## 红线提醒
- 不要把 token **覆盖** Tailwind 默认色（避免污染团队既有项目）— 只用 `extend`
- `fontFamily` 必须**显式**列入 `extend`（typography 字段是字符串描述，jq 不能直接转换）
- `radius.lg = 14` 不要无脑当全局 radius — 必须搭配多档（红线 #9）
## OKLCH 兼容
Tailwind v3.4+ 直接支持 oklch，无需额外配置。Tailwind v3.3- 需要 `@property` polyfill 或退到 hex fallback：
```bash
# hex fallback 版
jq '{ theme: { extend: { colors: .colorHex } } }' tokens/bold-minimal.json
```
FILE:tokens/mobile-native-harmony.json
{
"name": "mobile-native-harmony",
"displayName": "MOBILE-NATIVE · HarmonyOS 鸿蒙",
"version": "1.0.0",
"color": {
"paper": "oklch(0.97 0.005 240)",
"paper2": "oklch(0.94 0.008 240)",
"ink": "oklch(0.18 0.01 260)",
"mute": "oklch(0.50 0.01 260)",
"cyan": "oklch(0.78 0.12 220)",
"orange": "oklch(0.78 0.13 55)",
"green": "oklch(0.78 0.14 145)",
"pink": "oklch(0.82 0.10 5)"
},
"colorHex": {
"paper": "#f4f5f7",
"paper2": "#eaedf2",
"ink": "#23262e",
"mute": "#76798a",
"cyan": "#7ac8d4",
"orange": "#e0a877",
"green": "#9bc88a",
"pink": "#e7b6b6"
},
"typography": {
"display": "Noto Sans SC 700/900",
"body": "Noto Sans SC 400/500",
"numeric": "DM Sans 500/700"
},
"spacing": { "xs": 4, "sm": 8, "md": 16, "lg": 24, "xl": 32, "2xl": 56 },
"radius": { "control": 14, "tile": 24, "card": 28, "hero": 32, "pill": 999 },
"shadow": {
"tile": "0 6px 18px oklch(0.78 0.12 220 / .15)"
},
"redLineWaiver": ["灵动色块多色：4 色同明度 L≈0.78 + 同低饱和 C≈0.10–0.14 + 不同色相，避免某色一统天下"],
"examplePath": "../examples/mobile-native/harmony/index.html",
"motion": {
"duration": { "fast": 200, "normal": 300, "slow": 450 },
"easing": {
"harmonySpring": "cubic-bezier(0.2, 0, 0, 1)",
"fluid": "cubic-bezier(0.36, 0, 0.66, 1)",
"emphasized": "cubic-bezier(0.05, 0.7, 0.1, 1)"
},
"stagger": { "normal": 80 },
"philosophy": "鸿蒙流畅感 — fluid easing 模仿 ArkUI 的灵动效果；4 灵动色块切换 normal 300ms（比 iOS 偏快），保持响应敏捷"
}
}
FILE:tokens/mobile-native-ios.json
{
"name": "mobile-native-ios",
"displayName": "MOBILE-NATIVE · iOS HIG",
"version": "1.0.0",
"color": {
"paper": "oklch(0.985 0.005 75)",
"groupedBg": "oklch(0.94 0.005 75)",
"ink": "oklch(0.18 0.005 280)",
"mute": "oklch(0.55 0.01 280)",
"sep": "oklch(0.88 0.005 280)",
"accent": "oklch(0.66 0.16 50)",
"card": "oklch(1 0 0)"
},
"colorHex": {
"paper": "#fafaf6",
"groupedBg": "#efefe9",
"ink": "#2c2c33",
"mute": "#85858a",
"sep": "#dddde0",
"accent": "#cf7a26",
"card": "#ffffff"
},
"typography": {
"display": "Manrope 800",
"body": "IBM Plex Sans 400/500/600"
},
"spacing": { "xs": 4, "sm": 8, "md": 16, "lg": 20, "xl": 32, "2xl": 64 },
"radius": { "icon": 8, "button": 7, "list": 14, "fab": 18, "pill": 999 },
"shadow": {
"navbarBlur": "saturate(180%) blur(20px)",
"card": "0 1px 2px oklch(0 0 0 / .08), 0 0 0 .5px oklch(0 0 0 / .04)"
},
"redLineWaiver": ["accent 用自有暖橙替代 system blue #007AFF（红线 #8 合规：Apple 自家 App 也都用品牌色而非系统蓝）"],
"examplePath": "../examples/mobile-native/ios/index.html",
"motion": {
"duration": { "fast": 200, "normal": 350, "slow": 500 },
"easing": {
"iosSpring": "cubic-bezier(0.32, 0.72, 0, 1)",
"iosDefault": "cubic-bezier(0.25, 0.1, 0.25, 1)"
},
"stagger": { "normal": 70 },
"philosophy": "Apple HIG spring — iosSpring 模仿 SwiftUI 的 .interactiveSpring；模态弹出 350ms、tab 切换 200ms"
}
}
FILE:tokens/mobile-native-md3.json
{
"name": "mobile-native-md3",
"displayName": "MOBILE-NATIVE · Material Design 3",
"version": "1.0.0",
"seed": "oklch(0.55 0.13 175)",
"color": {
"primary": "oklch(0.55 0.13 175)",
"onPrimary": "oklch(0.99 0.01 175)",
"primaryContainer": "oklch(0.88 0.06 175)",
"onPrimaryContainer": "oklch(0.20 0.05 175)",
"secondaryContainer": "oklch(0.92 0.04 60)",
"onSecondaryContainer": "oklch(0.25 0.05 60)",
"tertiaryContainer": "oklch(0.90 0.05 320)",
"onTertiaryContainer": "oklch(0.25 0.06 320)",
"surface": "oklch(0.985 0.003 175)",
"surfaceVariant": "oklch(0.93 0.01 175)",
"onSurface": "oklch(0.18 0.01 175)",
"onSurfaceVariant": "oklch(0.45 0.01 175)",
"outline": "oklch(0.72 0.01 175)"
},
"colorHex": {
"primary": "#3a8884",
"onPrimary": "#fafefd",
"primaryContainer": "#bee0dc",
"onPrimaryContainer": "#193533",
"secondaryContainer": "#eddfcb",
"onSecondaryContainer": "#3e3322",
"tertiaryContainer": "#e3d6e1",
"onTertiaryContainer": "#3e2538",
"surface": "#fafdfc",
"surfaceVariant": "#e2eceb",
"onSurface": "#1c2625",
"onSurfaceVariant": "#646e6d",
"outline": "#a5b3b1"
},
"typography": {
"display": "DM Sans 700",
"body": "IBM Plex Sans 400/500"
},
"spacing": { "xs": 4, "sm": 8, "md": 16, "lg": 24, "xl": 32, "2xl": 56 },
"radius": { "chip": 8, "card": 16, "fab": 18, "navPill": 16, "topBar": 24 },
"shadow": {
"fab": "0 6px 20px oklch(0 0 0 / .18), 0 1px 3px oklch(0 0 0 / .12)"
},
"redLineWaiver": ["seed 自选 oklch(0.55 0.13 175) 青绿，避开 Material 默认紫；多档 radius (8/16/18/24) 满足红线 #9"],
"examplePath": "../examples/mobile-native/md3/index.html",
"motion": {
"duration": {
"short1": 50, "short2": 100, "short3": 150, "short4": 200,
"medium1": 250, "medium2": 300, "medium3": 350, "medium4": 400,
"long1": 450, "long2": 500, "long3": 550, "long4": 600
},
"easing": {
"emphasized": "cubic-bezier(0.2, 0, 0, 1)",
"emphasizedDecelerate": "cubic-bezier(0.05, 0.7, 0.1, 1)",
"emphasizedAccelerate": "cubic-bezier(0.3, 0, 0.8, 0.15)",
"standard": "cubic-bezier(0.2, 0, 0, 1)"
},
"stagger": { "normal": 75 },
"philosophy": "Material Design 3 完整 motion token — 12 档 duration + 4 档 emphasized easing 是 MD3 spec 一比一复刻；用 emphasized 给主动作（FAB / 抽屉），standard 给次要"
}
}
FILE:tokens/organic.json
{
"name": "organic",
"displayName": "ORGANIC · 有机自然",
"version": "1.0.0",
"color": {
"paper": "oklch(0.96 0.025 85)",
"ink": "oklch(0.28 0.04 50)",
"clay": "oklch(0.62 0.13 45)",
"moss": "oklch(0.45 0.10 145)",
"sky": "oklch(0.78 0.07 220)"
},
"colorHex": {
"paper": "#f7eedb",
"ink": "#574230",
"clay": "#c97f3a",
"moss": "#5b8a3b",
"sky": "#9bbed4"
},
"typography": {
"display": "Caveat 600",
"body": "Source Serif 4 400"
},
"spacing": { "xs": 4, "sm": 8, "md": 16, "lg": 32, "xl": 48, "2xl": 80 },
"radius": {
"irregular": "48% 52% 55% 45% / 50% 50% 50% 50%",
"soft": 24,
"pill": 999
},
"shadow": {
"subtle": "0 2px 8px oklch(0.62 0.13 45 / .12)",
"card": "0 8px 32px oklch(0.62 0.13 45 / .15)"
},
"redLineWaiver": ["irregular radius 是 hallmark：每个圆角不一样（4 段不同百分比），避免统一 16px 工业感（红线 #9 合规）"],
"examplePath": "../examples/organic/index.html",
"motion": {
"duration": { "fast": 300, "normal": 500, "slow": 800 },
"easing": {
"spring": "cubic-bezier(0.34, 1.56, 0.64, 1)",
"decelerate": "cubic-bezier(0, 0, 0.2, 1)",
"wave": "cubic-bezier(0.42, 0, 0.58, 1)"
},
"stagger": { "loose": 140 },
"philosophy": "弹性 + 摇曳 — spring easing 让卡片落下时有一次轻微回弹（1.56 超调）、stagger 140ms 像水面波纹扩散；duration 偏长配合手作慢节奏"
}
}
FILE:tokens/retro-future.json
{
"name": "retro-future",
"displayName": "RETRO-FUTURE · 复古未来",
"version": "1.0.0",
"color": {
"bg": "oklch(0.13 0.04 280)",
"ink": "oklch(0.96 0.04 110)",
"neonCyan": "oklch(0.85 0.18 195)",
"neonMagenta": "oklch(0.72 0.25 5)",
"rule": "oklch(0.35 0.1 280)"
},
"colorHex": {
"bg": "#1c1a3a",
"ink": "#f8f3c4",
"neonCyan": "#2cd9d5",
"neonMagenta": "#ee2657",
"rule": "#3a3170"
},
"typography": {
"display": "Major Mono Display 400",
"body": "VT323 400"
},
"spacing": { "xs": 4, "sm": 8, "md": 16, "lg": 32, "xl": 48, "2xl": 96 },
"radius": { "none": 0, "sm": 0, "md": 0, "lg": 4 },
"shadow": {
"neonCyan": "0 0 8px oklch(0.85 0.18 195), 0 0 24px oklch(0.85 0.18 195 / .5)",
"neonMagenta": "0 0 8px oklch(0.72 0.25 5), 0 0 24px oklch(0.72 0.25 5 / .5)"
},
"redLineWaiver": ["bg 用深紫蓝纯色（非渐变模糊），不犯红线 #2 / #11；霓虹效果靠 text-shadow 双层叠加，不靠 backdrop-blur（红线 #10 合规）"],
"examplePath": "../examples/retro-future/index.html",
"motion": {
"duration": { "blink": 1000, "fast": 200 },
"easing": {
"step2": "steps(2, end)",
"linear": "linear"
},
"stagger": { "tight": 50 },
"philosophy": "CRT 闪烁 + 无缓动 — 用 step2 模拟显像管刷新、1s blink 周期循环（光标 / 状态指示）；继承 brutalist 禁缓动函数"
}
}
一个有记忆、能学习、会教方法的小红书创作助手。两套打分体系叠加：①工程师流（标题/首段/排版/emoji/话题/合规）②Allen 流（留白度/AI腔/带读者/共鸣度/邀请语 — 来自司志远 Allen 三课五技法）。配以个人风格档案、规则覆盖、写作教练、对话式选题、造词工具、栏目化设计、多读者模拟、周复盘、A/...
---
name: huo15-xiaohongshu
displayName: 火一五小红书创作伙伴（含 Allen 流）
description: 一个有记忆、能学习、会教方法的小红书创作助手。两套打分体系叠加：①工程师流（标题/首段/排版/emoji/话题/合规）②Allen 流（留白度/AI腔/带读者/共鸣度/邀请语 — 来自司志远 Allen 三课五技法）。配以个人风格档案、规则覆盖、写作教练、对话式选题、造词工具、栏目化设计、多读者模拟、周复盘、A/B 测试、写作训练。可一键切换 Allen / engineer / balanced 三种风格预设。绝不自动化发布。触发词：小红书、xhs、写小红书、小红书文案、爆款文案、小红书助手、Allen 流、xiaohongshu。
version: 3.0.0
aliases:
- 火一五小红书技能
- 火一五小红书创作伙伴
- 火一五小红书全流程创作技能
- 小红书全流程
- 小红书助手
- 小红书写作
- 小红书文案
- 小红书选题
- 小红书发布
- 小红书运营
- 小红书复盘
- 小红书教练
- 写小红书
- 写xhs
- xhs
- xiaohongshu
- 小红书分析
- 小红书抓取
dependencies:
python-packages:
- requests
- jieba # 可选
- pandas # 可选
- anthropic # 可选 — 教练 LLM 增强
---
# 火一五小红书创作伙伴 v3.0（Allen 流升级）
> **从"工具集"到"创作助手"** — 助手记得你是谁、写过什么、什么风格、
> 哪些规则你不在意。所有打分 / 建议 / 选题都按你自己的画像调。
> 青岛火一五信息科技有限公司
---
## 一、定位与边界（先读）
**这个技能能做什么：**
1. **创作伙伴** — 一个有记忆、能学习的助手，按你自己的画像调每一条建议。
2. **调研** — 抓同行爆款笔记 + 离线分析，找选题方向。
3. **选题** — 基于种子词 / 抓取数据 / 多轮对话，生成选题清单。
4. **创作** — 11 种标题公式 + 7 种正文骨架 + 你自己常用的口头禅，给"骨架草稿"。
5. **优化** — 6 维打分（按你画像加权）+ 教练诊断（为什么 + 怎么改 + 例子）。
6. **合规** — 扫绝对化词、医疗承诺、站外导流、诱导互动、用户自定义敏感词。
7. **发布辅助** — 剪贴板打包 + 发布前 10 项检查表 + 本地日志。
8. **复盘** — 发布后 7 天互动快照 + 周/月复盘报告 + 长线成长建议。
9. **训练** — 命题练习、改写训练、A/B 测试 — 把"写"当成可练习的肌肉。
**这个技能不做什么（重要）：**
- ❌ **不替你按"发布"按钮** — 自动化发布会立刻被风控识别，账号轻则限流重则封禁。
- ❌ 不做点赞 / 关注 / 评论 / 私信自动化（同上）。
- ❌ 不批量翻页采集（搜索只取首页，主页只取 preview）。
- ❌ 不强制调用大模型 — 所有打分 / 诊断 / 选题离线规则可跑；
教练**可选**用 LLM 增强（设置 `XHS_LLM_PROVIDER=anthropic`）。
**核心原则：** 个人号最贵的资产是"信任画像"，比省 30 秒发布时间值钱多了。
---
## 一·五、v2.5 创作助手（核心新增）
> v2.0 给的是"工具堆"，v2.5 给的是"助手"。
> 区别在于 — 助手**记得你是谁、写过什么、什么风格、哪些规则你不在意**。
### 一站式入口：`assistant.py`
```bash
python3 scripts/assistant.py # 看状态 + 推荐下一步
python3 scripts/assistant.py next # 直接执行最优推荐
python3 scripts/assistant.py init ... # 第一次建风格档案
python3 scripts/assistant.py brainstorm # 5 轮对话收敛选题
python3 scripts/assistant.py write 干皮护肤 # 在风格约束下起草
python3 scripts/assistant.py coach draft.md # 教练诊断
python3 scripts/assistant.py polish draft.md # 打分模式
python3 scripts/assistant.py publish draft.md# 发布前流程
python3 scripts/assistant.py review # 周/月复盘
python3 scripts/assistant.py learn disable=emoji add-sensitive=卷王 # 教助手新规则
python3 scripts/assistant.py evolve # 基于历史 feedback 自动演进规则
```
### 三个核心模块
#### 1. 风格档案（StyleProfile）— 让产出"像你写的"
从 1~5 篇 baseline 自动学习：标题长度、正文段落、emoji 密度、口头禅、
偏好的公式 / 骨架、高频话题。后续所有生成都套用这个画像。
```bash
# 用代表作建立档案
python3 scripts/assistant.py init \
--persona "30+ 干皮女生" --voice casual --niche "护肤" \
--baseline note1.json note2.md note3.json
# 查看
python3 scripts/profile_init.py show
# 追加（每周把最爆的 1 篇加进来）
python3 scripts/profile_init.py add latest_hit.json
```
存档：`~/.xiaohongshu/profile/`（个人私有，跨 skill 可共用，不入 git）。
#### 2. 规则覆盖（RuleOverride）— 助手会"学"
用户教过的助手记住，下次自动应用。
```bash
# 教："我以后不要 emoji 检查"
python3 scripts/assistant.py learn disable=emoji
# 教："给我加这些自定义敏感词"
python3 scripts/assistant.py learn add-sensitive=卷王 add-sensitive=躺平
# 教："医生我能用'治愈'"
python3 scripts/assistant.py learn allow=治愈
# 演进 — 基于 coach 反馈自动调整
python3 scripts/assistant.py evolve
```
最终规则 = `data/默认 ⊕ profile/rules.json`。
#### 3. 写作教练（Coach）— 不只打分，给"为什么 + 怎么改 + 例子"
```bash
python3 scripts/coach.py --in draft.md
```
每条诊断包含：
- **what** — 哪里有问题（一句话）
- **why** — 为什么有问题（原理 / 数据）
- **how** — 怎么改（具体操作）
- **example** — 改后的样子
附带"风格偏离提醒"（你自己 baseline 长 18 字，这条 28 字了）+
"长线成长建议"（最近 5 篇平均分比早期 10 篇低 5 分，注意是否飘了）。
可选 LLM 增强：设置 `XHS_LLM_PROVIDER=anthropic` + 安装 `anthropic` SDK，
教练会调一次模型把 how/example 写得更具体。
### 闭环数据资产
```
~/.xiaohongshu/
├── posts.jsonl # 起草历史 (publish_helper 写)
├── snapshots.jsonl # 互动快照 (track_post 写)
└── profile/
├── style.json # 风格档案
├── rules.json # 规则覆盖
├── feedback.jsonl # 用户对建议的反馈
├── practice.jsonl # 命题/改写练习
├── ab_tests.jsonl # A/B 测试
├── baseline/ # 1~5 篇代表作
└── reviews/ # 周/月复盘报告归档
```
---
## 一·六、v3.0 Allen 流升级（哲学家视角）
> v2.5 的"工程师视角"打分（公式 / 钩子 / 排版 / 合规）+
> v3.0 新增的**「Allen 流」**（留白 / AI腔 / 教带 / 共鸣 / 邀请语）。
> 来源：司志远 Allen 的小红书文案教学（三课 + 五技法 + 11 案例）。
### Allen 5 个新维度
| 维度 | 这是在看 | Allen 课 |
|---|---|---|
| **留白度** breath | 句子是否给读者填情绪的空间 | 第一课：呼吸感 |
| **去 AI 腔** ai_speak | "汇报化 / 模板化 / 装腔"词检测 | 实战教训：避汇报化 |
| **带读者** teach_vs_lead | "你应该" 还是 "你可以试试" | 第一课：教 → 带 |
| **共鸣度** resonance | 共同记忆 vs 冷知识 / 装文化 | 实战教训：共鸣 vs 冷信息 |
| **邀请语** invitation | 互动是任务指令还是 "这里有个局" | 第三课：站文案里 |
### 三个风格预设（一键切换）
```bash
python3 scripts/profile_init.py preset --list
# allen — Allen 流（品牌 / 情感共鸣赛道）
# engineer — 工程师流（干货 / 教程 / 工具）
# balanced — 平衡流（默认）
python3 scripts/assistant.py preset allen
```
| 预设 | 工程权重 | Allen 权重 | 适合 |
|---|---|---|---|
| `allen` | 50% | 50% | 品牌号、情感号、生活号 |
| `engineer` | 100% | 0% | 干货号、教程、工具测评 |
| `balanced` | 70% | 30% | 综合个人号（默认） |
### 三个新 CLI
#### 1. `critique.py` — Allen 风格诊断
```bash
python3 scripts/critique.py --in draft.md
python3 scripts/critique.py --in draft.md --merged # 工程 + Allen 综合分
python3 scripts/critique.py --in draft.md --disable breath ai_speak # 关掉某维度
```
#### 2. `coin_word.py` — 造词工具（Allen 待修炼之一）
```bash
python3 scripts/coin_word.py --brand "尽兴" --value "活得舒服" --n 8
```
输出三种模式：谐音造词 / 概念迁移（生物/建筑/物理/音乐/电影/厨房）/ 形式包装。
设置 `XHS_LLM_PROVIDER=anthropic` 后会调一次模型补优质候选。
#### 3. `series_design.py` — 栏目化 + 互动阶梯
```bash
python3 scripts/series_design.py --theme "尽兴" --persona "30+ 都市女性"
```
输出：
- 5 类栏目名候选（时间型 / 动作型 / 形式型 / 活动型 / 情绪型）
- 5 级互动阶梯（关注 → 评论 → 发图 → 被收录 → 带走大礼）
- 12 个月 IP 节奏建议（启动 / 召集 / 收录 / 实物 / 借势 / 联动 / 沉淀 / 跨界）
#### 4. `reader_simulate.py` — 多读者画像走全文（Allen 第三课落地）
```bash
python3 scripts/reader_simulate.py --in draft.md
```
模拟 6 种典型读者画像（30+ 干皮 / 互联网打工人 / 新手妈妈 / 大学生 / i 人独居 / 二线自由职业）
读完后【开头 / 中段 / 结尾】的情绪曲线 + 是否会做后续动作（stay/like/save/comment/follow）。
### 三份新数据资产
| 文件 | 内容 |
|---|---|
| [data/allen_method.md](data/allen_method.md) | Allen 三课 + 五技法 + 11 案例 + 关键认知转变表 |
| [data/ai_speak_patterns.json](data/ai_speak_patterns.json) | AI 腔黑名单 ~80 条（汇报化/模板化/懂行装腔/夸大煽情/教读者腔/AI 高频开头） |
| [data/seasonal_themes.md](data/seasonal_themes.md) | 24 节气 + 现代节日 + 小红书伪节日的"已存在画面"清单 |
### Allen 哲学心法（速查）
> 1. 「好文案不是写出来的，是留出来的。」— 留白
>
> 2. 「站文案里面读文案，不是站在外面分析。」— 第三课
>
> 3. 「卖的是身份认同，不是商品本身。」— 第二课
>
> 4. 「文案 = 为读者铺设一条通往情绪的路径，然后让路本身消失。」
### coach.py 也升级了
`coach.py` 新增 Allen 美学诊断维度（include_allen=True 默认开），
对每个低于 7 分的 Allen 维度自动产出 (what, why, how, example) 四件套。
不开 `XHS_LLM_PROVIDER` 也完全可用。
### 工程 vs Allen — 不替代是叠加
```
最终分 = 工程分 × (1 - allen_weight) + Allen 分 × allen_weight
allen_weight 由 preset 决定：
- engineer: 0.0 (纯工程)
- balanced: 0.3 (默认)
- allen: 0.5 (一半 Allen)
```
干货账号请用 engineer；品牌 / 情感共鸣账号用 allen。
---
## 二、整体工作流（推荐顺序）
```
╔══════════════════════════════════════╗
║ assistant.py — 一站式入口 ║
║ status / next / init / write / ║
║ coach / publish / review / learn ║
╚════════════════╤═════════════════════╝
↓
┌─────────────────────────────────────────────────────────────────┐
│ 0. 建档案 ─→ profile_init.py（1~5 篇 baseline，自动学习风格） │
│ ↓ │
│ 1. 调研 ─→ scrape-{search,note,user}.py（Cookie + 强节流） │
│ ↓ │
│ 2. 分析 ─→ analyze-notes.py │
│ ↓ │
│ 3. 选题 ─→ brainstorm.py（对话式）/ topic_ideas.py（一次性） │
│ ↓ │
│ 4. 创作 ─→ write_post.py（标题 + 骨架占位，套你的 profile） │
│ ↓ Claude / 你填具体内容 │
│ 5a. 教练 ─→ coach.py（为什么 + 怎么改 + 例子 + 风格偏离） │
│ 5b. 打分 ─→ polish_post.py（6 维打分，按 profile 调权重） │
│ ↓ │
│ 6. 合规 ─→ compliance_check.py │
│ ↓ │
│ 7. 发布 ─→ publish_helper.py（剪贴板 + 10 项检查表） │
│ ↓ 你打开 App，粘贴并按发布 │
│ 8. 跟踪 ─→ track_post.py（24h/3d/7d 互动快照） │
│ ↓ │
│ 9. 复盘 ─→ weekly_review.py（周/月报告 + 下周建议） │
│ ↓ │
│ 10. 训练 ─→ practice.py（命题 / 改写）/ ab_test.py（A/B 对比） │
│ ↓ │
│ ↻ 助手学习 ─→ assistant.py learn / evolve（规则演进） │
└─────────────────────────────────────────────────────────────────┘
```
每一步都是独立 CLI，**也可以一律走 `assistant.py`** 让它根据上下文路由。
---
## 三、防封号原则（依然适用）
> 小红书风控很严。违反任何一条都可能导致账号被限流、封禁或要求验证。
1. **用自己的 Cookie**。脚本不做登录自动化 — 输密码 / 刷验证码会被立刻识别。
浏览器登录后从 DevTools → Application → Cookies 复制完整字符串。
2. **不共享 Cookie**。多账号别在同一台设备混用。
3. **节奏第一**。每次请求随机 3~7 秒延时，单会话 30 次封顶。
4. **会话间隔 10~30 分钟**。
5. **日请求不超过 100 次**。
6. **不自动写**。脚本无任何 post / like / follow / comment 接口。
7. **风控即退出**。460 / 461 / 403 / "captcha" / 重定向登录 → 立刻停 30 分钟。
8. **不翻页批量抓**。
---
## 四、准备工作
### 4.1 安装
```bash
pip install requests
pip install jieba pandas # 可选，分析更准
```
### 4.2 获取 Cookie（3 分钟）
1. 浏览器打开 https://www.xiaohongshu.com 正常登录；
2. F12 → Application → Cookies → 选 `https://www.xiaohongshu.com`；
3. 全选复制，拼成 `name1=value1; name2=value2; ...`；
4. 关键字段：`web_session` / `a1` / `webId` / `xsecappid`；
5. 导出环境变量：
```bash
export XHS_COOKIE='web_session=...; a1=...; webId=...; xsecappid=xhs-pc-web; ...'
```
### 4.3 自检
```bash
python3 scripts/safety_check.py
```
应当看到 `✓ __INITIAL_STATE__ 解析成功`，否则先别跑抓取。
---
## 五、命令速查 — 调研与分析（v1.0 已有）
### 5.1 抓单篇笔记
```bash
python3 scripts/scrape-note.py --url "https://www.xiaohongshu.com/explore/64abc...?xsec_token=xxx" \
--out /tmp/note.json
```
### 5.2 抓用户主页
```bash
python3 scripts/scrape-user.py --url "https://www.xiaohongshu.com/user/profile/5f123..." \
--out /tmp/user.json
```
### 5.3 关键词搜索（首页）
```bash
python3 scripts/scrape-search.py --keyword 秋冬护肤 --out /tmp/search.json
```
### 5.4 离线分析
```bash
# 多篇合并
for id in 64abc 64abd 64abe; do
python3 scripts/scrape-note.py --note-id $id >> notes.jsonl
done
python3 scripts/analyze-notes.py --input notes.jsonl --out report.md
```
样例数据：`examples/sample_notes.jsonl`（5 条，可直接 analyze 跑通）。
---
## 六、命令速查 — 创作与发布（v2.0 新增）
### 6.1 选题灵感
```bash
# 完全靠公式
python3 scripts/topic_ideas.py --seed "干皮护肤" --persona "30+ 干皮女生" --n 10
# 结合抓取数据（同行高频关键词、话题、爆款标题）
python3 scripts/topic_ideas.py --seed "干皮护肤" --notes notes.jsonl --n 10 --format md --out ideas.md
```
### 6.2 生成标题候选
```bash
# 列出所有公式 / 骨架代号
python3 scripts/write_post.py list
# 用指定公式生成标题（每种公式 2 条）
python3 scripts/write_post.py titles --topic "干皮护肤" --persona "30+" \
--payoff "稳油不闷痘" --formulas T1,T2,T5 --n 2
```
### 6.3 渲染正文骨架
```bash
python3 scripts/write_post.py skeleton --code S1
```
### 6.4 一键产出 markdown 草稿
```bash
python3 scripts/write_post.py draft --topic "干皮护肤" --persona "30+" \
--payoff "稳油不闷痘" --formula T2 --skeleton S1 \
--tags "护肤,干皮护肤,30岁护肤,敏感肌护肤" \
--cover-hint "护肤品平铺 + 手写标题字" \
--out draft.md
```
输出的 `draft.md` 是骨架占位，让 Claude / 你接着把 `{hook}` `{step1_label}` 等填进去。
### 6.5 文案打分 + 修改建议
```bash
python3 scripts/polish_post.py --in draft.md
# 或直接传字符串
python3 scripts/polish_post.py --title "..." --content "..." --tags "护肤,干皮护肤"
```
输出 6 个子项分（标题 / 首段 / 排版 / emoji / 话题 / 合规），每项 0~10，加权出 0~100 总分。
**总分 ≥ 80 可发；60~80 建议优化；<60 建议重写。**
### 6.6 合规扫描（发布前必跑）
```bash
python3 scripts/compliance_check.py --in draft.md
```
退出码：
- `0` — 完全干净
- `1` — 中风险（建议改）
- `2` — 高风险（必须改）
可串到 CI / pre-publish hook。
### 6.7 发布辅助
```bash
# 一站式：跑打分 + 复制到剪贴板 + 打印检查表 + 写本地日志
python3 scripts/publish_helper.py --in draft.md \
--log ~/.xiaohongshu/posts.jsonl
# 跳过打分（确认过了想直接发）
python3 scripts/publish_helper.py --in draft.md --skip-score
```
复制完成后：**打开小红书 App → 粘贴 → 选图 → 你点发布按钮。** 脚本到这就停。
### 6.8 发布后跟踪
```bash
# 1) 发布完拿到 note_id 后，回填到日志
python3 scripts/track_post.py register --uid abc123 --note-id 64abcd... --xsec-token xxx
# 2) 拉一次互动快照
python3 scripts/track_post.py snapshot --note-id 64abcd... --xsec-token xxx
# 3) 给所有跟踪期内的笔记一次性快照（节流）
python3 scripts/track_post.py snapshot-all
# 4) 看跟踪报告
python3 scripts/track_post.py report --out tracking.md
```
---
## 七、Python API（创作向）
```python
import sys; sys.path.insert(0, 'scripts')
from xhs_writer import (
Draft, generate_titles, render_skeleton, score_post, make_draft,
load_draft, save_draft, load_sensitive_words,
)
# 1. 生成标题候选
titles = generate_titles("干皮护肤", persona="30+ 干皮女生",
payoff="稳油不闷痘", formulas=["T1", "T2", "T5"], n_each=2)
for t in titles:
print(t["formula"], t["title"])
# 2. 一键骨架草稿
draft = make_draft("干皮护肤", persona="30+", payoff="稳油不闷痘",
formula="T2", skeleton="S1",
tags=["护肤", "干皮护肤", "30岁护肤"])
print(draft.to_markdown())
# 3. 自己填好后打分
draft.content = "..." # 填好的正文
score = score_post(draft.title, draft.content, draft.tags)
print(score.total, score.suggestions)
# 4. 保存为 markdown
save_draft(draft, "draft.md")
```
主要接口：
| 模块 | 函数/类 | 说明 |
|------|--------|------|
| `xhs_writer` | `generate_titles(topic, persona, payoff, formulas, n_each)` | 标题候选（11 种公式） |
| `xhs_writer` | `render_skeleton(code, fields)` | 渲染正文骨架 |
| `xhs_writer` | `make_draft(topic, ...)` | 一键骨架草稿 |
| `xhs_writer` | `score_post(title, content, tags)` | 6 维打分 |
| `xhs_writer` | `Draft` | 草稿数据结构（to_markdown / to_clipboard_text） |
| `xhs_writer` | `load_draft(path)` / `save_draft(draft, path)` | IO |
| `xhs_writer` | `load_sensitive_words()` | 敏感词列表 |
调研抓取 / 离线分析的 API 见 v1.0 部分（`xhs_client` / `xhs_parser` / `xhs_analyzer`）。
---
## 八、数据资产（data/）
| 文件 | 内容 |
|------|------|
| `data/title_templates.md` | 11 种爆款标题公式（T1~T11）+ 适用 + 踩坑 + 示例 |
| `data/content_structures.md` | 7 种正文骨架（S1~S7）+ 适用 + 字段说明 |
| `data/emoji_palette.md` | emoji 调色板 + 类目向 + 用量建议 |
| `data/hashtag_topics.md` | 话题标签库（大词/中词/小词）+ 选题工作流 |
| `data/community_rules.md` | 平台社区规则要点 + 红线清单 + 发布前 checklist |
| `data/sensitive_words.txt` | 敏感词列表（广告法 + 平台风控） |
这些文件不止给脚本读，也是 Claude 在调用本技能时的"参考手册" — 当用户问
「30 岁干皮怎么写选题？」，Claude 应该读 `title_templates.md` + `hashtag_topics.md` 给出答案。
---
## 九、典型场景示例
### 场景 A0：第一次用助手（建档案）
```bash
# 1) 准备 1~5 篇你的代表作（json/md 都行）
# 2) 一键建档
python3 scripts/assistant.py init \
--persona "30+ 干皮女生" --voice casual --niche "护肤" \
--baseline note1.json note2.md note3.json
# 3) 看状态 — 助手会告诉你接下来该干什么
python3 scripts/assistant.py status
```
### 场景 A1：让助手主导整周创作
```bash
# 周一早上
python3 scripts/assistant.py status # 看推荐
python3 scripts/assistant.py next # 直接执行（如：跑 brainstorm）
# 周内每篇笔记
python3 scripts/assistant.py write 干皮护肤 # 起草
python3 scripts/assistant.py coach draft.md # 教练诊断
python3 scripts/assistant.py publish draft.md # 发布前流程
# 周末
python3 scripts/assistant.py review # 周复盘
python3 scripts/assistant.py learn disable=emoji # 教助手新规则
python3 scripts/assistant.py evolve # 让助手吸收过去一周的反馈
```
### 场景 A：从零开始写一篇（手动版）
```bash
# 1) 调研竞品 (3~5 篇就够)
python3 scripts/scrape-note.py --url "https://..." >> notes.jsonl
# 2) 找选题
python3 scripts/topic_ideas.py --seed "干皮护肤" --notes notes.jsonl --n 10 \
--format md --out ideas.md
# 3) 选一条选题，生成草稿骨架
python3 scripts/write_post.py draft --topic "干皮护肤" --persona "30+" \
--formula T2 --skeleton S1 --tags "护肤,干皮护肤,30岁护肤" --out draft.md
# 4) 把 {hook} {step1_label} 等占位替换成真实内容（手动 or 让 Claude 写）
# 5) 打分 + 改
python3 scripts/polish_post.py --in draft.md
# 6) 合规扫
python3 scripts/compliance_check.py --in draft.md
# 7) 发布
python3 scripts/publish_helper.py --in draft.md --log ~/.xiaohongshu/posts.jsonl
# 8) 发布后回填 note_id + 拍快照
python3 scripts/track_post.py register --uid xxxxx --note-id 64abc... --xsec-token xxx
python3 scripts/track_post.py snapshot --note-id 64abc... --xsec-token xxx
```
### 场景 B：已经有草稿，想做一次完整发布前检查
```bash
python3 scripts/polish_post.py --in my_draft.md # 打分
python3 scripts/compliance_check.py --in my_draft.md # 合规
python3 scripts/publish_helper.py --in my_draft.md # 准备发布
```
### 场景 C：批量看上周发的 5 条笔记表现
```bash
python3 scripts/track_post.py snapshot-all # 一次性给跟踪期内所有笔记拍快照
python3 scripts/track_post.py report # 查看报告
```
---
## 十、常见错误对照
| 错误 | 含义 | 处理 |
|------|------|------|
| `LoginRequired: HTTP 401` | Cookie 过期 | 重新浏览器登录 + 重新 export |
| `RateLimited: HTTP 460/461` | 频率风控 | **立即停止**，至少等 30 分钟 |
| `BlockedByCaptcha: HTTP 403` / 出现 verify | 需要滑块 | 浏览器过验证 + 等 30 分钟 |
| `NotFound: HTTP 404` | 笔记被删 / id 错 | 换一个 |
| `没找到 __INITIAL_STATE__` | HTML 结构变了 / 重定向 | `--save-html` 看原始页 |
| `polish_post 退出码 2` | 文案分 < 60 | 看 suggestions 修改 |
| `compliance_check 退出码 2` | 高风险违规 | 必须改（联系方式 / 绝对化词） |
---
## 十一、触发词
- 小红书 / xhs / xiaohongshu / red
- 写小红书 / 小红书文案 / 爆款文案 / 小红书选题
- 小红书调研 / 小红书分析 / 同行分析
- 小红书发布 / 发小红书 / 小红书运营
- 小红书复盘 / 小红书数据
---
## 十二、版本历史
- **v3.0.0（当前，2026-04-27）** — Allen 流升级（哲学家视角）
- 新增 5 个 Allen 美学维度：留白度 / 去 AI 腔 / 带读者 / 共鸣度 / 邀请语
- 新增 3 个 CLI：`critique.py` / `coin_word.py` / `series_design.py` / `reader_simulate.py`
- 新增 3 份数据资产：`allen_method.md` / `ai_speak_patterns.json` / `seasonal_themes.md`
- 新增风格预设：`allen` / `engineer` / `balanced` 三种一键切换
- 工程打分 + Allen 美学**叠加**而不替代，权重按预设
- coach.py 整合 Allen 维度，对低于 7 分的维度自动产出 4 件套
- 来源：司志远（Allen）2026-04-23~27 三课 + 五技法 + 11 案例教学
- **v2.5.x** — 从"工具堆"升级为"创作助手"
- **新增个性化**：StyleProfile 风格档案（从 baseline 自动学习语调/长度/emoji/口头禅）
- **新增可学习**：RuleOverride 规则覆盖（用户教过的助手记住） + evolve 自动演进
- **新增写作教练** `coach.py`：每条问题给 (what, why, how, example) 四件套，
支持可选 LLM 增强（XHS_LLM_PROVIDER=anthropic）
- **新增对话式选题** `brainstorm.py`：5 轮对话从模糊到具体
- **新增周复盘** `weekly_review.py`：自动汇总 7/30 天产出 + 互动 + 反馈 + 下周建议
- **新增写作训练** `practice.py`：命题练习 + 改写训练 + 历史成绩
- **新增 A/B 测试** `ab_test.py`：同选题 2 版各发，互动数据告诉你哪版赢
- **新增主入口** `assistant.py`：根据上下文推荐下一步，把全流程串起来
- **打分系统升级**：score_post 接收 RuleOverride，按个人画像加权
- **个人档案**位于 `~/.xiaohongshu/profile/`（用户私有，跨 skill 共用）
- **v2.0.0** — 全流程创作能力大改版
- 新增 7 个 CLI：选题 / 创作 / 打分 / 合规 / 发布辅助 / 跟踪
- 新增 6 份数据资产：标题公式 / 正文骨架 / emoji 调色板 /
话题标签库 / 社区规则 / 敏感词
- **v1.0.x** — 抓取 + 分析能力首版（详见上版 README）
---
## 十三、设计哲学
1. **个人号 / 小团队**为目标用户，不是 MCN 批量号工厂。
2. **规则可解释** — 标题公式、骨架、打分项都明文写在 data/ 里，Claude 和你都能读。
3. **不依赖大模型** — 所有脚本零 API 成本可跑；让 Claude 来调它们的产物。
4. **半自动 ≠ 全自动** — 发布按钮永远在人手里，账号才安全。
5. **闭环优于单点** — 调研 / 写 / 发 / 复盘任何一环少了，都做不长。
---
**重要免责：** 本技能仅用于合规、针对公开可见内容的个人调研与创作辅助。
请尊重 xiaohongshu.com 的服务条款和当地法律法规。
商业批量采集、内容搬运、绕过风控、自动化发布 / 互动等行为均**不在本技能支持范围内**。
**技术支持：** 青岛火一五信息科技有限公司
FILE:_meta.json
{
"ownerId": "kn7byevkn40d6z4p7ghdb097z983tj33",
"slug": "huo15-xiaohongshu",
"version": "3.0.0"
}
FILE:data/ai_speak_patterns.json
{
"_meta": {
"name": "AI 腔黑名单 + 替换库",
"source": "Allen 教学（2026-04-27） + 实战批改总结",
"principle": "汇报化、模板化、'懂行'感的词都要避免；用 '人话' 替换。",
"scope": "正文 + 标题，不限赛道"
},
"categories": {
"汇报化词汇": {
"description": "白领开会汇报味，写到小红书显得疏离。",
"patterns": [
{"bad": "有效率", "good": ["快一点", "顺手", "麻利"], "why": "'效率' 是 KPI 思维，读者来小红书是放松不是上班"},
{"bad": "提升", "good": ["变得更...", "更好一点", "往上走"], "why": "'提升 X' 是 PPT 模板腔"},
{"bad": "优化", "good": ["改一改", "调一下", "换个法子"], "why": "白领黑话"},
{"bad": "进行", "good": [""], "why": "中文不需要 '进行' 这个虚词，删掉就好"},
{"bad": "赋能", "good": ["让你能", "帮你"], "why": "互联网黑话之首"},
{"bad": "闭环", "good": ["完整一遍", "从头到尾"], "why": "PPT 词"},
{"bad": "颗粒度", "good": ["细节", "具体到哪一层"], "why": "懂行感装腔"},
{"bad": "对齐", "good": ["统一", "说清楚", "聊一致"], "why": "互联网黑话"},
{"bad": "复盘", "good": ["回头看看", "我又想了一下"], "why": "工作味重；个人号写'回头看看'更亲切"},
{"bad": "迭代", "good": ["改了几版", "一版一版来"], "why": "工程师腔"},
{"bad": "落地", "good": ["真做下来", "做完", "搞定"], "why": "甲方腔"},
{"bad": "拉齐", "good": ["告诉大家", "对一下"], "why": "OKR 腔"},
{"bad": "抓手", "good": ["切入点", "从哪开始", "突破口"], "why": "互联网黑话"},
{"bad": "打法", "good": ["做法", "套路", "招数"], "why": "甲方腔"},
{"bad": "心智", "good": ["让人觉得", "印象", "认知"], "why": "营销人黑话"},
{"bad": "种草", "good": ["让你想买", "看完想入"], "why": "用得太多已成营销标签"},
{"bad": "破圈", "good": ["让更多人看见", "被人看见"], "why": "运营黑话"},
{"bad": "降维打击", "good": ["完全不在一个层面", "差太远"], "why": "装腔"},
{"bad": "双向奔赴", "good": ["互相喜欢", "都在靠近"], "why": "媒体造词，已被用烂"},
{"bad": "顶流", "good": ["最火的", "最受关注"], "why": "媒体词"}
]
},
"模板化句式": {
"description": "AI 写小红书最容易出现的几种公式句。",
"patterns": [
{"bad": "首先...其次...最后", "good": ["想到几件事", "我把这天写下来"], "why": "教科书结构"},
{"bad": "总结一下", "good": ["大概就是这些", ""], "why": "总分总结构是论文不是生活"},
{"bad": "综上所述", "good": [""], "why": "禁用"},
{"bad": "众所周知", "good": [""], "why": "AI 腔最大特征之一"},
{"bad": "通过...可以...", "good": ["这样能", "做了之后"], "why": "公文腔"},
{"bad": "在我看来", "good": ["我觉得", "我自己是"], "why": "略装"},
{"bad": "值得一提的是", "good": ["还有", "对了"], "why": "AI 经典开头词"},
{"bad": "不仅...而且...", "good": ["还", "另外"], "why": "教科书句式"},
{"bad": "众所周知", "good": ["大家都知道", "你应该听过"], "why": "AI 滥用"},
{"bad": "毋庸置疑", "good": [""], "why": "禁用"},
{"bad": "一言以蔽之", "good": [""], "why": "装腔"},
{"bad": "事实上", "good": ["其实", "我后来发现"], "why": "AI 高频"},
{"bad": "必不可少", "good": ["少不了", "都得有"], "why": "公文"},
{"bad": "至关重要", "good": ["很关键", "很要紧"], "why": "公文"},
{"bad": "举足轻重", "good": [""], "why": "成语堆砌典型"},
{"bad": "锦上添花", "good": ["更好了", "更妙的是"], "why": "成语堆砌"}
]
},
"懂行感装腔": {
"description": "想显得专业反而显得疏远。",
"patterns": [
{"bad": "据相关数据显示", "good": ["我看到一个数字", "有个数据是"], "why": "新闻腔"},
{"bad": "研究表明", "good": ["有研究说", "看到一篇"], "why": "学术腔"},
{"bad": "广泛认为", "good": ["很多人觉得"], "why": "公文"},
{"bad": "深度赋能", "good": [""], "why": "互联网黑话之 boss"},
{"bad": "全方位", "good": ["每个角度", "里里外外"], "why": "广告腔"},
{"bad": "全维度", "good": [""], "why": "互联网黑话"},
{"bad": "立体化", "good": ["多面"], "why": "公文"},
{"bad": "矩阵", "good": ["一组", "几个"], "why": "运营黑话"},
{"bad": "生态", "good": ["环境", "圈子"], "why": "互联网黑话被用烂"},
{"bad": "底层逻辑", "good": ["道理是", "其实就是"], "why": "知识付费腔"}
]
},
"夸大煽情": {
"description": "情绪被推得过头，反而像营销号。",
"patterns": [
{"bad": "绝绝子", "good": ["真的太好了", "真喜欢"], "why": "网络梗用滥"},
{"bad": "yyds", "good": ["真的爱", "心头好"], "why": "梗用滥"},
{"bad": "太爱了！！！", "good": ["真的喜欢", "我很爱"], "why": "三个感叹号 = 营销号"},
{"bad": "冲冲冲", "good": [""], "why": "营销带货号"},
{"bad": "无敌好用", "good": ["真的好用", "我用着很顺"], "why": "广告法"},
{"bad": "封神", "good": ["真不错", "我觉得很好"], "why": "梗用滥"},
{"bad": "我哭死", "good": ["真的会感动", "心头一动"], "why": "网络梗"},
{"bad": "宝子们", "good": ["大家", "你"], "why": "带货腔"},
{"bad": "家人们", "good": ["你", "大家"], "why": "带货腔"}
]
},
"教读者腔": {
"description": "Allen 第一课：从'教读者' → '带读者'。",
"patterns": [
{"bad": "你应该", "good": ["你可以试试", "也许"], "why": "教读者"},
{"bad": "你必须", "good": ["建议你", "如果你想"], "why": "教读者"},
{"bad": "记住", "good": ["有时候", "我自己是"], "why": "教读者"},
{"bad": "划重点", "good": ["有件事我想说", "唯一一点"], "why": "教读者"},
{"bad": "敲黑板", "good": ["这个值得多想一下"], "why": "教读者，且过时"},
{"bad": "答案是", "good": ["我后来发现", "我想到的是"], "why": "教读者"},
{"bad": "正确的做法", "good": ["我自己是这么做的", "可以试试"], "why": "教读者"},
{"bad": "误区", "good": ["我之前以为", "曾经走偏的是"], "why": "教读者"},
{"bad": "三大要点", "good": ["几件事", "三个我自己的体会"], "why": "教读者"},
{"bad": "干货分享", "good": [""], "why": "标签化，让人警觉"}
]
},
"AI 高频开头": {
"description": "几乎只有 AI 才会这样开头。",
"patterns": [
{"bad": "亲爱的朋友们", "good": ["你", "大家好呀"], "why": "AI 客套"},
{"bad": "在这个", "good": [""], "why": "'在这个 X 的时代' 全删"},
{"bad": "随着", "good": [""], "why": "'随着 X 的发展' 公文开头"},
{"bad": "在当今", "good": [""], "why": "公文开头"},
{"bad": "当今社会", "good": [""], "why": "公文开头"}
]
}
},
"structural_smells": {
"_description": "整体的 '气味'，靠正则可能查不到，靠人或 LLM 判：",
"items": [
"每段开头都是名词词组（'其一''其二'）",
"每个观点都给三条理由",
"结尾必有 '让我们一起...' 之类的呼吁",
"动不动就 '总结一下' 然后罗列",
"用比喻必带 '就像 X 一样'",
"情绪表达靠形容词堆砌（'非常''特别''极其'）",
"人话该用感叹号的地方用句号"
]
}
}
FILE:data/allen_method.md
# Allen 文案方法论 — 三课 · 五技法 · 十一案例
> 来源：司志远（Allen），品牌广告营销导师（70%）+ 哲学家（30%）
> 教学时间：2026-04-23 ~ 2026-04-27
> 学习者：贾维斯（JARVIS）
---
## 总纲：四句心法
1. **「好文案不是写出来的，是留出来的。」**
留出空间让读者自己往里填情绪，不要把所有话说完。
2. **「站文案里面读文案，不是站在外面分析。」**
交互引导（关注/发图/去 H5）不是任务清单，是邀请语 — "这里有个局，你可以来"。
3. **「卖的是身份认同，不是商品本身。」**
不说产品，说人心中那根弦。把"卖"包装成"懂你"。
4. **「文案 = 为读者铺设一条通往情绪的路径，然后让路本身消失。」**
终点不一定是转化，有时只是种下一颗种子。
---
## 第一课：句子要有「呼吸感」
### 旧问题
- 信息密度太高，像说明书
- 总分总结构、逻辑闭环 — 读者没空间填情绪
### 关键转变
**从「教读者」 → 「带读者」。**
| 教读者（旧） | 带读者（新） |
|---|---|
| 你应该 / 你必须 | 你可以试试 / 我自己 |
| 这是因为... | 有时候 / 也许是 |
| 总结一下 | 我把这天记下来 |
| 三大要点 | 一些不太成熟的想法 |
### 技法
- **断句 + 留白**，不是连续推演
- **金句法**：`「上班就是不合理的超自然现象」` — 一句反常识的话制造情绪共识，**不解释道理**
- 写完一段，想"读者读到这里能不能停一下"
### 自检问题
> "我这一段，是把答案给读者了，还是把空间留给他了？"
---
## 第二课：把「卖」包装成「懂你」
### 核心
**不说产品，说人心中的那根弦。**
### 「不必 X，就能 Y」结构
公式：先**卸焦虑**，再给答案。
| 例子 | 卸的焦虑 (X) | 给的答案 (Y) |
|---|---|---|
| 不必去远方，就能放空自己 | 必须出远门 | 心态切换就够了 |
| 不必精致，就能美 | 完美主义 | 有自己的样子 |
| 不必准备好，就能开始 | 等待时机 | 行动本身有意义 |
### 造词三层结构
以「**薯你会买**」为例（小红书双 11 品牌词）：
| 层 | 内容 |
|---|---|
| 1️⃣ 品牌资产 | "薯" — 小红书代号 |
| 2️⃣ 对话感 | "你" — 直接对读者说话 |
| 3️⃣ 价值观 | "会买 = 会生活" — 卖的是身份认同 |
### 概念迁移造词
- **「人类丰容」**：把动物行为学概念（动物丰容 enrichment）搬到人的日常
→ 给生活加新鲜感，不只是动物才需要
- **「人生尽兴指南」**：把"指南"这种说明书形式包装情绪态度
### 自检问题
> "我现在写的，是产品功能，还是用户心里那根弦？"
---
## 第三课：站在文案里面读文案
### 旧问题
- 站在外面分析，不是在里面感受
- 把文案当任务清单拆解（关注 → 发图 → 去 H5），跳过了"感受邀请"这一步
### 关键纠正
**互动引导词不是给读者执行的命令，是让读者感受到「这里有个局，你可以来」的邀请语。**
| 任务指令（旧理解） | 邀请语（新理解） |
|---|---|
| "请关注我" | "如果你也喜欢这种感觉" |
| "评论 666 抽奖" | "你的那一天是怎样的？告诉我" |
| "扫码私聊" | "如果你也在找答案，这里有个落脚点" |
### 关键认知
> "那不是文案，是一次有脚本的见面。"
文案是**情境**，不是**说服**。
---
## 五大核心技法
### 1️⃣ 场景构建代替功能描述
不描述产品特性，**让场景激发想象，让想象产生情绪**。
```
不写产品 → 写场景 → 场景激发想象 → 想象产生情绪 → 情绪种下认知
↑
读者自己走完这段路
```
### 2️⃣ 概念迁移与造词
跨领域借用：动物行为学 → 人；建筑词 → 心境；说明书 → 态度。
### 3️⃣ 栏目化内容设计
把单次活动变成可持续的内容 IP：
- "周三存档" — 友情记忆相册
- "尽兴放映厅" — 三月活动汇总
- "尽兴请回答" — 问答专栏
栏目名 = 品牌的内容资产，越久越值钱。
### 4️⃣ 互动阶梯设计
从低到高递进：
```
关注（最低门槛）
→ 评论（自我表达）
→ 发 Live 图（参与共创）
→ 被收录（获得认可）
→ 带走大礼（实物奖励，最高位）
```
每一层都有"为什么会做"的情绪驱动力，不是命令。
### 5️⃣ emoji 当标点符号（功能性运用）
不是随意点缀，每个 emoji 扮演**功能角色**：
| emoji | 功能 |
|---|---|
| ✨ | 吸睛 / 开场 |
| 🎯 | 定调 / 主题 |
| ⛅ | 过渡 / 切场 |
| 🎉 | 点明规则 |
| 🎁 | 引出奖励 |
---
## 案例库（11 个，按出现顺序）
### 案例 1 — 共情痛点型「上班就是不合理的超自然现象」
- **技法**：断句 + 留白
- **要点**：金句独立成段，不解释
- **核心**：制造情绪共识，不讲道理
### 案例 2 — 小红书双 11「薯你会买」
- **技法**：品牌造词 + 四场景创意
- **结构**：「不必 X，就能 Y」
- **场景**：OOTD / 居家 / 化妆 / 护肤
- **要点**：没说的比说了的重要
### 案例 3 — 人生尽兴指南 · 地球日 + 读书日
- **技法**：互动交互式文案
- **结构**：双事件 × 双选择 × 单品牌
- **互动阶梯**：关注 → 发 Live → 被收录 → 带走大礼
### 案例 4 — 人生尽兴指南 · 周三存档（友情记忆）
- **技法**：栏目化设计
- **要点**：把活动变成可延续的栏目 IP
### 案例 5 — 人类丰容（植树节）
- **技法**：概念迁移
- **核心**：动物行为学借用到日常生活
### 案例 6 — 联动余承恩（KOL 借势）
- **技法**：栏目化 + KOL 借势
- **要点**：心动就行动，不等准备好
### 案例 7 — 外滩元境（地产·心智种草）
- **技法**：不追求转化的文案
- **核心**：环境/人文/人居理念让你尽兴
- **要点**：有些文案的终点是种下一颗种子
### 案例 8 — 春分「什么也不赶」
- **技法**：节气借势
- **要点**：节日只取一个零件，允许慢下来
### 案例 9 — 三月尽兴放映厅
- **技法**：内容延展性
- **关键纠正**：不是"收"网帖，是"开"— 展示一个灵感能延伸出多少种可能
### 案例 10 — 植树节 · 种一颗小尽兴
- **技法**：从实到虚递进
- **要点**：从种树（实）到种心情（虚）
### 案例 11 — 惊蛰 · 领取春日礼
- **技法**：动词意境指向
- **关键认知**：动词不一定要驱动行为
- "领取" 不是去拿一个实物
- 是把春日的阳光、花香、微风**当作礼物**，打开感官接收
---
## 核心法则速查
### 关于动词
- 动词的使用权利**不在于**它能不能驱动行为
- 在于它能不能在读者脑海里**造画面 / 意境**
- 动词可以指向意境、画面、场景，不一定指向具体动作
### 关于节气
- 节气（惊蛰/春分/植树节）**自带画面感**
- 品牌不需要从头造场景
- 只需要**借用**它已经存在的情绪
- 写法：拆解节日，**只取一个对自己有用的零件**
### 关于"长出来"
- ❌ 旧理解：内容是"收"的，把散落的互动汇聚成闭环
- ✅ 新理解：内容是"长"的，展示一个灵感能延伸出多少种可能性
- 一个灵感的真正价值不在它本身有多好，在它能延伸出多少新的可能性
### 关于场景共鸣（实战教训）
> 场景共鸣**不是**选有"时间感"的事物
> **是**选**人人都有过的共同记忆**
| 类型 | 例子 | 评价 |
|---|---|---|
| 知识 → 冷信息 | 井水西瓜 / 蝉七年 / 古人喝什么 | ❌ 装文化，无共鸣 |
| 体验 → 真共鸣 | 草地 / 树荫 / 风扇声 / 窗帘飘起来 | ✅ 谁都有过 |
### 关于措辞（避 AI 腔）
- ❌ 汇报化词汇："有效率""提升""进行优化""赋能"
- ✅ 人话："慢慢来""变得更好""改一改""帮帮我"
---
## 「同一个 slogán，六个切面」原则
| 案例 | 主题 | 打的人群 | 互动形式 |
|---|---|---|---|
| ① 地球日+读书日 | 在自然和书里找到自己 | 独处的人 | 发 Live |
| ② 周三存档 | 和朋友的共同记忆 | 重感情的人 | 评论+艾特 |
| ③ 人类丰容 | 给生活加新鲜感 | 日常被掏空的人 | 发 Live |
| ④ 联动余承恩 | 心动就行动 | 粉丝+行动派 | 带话题 |
| ⑤ 外滩元境 | 在好环境里自然尽兴 | 有居住理想的人 | 无互动（种草） |
| ⑥ 薯你会买 | 懂买=懂生活 | 消费决策者 | 品牌造势 |
> **同一个 slogán，六个切面，六种受众。**
> 文案本身不变，不同人群看到的是自己想看的那一面。
> 这不是缺陷，是力量。
---
## 实战水位评估表
```
结构：✅ 交互：✅ 场景：❌ 措辞：❌
```
**已掌握：**
- 场景感构建
- 情绪反差设计
- 拟人化表达
- 括号体运用
**待修炼：**
- 造词能力（如「薯你会买」）
- 造活动能力（如「尽兴放映厅」）
- 造体系能力（完整品牌表达）
- 句子的「热度」（旁观者洞察 → 参与者邀请）
- 借势能力（KOL 联动放大）
- 栏目化设计
---
## 关键认知转变
| 之前（错的） | 现在（对的） |
|---|---|
| 先想卖点 | 先想读者有什么情绪 |
| 用功能证明价值 | 用场景触发想象 |
| 告诉读者「这个很好」 | 让读者自己觉得「这是我要的」 |
| 写成文字推给读者 | 铺设情绪路径让读者自己走完 |
| 站在外面分析文案 | 站在文案里面感受它 |
| 把文案当题做找规律 | 读文案不找标准答案 |
| 内容收尾 | 内容长出新东西 |
| 节气从头造场景 | 借用节气已存在的画面 |
| 动词驱动行为 | 动词造画面/意境 |
---
## 关联条目
- 数据：[ai_speak_patterns.json](ai_speak_patterns.json) — 措辞 AI 腔黑名单
- 数据：[seasonal_themes.md](seasonal_themes.md) — 节气借势画面库
- 脚本：[xhs_aesthetic.py](../scripts/xhs_aesthetic.py) — 留白 / 共鸣 / 邀请语五维
- 脚本：[critique.py](../scripts/critique.py) — Allen 风格诊断
- 脚本：[coin_word.py](../scripts/coin_word.py) — 造词工具
- 脚本：[series_design.py](../scripts/series_design.py) — 栏目化设计
- 脚本：[reader_simulate.py](../scripts/reader_simulate.py) — 多读者模拟
*指导人：司志远（Allen）· 学习者：贾维斯*
FILE:data/community_rules.md
# 小红书社区规则要点（写文案前必读）
> 这不是法律意见，也不替代官方公告 — 只是一份**实战导向**的红线清单，
> 帮你避免「写完不能发」「发了被限流」「号被封了才知道」。
>
> 来源：小红书《社区公约》《互动规范》《商业笔记规范》《医疗、医美、美妆备案规则》(摘要总结)，
> 加上一线创作者的实际反馈。
>
> 信息为 2026 年 4 月版本，规则随时调整 — 重要内容请以平台官方为准。
---
## 一、绝对化用语（广告法 + 平台风控双重风险）
法律层面这些词在商业内容里直接违法，平台层面会触发限流。
```
最、第一、首选、首屈一指、独家、唯一、绝对、100%、永远、永久、
顶级、终极、最佳、最好、最强、最大、最高、最低、最便宜、最划算、
保证、绝对有效、立刻见效、立竿见影、根除、根治、痊愈、治愈、永不复发、
完美、完胜、全国第一、全球首发、史无前例、空前绝后
```
**替代写法：**
- 「最有效」→「我体验下来最满意的」「亲测对我有用」
- 「保证瘦」→「我自己 7 天瘦了 X 斤」
- 「100% 不长痘」→「我用了 3 个月没再爆痘」
**关键：** 所有"客观保证"都要变成"我的主观经验"。
---
## 二、医疗 / 医美 / 保健 类红线
**不能写的：**
- 任何"治疗""疗效""治愈""根除"+病症
- 暗示药效：「比药都管用」「替代医生」
- 推荐处方药 / 保健品声称疗效
- 整形医院、医美机构需要资质且必须挂"商业笔记"
**必须挂"商业笔记"的：**
- 医美 / 医疗机构相关内容
- 减肥产品、保健品（不挂会被限流 + 罚款）
- 含品牌方提供的样品 / 报酬 (即使是 KOC)
**建议：** 涉及健康话题写"我自己的体验"，不写"建议大家"。
---
## 三、教育 / 金融 / 法律
- **教育：** 不承诺"包过""保提分""轻松上岸"，K12 学科类基本不让发
- **金融：** 不能荐股、不能承诺收益率、不能写"无风险""稳赚"
- **法律：** 个案咨询不写绝对结论，提示「具体咨询律师」
---
## 四、互动行为红线
**不能在文案里写：**
- 「关注我私信领」「评论 666 抽奖」（诱导互动）
- 「加微信详聊」「+V xxx」（导流站外）
- 「点赞过 100 我就更下一篇」（变相诱导）
**改成：**
- 「想要的扣 1，我会更下一篇」（被允许的互动鼓励）
- 「评论区聊聊你的想法」（不绑定动作的开放邀请）
---
## 五、导流站外（重点中的重点）
平台严打"截流到微信 / 淘宝 / 抖音 / B 站"。
**会被识别 + 限流的：**
- 文案 / 图片里出现「公众号」「VX」「+v」「微信号」「v❤」
- 手机号、QQ 号、微信号（包括各种谐音「威❤」「v 一下」）
- 二维码（哪怕是模糊的）
- 引导到「主页有联系方式」「简介有 wx」
**合规做法：**
- 私信回复关键词 → 平台允许，但**不能在文案明示**「私信"X"领」
- 走「店铺」「专业号 / 蒲公英」官方导流通道
---
## 六、商业笔记 / 蒲公英
**判定为商业笔记的内容：**
- 与品牌方有金钱 / 实物 / 资源置换关系
- 出现品牌 logo / 包装 / 名称且明显推荐
- 直接 @ 品牌官方账号
**不申报商业笔记会有什么后果？**
1. 笔记被限流 / 不进推荐池
2. 重复违规 → 账号扣分 / 30 天禁商
3. 严重 → 永久封号
**建议：**
- 接广必须走蒲公英平台
- 自己买的东西分享，写「自购」「我自己买的」并带 `#自购分享` 话题
---
## 七、敏感人群保护
- **未成年：** 不出现完整面部特写（除非自己孩子且不涉及隐私）
- **他人肖像：** 路人入镜需打码或同意
- **色情擦边：** 内衣 / 泳装 / 性暗示 — 一旦被举报基本掉粉 + 限流
---
## 八、内容质量风险
**会被识别为低质 / 营销号的特征：**
- 全图水印、全图重复、全图低清
- 标题党 + 正文不兑现
- 大量复制粘贴他人内容（搬运）
- 同一账号短时间发 20+ 篇模板化内容
- 评论用脚本刷（直接封号）
---
## 九、违禁内容（碰即封号）
- 政治敏感、宗教敏感
- 色情、暴力、血腥
- 赌博、毒品、灰产
- 谣言、虚假宣传、虚假数据
- 侵权（他人作品 / 影视截图大段使用）
---
## 十、申诉与冷启动
- 笔记被限流：私信小助手 / 走「创作助手」反馈
- 账号扣分：在「我」→「设置」→「账号状态」查看，可申诉
- **新号期：** 头 7 天内容**不要带任何品牌词**，建立账号画像
- **限流期：** 不要继续发同质内容，停 3~5 天，发不同方向的笔记拉新画像
---
## 实战检查清单（发布前过一遍）
- [ ] 标题没有「最 / 第一 / 100% / 必」等绝对化词
- [ ] 正文没有「微信 / VX / 公众号 / +v / 二维码」
- [ ] 不诱导「关注私信」「点赞抽奖」
- [ ] 涉品牌的内容已申报商业笔记 / 加 #自购 标签
- [ ] 医疗 / 健康 / 金融避开"治疗""保证""稳赚"
- [ ] 路人面部已打码
- [ ] 没有引用未授权的影视截图
- [ ] 话题数量 3~6 个，没有禁用词
跑 `python3 scripts/compliance_check.py` 自动检查一遍。
FILE:data/content_structures.md
# 小红书正文骨架库
> 标题决定打开率，正文决定收藏 / 转发 / 完播。
> 小红书阅读场景是「滑动式扫读」，正文必须**前 3 行抓住注意力**。
>
> 黄金长度区间：
> - 干货 / 教程类：**400~800 字**
> - 生活 / 故事类：**200~500 字**
> - 测评 / 推荐类：**300~600 字**
>
> 段落要短：**每段 1~3 句**，每行不超过 22 字（手机一屏一行）。
---
## 骨架 S1：钩子—痛点—方案—金句
适用：干货、技能、效率工具
```
[钩子段] 1~2 句话点出"你曾经也...？"
[痛点段] 放大 2~3 个具体场景
[转折/破题] "其实只要 X 就能..."
[方案 1] 动作 + 为什么 + 怎么做
[方案 2] ...
[方案 3] ...
[补充避坑] 1~2 个反例
[金句结尾] 一句记得住的话 + 引导互动
```
**示例：** 一篇关于「早起」的笔记，把每一步分成 emoji + 一句话。
---
## 骨架 S2：故事—感悟—行动
适用：人生记录、情感共鸣、轻量观点输出
```
[场景] 时间 / 地点 / 人物 1~2 句带入
[冲突/转折] 发生了什么意料之外
[感受] 情绪 + 当时的想法
[反思/觉察] 我意识到了 X
[可迁移的行动] 你也可以试试 ...
[互动钩子] "你有过类似的时刻吗？"
```
**例：** "上周辞职那天，我才发现自己 5 年没好好吃过饭。"
---
## 骨架 S3：测评—对比—结论
适用：产品测评、好物推荐、避雷帖
```
[问题/动机] 为什么需要这个
[选品标准] 我看重什么（建立专业度）
[3~5 款对比] 每款：外观 / 体验 / 价格 / 适合谁
[排名 / 推荐] 第一名是谁，原因
[避雷] 哪款别买，为什么
[选购建议] 不同预算 / 不同人群怎么选
```
**踩坑：** 测评类必须带使用细节（哪天买的、用了多久），否则被识别为软广。
---
## 骨架 S4：清单/列表型
适用：盘点、合集、推荐
```
[开篇] "我整理了 ..."
[列表项 1] 标题 + 1~2 句描述 + 适用场景
[列表项 2] ...
... (5~10 项)
[排序说明] (可选) 我最喜欢的是 X，因为 ...
[互动钩子] "你还知道哪些？评论区告诉我"
```
每项控制在 30~80 字，可用 emoji 做项目符号（🔹 ✨ ▫️）。
---
## 骨架 S5：保姆级教程
适用：步骤分明的技能教学
```
[结果先看] 完成后能做到什么 / 配图展示效果
[准备工作] 需要的工具 / 时间 / 前置条件
[步骤 1] "Step 1: 做什么" + 关键截图描述
[步骤 2] ...
[步骤 N] ...
[常见问题] Q1 / Q2 / Q3
[进阶玩法] (可选) 学会基础后还能 ...
```
**关键：** 步骤必须能复现 — 写错一步用户掉粉。
---
## 骨架 S6：观点 / 反共识
适用：行业洞察、独立思考、专业输出
```
[亮明观点] "我认为 ..." (一句话)
[反共识声明] 大多数人觉得 X，但我觉得 Y
[3 条理由] 为什么 — 数据 / 经验 / 案例 各 1
[反方反驳] "也许有人会说 ...，我的回应是 ..."
[呼吁] "如果你也这么想，欢迎在评论区交流"
```
风险：观点越鲜明越容易爆，但也容易被攻击；要有数据/经验兜底。
---
## 骨架 S7：日记/Vlog 文案
适用：日常生活、城市探索、咖啡馆探店
```
[时间地点] "周三下午 / 上海愚园路"
[流水但有筛选] 只写让你停顿的 3~5 个瞬间
[感官细节] 1 句视觉 + 1 句嗅觉/听觉
[小思考] 一两句 not too deep 的感慨
[hashtag 收尾] #当代年轻人生活 之类
```
**关键：** 不用追求完整，追求"颗粒感"。
---
## 通用排版原则
1. **首段 = 黄金 3 行**：80% 用户只读这里，必须有钩子。
2. **emoji 当项目符号**：✨🔥💡📌 比"1.2.3."更小红书。
3. **段间空行**：手机阅读没空行就是大字报。
4. **避免长段落**：超过 4 行立刻拆。
5. **关键句加粗**：`**这里是重点**` 让扫读的人也抓得住。
6. **结尾留钩子**：问题 / 福利 / 下一篇预告 — 三选一。
7. **话题 3~6 个**：少了不被推，多了像营销号。
8. **@ 谁不重要，关键词重要**：别 @ 大 V 求曝光，把品牌词 / 行业词写进话题。
---
## 行尾互动钩子模板
- "你最近有过类似的时刻吗？评论区告诉我"
- "想看 [下一篇主题] 的扣 1，我下周更"
- "评论区说说你卡在哪一步，我帮你看看"
- "你还知道哪些？补充一下让大家少踩坑"
- "如果对你有帮助，点个收藏，下次想用就找得到啦"
---
## 骨架 → 代号 速查表
| 代号 | 骨架名 | 适用 |
|------|--------|------|
| `S1` | 钩子-痛点-方案-金句 | 干货 / 技能 / 工具 |
| `S2` | 故事-感悟-行动 | 人生记录 / 情感 |
| `S3` | 测评-对比-结论 | 产品测评 / 好物 |
| `S4` | 清单 / 列表 | 盘点 / 合集 |
| `S5` | 保姆级教程 | 步骤教学 |
| `S6` | 观点 / 反共识 | 行业洞察 / 输出 |
| `S7` | 日记 / Vlog | 日常 / 探店 |
FILE:data/emoji_palette.md
# 小红书 emoji 调色板
> emoji 是小红书"视觉节奏"的一部分。用对了能让正文有呼吸感、关键句更突出；
> 用错了显得幼稚、像营销号。
>
> **原则：每段 0~2 个，全文不超过 15 个。**
---
## 通用强调
- ✨ — 亮点 / 推荐 / "重点来了"
- 🔥 — 热门 / 爆款 / 高互动
- 💡 — 灵感 / 提示 / 知识点
- 📌 — 收藏 / 重点 / "钉一下"
- ⚡ — 速度 / 高效 / 立即
- 🎯 — 目标 / 精准 / "对症下药"
- ✅ — 推荐 / 正确做法
- ❌ — 避雷 / 错误做法
- ⚠️ — 警告 / 注意
## 情绪/共情
- 😭 — 委屈 / 共情 / "真的会哭"
- 🥹 — 感动 / 微酸
- 😩 — 累 / 苦
- 🤡 — 自嘲
- 🥲 — 苦笑
- 🫠 — 融化 / 摆烂
- 😮💨 — 松一口气
- 🤔 — 思考
- 🥺 — 求互动 / 撒娇
## 动作
- 👉 — 引出下文
- 👇 — 看下面
- ✍️ — 记笔记
- 🙋♀️ — 提问 / 我来
- 🙌 — 赞同 / 庆祝
- 💬 — 评论 / 对话
## 类目向
### 美妆护肤
🌸 ✨ 💄 💧 🌿 🧴
### 穿搭
👗 👜 👟 🧥 🪞 🛍️
### 美食
🍳 🍰 🥗 🍜 ☕ 🍷 🍱
### 旅行
✈️ 📷 🗺️ 🏝️ ⛰️ 🎒 🌅
### 健身/瘦身
🏃♀️ 💪 🧘♀️ 🥗 💦 ⏱️
### 数码/办公
💻 📱 ⌨️ 📊 📝 ⚙️ 📂
### 育儿
👶 🍼 🎈 📚 🧸
### 家居
🏠 🛋️ 🪴 🕯️ 🛏️ 🧹
### 学习/职场
📚 ✏️ 🎓 💼 📈 ⏰ 🧠
### 情感/生活
☕ 🌙 ⭐ 🌷 📖 🎵 🌧️
---
## 用法对照
### ✅ 好用法
```
✨ 重点来了
我用了 3 个月，皮肤真的变稳了
📌 三件最有效的事：
1. 早睡（11 点前）
2. 多喝温水
3. 不熬夜刷手机
```
### ❌ 别这样
```
🔥🔥🔥🔥🔥 重点重点重点 ✨✨✨✨ 来了来了来了 💯💯💯
```
— 同一个 emoji 重复 3 次以上，立刻像低质营销号。
```
我☕️今天☀️去📍了一家🥐很好吃的店🍰真的很喜欢💕
```
— 每两个字一个 emoji，让人无法阅读。
---
## 数量建议
| 内容类型 | emoji 数量 | 备注 |
|---------|-----------|------|
| 标题 | 0~2 个 | 0 也完全 OK |
| 首段（钩子） | 0~1 个 | 注意力集中 |
| 列表项 | 1 个 / 项 | 当项目符号 |
| 普通段落 | 0~1 个 | 强调用 |
| 结尾钩子 | 1~2 个 | 营造氛围 |
| **全文上限** | **15 个** | 超过就过载 |
---
## 当代审美避雷
以下 emoji 在 2026 年小红书已经"老气"：
- 💯 (一百分) — 营销味重
- 🌟⭐️ 重复 (3 个以上) — 低质感
- 🤩 — 略微浮夸
- 🥰 — 略微夹
- 💕💖❤️ 滥用 — 代际感强（更年轻群体不太用）
更年轻、更"小红书感"的替代：
- ✨ 替代 💯
- 🥹 替代 🥰
- 🫶🏻 替代 ❤️
- 🌷 替代 💐
FILE:data/hashtag_topics.md
# 小红书话题标签库
> 话题（# 号）是小红书最重要的分发信号之一 — 平台主要靠它把笔记
> 推送给「关注该话题」「常看该话题」的用户。
>
> **黄金数量：3~6 个。**少了不被推；多了像营销号反而被压。
>
> **结构原则：1 大词 + 2~3 中词 + 1~2 小词**。
> - 大词（百万+ 篇）：保证基础流量池
> - 中词（10~100 万篇）：精准受众
> - 小词（万级以下 / 自创）：垂直 / 个人 IP
---
## 通用大词（百万级，谨慎使用，竞争激烈）
`#今日穿搭` `#日常` `#好物分享` `#美食` `#旅行` `#护肤`
`#减肥` `#健身` `#学习` `#职场` `#育儿` `#家居`
`#vlog` `#早八` `#周末日常` `#生活记录`
---
## 类目热门话题
### 美妆护肤
`#护肤` `#敏感肌护肤` `#油皮护肤` `#干皮护肤` `#平价护肤`
`#护肤心得` `#美白攻略` `#抗老` `#油皮亲妈`
`#彩妆教程` `#新手化妆` `#日常妆` `#约会妆`
`#口红试色` `#粉底液测评` `#眼影教程`
### 穿搭
`#今日穿搭` `#穿搭分享` `#穿搭灵感` `#OOTD`
`#小个子穿搭` `#梨形身材穿搭` `#微胖穿搭` `#腿粗穿搭`
`#日系穿搭` `#韩系穿搭` `#法式穿搭` `#美式复古`
`#职场穿搭` `#约会穿搭` `#通勤穿搭`
`#平价穿搭` `#百元穿搭` `#淘宝穿搭`
### 美食
`#美食` `#美食探店` `#美食分享` `#家常菜`
`#减脂餐` `#早餐` `#一人食` `#带饭`
`#烘焙` `#甜品` `#咖啡` `#茶饮`
`#上海美食` `#北京美食` `#成都美食` `#深圳美食`
`#探店` `#苍蝇馆子` `#深夜食堂`
### 旅行
`#旅行` `#旅行攻略` `#穷游` `#周末去哪儿`
`#国内旅行` `#出境游` `#日本旅行` `#东南亚旅行`
`#自驾游` `#徒步` `#露营`
`#上海周边游` `#北京周边游` `#成都周边游`
`#小众旅行地` `#冷门景点`
### 健身瘦身
`#健身` `#健身打卡` `#减肥` `#减脂`
`#普拉提` `#瑜伽` `#跑步` `#力量训练`
`#健身餐` `#减脂餐食谱`
`#产后修复` `#腹部塑形` `#改善体态`
### 数码 / 办公
`#数码好物` `#数码测评` `#苹果` `#安卓`
`#mac使用技巧` `#iPad` `#笔记本电脑推荐`
`#效率工具` `#提升效率` `#职场效率`
`#notion` `#flomo` `#obsidian`
`#chatgpt` `#ai工具` `#AI绘画` `#claude`
### 育儿
`#育儿` `#育儿日常` `#新手妈妈` `#科学育儿`
`#辅食` `#早教` `#亲子游戏` `#绘本推荐`
`#母婴好物` `#宝宝护理`
### 家居
`#家居` `#家居分享` `#家居装修` `#装修攻略`
`#收纳` `#收纳整理` `#断舍离`
`#出租屋改造` `#小户型` `#极简家居`
`#家居好物` `#平价家居`
### 学习
`#学习` `#学习方法` `#学习打卡` `#学习计划`
`#考研` `#考公` `#英语学习` `#雅思` `#托福`
`#读书笔记` `#读书分享` `#自我提升`
### 职场
`#职场` `#职场干货` `#职场穿搭` `#职场新人`
`#面试` `#简历` `#跳槽` `#转行`
`#副业` `#自由职业` `#数字游民`
`#互联网打工人` `#国企` `#公务员`
### 情感 / 生活
`#情感` `#恋爱` `#相亲` `#脱单`
`#一个人生活` `#独处` `#内向`
`#原生家庭` `#自我成长` `#心理学`
`#emo文学` `#松弛感`
### 创作者 / 副业
`#自媒体` `#小红书运营` `#博主日常`
`#文案技巧` `#爆款文案`
`#副业推荐` `#搞钱` `#被动收入`
---
## 自创话题策略
如果你做长期 IP，**自创一个个人 hashtag**：
- 形式：`#{你的昵称}的{品类}` 或 `#{昵称}{动作}`
- 示例：`#火一五日报` `#某某选品` `#某某vlog周记`
第一周笔记数 = 1 时也要带，沉淀久了就是品牌资产。
---
## 选话题工作流
1. 打开小红书 App，搜索你写的关键词，看排行靠前的笔记**用了哪些话题**。
2. 对比同一类目下的多个爆款，找出**共同出现 ≥3 次**的话题。
3. 那些就是该类目当下的"分发友好词"。
4. 加上 1 个自创个人话题。
---
## 已知限流话题（避开）
- 含品牌名 + 黑产词："xx代购""xx全网最低"
- 平台敏感词组合："互推""涨粉""加微信"
- 时间紧迫词组合："最后一天""仅剩 3 名"
- 绝对化用语：见 `data/sensitive_words.txt`
避开这些，再做事半功倍的内容。
---
## 速查：每篇笔记的 6 个槽位（推荐）
```
#{大词1，类目根词}
#{中词1，垂直细分}
#{中词2，使用场景}
#{中词3，受众身份}
#{小词，自创/长尾}
#{小词，活动/挑战赛}（可选）
```
例：一篇关于「30 岁干皮护肤」的笔记
```
#护肤
#干皮护肤
#30岁护肤
#敏感肌护肤
#火一五护肤笔记
#早C晚A
```
FILE:data/seasonal_themes.md
# 节气 / 节日借势画面库
> 来源：Allen 教学 — 「节气自带画面感，品牌不需要从头造场景，只需要借用它已经存在的情绪。」
>
> **核心法则：拆解节日，只取一个对自己有用的零件。**
>
> 怎么用：写节气文案前先来这里挑 1 个画面 / 1 个情绪 / 1 个动作，然后让品牌
> 自己的价值观从这个零件**长出来**。
---
## 二十四节气（按时间）
### 立春（2 月初）
- **存量画面**：冰开始融、第一片嫩芽、风变软、太阳更亮一会儿
- **情绪**：盼头、试着开始、苦尽甘来
- **动作**：散步、开窗、换薄衣服
- **可借的零件**：「试着开始一件事」「窗边坐一会儿」
### 雨水（2 月中下）
- **存量画面**：屋檐滴水、湿润的空气、地上反光
- **情绪**：润、湿润、被滋养
- **动作**：撑伞、听雨、煮一杯热的
- **可借的零件**：「润物细无声」「让自己也被润一下」
### 惊蛰（3 月初）
- **存量画面**：第一声春雷、虫子动了、土壤松动
- **情绪**：被唤醒、动起来、新生
- **动作**：抖一抖、伸懒腰、走出门
- **Allen 案例**：「领取春日礼」— 不是去拿礼物，是把阳光花香当作礼物**接收**
### 春分（3 月下）
- **存量画面**：白天和黑夜一样长、平衡、燕子回来
- **情绪**：刚刚好、平衡、不急不慢
- **动作**：晒太阳、慢一点、什么也不赶
- **Allen 案例**：「什么也不赶」— 允许慢下来
### 清明（4 月初）
- **存量画面**：青草地、踏青、思念、纸钱燃烧的烟
- **情绪**：怀念、清新、追溯、和过去和解
- **动作**：扫墓、踏青、吃青团
- **可借的零件**：「想念一个人」「在草地上坐一会儿」
- ⚠️ 涉死亡相关，商业品牌慎用
### 谷雨（4 月下）
- **存量画面**：春雨密、谷物喜雨、桃花落、牡丹开
- **情绪**：饱满、即将完成、积蓄
- **动作**：喝春茶、看花、做一件慢事
- **可借的零件**：「事情快好了的那种感觉」
### 立夏（5 月初）
- **存量画面**：知了试啼、风渐热、第一根冰棍
- **情绪**：换季、变热的不耐烦、希望
- **动作**：换凉席、开西瓜、剪头发
### 小满（5 月下）
- **存量画面**：麦穗灌浆但还没熟、小有所成、不到饱和
- **情绪**：满 vs 未满、节制、刚好
- **动作**：留三分、不着急
- **可借的零件**：「人生小满就够了」（已被用滥，慎用）
### 芒种（6 月初）
- **存量画面**：抢收抢种、密集的劳作、最忙的节气
- **情绪**：忙、抢时间、紧迫
- **动作**：早起、连轴转
### 夏至（6 月下）
- **存量画面**：白天最长、黑夜最短、绿荫最浓
- **情绪**：达到顶峰、转折前的极致
- **动作**：吃面条、看星星
### 小暑 / 大暑（7 月）
- **存量画面**：知了拼命叫、柏油被晒软、风扇嗡嗡响、午后窗帘飘起来
- **情绪**：热得发懒、小时候、光着脚踩地砖
- **动作**：吹空调、吃冰棍、睡午觉
- **共鸣体验**：风扇声、午后蝉鸣、窗帘飘起来 ✅
- **冷知识勿用**：井水冰西瓜（已脱离当代经验）❌
### 立秋（8 月初）
- **存量画面**：第一片黄叶、风变干、夜里凉
- **情绪**：松一口气、要收一收了
- **动作**：换薄外套、整理东西
### 处暑（8 月下）
- **存量画面**：暑气退、月亮明、晚风
- **情绪**：终于凉了、从极致往回走
### 白露 / 秋分 / 寒露
- **存量画面**：清晨草上的露珠、桂花、薄雾、第一件长袖
- **情绪**：清、爽、淡淡的甜
- **动作**：晾被子、煲汤、闻桂花
- **可借的零件**：「桂花飘的时候」「清晨第一口冷空气」
### 霜降 / 立冬
- **存量画面**：第一次霜、白菜上市、围巾出场
- **情绪**：要藏起来一点、内向
### 小雪 / 大雪
- **存量画面**：第一场雪、玻璃上的雾、热饮
- **情绪**：安静、温柔、抱团
- **动作**：堆雪人（北方）/ 期待雪（南方）
### 冬至
- **存量画面**：饺子、汤圆、最长的夜
- **情绪**：团聚、家、吃顿好的
- **动作**：打电话回家
### 小寒 / 大寒
- **存量画面**：呼吸成白雾、最冷的清晨、窝在被子里
- **情绪**：藏、慢、躺平有理
- **动作**：捂手、煮饭、不出门
---
## 现代节日
### 春节（农历正月初一）
- **存量画面**：年夜饭、春晚、放鞭炮（南方少了）、地铁站归乡的行李、村口的红灯笼
- **情绪**：团圆、归属、累但值得
- **共鸣体验**：抢着结账、爸妈往你包里塞东西 ✅
- **冷知识慎用**：传统年俗"扫尘""贴福"（已和大多数 90/00 后断层）
### 元宵节
- **存量画面**：汤圆、灯笼、月圆
- **情绪**：团圆的尾声、热闹收场
### 情人节（2.14 / 七夕）
- **存量画面**：礼物挑了又挑、餐厅排队、共享伞
- **情绪**：被在意、想被在意、有点别扭
- **共鸣体验**：偷偷看对方反应
### 三八妇女节
- **存量画面**：自我犒赏、不再是别人的角色
- **情绪**：自由、为自己花钱、被看见
- **可借的零件**：「先做自己 5 分钟」
### 五一 / 国庆
- **存量画面**：高铁挤、景点人山人海、旅行 vs 在家躺
- **情绪**：解放、计划、矛盾（出去 or 不出去）
- **共鸣体验**：算景点门票、抢车票
### 母亲节 / 父亲节
- **存量画面**：电话、寄东西回家、合照
- **情绪**：内疚 + 在意
- **可借的零件**：「打个电话比什么都好」
### 教师节
- **存量画面**：感谢老师、想起小时候
- **情绪**：怀念、来不及说
### 中秋
- **存量画面**：月亮、月饼盒、家人围坐
- **情绪**：团圆 / 没法团圆
- **共鸣体验**：吃不完的月饼、视频通话
### 双 11 / 双 12 / 618
- **存量画面**：购物车、凌晨抢、买完的复杂心情
- **情绪**：纠结、买不买、买完后悔 / 不后悔
- **Allen 案例**：「薯你会买」— "会买 = 会生活"，把购物变成价值观
### 圣诞 / 元旦
- **存量画面**：圣诞树、礼物、新年第一杯咖啡
- **情绪**：仪式感、又一年
- **共鸣体验**：写新年清单又没做完
### 万圣节
- **存量画面**：南瓜、cosplay、聚会
- **情绪**：扮演别的自己
### 地球日（4.22）
- **存量画面**：青草、回收、共享单车
- **Allen 案例**：与读书日联动 — "在自然和书里找到自己"
### 读书日（4.23）
- **存量画面**：书脊、咖啡馆、安静
- **情绪**：独处、被句子击中
---
## 小红书"伪节日"现象（创造文案 IP 的入口）
### 仪式感场景
- 周一咖啡 / 周三发呆 / 周五解放 / 周日万事难
- 月初存档 / 月末复盘
- 季末换衣 / 换香水
### 时间型场景
- 5 点起床 / 7 点早八 / 12 点午休 / 18 点下班 / 23 点睡前
### 状态型节日
- 离职日 / 入职第一天 / 生理期 / 失眠夜 / 感冒天
- 这些都不是真节日，但**自带情绪共鸣**，是文案天然的入口
---
## 共鸣 vs 冷知识对照表（Allen 实战教训）
| ❌ 冷知识（装文化） | ✅ 共鸣体验（人人有过） |
|---|---|
| 井水西瓜 | 冰箱里捞冰镇西瓜 |
| 蝉七年才爬出来 | 夏夜窗外蝉鸣 |
| 古人立春咬萝卜 | 春天第一次脱长袖 |
| 节气物候图 | 早晨闻到不一样的风 |
| "二十四番花信风" | 桂花来的那一天 |
**判断准则：** "我的奶奶 / 妈妈 / 邻居孩子 / 公司同事，他们都有过这个画面吗？"
- 大部分人都有 → 共鸣
- 需要查百科才理解 → 冷知识
---
## 怎么使用本库
1. **写文案前，挑 1 个零件**（不是全套）：
- 1 个画面（视觉锚点）
- 1 个情绪（共鸣点）
- 1 个动作（轻量行为）
2. **品牌价值观**和这个零件**自然挂钩**（不要硬挂）
3. **不要写百科介绍**（节气是什么、起源、习俗）— 那是干货号该做的，不是品牌号
> "节气是存量画面，你的事是借一个画面来。"
> — Allen，2026-04-27 夜校
FILE:data/sensitive_words.txt
# 小红书 + 广告法敏感词清单
# 一行一个词。# 开头是注释。
# 用法：scripts/compliance_check.py 会按行扫描文案命中。
# 分类只是给人看，扫描不区分类别。
# ---------- 广告法绝对化用语 ----------
最佳
最好
最优
最强
最先进
最大
最高
最低
最便宜
最划算
最赚钱
第一
首选
首屈一指
独家
唯一
绝对
100%
百分百
顶级
终极
完美
史上
史无前例
空前绝后
全网最低
全国第一
全球首发
全球首款
极致
极品
权威
首席
首发
独有
独创
# ---------- 承诺类（保证/必/永远） ----------
保证
绝对有效
必瘦
必白
必爽
必胜
必读
必看
立刻见效
立竿见影
当天见效
一夜
一秒
永远
永久
永不复发
不复胖
零反弹
零失败
完胜
碾压
吊打
# ---------- 医疗医美禁用 ----------
治愈
治疗
痊愈
根治
根除
特效
神效
奇效
药到病除
祖传秘方
有效率
治愈率
诊断
医院推荐
医生推荐
权威认证
医学验证
# ---------- 金融禁用 ----------
保本
稳赚
稳赚不赔
保收益
高收益
零风险
无风险
躺赚
日入过万
月入十万
财富自由
被动收入翻倍
# ---------- 教育禁用 ----------
包过
保过
保上岸
轻松上岸
保提分
保学会
绝对涨分
名校保送
名校直通
学霸养成
# ---------- 站外导流 ----------
微信号
微信群
公众号
+v
加v
威❤
威信
微❤
v我
扣我
qq群
QQ:
二维码
联系方式
私信加我
主页有联系
简介看
评论解锁
私我领
私信领
# ---------- 诱导互动 ----------
关注私信领
评论666
评论抽奖
点赞过百更新
转发抽奖
求关注
求点赞
求转发
# ---------- 平台敏感行为词 ----------
搬运
互推
互赞
互粉
涨粉
刷粉
买粉
机刷
# ---------- 色情/擦边 ----------
擦边
软色情
约炮
裸聊
情趣
两性
# ---------- 灰产/违法 ----------
代购微信
代刷
代写
代发
代写论文
代过CET
赌博
博彩
高仿
A货
精仿
山寨
走私
水货
# ---------- 商业笔记必报 ----------
品牌赞助
品牌寄送
官方合作
免费试用（领取）
# ---------- 谣言/夸大 ----------
央视报道
人民日报推荐
官方认证
央视推荐
特价中
最后一天
仅剩
名额有限
限时秒杀
FILE:data/title_templates.md
# 小红书爆款标题公式库
> 标题决定 60% 的打开率。这里给的不是模板填空，而是 11 种被 *反复验证*
> 的结构 — 每条带「适用场景」「踩坑提示」与「示例」。
>
> 标题黄金区间：**16~22 字**。短于 12 字钩子不足，长于 26 字会被首页截断。
---
## 1. 数字对比型 — 「3 个 / 5 招 / 7 天」
**结构：** `{数字} + {名词复数} + {结果/反差}`
**适用：** 干货、清单、教程、装备测评
**示例：**
- `3 个被严重低估的 Notion 模板，写作效率翻倍`
- `5 招让你的小红书首图点击率涨 30%`
- `7 天瘦了 4 斤，方法不饿不躁不反弹`
**踩坑：** 数字别太大（"100 招"会显得水），3/5/7/10 最稳。
---
## 2. 痛点共情型 — 「为什么 / 怎么办 / 真的不能再...」
**结构：** `{受众身份/场景} + {普遍困境} + {暗示有解法}`
**适用：** 职场、婚恋、育儿、心理、健康
**示例：**
- `30+ 互联网打工人，为什么周末越休越累`
- `内向人怎么办：3 个不靠社交也能交朋友的笨办法`
- `身高 158 真的不能再瞎穿了，记住这 4 个比例`
**踩坑：** 不要堆消极词（"崩溃""绝望""毁了"过度会被限流），点到即止。
---
## 3. 反差冲突型 — 「我以为 X，结果 Y」
**结构：** `{原认知} + {实际结果}` 或 `{看似 A} + {其实 B}`
**适用：** 测评、避雷、知识纠偏、个人经历
**示例：**
- `我以为是无效护肤，用一周脸亮了一个度`
- `985 毕业 3 年回老家，发现这才是体面的活法`
- `她看着普通，月入 8 万 — 我研究了她 200 条笔记`
---
## 4. 悬念钩子型 — 「居然 / 没想到 / 这一招」
**结构：** `{动作 / 现象} + {居然/没想到} + {反常识结果}`
**适用：** 故事、生活记录、产品体验
**示例：**
- `把厨房纸放进冰箱冷藏室，居然能省 30 块电费`
- `没想到通勤地铁上能背完 1 万个单词，方法很笨`
- `这一招让我家娃自己写作业，亲测 21 天`
**踩坑：** 别学营销号搞标题党 — 正文必须真的能兑现，否则掉粉很快。
---
## 5. 身份代入型 — 「作为 X 我...」
**结构：** `作为 {身份} + {私家经验/独家视角}`
**适用：** 职业经验、内行人视角、避雷指南
**示例：**
- `作为前苹果 Genius，5 年修过的 iPhone 我来劝你别买这两款`
- `作为月经常年不准的女生，亲测有效的 3 件小事`
- `作为 30 岁不婚不育的女人，我活得比想象中爽`
---
## 6. 福利免费型 — 「免费 / 0 元 / 白嫖」
**结构：** `{福利量词} + {领取方式暗示}` 或 `{0 元} + {获得物}`
**适用：** 资源分享、工具推荐、薅羊毛
**示例：**
- `免费！3000+ 张高清手机壁纸，分类整理好了`
- `0 元学完 Python 入门，B 站这 3 个 UP 主真的够用`
- `白嫖了 12 个 GPT 高质量提示词模板，已分门别类`
**踩坑：** 不要在标题写「关注私信领」 — 会被判定诱导互动，限流。
---
## 7. 时间节点型 — 「今年/季节/年龄段」
**结构：** `{时间节点} + {应对策略}` 或 `{年龄阶段} + {建议}`
**适用：** 应季内容、年龄向、节日营销
**示例：**
- `2026 年清明假期 3 天去哪儿，避开人挤的 5 个冷门地`
- `28 岁前一定要做的 5 件事，越早做越省钱`
- `换季敏感肌救命指南：从 3 月到 6 月的 4 步走`
---
## 8. 提问诱发型 — 「为什么 / 是不是 / 真的吗」
**结构：** 直接抛一个能勾起好奇的问题
**适用：** 知识分享、热点解读、观点输出
**示例：**
- `为什么有些人下班后还充满精力？我观察了 30 个人`
- `身材好的人是不是真的天生易瘦？我体检数据给你看`
- `网传"早上空腹吃苹果排毒"，是真是假`
---
## 9. 极端结果型 — 「最 / 第一次 / 唯一」
**结构：** `{最高级} + {名词}` 或 `{唯一/第一次} + {经历}`
**适用：** 测评、推荐、个人经历
**示例：**
- `这是我用过最舒服的内裤，回购 8 次不夸张`
- `第一次去成都，被这家苍蝇馆子封神了`
- `30 岁前唯一让我后悔没早做的一件事`
**踩坑：** "最"字号在医美/食品/保健类会触发广告法，避开使用。
---
## 10. 步骤指南型 — 「保姆级 / 手把手 / 从零」
**结构：** `{保姆级/手把手} + {目标/技能}` 或 `{从零到一} + {产出}`
**适用：** 教程、SOP、技能传授
**示例：**
- `保姆级！从零开始装修小户型，预算 8 万怎么花`
- `手把手教你 30 天写出第一篇爆款笔记，含模板`
- `小白入门 ChatGPT，记住这 5 步少走 1 年弯路`
---
## 11. 故事开场型 — 「上周 / 那天 / 朋友说」
**结构：** `{时间/场景} + {小事件} + {引发的思考或解法}`
**适用：** 生活记录、感悟、case study
**示例：**
- `上周裸辞回老家，每天花 50 块也很开心`
- `那天加班到 11 点，发现真的不必为公司这么拼`
- `朋友说我变了，原来是因为这一个习惯`
---
## 通用加分项
1. **emoji 节制**：标题 0~2 个 emoji 最好，超过 3 个会显廉价（食品/穿搭赛道可放宽）。
2. **数字阿拉伯优先**：「3」比「三」更醒目，但成语词组用汉字（"三天打鱼"）。
3. **冒号断句**：`关键词：解释` 比一长串更易扫读。
4. **避免敏感词**：见 `data/sensitive_words.txt`。
5. **首图与标题对齐**：钩子是标题的，但兑现的是首图 — 两者要一致。
---
## 公式 → 代号 速查表
| 代号 | 公式名 | 一句话 |
|------|--------|--------|
| `T1` | 数字对比 | 数字+名词+反差 |
| `T2` | 痛点共情 | 身份+困境+暗解法 |
| `T3` | 反差冲突 | 我以为 X 结果 Y |
| `T4` | 悬念钩子 | 动作+居然+反常识 |
| `T5` | 身份代入 | 作为 X 我... |
| `T6` | 福利免费 | 0 元/免费+获得物 |
| `T7` | 时间节点 | 时间+策略 |
| `T8` | 提问诱发 | 一句疑问 |
| `T9` | 极端结果 | 最/第一次/唯一 |
| `T10` | 步骤指南 | 保姆级/手把手 |
| `T11` | 故事开场 | 时间+小事件+感悟 |
FILE:examples/sample_draft.md
# 30+ 干皮女生，护肤踩了 5 年的坑我都给你讲明白
✨ 你是不是也遇到过这种情况？
明明面霜一抹再抹，下午脸还是又紧又起皮。
夏天加班回家，T 区出油 U 区脱皮，化妆卡粉到怀疑人生。
试过堆叠 8 层精华，结果第二天闷出闭口。
**其实只要换一个思路 — 干皮护肤的核心不是"堆"，是"封"。**
📌 早晨：清水洁面 + 一片湿敷
我之前一直用洗面奶，干皮越洗越紧。
现在早起就清水洗脸，1 张化妆水湿敷 3 分钟，比任何乳液都管用。
📌 上妆前：油 + 水按压打底
2 滴角鲨烷在掌心，按压上脸；
再喷 2 下喷雾按压一次。
妆面服帖一整天，几乎不卡粉。
📌 晚上：先油卸妆 + 厚敷面霜
油卸的关键是不带走皮脂膜；
面霜抹完用手心捂 30 秒，让分子真的吃进去。
⚠️ 避雷：去角质频率别超过 1 周 1 次,
干皮的屏障比想象中脆弱，过度去角质 = 直接破防。
亲测 3 个月,皮肤稳定到不用化妆,真的舒服。
你卡在哪一步？评论区告诉我，我帮你看看 🌷
#护肤 #干皮护肤 #敏感肌护肤 #30岁护肤 #早C晚A #火一五护肤笔记
FILE:scripts/ab_test.py
#!/usr/bin/env python3
"""火一五小红书"同选题 A/B 测试" — 两版各发，看哪版赢。
为什么需要 A/B
==============
个人号最常见的纠结是"标题用 T2 还是 T3 / 首图选 X 还是 Y / 长版 vs 短版"。
猜没意义 — 同一周内分开 24~48 小时各发一版，让数据告诉你。
工作流
======
1. **plan** — 给定主题，生成两版草稿（用不同公式 / 骨架）
2. **register** — 两版都发布完后，登记两个 note_id 到一个 ab 实验组
3. **compare** — 拉双方快照，输出对比报告（哪版赢、赢在哪）
记录在 `~/.xiaohongshu/profile/ab_tests.jsonl`。
用法
----
# 1) 计划
python3 ab_test.py plan --topic "干皮护肤" --persona "30+ 干皮女生" \\
--variant-a "T2,S1" --variant-b "T3,S6" --out-dir /tmp/ab1
# 2) 两版分别发布完后登记
python3 ab_test.py register --test-id ab_001 \\
--note-a 64aaa... --xsec-a tokenA \\
--note-b 64bbb... --xsec-b tokenB
# 3) 比较（24~72h 后跑）
python3 ab_test.py compare --test-id ab_001
"""
from __future__ import annotations
import argparse
import datetime as dt
import json
import sys
from pathlib import Path
from typing import Any, Dict, List, Optional
sys.path.insert(0, str(Path(__file__).parent))
from xhs_profile import ProfileStore # noqa: E402
from xhs_writer import make_draft, save_draft # noqa: E402
def ab_log_path(store: ProfileStore) -> Path:
return store.root / "ab_tests.jsonl"
def load_ab(store: ProfileStore) -> List[Dict[str, Any]]:
p = ab_log_path(store)
if not p.exists():
return []
out = []
for line in p.read_text(encoding="utf-8").splitlines():
if line.strip():
try:
out.append(json.loads(line))
except json.JSONDecodeError:
pass
return out
def save_ab(store: ProfileStore, entries: List[Dict[str, Any]]) -> None:
p = ab_log_path(store)
p.parent.mkdir(parents=True, exist_ok=True)
with p.open("w", encoding="utf-8") as f:
for e in entries:
f.write(json.dumps(e, ensure_ascii=False) + "\n")
def _next_test_id(entries: List[Dict[str, Any]]) -> str:
n = len(entries) + 1
return f"ab_{n:03d}"
def _parse_variant(v: str) -> Dict[str, str]:
"""变体格式 'T2,S1' 或 'T3,S6'。"""
parts = [p.strip() for p in v.split(",")]
return {"formula": parts[0] if parts else "T2",
"skeleton": parts[1] if len(parts) > 1 else "S1"}
# ---------- 子命令 ----------
def cmd_plan(args: argparse.Namespace) -> int:
store = ProfileStore()
profile = store.load_style()
persona = args.persona or profile.persona
payoff = args.payoff or ""
tags = args.tags.split(",") if args.tags else profile.common_tags[:5]
a = _parse_variant(args.variant_a)
b = _parse_variant(args.variant_b)
draft_a = make_draft(args.topic, persona=persona, payoff=payoff,
formula=a["formula"], skeleton=a["skeleton"], tags=tags)
draft_b = make_draft(args.topic, persona=persona, payoff=payoff,
formula=b["formula"], skeleton=b["skeleton"], tags=tags)
out_dir = Path(args.out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
path_a = out_dir / "variant_a.md"
path_b = out_dir / "variant_b.md"
save_draft(draft_a, str(path_a))
save_draft(draft_b, str(path_b))
entries = load_ab(store)
test_id = _next_test_id(entries)
entry = {
"test_id": test_id,
"topic": args.topic,
"persona": persona,
"payoff": payoff,
"variants": {
"A": {**a, "draft_path": str(path_a), "note_id": "", "xsec_token": ""},
"B": {**b, "draft_path": str(path_b), "note_id": "", "xsec_token": ""},
},
"created_at": dt.datetime.now().isoformat(timespec="seconds"),
"compared_at": "",
"winner": "",
}
entries.append(entry)
save_ab(store, entries)
print(f"✓ A/B 实验 {test_id} 已创建")
print(f" 主题：{args.topic} 受众：{persona}")
print(f" A：{a['formula']} + {a['skeleton']} → {path_a}")
print(f" B：{b['formula']} + {b['skeleton']} → {path_b}")
print(f"\n下一步：")
print(f" 1. 完善两版草稿、跑 polish/coach")
print(f" 2. 间隔 24~48 小时分别发布")
print(f" 3. python3 scripts/ab_test.py register --test-id {test_id} --note-a ... --note-b ...")
return 0
def cmd_register(args: argparse.Namespace) -> int:
store = ProfileStore()
entries = load_ab(store)
found = None
for e in entries:
if e["test_id"] == args.test_id:
found = e
break
if not found:
print(f"❌ 没有 test_id={args.test_id}", file=sys.stderr)
return 1
if args.note_a:
found["variants"]["A"]["note_id"] = args.note_a
found["variants"]["A"]["xsec_token"] = args.xsec_a or ""
found["variants"]["A"]["published_at"] = dt.datetime.now().isoformat(timespec="seconds")
if args.note_b:
found["variants"]["B"]["note_id"] = args.note_b
found["variants"]["B"]["xsec_token"] = args.xsec_b or ""
found["variants"]["B"]["published_at"] = dt.datetime.now().isoformat(timespec="seconds")
save_ab(store, entries)
print(f"✓ 已登记 {args.test_id}")
return 0
def cmd_compare(args: argparse.Namespace) -> int:
store = ProfileStore()
entries = load_ab(store)
test = next((e for e in entries if e["test_id"] == args.test_id), None)
if not test:
print(f"❌ 没有 test_id={args.test_id}", file=sys.stderr)
return 1
a = test["variants"]["A"]
b = test["variants"]["B"]
if not (a.get("note_id") and b.get("note_id")):
print("❌ 还有变体没登记 note_id", file=sys.stderr)
return 1
# 拉快照
if not args.skip_snapshot:
from xhs_client import XHSClient, load_cookie_from_env
from xhs_parser import note_to_dict, parse_note_page
try:
client = XHSClient(cookie=load_cookie_from_env())
for v in (a, b):
html = client.get_explore_page(note_id=v["note_id"],
xsec_token=v.get("xsec_token") or None)
note = parse_note_page(html, note_id=v["note_id"])
if note:
nd = note_to_dict(note)
v["latest_engagement"] = (nd["interactions"]["liked_count"]
+ nd["interactions"]["collected_count"]
+ nd["interactions"]["comment_count"])
v["liked"] = nd["interactions"]["liked_count"]
v["collected"] = nd["interactions"]["collected_count"]
v["comment"] = nd["interactions"]["comment_count"]
except Exception as e:
print(f"⚠️ 抓取失败，使用上次记录的快照（如有）：{e}", file=sys.stderr)
# 比较
eng_a = a.get("latest_engagement", 0)
eng_b = b.get("latest_engagement", 0)
winner = "A" if eng_a > eng_b else "B" if eng_b > eng_a else "TIE"
test["winner"] = winner
test["compared_at"] = dt.datetime.now().isoformat(timespec="seconds")
save_ab(store, entries)
print("=" * 50)
print(f"🏆 A/B 实验 {args.test_id} 对比")
print("=" * 50)
print(f" 主题：{test['topic']}")
print()
print(f" A [{a['formula']}+{a['skeleton']}] 互动 {eng_a}")
print(f" ({a.get('liked',0)}赞 / {a.get('collected',0)}藏 / {a.get('comment',0)}评)")
print(f" B [{b['formula']}+{b['skeleton']}] 互动 {eng_b}")
print(f" ({b.get('liked',0)}赞 / {b.get('collected',0)}藏 / {b.get('comment',0)}评)")
print()
if winner == "TIE":
print(" → 两版表现相近，无明显差异")
else:
loser = "B" if winner == "A" else "A"
loser_eng = eng_b if winner == "A" else eng_a
winner_eng = eng_a if winner == "A" else eng_b
gap = (winner_eng - loser_eng) / max(1, loser_eng) * 100
print(f" 🏆 {winner} 胜出（互动高 {gap:.0f}%）")
win = test["variants"][winner]
print(f" 建议把 {win['formula']}+{win['skeleton']} 加入风格档案的 favorite_*")
return 0
def cmd_list(args: argparse.Namespace) -> int:
store = ProfileStore()
entries = load_ab(store)
if not entries:
print("（还没有 A/B 实验）")
return 0
print("== A/B 实验历史 ==\n")
for e in entries:
a, b = e["variants"]["A"], e["variants"]["B"]
status = e.get("winner") or ("待比较" if a.get("note_id") and b.get("note_id") else "起草中")
print(f" {e['test_id']} [{e['topic'][:20]}] "
f"A={a['formula']}+{a['skeleton']} B={b['formula']}+{b['skeleton']} "
f"→ {status}")
return 0
def main() -> int:
p = argparse.ArgumentParser(prog="ab_test.py", description="同选题 A/B 测试")
sub = p.add_subparsers(dest="cmd", required=True)
pp = sub.add_parser("plan", help="规划一个 A/B 实验")
pp.add_argument("--topic", required=True)
pp.add_argument("--persona", default="")
pp.add_argument("--payoff", default="")
pp.add_argument("--variant-a", required=True, help="变体 A：'T2,S1'")
pp.add_argument("--variant-b", required=True, help="变体 B：'T3,S6'")
pp.add_argument("--tags", default="")
pp.add_argument("--out-dir", required=True)
pp.set_defaults(func=cmd_plan)
pr = sub.add_parser("register", help="登记两版 note_id")
pr.add_argument("--test-id", required=True)
pr.add_argument("--note-a", default="")
pr.add_argument("--note-b", default="")
pr.add_argument("--xsec-a", default="")
pr.add_argument("--xsec-b", default="")
pr.set_defaults(func=cmd_register)
pc = sub.add_parser("compare", help="拉快照 + 输出对比")
pc.add_argument("--test-id", required=True)
pc.add_argument("--skip-snapshot", action="store_true",
help="不抓快照，用现有数据比较")
pc.set_defaults(func=cmd_compare)
pl = sub.add_parser("list", help="列出所有实验")
pl.set_defaults(func=cmd_list)
args = p.parse_args()
return args.func(args)
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/analyze-notes.py
#!/usr/bin/env python3
"""离线分析已抓取的笔记数据集。
输入：scrape-note / scrape-user / 手工整理后得到的 JSON / JSONL。
输出：Markdown 报告（默认），或者完整 JSON。
python3 analyze-notes.py --input notes.jsonl --format md --out report.md
python3 analyze-notes.py --input notes.json --format json --out report.json
"""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent))
from xhs_analyzer import ( # noqa: E402
full_report,
load_notes,
report_to_markdown,
)
def main() -> int:
ap = argparse.ArgumentParser(description="离线分析小红书笔记数据集")
ap.add_argument("--input", "-i", required=True, help="JSON 数组或 JSONL 文件")
ap.add_argument("--format", choices=["md", "json"], default="md")
ap.add_argument("--out", "-o", default="", help="输出路径；省略则 stdout")
args = ap.parse_args()
notes = load_notes(args.input)
if not notes:
print("输入数据为空或解析失败。", file=sys.stderr)
return 1
# 支持 {"results": [...]} 这种 wrapper
if isinstance(notes, list) and notes and isinstance(notes[0], dict) and "results" in notes[0] and "note_id" not in notes[0]:
notes = notes[0]["results"]
report = full_report(notes)
if args.format == "json":
output = json.dumps(report, ensure_ascii=False, indent=2)
else:
output = report_to_markdown(report)
if args.out:
Path(args.out).write_text(output, encoding="utf-8")
print(f"✓ 报告已写入 {args.out}（样本 {report['sample_size']} 条）")
else:
print(output)
return 0
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/assistant.py
#!/usr/bin/env python3
"""火一五小红书"创作伙伴" — 一个把所有能力串起来的主入口。
定位
====
**这才是这个技能的"助手"。** 单点 CLI 是"工具"，这个脚本是"工作流"。
它做三件事：
1. **看上下文** — 读 profile / posts / snapshots / feedback，知道你在哪一步。
2. **推荐下一步** — 你"刚来"还是"在写"还是"发完了"，给不一样的建议。
3. **路由** — 直接帮你跑下一步该跑的脚本。
子命令
======
- `status` — 一句话：你现在该干什么
- `next` — 推荐下一步 + 直接执行
- `init` — 引导第一次建档案（profile_init.py init 的友好包装）
- `brainstorm` — 进入对话式选题
- `write <topic>` — 在风格档案约束下起草
- `coach <draft>` — 教练模式
- `polish <draft>` — 打分模式（轻量）
- `publish <draft>` — 进入发布前流程
- `review` — 周复盘
- `learn <kv>` — 教助手新规则（profile_init.py rules 的友好包装）
`status` 与 `next` 的判断依据
==============================
- 没有 profile → 引导建 baseline
- 有 profile 没起草过 → 推荐 brainstorm
- 有起草中（最近 24h 内）→ 推荐 coach / polish 那篇
- 有发布超 24h 但未拍快照 → 推荐 track snapshot
- 距上次复盘 ≥ 7 天 → 推荐 weekly_review
"""
from __future__ import annotations
import argparse
import datetime as dt
import os
import subprocess
import sys
from pathlib import Path
from typing import Any, Dict, List, Optional
sys.path.insert(0, str(Path(__file__).parent))
from xhs_profile import ProfileStore # noqa: E402
_SCRIPTS_DIR = Path(__file__).resolve().parent
_PY = sys.executable
def _run(*args: str) -> int:
return subprocess.call([_PY, str(_SCRIPTS_DIR / args[0]), *args[1:]])
# =====================================================================
# 上下文判断
# =====================================================================
def detect_context() -> Dict[str, Any]:
store = ProfileStore()
has_profile = store.style_path.exists()
profile = store.load_style() if has_profile else None
posts = store.load_posts()
snaps = store.load_snapshots()
reviews = sorted(store.reviews_dir.glob("review_*.md")) if store.reviews_dir.exists() else []
now = dt.datetime.now()
last_drafted: Optional[Dict[str, Any]] = None
last_drafted_at: Optional[dt.datetime] = None
for p in posts:
d = _parse(p.get("drafted_at"))
if d and (not last_drafted_at or d > last_drafted_at):
last_drafted_at, last_drafted = d, p
last_review_at: Optional[dt.datetime] = None
if reviews:
# 文件名形如 review_20260427_1820_7d.md
for r in reviews:
try:
ts = r.stem.split("_")[1] + r.stem.split("_")[2]
last_review_at = dt.datetime.strptime(ts, "%Y%m%d%H%M")
except (IndexError, ValueError):
continue
# 未拍快照的发布
snap_note_ids = {s.get("note_id") for s in snaps}
pending_snap = [
p for p in posts
if p.get("note_id") and p["note_id"] not in snap_note_ids
and _hours_since(_parse(p.get("published_at"))) >= 24
]
return {
"has_profile": has_profile,
"profile": profile,
"post_count": len(posts),
"snapshot_count": len(snaps),
"last_drafted": last_drafted,
"last_drafted_at": last_drafted_at,
"last_drafted_hours_ago": _hours_since(last_drafted_at),
"pending_snap": pending_snap,
"last_review_at": last_review_at,
"days_since_review": (now - last_review_at).days if last_review_at else None,
}
def _parse(s: Optional[str]) -> Optional[dt.datetime]:
if not s:
return None
try:
return dt.datetime.fromisoformat(s)
except ValueError:
return None
def _hours_since(d: Optional[dt.datetime]) -> float:
if not d:
return 1e9
return (dt.datetime.now() - d).total_seconds() / 3600
# =====================================================================
# 推荐下一步
# =====================================================================
def recommend(ctx: Dict[str, Any]) -> List[Dict[str, str]]:
"""返回一组推荐 [{"label": ..., "command": ..., "why": ...}]，越靠前越优先。"""
rec: List[Dict[str, str]] = []
if not ctx["has_profile"]:
rec.append({
"label": "🌱 建立你的风格档案（5 分钟）",
"command": "assistant.py init",
"why": "助手还不认识你 — 给我 1~5 篇你的代表作，我会自动学习你的语调、长度、emoji 习惯。",
})
return rec
# 有未拍快照的发布
if ctx["pending_snap"]:
for p in ctx["pending_snap"][:3]:
note_id = p["note_id"]
rec.append({
"label": f"📊 拍快照：{p.get('title', '')[:25]}",
"command": f"track_post.py snapshot --note-id {note_id} --xsec-token {p.get('xsec_token','')}",
"why": f"这篇发布超过 24h 还没拉表现数据。",
})
# 距上次复盘 ≥ 7 天
if ctx["days_since_review"] is None or ctx["days_since_review"] >= 7:
rec.append({
"label": "🔁 跑一份周复盘",
"command": "assistant.py review",
"why": "上次复盘已超 7 天 — 不复盘的话，'哪些选题真有用' 会变成猜。",
})
# 起草中
if ctx["last_drafted"] and ctx["last_drafted_hours_ago"] < 24:
rec.append({
"label": f"🏋️ 给最近这篇做个教练诊断",
"command": "coach.py --in <你的最新草稿>",
"why": f"{ctx['last_drafted_hours_ago']:.0f}h 前起草了一篇 — 教练能告诉你哪里还可以再打磨。",
})
# 没起草过 / 起草过但最近一周没动
if not ctx["last_drafted"] or ctx["last_drafted_hours_ago"] > 7 * 24:
rec.append({
"label": "🧠 找个新选题",
"command": "assistant.py brainstorm",
"why": "最近没起草新选题。5 轮对话帮你收敛。",
})
return rec or [{
"label": "✓ 看起来一切顺利",
"command": "assistant.py status",
"why": "没有特别紧迫的事 — 想写就跑 brainstorm 或 write。",
}]
# =====================================================================
# 命令实现
# =====================================================================
def cmd_status(args: argparse.Namespace) -> int:
ctx = detect_context()
p = ctx["profile"]
print("━" * 60)
print("📒 火一五小红书创作伙伴 — 状态")
print("━" * 60)
if ctx["has_profile"] and p:
print(f" 人设：{p.persona or '(未设置)'} 赛道：{p.niche or '(未设置)'} "
f"样本：{p.sample_count} 篇")
print(f" 历史起草：{ctx['post_count']} 篇 快照：{ctx['snapshot_count']} 条")
if ctx["last_drafted_at"]:
print(f" 最近起草：{ctx['last_drafted_hours_ago']:.0f}h 前 — "
f"{ctx['last_drafted'].get('title', '')[:30]}")
if ctx["last_review_at"]:
print(f" 上次复盘：{ctx['days_since_review']} 天前")
if ctx["pending_snap"]:
print(f" ⚠️ {len(ctx['pending_snap'])} 篇发布超 24h 未拍快照")
else:
print(" ❗ 还没建立风格档案")
print()
print("🧭 接下来推荐：")
for r in recommend(ctx)[:3]:
print(f" {r['label']}")
print(f" ↳ {r['why']}")
print(f" $ python3 scripts/{r['command']}")
print()
return 0
def cmd_next(args: argparse.Namespace) -> int:
ctx = detect_context()
rec = recommend(ctx)
if not rec:
print("没有特别推荐。")
return 0
top = rec[0]
print(f"🧭 推荐执行：{top['label']}")
print(f" ↳ {top['why']}")
# 直接跑（除了占位类推荐）
cmd_parts = top["command"].split()
if not cmd_parts or "<" in top["command"]:
# 含尖括号占位的 — 不能直接跑
print(f" $ python3 scripts/{top['command']}")
print(" （需要参数，请手动执行上面的命令）")
return 0
if not args.dry_run:
return _run(*cmd_parts)
print(f" $ python3 scripts/{top['command']} (dry-run)")
return 0
def cmd_init(args: argparse.Namespace) -> int:
"""引导建立 baseline — 包装 profile_init.py init。"""
extra = []
if args.persona:
extra += ["--persona", args.persona]
if args.voice:
extra += ["--voice", args.voice]
if args.niche:
extra += ["--niche", args.niche]
if args.baseline:
extra += ["--baseline", *args.baseline]
if args.note_id:
extra += ["--note-id", *args.note_id]
if args.xsec_token:
extra += ["--xsec-token", args.xsec_token]
return _run("profile_init.py", "init", *extra)
def cmd_brainstorm(args: argparse.Namespace) -> int:
extra = []
if args.seed:
extra += ["--seed", args.seed]
if args.format:
extra += ["--format", args.format]
if args.out:
extra += ["--out", args.out]
return _run("brainstorm.py", *extra)
def cmd_write(args: argparse.Namespace) -> int:
"""根据风格档案预填一些参数。"""
profile = ProfileStore().load_style()
extra = ["--topic", args.topic]
if args.persona or profile.persona:
extra += ["--persona", args.persona or profile.persona]
if args.payoff:
extra += ["--payoff", args.payoff]
formula = args.formula or _favorite_formula(profile) or "T2"
skeleton = args.skeleton or _favorite_skeleton(profile) or "S1"
extra += ["--formula", formula, "--skeleton", skeleton]
if profile.common_tags:
extra += ["--tags", ",".join(profile.common_tags[:5])]
if args.out:
extra += ["--out", args.out]
return _run("write_post.py", "draft", *extra)
def _favorite_formula(profile) -> Optional[str]:
if profile and profile.favorite_formulas:
return max(profile.favorite_formulas, key=profile.favorite_formulas.get)
return None
def _favorite_skeleton(profile) -> Optional[str]:
if profile and profile.favorite_skeletons:
return max(profile.favorite_skeletons, key=profile.favorite_skeletons.get)
return None
def cmd_coach(args: argparse.Namespace) -> int:
extra = ["--in", args.draft]
if args.format:
extra += ["--format", args.format]
if args.out:
extra += ["--out", args.out]
if args.feedback:
for f in args.feedback:
extra += ["--feedback", f]
return _run("coach.py", *extra)
def cmd_polish(args: argparse.Namespace) -> int:
return _run("polish_post.py", "--in", args.draft)
def cmd_critique(args: argparse.Namespace) -> int:
extra = ["--in", args.draft]
if args.merged:
extra.append("--merged")
if args.allen_weight is not None:
extra += ["--allen-weight", str(args.allen_weight)]
if args.format:
extra += ["--format", args.format]
if args.out:
extra += ["--out", args.out]
return _run("critique.py", *extra)
def cmd_coin(args: argparse.Namespace) -> int:
extra = ["--brand", args.brand]
if args.value:
extra += ["--value", args.value]
if args.n:
extra += ["--n", str(args.n)]
return _run("coin_word.py", *extra)
def cmd_series(args: argparse.Namespace) -> int:
extra = ["--theme", args.theme]
if args.persona:
extra += ["--persona", args.persona]
if args.n:
extra += ["--n", str(args.n)]
return _run("series_design.py", *extra)
def cmd_simulate(args: argparse.Namespace) -> int:
return _run("reader_simulate.py", "--in", args.draft)
def cmd_publish(args: argparse.Namespace) -> int:
extra = ["--in", args.draft, "--log", str(ProfileStore().posts_path)]
return _run("publish_helper.py", *extra)
def cmd_review(args: argparse.Namespace) -> int:
extra = []
if args.days:
extra += ["--days", str(args.days)]
if args.out:
extra += ["--out", args.out]
return _run("weekly_review.py", *extra)
def cmd_learn(args: argparse.Namespace) -> int:
"""教助手一条规则。
支持的简化语法：
- `disable=emoji` → 禁用 emoji 检查
- `add-sensitive=卷王` → 加敏感词
- `allow=最佳` → 解禁某词
- `max-emoji=4` → 单篇上限
"""
extra = []
for kv in args.rules:
if "=" not in kv:
continue
k, v = kv.split("=", 1)
k = k.strip().lower()
v = v.strip()
if k == "disable":
extra += ["--disable", v]
elif k == "enable":
extra += ["--enable", v]
elif k == "add-sensitive":
extra += ["--add-sensitive", v]
elif k == "remove-sensitive":
extra += ["--remove-sensitive", v]
elif k == "allow":
extra += ["--allow", v]
elif k == "max-emoji":
extra += ["--max-emoji", v]
elif k == "weight":
extra += ["--weight", v]
elif k == "prefer-emoji":
extra += ["--prefer-emoji", v]
if not extra:
print("用法举例：assistant.py learn disable=emoji add-sensitive=卷王", file=sys.stderr)
return 1
return _run("profile_init.py", "rules", *extra)
def cmd_evolve(args: argparse.Namespace) -> int:
return _run("profile_init.py", "evolve", "--threshold", str(args.threshold))
def cmd_preset(args: argparse.Namespace) -> int:
if args.list:
return _run("profile_init.py", "preset", "--list")
if not args.name:
print("用法：assistant.py preset <allen|engineer|balanced> 或 --list 查看", file=sys.stderr)
return 1
return _run("profile_init.py", "preset", args.name)
# =====================================================================
# 入口
# =====================================================================
def build_parser() -> argparse.ArgumentParser:
p = argparse.ArgumentParser(
prog="assistant.py",
description="火一五小红书创作伙伴 — 一个入口把所有能力串起来",
)
sub = p.add_subparsers(dest="cmd")
sub.add_parser("status", help="状态 + 推荐").set_defaults(func=cmd_status)
pn = sub.add_parser("next", help="按推荐执行下一步")
pn.add_argument("--dry-run", action="store_true")
pn.set_defaults(func=cmd_next)
pi = sub.add_parser("init", help="引导建立风格档案")
pi.add_argument("--persona", default="")
pi.add_argument("--voice", choices=["casual", "formal", "playful", "pro"], default="")
pi.add_argument("--niche", default="")
pi.add_argument("--baseline", nargs="*", default=[])
pi.add_argument("--note-id", nargs="*", default=[])
pi.add_argument("--xsec-token", default="")
pi.set_defaults(func=cmd_init)
pb = sub.add_parser("brainstorm", help="对话式选题")
pb.add_argument("--seed", default="")
pb.add_argument("--format", choices=["text", "md", "json"], default="")
pb.add_argument("--out", default="")
pb.set_defaults(func=cmd_brainstorm)
pw = sub.add_parser("write", help="在风格约束下起草")
pw.add_argument("topic")
pw.add_argument("--persona", default="")
pw.add_argument("--payoff", default="")
pw.add_argument("--formula", default="")
pw.add_argument("--skeleton", default="")
pw.add_argument("--out", default="")
pw.set_defaults(func=cmd_write)
pc = sub.add_parser("coach", help="教练诊断")
pc.add_argument("draft")
pc.add_argument("--format", choices=["text", "md", "json"], default="")
pc.add_argument("--out", default="")
pc.add_argument("--feedback", action="append", default=[])
pc.set_defaults(func=cmd_coach)
pl = sub.add_parser("polish", help="打分模式（轻量）")
pl.add_argument("draft")
pl.set_defaults(func=cmd_polish)
pcr = sub.add_parser("critique", help="Allen 风格诊断（留白/AI腔/带读者/共鸣/邀请语）")
pcr.add_argument("draft")
pcr.add_argument("--merged", action="store_true", help="同时合并工程打分")
pcr.add_argument("--allen-weight", type=float, default=None)
pcr.add_argument("--format", choices=["text", "md", "json"], default="")
pcr.add_argument("--out", default="")
pcr.set_defaults(func=cmd_critique)
pco = sub.add_parser("coin", help="造词工具（Allen 待修炼方向之一）")
pco.add_argument("--brand", required=True)
pco.add_argument("--value", default="")
pco.add_argument("--n", type=int, default=8)
pco.set_defaults(func=cmd_coin)
pse = sub.add_parser("series", help="栏目化设计 + 互动阶梯")
pse.add_argument("--theme", required=True)
pse.add_argument("--persona", default="")
pse.add_argument("--n", type=int, default=5)
pse.set_defaults(func=cmd_series)
psm = sub.add_parser("simulate", help="模拟多读者画像走完文案的情绪流")
psm.add_argument("draft")
psm.set_defaults(func=cmd_simulate)
pp = sub.add_parser("publish", help="进入发布前流程")
pp.add_argument("draft")
pp.set_defaults(func=cmd_publish)
pr = sub.add_parser("review", help="周/月复盘")
pr.add_argument("--days", type=int, default=7)
pr.add_argument("--out", default="")
pr.set_defaults(func=cmd_review)
pe = sub.add_parser("learn", help="教助手一条规则（短语法）")
pe.add_argument("rules", nargs="+",
help="如 disable=emoji add-sensitive=卷王 allow=最佳 max-emoji=4")
pe.set_defaults(func=cmd_learn)
pv = sub.add_parser("evolve", help="基于 feedback 自动演进规则")
pv.add_argument("--threshold", type=int, default=3)
pv.set_defaults(func=cmd_evolve)
pps = sub.add_parser("preset", help="切风格预设：allen / engineer / balanced")
pps.add_argument("name", nargs="?", default="")
pps.add_argument("--list", action="store_true")
pps.set_defaults(func=cmd_preset)
return p
def main() -> int:
args = build_parser().parse_args()
if not getattr(args, "cmd", None):
# 默认 = status
return cmd_status(argparse.Namespace())
return args.func(args)
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/brainstorm.py
#!/usr/bin/env python3
"""火一五小红书"对话式选题"助手 — 多轮收敛找选题。
和 topic_ideas.py 的差别
========================
- topic_ideas 是"一次性输出 N 条" — 你拿走自己挑。
- brainstorm 是"和你对话 5 轮" — 把模糊感觉收敛到具体可写的选题。
工作流
======
1. 启动 → 检查近况：你最近写了什么？粉丝在评论什么？最近一周关注了什么？
2. 收集 → 主题模糊度 → 受众身份 → 利益点 → 反差 / 角度 → 排序
3. 输出 → 3 个候选选题，每个带：标题 / 公式 / 骨架 / 第一段钩子 / 推荐配图
支持非交互模式（脚本式 / Claude 调用）：把一系列回答用 --turn 传进来。
用法
----
# 交互模式
python3 brainstorm.py
# 一句话模糊种子（仍然进交互）
python3 brainstorm.py --seed "想写点关于副业的"
# 非交互模式（每个 --turn 是一个回答）
python3 brainstorm.py --seed "副业" \\
--turn "30 岁互联网打工人" \\
--turn "想写'下班后的另一种活法'" \\
--turn "不想说赚多少钱，想说时间自由" \\
--format md --out ideas.md
"""
from __future__ import annotations
import argparse
import json
import sys
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Dict, List, Optional
sys.path.insert(0, str(Path(__file__).parent))
from xhs_profile import ProfileStore, StyleProfile # noqa: E402
from xhs_writer import generate_titles # noqa: E402
# ---------- 5 轮对话 ----------
@dataclass
class Turn:
prompt: str
field_name: str
examples: List[str] = field(default_factory=list)
_TURNS = [
Turn(
prompt="你这次想写的主题是什么？（可以模糊，比如'副业'、'秋冬护肤'）",
field_name="topic",
examples=["秋冬干皮护肤", "下班后的副业", "30+ 互联网职场"],
),
Turn(
prompt="目标读者是谁？（年龄/身份/痛点 — 越具体越好）",
field_name="persona",
examples=["30+ 干皮女生", "刚毕业 1 年想换工作的", "二胎宝妈"],
),
Turn(
prompt="读者读完能得到什么？（避免'变美/变好'这种空话）",
field_name="payoff",
examples=["不再下午脸起皮", "副业月入 3000 不影响主业", "通勤地铁上背完单词"],
),
Turn(
prompt="你最想表达的核心观点 / 反差是什么？（一句话）",
field_name="angle",
examples=["其实不堆产品，皮肤反而更稳",
"副业不是为了赚钱，是为了证明'我不止这一种活法'",
"通勤时间是被低估的成长池"],
),
Turn(
prompt="有什么 '读者可能反对' 的点？（你打算怎么回应？）",
field_name="objection",
examples=["读者可能说'又是鸡汤'，我会用具体数据回应",
"可能说'这样很卷'，我会反过来说为什么不卷"],
),
]
# ---------- 收集回答 ----------
def collect_interactive(seed: str = "") -> Dict[str, str]:
answers: Dict[str, str] = {}
if seed:
answers["topic"] = seed
print(f"💬 已收到种子：{seed}\n")
for t in _TURNS:
if t.field_name in answers:
continue
print(f"❓ {t.prompt}")
if t.examples:
print(f" 例子：{' / '.join(t.examples[:2])}")
ans = input("> ").strip()
if not ans:
ans = "" # 允许跳过
answers[t.field_name] = ans
print()
return answers
def collect_from_args(seed: str, turns: List[str]) -> Dict[str, str]:
answers: Dict[str, str] = {}
if seed:
answers["topic"] = seed
fields_left = [t.field_name for t in _TURNS if t.field_name not in answers]
for ans, name in zip(turns, fields_left):
answers[name] = ans
for name in fields_left:
answers.setdefault(name, "")
return answers
# ---------- 收敛产出选题 ----------
def synthesize_ideas(answers: Dict[str, str], profile: Optional[StyleProfile]) -> List[Dict[str, Any]]:
topic = answers.get("topic") or "新选题"
persona = answers.get("persona") or (profile.persona if profile else "")
payoff = answers.get("payoff") or ""
angle = answers.get("angle") or ""
objection = answers.get("objection") or ""
# 基于回答匹配最合适的 3 种公式
formulas: List[str] = []
if angle:
formulas.append("T3") # 反差冲突
formulas.append("T6") # 观点（用 S6 骨架）
if "为什么" in (angle + topic):
formulas.append("T8") # 提问
if persona:
formulas.append("T5") # 身份代入
if payoff:
formulas.append("T1") # 数字对比
formulas.append("T2") # 痛点共情兜底
# 去重保留前 3
seen = []
for f in formulas:
if f not in seen:
seen.append(f)
formulas = seen[:3]
skeletons_for_formula = {
"T1": "S1", "T2": "S1", "T3": "S6", "T5": "S2",
"T6": "S6", "T8": "S6", "T10": "S5",
}
ideas = []
for code in formulas:
cand = generate_titles(topic, persona=persona, payoff=payoff,
formulas=[code], n_each=1)
if not cand:
continue
skeleton = skeletons_for_formula.get(code, "S1")
first_hook = _build_first_hook(answers)
cover = _suggest_cover(answers, profile)
ideas.append({
"title": cand[0]["title"],
"formula": code,
"skeleton": skeleton,
"first_paragraph_hook": first_hook,
"cover_hint": cover,
"objection_response_hint": (
f"在文末或正文后段 1~2 句回应：{objection}" if objection else ""
),
"tags_seed": _tags_seed(topic, profile),
})
return ideas
def _build_first_hook(answers: Dict[str, str]) -> str:
persona = answers.get("persona", "").strip()
payoff = answers.get("payoff", "").strip()
angle = answers.get("angle", "").strip()
if angle and persona:
return f"✨ 作为 {persona}，我有个不太主流的看法：{angle}"
if persona and payoff:
return f"✨ {persona}是不是也想过 {payoff}？我自己尝试了 30 天，结果出乎意料"
if angle:
return f"💡 {angle}"
if payoff:
return f"✨ 你是不是也想 {payoff}？"
return "✨ 你是不是也遇到过这种情况？"
def _suggest_cover(answers: Dict[str, str], profile: Optional[StyleProfile]) -> str:
topic = answers.get("topic", "")
persona = answers.get("persona", "")
base = f"{topic} 主题图（手写字 + 简洁背景）"
if persona:
base += f"，标题字号大，提及'{persona[:8]}'"
return base
def _tags_seed(topic: str, profile: Optional[StyleProfile]) -> List[str]:
out = [topic.strip()] if topic else []
if profile and profile.common_tags:
out += [t for t in profile.common_tags[:5] if t not in out]
return out[:5]
# ---------- 渲染 ----------
def render_text(answers: Dict[str, str], ideas: List[Dict[str, Any]]) -> str:
parts = ["🧠 选题对话总结\n"]
for k in ["topic", "persona", "payoff", "angle", "objection"]:
if answers.get(k):
parts.append(f" {k:<10}: {answers[k]}")
parts.append("")
parts.append("=" * 50)
parts.append(f"💡 收敛出 {len(ideas)} 个候选选题：\n")
for i, idea in enumerate(ideas, 1):
parts.append(f"{i}. [{idea['formula']} + {idea['skeleton']}] {idea['title']}")
parts.append(f" 首段钩子: {idea['first_paragraph_hook']}")
parts.append(f" 封面建议: {idea['cover_hint']}")
parts.append(f" 推荐话题: {' '.join('#' + t for t in idea['tags_seed'])}")
if idea.get("objection_response_hint"):
parts.append(f" 反方回应: {idea['objection_response_hint']}")
parts.append("")
parts.append("=" * 50)
parts.append("下一步：")
parts.append(f" python3 scripts/write_post.py draft --topic '{answers.get('topic','')}' \\")
parts.append(f" --formula {ideas[0]['formula'] if ideas else 'T2'} "
f"--skeleton {ideas[0]['skeleton'] if ideas else 'S1'} \\")
parts.append(f" --persona '{answers.get('persona','')}' \\")
parts.append(f" --payoff '{answers.get('payoff','')}' --out draft.md")
return "\n".join(parts)
def render_md(answers: Dict[str, str], ideas: List[Dict[str, Any]]) -> str:
parts = ["# 选题对话产出\n", "## 收敛要点\n"]
for k, label in [("topic", "主题"), ("persona", "受众"),
("payoff", "利益点"), ("angle", "核心角度"),
("objection", "反方")]:
if answers.get(k):
parts.append(f"- **{label}**：{answers[k]}")
parts.append("\n## 候选选题\n")
for i, idea in enumerate(ideas, 1):
parts.append(f"### {i}. {idea['title']}\n")
parts.append(f"- **公式 / 骨架**：{idea['formula']} + {idea['skeleton']}")
parts.append(f"- **首段钩子**：{idea['first_paragraph_hook']}")
parts.append(f"- **封面建议**：{idea['cover_hint']}")
parts.append(f"- **推荐话题**：{' '.join('#' + t for t in idea['tags_seed'])}")
if idea.get("objection_response_hint"):
parts.append(f"- **反方回应**：{idea['objection_response_hint']}")
parts.append("")
return "\n".join(parts) + "\n"
def main() -> int:
p = argparse.ArgumentParser(prog="brainstorm.py", description="对话式选题")
p.add_argument("--seed", default="", help="模糊种子主题")
p.add_argument("--turn", action="append", default=[],
help="非交互：按 _TURNS 顺序传回答，可重复")
p.add_argument("--format", choices=["text", "md", "json"], default="text")
p.add_argument("--out", default="")
p.add_argument("--no-profile", action="store_true")
args = p.parse_args()
profile = None
if not args.no_profile:
profile = ProfileStore().load_style()
if args.turn:
answers = collect_from_args(args.seed, args.turn)
else:
answers = collect_interactive(args.seed)
ideas = synthesize_ideas(answers, profile)
if args.format == "json":
out_text = json.dumps({"answers": answers, "ideas": ideas},
ensure_ascii=False, indent=2)
elif args.format == "md":
out_text = render_md(answers, ideas)
else:
out_text = render_text(answers, ideas)
if args.out:
Path(args.out).write_text(out_text, encoding="utf-8")
print(f"✓ 已写入 {args.out}", file=sys.stderr)
else:
print(out_text)
return 0
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/coach.py
#!/usr/bin/env python3
"""火一五小红书写作教练 — 不只打分，给"为什么 + 怎么改 + 例子"。
和 polish_post.py 的差别
========================
- polish 是"打分员"：每个维度 0~10，给一句修改建议。
- coach 是"教练"：每条问题展开成 (what, why, how, example)，
并对照你的风格档案做"风格偏离"提醒，给长线成长建议。
LLM 增强（可选）
================
设置 `XHS_LLM_PROVIDER=anthropic` + 安装 anthropic SDK 后，
教练会调一次 LLM 把建议写得更具体。未设置时纯规则离线跑。
用法
----
# 标准教练 — 读 profile + rules
python3 coach.py --in draft.md
# 详细 markdown 报告
python3 coach.py --in draft.md --format md --out coach_report.md
# 同时记录用户反馈（accept/reject 某条建议）
python3 coach.py --in draft.md --feedback rule_key=reaction \\
--feedback emoji=reject --feedback first_lines=accept
# 不读个人档案（纯通用打分）
python3 coach.py --in draft.md --no-profile
"""
from __future__ import annotations
import argparse
import datetime as dt
import json
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent))
from xhs_coach import CoachReport, coach # noqa: E402
from xhs_profile import Feedback, ProfileStore # noqa: E402
from xhs_writer import load_draft # noqa: E402
_SEVERITY_ICON = {"high": "🔴", "medium": "🟡", "low": "🔵"}
_WHERE_LABEL = {
"title": "标题",
"first_lines": "首段",
"structure": "结构",
"layout": "排版",
"style": "风格偏离",
"allen": "Allen 美学",
}
def render_text(report: CoachReport) -> str:
parts = []
parts.append(f"🏋️ 写作教练报告 — 总分 {report.overall}/100")
parts.append("")
if not report.diagnoses:
parts.append("✓ 没发现明显问题，可以直接进 polish_post.py 做最后体检。")
else:
parts.append(f"发现 {len(report.diagnoses)} 处可优化：\n")
for i, d in enumerate(report.diagnoses, 1):
icon = _SEVERITY_ICON.get(d.severity, "•")
label = _WHERE_LABEL.get(d.where, d.where)
parts.append(f"{icon} 【{label}】{d.what}")
parts.append(f" ▸ 为什么：{d.why}")
parts.append(f" ▸ 怎么改：{d.how}")
if d.example:
parts.append(f" ▸ 示例 ：{d.example}")
parts.append("")
if report.growth_hints:
parts.append("─" * 50)
parts.append("📈 长线成长建议")
for h in report.growth_hints:
parts.append(f" {h}")
return "\n".join(parts)
def render_md(report: CoachReport) -> str:
parts = [f"# 写作教练报告\n\n**总分：{report.overall}/100**\n"]
if report.breakdown:
parts.append("## 子项分\n")
for k, v in report.breakdown.items():
label = _WHERE_LABEL.get(k, k)
parts.append(f"- {label}：{v}/10")
parts.append("")
if report.diagnoses:
parts.append("## 诊断与建议\n")
for i, d in enumerate(report.diagnoses, 1):
icon = _SEVERITY_ICON.get(d.severity, "•")
label = _WHERE_LABEL.get(d.where, d.where)
parts.append(f"### {i}. {icon} 【{label}】{d.what}\n")
parts.append(f"**为什么：** {d.why}\n")
parts.append(f"**怎么改：** {d.how}\n")
if d.example:
parts.append(f"**示例：**\n\n```\n{d.example}\n```\n")
if report.growth_hints:
parts.append("## 长线成长\n")
for h in report.growth_hints:
parts.append(f"- {h}")
return "\n".join(parts) + "\n"
def main() -> int:
p = argparse.ArgumentParser(prog="coach.py", description="小红书写作教练")
p.add_argument("--in", dest="path", required=True, help="草稿路径 (.md 或 .json)")
p.add_argument("--format", choices=["text", "md", "json"], default="text")
p.add_argument("--out", default="")
p.add_argument("--no-profile", action="store_true", help="不读个人档案")
p.add_argument("--no-llm", action="store_true", help="禁用 LLM 增强（即使设置了 XHS_LLM_PROVIDER）")
p.add_argument("--feedback", action="append", default=[],
help="对某条建议反馈，格式 rule_key=accept|reject|ignore，可重复")
args = p.parse_args()
draft = load_draft(args.path)
profile = None
rules = None
feedback_log = []
post_history = []
store: ProfileStore | None = None
if not args.no_profile:
store = ProfileStore()
profile = store.load_style()
rules = store.load_rules()
feedback_log = [fb.to_dict() for fb in store.load_feedback()]
post_history = store.load_posts()
report = coach(
draft, profile=profile, rules=rules,
feedback_log=feedback_log, post_history=post_history,
enrich_llm=not args.no_llm,
)
# 记录反馈
if args.feedback and store is not None:
now = dt.datetime.now().isoformat(timespec="seconds")
for kv in args.feedback:
if "=" not in kv:
continue
rule_key, reaction = kv.split("=", 1)
store.append_feedback(Feedback(
at=now,
rule_key=rule_key.strip(),
suggestion="(via coach.py)",
reaction=reaction.strip(),
))
print(f"✓ 已记录 {len(args.feedback)} 条反馈到 {store.feedback_path}", file=sys.stderr)
# 输出
if args.format == "json":
text = json.dumps(report.to_dict(), ensure_ascii=False, indent=2)
elif args.format == "md":
text = render_md(report)
else:
text = render_text(report)
if args.out:
Path(args.out).write_text(text, encoding="utf-8")
print(f"✓ 已写入 {args.out}", file=sys.stderr)
else:
print(text)
# 退出码
high_count = sum(1 for d in report.diagnoses if d.severity == "high")
return 0 if high_count == 0 and report.overall >= 70 else 1
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/coin_word.py
#!/usr/bin/env python3
"""火一五小红书"造词工具" — Allen 待修炼方向之一。
Allen 教训
==========
- **「薯你会买」** = 品牌资产（薯）+ 对话感（你）+ 价值观（会买=会生活）
- **「人类丰容」** = 概念迁移（动物丰容 → 人）
- **「人生尽兴指南」** = 形式包装（指南 = 说明书形式）+ 情绪态度
造词三种模式：
1. **谐音造词** — 把品牌发音嵌进熟语
2. **概念迁移** — 跨领域借用（生物 / 建筑 / 心理 / 物理）
3. **形式包装** — 用说明书 / 指南 / 报告 / 档案等形式装情绪
不依赖 LLM，纯规则候选；同时支持 LLM 增强（XHS_LLM_PROVIDER=anthropic）。
用法
----
# 给品牌词造词候选
python3 coin_word.py --brand "小红薯" --value "会买=会生活" --n 8
# 输出 JSON
python3 coin_word.py --brand "尽兴" --value "活得舒服" --format json
"""
from __future__ import annotations
import argparse
import json
import os
import sys
from pathlib import Path
from typing import List, Dict, Any
sys.path.insert(0, str(Path(__file__).parent))
# ---------- 模板库 ----------
_HOMOPHONE_TEMPLATES = [
"{b}你会{v}", # 薯你会买
"{b}的就是好",
"看{b}就懂{v}",
"{b}味人生",
"和{b}相处{v}",
"{b}是个好习惯",
]
_CROSS_DOMAIN = {
"生物": [("丰容", "给环境加新鲜感"), ("反刍", "回味"),
("光合", "靠自己回血"), ("脱壳", "蜕变"),
("筑巢", "营造空间")],
"建筑": [("留白", "空间感"), ("骨架", "结构感"),
("肌理", "细节感"), ("透光", "氛围")],
"物理": [("惯性", "状态延续"), ("反作用力", "回弹"),
("熵减", "整理感"), ("折射", "看见不同面")],
"音乐": [("低音", "底色"), ("和弦", "组合"),
("休止符", "停一停"), ("回旋", "重复")],
"电影": [("长镜头", "完整感"), ("蒙太奇", "拼接"),
("反转", "剧情"), ("彩蛋", "惊喜")],
"厨房": [("文火", "慢"), ("出锅", "完成感"),
("回锅", "再来一遍"), ("撒盐", "提味")],
}
_FORMAT_WRAPPERS = [
"{title}指南", "{title}手册", "{title}白皮书",
"{title}小词典", "{title}存档", "{title}剧本",
"{title}档案", "{title}日记", "{title}图鉴",
"{title}修炼", "{title}入门", "{title}百宝书",
"{title}清单", "{title}年报", "{title}地图",
]
def coin_homophone(brand: str, value: str = "") -> List[Dict[str, str]]:
"""谐音造词。"""
out = []
if not brand:
return out
short = brand[0] # 取首字
val_short = (value.split("=")[0] if "=" in value else value)[:2] # 取前 2 字
for t in _HOMOPHONE_TEMPLATES:
word = t.format(b=short, v=val_short or brand)
out.append({
"word": word,
"type": "谐音造词",
"explain": f"借用'{short}'的发音，把品牌嵌进对话感的熟语",
})
return out
def coin_cross_domain(brand: str, value: str = "") -> List[Dict[str, str]]:
"""概念迁移造词。"""
out = []
for domain, items in _CROSS_DOMAIN.items():
for term, desc in items[:2]:
word = f"{brand}{term}"
out.append({
"word": word,
"type": f"概念迁移（{domain}）",
"explain": f"把'{domain}'里的'{term}'（{desc}）借用到品牌语境",
})
return out
def coin_format_wrap(brand: str, value: str = "") -> List[Dict[str, str]]:
"""形式包装：用说明书/指南等形式装情绪。"""
out = []
title_base = brand if value == "" else f"{brand}{value.split('=')[0][:2]}"
for t in _FORMAT_WRAPPERS:
word = t.format(title=title_base)
out.append({
"word": word,
"type": "形式包装",
"explain": f"借'{t.split('{title}')[1] if '{title}' in t else t}'这种形式包装情绪态度",
})
return out
# ---------- LLM 增强（可选） ----------
def llm_enhance(brand: str, value: str, candidates: List[Dict[str, str]]) -> List[Dict[str, str]]:
provider = os.environ.get("XHS_LLM_PROVIDER", "").lower()
if provider != "anthropic":
return candidates
try:
from anthropic import Anthropic
client = Anthropic()
sys_prompt = (
"你是品牌创意文案，参考 Allen 的方法（如'薯你会买''人类丰容''人生尽兴指南'）。"
"针对给定品牌词和价值观，给出 6 个新造词候选 — 三种模式各 2 个："
"①谐音造词 ②概念迁移 ③形式包装。返回 JSON 数组，每条 {word, type, explain}。"
)
msg = client.messages.create(
model=os.environ.get("XHS_LLM_MODEL", "claude-haiku-4-5-20251001"),
max_tokens=800,
system=sys_prompt,
messages=[{"role": "user",
"content": json.dumps({"brand": brand, "value": value}, ensure_ascii=False)}],
)
text = msg.content[0].text if msg.content else ""
try:
data = json.loads(text)
if isinstance(data, list):
return data
except Exception:
return candidates
except Exception:
return candidates
return candidates
# ---------- 主流程 ----------
def render_text(brand: str, value: str, candidates: List[Dict[str, str]]) -> str:
parts = [f"💡 「{brand}」造词候选（价值观：{value or '未指定'}）", ""]
by_type: Dict[str, List[Dict[str, str]]] = {}
for c in candidates:
by_type.setdefault(c["type"], []).append(c)
for t, items in by_type.items():
parts.append(f"## {t}")
for c in items:
parts.append(f" • **{c['word']}** — {c['explain']}")
parts.append("")
parts.append("─" * 50)
parts.append("Allen 标准（参考 data/allen_method.md）：")
parts.append(" ① 品牌资产嵌入 ② 对话感 ③ 价值观可解读")
parts.append(" 好的造词三层都满足；选一个最贴你品牌底色的去用。")
return "\n".join(parts)
def main() -> int:
p = argparse.ArgumentParser(prog="coin_word.py", description="品牌造词")
p.add_argument("--brand", required=True, help="品牌词，如：小红薯 / 尽兴")
p.add_argument("--value", default="", help="价值观，如：会买=会生活")
p.add_argument("--n", type=int, default=12, help="返回多少候选")
p.add_argument("--format", choices=["text", "json"], default="text")
p.add_argument("--no-llm", action="store_true")
args = p.parse_args()
candidates: List[Dict[str, str]] = []
candidates += coin_homophone(args.brand, args.value)[:3]
candidates += coin_cross_domain(args.brand, args.value)[:6]
candidates += coin_format_wrap(args.brand, args.value)[:3]
if not args.no_llm:
candidates = llm_enhance(args.brand, args.value, candidates)
candidates = candidates[:args.n]
if args.format == "json":
print(json.dumps({"brand": args.brand, "value": args.value, "candidates": candidates},
ensure_ascii=False, indent=2))
else:
print(render_text(args.brand, args.value, candidates))
return 0
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/compliance_check.py
#!/usr/bin/env python3
"""火一五小红书文案合规扫描 — 发布前必跑。
检测维度
========
1. **绝对化用语**（《广告法》第 9 条 + 平台限流红线）
2. **医疗 / 金融 / 教育**承诺词
3. **站外导流**（微信 / V / QQ / 二维码）
4. **诱导互动**（关注私信 / 评论 666 抽奖等）
5. **平台敏感**（搬运 / 互推 / 涨粉 / 刷粉）
6. **手机号 / 微信号 / QQ 号**正则识别
7. **话题数量**（< 3 或 > 8 都不健康）
数据源：data/sensitive_words.txt + 内置正则。
用法
----
# 扫一篇 markdown 草稿
python3 compliance_check.py --in draft.md
# 扫纯文本
python3 compliance_check.py --text "免费！100% 包过，加微信详聊"
# 输出 JSON 给 pipeline
python3 compliance_check.py --in draft.md --format json
退出码
------
- 0：完全干净
- 1：有警告（轻度，不一定限流）
- 2：有违规（高风险，必须改）
"""
from __future__ import annotations
import argparse
import json
import re
import sys
from pathlib import Path
from typing import Dict, List
sys.path.insert(0, str(Path(__file__).parent))
from xhs_writer import Draft, load_draft, load_sensitive_words # noqa: E402
# ---------- 正则规则（高风险） ----------
_RULES_HIGH: Dict[str, re.Pattern] = {
"phone": re.compile(r"(?<!\d)(1[3-9]\d{9}|0\d{2,3}[-\s]?\d{7,8})(?!\d)"),
"qq": re.compile(r"(?:Q+|q+|扣扣|企鹅)\s*[:：]?\s*\d{5,12}"),
"wechat_id": re.compile(r"(?:微信号|WX|wx|VX|vx|V[xX]|微信)\s*[:：]?\s*[A-Za-z][\w\-]{4,19}"),
"url_external": re.compile(r"\b(?:taobao|tmall|jd|pinduoduo|douyin|kuaishou|bilibili)\.com\b", re.I),
}
# ---------- 正则规则（中风险） ----------
_RULES_MEDIUM: Dict[str, re.Pattern] = {
"induce_follow": re.compile(r"(关注私信|私信领取|关注后私信|私我领|评论\s*\d{2,3}\s*(?:抽奖|送)|点赞过\s*\d+\s*(?:更新|更))"),
"off_platform_hint": re.compile(r"(主页有(?:联系|微信|VX|号)|简介(?:有|看)(?:微信|VX|联系)|评论解锁)"),
}
def scan_text(text: str, sensitive: List[str]) -> Dict[str, List[Dict[str, str]]]:
"""返回三档命中：high / medium / low (敏感词)。"""
result = {"high": [], "medium": [], "low": []}
for name, pat in _RULES_HIGH.items():
for m in pat.finditer(text):
result["high"].append({
"rule": name,
"match": m.group(0),
"pos": m.start(),
})
for name, pat in _RULES_MEDIUM.items():
for m in pat.finditer(text):
result["medium"].append({
"rule": name,
"match": m.group(0),
"pos": m.start(),
})
# 敏感词逐一扫
for w in sensitive:
if not w:
continue
idx = text.find(w)
if idx >= 0:
result["low"].append({
"rule": "sensitive_word",
"match": w,
"pos": idx,
})
return result
def render(result: Dict, tag_count: int) -> str:
out = []
high = result["high"]
medium = result["medium"]
low = result["low"]
out.append("=== 小红书文案合规扫描 ===")
out.append("")
if not (high or medium or low) and 3 <= tag_count <= 6:
out.append("✓ 干净，可以发布")
return "\n".join(out)
if high:
out.append(f"❌ 高风险（必须改）— {len(high)} 处")
for h in high:
out.append(f" • [{h['rule']}] {h['match']!r}")
out.append("")
if medium:
out.append(f"⚠️ 中风险（建议改）— {len(medium)} 处")
for h in medium:
out.append(f" • [{h['rule']}] {h['match']!r}")
out.append("")
if low:
# 去重，只展示前 20
seen = set()
unique = []
for h in low:
if h["match"] in seen:
continue
seen.add(h["match"])
unique.append(h)
out.append(f"⚠️ 敏感词（建议改）— {len(unique)} 个")
for h in unique[:20]:
out.append(f" • {h['match']!r}")
if len(unique) > 20:
out.append(f" ...省略 {len(unique) - 20} 个")
out.append("")
if tag_count < 3:
out.append(f"⚠️ 话题数 {tag_count} 个（建议 3~6 个，否则推荐量很小）")
elif tag_count > 8:
out.append(f"⚠️ 话题数 {tag_count} 个（超过 8 个会被识别为营销号）")
out.append("")
out.append("修改思路：")
out.append(" • 绝对化词 → 主观表达（'我用着觉得' / '亲测' / '我自己'）")
out.append(" • 联系方式 → 删除 / 走私信关键词回复（不在文案明示）")
out.append(" • 医疗承诺 → 改成 '我自己的体验'，避开 '治愈/根治'")
out.append(" • 诱导互动 → 改成 '评论区聊聊你的想法' 等开放邀请")
return "\n".join(out)
def main() -> int:
p = argparse.ArgumentParser(prog="compliance_check.py", description="小红书文案合规扫描")
p.add_argument("--in", dest="path", default="", help="草稿路径 (.md 或 .json)")
p.add_argument("--text", default="", help="直接传纯文本扫描")
p.add_argument("--format", choices=["text", "json"], default="text")
args = p.parse_args()
if args.path:
draft = load_draft(args.path)
text = f"{draft.title}\n{draft.content}\n{' '.join(draft.tags)}"
tag_count = len([t for t in draft.tags if t.strip()])
elif args.text:
text = args.text
tag_count = len(re.findall(r"#([\w一-鿿]+)", text))
else:
print("需要 --in 或 --text", file=sys.stderr)
return 1
sensitive = load_sensitive_words()
result = scan_text(text, sensitive)
if args.format == "json":
print(json.dumps({
"high": result["high"],
"medium": result["medium"],
"low": result["low"],
"tag_count": tag_count,
}, ensure_ascii=False, indent=2))
else:
print(render(result, tag_count))
if result["high"]:
return 2
if result["medium"] or result["low"]:
return 1
if not (3 <= tag_count <= 6) and tag_count > 0:
return 1
return 0
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/critique.py
#!/usr/bin/env python3
"""火一五小红书 "Allen 风格诊断" — 哲学家视角的文案体检。
和 polish_post / coach 的差别
============================
- polish 是工程师视角：标题钩子 / 首段抓力 / 排版 / emoji / 话题 / 合规。
- coach 是教练视角：what/why/how/example，覆盖工程维度。
- critique 是 Allen 视角：留白 / AI腔 / 教带 / 共鸣 / 邀请语，看"文案的气韵"。
什么时候用哪个？
================
- **干货 / 教程 / 工具类**：polish 即可，不开 critique（过度强调留白会让步骤教学失真）
- **生活 / 情绪 / 品牌 / 情感共鸣类**：coach + critique 一起用最有效
- **要发出去前**：polish 验工程合规 → critique 验美学 → publish_helper
用法
----
# 标准 Allen 诊断
python3 critique.py --in draft.md
# 合并工程 + Allen 综合分
python3 critique.py --in draft.md --merged
# 关闭某个维度（写到 profile/rules.json 也可以）
python3 critique.py --in draft.md --disable breath ai_speak
# 输出 markdown 报告
python3 critique.py --in draft.md --format md --out critique.md
# JSON for pipeline
python3 critique.py --in draft.md --format json
"""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent))
from xhs_aesthetic import ( # noqa: E402
AestheticScore,
aesthetic_score,
merge_with_engineering_score,
)
from xhs_profile import ProfileStore # noqa: E402
from xhs_writer import load_draft, score_post # noqa: E402
_LABEL_ORDER = ["breath", "ai_speak", "teach_vs_lead", "resonance", "invitation"]
_LABELS = {
"breath": "留白度",
"ai_speak": "去 AI 腔",
"teach_vs_lead": "带读者",
"resonance": "共鸣度",
"invitation": "邀请语",
}
def render_text(score: AestheticScore, merged: dict | None = None) -> str:
parts = []
parts.append("=" * 50)
parts.append(f"🎨 Allen 风格诊断 — 总分 {score.total}/100")
parts.append("=" * 50)
parts.append("")
parts.append("各维度（0~10）:")
for k in _LABEL_ORDER:
if k not in score.by_dim:
continue
v = score.by_dim[k]["score"]
bar = "█" * v + "░" * (10 - v)
parts.append(f" {_LABELS[k]:<10} {bar} {v}/10")
parts.append("")
if merged:
parts.append("─" * 50)
parts.append(f"🧮 综合分（工程 × {1 - merged['aesthetic_weight']:.0%} + "
f"Allen × {merged['aesthetic_weight']:.0%}）：**{merged['final']}/100**")
parts.append(f" 工程分: {merged['engineering']}/100 Allen 分: {merged['aesthetic']}/100")
parts.append("")
if score.issues:
parts.append("📋 命中问题（按维度）:")
for it in score.issues:
parts.append(f" • {it}")
parts.append("")
if score.suggestions:
parts.append("💡 改写建议:")
for s in score.suggestions:
parts.append(f" → {s}")
parts.append("")
parts.append("─" * 50)
parts.append("📚 配套阅读:")
parts.append(" • Allen 三课与五技法：data/allen_method.md")
parts.append(" • AI 腔黑名单 + 替换：data/ai_speak_patterns.json")
parts.append(" • 节气借势画面库：data/seasonal_themes.md")
return "\n".join(parts)
def render_md(score: AestheticScore, merged: dict | None = None) -> str:
parts = [f"# Allen 风格诊断报告\n\n**总分：{score.total}/100**\n"]
parts.append("## 各维度\n")
for k in _LABEL_ORDER:
if k not in score.by_dim:
continue
v = score.by_dim[k]["score"]
parts.append(f"- {_LABELS[k]}：**{v}/10**")
parts.append("")
if merged:
parts.append("## 综合分（与工程打分合并）\n")
parts.append(f"- **最终分：{merged['final']}/100**")
parts.append(f"- 工程分：{merged['engineering']}（权重 {1 - merged['aesthetic_weight']:.0%}）")
parts.append(f"- Allen 分：{merged['aesthetic']}（权重 {merged['aesthetic_weight']:.0%}）")
parts.append("")
if score.issues:
parts.append("## 命中问题\n")
for it in score.issues:
parts.append(f"- {it}")
parts.append("")
if score.suggestions:
parts.append("## 改写建议\n")
for s in score.suggestions:
parts.append(f"- {s}")
parts.append("")
return "\n".join(parts) + "\n"
def main() -> int:
p = argparse.ArgumentParser(prog="critique.py", description="Allen 风格诊断")
p.add_argument("--in", dest="path", required=True)
p.add_argument("--format", choices=["text", "md", "json"], default="text")
p.add_argument("--out", default="")
p.add_argument("--disable", nargs="*", default=[],
help="要跳过的维度：breath / ai_speak / teach_vs_lead / resonance / invitation")
p.add_argument("--merged", action="store_true",
help="同时跑工程打分并合并（默认权重 Allen 0.4）")
p.add_argument("--allen-weight", type=float, default=0.4,
help="Allen 在综合分里的权重（0~1，配合 --merged）")
p.add_argument("--no-profile", action="store_true",
help="不读 profile/rules.json 的 disabled_aesthetic")
args = p.parse_args()
draft = load_draft(args.path)
# 收集要禁用的维度（CLI 优先 + profile 补充）
disabled = list(args.disable)
if not args.no_profile:
rules = ProfileStore().load_rules()
for k in (rules.disabled_checks or []):
# 复用 disabled_checks，前缀 aesthetic: 表示 Allen 维度
if k.startswith("aesthetic:"):
key = k.split(":", 1)[1]
if key not in disabled:
disabled.append(key)
score = aesthetic_score(draft.title, draft.content, disabled=disabled)
merged = None
if args.merged:
rules = None if args.no_profile else ProfileStore().load_rules()
eng = score_post(draft.title, draft.content, draft.tags, rules=rules)
merged = merge_with_engineering_score(
eng.breakdown, eng.total, score, aesthetic_weight=args.allen_weight,
)
if args.format == "json":
out_text = json.dumps({
"aesthetic": score.to_dict(),
"merged": merged,
}, ensure_ascii=False, indent=2)
elif args.format == "md":
out_text = render_md(score, merged)
else:
out_text = render_text(score, merged)
if args.out:
Path(args.out).write_text(out_text, encoding="utf-8")
print(f"✓ 已写入 {args.out}", file=sys.stderr)
else:
print(out_text)
# 退出码：分 < 60 视为失败
return 0 if score.total >= 60 else 1
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/polish_post.py
#!/usr/bin/env python3
"""火一五小红书文案打分 + 优化建议。
输入：一篇笔记草稿（markdown 或 json）。
输出：6 维打分（0~100）+ 命中问题 + 修改建议。
用法
----
# markdown 草稿（# 标题，正文，最后行带 # 话题）
python3 polish_post.py --in draft.md
# JSON 草稿（xhs_writer.Draft.to_dict 格式）
python3 polish_post.py --in draft.json
# 直接传字符串
python3 polish_post.py --title "标题" --content "正文..." --tags "护肤,干皮护肤"
# 输出 JSON 给后续 pipeline 用
python3 polish_post.py --in draft.md --format json
"""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent))
from xhs_writer import Draft, load_draft, score_post # noqa: E402
from xhs_profile import ProfileStore # noqa: E402
def render_text(score) -> str:
out = []
out.append(f"=== 打分结果（0~100）：{score.total} ===")
out.append("")
grade = "🎉 可发布" if score.total >= 80 else "⚠️ 建议优化" if score.total >= 60 else "❌ 重写"
out.append(f"等级：{grade}")
out.append("")
out.append("子项分（0~10）:")
labels = {
"title": "标题钩子",
"first_lines": "首段抓力",
"layout": "段落排版",
"emoji": "emoji 节奏",
"hashtags": "话题数量",
"compliance": "合规性",
}
for k, v in score.breakdown.items():
bar = "█" * v + "░" * (10 - v)
out.append(f" {labels.get(k, k):<10} {bar} {v}/10")
out.append("")
if score.issues:
out.append("命中问题：")
for x in score.issues:
out.append(f" • {x}")
out.append("")
if score.suggestions:
out.append("修改建议：")
for x in score.suggestions:
out.append(f" → {x}")
return "\n".join(out)
def main() -> int:
p = argparse.ArgumentParser(prog="polish_post.py", description="小红书文案打分 + 优化建议")
p.add_argument("--in", dest="path", default="", help="草稿文件路径 (.md 或 .json)")
p.add_argument("--title", default="", help="不传文件时直接传标题")
p.add_argument("--content", default="", help="不传文件时直接传正文")
p.add_argument("--tags", default="", help="不传文件时直接传话题（逗号分隔）")
p.add_argument("--format", choices=["text", "json"], default="text")
p.add_argument("--no-profile", action="store_true",
help="跳过个人规则覆盖（profile/rules.json），用纯默认规则")
args = p.parse_args()
if args.path:
draft = load_draft(args.path)
elif args.title or args.content:
tags = [t.strip() for t in args.tags.split(",") if t.strip()]
draft = Draft(title=args.title, content=args.content, tags=tags)
else:
print("必须提供 --in 或 (--title + --content)", file=sys.stderr)
return 1
rules = None
if not args.no_profile:
rules = ProfileStore().load_rules()
score = score_post(draft.title, draft.content, draft.tags, rules=rules)
if args.format == "json":
print(json.dumps({
"draft": draft.to_dict(),
"score": score.to_dict(),
}, ensure_ascii=False, indent=2))
else:
print(render_text(score))
# 退出码：60 分以下视为失败，方便 CI / pipeline 检查
return 0 if score.total >= 60 else 2
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/practice.py
#!/usr/bin/env python3
"""火一五小红书写作训练 — 把"写"当成可练习的肌肉。
为什么需要练习
==============
看再多模板也写不好，得动手。这个脚本提供两种训练：
1. **`prompt`** — 命题练习
助手给你一个主题 + 一个公式 + 一个场景约束，你写一条标题或一段开头。
写完跑 `practice.py grade` 给你打分 + 对照参考。
2. **`rewrite`** — 改写训练
助手随机抽一段"反面教材"（或你过往得分低的笔记片段），让你改写。
提交后对比你的版本和"AI 默认改法"，告诉你为什么哪种好。
3. **`drill`** — 题库训练
把所有命题串成一个题库（每天 1 题），可批量回顾历史成绩。
记录在 `~/.xiaohongshu/profile/practice.jsonl`。
用法
----
# 出 1 道命题
python3 practice.py prompt
# 提交答案
python3 practice.py grade --task-id 5 --answer "你的标题或段落"
# 改写训练
python3 practice.py rewrite
# 看历史
python3 practice.py history --days 30
"""
from __future__ import annotations
import argparse
import datetime as dt
import json
import random
import sys
from pathlib import Path
from typing import Any, Dict, List
sys.path.insert(0, str(Path(__file__).parent))
from xhs_profile import ProfileStore # noqa: E402
from xhs_writer import ( # noqa: E402
Draft,
generate_titles,
score_post,
score_title,
)
# ---------- 命题题库 ----------
_PRACTICE_PROMPTS = [
{"topic": "干皮护肤", "persona": "30+ 干皮女生", "constraint": "标题 ≤ 22 字，使用 T2 公式（痛点共情）"},
{"topic": "副业", "persona": "互联网打工人", "constraint": "首段 3 行，必须命中 '代入式提问' 钩子"},
{"topic": "通勤", "persona": "上班族", "constraint": "用 T11 故事开场，标题以 '上周' 开头"},
{"topic": "减脂餐", "persona": "微胖", "constraint": "标题 + 首 50 字，无 emoji，靠文字钩子"},
{"topic": "Notion 模板", "persona": "学生党", "constraint": "T6 福利免费公式，标题不能用 '最/唯一/第一'"},
{"topic": "护肤避雷", "persona": "敏感肌", "constraint": "T3 反差冲突，要有 '我以为...结果...'"},
{"topic": "亲子绘本", "persona": "新手妈妈", "constraint": "T10 步骤指南，副标题写 '3 步'"},
{"topic": "断舍离", "persona": "出租屋住户", "constraint": "T1 数字对比，限制 18 字"},
{"topic": "面试自我介绍", "persona": "应届生", "constraint": "T5 身份代入，避开 '最强/保过'"},
{"topic": "周末独处", "persona": "i 人", "constraint": "T7 时间节点 + 故事感"},
]
_REWRITE_BAD_SAMPLES = [
{
"bad": "100% 有效！秋冬最强护肤大法，绝对让你皮肤变好！",
"issues": ["100%", "最强", "绝对", "无具体方法"],
"good_hint": "把'绝对/最强'换成'我亲测 3 个月'，给 3 个具体步骤。",
},
{
"bad": "宝子们我跟你说真的，这个东西真的太好用了，不买真的会后悔，都给我冲！！！",
"issues": ["营销味", "无信息量", "诱导购买"],
"good_hint": "把'冲'改成具体使用细节 + 适合谁不适合谁。",
},
{
"bad": "副业月入 3 万的方法，加我 vx 私信领",
"issues": ["导流站外", "夸大收益"],
"good_hint": "改成'我自己副业 3 个月的真实流水 + 几条踩坑'，不导流。",
},
{
"bad": "护肤其实很简单，按部就班就行，没什么难的。",
"issues": ["首段无钩子", "无具体场景", "信息密度低"],
"good_hint": "把'按部就班'改成具体场景：'你是不是也每天早晨手忙脚乱涂 5 层？其实只要...'",
},
]
# ---------- 存储 ----------
def practice_log_path(store: ProfileStore) -> Path:
return store.root / "practice.jsonl"
def append_practice(store: ProfileStore, entry: Dict[str, Any]) -> None:
p = practice_log_path(store)
with p.open("a", encoding="utf-8") as f:
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
def load_practice(store: ProfileStore) -> List[Dict[str, Any]]:
p = practice_log_path(store)
if not p.exists():
return []
out = []
for line in p.read_text(encoding="utf-8").splitlines():
if line.strip():
try:
out.append(json.loads(line))
except json.JSONDecodeError:
pass
return out
# ---------- 子命令 ----------
def cmd_prompt(args: argparse.Namespace) -> int:
store = ProfileStore()
history = load_practice(store)
task_id = len(history) + 1
prompt = random.choice(_PRACTICE_PROMPTS)
entry = {
"task_id": task_id,
"kind": "prompt",
"issued_at": dt.datetime.now().isoformat(timespec="seconds"),
"topic": prompt["topic"],
"persona": prompt["persona"],
"constraint": prompt["constraint"],
"answer": "",
"score": None,
}
append_practice(store, entry)
print(f"📝 命题练习 #{task_id}\n")
print(f" 主题约束 : {prompt['topic']}")
print(f" 目标读者 : {prompt['persona']}")
print(f" 写作约束 : {prompt['constraint']}\n")
print(f"提交答案：")
print(f" python3 scripts/practice.py grade --task-id {task_id} --answer '你的回答'")
return 0
def cmd_grade(args: argparse.Namespace) -> int:
store = ProfileStore()
history = load_practice(store)
if args.task_id < 1 or args.task_id > len(history):
print(f"❌ task_id 无效（{args.task_id}）", file=sys.stderr)
return 1
task = history[args.task_id - 1]
answer = args.answer.strip()
if not answer:
print("❌ 答案为空", file=sys.stderr)
return 1
# 简单判分：如果答案像"标题"（短）→ 用 score_title；否则按 score_post 当正文
if len(answer) < 35 and "\n" not in answer:
s, issues, suggestions = score_title(answer)
score = int(s * 10)
breakdown = {"title": s}
# 给参考标题
ref = generate_titles(task["topic"], persona=task["persona"], formulas=["T2", "T3"], n_each=1)
ref_text = " / ".join(t["title"] for t in ref)
else:
ps = score_post("(your-title)" if len(answer) > 35 else answer, answer, [])
score = ps.total
breakdown = ps.breakdown
suggestions = ps.suggestions
ref_text = "（用 brainstorm.py 看更完整的参考骨架）"
# 更新记录
task["answer"] = answer
task["score"] = score
task["graded_at"] = dt.datetime.now().isoformat(timespec="seconds")
history[args.task_id - 1] = task
# 全量 rewrite（小数据量可接受）
p = practice_log_path(store)
with p.open("w", encoding="utf-8") as f:
for h in history:
f.write(json.dumps(h, ensure_ascii=False) + "\n")
# 输出
print(f"📊 命题 #{args.task_id} 评分：{score}/100")
print(f" 主题：{task['topic']} 约束：{task['constraint']}")
print(f" 你的回答：{answer[:80]}{'...' if len(answer) > 80 else ''}")
print()
if breakdown:
for k, v in breakdown.items():
print(f" {k:<14} {'█' * v}{'░' * (10 - v)} {v}/10")
print()
if suggestions:
print("💡 改进建议：")
for s in suggestions[:5]:
print(f" • {s}")
print()
print(f"📚 参考写法：{ref_text}")
return 0
def cmd_rewrite(args: argparse.Namespace) -> int:
store = ProfileStore()
history = load_practice(store)
task_id = len(history) + 1
sample = random.choice(_REWRITE_BAD_SAMPLES)
entry = {
"task_id": task_id,
"kind": "rewrite",
"issued_at": dt.datetime.now().isoformat(timespec="seconds"),
"bad": sample["bad"],
"issues": sample["issues"],
"good_hint": sample["good_hint"],
"answer": "",
"score": None,
}
append_practice(store, entry)
print(f"🔧 改写练习 #{task_id}\n")
print(f"原文（反面教材）：")
print(f" > {sample['bad']}\n")
print(f"已知问题：{', '.join(sample['issues'])}\n")
print(f"提示：{sample['good_hint']}\n")
print(f"提交：python3 scripts/practice.py grade --task-id {task_id} --answer '你的改写版本'")
return 0
def cmd_history(args: argparse.Namespace) -> int:
store = ProfileStore()
history = load_practice(store)
if not history:
print("（还没练过题。从 `practice.py prompt` 或 `practice.py rewrite` 开始）")
return 0
cutoff = dt.datetime.now() - dt.timedelta(days=args.days)
recent = [h for h in history if h.get("issued_at", "") >= cutoff.isoformat(timespec="seconds")]
print(f"📚 最近 {args.days} 天练习：{len(recent)} 题")
print()
if recent:
graded = [h for h in recent if h.get("score") is not None]
if graded:
avg = sum(h["score"] for h in graded) / len(graded)
best = max(graded, key=lambda x: x["score"])
print(f" 已评分 {len(graded)} 题，平均 {avg:.1f}，最高 {best['score']}")
print(f" 最高分题目：#{best['task_id']} - {best.get('topic') or '改写'}")
print()
for h in recent[-10:]:
tag = "📝" if h["kind"] == "prompt" else "🔧"
score = h.get("score", "?")
topic = h.get("topic") or h.get("bad", "")[:30]
print(f" {tag} #{h['task_id']} ({score}分) {topic}")
return 0
def main() -> int:
p = argparse.ArgumentParser(prog="practice.py", description="小红书写作训练")
sub = p.add_subparsers(dest="cmd", required=True)
sub.add_parser("prompt", help="出一道命题").set_defaults(func=cmd_prompt)
sub.add_parser("rewrite", help="改写训练").set_defaults(func=cmd_rewrite)
pg = sub.add_parser("grade", help="提交答案 + 评分")
pg.add_argument("--task-id", type=int, required=True)
pg.add_argument("--answer", required=True)
pg.set_defaults(func=cmd_grade)
ph = sub.add_parser("history", help="查看练习历史")
ph.add_argument("--days", type=int, default=30)
ph.set_defaults(func=cmd_history)
args = p.parse_args()
return args.func(args)
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/profile_init.py
#!/usr/bin/env python3
"""火一五小红书创作者画像初始化 / 管理。
工作模式
========
1. **`init`** — 引导用户从 1~5 篇代表作建立 baseline，自动提取风格档案。
2. **`add`** — 追加一篇 baseline（已有档案的情况下）。
3. **`show`** — 查看当前风格档案 + 规则覆盖。
4. **`rules`** — 编辑规则覆盖（disabled / weights / custom_sensitive）。
5. **`evolve`** — 根据历史 feedback 自动演进规则。
6. **`reset`** — 删除整个档案（需 --confirm）。
存档位置：`~/.xiaohongshu/profile/`（可被 XHS_PROFILE_DIR 覆盖）。
用法
----
# 第一次：从已有的 .json 笔记建 baseline
python3 profile_init.py init --persona "30+ 干皮女生" --voice casual \\
--niche "护肤" --baseline note1.json note2.json note3.json
# 也支持直接抓取（要 Cookie）
python3 profile_init.py init --persona "..." --note-id 64abc...
# 查看
python3 profile_init.py show
# 教助手一条规则（"我以后不要 emoji"）
python3 profile_init.py rules --disable emoji
# 加自定义敏感词
python3 profile_init.py rules --add-sensitive "卷王" --add-sensitive "原谅色"
"""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent))
from xhs_profile import ( # noqa: E402
Feedback,
ProfileStore,
RuleOverride,
StyleProfile,
derive_style,
evolve_rules,
)
from xhs_writer import load_draft # noqa: E402
# ---------- 工具：把多种输入归一化成 baseline 用的 dict ----------
def _load_input(p: str) -> dict:
path = Path(p)
if not path.exists():
raise FileNotFoundError(p)
if path.suffix.lower() == ".json":
return json.loads(path.read_text(encoding="utf-8"))
# markdown 草稿
draft = load_draft(p)
return draft.to_dict()
# ---------- 子命令 ----------
def cmd_init(args: argparse.Namespace) -> int:
store = ProfileStore()
if store.style_path.exists() and not args.force:
print(f"⚠️ 已存在档案：{store.style_path}")
print(" 要重新初始化加 --force；要追加用 `profile_init.py add`")
return 1
samples = []
for p in args.baseline:
try:
samples.append(_load_input(p))
except Exception as e:
print(f"❌ 读不了 {p}: {e}", file=sys.stderr)
return 1
# 可选：直接从平台抓
if args.note_id:
try:
from xhs_client import XHSClient, load_cookie_from_env
from xhs_parser import note_to_dict, parse_note_page
client = XHSClient(cookie=load_cookie_from_env())
for nid in args.note_id:
html = client.get_explore_page(note_id=nid, xsec_token=args.xsec_token or None)
note = parse_note_page(html, note_id=nid)
if note:
samples.append(note_to_dict(note))
except Exception as e:
print(f"⚠️ 抓取失败（继续用本地样本）：{e}", file=sys.stderr)
if not samples:
print("❌ 没有 baseline 样本（用 --baseline file1.json file2.md ... 或 --note-id ...）",
file=sys.stderr)
return 1
# 持久化样本
for s in samples:
store.add_baseline(s)
# 提取风格
profile = derive_style(samples)
if args.persona:
profile.persona = args.persona
if args.voice:
profile.voice = args.voice
if args.niche:
profile.niche = args.niche
store.save_style(profile)
# 默认规则覆盖留空，让用户后续教
if not store.rules_path.exists():
store.save_rules(RuleOverride())
print(f"✓ 初始化完成（{len(samples)} 篇样本）")
print(f" 档案位置：{store.root}")
_print_profile_summary(profile)
print("\n下一步：")
print(" python3 scripts/assistant.py # 进入对话助手")
print(" python3 scripts/profile_init.py show")
return 0
def cmd_add(args: argparse.Namespace) -> int:
store = ProfileStore()
samples = []
for p in args.baseline:
try:
samples.append(_load_input(p))
except Exception as e:
print(f"❌ 读不了 {p}: {e}", file=sys.stderr)
return 1
for s in samples:
store.add_baseline(s)
# 重新算
all_baselines = store.load_baselines()
profile = store.load_style()
new_profile = derive_style(all_baselines)
# 保留用户手填的字段
new_profile.persona = profile.persona
new_profile.voice = profile.voice
new_profile.niche = profile.niche or new_profile.niche
new_profile.avoid_words = profile.avoid_words
store.save_style(new_profile)
print(f"✓ 已追加 {len(samples)} 篇，重新提取风格档案。当前样本数：{new_profile.sample_count}")
return 0
def cmd_show(args: argparse.Namespace) -> int:
store = ProfileStore()
if not store.style_path.exists():
print("（尚未初始化档案。先跑 `profile_init.py init` ）")
return 0
profile = store.load_style()
rules = store.load_rules()
if args.format == "json":
print(json.dumps({
"style": profile.to_dict(),
"rules": rules.to_dict(),
"baseline_count": len(store.load_baselines()),
"feedback_count": len(store.load_feedback()),
}, ensure_ascii=False, indent=2))
return 0
_print_profile_summary(profile)
print()
_print_rules_summary(rules)
print(f"\n📁 档案位置：{store.root}")
return 0
def cmd_rules(args: argparse.Namespace) -> int:
store = ProfileStore()
rules = store.load_rules()
if args.disable:
for k in args.disable:
if k not in rules.disabled_checks:
rules.disabled_checks.append(k)
if args.enable:
rules.disabled_checks = [k for k in rules.disabled_checks if k not in args.enable]
if args.weight:
for kv in args.weight:
if "=" not in kv:
continue
k, v = kv.split("=", 1)
try:
rules.weights[k.strip()] = float(v)
except ValueError:
continue
if args.add_sensitive:
for w in args.add_sensitive:
if w not in rules.custom_sensitive:
rules.custom_sensitive.append(w)
if args.remove_sensitive:
rules.custom_sensitive = [w for w in rules.custom_sensitive if w not in args.remove_sensitive]
if args.allow:
for w in args.allow:
if w not in rules.allowed_words:
rules.allowed_words.append(w)
if args.unallow:
rules.allowed_words = [w for w in rules.allowed_words if w not in args.unallow]
if args.max_emoji is not None:
rules.max_emoji_per_post = args.max_emoji
if args.prefer_emoji is not None:
rules.prefer_emoji = args.prefer_emoji
store.save_rules(rules)
_print_rules_summary(rules)
print(f"\n✓ 已写入 {store.rules_path}")
return 0
_PRESETS = {
"allen": {
"name": "Allen 流（品牌 / 情感共鸣赛道）",
"rules": {
"weights": {
"compliance": 0.20, # 合规权重微降
"emoji": 0.05, # emoji 工程指标权重降
},
"disabled_checks": [], # 不禁用工程项，只是 Allen 美学加权
},
"aesthetic_weights": {
"breath": 0.25,
"ai_speak": 0.20,
"teach_vs_lead": 0.20,
"resonance": 0.20,
"invitation": 0.15,
},
"merge_aesthetic_weight": 0.5, # 综合分时 Allen 美学占一半
"phrases": ["其实", "我自己", "我体感", "亲测", "我之前以为"],
},
"engineer": {
"name": "工程师流（干货 / 教程 / 工具）",
"rules": {
"disabled_checks": ["aesthetic:breath", "aesthetic:teach_vs_lead",
"aesthetic:resonance"], # 关掉 Allen 美学维度
},
"merge_aesthetic_weight": 0.0,
},
"balanced": {
"name": "平衡流（默认）",
"rules": {},
"merge_aesthetic_weight": 0.3,
},
}
def cmd_preset(args: argparse.Namespace) -> int:
"""切换风格预设。"""
if args.list:
print("可用预设：")
for k, v in _PRESETS.items():
print(f" {k:<10} — {v['name']}")
return 0
preset = _PRESETS.get(args.name)
if not preset:
print(f"❌ 未知预设：{args.name}（可用：{', '.join(_PRESETS)}）", file=sys.stderr)
return 1
store = ProfileStore()
rules = store.load_rules()
# 合并预设到当前 rules
p_rules = preset.get("rules", {})
if "weights" in p_rules:
rules.weights = {**rules.weights, **p_rules["weights"]}
if "disabled_checks" in p_rules:
for k in p_rules["disabled_checks"]:
if k not in rules.disabled_checks:
rules.disabled_checks.append(k)
if "phrases" in preset:
rules.custom_phrases = preset["phrases"]
# 把 aesthetic_weights 和 merge_aesthetic_weight 写进 rules.weights 的特殊键
if "aesthetic_weights" in preset:
for k, v in preset["aesthetic_weights"].items():
rules.weights[f"aesthetic:{k}"] = v
if "merge_aesthetic_weight" in preset:
rules.weights["_merge_aesthetic_weight"] = preset["merge_aesthetic_weight"]
store.save_rules(rules)
print(f"✓ 已切换到预设：{preset['name']}")
_print_rules_summary(rules)
return 0
def cmd_evolve(args: argparse.Namespace) -> int:
store = ProfileStore()
before = store.load_rules()
after = evolve_rules(store, threshold=args.threshold)
added = set(after.disabled_checks) - set(before.disabled_checks)
if added:
print(f"✓ 自动禁用了：{', '.join(sorted(added))}")
print(" 原因：相同 rule_key 在 feedback 里连续被 reject 达到阈值。")
else:
print("（没有需要演进的规则。）")
return 0
def cmd_reset(args: argparse.Namespace) -> int:
if not args.confirm:
print("⚠️ 这会删除 ~/.xiaohongshu/profile/ 下所有档案。加 --confirm 真的要执行。",
file=sys.stderr)
return 1
import shutil
store = ProfileStore()
if store.root.exists():
shutil.rmtree(store.root)
print(f"✓ 已删除 {store.root}")
return 0
# ---------- 打印 ----------
def _print_profile_summary(profile: StyleProfile) -> None:
print("=" * 60)
print("📋 风格档案")
print("=" * 60)
print(f" 人设 : {profile.persona or '(未设置)'}")
print(f" 语调 : {profile.voice}")
print(f" 赛道 : {profile.niche or '(未设置)'}")
print(f" 样本数 : {profile.sample_count}")
print(f" 平均标题长 : {profile.avg_title_len} 字 范围 {profile.title_len_range}")
print(f" 平均正文 : {profile.avg_content_len} 字 / {profile.avg_paragraphs} 段")
print(f" emoji/篇 : {profile.emoji_per_post}")
if profile.favorite_emojis:
print(f" 常用 emoji : {' '.join(profile.favorite_emojis)}")
if profile.favorite_formulas:
items = sorted(profile.favorite_formulas.items(), key=lambda x: -x[1])
print(f" 偏好公式 : {', '.join(f'{k}({v})' for k, v in items)}")
if profile.favorite_skeletons:
items = sorted(profile.favorite_skeletons.items(), key=lambda x: -x[1])
print(f" 偏好骨架 : {', '.join(f'{k}({v})' for k, v in items)}")
if profile.common_tags:
print(f" 高频话题 : {' '.join('#' + t for t in profile.common_tags[:8])}")
if profile.common_phrases:
print(f" 口头禅 : {', '.join(profile.common_phrases)}")
def _print_rules_summary(rules: RuleOverride) -> None:
print("=" * 60)
print("🛠️ 规则覆盖")
print("=" * 60)
print(f" 禁用的检查项 : {rules.disabled_checks or '(无)'}")
if rules.weights:
print(f" 权重覆盖 : {rules.weights}")
if rules.custom_sensitive:
print(f" 自定义敏感词 : {rules.custom_sensitive}")
if rules.allowed_words:
print(f" 解禁词 : {rules.allowed_words}")
if rules.max_emoji_per_post is not None:
print(f" emoji 上限 : {rules.max_emoji_per_post}")
if rules.prefer_emoji is not None:
print(f" 偏好 emoji : {rules.prefer_emoji}")
if rules.custom_phrases:
print(f" 自创口头禅 : {rules.custom_phrases}")
# ---------- 入口 ----------
def build_parser() -> argparse.ArgumentParser:
p = argparse.ArgumentParser(prog="profile_init.py", description="创作者风格档案 / 规则管理")
sub = p.add_subparsers(dest="cmd", required=True)
pi = sub.add_parser("init", help="第一次建立风格档案")
pi.add_argument("--persona", default="", help="人设，如：30+ 干皮女生")
pi.add_argument("--voice", choices=["casual", "formal", "playful", "pro"], default="casual")
pi.add_argument("--niche", default="", help="赛道，如：护肤")
pi.add_argument("--baseline", nargs="*", default=[], help=".json 笔记 / .md 草稿（1~5 篇）")
pi.add_argument("--note-id", nargs="*", default=[], help="或直接给 note_id（要 Cookie）")
pi.add_argument("--xsec-token", default="")
pi.add_argument("--force", action="store_true", help="覆盖已有档案")
pi.set_defaults(func=cmd_init)
pa = sub.add_parser("add", help="追加 baseline 样本")
pa.add_argument("baseline", nargs="+", help="新样本路径")
pa.set_defaults(func=cmd_add)
ps = sub.add_parser("show", help="查看档案")
ps.add_argument("--format", choices=["text", "json"], default="text")
ps.set_defaults(func=cmd_show)
pr = sub.add_parser("rules", help="编辑规则覆盖")
pr.add_argument("--disable", nargs="*", default=[],
help="禁用的检查项（title/first_lines/layout/emoji/hashtags/compliance）")
pr.add_argument("--enable", nargs="*", default=[], help="重新启用某个检查项")
pr.add_argument("--weight", nargs="*", default=[], help="设置权重，如 emoji=0.05")
pr.add_argument("--add-sensitive", nargs="*", default=[], help="加自定义敏感词")
pr.add_argument("--remove-sensitive", nargs="*", default=[], help="移除自定义敏感词")
pr.add_argument("--allow", nargs="*", default=[], help="解禁某些默认敏感词")
pr.add_argument("--unallow", nargs="*", default=[], help="撤销解禁")
pr.add_argument("--max-emoji", type=int, default=None, help="单篇 emoji 上限")
pr.add_argument("--prefer-emoji", type=lambda x: x.lower() == "true", default=None,
help="是否偏好 emoji (true/false)")
pr.set_defaults(func=cmd_rules)
pe = sub.add_parser("evolve", help="基于 feedback 自动演进规则")
pe.add_argument("--threshold", type=int, default=3, help="连续 reject N 次后自动禁用")
pe.set_defaults(func=cmd_evolve)
pp = sub.add_parser("preset", help="切换风格预设：allen / engineer / balanced")
pp.add_argument("name", nargs="?", default="", help="预设名")
pp.add_argument("--list", action="store_true")
pp.set_defaults(func=cmd_preset)
px = sub.add_parser("reset", help="删除整个档案")
px.add_argument("--confirm", action="store_true")
px.set_defaults(func=cmd_reset)
return p
def main() -> int:
args = build_parser().parse_args()
return args.func(args)
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/publish_helper.py
#!/usr/bin/env python3
"""火一五小红书"半自动发布"助手 — 不做自动化发布。
为什么不做自动化发布
====================
1. 小红书发布接口需要 X-s/X-t 签名 + 设备指纹 + 风控验证；
2. 自动化发布会立刻被识别为机器行为，账号轻则限流、重则永久封禁；
3. 即使绕过当前风控，一次平台升级你就要重写所有签名，账号还得跟着重置；
4. 个人号的核心资产是"信任画像" — 不值得为节省 30 秒发布时间冒险。
所以本脚本做的是 **"发布前把所有事做好"**：
- 跑合规扫描 + 打分；
- 整合标题 / 正文 / 话题 / 配图说明，复制到剪贴板；
- 给一份"打开 App 后的操作清单"；
- 记录到本地发布日志，方便后续 track_post.py 跟踪。
你只需要：
1. 跑这个脚本；
2. 打开小红书 App，粘贴；
3. 选好图，按发布 — 完事。
用法
----
# 用一个准备好的草稿
python3 publish_helper.py --in draft.md
# 跳过打分（快速复制走人）
python3 publish_helper.py --in draft.md --skip-score
# 同时记录到日志
python3 publish_helper.py --in draft.md --log ~/.xiaohongshu/posts.jsonl
"""
from __future__ import annotations
import argparse
import datetime as dt
import json
import os
import platform
import subprocess
import sys
import uuid
from pathlib import Path
from typing import Optional
sys.path.insert(0, str(Path(__file__).parent))
from xhs_writer import Draft, load_draft, score_post # noqa: E402
# ---------- 剪贴板（跨平台） ----------
def copy_to_clipboard(text: str) -> bool:
sysname = platform.system()
try:
if sysname == "Darwin":
subprocess.run(["pbcopy"], input=text.encode("utf-8"), check=True)
return True
if sysname == "Linux":
for cmd in (["xclip", "-selection", "clipboard"], ["xsel", "--clipboard", "--input"]):
try:
subprocess.run(cmd, input=text.encode("utf-8"), check=True)
return True
except (FileNotFoundError, subprocess.CalledProcessError):
continue
return False
if sysname == "Windows":
subprocess.run(["clip"], input=text.encode("utf-16"), check=True)
return True
except Exception:
return False
return False
# ---------- 检查表 ----------
_CHECKLIST = """
📋 发布前最后检查（请逐条确认）
□ 1. 配图至少 3 张，首图清晰、信息密度合适
□ 2. 配图无水印 / 别人的 logo / 截图侵权
□ 3. 路人面部已打码（如果有路人入镜）
□ 4. 视频笔记：前 3 秒有钩子，封面字号大
□ 5. 标题已粘贴 → 检查没被截断
□ 6. 正文已粘贴 → emoji 显示正常、空行没丢
□ 7. 话题 # 已选 3~6 个（粘贴的话题可能要在 App 里重新选）
□ 8. 地点 / 商品标签按需添加（自购请打 #自购分享）
□ 9. 是否商业合作？是 → 走"蒲公英"申报
□ 10. 发布时段：参考你账号的最佳时段（python3 analyze-notes.py）
"""
# ---------- 发布日志 ----------
def append_log(log_path: str, draft: Draft, score_total: int) -> str:
"""在本地追加一条发布日志（jsonl），返回生成的 post_uid。"""
log = Path(os.path.expanduser(log_path))
log.parent.mkdir(parents=True, exist_ok=True)
post_uid = uuid.uuid4().hex[:10]
entry = {
"post_uid": post_uid,
"title": draft.title,
"tags": draft.tags,
"score": score_total,
"drafted_at": dt.datetime.now().isoformat(timespec="seconds"),
"note_id": "", # 用户发布后回填，给 track_post.py 用
}
with log.open("a", encoding="utf-8") as f:
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
return post_uid
# ---------- 主流程 ----------
def main() -> int:
p = argparse.ArgumentParser(prog="publish_helper.py", description="发布前一站式准备")
p.add_argument("--in", dest="path", required=True, help="草稿路径 (.md 或 .json)")
p.add_argument("--skip-score", action="store_true", help="跳过打分，直接复制")
p.add_argument("--skip-clipboard", action="store_true", help="不复制到剪贴板")
p.add_argument("--log", default="", help="发布日志路径（jsonl）；不填则不记录")
p.add_argument("--copy-mode", choices=["body", "all"], default="body",
help="body = 只复制正文+话题；all = 复制标题+正文+话题")
args = p.parse_args()
draft = load_draft(args.path)
if not draft.title:
print("❌ 草稿没有标题，请补上 (markdown 用 # 开头)", file=sys.stderr)
return 1
if not draft.content.strip():
print("❌ 草稿没有正文", file=sys.stderr)
return 1
# 1. 打分（除非跳过）
score_total = 0
if not args.skip_score:
score = score_post(draft.title, draft.content, draft.tags)
score_total = score.total
print(f"📊 文案分：{score.total}/100")
if score.total < 60:
print("\n⚠️ 分数偏低，建议先跑 polish_post.py 看修改建议：")
print(f" python3 polish_post.py --in {args.path}")
ans = input("仍然继续准备发布？[y/N] ").strip().lower()
if ans != "y":
return 1
# 2. 拼好剪贴板内容
if args.copy_mode == "all":
clipboard = f"{draft.title}\n\n{draft.to_clipboard_text()}"
else:
clipboard = draft.to_clipboard_text()
# 3. 复制
if not args.skip_clipboard:
ok = copy_to_clipboard(clipboard)
if ok:
chars = len(clipboard)
print(f"✓ 已复制 {chars} 字到剪贴板（{args.copy_mode} 模式）")
else:
print("⚠️ 剪贴板复制失败，下面手动复制：\n")
print("-" * 40)
print(clipboard)
print("-" * 40)
# 4. 记日志
if args.log:
post_uid = append_log(args.log, draft, score_total)
print(f"✓ 已记录到 {args.log}（post_uid: {post_uid}）")
print(f" 发布完成后回填 note_id：python3 track_post.py register --uid {post_uid} --note-id <id>")
# 5. 显示标题（提醒：标题需要单独粘贴）
print(f"\n📝 标题（请单独粘贴到 App 标题框）：")
print(f" {draft.title}")
if draft.cover_hint:
print(f"\n🖼️ 封面建议：{draft.cover_hint}")
if draft.image_hints:
print("\n🖼️ 配图建议：")
for i, h in enumerate(draft.image_hints, 1):
print(f" 图{i}：{h}")
# 6. Checklist
print(_CHECKLIST)
print("👉 现在打开小红书 App / 创作中心，粘贴并发布。")
return 0
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/reader_simulate.py
#!/usr/bin/env python3
"""火一五小红书"模拟读者画像走全文" — 落地 Allen 第三课。
Allen 第三课
============
**「站文案里面读文案，不是站在外面分析。」**
这个脚本不让你"分析文案"，而是模拟 6 种典型读者画像走完全文，
给出每个画像在【开头 / 中段 / 结尾】的预期情绪曲线 + 是否会做后续动作。
工作模式
========
1. **规则模式**（默认）：根据画像 + 文案语言特征推断情绪曲线
2. **LLM 模式**（XHS_LLM_PROVIDER=anthropic）：调一次 LLM 让"读者"真的读
输出：每个读者画像三段情绪 + 总反应（停留 / 下滑 / 互动 / 收藏 / 关注）
用法
----
# 默认 6 个画像
python3 reader_simulate.py --in draft.md
# 指定画像
python3 reader_simulate.py --in draft.md --persona "30+ 干皮女生" "新手妈妈"
# JSON pipeline
python3 reader_simulate.py --in draft.md --format json
"""
from __future__ import annotations
import argparse
import json
import os
import re
import sys
from dataclasses import asdict, dataclass, field
from pathlib import Path
from typing import Any, Dict, List, Optional
sys.path.insert(0, str(Path(__file__).parent))
from xhs_writer import Draft, load_draft # noqa: E402
# ---------- 默认读者画像 ----------
_DEFAULT_PERSONAS = [
{"name": "30+ 干皮女生", "cares_about": ["护肤", "情绪", "自我犒赏"], "warm_to": ["亲测", "30+", "干皮", "妈妈"]},
{"name": "互联网打工人", "cares_about": ["效率", "副业", "下班"], "warm_to": ["早八", "周三", "通勤", "副业"]},
{"name": "新手妈妈", "cares_about": ["育儿", "省时", "情绪"], "warm_to": ["宝宝", "辅食", "睡眠", "陪伴"]},
{"name": "大学生", "cares_about": ["学习", "省钱", "氛围感"], "warm_to": ["白嫖", "宿舍", "考研", "图书馆"]},
{"name": "i 人独居者", "cares_about": ["独处", "周末", "氛围"], "warm_to": ["一个人", "独处", "周末", "在家"]},
{"name": "二线小城自由职业", "cares_about": ["慢生活", "副业", "降级"], "warm_to": ["回老家", "数字游民", "县城"]},
]
_HOOK_WORDS = {
"代入型": ["你是不是", "你也", "你最近", "你那一天"],
"数据型": ["%", "天", "斤", "块", "万"],
"反差型": ["我以为", "本以为", "结果", "没想到"],
"故事型": ["上周", "那天", "今天", "周一", "周五"],
"权威型": ["作为", "亲测", "我自己", "我体感"],
}
_LEAD_VS_TEACH_TEACH = ["你应该", "你必须", "记住", "划重点", "敲黑板"]
_LEAD_VS_TEACH_LEAD = ["你可以试试", "我自己", "我体感", "也许", "或许", "你呢"]
# ---------- 数据结构 ----------
@dataclass
class ReaderTrace:
persona: str
opening_emotion: str # 开头读完的情绪
midway_emotion: str # 中段读完的情绪
ending_emotion: str # 结尾读完的情绪
will_continue: bool # 会读到底吗
will_interact: List[str] # 会做哪些动作 [stay/comment/like/save/follow/share]
pull_quote: str # 这个读者最被打动的一句
def to_dict(self) -> Dict[str, Any]:
return asdict(self)
# ---------- 规则引擎 ----------
def _split_thirds(text: str) -> tuple[str, str, str]:
lines = [ln for ln in text.splitlines() if ln.strip()]
if not lines:
return "", "", ""
n = len(lines)
a = "\n".join(lines[: max(1, n // 3)])
b = "\n".join(lines[n // 3: 2 * n // 3])
c = "\n".join(lines[2 * n // 3:])
return a, b, c
def _has_warm(text: str, words: List[str]) -> bool:
return any(w in text for w in words)
def _pick_pull_quote(text: str) -> str:
"""挑一句最有"金句感"的当 pull quote。"""
lines = [ln.strip() for ln in text.splitlines() if 8 < len(ln.strip()) < 40]
if not lines:
return ""
# 偏好独立成段、短而有反差词的
for ln in lines:
if any(w in ln for w in ["其实", "我以为", "原来", "没想到", "亲测", "你也"]):
return ln
return lines[0]
def simulate_reader_rules(draft: Draft, persona: Dict[str, Any]) -> ReaderTrace:
full = f"{draft.title}\n{draft.content}"
a, b, c = _split_thirds(draft.content)
# 开头判断：是否有钩子
hook_hit = any(any(w in (draft.title + a) for w in ws) for ws in _HOOK_WORDS.values())
warm_hit = _has_warm(full, persona["warm_to"])
teach_count = sum(full.count(w) for w in _LEAD_VS_TEACH_TEACH)
lead_count = sum(full.count(w) for w in _LEAD_VS_TEACH_LEAD)
# 开头情绪
if hook_hit and warm_hit:
opening = "✨ 被勾住，想继续读"
elif hook_hit:
opening = "🤔 有点好奇，但题材不是我特别关心的"
elif warm_hit:
opening = "👀 题材有关联，开头平淡"
else:
opening = "😴 没钩到我，可能下滑"
# 中段情绪
if teach_count > lead_count + 2:
midway = "🥱 教师讲课感，开始走神"
elif lead_count >= 2:
midway = "🌷 像在听朋友说话，舒服"
else:
midway = "🙂 节奏中规中矩"
# 结尾情绪 + 是否互动
invite = bool(re.search(r"你呢|你那一天|留个|评论区聊聊|你最近|如果你也", c))
if invite:
ending = "💬 想说点什么"
elif "总结" in c or "划重点" in c:
ending = "📋 像被检查作业，没动力互动"
else:
ending = "🌒 自然结束，没特别冲动"
will_continue = (opening != "😴 没钩到我，可能下滑")
actions: List[str] = []
if will_continue:
actions.append("stay")
if midway == "🌷 像在听朋友说话，舒服":
actions.append("like")
if invite:
actions.append("comment")
if warm_hit and lead_count >= 2:
actions.append("save")
if warm_hit and lead_count >= 3:
actions.append("follow")
return ReaderTrace(
persona=persona["name"],
opening_emotion=opening,
midway_emotion=midway,
ending_emotion=ending,
will_continue=will_continue,
will_interact=actions,
pull_quote=_pick_pull_quote(draft.content),
)
# ---------- LLM 模式 ----------
def simulate_reader_llm(draft: Draft, persona: Dict[str, Any]) -> Optional[ReaderTrace]:
if os.environ.get("XHS_LLM_PROVIDER", "").lower() != "anthropic":
return None
try:
from anthropic import Anthropic
client = Anthropic()
sys_prompt = (
f"你现在是「{persona['name']}」，关心：{', '.join(persona['cares_about'])}。"
f"你打开小红书随意浏览，遇到这篇笔记。请如实给出三段情绪反馈："
"①开头三句你是什么感觉；②中段你的注意力如何；③结尾你会做什么动作。"
"返回 JSON: {opening_emotion, midway_emotion, ending_emotion, will_continue (bool), "
"will_interact (array of stay/like/save/comment/follow/share), pull_quote (你最被打动的那句话)}"
)
msg = client.messages.create(
model=os.environ.get("XHS_LLM_MODEL", "claude-haiku-4-5-20251001"),
max_tokens=600,
system=sys_prompt,
messages=[{"role": "user",
"content": f"标题：{draft.title}\n\n正文：\n{draft.content[:1500]}"}],
)
text = msg.content[0].text if msg.content else ""
try:
data = json.loads(text)
return ReaderTrace(
persona=persona["name"],
opening_emotion=data.get("opening_emotion", ""),
midway_emotion=data.get("midway_emotion", ""),
ending_emotion=data.get("ending_emotion", ""),
will_continue=bool(data.get("will_continue", True)),
will_interact=list(data.get("will_interact", [])),
pull_quote=data.get("pull_quote", ""),
)
except Exception:
return None
except Exception:
return None
def simulate_reader(draft: Draft, persona: Dict[str, Any], use_llm: bool = True) -> ReaderTrace:
if use_llm:
llm = simulate_reader_llm(draft, persona)
if llm:
return llm
return simulate_reader_rules(draft, persona)
# ---------- 渲染 ----------
def render_text(traces: List[ReaderTrace]) -> str:
parts = []
parts.append("👥 多读者模拟（Allen 第三课：站文案里面读）\n")
for t in traces:
parts.append(f"━━━ {t.persona} ━━━")
parts.append(f" 开头：{t.opening_emotion}")
parts.append(f" 中段：{t.midway_emotion}")
parts.append(f" 结尾：{t.ending_emotion}")
parts.append(f" 读完会做：{', '.join(t.will_interact) if t.will_interact else '（什么都不做）'}")
if t.pull_quote:
parts.append(f" 🎯 最被打动：「{t.pull_quote}」")
parts.append("")
# 总结
will_stay = sum(1 for t in traces if t.will_continue)
will_save = sum(1 for t in traces if "save" in t.will_interact)
will_comment = sum(1 for t in traces if "comment" in t.will_interact)
parts.append("─" * 50)
parts.append(f"📊 {will_stay}/{len(traces)} 个读者会读完 / "
f"{will_save} 收藏 / {will_comment} 评论")
if will_stay <= len(traces) // 2:
parts.append("⚠️ 多数读者读不完 — 检查开头钩子和中段是否有'教读者'语气")
return "\n".join(parts)
def main() -> int:
p = argparse.ArgumentParser(prog="reader_simulate.py", description="多读者画像模拟")
p.add_argument("--in", dest="path", required=True)
p.add_argument("--persona", nargs="*", default=[],
help="只模拟指定画像（按名称）；空 = 全部 6 个")
p.add_argument("--format", choices=["text", "json"], default="text")
p.add_argument("--out", default="")
p.add_argument("--no-llm", action="store_true")
args = p.parse_args()
draft = load_draft(args.path)
if args.persona:
chosen = [p for p in _DEFAULT_PERSONAS if p["name"] in args.persona]
if not chosen:
chosen = [{"name": n, "cares_about": [], "warm_to": []} for n in args.persona]
else:
chosen = _DEFAULT_PERSONAS
traces = [simulate_reader(draft, p, use_llm=not args.no_llm) for p in chosen]
if args.format == "json":
out_text = json.dumps([t.to_dict() for t in traces], ensure_ascii=False, indent=2)
else:
out_text = render_text(traces)
if args.out:
Path(args.out).write_text(out_text, encoding="utf-8")
print(f"✓ 已写入 {args.out}", file=sys.stderr)
else:
print(out_text)
return 0
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/safety_check.py
#!/usr/bin/env python3
"""抓取前做一次自检：Cookie 是否有效、是否会被风控拦、当前节奏是否合理。
用法：
export XHS_COOKIE='...'
python3 safety_check.py
"""
from __future__ import annotations
import os
import sys
import time
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent))
from xhs_client import XHSClient, load_cookie_from_env, XHSError, BlockedByCaptcha, LoginRequired # noqa: E402
from xhs_parser import extract_initial_state # noqa: E402
def main() -> int:
print("=== 火一五小红书抓取自检 ===\n")
# 1. Cookie 检查
try:
cookie = load_cookie_from_env()
except LoginRequired as e:
print("❌", e)
return 1
cookie_count = len([c for c in cookie.split(";") if c.strip()])
print(f"✓ Cookie 字段数：{cookie_count}（通常 10+）")
for key in ("web_session", "a1", "webId", "xsecappid"):
found = any(c.strip().startswith(key + "=") for c in cookie.split(";"))
mark = "✓" if found else "⚠"
print(f" {mark} 包含 {key}" + ("" if found else "（缺失，部分接口可能不稳定）"))
# 2. 访问首页看一下风控
print("\n--- 访问 xiaohongshu.com 首页 ---")
client = XHSClient(cookie=cookie, min_delay=1, max_delay=2, max_requests_per_session=5)
try:
resp = client.session.get("https://www.xiaohongshu.com/explore", timeout=10,
headers=client._base_headers())
if resp.status_code == 200:
print(f"✓ HTTP 200 — 首页可达（{len(resp.text)} bytes）")
else:
print(f"⚠ HTTP {resp.status_code} — 可能已经被限制")
state = extract_initial_state(resp.text)
if state:
print(f"✓ __INITIAL_STATE__ 解析成功（顶层 key {len(state)} 个）")
else:
print("⚠ 未找到 __INITIAL_STATE__ — HTML 结构可能变了，或页面被重定向")
except XHSError as e:
print(f"❌ {e}")
return 1
except Exception as e:
print(f"❌ 网络错误：{e}")
return 1
# 3. 节奏建议
print("\n--- 节奏建议 ---")
print(" • 单次会话建议不超过 30 次请求（默认 max_requests_per_session=30）")
print(" • 每次请求间隔 3~7 秒（默认 min_delay=3, max_delay=7）")
print(" • 两次会话之间至少间隔 10~30 分钟")
print(" • 一天内对同一账号不要超过 4~5 次脚本访问")
print(" • 任何 460/461/403 或验证码提示，立即停手，进浏览器手动操作恢复")
print("\n✓ 自检完成。")
return 0
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/scrape-note.py
#!/usr/bin/env python3
"""抓单条笔记详情。
用法：
export XHS_COOKIE='a=b; c=d; ...' # 登录后复制浏览器 Cookie
python3 scrape-note.py --note-id 64abc... --out /tmp/note.json
python3 scrape-note.py --url "https://www.xiaohongshu.com/explore/64abc...?xsec_token=xxx" \
--out /tmp/note.json
"""
from __future__ import annotations
import argparse
import json
import os
import re
import sys
from pathlib import Path
from urllib.parse import parse_qs, urlparse
sys.path.insert(0, str(Path(__file__).parent))
from xhs_client import XHSClient, load_cookie_from_env, XHSError # noqa: E402
from xhs_parser import parse_note_page, note_to_dict # noqa: E402
def _parse_url(url: str):
"""从 URL 抽取 note_id / xsec_token。"""
m = re.search(r"/explore/([0-9a-zA-Z]+)", url)
note_id = m.group(1) if m else ""
qs = parse_qs(urlparse(url).query)
xsec = (qs.get("xsec_token") or [""])[0]
xsec_src = (qs.get("xsec_source") or ["pc_feed"])[0]
return note_id, xsec, xsec_src
def main() -> int:
ap = argparse.ArgumentParser(description="抓单条小红书笔记")
ap.add_argument("--note-id")
ap.add_argument("--url")
ap.add_argument("--xsec-token", default="")
ap.add_argument("--xsec-source", default="pc_feed")
ap.add_argument("--out", "-o", default="", help="JSON 输出路径，省略则 stdout")
ap.add_argument("--save-html", default="", help="同时保存原始 HTML（调试用）")
ap.add_argument("--min-delay", type=float, default=3.0)
ap.add_argument("--max-delay", type=float, default=7.0)
args = ap.parse_args()
note_id, xsec, xsec_src = args.note_id or "", args.xsec_token, args.xsec_source
if args.url:
parsed_id, parsed_xsec, parsed_src = _parse_url(args.url)
note_id = note_id or parsed_id
xsec = xsec or parsed_xsec
xsec_src = parsed_src or xsec_src
if not note_id:
print("必须提供 --note-id 或 --url", file=sys.stderr)
return 2
try:
cookie = load_cookie_from_env()
except Exception as e:
print(str(e), file=sys.stderr)
return 2
client = XHSClient(cookie=cookie, min_delay=args.min_delay, max_delay=args.max_delay)
try:
html = client.get_explore_page(note_id, xsec_token=xsec, xsec_source=xsec_src)
except XHSError as e:
print(f"抓取失败：{e}", file=sys.stderr)
return 1
if args.save_html:
Path(args.save_html).write_text(html, encoding="utf-8")
note = parse_note_page(html, note_id=note_id)
if not note:
print("解析失败 — HTML 里没找到 __INITIAL_STATE__，可能 Cookie 失效或被风控。", file=sys.stderr)
if not args.save_html:
print("加 --save-html /tmp/raw.html 查看原始页面。", file=sys.stderr)
return 1
data = note_to_dict(note)
text = json.dumps(data, ensure_ascii=False, indent=2)
if args.out:
Path(args.out).write_text(text, encoding="utf-8")
print(f"✓ 已写入 {args.out}（标题：{note.title[:40] or '(空)'}）")
else:
print(text)
return 0
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/scrape-search.py
#!/usr/bin/env python3
"""搜索笔记关键词 — 只拉第 1 页的推荐结果，避免翻页触发风控。
export XHS_COOKIE='...'
python3 scrape-search.py --keyword 宠物用品 --out /tmp/search.json
"""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent))
from xhs_client import XHSClient, load_cookie_from_env, XHSError # noqa: E402
from xhs_parser import parse_search_page # noqa: E402
def main() -> int:
ap = argparse.ArgumentParser(description="搜索小红书笔记关键词")
ap.add_argument("--keyword", required=True)
ap.add_argument("--out", "-o", default="")
ap.add_argument("--save-html", default="")
ap.add_argument("--min-delay", type=float, default=3.0)
ap.add_argument("--max-delay", type=float, default=7.0)
args = ap.parse_args()
try:
cookie = load_cookie_from_env()
except Exception as e:
print(str(e), file=sys.stderr)
return 2
client = XHSClient(cookie=cookie, min_delay=args.min_delay, max_delay=args.max_delay)
try:
html = client.get_search_page(args.keyword)
except XHSError as e:
print(f"抓取失败：{e}", file=sys.stderr)
return 1
if args.save_html:
Path(args.save_html).write_text(html, encoding="utf-8")
results = parse_search_page(html)
data = {"keyword": args.keyword, "count": len(results), "results": results}
text = json.dumps(data, ensure_ascii=False, indent=2)
if args.out:
Path(args.out).write_text(text, encoding="utf-8")
print(f"✓ 已写入 {args.out}（命中 {len(results)} 条）")
else:
print(text)
if not results:
print("⚠ 没有解析到结果。如果 Cookie 没过期、HTML 也正常返回，可能是搜索页的数据结构有新变化，"
"请加 --save-html 导出看看。", file=sys.stderr)
return 0
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/scrape-user.py
#!/usr/bin/env python3
"""抓用户主页 + 主页列出的笔记预览。
export XHS_COOKIE='...'
python3 scrape-user.py --user-id 5f123abc... --out /tmp/user.json
python3 scrape-user.py --url "https://www.xiaohongshu.com/user/profile/5f123abc..." \
--out /tmp/user.json
"""
from __future__ import annotations
import argparse
import json
import re
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent))
from xhs_client import XHSClient, load_cookie_from_env, XHSError # noqa: E402
from xhs_parser import parse_user_page, profile_to_dict # noqa: E402
def _id_from_url(url: str) -> str:
m = re.search(r"/user/profile/([0-9a-zA-Z]+)", url)
return m.group(1) if m else ""
def main() -> int:
ap = argparse.ArgumentParser(description="抓小红书用户主页")
ap.add_argument("--user-id")
ap.add_argument("--url")
ap.add_argument("--out", "-o", default="")
ap.add_argument("--save-html", default="")
ap.add_argument("--min-delay", type=float, default=3.0)
ap.add_argument("--max-delay", type=float, default=7.0)
args = ap.parse_args()
user_id = args.user_id or (_id_from_url(args.url) if args.url else "")
if not user_id:
print("必须提供 --user-id 或 --url", file=sys.stderr)
return 2
try:
cookie = load_cookie_from_env()
except Exception as e:
print(str(e), file=sys.stderr)
return 2
client = XHSClient(cookie=cookie, min_delay=args.min_delay, max_delay=args.max_delay)
try:
html = client.get_user_page(user_id)
except XHSError as e:
print(f"抓取失败：{e}", file=sys.stderr)
return 1
if args.save_html:
Path(args.save_html).write_text(html, encoding="utf-8")
profile = parse_user_page(html)
if not profile:
print("解析失败 — 可能 Cookie 失效或被风控。", file=sys.stderr)
return 1
data = profile_to_dict(profile)
text = json.dumps(data, ensure_ascii=False, indent=2)
if args.out:
Path(args.out).write_text(text, encoding="utf-8")
print(f"✓ 已写入 {args.out}（昵称：{profile.nickname or '(未识别)'}，"
f"主页笔记预览 {len(profile.recent_notes)} 条）")
else:
print(text)
return 0
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/series_design.py
#!/usr/bin/env python3
"""火一五小红书"栏目化设计 + 互动阶梯" — Allen 待修炼方向之一。
Allen 教训
==========
- 单次活动 → **可持续的内容 IP**（"周三存档"、"尽兴放映厅"、"尽兴请回答"）
- 互动是**邀请阶梯**，不是任务清单：
关注 → 评论 → 发图 → 被收录 → 带走大礼
- 「不是收，是开」 — 一个灵感能延伸出多少种可能性
输出
====
1. 5~8 个栏目名候选（按风格分类）
2. 配套的互动阶梯设计（5 级）
3. 12 个月节奏建议（怎么把单次变成长期 IP）
用法
----
python3 series_design.py --theme "尽兴" --persona "30+ 都市女性"
python3 series_design.py --theme "下班后" --persona "互联网打工人" --n 8
"""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
from typing import Dict, List
sys.path.insert(0, str(Path(__file__).parent))
# ---------- 栏目名模板 ----------
_SERIES_TEMPLATES = {
"时间型（强韵律）": [
"{theme}周三存档",
"{theme}周日小记",
"{theme}月初剪贴簿",
"{theme}月末复盘册",
"周一{theme}信",
],
"动作型（轻量）": [
"{theme}请回答",
"{theme}小练习",
"{theme}笔记本",
"{theme}打开方式",
"{theme}收录册",
],
"形式型（包装）": [
"{theme}图鉴",
"{theme}指南",
"{theme}百宝书",
"{theme}白皮书",
"{theme}小词典",
],
"活动型（高门槛）": [
"{theme}放映厅",
"{theme}电台",
"{theme}市集",
"{theme}季报",
"{theme}年鉴",
],
"情绪型（共鸣）": [
"{theme}的瞬间",
"{theme}时刻",
"{theme}清单",
"{theme}存档",
"{theme}地图",
],
}
def generate_series_names(theme: str, n: int = 8) -> List[Dict[str, str]]:
out: List[Dict[str, str]] = []
for category, templates in _SERIES_TEMPLATES.items():
for t in templates[:2]:
out.append({
"name": t.format(theme=theme),
"category": category,
})
return out[:n]
# ---------- 互动阶梯 ----------
_LADDER_TEMPLATE = [
{
"level": 1,
"name": "关注（最低门槛）",
"invitation_pattern": "如果你也喜欢这种感觉",
"for_reader": "几乎零成本，只是举一下手",
"drives": "持续看到你的下一篇",
},
{
"level": 2,
"name": "评论（自我表达）",
"invitation_pattern": "你呢？/ 你那一天是怎样的？",
"for_reader": "把自己的故事说出来一点",
"drives": "归属感 — 这是个能聊的地方",
},
{
"level": 3,
"name": "发图 / 发笔记（参与共创）",
"invitation_pattern": "拍一张你自己的 X，在我这里留个落脚点",
"for_reader": "贡献内容，参与品牌叙事",
"drives": "我的作品也是这个故事的一部分",
},
{
"level": 4,
"name": "被收录（被认可）",
"invitation_pattern": "我会精选 N 篇放到下一期",
"for_reader": "得到肯定 / 被看见",
"drives": "我没白发，我被认真对待",
},
{
"level": 5,
"name": "带走大礼（实物）",
"invitation_pattern": "前 N 名收录者，会有 X 寄到家里",
"for_reader": "实体感、仪式感",
"drives": "故事到真实生活的延伸",
},
]
def build_ladder(theme: str, persona: str = "") -> List[Dict[str, str]]:
"""根据主题填充邀请语模板。"""
out = []
for step in _LADDER_TEMPLATE:
s = dict(step)
s["invitation_pattern"] = s["invitation_pattern"].replace(
"X", theme).replace("Y", persona or "你的故事")
out.append(s)
return out
# ---------- 12 个月节奏 ----------
def yearly_cadence(theme: str) -> List[Dict[str, str]]:
"""把单次栏目变成长期 IP 的 12 个月节奏建议。"""
return [
{"month": "M1-M3 启动期", "task": f"每周固定一次「{theme}」相关笔记，找到稳定钩子"},
{"month": "M2-M4 召集期", "task": "首篇正式栏目化笔记，给出明确互动阶梯（1~3 级）"},
{"month": "M3-M5 收录期", "task": "每月一次精选收录帖（Level 4），给读者被看见的位置"},
{"month": "M4-M6 实物期", "task": "做一次小实物联动（明信片/书签/贴纸），升 Level 5"},
{"month": "M6-M7 借势期", "task": "结合一个节气 / 节日（参考 seasonal_themes.md）做主题季"},
{"month": "M7-M9 联动期", "task": "邀 1~2 个相邻 IP 联动（KOL / 兄弟品牌），扩圈"},
{"month": "M9-M10 沉淀期", "task": "把过去 9 个月的精华做成栏目'白皮书 / 年鉴'，沉淀"},
{"month": "M10-M12 跨界期", "task": "把栏目从内容延伸到实物 / 服务 / 周边"},
]
# ---------- 渲染 ----------
def render_text(theme: str, persona: str, names: List, ladder: List, cadence: List) -> str:
parts = []
parts.append(f"📚 「{theme}」栏目化设计")
if persona:
parts.append(f" 面向：{persona}")
parts.append("")
parts.append("=" * 60)
parts.append("一、栏目名候选\n")
by_cat: Dict[str, List[str]] = {}
for n in names:
by_cat.setdefault(n["category"], []).append(n["name"])
for cat, items in by_cat.items():
parts.append(f" {cat}:")
for it in items:
parts.append(f" • {it}")
parts.append("")
parts.append("=" * 60)
parts.append("二、互动阶梯（5 级）\n")
for step in ladder:
parts.append(f" Level {step['level']}: {step['name']}")
parts.append(f" 邀请语模板：{step['invitation_pattern']}")
parts.append(f" 读者得到 ：{step['for_reader']}")
parts.append(f" 驱动情绪 ：{step['drives']}")
parts.append("")
parts.append("=" * 60)
parts.append("三、12 个月节奏\n")
for c in cadence:
parts.append(f" • {c['month']}：{c['task']}")
parts.append("")
parts.append("─" * 60)
parts.append("Allen 提醒：")
parts.append(" • 不是收，是开 — 栏目是延展性的容器，不是收尾的圈")
parts.append(" • 一个灵感的真正价值不在它本身有多好，在它能延伸出多少新的可能性")
parts.append(" • 互动阶梯每一级都要有'读者为什么会做'的情绪驱动力，不是命令")
return "\n".join(parts)
def main() -> int:
p = argparse.ArgumentParser(prog="series_design.py", description="栏目化 + 互动阶梯")
p.add_argument("--theme", required=True, help="主题词，如：尽兴 / 下班后 / 早八")
p.add_argument("--persona", default="", help="目标读者画像")
p.add_argument("--n", type=int, default=8, help="栏目名候选数")
p.add_argument("--format", choices=["text", "json"], default="text")
args = p.parse_args()
names = generate_series_names(args.theme, args.n)
ladder = build_ladder(args.theme, args.persona)
cadence = yearly_cadence(args.theme)
if args.format == "json":
print(json.dumps({
"theme": args.theme, "persona": args.persona,
"names": names, "ladder": ladder, "cadence": cadence,
}, ensure_ascii=False, indent=2))
else:
print(render_text(args.theme, args.persona, names, ladder, cadence))
return 0
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/topic_ideas.py
#!/usr/bin/env python3
"""火一五小红书选题灵感生成器。
工作模式
========
1. 输入：种子关键词 + （可选）已抓取的同行笔记数据集 (.json/.jsonl)。
2. 处理：从数据集里提取爆款笔记的高频关键词 + 话题标签 + 标题模式。
3. 输出：N 条"标题 + 角度 + 推荐骨架 + 推荐话题"的选题清单。
如果不传数据集，就只用内置的 11 种标题公式 + 通用角度组合，给出种子选题。
用法
----
# 基于已抓取的笔记数据生成选题
python3 topic_ideas.py --seed "干皮护肤" --notes notes.jsonl --n 10
# 没数据，只靠公式
python3 topic_ideas.py --seed "干皮护肤" --persona "30+ 干皮女生" --n 10
# 输出 markdown 报告
python3 topic_ideas.py --seed "副业" --notes notes.jsonl --format md --out ideas.md
"""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
from typing import Any, Dict, List, Optional
sys.path.insert(0, str(Path(__file__).parent))
from xhs_analyzer import keyword_frequency, load_notes, tag_frequency, top_notes # noqa: E402
from xhs_writer import generate_titles # noqa: E402
# ---------- 角度（与公式正交的"内容方向"） ----------
_ANGLES = [
("误区纠正", "大多数人 X 是错的，正确做法是 Y", "S6"),
("入门 SOP", "从零做 X 的 5 个步骤", "S5"),
("产品对比", "X 类目里 3 款值得买的", "S3"),
("时间维度", "X 这件事，3 天/30 天/3 个月分别是什么样", "S1"),
("身份代入", "作为 Y，做 X 我有 5 句心里话", "S2"),
("反面避雷", "X 这件事，最坑的 3 个错", "S1"),
("清单整理", "X 相关的 7 个工具/资源/技巧", "S4"),
("自我经历", "我做 X 的 30 天，发生了什么", "S2"),
("反共识", "我有个不太主流的看法 — 关于 X", "S6"),
("场景代入", "X 这一刻的细节，我记了下来", "S7"),
]
def derive_personas(notes: List[Dict[str, Any]]) -> List[str]:
"""从抓的爆款笔记里粗略提取受众身份词（出现 >= 2 次的）。"""
if not notes:
return []
candidates = ["女生", "男生", "学生", "职场", "宝妈", "30+", "30 岁", "新手",
"小白", "干皮", "油皮", "敏感肌", "微胖", "小个子", "梨形",
"i 人", "e 人", "内向", "副业", "上班族", "自由职业"]
hits = {}
for n in notes:
blob = (n.get("title", "") or "") + (n.get("content", "") or "")
for c in candidates:
if c in blob:
hits[c] = hits.get(c, 0) + 1
return [k for k, v in sorted(hits.items(), key=lambda x: -x[1]) if v >= 2][:5]
def gather_signals(notes_path: str) -> Dict[str, Any]:
"""从抓取的数据集中提取选题信号。"""
notes = load_notes(notes_path)
return {
"sample_size": len(notes),
"keywords": keyword_frequency(notes, top=30),
"tags": tag_frequency(notes, top=20),
"top_notes": top_notes(notes, 5),
"personas": derive_personas(notes),
}
def build_ideas(
seed: str,
n: int = 10,
*,
persona: str = "",
payoff: str = "",
signals: Optional[Dict[str, Any]] = None,
) -> List[Dict[str, Any]]:
"""生成选题清单。"""
ideas: List[Dict[str, Any]] = []
# 优先用数据集里挖出来的受众词
personas: List[str] = []
if persona:
personas.append(persona)
if signals:
personas += [p for p in signals.get("personas", []) if p not in personas]
if not personas:
personas = ["", "新手", "30+"]
# 推荐话题：seed + 数据集里 top 5 标签
tag_pool = [seed]
if signals:
for tag, _cnt in signals.get("tags", [])[:5]:
if tag not in tag_pool:
tag_pool.append(tag)
# 角度 × 公式 交叉，挑前 n 个
formulas = ["T2", "T1", "T3", "T5", "T10", "T8", "T6", "T11"]
pairs = [(angle, formulas[i % len(formulas)]) for i, angle in enumerate(_ANGLES)]
for i, (angle, formula) in enumerate(pairs):
if len(ideas) >= n:
break
p = personas[i % len(personas)] if personas else ""
titles = generate_titles(seed, persona=p, payoff=payoff, formulas=[formula], n_each=1)
if not titles:
continue
skeleton = angle[2]
ideas.append({
"idea_no": len(ideas) + 1,
"angle": angle[0],
"angle_hint": angle[1].replace("X", seed).replace("Y", p or "你"),
"title": titles[0]["title"],
"formula": formula,
"skeleton": skeleton,
"tags": tag_pool[:5],
})
return ideas
def render_markdown(seed: str, ideas: List[Dict[str, Any]], signals: Optional[Dict[str, Any]]) -> str:
parts = [f"# 「{seed}」选题清单（{len(ideas)} 条）", ""]
if signals:
parts.append(f"_基于 {signals['sample_size']} 条同行笔记数据_")
if signals.get("personas"):
parts.append(f"- 高频受众：{', '.join(signals['personas'])}")
if signals.get("tags"):
top_tags = ", ".join(f"#{t}({c})" for t, c in signals["tags"][:8])
parts.append(f"- 高频话题：{top_tags}")
if signals.get("top_notes"):
parts.append("- 爆款参考：")
for t in signals["top_notes"]:
parts.append(f" - [{t['engagement']} 互动] {t['title']}")
parts.append("")
for idx, idea in enumerate(ideas, 1):
parts.append(f"## {idx}. [{idea['angle']}] {idea['title']}")
parts.append("")
parts.append(f"- **角度**：{idea['angle_hint']}")
parts.append(f"- **标题公式**：{idea['formula']}")
parts.append(f"- **正文骨架**：{idea['skeleton']}（详见 data/content_structures.md）")
parts.append(f"- **推荐话题**：{' '.join('#' + t for t in idea['tags'])}")
parts.append("")
parts.append("---")
parts.append("")
parts.append("下一步：")
parts.append(f" python3 write_post.py draft --topic '{seed}' --formula T2 --skeleton S1 --out draft.md")
return "\n".join(parts) + "\n"
def main() -> int:
p = argparse.ArgumentParser(prog="topic_ideas.py", description="小红书选题灵感生成")
p.add_argument("--seed", required=True, help="种子关键词")
p.add_argument("--persona", default="", help="目标受众身份")
p.add_argument("--payoff", default="", help="利益点")
p.add_argument("--notes", default="", help="(可选) 已抓取的笔记数据 .json/.jsonl")
p.add_argument("--n", type=int, default=10, help="生成几条选题")
p.add_argument("--format", choices=["md", "json", "text"], default="text")
p.add_argument("--out", default="", help="输出文件；不填打印到 stdout")
args = p.parse_args()
signals: Optional[Dict[str, Any]] = None
if args.notes:
try:
signals = gather_signals(args.notes)
except FileNotFoundError:
print(f"数据文件不存在：{args.notes}", file=sys.stderr)
return 1
ideas = build_ideas(args.seed, n=args.n, persona=args.persona,
payoff=args.payoff, signals=signals)
if args.format == "md":
out_text = render_markdown(args.seed, ideas, signals)
elif args.format == "json":
out_text = json.dumps({"seed": args.seed, "ideas": ideas, "signals": signals},
ensure_ascii=False, indent=2)
else:
out_text = "\n".join(f"{i['idea_no']}. [{i['angle']} / {i['formula']}+{i['skeleton']}] {i['title']}"
for i in ideas)
if args.out:
Path(args.out).write_text(out_text, encoding="utf-8")
print(f"✓ 已输出到 {args.out}", file=sys.stderr)
else:
print(out_text)
return 0
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/track_post.py
#!/usr/bin/env python3
"""火一五小红书发布后跟踪 — 记录笔记的 1 天 / 3 天 / 7 天表现。
为什么需要跟踪
==============
小红书的"赛马机制"会在发布后 24~72 小时给一波小流量，
笔记表现 (互动率) 决定后续是否进推荐池。如果你不记录，
就只能凭感觉判断哪些选题真的有效。
工作模式
========
1. **register**：把发布完成的笔记 (note_id) 关联到 publish_helper 生成的 post_uid。
2. **snapshot**：跑一次抓取，给指定笔记拍个"互动快照"（liked/collected/comment）。
3. **report**：列出所有跟踪中的笔记 + 各时间点的快照对比。
数据存储
========
默认 `~/.xiaohongshu/posts.jsonl`（与 publish_helper.py 默认一致）。
快照存到 `~/.xiaohongshu/snapshots.jsonl`。
用法
----
# 1) 发布后回填 note_id
python3 track_post.py register --uid abc123 --note-id 64abcd... \\
--xsec-token xxx
# 2) 拉一次快照（手动触发，不自动定时）
python3 track_post.py snapshot --note-id 64abcd... --xsec-token xxx
# 3) 看表现
python3 track_post.py report
# 4) 拉所有还在跟踪期的笔记快照
python3 track_post.py snapshot-all
"""
from __future__ import annotations
import argparse
import datetime as dt
import json
import os
import sys
from pathlib import Path
from typing import Any, Dict, List, Optional
sys.path.insert(0, str(Path(__file__).parent))
from xhs_client import ( # noqa: E402
BlockedByCaptcha,
LoginRequired,
RateLimited,
XHSClient,
XHSError,
load_cookie_from_env,
)
from xhs_parser import note_to_dict, parse_note_page # noqa: E402
DEFAULT_LOG = "~/.xiaohongshu/posts.jsonl"
DEFAULT_SNAPSHOTS = "~/.xiaohongshu/snapshots.jsonl"
# ---------- IO ----------
def _read_jsonl(path: str) -> List[Dict[str, Any]]:
p = Path(os.path.expanduser(path))
if not p.exists():
return []
out = []
for line in p.read_text(encoding="utf-8").splitlines():
line = line.strip()
if not line:
continue
try:
out.append(json.loads(line))
except json.JSONDecodeError:
pass
return out
def _append_jsonl(path: str, entry: Dict[str, Any]) -> None:
p = Path(os.path.expanduser(path))
p.parent.mkdir(parents=True, exist_ok=True)
with p.open("a", encoding="utf-8") as f:
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
def _rewrite_jsonl(path: str, entries: List[Dict[str, Any]]) -> None:
p = Path(os.path.expanduser(path))
p.parent.mkdir(parents=True, exist_ok=True)
with p.open("w", encoding="utf-8") as f:
for e in entries:
f.write(json.dumps(e, ensure_ascii=False) + "\n")
# ---------- 子命令 ----------
def cmd_register(args: argparse.Namespace) -> int:
posts = _read_jsonl(args.log)
found = False
for p in posts:
if p.get("post_uid") == args.uid:
p["note_id"] = args.note_id
p["xsec_token"] = args.xsec_token or ""
p["published_at"] = dt.datetime.now().isoformat(timespec="seconds")
found = True
break
if not found:
print(f"❌ 没找到 post_uid={args.uid}，请先用 publish_helper.py 准备并 --log", file=sys.stderr)
return 1
_rewrite_jsonl(args.log, posts)
print(f"✓ 已关联 note_id={args.note_id}")
return 0
def cmd_snapshot(args: argparse.Namespace) -> int:
try:
cookie = load_cookie_from_env()
except LoginRequired as e:
print(f"❌ {e}", file=sys.stderr)
return 1
client = XHSClient(cookie=cookie)
return _do_snapshot(client, args.note_id, args.xsec_token, args.snapshots)
def _do_snapshot(client: XHSClient, note_id: str, xsec_token: str, snapshots_path: str) -> int:
try:
html = client.get_explore_page(note_id=note_id, xsec_token=xsec_token or None)
except (RateLimited, BlockedByCaptcha) as e:
print(f"❌ 风控触发：{e}", file=sys.stderr)
return 2
except XHSError as e:
print(f"❌ {e}", file=sys.stderr)
return 1
note = parse_note_page(html, note_id=note_id)
if not note:
print("❌ 解析失败，可能页面结构变化", file=sys.stderr)
return 1
nd = note_to_dict(note)
inter = nd.get("interactions", {}) or {}
snap = {
"note_id": note_id,
"snapshot_at": dt.datetime.now().isoformat(timespec="seconds"),
"liked": int(inter.get("liked_count", 0) or 0),
"collected": int(inter.get("collected_count", 0) or 0),
"comment": int(inter.get("comment_count", 0) or 0),
"shared": int(inter.get("shared_count", 0) or 0),
}
_append_jsonl(snapshots_path, snap)
print(f"✓ 快照：{snap['liked']}赞 / {snap['collected']}藏 / {snap['comment']}评")
return 0
def cmd_snapshot_all(args: argparse.Namespace) -> int:
posts = _read_jsonl(args.log)
active = [p for p in posts if p.get("note_id") and _within_tracking_period(p, args.days)]
if not active:
print("没有需要快照的笔记（已过 7 天跟踪期或未填 note_id）。")
return 0
try:
cookie = load_cookie_from_env()
except LoginRequired as e:
print(f"❌ {e}", file=sys.stderr)
return 1
client = XHSClient(cookie=cookie)
fail = 0
for p in active:
print(f"\n→ {p.get('title', '')[:30]}（{p['note_id']}）")
rc = _do_snapshot(client, p["note_id"], p.get("xsec_token", ""), args.snapshots)
if rc != 0:
fail += 1
if rc == 2:
# 风控立即停手
print("⛔ 触发风控，停止后续快照")
break
print(f"\n完成 — {len(active) - fail} 成功 / {fail} 失败")
return 0 if fail == 0 else 1
def _within_tracking_period(post: Dict[str, Any], days: int = 7) -> bool:
pub = post.get("published_at") or post.get("drafted_at")
if not pub:
return False
try:
d = dt.datetime.fromisoformat(pub)
except ValueError:
return False
return (dt.datetime.now() - d).total_seconds() < days * 86400
def cmd_report(args: argparse.Namespace) -> int:
posts = _read_jsonl(args.log)
snaps = _read_jsonl(args.snapshots)
by_note: Dict[str, List[Dict[str, Any]]] = {}
for s in snaps:
by_note.setdefault(s.get("note_id", ""), []).append(s)
for k in by_note:
by_note[k].sort(key=lambda x: x.get("snapshot_at", ""))
if not posts:
print("还没有发布日志。先用 publish_helper.py --log <path>")
return 0
parts = ["# 火一五小红书发布跟踪报告\n"]
parts.append(f"_共 {len(posts)} 条发布记录_\n")
for p in sorted(posts, key=lambda x: x.get("drafted_at", ""), reverse=True):
parts.append(f"## {p.get('title', '(无标题)')}")
parts.append("")
parts.append(f"- 起草：{p.get('drafted_at', '?')}")
parts.append(f"- 发布：{p.get('published_at', '未发布')}")
parts.append(f"- 文案分：{p.get('score', '?')}/100")
parts.append(f"- note_id：{p.get('note_id') or '(待填)'}")
snaps_for = by_note.get(p.get("note_id", ""), [])
if snaps_for:
parts.append("")
parts.append("| 时间 | 点赞 | 收藏 | 评论 | 互动合计 |")
parts.append("|---|---|---|---|---|")
for s in snaps_for:
total = s["liked"] + s["collected"] + s["comment"]
parts.append(f"| {s['snapshot_at']} | {s['liked']} | {s['collected']} | {s['comment']} | **{total}** |")
parts.append("")
out = "\n".join(parts)
if args.out:
Path(args.out).write_text(out, encoding="utf-8")
print(f"✓ 报告已写入 {args.out}", file=sys.stderr)
else:
print(out)
return 0
# ---------- 入口 ----------
def build_parser() -> argparse.ArgumentParser:
p = argparse.ArgumentParser(prog="track_post.py", description="发布后跟踪笔记表现")
sub = p.add_subparsers(dest="cmd", required=True)
pr = sub.add_parser("register", help="发布后回填 note_id")
pr.add_argument("--uid", required=True, help="publish_helper.py 给的 post_uid")
pr.add_argument("--note-id", required=True, help="发布完成后的笔记 ID")
pr.add_argument("--xsec-token", default="", help="xsec_token (URL 里取)")
pr.add_argument("--log", default=DEFAULT_LOG)
pr.set_defaults(func=cmd_register)
ps = sub.add_parser("snapshot", help="给指定笔记拍快照")
ps.add_argument("--note-id", required=True)
ps.add_argument("--xsec-token", default="")
ps.add_argument("--snapshots", default=DEFAULT_SNAPSHOTS)
ps.set_defaults(func=cmd_snapshot)
psa = sub.add_parser("snapshot-all", help="给跟踪期内所有笔记拍快照（节流）")
psa.add_argument("--days", type=int, default=7, help="跟踪期天数（默认 7 天）")
psa.add_argument("--log", default=DEFAULT_LOG)
psa.add_argument("--snapshots", default=DEFAULT_SNAPSHOTS)
psa.set_defaults(func=cmd_snapshot_all)
pre = sub.add_parser("report", help="生成跟踪报告")
pre.add_argument("--log", default=DEFAULT_LOG)
pre.add_argument("--snapshots", default=DEFAULT_SNAPSHOTS)
pre.add_argument("--out", default="")
pre.set_defaults(func=cmd_report)
return p
def main() -> int:
args = build_parser().parse_args()
return args.func(args)
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/weekly_review.py
#!/usr/bin/env python3
"""火一五小红书周/月创作复盘 — 自动生成。
数据来源
========
- `~/.xiaohongshu/posts.jsonl` — publish_helper 写的起草日志
- `~/.xiaohongshu/snapshots.jsonl` — track_post 写的互动快照
- `~/.xiaohongshu/profile/feedback.jsonl` — coach 的反馈日志
输出
====
一份 markdown 复盘，包含：
1. 时段内起草 / 发布 / 跟踪状况
2. 互动表现：最爆 / 最沉的笔记
3. 风格画像变化（如果 baseline 重新跑过）
4. 反馈总结：你最常 reject 哪类建议
5. 下周建议：哪类选题该多做 / 该少做
用法
----
python3 weekly_review.py # 最近 7 天
python3 weekly_review.py --days 30 # 最近 30 天（月度）
python3 weekly_review.py --out review.md # 保存到文件 + 写入 reviews/
"""
from __future__ import annotations
import argparse
import collections
import datetime as dt
import json
import sys
from pathlib import Path
from typing import Any, Dict, List
sys.path.insert(0, str(Path(__file__).parent))
from xhs_profile import ProfileStore # noqa: E402
def _parse_dt(s: str) -> dt.datetime | None:
if not s:
return None
try:
return dt.datetime.fromisoformat(s)
except ValueError:
return None
def _within(s: str, days: int) -> bool:
d = _parse_dt(s)
if not d:
return False
return (dt.datetime.now() - d).total_seconds() < days * 86400
def build_review(store: ProfileStore, days: int) -> str:
posts = store.load_posts()
snaps = store.load_snapshots()
feedback = [fb.to_dict() for fb in store.load_feedback()]
profile = store.load_style()
in_range = [p for p in posts if _within(p.get("drafted_at", ""), days)]
drafted = len(in_range)
published = sum(1 for p in in_range if p.get("note_id"))
# 最新快照 by note_id
latest_snap: Dict[str, Dict[str, Any]] = {}
for s in snaps:
nid = s.get("note_id", "")
if not nid:
continue
prev = latest_snap.get(nid)
if not prev or s.get("snapshot_at", "") > prev.get("snapshot_at", ""):
latest_snap[nid] = s
# 排互动
perf: List[Dict[str, Any]] = []
for p in in_range:
nid = p.get("note_id", "")
if nid and nid in latest_snap:
s = latest_snap[nid]
engagement = s["liked"] + s["collected"] + s["comment"]
perf.append({
"title": p.get("title", "(无)"),
"score": p.get("score", 0),
"liked": s["liked"], "collected": s["collected"],
"comment": s["comment"], "engagement": engagement,
"drafted_at": p.get("drafted_at", ""),
})
perf.sort(key=lambda x: -x["engagement"])
# 反馈总结
fb_in_range = [f for f in feedback if _within(f.get("at", ""), days)]
rejected: collections.Counter = collections.Counter()
accepted: collections.Counter = collections.Counter()
for f in fb_in_range:
key = (f.get("rule_key") or "").split(":", 1)[0]
if f.get("reaction") == "reject":
rejected[key] += 1
elif f.get("reaction") == "accept":
accepted[key] += 1
# 渲染
parts = []
title_period = "周" if days <= 7 else f"近{days}天"
parts.append(f"# 火一五小红书 {title_period}创作复盘\n")
parts.append(f"_生成于 {dt.datetime.now().isoformat(timespec='minutes')}_\n")
parts.append(f"_档案：{store.root}_\n")
parts.append("## 一、产出概况\n")
parts.append(f"- 起草：**{drafted}** 篇")
parts.append(f"- 发布（已回填 note_id）：**{published}** 篇")
parts.append(f"- 拉到快照：**{len(perf)}** 篇\n")
if perf:
parts.append("## 二、互动表现\n")
parts.append("| 排名 | 标题 | 文案分 | 点赞 | 收藏 | 评论 | 互动合计 |")
parts.append("|---|---|---|---|---|---|---|")
for i, p in enumerate(perf[:10], 1):
parts.append(f"| {i} | {p['title'][:30]} | {p['score']} | "
f"{p['liked']} | {p['collected']} | {p['comment']} | **{p['engagement']}** |")
parts.append("")
# 爆款 vs 平均
avg = sum(x["engagement"] for x in perf) / len(perf)
top1 = perf[0]
if top1["engagement"] > avg * 2:
parts.append(f"📈 **{top1['title']}** 互动 {top1['engagement']}，"
f"是平均（{avg:.0f}）的 {top1['engagement']/max(1,avg):.1f} 倍 — 这条值得复盘共性。\n")
if rejected or accepted:
parts.append("## 三、教练反馈\n")
if rejected:
parts.append(f"- 你最常 reject 的检查项：")
for k, v in rejected.most_common(5):
hint = " ← 可考虑跑 `profile_init.py evolve` 永久禁用" if v >= 3 else ""
parts.append(f" - {k}（{v} 次）{hint}")
if accepted:
parts.append(f"- 你最常 accept 的检查项：")
for k, v in accepted.most_common(5):
parts.append(f" - {k}（{v} 次）")
parts.append("")
if profile.sample_count:
parts.append("## 四、当前风格画像\n")
parts.append(f"- 人设 / 赛道：{profile.persona or '?'} / {profile.niche or '?'}")
parts.append(f"- 标题 {profile.avg_title_len} 字 / 正文 {profile.avg_content_len} 字 / "
f"{profile.emoji_per_post} emoji 每篇")
if profile.favorite_formulas:
top_f = sorted(profile.favorite_formulas.items(), key=lambda x: -x[1])[:3]
parts.append(f"- 偏好公式：{', '.join(f'{k}({v})' for k, v in top_f)}")
parts.append("")
# 下周建议
parts.append("## 五、下周建议\n")
if drafted < 3:
parts.append("- 🐢 起草数 < 3 篇 — 节奏偏慢，建议跑 `brainstorm.py` 找 1~2 个新选题")
elif drafted > 7:
parts.append("- 🚀 起草数偏多 — 留意是否有 '模板化' 风险，每一篇都问自己 '读者凭什么收藏'")
if perf and perf[-1]["engagement"] < perf[0]["engagement"] / 5:
parts.append("- 📊 最低和最高表现差距过大 — 复盘最低那一篇是哪个环节没做到位")
if rejected and max(rejected.values()) >= 3:
top_rej = rejected.most_common(1)[0][0]
parts.append(f"- 🛠 反复拒绝「{top_rej}」检查 — 跑 `profile_init.py evolve` 让助手学到")
parts.append("- 把这周最爆的 1 篇加到 baseline：`profile_init.py add path/to/note.json`")
return "\n".join(parts) + "\n"
def main() -> int:
p = argparse.ArgumentParser(prog="weekly_review.py", description="周/月创作复盘")
p.add_argument("--days", type=int, default=7, help="时间窗口（天）")
p.add_argument("--out", default="", help="输出路径（不填打印到 stdout，并自动写入 reviews/）")
args = p.parse_args()
store = ProfileStore()
text = build_review(store, args.days)
# 总是存一份到 reviews/
fname = f"review_{dt.datetime.now().strftime('%Y%m%d_%H%M')}_{args.days}d.md"
saved = store.reviews_dir / fname
saved.write_text(text, encoding="utf-8")
if args.out:
Path(args.out).write_text(text, encoding="utf-8")
print(f"✓ 报告已写入 {args.out}", file=sys.stderr)
else:
print(text)
print(f"✓ 已归档到 {saved}", file=sys.stderr)
return 0
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/write_post.py
#!/usr/bin/env python3
"""火一五小红书文案生成器 — 给 Claude / 用户一份"骨架草稿"。
工作模式
========
本脚本不调大模型，只输出**骨架 + 候选标题 + 话题建议**；
真正的内容填充由调用它的 LLM（或你本人）完成。
这样设计的好处：
1. 离线可跑、零依赖；
2. Claude 拿到骨架后可以一次性看清"标题公式 + 段落结构 + 强占位"，
产出会更结构化、不容易跑偏；
3. 不会假装替你"写完"再让你改 — 反而拖慢。
用法
====
# 1) 列出标题候选（多公式 × 多个）
python3 write_post.py titles --topic "干皮护肤" --persona "30+ 干皮女生" \\
--payoff "稳油不闷痘" --formulas T1,T3,T5 --n 2
# 2) 渲染正文骨架
python3 write_post.py skeleton --code S1
# 3) 一键产出 markdown 草稿（标题 + 骨架 + 话题占位）
python3 write_post.py draft --topic "干皮护肤" --persona "30+" \\
--payoff "稳油不闷痘" --formula T2 --skeleton S1 \\
--tags "护肤,干皮护肤,30岁护肤" --out draft.md
"""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent))
from xhs_writer import ( # noqa: E402
Draft,
generate_titles,
get_skeleton,
make_draft,
render_skeleton,
save_draft,
)
def cmd_titles(args: argparse.Namespace) -> int:
formulas = [f.strip() for f in args.formulas.split(",")] if args.formulas else None
titles = generate_titles(
args.topic,
persona=args.persona,
payoff=args.payoff,
formulas=formulas,
n_each=args.n,
)
if args.format == "json":
print(json.dumps(titles, ensure_ascii=False, indent=2))
else:
for t in titles:
print(f"[{t['formula']}] {t['title']}")
return 0
def cmd_skeleton(args: argparse.Namespace) -> int:
fields = {}
if args.fields:
try:
fields = json.loads(args.fields)
except json.JSONDecodeError as e:
print(f"--fields 不是合法 JSON: {e}", file=sys.stderr)
return 1
if fields:
print(render_skeleton(args.code, fields))
else:
for line in get_skeleton(args.code):
print(line)
return 0
def cmd_draft(args: argparse.Namespace) -> int:
tags = [t.strip() for t in args.tags.split(",")] if args.tags else []
draft = make_draft(
args.topic,
persona=args.persona,
payoff=args.payoff,
formula=args.formula,
skeleton=args.skeleton,
tags=tags,
)
if args.cover_hint:
draft.cover_hint = args.cover_hint
if args.image_hints:
draft.image_hints = [h.strip() for h in args.image_hints.split("|") if h.strip()]
text = draft.to_markdown()
if args.out:
save_draft(draft, args.out)
print(f"✓ 草稿已保存到 {args.out}", file=sys.stderr)
else:
print(text)
return 0
def cmd_list(args: argparse.Namespace) -> int:
"""列出所有可用的标题公式 / 正文骨架代号。"""
print("== 标题公式 ==")
print(" T1 数字对比 T2 痛点共情 T3 反差冲突 T4 悬念钩子")
print(" T5 身份代入 T6 福利免费 T7 时间节点 T8 提问诱发")
print(" T9 极端结果 T10 步骤指南 T11 故事开场")
print()
print("== 正文骨架 ==")
print(" S1 钩子-痛点-方案-金句 S2 故事-感悟-行动")
print(" S3 测评-对比-结论 S4 清单/列表")
print(" S5 保姆级教程 S6 观点/反共识")
print(" S7 日记/Vlog")
print()
print("详见 data/title_templates.md 和 data/content_structures.md")
return 0
def build_parser() -> argparse.ArgumentParser:
p = argparse.ArgumentParser(prog="write_post.py", description="火一五小红书文案生成（骨架版）")
sub = p.add_subparsers(dest="cmd", required=True)
pt = sub.add_parser("titles", help="生成标题候选")
pt.add_argument("--topic", required=True, help="主题关键词，如：干皮护肤")
pt.add_argument("--persona", default="", help="目标受众，如：30+ 干皮女生")
pt.add_argument("--payoff", default="", help="利益点 / 结果，如：稳油不闷痘")
pt.add_argument("--formulas", default="", help="公式代号，逗号分隔；空 = 全部 (T1~T11)")
pt.add_argument("--n", type=int, default=2, help="每种公式生成几条")
pt.add_argument("--format", choices=["text", "json"], default="text")
pt.set_defaults(func=cmd_titles)
ps = sub.add_parser("skeleton", help="打印正文骨架")
ps.add_argument("--code", required=True, help="骨架代号 S1~S7")
ps.add_argument("--fields", default="", help="JSON: {\"hook\":\"...\"} 用于填充占位")
ps.set_defaults(func=cmd_skeleton)
pd = sub.add_parser("draft", help="一键生成 markdown 草稿")
pd.add_argument("--topic", required=True)
pd.add_argument("--persona", default="")
pd.add_argument("--payoff", default="")
pd.add_argument("--formula", default="T2", help="标题公式 T1~T11")
pd.add_argument("--skeleton", default="S1", help="正文骨架 S1~S7")
pd.add_argument("--tags", default="", help="话题列表，逗号分隔，不带 #")
pd.add_argument("--cover-hint", default="", help="封面图说明")
pd.add_argument("--image-hints", default="", help="多张配图说明，| 分隔")
pd.add_argument("--out", default="", help="输出 .md 或 .json；不填打印到 stdout")
pd.set_defaults(func=cmd_draft)
pl = sub.add_parser("list", help="列出所有公式 / 骨架代号")
pl.set_defaults(func=cmd_list)
return p
def main() -> int:
args = build_parser().parse_args()
return args.func(args)
if __name__ == "__main__":
sys.exit(main())
FILE:scripts/xhs_aesthetic.py
"""火一五小红书"Allen 美学诊断"核心库。
来源：司志远（Allen）的小红书文案教学体系（三课 + 五技法 + 11 案例）。
区别于 v2.0 的"工程师视角"打分，这里是"哲学家视角"评估。
五个新维度
==========
1. **breath_score 留白度** — 句子是否有呼吸空间，让读者自己填情绪
2. **ai_speak_score AI 腔指数** — 汇报化 / 模板化 / 装腔词检测
3. **teach_vs_lead 教 vs 带** — "你应该" 还是 "你可以试试"
4. **resonance_score 共鸣度** — 共同记忆 vs 冷知识 / 装文化
5. **invitation_score 邀请语** — 互动是任务指令还是 "这里有个局"
每一项都是 0~10 分 + 命中样本 + 改写建议。
设计原则
========
- 每一项**单独可关**（disabled_checks 写进 profile/rules.json）
- 默认**只在用户开 Allen-mode 时生效**（避免误伤工程类干货号）
- 所有判断有可解释规则（不依赖 LLM），同时支持 LLM 增强
"""
from __future__ import annotations
import json
import re
from dataclasses import asdict, dataclass, field
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
_DATA_DIR = Path(__file__).resolve().parent.parent / "data"
# =====================================================================
# 数据加载
# =====================================================================
def _load_ai_speak_patterns() -> List[Dict[str, Any]]:
p = _DATA_DIR / "ai_speak_patterns.json"
if not p.exists():
return []
try:
data = json.loads(p.read_text(encoding="utf-8"))
except json.JSONDecodeError:
return []
out: List[Dict[str, Any]] = []
for cat_name, cat in (data.get("categories") or {}).items():
for item in cat.get("patterns") or []:
out.append({
"category": cat_name,
"bad": item.get("bad", ""),
"good": item.get("good", []),
"why": item.get("why", ""),
})
return out
# =====================================================================
# 1. 留白度（Breath）
# =====================================================================
_NO_BREATH_MARKERS = [
# 信息密度过高的特征
re.compile(r"，[^，。！？\n]{30,}"), # 一句话超 30 字不停顿
re.compile(r"[1-9][\.、] ?\S+"), # 显式编号「1. 2. 3.」 — 教科书结构
re.compile(r"首先[^。]+。.*其次[^。]+。"), # 首先...其次...
re.compile(r"综上|综合来看|总结一下|总而言之"),
]
_BREATH_MARKERS = [
# 留白的特征
re.compile(r"^[—…]{2,}\s*$", re.MULTILINE), # 单独一行省略号或破折号
re.compile(r"\n\s*\n"), # 空行
re.compile(r"[。？！]\s*$", re.MULTILINE), # 短句独立成段
]
def score_breath(content: str) -> Tuple[int, List[str], List[str]]:
"""留白度 0~10。看句子能不能让读者停下来。"""
if not content.strip():
return 0, ["正文为空"], []
issues: List[str] = []
suggestions: List[str] = []
score = 6
# 长句过多
long_phrases = sum(1 for ln in content.splitlines()
for _ in re.finditer(r"，[^，。！？\n]{30,}", ln))
if long_phrases >= 3:
score -= 2
issues.append(f"有 {long_phrases} 处一句话超 30 字不停顿，信息密度过高（说明书感）")
suggestions.append("把长句子在自然停顿处断开 — 一个意思一行")
# 显式编号 1.2.3
if len(re.findall(r"[1-9][\.、] ?\S+", content)) >= 3:
score -= 1
issues.append("出现 1. 2. 3. 显式编号 — 教科书结构，不像小红书的'松散感'")
suggestions.append("把数字编号换成 emoji 项目符号或自然过渡（'还有'/'另外'/'对了'）")
# 总分总
if re.search(r"首先[^。]+。.*其次", content) or "综上" in content or "总而言之" in content:
score -= 2
issues.append("出现总分总结构关键词 — 论文腔，不是生活感")
suggestions.append("改成'我把这天写下来'或者直接列散点，不要框")
# 空行
paragraphs = [p for p in content.split("\n\n") if p.strip()]
blanks = content.count("\n\n")
if paragraphs and blanks / max(1, len(paragraphs)) < 0.5:
score -= 1
issues.append("段间空行偏少，文字像挤在一起，没有呼吸口")
suggestions.append("每 2~3 句之间加一个空行，让读者有眼睛能落的地方")
# 加分项：关键句独立成段
short_punch = sum(1 for ln in content.splitlines()
if 6 < len(ln.strip()) < 25 and ln.strip().endswith(("。", "！", "？", "—")))
if short_punch >= 2:
score += 1
# 加分项：留白符号
if re.search(r"^—{2,}\s*$|^…{2,}\s*$", content, re.MULTILINE):
score += 1
return max(0, min(10, score)), issues, suggestions
# =====================================================================
# 2. AI 腔指数（AI-speak）
# =====================================================================
def score_ai_speak(text: str) -> Tuple[int, List[str], List[str]]:
"""AI 腔检测 0~10（高分 = 没什么 AI 腔，低分 = AI 腔严重）。"""
patterns = _load_ai_speak_patterns()
if not patterns:
return 7, [], []
hits: List[Dict[str, Any]] = []
for p in patterns:
bad = p["bad"]
if not bad:
continue
if bad in text:
hits.append(p)
issues: List[str] = []
suggestions: List[str] = []
score = 10
score -= min(8, len(hits))
# 限制：只展示前 8 条最相关的
seen_bad = set()
for h in hits:
if h["bad"] in seen_bad:
continue
seen_bad.add(h["bad"])
good_options = [g for g in h["good"] if g]
rep = " / ".join(good_options) if good_options else "（删掉它）"
issues.append(f"AI 腔：{h['bad']!r}（{h['category']}）")
suggestions.append(f"{h['bad']!r} → {rep} // {h['why']}")
if len(issues) >= 8:
break
return max(0, min(10, score)), issues, suggestions
# =====================================================================
# 3. 教 vs 带（Teach vs Lead）
# =====================================================================
_TEACH_PATTERNS = [
(re.compile(r"你应该|你必须|你需要|你要"), "命令式"),
(re.compile(r"记住|划重点|敲黑板|注意了|划个重点"), "教官式"),
(re.compile(r"答案是|正确的做法|标准做法|最佳实践"), "标准答案式"),
(re.compile(r"^三大|^五大|^几大要点"), "教科书式"),
(re.compile(r"误区[一二三]?[:：]|常见误区"), "纠错式"),
(re.compile(r"切记|千万不要|绝对不能"), "警告式"),
]
_LEAD_PATTERNS = [
(re.compile(r"你可以试试|不妨"), "邀请式"),
(re.compile(r"我自己|我体感|我之前"), "自身经历式"),
(re.compile(r"也许|或许|有时候|可能"), "留余地式"),
(re.compile(r"我把这[天份]?记下来|我想到|我后来发现"), "记录式"),
(re.compile(r"你呢|你那边怎么样"), "回声式"),
]
def score_teach_vs_lead(text: str) -> Tuple[int, List[str], List[str]]:
"""0~10：高分 = 带读者，低分 = 教读者。"""
teach_hits: List[str] = []
lead_hits: List[str] = []
for pat, kind in _TEACH_PATTERNS:
for m in pat.finditer(text):
teach_hits.append(f"{m.group(0)} ({kind})")
for pat, kind in _LEAD_PATTERNS:
for m in pat.finditer(text):
lead_hits.append(f"{m.group(0)} ({kind})")
score = 5 + len(set(lead_hits)) - len(set(teach_hits))
score = max(0, min(10, score))
issues: List[str] = []
suggestions: List[str] = []
if teach_hits:
# 去重前 5
seen = set()
unique = []
for h in teach_hits:
if h in seen:
continue
seen.add(h)
unique.append(h)
issues.append(f"教读者腔 {len(unique)} 处：{', '.join(unique[:5])}")
suggestions.append(
"Allen 第一课：从'教'到'带'。"
"把 '你应该 / 必须 / 记住' 改成 '我自己 / 不妨 / 有时候'，"
"把答案语气改成留白，让读者自己填进去。"
)
if not lead_hits and not teach_hits:
suggestions.append("没有明显的'带读者'语气词，也没有'教读者' — 偏中性。"
"可以加 1~2 处 '我自己是这么..' / '不妨试试' 拉近。")
return score, issues, suggestions
# =====================================================================
# 4. 共鸣度（Resonance）
# =====================================================================
# 装文化 / 冷知识标志（命中扣分）
_COLD_MARKERS = [
"井水", "蝉七年", "二十四番", "文人雅士", "古人云", "诗云",
"据《", "《本草", "《诗经》", "节气习俗", "封建社会",
"考据", "传统认为", "民俗记载",
]
# 共同记忆体验词（命中加分）
_WARM_MARKERS = [
"风扇", "凉席", "冰箱", "外婆", "奶奶", "妈妈", "阳台", "巷子",
"早八", "下班", "通勤", "周三", "周五", "周日",
"草地", "树荫", "晚风", "蝉鸣", "雨声", "桂花", "栀子",
"电梯", "地铁", "便利店", "小卖部", "校服",
"伸懒腰", "搓搓手", "踢被子", "打哈欠", "揉眼睛",
"热水袋", "冰可乐", "绿豆汤", "冰棍", "麻辣烫",
"翻箱倒柜", "猫咪", "狗子",
]
def score_resonance(text: str) -> Tuple[int, List[str], List[str]]:
"""共鸣度 0~10。"""
cold = [w for w in _COLD_MARKERS if w in text]
warm = [w for w in _WARM_MARKERS if w in text]
score = 5 + min(4, len(set(warm))) - min(4, len(set(cold)))
score = max(0, min(10, score))
issues: List[str] = []
suggestions: List[str] = []
if cold:
issues.append(f"装文化 / 冷知识词 {len(set(cold))} 处：{', '.join(set(cold))[:60]}")
suggestions.append(
"Allen 教训：场景共鸣不是选有'时间感'的事物，是选**人人都有过的共同记忆**。"
"把 '井水西瓜' 换成 '冰箱里捞冰镇西瓜'，把 '蝉七年' 换成 '夏夜窗外蝉鸣'。"
)
if not warm and not cold:
suggestions.append(
"没找到具象的共鸣体验词。"
"试着写 1~2 个**画面**（草地/晚风/凉席/翻箱倒柜），让读者能看见自己。"
)
return score, issues, suggestions
# =====================================================================
# 5. 邀请语（Invitation）
# =====================================================================
_COMMAND_TONE = [
re.compile(r"必须关注"),
re.compile(r"赶紧关注"),
re.compile(r"立刻"),
re.compile(r"现在就"),
re.compile(r"马上"),
re.compile(r"快冲|冲冲冲"),
re.compile(r"\d+小时内"),
]
_INVITATION_TONE = [
re.compile(r"如果你也"),
re.compile(r"要是你"),
re.compile(r"你那一天"),
re.compile(r"你呢"),
re.compile(r"评论区聊聊"),
re.compile(r"你的[\S]{1,4}是怎样"),
re.compile(r"留个[^\n]{1,8}给我"),
]
def score_invitation(text: str) -> Tuple[int, List[str], List[str]]:
"""互动语气：邀请 vs 任务指令。0~10。"""
cmd: List[str] = []
inv: List[str] = []
for pat in _COMMAND_TONE:
cmd.extend(m.group(0) for m in pat.finditer(text))
for pat in _INVITATION_TONE:
inv.extend(m.group(0) for m in pat.finditer(text))
score = 5 + min(4, len(set(inv))) - min(4, len(set(cmd)))
score = max(0, min(10, score))
issues: List[str] = []
suggestions: List[str] = []
if cmd:
issues.append(f"命令式互动语 {len(set(cmd))} 处：{', '.join(set(cmd))}")
suggestions.append(
"Allen 第三课：互动不是任务指令，是邀请语。"
"「赶紧关注」→「如果你也喜欢这种感觉」；「立刻参与」→「这里有个落脚点」。"
)
if not inv:
suggestions.append(
"正文里没有'邀请语' — 读者读完，没有被'拉进来'的位置。"
"末段加一句：'你呢？' / '你那一天是怎样的，留个故事给我'。"
)
return score, issues, suggestions
# =====================================================================
# 综合
# =====================================================================
@dataclass
class AestheticScore:
breath: int
ai_speak: int
teach_vs_lead: int
resonance: int
invitation: int
total: int # 加权总分 0~100
issues: List[str] = field(default_factory=list)
suggestions: List[str] = field(default_factory=list)
by_dim: Dict[str, Dict[str, Any]] = field(default_factory=dict)
def to_dict(self) -> Dict[str, Any]:
return asdict(self)
_DIM_LABELS = {
"breath": "留白度",
"ai_speak": "去 AI 腔",
"teach_vs_lead": "带读者",
"resonance": "共鸣度",
"invitation": "邀请语",
}
_DEFAULT_AESTHETIC_WEIGHTS = {
"breath": 0.25,
"ai_speak": 0.20,
"teach_vs_lead": 0.20,
"resonance": 0.20,
"invitation": 0.15,
}
def aesthetic_score(
title: str,
content: str,
*,
disabled: Optional[List[str]] = None,
weights: Optional[Dict[str, float]] = None,
) -> AestheticScore:
full = f"{title}\n{content}"
disabled = set(disabled or [])
weights = weights or _DEFAULT_AESTHETIC_WEIGHTS
by_dim: Dict[str, Dict[str, Any]] = {}
issues_all: List[str] = []
suggestions_all: List[str] = []
def _run(name: str, fn, arg):
if name in disabled:
return None
s, i, sg = fn(arg)
by_dim[name] = {"score": s, "issues": i, "suggestions": sg}
issues_all.extend(f"[{_DIM_LABELS[name]}] {x}" for x in i)
suggestions_all.extend(f"[{_DIM_LABELS[name]}] {x}" for x in sg)
return s
breath = _run("breath", score_breath, content)
ai = _run("ai_speak", score_ai_speak, full)
tvl = _run("teach_vs_lead", score_teach_vs_lead, full)
res = _run("resonance", score_resonance, full)
inv = _run("invitation", score_invitation, content)
# 归一化加权
norm: Dict[str, float] = {}
s_total_w = sum(w for k, w in weights.items() if k in by_dim) or 1.0
for k, w in weights.items():
if k in by_dim:
norm[k] = w / s_total_w
total = int(round(sum(by_dim[k]["score"] / 10 * norm[k] for k in by_dim) * 100))
return AestheticScore(
breath=breath if breath is not None else 0,
ai_speak=ai if ai is not None else 0,
teach_vs_lead=tvl if tvl is not None else 0,
resonance=res if res is not None else 0,
invitation=inv if inv is not None else 0,
total=total,
issues=issues_all,
suggestions=suggestions_all,
by_dim=by_dim,
)
# =====================================================================
# Allen-mode：与 score_post 整合的合并打分
# =====================================================================
def merge_with_engineering_score(
eng_breakdown: Dict[str, int],
eng_total: int,
aesthetic: AestheticScore,
*,
aesthetic_weight: float = 0.4,
) -> Dict[str, Any]:
"""把工程打分（v2.0 那套 6 维）和 Allen 打分混合。
aesthetic_weight = 0 → 纯工程分；= 1 → 纯 Allen 分。
默认 0.4 — Allen 占四成。
"""
eng = max(0, min(100, eng_total))
aes = max(0, min(100, aesthetic.total))
final = int(round(eng * (1 - aesthetic_weight) + aes * aesthetic_weight))
return {
"final": final,
"engineering": eng,
"aesthetic": aes,
"aesthetic_weight": aesthetic_weight,
"engineering_breakdown": eng_breakdown,
"aesthetic_breakdown": {k: v["score"] for k, v in aesthetic.by_dim.items()},
}
FILE:scripts/xhs_analyzer.py
"""小红书数据分析 — 输入是本地已抓取好的 JSON / JSONL 数据集，离线跑。
分析维度：
- engagement：互动率（liked + collected + comment）/ 预估曝光（粉丝数或中位数）
- 关键词：标题 / 正文 / 话题标签 的高频词
- 时段：发布时间的小时 × 星期分布
- 爆款特征：高互动笔记的长度、图片数、话题数对比中位数
- 同行对比：多账号间的互动中位数、发帖频次
所有函数都接受 `notes: List[Dict]`（对齐 xhs_parser.Note 的结构），返回纯 dict / DataFrame。
"""
from __future__ import annotations
import collections
import datetime as dt
import json
import os
import re
import statistics as stats
from typing import Any, Dict, Iterable, List, Optional, Tuple
# 可选 pandas
try:
import pandas as pd # type: ignore
HAS_PANDAS = True
except ImportError:
HAS_PANDAS = False
# --------- 加载 ---------
def load_notes(path: str) -> List[Dict[str, Any]]:
"""支持 .json（一个数组）和 .jsonl（逐行）。"""
if not os.path.exists(path):
raise FileNotFoundError(path)
with open(path, encoding="utf-8") as f:
text = f.read().strip()
if not text:
return []
if text.startswith("["):
data = json.loads(text)
return data if isinstance(data, list) else []
# jsonl
out = []
for line in text.splitlines():
line = line.strip()
if not line:
continue
try:
out.append(json.loads(line))
except json.JSONDecodeError:
pass
return out
def dump_notes(notes: List[Dict[str, Any]], path: str, jsonl: bool = False) -> None:
with open(path, "w", encoding="utf-8") as f:
if jsonl:
for n in notes:
f.write(json.dumps(n, ensure_ascii=False) + "\n")
else:
json.dump(notes, f, ensure_ascii=False, indent=2)
# --------- 规范化访问 ---------
def _inter(note: Dict[str, Any]) -> Dict[str, int]:
i = note.get("interactions") or {}
return {
"liked": int(i.get("liked_count", 0) or 0),
"collected": int(i.get("collected_count", 0) or 0),
"comment": int(i.get("comment_count", 0) or 0),
"shared": int(i.get("shared_count", 0) or 0),
}
def _engagement(note: Dict[str, Any]) -> int:
i = _inter(note)
return i["liked"] + i["collected"] + i["comment"] + i["shared"]
def _published_dt(note: Dict[str, Any]) -> Optional[dt.datetime]:
ts = note.get("raw_time")
if isinstance(ts, (int, float)) and ts > 0:
try:
return dt.datetime.fromtimestamp(ts / 1000)
except (OverflowError, OSError, ValueError):
return None
iso = note.get("published_at", "")
if iso:
try:
return dt.datetime.fromisoformat(iso)
except ValueError:
return None
return None
# --------- 互动分析 ---------
def engagement_summary(notes: Iterable[Dict[str, Any]]) -> Dict[str, Any]:
values = [_engagement(n) for n in notes]
if not values:
return {"count": 0}
return {
"count": len(values),
"mean": round(stats.mean(values), 1),
"median": stats.median(values),
"p90": _quantile(values, 0.9),
"max": max(values),
"min": min(values),
"stdev": round(stats.pstdev(values), 1) if len(values) > 1 else 0,
}
def _quantile(values: List[int], q: float) -> int:
s = sorted(values)
idx = int(q * (len(s) - 1))
return s[idx]
def top_notes(notes: List[Dict[str, Any]], n: int = 10) -> List[Dict[str, Any]]:
ranked = sorted(notes, key=_engagement, reverse=True)
out = []
for note in ranked[:n]:
inter = _inter(note)
out.append({
"note_id": note.get("note_id", ""),
"title": (note.get("title") or note.get("content", ""))[:60],
"liked": inter["liked"],
"collected": inter["collected"],
"comment": inter["comment"],
"engagement": _engagement(note),
"url": note.get("url") or f"https://www.xiaohongshu.com/explore/{note.get('note_id', '')}",
})
return out
# --------- 关键词 / 话题 ---------
_PUNCT_RE = re.compile(r"[\s\.,;:!?\"'()\[\]{}<>/\\|\-_=+*&^%$#@~`—…！？，。、：；""''（）【】《》〈〉「」·]+")
def keyword_frequency(notes: Iterable[Dict[str, Any]], *, use_jieba: bool = True,
min_len: int = 2, top: int = 30,
stopwords: Optional[Iterable[str]] = None) -> List[Tuple[str, int]]:
"""标题 + 正文 + 话题 的词频统计。有 jieba 用 jieba，否则退化为按标点切。"""
stop = set(stopwords or _DEFAULT_STOPWORDS)
counter: collections.Counter = collections.Counter()
text_parts: List[str] = []
for note in notes:
text_parts.append(note.get("title", "") or "")
text_parts.append(note.get("content", "") or "")
text_parts.extend(note.get("tags", []) or [])
blob = "\n".join(text_parts)
tokens: List[str] = []
if use_jieba:
try:
import jieba # type: ignore
tokens = list(jieba.cut_for_search(blob))
except ImportError:
tokens = []
if not tokens:
# 退化方案：按标点/空格切
tokens = _PUNCT_RE.split(blob)
for tok in tokens:
tok = tok.strip().lower()
if len(tok) < min_len or tok in stop or tok.isdigit():
continue
counter[tok] += 1
return counter.most_common(top)
_DEFAULT_STOPWORDS = {
"the", "a", "an", "of", "in", "on", "and", "or", "is", "are", "to", "for", "with", "at",
"我", "你", "他", "她", "它", "我们", "你们", "他们", "的", "了", "吧", "啊", "呀", "哦",
"这", "那", "都", "也", "就", "还", "再", "说", "说一", "但是", "而且", "所以", "因为",
"什么", "怎么", "如何", "为什么", "以及", "一个", "一下", "一点", "一些", "真的", "可以", "能够",
"分享", "今天", "一下", "大家", "朋友", "宝贝", "姐妹", "姐妹们",
"http", "https", "www", "com", "cn", "xhs",
}
def tag_frequency(notes: Iterable[Dict[str, Any]], top: int = 30) -> List[Tuple[str, int]]:
counter: collections.Counter = collections.Counter()
for note in notes:
for tag in note.get("tags", []) or []:
if tag:
counter[tag.strip()] += 1
return counter.most_common(top)
# --------- 时间分布 ---------
def posting_time_heatmap(notes: Iterable[Dict[str, Any]]) -> Dict[str, Dict[int, int]]:
"""星期 × 小时 发布频次。星期用中文名。"""
week = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
grid: Dict[str, Dict[int, int]] = {w: {h: 0 for h in range(24)} for w in week}
for note in notes:
d = _published_dt(note)
if not d:
continue
grid[week[d.weekday()]][d.hour] += 1
return grid
def best_posting_windows(notes: Iterable[Dict[str, Any]], top: int = 5) -> List[Dict[str, Any]]:
"""按 (星期 × 小时) 的笔记平均互动排序，给发文时段建议。"""
buckets: Dict[Tuple[int, int], List[int]] = collections.defaultdict(list)
for note in notes:
d = _published_dt(note)
if not d:
continue
buckets[(d.weekday(), d.hour)].append(_engagement(note))
ranked = []
for (w, h), vals in buckets.items():
if len(vals) < 2:
continue
ranked.append({
"weekday": ["周一", "周二", "周三", "周四", "周五", "周六", "周日"][w],
"hour": h,
"count": len(vals),
"median_engagement": stats.median(vals),
"mean_engagement": round(stats.mean(vals), 1),
})
ranked.sort(key=lambda x: x["median_engagement"], reverse=True)
return ranked[:top]
# --------- 爆款特征 ---------
def viral_pattern(notes: List[Dict[str, Any]], percentile: float = 0.8) -> Dict[str, Any]:
"""对比 top 20% 笔记与其他笔记的特征差异。"""
if len(notes) < 5:
return {"note": "样本太少，至少 5 条"}
scored = sorted(notes, key=_engagement, reverse=True)
cut = max(1, int(len(scored) * (1 - percentile)))
top = scored[:cut]
rest = scored[cut:]
def _stats(group: List[Dict[str, Any]]) -> Dict[str, Any]:
if not group:
return {}
titles = [len(n.get("title", "") or "") for n in group]
contents = [len(n.get("content", "") or "") for n in group]
imgs = [len(n.get("images", []) or []) for n in group]
tags = [len(n.get("tags", []) or []) for n in group]
return {
"count": len(group),
"title_len_med": int(stats.median(titles)) if titles else 0,
"content_len_med": int(stats.median(contents)) if contents else 0,
"images_med": int(stats.median(imgs)) if imgs else 0,
"tags_med": int(stats.median(tags)) if tags else 0,
"engagement_med": int(stats.median([_engagement(n) for n in group])),
}
return {
"top": _stats(top),
"rest": _stats(rest),
}
# --------- 综合报告 ---------
def full_report(notes: List[Dict[str, Any]]) -> Dict[str, Any]:
return {
"sample_size": len(notes),
"engagement_summary": engagement_summary(notes),
"top_notes": top_notes(notes, 10),
"keyword_top30": keyword_frequency(notes, top=30),
"tag_top30": tag_frequency(notes, top=30),
"posting_time_heatmap": posting_time_heatmap(notes),
"best_windows": best_posting_windows(notes, 5),
"viral_pattern": viral_pattern(notes),
}
def report_to_markdown(report: Dict[str, Any]) -> str:
"""把分析报告渲染成 Markdown，方便直接写到笔记里。"""
parts: List[str] = []
parts.append(f"# 小红书数据分析报告\n\n样本量：**{report['sample_size']}** 条笔记\n")
es = report["engagement_summary"]
if es.get("count"):
parts.append("## 互动概览\n")
parts.append(f"- 平均互动：**{es['mean']}**")
parts.append(f"- 中位数：{es['median']}")
parts.append(f"- P90：{es['p90']}")
parts.append(f"- 最高：{es['max']} / 最低：{es['min']}\n")
parts.append("## Top 10 笔记\n")
parts.append("| 排名 | 标题 | 点赞 | 收藏 | 评论 | 互动合计 |")
parts.append("|---|---|---|---|---|---|")
for idx, t in enumerate(report["top_notes"], 1):
title = (t["title"] or "(无标题)").replace("|", "/")
parts.append(f"| {idx} | {title} | {t['liked']} | {t['collected']} | {t['comment']} | **{t['engagement']}** |")
parts.append("\n## Top 30 关键词\n")
parts.append(", ".join(f"`{k}`({v})" for k, v in report["keyword_top30"][:30]))
parts.append("\n\n## Top 30 话题标签\n")
parts.append(", ".join(f"#{k}({v})" for k, v in report["tag_top30"][:30]))
parts.append("\n\n## 最佳发文时段（按中位互动）\n")
for w in report["best_windows"]:
parts.append(f"- {w['weekday']} {w['hour']:02d}:00 — 中位互动 {w['median_engagement']}（{w['count']} 条样本）")
vp = report["viral_pattern"]
if isinstance(vp, dict) and "top" in vp and "rest" in vp:
parts.append("\n## 爆款 vs 普通")
parts.append(f"- 爆款 ({vp['top']['count']} 条): 标题 {vp['top']['title_len_med']} 字 / 正文 "
f"{vp['top']['content_len_med']} 字 / {vp['top']['images_med']} 图 / "
f"{vp['top']['tags_med']} 话题 / 互动中位 {vp['top']['engagement_med']}")
parts.append(f"- 普通 ({vp['rest']['count']} 条): 标题 {vp['rest']['title_len_med']} 字 / 正文 "
f"{vp['rest']['content_len_med']} 字 / {vp['rest']['images_med']} 图 / "
f"{vp['rest']['tags_med']} 话题 / 互动中位 {vp['rest']['engagement_med']}")
return "\n".join(parts) + "\n"
FILE:scripts/xhs_client.py
"""火一五小红书客户端 — 尊重频率 / 防封号的 HTTP 层。
设计原则
========
1. **用用户自己的登录 Cookie**：脚本不做登录自动化（输入密码 / 刷验证码都会被风控识别）。用户在
正常浏览器里登录后，把 Cookie 复制过来用。
2. **浏览器式请求**：Header 与真实 Chrome/手 Q 保持一致；不伪造 User-Agent 之外的指纹。
3. **人类化节奏**：每次请求之间加入 2~8 秒随机延时 + 任务级 max-requests 上限；
绝不在短时间内密集抓取。
4. **错误即退出**：遇到 460 / 461 / 403 / 出现 "verify" / "captcha" / "登录" 字样，
立即停止并抛出 RateLimited / BlockedByCaptcha，不做盲目重试。
5. **只读 / 小规模**：目标是给个人号做"选题调研、同行分析"，而不是批量搬运。
6. **不写**：从不调用 post / like / follow / comment 接口，避免触发行为风控。
使用
====
from xhs_client import XHSClient, load_cookie_from_env
client = XHSClient(cookie=load_cookie_from_env(), min_delay=3, max_delay=7)
html = client.get_explore_page(note_id='64abcd...')
实现说明
========
- 目前使用 **web 网页端** + Cookie 的方式，解析 `window.__INITIAL_STATE__` / `window.__INITIAL_SSR_STATE__`。
- API 接口（/api/sns/web/v1/...）需要 X-s / X-t 签名。本模块预留 `_sign_request` 钩子，
但默认不启用 — 风险大、容易封号，不推荐。
"""
from __future__ import annotations
import json
import logging
import os
import random
import time
from dataclasses import dataclass, field
from typing import Any, Dict, Optional
from urllib.parse import urlparse
try:
import requests
except ImportError as exc: # pragma: no cover
raise SystemExit("需要 requests: pip install requests") from exc
LOG = logging.getLogger("xhs_client")
if not LOG.handlers:
_h = logging.StreamHandler()
_h.setFormatter(logging.Formatter("[%(levelname)s] %(message)s"))
LOG.addHandler(_h)
LOG.setLevel(logging.INFO)
# -------- 例外 --------
class XHSError(Exception):
"""小红书客户端基础错误。"""
class RateLimited(XHSError):
"""触发频率限制 — 必须停止抓取一段时间。"""
class BlockedByCaptcha(XHSError):
"""触发滑块 / 图形验证 — 必须人工处理，脚本不自救。"""
class LoginRequired(XHSError):
"""Cookie 失效或未登录。"""
class NotFound(XHSError):
"""笔记 / 用户不存在或已被删除。"""
# -------- 工具 --------
def load_cookie_from_env(var: str = "XHS_COOKIE") -> str:
"""从环境变量读 Cookie。"""
cookie = os.environ.get(var, "").strip()
if not cookie:
raise LoginRequired(
f"未设置环境变量 {var}。请先在浏览器登录小红书，复制完整 Cookie 字符串后执行：\n"
f" export {var}='a=b; c=d; ...'\n"
"注意：这是你本人的登录态，切勿分享给他人。"
)
return cookie
_DEFAULT_UAS = [
# 桌面 Chrome（最安全、最常见）
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 "
"(KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36",
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
"(KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36",
# 手机端 Safari
"Mozilla/5.0 (iPhone; CPU iPhone OS 17_5 like Mac OS X) AppleWebKit/605.1.15 "
"(KHTML, like Gecko) Version/17.0 Mobile/15E148 Safari/604.1",
]
# -------- 客户端 --------
@dataclass
class XHSClient:
"""小红书网页端客户端。用用户 Cookie，强节流 + 风控检测。"""
cookie: str
user_agent: Optional[str] = None
min_delay: float = 3.0 # 最短随机等待（秒）
max_delay: float = 7.0 # 最长随机等待（秒）
timeout: float = 15.0
max_requests_per_session: int = 30 # 单次会话抓取上限
proxies: Optional[Dict[str, str]] = None
session: requests.Session = field(init=False)
_request_count: int = field(init=False, default=0)
_last_request_at: float = field(init=False, default=0.0)
def __post_init__(self) -> None:
self.session = requests.Session()
if not self.user_agent:
self.user_agent = random.choice(_DEFAULT_UAS)
self.session.headers.update(self._base_headers())
self._apply_cookie(self.cookie)
if self.proxies:
self.session.proxies.update(self.proxies)
# ----- headers / cookie -----
def _base_headers(self) -> Dict[str, str]:
return {
"User-Agent": self.user_agent,
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
"Accept-Encoding": "gzip, deflate, br",
"Cache-Control": "no-cache",
"Pragma": "no-cache",
"Referer": "https://www.xiaohongshu.com/",
"Upgrade-Insecure-Requests": "1",
"sec-ch-ua": '"Chromium";v="125", "Not.A/Brand";v="24"',
"sec-ch-ua-mobile": "?0",
"sec-ch-ua-platform": '"macOS"',
}
def _apply_cookie(self, cookie_str: str) -> None:
"""把字符串形式的 Cookie 拆到 session 里。"""
for chunk in cookie_str.split(";"):
chunk = chunk.strip()
if not chunk or "=" not in chunk:
continue
name, value = chunk.split("=", 1)
self.session.cookies.set(name.strip(), value.strip(), domain=".xiaohongshu.com")
# ----- 节流 -----
def _throttle(self) -> None:
self._request_count += 1
if self._request_count > self.max_requests_per_session:
raise RateLimited(
f"本次会话已发起 {self._request_count - 1} 个请求，超过上限 "
f"{self.max_requests_per_session}。请换个时间再抓，避免触发风控。"
)
wait = random.uniform(self.min_delay, self.max_delay)
elapsed = time.time() - self._last_request_at
if elapsed < wait and self._last_request_at > 0:
time.sleep(wait - elapsed)
self._last_request_at = time.time()
# ----- 核心 GET -----
def _get(self, url: str, params: Optional[Dict[str, Any]] = None,
extra_headers: Optional[Dict[str, str]] = None) -> requests.Response:
self._throttle()
headers = {}
if extra_headers:
headers.update(extra_headers)
LOG.info("GET %s", url)
resp = self.session.get(url, params=params, headers=headers, timeout=self.timeout)
self._check_response(resp)
return resp
def _check_response(self, resp: requests.Response) -> None:
"""识别常见的风控信号。"""
status = resp.status_code
text_head = resp.text[:2048] if resp.text else ""
low = text_head.lower()
if status in (460, 461):
raise RateLimited(f"HTTP {status} — 小红书频率限制，立即停止。")
if status in (401,):
raise LoginRequired("HTTP 401 — Cookie 失效，需要重新登录获取。")
if status == 403:
raise BlockedByCaptcha("HTTP 403 — 被拒绝，通常是风控，请到浏览器过一下滑块。")
if status == 404:
raise NotFound("HTTP 404 — 内容不存在。")
if status >= 500:
raise XHSError(f"HTTP {status} — 小红书服务端错误。")
# HTML 层面的风控识别
captcha_markers = ("captcha", "verify", "滑块", "行为验证", "/verify/")
if any(m in low for m in captcha_markers) and "search" not in resp.url:
raise BlockedByCaptcha("响应体出现验证码提示，已被风控，请到浏览器处理。")
# 如果重定向到登录页
if "login" in urlparse(resp.url).path.lower():
raise LoginRequired("被重定向到登录页，Cookie 可能已失效。")
# ----- 公开方法：网页端读取 -----
def get_explore_page(self, note_id: str, xsec_token: Optional[str] = None,
xsec_source: str = "pc_feed") -> str:
"""抓取笔记详情页 HTML。解析 `window.__INITIAL_STATE__` 见 xhs_parser。"""
url = f"https://www.xiaohongshu.com/explore/{note_id}"
params: Dict[str, Any] = {}
if xsec_token:
params["xsec_token"] = xsec_token
params["xsec_source"] = xsec_source
resp = self._get(url, params=params)
return resp.text
def get_user_page(self, user_id: str) -> str:
"""抓取用户主页 HTML。解析 user profile + 已发布笔记列表 preview。"""
url = f"https://www.xiaohongshu.com/user/profile/{user_id}"
resp = self._get(url)
return resp.text
def get_search_page(self, keyword: str, source: str = "web_search_result_notes") -> str:
"""搜索笔记页（网页端）。"""
from urllib.parse import quote
url = f"https://www.xiaohongshu.com/search_result?keyword={quote(keyword)}&source={source}"
resp = self._get(url)
return resp.text
# ----- 状态 -----
@property
def request_count(self) -> int:
return self._request_count
def cool_down(self, minutes: float = 10) -> None:
"""主动冷却 — 任务之间手动调用一下。"""
LOG.info("cool down %.1f min ...", minutes)
time.sleep(minutes * 60)
self._request_count = 0
FILE:scripts/xhs_coach.py
"""火一五小红书"写作教练"核心库。
定位
====
比 polish_post 多一层："为什么这里不好 + 应该怎么改 + 参考是什么样"。
不是替你写，是教你为什么这样写更好。
诊断维度
========
1. **风格偏离** — 标题/段落/emoji 是否偏离用户自己的 baseline
2. **公式诊断** — 这个标题命中了哪种公式？为什么没用上更适合的？
3. **首段抓力** — 钩子词命中了吗？前 3 行能不能让人停？
4. **结构判断** — 这段正文像哪种骨架？该补什么？
5. **类比参考** — 从用户 baseline 里找相似主题的"好"样本对照
6. **演进 / 学习** — 反复同类问题 → 标记为成长方向
输出
====
一组 `Diagnosis(what, why, how, example)`：
- `what` — 哪里有问题（一句话）
- `why` — 为什么有问题（原理 / 数据）
- `how` — 怎么改（一两个具体操作）
- `example` — 改后的样子（可选）
LLM 钩子
========
默认靠规则推理。如果环境变量 `XHS_LLM_PROVIDER` 设置（如 anthropic），
且安装了对应 SDK，会调一次 LLM 把「why / how / example」生成得更具体。
未设置时完全离线。
"""
from __future__ import annotations
import os
import re
from dataclasses import asdict, dataclass, field
from typing import Any, Dict, List, Optional
from xhs_profile import RuleOverride, StyleProfile
from xhs_writer import (
Draft,
_EMOJI_RE,
_count_emoji,
score_post,
)
# =====================================================================
# 数据结构
# =====================================================================
@dataclass
class Diagnosis:
where: str # "title" / "first_lines" / "emoji" / "structure" / "style"
severity: str # "high" / "medium" / "low"
what: str
why: str
how: str
example: str = ""
def to_dict(self) -> Dict[str, Any]:
return asdict(self)
@dataclass
class CoachReport:
overall: int # 0~100，重用 score_post 总分
breakdown: Dict[str, int]
diagnoses: List[Diagnosis]
growth_hints: List[str] # 长线成长建议（基于历史 feedback）
def to_dict(self) -> Dict[str, Any]:
return {
"overall": self.overall,
"breakdown": self.breakdown,
"diagnoses": [d.to_dict() for d in self.diagnoses],
"growth_hints": self.growth_hints,
}
# =====================================================================
# 标题诊断 — 公式 + 风格
# =====================================================================
_FORMULA_FEATURES = {
"T1": ("数字对比", re.compile(r"^\s*\d+\s*[个件招种]")),
"T2": ("痛点共情", re.compile(r"为什么|怎么办|不能再")),
"T3": ("反差冲突", re.compile(r"我以为|没想到|看着.+其实")),
"T4": ("悬念钩子", re.compile(r"居然|没想到|这一招")),
"T5": ("身份代入", re.compile(r"^作为")),
"T6": ("福利免费", re.compile(r"免费|0\s?元|白嫖")),
"T7": ("时间节点", re.compile(r"\d+\s*年|\d+\s*岁|换季|今年")),
"T8": ("提问诱发", re.compile(r"是不是|为什么|真的吗|是真是假|\?$|？$")),
"T9": ("极端结果", re.compile(r"最|第一次|唯一")),
"T10": ("步骤指南", re.compile(r"保姆级|手把手|从零")),
"T11": ("故事开场", re.compile(r"上周|那天|朋友说|前几天")),
}
def detect_formula(title: str) -> Optional[str]:
for code, (_, pat) in _FORMULA_FEATURES.items():
if pat.search(title):
return code
return None
def diagnose_title(title: str, profile: StyleProfile) -> List[Diagnosis]:
diags: List[Diagnosis] = []
n = len(title)
# 长度
if n < 12:
diags.append(Diagnosis(
where="title", severity="high",
what=f"标题只有 {n} 字，钩子不足",
why="小红书首页标题黄金区间是 16~22 字，太短信息密度不够、读者一滑就走。",
how="加上 ① 数字（3/5/7） ② 利益点 ③ 反差词。例如 '5 个 + 名词复数 + 结果'",
example=f"3 个被严重低估的 {profile.niche or '小习惯'}，{profile.persona or '我'} 体感真的不一样",
))
elif n > 28:
diags.append(Diagnosis(
where="title", severity="medium",
what=f"标题 {n} 字偏长",
why="超过 26~28 字会被首页截断，截掉的部分往往就是关键词。",
how="把利益点收口成 1 句话，去掉重复修饰词。",
))
# 公式识别
formula = detect_formula(title)
if not formula:
diags.append(Diagnosis(
where="title", severity="medium",
what="标题没有明显的钩子模式",
why=("爆款标题 90% 命中至少一种公式（数字、痛点、反差、悬念等）。"
"无公式 = 平淡叙述句 = 容易被算法和读者一起忽略。"),
how="选一个公式重写，比如 '为什么...怎么办'（痛点）/ '我以为 X 结果 Y'（反差）",
example="见 data/title_templates.md",
))
else:
# 用了公式，但是不是用户自己擅长的？
favs = profile.favorite_formulas or {}
if favs and formula not in favs:
top = max(favs, key=favs.get)
diags.append(Diagnosis(
where="title", severity="low",
what=f"用的公式是 {formula}，但你 baseline 里最常用的是 {top}",
why="保持公式一致性会让账号画像更清晰，读者形成预期。同时不强求 — 偶尔变换是好事。",
how=f"如果想稳，参考 baseline 里 {top} 公式的标题；想突破，继续用 {formula}。",
))
# emoji 浮夸
e = _count_emoji(title)
if e > 3:
diags.append(Diagnosis(
where="title", severity="medium",
what=f"标题 {e} 个 emoji 偏多",
why="emoji 多的标题会被识别为低质营销号，掉权重。",
how="保留 0~2 个最贴主题的，其他删掉。",
))
return diags
# =====================================================================
# 首段诊断
# =====================================================================
_HOOK_PATTERNS = [
("代入式提问", re.compile(r"你是不是|你也|你有过|你最近")),
("数据冲击", re.compile(r"\d+%|\d+\s?(?:年|个月|天|斤|块|万|w)")),
("反差陈述", re.compile(r"我以为|本以为|没想到|结果")),
("场景白描", re.compile(r"上周|那天|今天|周\w|早上|晚上")),
]
def diagnose_first_lines(content: str, profile: StyleProfile) -> List[Diagnosis]:
diags: List[Diagnosis] = []
head = "\n".join([ln for ln in content.splitlines() if ln.strip()][:3])
if len(head) < 30:
diags.append(Diagnosis(
where="first_lines", severity="high",
what="首段不到 30 字",
why="80% 用户只看首 3 行就决定要不要继续读。这里不抓住，后面写得再好也白搭。",
how="补 1~2 个具体场景或数字，让读者感到'就是说我'。",
example="✨ 你是不是也遇到过这种情况？\n下午脸又紧又起皮，妆面卡得怀疑人生。",
))
return diags
matched = [name for name, pat in _HOOK_PATTERNS if pat.search(head)]
if not matched:
diags.append(Diagnosis(
where="first_lines", severity="medium",
what="首段没有可识别的钩子模式",
why="爆款首段 4 选 1：① 代入式提问 ② 数据冲击 ③ 反差陈述 ④ 场景白描。"
"缺了钩子，读者会把它当成普通介绍直接划走。",
how="选一种钩子重写第一句。",
example="✨ 你是不是也每次到下午就脸又紧又起皮？/ 上周第一次试这个方法，没想到真的有用。",
))
return diags
# =====================================================================
# 结构 / 排版诊断
# =====================================================================
_SKELETON_HINTS = {
"S1": ("钩子-痛点-方案-金句", ["📌", "✨", "其实", "方案", "三件"]),
"S3": ("测评对比", ["对比", "推荐", "回购", "避雷", "🔹"]),
"S4": ("清单", ["1️⃣", "2️⃣", "整理了", "盘点"]),
"S5": ("教程", ["Step 1", "保姆级", "步骤", "📦"]),
"S6": ("观点", ["我认为", "不主流", "反共识", "理由"]),
"S7": ("Vlog", ["☕", "🌙", "周末", "下午"]),
}
def detect_skeleton(content: str) -> Optional[str]:
best = None
best_hits = 0
for code, (_name, markers) in _SKELETON_HINTS.items():
hits = sum(1 for m in markers if m in content)
if hits > best_hits:
best, best_hits = code, hits
return best if best_hits >= 2 else None
def diagnose_structure(content: str, profile: StyleProfile) -> List[Diagnosis]:
diags: List[Diagnosis] = []
skeleton = detect_skeleton(content)
if not skeleton:
diags.append(Diagnosis(
where="structure", severity="medium",
what="正文没有可识别的骨架",
why=("读者扫读时靠 '视觉锚点'（emoji 项目符号 / 步骤词 / 小标题）抓信息。"
"无骨架 = 一团文字 = 收藏率低。"),
how="选一种骨架（S1~S7）重排：每段 1~3 句，关键句用 📌/✨ 当锚点。",
example="见 data/content_structures.md",
))
# 段落
lines = content.splitlines()
long_paragraphs = [ln for ln in lines if len(ln) > 80]
if len(long_paragraphs) > 2:
diags.append(Diagnosis(
where="layout", severity="medium",
what=f"有 {len(long_paragraphs)} 段超过 80 字",
why="手机一屏一行约 22 字，超过 80 字 = 4 屏一坨，扫读体验崩。",
how="把长段在自然停顿处拆成 2~3 行，加空行。",
))
return diags
# =====================================================================
# 风格偏离 — 拿用户自己的 baseline 当尺
# =====================================================================
def diagnose_style_drift(draft: Draft, profile: StyleProfile) -> List[Diagnosis]:
diags: List[Diagnosis] = []
if profile.sample_count < 1:
return diags
# 标题长度偏离
if draft.title:
tl = len(draft.title)
lo, hi = profile.title_len_range
if not (lo - 2 <= tl <= hi + 2):
diags.append(Diagnosis(
where="style", severity="low",
what=f"标题 {tl} 字，偏离你常用范围 {lo}~{hi}",
why="账号画像稳定的核心是 '读者预期一致' — 标题长短跳脱会让算法重新打标。",
how=f"压缩或扩展到 {lo}~{hi} 字（如果有意尝试新风格可忽略）。",
))
# emoji 偏离
e = _count_emoji(draft.content)
if profile.emoji_per_post > 0 and (e < 0.4 * profile.emoji_per_post or e > 2.0 * profile.emoji_per_post):
diags.append(Diagnosis(
where="style", severity="low",
what=f"全文 emoji {e} 个，与你常态（{profile.emoji_per_post}）差较多",
why="emoji 是你账号 '语气' 的一部分，跳脱也会影响识别度。",
how=f"调到 {int(profile.emoji_per_post * 0.6)}~{int(profile.emoji_per_post * 1.4)} 之间。",
))
# 口头禅
if profile.common_phrases:
any_phrase = any(p in (draft.content + draft.title) for p in profile.common_phrases)
if not any_phrase:
diags.append(Diagnosis(
where="style", severity="low",
what="文案里没出现你常用的口头禅",
why=f"baseline 里常出现 {', '.join(profile.common_phrases[:3])}，"
"这些是读者认你的 IP 标记。",
how=f"自然地加 1 处，比如开头或转折。",
))
return diags
# =====================================================================
# 长线成长建议（看反馈历史 + 快照表现）
# =====================================================================
def derive_growth_hints(feedback_log: List[Dict[str, Any]],
post_history: List[Dict[str, Any]]) -> List[str]:
out: List[str] = []
if not feedback_log and not post_history:
return out
# 反馈：哪类规则反复被 reject
by_rule: Dict[str, int] = {}
for fb in feedback_log:
if fb.get("reaction") == "reject":
k = (fb.get("rule_key") or "").split(":", 1)[0]
by_rule[k] = by_rule.get(k, 0) + 1
if by_rule:
top = max(by_rule, key=by_rule.get)
if by_rule[top] >= 3:
out.append(f"📈 你已经多次拒绝「{top}」检查。可以跑 "
"`profile_init.py evolve` 让助手自动禁用它。")
# 历史分数趋势
scored = [p for p in post_history if isinstance(p.get("score"), (int, float))]
if len(scored) >= 5:
recent = sum(p["score"] for p in scored[-5:]) / 5
early = sum(p["score"] for p in scored[:5]) / 5
if recent > early + 5:
out.append(f"📈 最近 5 篇平均分 {recent:.0f}，比早期 5 篇 {early:.0f} 高 — 在进步。")
elif early > recent + 5:
out.append(f"📉 最近 5 篇平均分 {recent:.0f}，比早期 {early:.0f} 低 — 注意是否飘了。")
return out
# =====================================================================
# LLM 钩子（可选）
# =====================================================================
def _maybe_enrich_with_llm(draft: Draft, diagnoses: List[Diagnosis]) -> List[Diagnosis]:
"""如果设置了 XHS_LLM_PROVIDER，调一次 LLM 把每条 diag 的 how/example 写得更具体。
只是增强；失败静默回退到规则版本。
"""
provider = os.environ.get("XHS_LLM_PROVIDER", "").lower()
if not provider:
return diagnoses
try:
if provider == "anthropic":
return _enrich_anthropic(draft, diagnoses)
except Exception:
return diagnoses
return diagnoses
def _enrich_anthropic(draft: Draft, diagnoses: List[Diagnosis]) -> List[Diagnosis]:
try:
from anthropic import Anthropic # type: ignore
except ImportError:
return diagnoses
client = Anthropic()
sys_prompt = (
"你是小红书写作教练。给定一篇笔记草稿和一组诊断，"
"针对每条诊断，写一句更具体、更可操作的'how'，并给一行示例。"
"保留原始结构 (where/severity/what/why)，只改 how 和 example。"
)
payload = {
"draft": {"title": draft.title, "content": draft.content[:1200], "tags": draft.tags},
"diagnoses": [d.to_dict() for d in diagnoses],
}
msg = client.messages.create(
model=os.environ.get("XHS_LLM_MODEL", "claude-haiku-4-5-20251001"),
max_tokens=1500,
system=sys_prompt,
messages=[{"role": "user", "content": str(payload)}],
)
text = msg.content[0].text if msg.content else ""
# 简化：失败就直接回退
import json as _json
try:
data = _json.loads(text)
out = []
for raw, d in zip(data.get("diagnoses", []), diagnoses):
d.how = raw.get("how", d.how)
d.example = raw.get("example", d.example)
out.append(d)
return out
except Exception:
return diagnoses
# =====================================================================
# 顶层入口
# =====================================================================
def diagnose_allen(draft: Draft) -> List[Diagnosis]:
"""从 Allen 美学体系生成诊断 — 留白 / AI腔 / 教带 / 共鸣 / 邀请。"""
try:
from xhs_aesthetic import aesthetic_score
except ImportError:
return []
a = aesthetic_score(draft.title, draft.content)
out: List[Diagnosis] = []
label_map = {
"breath": ("留白度", "Allen 第一课：留白是给读者填情绪的空间"),
"ai_speak": ("AI 腔", "汇报化 / 模板化词汇会让读者觉得疏离"),
"teach_vs_lead": ("带读者", "Allen 第一课：从'教读者'到'带读者'"),
"resonance": ("共鸣度", "Allen 实战教训：场景共鸣 = 共同记忆，不是冷知识"),
"invitation": ("邀请语", "Allen 第三课：互动是邀请，不是任务指令"),
}
for key, info in a.by_dim.items():
if info["score"] >= 7:
continue # 高分项不诊断
what_label, why_base = label_map.get(key, (key, ""))
severity = "high" if info["score"] <= 3 else "medium" if info["score"] <= 5 else "low"
what = (info["issues"][0] if info["issues"] else f"{what_label} 偏低（{info['score']}/10）")
why = why_base
how = (info["suggestions"][0] if info["suggestions"] else "见 data/allen_method.md")
out.append(Diagnosis(
where="allen",
severity=severity,
what=f"【{what_label}】{what}",
why=why,
how=how,
example="",
))
return out
def coach(
draft: Draft,
profile: Optional[StyleProfile] = None,
rules: Optional[RuleOverride] = None,
feedback_log: Optional[List[Dict[str, Any]]] = None,
post_history: Optional[List[Dict[str, Any]]] = None,
*,
enrich_llm: bool = True,
include_allen: bool = True,
) -> CoachReport:
profile = profile or StyleProfile()
diagnoses: List[Diagnosis] = []
diagnoses += diagnose_title(draft.title, profile)
diagnoses += diagnose_first_lines(draft.content, profile)
diagnoses += diagnose_structure(draft.content, profile)
diagnoses += diagnose_style_drift(draft, profile)
if include_allen:
diagnoses += diagnose_allen(draft)
if enrich_llm:
diagnoses = _maybe_enrich_with_llm(draft, diagnoses)
score = score_post(draft.title, draft.content, draft.tags, rules=rules)
growth = derive_growth_hints(feedback_log or [], post_history or [])
return CoachReport(
overall=score.total,
breakdown=score.breakdown,
diagnoses=diagnoses,
growth_hints=growth,
)
FILE:scripts/xhs_parser.py
"""从小红书网页端 HTML 抽取结构化数据。
核心思路：小红书网页端把数据注入到 `window.__INITIAL_STATE__`（或 SSR 版本）里，
我们只需要把那段 JSON 解出来就行，不涉及 X-s 签名。
"""
from __future__ import annotations
import html as htmlmod
import json
import re
from dataclasses import asdict, dataclass, field
from typing import Any, Dict, List, Optional
# ----- 数据结构 -----
@dataclass
class Author:
user_id: str = ""
nickname: str = ""
avatar: str = ""
follower_count: Optional[int] = None
following_count: Optional[int] = None
note_count: Optional[int] = None
@dataclass
class Interactions:
liked_count: int = 0
collected_count: int = 0
comment_count: int = 0
shared_count: int = 0
@dataclass
class Note:
note_id: str = ""
title: str = ""
content: str = ""
note_type: str = "" # "normal" | "video"
images: List[str] = field(default_factory=list)
video_url: str = ""
tags: List[str] = field(default_factory=list)
at_users: List[str] = field(default_factory=list)
author: Author = field(default_factory=Author)
interactions: Interactions = field(default_factory=Interactions)
ip_location: str = ""
published_at: str = "" # ISO-like str if available
last_update_at: str = ""
url: str = ""
raw_time: Optional[int] = None # 毫秒时间戳
@dataclass
class UserProfile:
user_id: str = ""
nickname: str = ""
avatar: str = ""
description: str = ""
gender: str = ""
ip_location: str = ""
interactions: Dict[str, int] = field(default_factory=dict) # fans / follows / notes / liked
recent_notes: List[Dict[str, Any]] = field(default_factory=list)
tags: List[str] = field(default_factory=list)
# ----- 从 HTML 提取 state -----
_STATE_RE = re.compile(
r"window\.__INITIAL_STATE__\s*=\s*(\{.*?\})\s*</script>",
re.DOTALL,
)
_SSR_RE = re.compile(
r"window\.__INITIAL_SSR_STATE__\s*=\s*(\{.*?\})\s*</script>",
re.DOTALL,
)
def extract_initial_state(html_text: str) -> Dict[str, Any]:
"""解析 HTML 里的 __INITIAL_STATE__。小红书会把 undefined 写进 JSON，得替换一下。"""
for regex in (_STATE_RE, _SSR_RE):
m = regex.search(html_text)
if not m:
continue
blob = m.group(1)
# XHS 把 JS 的 undefined 直接塞进来了，json.loads 不认，替换成 null
blob = re.sub(r":\s*undefined([,\}])", r": null\1", blob)
blob = blob.replace(":undefined,", ":null,").replace(":undefined}", ":null}")
try:
return json.loads(blob)
except json.JSONDecodeError:
continue
return {}
# ----- note 解析 -----
def parse_note_page(html_text: str, note_id: str = "") -> Optional[Note]:
"""从 `https://www.xiaohongshu.com/explore/{id}` 页面解析出笔记。"""
state = extract_initial_state(html_text)
if not state:
return None
note_data = _locate_note_detail(state, note_id)
if not note_data:
return None
return _build_note(note_data, note_id=note_id)
def _locate_note_detail(state: Dict[str, Any], note_id: str) -> Optional[Dict[str, Any]]:
# 结构：state.note.noteDetailMap[id].note
note_tree = state.get("note") or {}
detail_map = note_tree.get("noteDetailMap") or {}
if note_id and note_id in detail_map:
entry = detail_map[note_id]
return entry.get("note") or entry
# 否则取第一个
for key, entry in detail_map.items():
if isinstance(entry, dict):
return entry.get("note") or entry
return None
def _build_note(d: Dict[str, Any], note_id: str = "") -> Note:
note = Note()
note.note_id = d.get("noteId") or d.get("id") or note_id
note.title = _clean_text(d.get("title", ""))
note.content = _clean_text(d.get("desc", ""))
note.note_type = d.get("type", "normal")
note.ip_location = d.get("ipLocation", "")
raw_time = d.get("time") or d.get("publishTime")
if isinstance(raw_time, (int, float)):
note.raw_time = int(raw_time)
note.published_at = _ts_to_iso(int(raw_time))
last_up = d.get("lastUpdateTime")
if isinstance(last_up, (int, float)):
note.last_update_at = _ts_to_iso(int(last_up))
note.url = f"https://www.xiaohongshu.com/explore/{note.note_id}"
# 图片
for img in d.get("imageList", []) or []:
url = img.get("urlDefault") or img.get("url")
if url:
note.images.append(url)
# 视频
video = d.get("video") or {}
media = video.get("media") or {}
for stream_list in (media.get("stream") or {}).values():
if isinstance(stream_list, list) and stream_list:
master = stream_list[0].get("masterUrl") or stream_list[0].get("backupUrls", [""])[0]
if master:
note.video_url = master
break
# 话题标签 / @
for tag in d.get("tagList", []) or []:
name = tag.get("name")
ttype = tag.get("type")
if not name:
continue
if ttype == "topic":
note.tags.append(name)
elif ttype == "mention":
note.at_users.append(name)
# 作者
user = d.get("user") or {}
note.author = Author(
user_id=user.get("userId") or user.get("id") or "",
nickname=_clean_text(user.get("nickname", user.get("nickName", ""))),
avatar=user.get("avatar", ""),
)
# 互动
inter = d.get("interactInfo") or {}
note.interactions = Interactions(
liked_count=_to_int(inter.get("likedCount")),
collected_count=_to_int(inter.get("collectedCount")),
comment_count=_to_int(inter.get("commentCount")),
shared_count=_to_int(inter.get("sharedCount", inter.get("shareCount", 0))),
)
return note
# ----- user 解析 -----
def parse_user_page(html_text: str) -> Optional[UserProfile]:
state = extract_initial_state(html_text)
if not state:
return None
user_tree = state.get("user") or {}
page = user_tree.get("userPageData") or user_tree.get("pageData") or user_tree
basic = page.get("basicInfo") or user_tree.get("basicInfo") or {}
inter = page.get("interactions") or []
tags = page.get("tags") or []
notes = page.get("notes") or []
if not basic:
# 有的主页结构 userPageData 直接就是基础信息
basic = page
profile = UserProfile(
user_id=basic.get("userId") or basic.get("redId") or "",
nickname=_clean_text(basic.get("nickname", "")),
avatar=basic.get("imageb", basic.get("images", "")),
description=_clean_text(basic.get("desc", "")),
gender=str(basic.get("gender", "")),
ip_location=basic.get("ipLocation", ""),
)
if isinstance(inter, list):
for item in inter:
k = item.get("type") or item.get("name")
v = item.get("count")
if k and v is not None:
profile.interactions[str(k)] = _to_int(v)
elif isinstance(inter, dict):
for k, v in inter.items():
profile.interactions[str(k)] = _to_int(v)
profile.tags = [t.get("name") for t in tags if isinstance(t, dict) and t.get("name")]
# 主页笔记列表（preview，信息有限但有 note_id、likes、封面）
if isinstance(notes, list):
for group in notes:
if isinstance(group, list):
for item in group:
preview = _note_preview(item)
if preview:
profile.recent_notes.append(preview)
elif isinstance(group, dict):
preview = _note_preview(group)
if preview:
profile.recent_notes.append(preview)
return profile
def _note_preview(raw: Dict[str, Any]) -> Optional[Dict[str, Any]]:
card = raw.get("noteCard") or raw
note_id = card.get("noteId") or raw.get("id") or card.get("id")
if not note_id:
return None
inter = card.get("interactInfo") or {}
return {
"note_id": note_id,
"title": _clean_text(card.get("displayTitle") or card.get("title") or ""),
"type": card.get("type", ""),
"cover": ((card.get("cover") or {}).get("urlDefault", "")),
"liked_count": _to_int(inter.get("likedCount")),
"url": f"https://www.xiaohongshu.com/explore/{note_id}",
}
# ----- search 解析 -----
def parse_search_page(html_text: str) -> List[Dict[str, Any]]:
state = extract_initial_state(html_text)
if not state:
return []
search = state.get("search") or {}
feeds = search.get("feeds") or search.get("feedList") or []
results = []
if isinstance(feeds, list):
for f in feeds:
if isinstance(f, dict):
preview = _note_preview(f)
if preview:
results.append(preview)
return results
# ----- 小工具 -----
def _clean_text(text: Any) -> str:
if not text:
return ""
if not isinstance(text, str):
text = str(text)
return htmlmod.unescape(text).strip()
def _to_int(val: Any) -> int:
if val is None:
return 0
if isinstance(val, (int, float)):
return int(val)
s = str(val).strip()
if not s:
return 0
# 处理 "1.2万" / "3000+" 这种
s = s.replace(",", "").replace("+", "")
mult = 1
if s.endswith("万"):
mult = 10000
s = s[:-1]
elif s.endswith("k") or s.endswith("K"):
mult = 1000
s = s[:-1]
try:
return int(float(s) * mult)
except ValueError:
return 0
def _ts_to_iso(ts_ms: int) -> str:
import datetime
try:
return datetime.datetime.fromtimestamp(ts_ms / 1000).isoformat(timespec="seconds")
except (OverflowError, OSError, ValueError):
return ""
# ----- dataclass → dict helper -----
def note_to_dict(note: Note) -> Dict[str, Any]:
return asdict(note)
def profile_to_dict(p: UserProfile) -> Dict[str, Any]:
return asdict(p)
FILE:scripts/xhs_profile.py
"""火一五小红书"创作者画像"核心库。
设计目标
========
让助手"记得这个创作者" — 他写过什么、什么风格、哪些规则适用、哪些不适用。
所有功能（打分 / 教练 / 选题 / 复盘）都从这里读取，让产出"像他自己写的"。
存档位置
========
**`~/.xiaohongshu/profile/`**（用户私有，不入 git/skills 包）。
跨 skill 共用 — 未来 huo15-blog / huo15-douyin 也能读到同一份创作者档案。
数据
====
- `style.json` — 自动从 baseline 笔记提取的风格特征
- `rules.json` — 用户的规则覆盖（"我不要这个"+"加这个"）
- `feedback.jsonl` — 用户对每条建议的反馈日志（用于规则演进）
- `baseline/` — 1~5 篇代表作样本（json 文件）
- `posts.jsonl` — 起草历史（publish_helper 写入）
- `snapshots.jsonl`— 互动快照（track_post 写入）
- `reviews/` — 周/月复盘报告
设计原则
========
- **默认规则在 `data/`（技能资产，所有人共享），覆盖在 `profile/rules.json`（个人化）。**
最终值 = `merge(default, override)`。
- 风格档案是"统计特征"，不是"硬规定" — 偏离时只是提醒，不是禁止。
- 规则覆盖支持"教学"流程：用户给反馈 → 反馈累积到阈值 → 自动调整 rules.json。
"""
from __future__ import annotations
import collections
import datetime as dt
import json
import os
import re
import statistics as stats
from dataclasses import asdict, dataclass, field
from pathlib import Path
from typing import Any, Dict, List, Optional, Set
# =====================================================================
# 路径
# =====================================================================
def profile_root(custom: Optional[str] = None) -> Path:
"""档案根目录：默认 ~/.xiaohongshu/profile/，可被环境变量覆盖。"""
if custom:
return Path(os.path.expanduser(custom))
env = os.environ.get("XHS_PROFILE_DIR")
if env:
return Path(os.path.expanduser(env))
return Path(os.path.expanduser("~/.xiaohongshu/profile"))
def ensure_root(root: Optional[Path] = None) -> Path:
p = root or profile_root()
p.mkdir(parents=True, exist_ok=True)
(p / "baseline").mkdir(exist_ok=True)
(p / "reviews").mkdir(exist_ok=True)
return p
# =====================================================================
# StyleProfile — 风格档案
# =====================================================================
@dataclass
class StyleProfile:
"""从 1~5 篇 baseline 提取的创作者特征。所有字段都是统计/偏好。"""
persona: str = "" # "30+ 干皮女生"，可选手填
voice: str = "casual" # casual / formal / playful / pro
niche: str = "" # "护肤"、"职场"，从 baseline 推断
avg_title_len: int = 18 # 平均标题长度
title_len_range: List[int] = field(default_factory=lambda: [16, 22])
avg_content_len: int = 400 # 平均正文字符数
avg_paragraphs: int = 8 # 平均段落数
avg_para_chars: int = 50 # 平均段落字符数
emoji_per_post: float = 6.0 # 平均每篇 emoji 数
favorite_emojis: List[str] = field(default_factory=list)
favorite_formulas: Dict[str, int] = field(default_factory=dict) # T1: 3 表示用过 3 次
favorite_skeletons: Dict[str, int] = field(default_factory=dict) # S1: 5
common_tags: List[str] = field(default_factory=list) # 高频话题（去重）
common_phrases: List[str] = field(default_factory=list) # "亲测" "我体感" 等口头禅
avoid_words: List[str] = field(default_factory=list) # 用户不爱用的词（手填）
sample_count: int = 0 # 用了几篇样本
last_updated: str = ""
def to_dict(self) -> Dict[str, Any]:
return asdict(self)
@classmethod
def from_dict(cls, d: Dict[str, Any]) -> "StyleProfile":
kwargs = {k: v for k, v in d.items() if k in cls.__dataclass_fields__}
return cls(**kwargs)
# =====================================================================
# RuleOverride — 规则覆盖
# =====================================================================
@dataclass
class RuleOverride:
"""用户对默认规则的覆盖。"""
# 子项权重覆盖（默认见 score_post 的 weights）
weights: Dict[str, float] = field(default_factory=dict)
# 完全禁用的检查项（不会扣分也不会建议）
disabled_checks: List[str] = field(default_factory=list)
# 用户额外的敏感词
custom_sensitive: List[str] = field(default_factory=list)
# 用户**允许**的词（覆盖 data/sensitive_words.txt 里的，比如医生确实需要"治愈"）
allowed_words: List[str] = field(default_factory=list)
# 用户偏好（影响生成）
prefer_emoji: Optional[bool] = None # None=不强制；True/False
prefer_question_title: Optional[bool] = None # 是否喜欢提问型标题
max_emoji_per_post: Optional[int] = None
custom_phrases: List[str] = field(default_factory=list) # 用户希望多用的口头禅
last_updated: str = ""
def to_dict(self) -> Dict[str, Any]:
return asdict(self)
@classmethod
def from_dict(cls, d: Dict[str, Any]) -> "RuleOverride":
kwargs = {k: v for k, v in d.items() if k in cls.__dataclass_fields__}
return cls(**kwargs)
# =====================================================================
# Feedback — 用户对建议的反馈
# =====================================================================
@dataclass
class Feedback:
"""用户对某条建议的反馈，会驱动 rules.json 自动演进。"""
at: str # ISO timestamp
rule_key: str # 如 "emoji"、"sensitive_word:最佳"
suggestion: str # 建议原文
reaction: str # accept / reject / ignore
note: str = ""
def to_dict(self) -> Dict[str, Any]:
return asdict(self)
# =====================================================================
# ProfileStore — 读写一体
# =====================================================================
class ProfileStore:
"""档案的统一读写入口。"""
def __init__(self, root: Optional[Path] = None) -> None:
self.root = ensure_root(root)
self.style_path = self.root / "style.json"
self.rules_path = self.root / "rules.json"
self.feedback_path = self.root / "feedback.jsonl"
self.baseline_dir = self.root / "baseline"
self.posts_path = self.root.parent / "posts.jsonl" # publish_helper 默认位置
self.snapshots_path = self.root.parent / "snapshots.jsonl" # track_post 默认位置
self.reviews_dir = self.root / "reviews"
# ---------- style ----------
def load_style(self) -> StyleProfile:
if not self.style_path.exists():
return StyleProfile()
try:
d = json.loads(self.style_path.read_text(encoding="utf-8"))
except (json.JSONDecodeError, OSError):
return StyleProfile()
return StyleProfile.from_dict(d)
def save_style(self, profile: StyleProfile) -> None:
profile.last_updated = dt.datetime.now().isoformat(timespec="seconds")
self.style_path.write_text(
json.dumps(profile.to_dict(), ensure_ascii=False, indent=2),
encoding="utf-8",
)
# ---------- rules ----------
def load_rules(self) -> RuleOverride:
if not self.rules_path.exists():
return RuleOverride()
try:
d = json.loads(self.rules_path.read_text(encoding="utf-8"))
except (json.JSONDecodeError, OSError):
return RuleOverride()
return RuleOverride.from_dict(d)
def save_rules(self, rules: RuleOverride) -> None:
rules.last_updated = dt.datetime.now().isoformat(timespec="seconds")
self.rules_path.write_text(
json.dumps(rules.to_dict(), ensure_ascii=False, indent=2),
encoding="utf-8",
)
# ---------- baseline ----------
def add_baseline(self, draft_or_note: Dict[str, Any]) -> Path:
"""加一篇 baseline 笔记。返回写入的文件路径。"""
idx = len(list(self.baseline_dir.glob("*.json"))) + 1
path = self.baseline_dir / f"sample_{idx:02d}.json"
path.write_text(
json.dumps(draft_or_note, ensure_ascii=False, indent=2),
encoding="utf-8",
)
return path
def load_baselines(self) -> List[Dict[str, Any]]:
out = []
for p in sorted(self.baseline_dir.glob("*.json")):
try:
out.append(json.loads(p.read_text(encoding="utf-8")))
except json.JSONDecodeError:
continue
return out
# ---------- feedback ----------
def append_feedback(self, fb: Feedback) -> None:
with self.feedback_path.open("a", encoding="utf-8") as f:
f.write(json.dumps(fb.to_dict(), ensure_ascii=False) + "\n")
def load_feedback(self) -> List[Feedback]:
if not self.feedback_path.exists():
return []
out = []
for line in self.feedback_path.read_text(encoding="utf-8").splitlines():
line = line.strip()
if not line:
continue
try:
d = json.loads(line)
out.append(Feedback(**{k: v for k, v in d.items() if k in Feedback.__dataclass_fields__}))
except (json.JSONDecodeError, TypeError):
continue
return out
# ---------- posts / snapshots（read-only 视图） ----------
def load_posts(self) -> List[Dict[str, Any]]:
if not self.posts_path.exists():
return []
out = []
for line in self.posts_path.read_text(encoding="utf-8").splitlines():
if line.strip():
try:
out.append(json.loads(line))
except json.JSONDecodeError:
pass
return out
def load_snapshots(self) -> List[Dict[str, Any]]:
if not self.snapshots_path.exists():
return []
out = []
for line in self.snapshots_path.read_text(encoding="utf-8").splitlines():
if line.strip():
try:
out.append(json.loads(line))
except json.JSONDecodeError:
pass
return out
# =====================================================================
# 从 baseline 笔记提取 StyleProfile
# =====================================================================
_EMOJI_RE = re.compile(
"["
"\U0001F300-\U0001F9FF"
"\U0001FA70-\U0001FAFF"
"\U00002600-\U000027BF"
"\U0001F000-\U0001F2FF"
"️]"
)
def _all_emojis(text: str) -> List[str]:
return _EMOJI_RE.findall(text)
def _extract_phrases(text: str) -> List[str]:
"""口头禅检测：常见的几种，命中即标记。"""
candidates = [
"亲测", "我自己", "我体感", "我之前", "我以为", "其实",
"保姆级", "手把手", "记一下", "整理了", "私心推荐",
"重点来了", "划重点", "敲黑板",
]
return [c for c in candidates if c in text]
def derive_style(baselines: List[Dict[str, Any]]) -> StyleProfile:
"""从已抓取/手动整理的笔记中提取风格档案。"""
if not baselines:
return StyleProfile()
title_lens: List[int] = []
content_lens: List[int] = []
para_counts: List[int] = []
para_lens: List[int] = []
emoji_counts: List[int] = []
emoji_pool: collections.Counter = collections.Counter()
formulas: collections.Counter = collections.Counter()
skeletons: collections.Counter = collections.Counter()
tags_pool: collections.Counter = collections.Counter()
phrases_pool: collections.Counter = collections.Counter()
for n in baselines:
title = (n.get("title") or "").strip()
content = n.get("content") or ""
if title:
title_lens.append(len(title))
if content:
content_lens.append(len(content))
paras = [p for p in content.splitlines() if p.strip()]
para_counts.append(len(paras))
para_lens.extend(len(p) for p in paras)
es = _all_emojis(title + "\n" + content)
emoji_counts.append(len(es))
emoji_pool.update(es)
for t in (n.get("tags") or []):
tags_pool[t.strip()] += 1
if n.get("formula"):
formulas[n["formula"]] += 1
if n.get("skeleton"):
skeletons[n["skeleton"]] += 1
phrases_pool.update(_extract_phrases(title + "\n" + content))
def _med(xs: List[int]) -> int:
return int(stats.median(xs)) if xs else 0
profile = StyleProfile(
avg_title_len=_med(title_lens) if title_lens else 18,
title_len_range=[min(title_lens), max(title_lens)] if title_lens else [16, 22],
avg_content_len=_med(content_lens) if content_lens else 400,
avg_paragraphs=_med(para_counts) if para_counts else 8,
avg_para_chars=_med(para_lens) if para_lens else 50,
emoji_per_post=round(stats.mean(emoji_counts), 1) if emoji_counts else 6.0,
favorite_emojis=[e for e, _ in emoji_pool.most_common(8)],
favorite_formulas=dict(formulas),
favorite_skeletons=dict(skeletons),
common_tags=[t for t, _ in tags_pool.most_common(15)],
common_phrases=[p for p, _ in phrases_pool.most_common(8)],
sample_count=len(baselines),
)
return profile
# =====================================================================
# 规则合并：default ⊕ override
# =====================================================================
_DEFAULT_WEIGHTS = {
"title": 0.25,
"first_lines": 0.20,
"layout": 0.10,
"emoji": 0.10,
"hashtags": 0.10,
"compliance": 0.25,
}
def effective_weights(rules: RuleOverride) -> Dict[str, float]:
"""合并默认权重 + 用户覆盖（含禁用 = 权重置 0）。返回归一化后的权重。"""
merged = dict(_DEFAULT_WEIGHTS)
for k, v in rules.weights.items():
if k in merged:
merged[k] = max(0.0, float(v))
for k in rules.disabled_checks:
if k in merged:
merged[k] = 0.0
total = sum(merged.values()) or 1.0
return {k: v / total for k, v in merged.items()}
def effective_sensitive_words(rules: RuleOverride, base: List[str]) -> List[str]:
"""合并：先去掉用户允许的词，再加上用户的额外敏感词。"""
allowed = set(rules.allowed_words or [])
out = [w for w in base if w not in allowed]
out.extend(w for w in (rules.custom_sensitive or []) if w not in allowed)
return out
# =====================================================================
# 规则演进：根据 feedback 自动调 rules
# =====================================================================
def evolve_rules(store: ProfileStore, *, threshold: int = 3) -> RuleOverride:
"""读 feedback.jsonl，把"连续 N 次 reject"的检查项自动写进 rules.json 的 disabled_checks。
规则：
- 同一个 rule_key 连续被 reject ≥ threshold → 加入 disabled_checks
- 一旦该 rule_key 出现 accept → 重置计数
- 已经 disabled 的不再重复加
"""
feedback = store.load_feedback()
rules = store.load_rules()
disabled = set(rules.disabled_checks)
counters: Dict[str, int] = {}
for fb in feedback:
# 只看大类（前缀切掉冒号后的细节）
key = fb.rule_key.split(":", 1)[0]
if fb.reaction == "reject":
counters[key] = counters.get(key, 0) + 1
elif fb.reaction == "accept":
counters[key] = 0
changed = False
for k, c in counters.items():
if c >= threshold and k not in disabled and k in _DEFAULT_WEIGHTS:
disabled.add(k)
changed = True
rules.disabled_checks = sorted(disabled)
if changed:
store.save_rules(rules)
return rules
FILE:scripts/xhs_writer.py
"""火一五小红书文案创作核心库。
负责三件事：
1. **生成（draft）**：根据主题 / 受众 / 骨架代号，给出标题候选 + 正文骨架填充。
2. **打分（score）**：从标题钩子、首段抓力、emoji 节奏、话题数、合规
等维度给一篇笔记打分。
3. **优化（polish）**：基于打分结果给出可执行的修改建议。
设计原则
========
- **不依赖大模型** — 所有生成逻辑都是规则驱动 + 模板填充，离线可跑。
这样脚本既是 skill 内部用的，也能让 Claude 在调用时拿到可解释的中间产物。
- **可被 LLM 复用** — 把"标题公式""正文骨架""敏感词"暴露成数据，
Claude 在生成时也能直接 import / 读取。
- **从不调用发布接口** — 任何写操作（发文 / 点赞 / 关注）都不在本模块。
"""
from __future__ import annotations
import json
import os
import random
import re
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
# 数据目录的解析：跑脚本时按相对路径找
_DATA_DIR = Path(__file__).resolve().parent.parent / "data"
# =====================================================================
# 数据加载
# =====================================================================
def _load_lines(path: Path) -> List[str]:
if not path.exists():
return []
out: List[str] = []
for line in path.read_text(encoding="utf-8").splitlines():
s = line.strip()
if not s or s.startswith("#"):
continue
out.append(s)
return out
def load_sensitive_words(path: Optional[Path] = None) -> List[str]:
return _load_lines(path or _DATA_DIR / "sensitive_words.txt")
# =====================================================================
# 标题模板（11 种公式 → 生成函数）
# =====================================================================
# 每个模板返回多个候选，让 Claude / 用户挑。
# 占位符：{topic} 主题词 / {persona} 受众身份 / {payoff} 利益点 / {number} 数字
def _pick_number(default: int = 3) -> int:
return random.choice([3, 5, 7])
_TITLE_GENERATORS: Dict[str, Any] = {}
def _register_title(code: str):
def deco(fn):
_TITLE_GENERATORS[code] = fn
return fn
return deco
@_register_title("T1") # 数字对比
def _t1(topic: str, persona: str, payoff: str) -> List[str]:
n = _pick_number()
base = [
f"{n} 个被严重低估的 {topic}，{payoff}",
f"{n} 招让你的 {topic} {payoff}",
f"{n} 件 {persona or '我'} 做了真的不后悔的 {topic} 小事",
]
return base
@_register_title("T2") # 痛点共情
def _t2(topic: str, persona: str, payoff: str) -> List[str]:
p = persona or "成年人"
return [
f"{p}，为什么 {topic} 总是越做越累",
f"{p} 怎么办：3 个不靠运气也能 {payoff or '改善'} 的笨办法",
f"{p} 真的不能再这么 {topic} 了，记住 4 个动作",
]
@_register_title("T3") # 反差冲突
def _t3(topic: str, persona: str, payoff: str) -> List[str]:
return [
f"我以为 {topic} 没用，结果 {payoff or '真香了'}",
f"看着普通的 {topic}，其实藏着 {payoff or '一个反常识'}",
f"被 {topic} 救了一次，才知道 {payoff or '之前都白折腾了'}",
]
@_register_title("T4") # 悬念钩子
def _t4(topic: str, persona: str, payoff: str) -> List[str]:
return [
f"把 {topic} 这样用，居然能 {payoff or '省一半时间'}",
f"没想到 {topic} 还能 {payoff or '这样玩'}，亲测有效",
f"这一招让 {topic} 的效率翻倍，方法很笨",
]
@_register_title("T5") # 身份代入
def _t5(topic: str, persona: str, payoff: str) -> List[str]:
p = persona or "前从业者"
return [
f"作为{p}，我来劝你别盲目入 {topic}",
f"作为常年研究 {topic} 的人，亲测有效的 3 件小事",
f"作为 30+ {p}，关于 {topic} 我有 5 句真心话",
]
@_register_title("T6") # 福利免费
def _t6(topic: str, persona: str, payoff: str) -> List[str]:
return [
f"免费！整理好的 {topic} 资料合集，分类清晰可用",
f"0 元学完 {topic}，这 3 个渠道真的够用",
f"{topic} 必备清单 — 不花冤枉钱版",
]
@_register_title("T7") # 时间节点
def _t7(topic: str, persona: str, payoff: str) -> List[str]:
return [
f"2026 年再做 {topic}，请先看完这 5 点",
f"{persona or '28 岁'} 之前一定要做的 {topic} 5 件事",
f"换季 {topic} 救命指南：从 0 到 1 的 4 步",
]
@_register_title("T8") # 提问诱发
def _t8(topic: str, persona: str, payoff: str) -> List[str]:
return [
f"为什么有些人做 {topic} 越做越轻松？我观察了 30 个案例",
f"{topic} 是不是真的有那么难？我自己试了 3 个月",
f"网传 {topic} 的几种说法，是真是假",
]
@_register_title("T9") # 极端结果（避绝对化用语，用主观）
def _t9(topic: str, persona: str, payoff: str) -> List[str]:
return [
f"这是我用过觉得舒服的 {topic}，回购了好几次",
f"第一次认真做 {topic}，被效果惊到了",
f"30 岁前唯一让我后悔没早做的 {topic} 这件事",
]
@_register_title("T10") # 步骤指南
def _t10(topic: str, persona: str, payoff: str) -> List[str]:
n = _pick_number(5)
return [
f"保姆级！从零开始做 {topic}，这 {n} 步少走弯路",
f"手把手教 {persona or '小白'} 入门 {topic}，含模板",
f"{topic} 入门 SOP：记住这 {n} 步就够了",
]
@_register_title("T11") # 故事开场
def _t11(topic: str, persona: str, payoff: str) -> List[str]:
return [
f"上周开始 {topic}，每天花 30 分钟也很值",
f"那天因为 {topic} 加班到半夜，我决定换种活法",
f"朋友说我变了，原来是开始 {topic} 之后",
]
def generate_titles(
topic: str,
*,
persona: str = "",
payoff: str = "",
formulas: Optional[List[str]] = None,
n_each: int = 1,
) -> List[Dict[str, str]]:
"""根据公式代号生成标题候选。
Args:
topic: 主题关键词，例如 "护肤""副业""减脂"。
persona: 目标受众身份，例如 "干皮女生""互联网打工人"。
payoff: 利益点 / 结果，例如 "效率翻倍""不踩坑"。
formulas: 想用的公式代号列表（T1 ~ T11）；空 = 全部。
n_each: 每种公式生成几条。
Returns:
[{"formula": "T1", "title": "..."}]
"""
keys = formulas or list(_TITLE_GENERATORS.keys())
out: List[Dict[str, str]] = []
for code in keys:
gen = _TITLE_GENERATORS.get(code)
if not gen:
continue
candidates = gen(topic, persona, payoff)
for t in candidates[:n_each]:
out.append({"formula": code, "title": t.strip()})
return out
# =====================================================================
# 正文骨架渲染（S1 ~ S7）
# =====================================================================
_BODY_SKELETONS: Dict[str, List[str]] = {
"S1": [
"✨ 你是不是也 {hook}？",
"我之前 {pain1}，",
"试过 {pain2}，{pain3}，最后发现都是治标不治本。",
"",
"其实只要 {breakthrough}，整件事就 {result}。",
"",
"📌 {step1_label}",
"{step1_detail}",
"",
"📌 {step2_label}",
"{step2_detail}",
"",
"📌 {step3_label}",
"{step3_detail}",
"",
"⚠️ {warning}",
"",
"{closing} {cta}",
],
"S2": [
"{scene}",
"",
"那一刻我突然意识到，{insight}。",
"",
"之前我一直 {old_pattern}，以为 {old_belief}。",
"其实 {new_realization}。",
"",
"如果你也在 {similar_situation}，可以试试 {action}。",
"",
"{closing}",
"",
"{cta}",
],
"S3": [
"✨ 这次给大家测了 {n} 款 {category}。",
"我筛选标准：{criteria}",
"",
"🔹 {item1_name}",
"{item1_detail}",
"",
"🔹 {item2_name}",
"{item2_detail}",
"",
"🔹 {item3_name}",
"{item3_detail}",
"",
"🏆 我自己最回购的是 {pick}，因为 {reason}。",
"",
"❌ 避雷：{avoid}",
"",
"{cta}",
],
"S4": [
"整理了 {n} 个 {topic}，分门别类放好了 ✨",
"",
"1️⃣ {item1}",
"2️⃣ {item2}",
"3️⃣ {item3}",
"4️⃣ {item4}",
"5️⃣ {item5}",
"",
"💡 我自己最常用的是 {favorite}。",
"",
"{cta}",
],
"S5": [
"今天写一份保姆级 {topic} 教程 ✍️",
"看完你能 {outcome}。",
"",
"📦 准备工作：{prep}",
"",
"Step 1：{step1}",
"{step1_note}",
"",
"Step 2：{step2}",
"{step2_note}",
"",
"Step 3：{step3}",
"{step3_note}",
"",
"❓ 常见问题",
"Q: {q1} A: {a1}",
"Q: {q2} A: {a2}",
"",
"{cta}",
],
"S6": [
"💡 我有个不太主流的观点：{opinion}",
"",
"大多数人觉得 {common_view}，但我的体感是 — {counter}。",
"",
"理由 1：{reason1}",
"理由 2：{reason2}",
"理由 3：{reason3}",
"",
"🤔 也许会有人说 {objection}，我的回应是 {response}。",
"",
"{cta}",
],
"S7": [
"{when} / {where}",
"",
"{moment1}",
"{moment2}",
"{moment3}",
"",
"{tiny_thought}",
"",
"{cta}",
],
}
def get_skeleton(code: str) -> List[str]:
if code not in _BODY_SKELETONS:
raise ValueError(f"未知正文骨架代号：{code}（可用：{', '.join(_BODY_SKELETONS)})")
return list(_BODY_SKELETONS[code])
def render_skeleton(code: str, fields: Optional[Dict[str, str]] = None) -> str:
"""把骨架代号 + 字段映射渲染成正文。未填的字段保留 `{name}`，让 LLM/作者后填。"""
lines = get_skeleton(code)
fields = fields or {}
rendered = []
for ln in lines:
try:
rendered.append(ln.format_map(_DefaultDict(fields)))
except Exception:
rendered.append(ln)
return "\n".join(rendered)
class _DefaultDict(dict):
"""缺失键时返回 `{key}` 占位，避免 KeyError。"""
def __missing__(self, key: str) -> str:
return "{" + key + "}"
# =====================================================================
# 文案打分
# =====================================================================
@dataclass
class PostScore:
"""一篇笔记的总分 + 子项分 + 修改建议。"""
total: int # 0 ~ 100
breakdown: Dict[str, int] = field(default_factory=dict) # 各子项 0~10
issues: List[str] = field(default_factory=list) # 警示
suggestions: List[str] = field(default_factory=list) # 修改建议
def to_dict(self) -> Dict[str, Any]:
return {
"total": self.total,
"breakdown": self.breakdown,
"issues": self.issues,
"suggestions": self.suggestions,
}
_EMOJI_RE = re.compile(
"["
"\U0001F300-\U0001F9FF"
"\U0001FA70-\U0001FAFF"
"\U00002600-\U000027BF"
"\U0001F000-\U0001F2FF"
"️]"
)
def _count_emoji(text: str) -> int:
return len(_EMOJI_RE.findall(text))
def score_title(title: str) -> Tuple[int, List[str], List[str]]:
"""标题打分。0~10。"""
score = 5
issues: List[str] = []
suggestions: List[str] = []
n = len(title)
if n < 10:
score -= 2
issues.append(f"标题过短（{n} 字），钩子不足")
suggestions.append("加上利益点 / 数字 / 反差词，扩到 16~22 字")
elif n > 30:
score -= 2
issues.append(f"标题过长（{n} 字），首页可能被截断")
suggestions.append("精简到 22 字以内")
elif 16 <= n <= 22:
score += 2
# 钩子词
hook_words = ["3 ", "5 ", "7 ", "为什么", "怎么办", "居然", "没想到", "亲测",
"保姆级", "手把手", "我以为", "作为", "免费", "0 元", "白嫖"]
if any(w in title for w in hook_words):
score += 2
# emoji
e = _count_emoji(title)
if e > 3:
score -= 2
issues.append(f"标题 emoji {e} 个，太多显廉价")
suggestions.append("减到 0~2 个")
return max(0, min(10, score)), issues, suggestions
def score_first_lines(content: str) -> Tuple[int, List[str], List[str]]:
"""首 3 行打分（决定是否被滑走）。0~10。"""
score = 5
issues: List[str] = []
suggestions: List[str] = []
lines = [ln for ln in content.splitlines() if ln.strip()]
if not lines:
return 0, ["正文为空"], ["补全正文"]
head = "\n".join(lines[:3])
if len(head) < 30:
score -= 2
issues.append("首段过短，钩子不够")
suggestions.append("首段加 1~2 个具体场景或数字")
# 钩子模式
hook_patterns = ["你是不是", "你也", "为什么", "我之前", "其实", "上周", "那天",
"我以为", "亲测", "整理了", "测了", "不主流", "💡", "✨", "🔥"]
if any(p in head for p in hook_patterns):
score += 3
else:
suggestions.append("首段可以加'你是不是也...'、'我之前也...'之类的代入钩子")
return max(0, min(10, score)), issues, suggestions
def score_paragraph_layout(content: str) -> Tuple[int, List[str], List[str]]:
"""段落排版打分 — 段落是否过长 / 有没有空行。0~10。"""
score = 6
issues: List[str] = []
suggestions: List[str] = []
lines = content.splitlines()
long_paragraphs = sum(1 for ln in lines if len(ln) > 80)
if long_paragraphs > 2:
score -= 3
issues.append(f"有 {long_paragraphs} 段超过 80 字，手机阅读会很累")
suggestions.append("把长段落拆短，每段控制在 1~3 句")
# 是否有空行
blank = sum(1 for ln in lines if not ln.strip())
text_lines = max(1, len(lines) - blank)
if blank / text_lines < 0.15:
score -= 2
issues.append("段间空行偏少，看起来像大字报")
suggestions.append("每 2~3 句加一个空行")
return max(0, min(10, score)), issues, suggestions
def score_emoji_density(content: str) -> Tuple[int, List[str], List[str]]:
"""emoji 节奏打分。0~10。"""
score = 7
issues: List[str] = []
suggestions: List[str] = []
e = _count_emoji(content)
chars = max(1, len(content))
rate = e / chars
if e == 0:
score -= 2
issues.append("全文没有 emoji，缺少视觉节奏")
suggestions.append("加 3~6 个 emoji 当项目符号 / 强调")
elif e > 18:
score -= 3
issues.append(f"全文 {e} 个 emoji，密度过高显廉价")
suggestions.append("精简到 6~12 个，关键句留就好")
elif rate > 0.05:
score -= 1
issues.append("emoji 密度偏高")
return max(0, min(10, score)), issues, suggestions
def score_hashtags(tags: List[str]) -> Tuple[int, List[str], List[str]]:
"""话题数量 + 质量。0~10。"""
score = 5
issues: List[str] = []
suggestions: List[str] = []
n = len([t for t in tags if t.strip()])
if n == 0:
score = 2
issues.append("没有话题，分发会很差")
suggestions.append("加 3~6 个相关 #话题（参考 data/hashtag_topics.md）")
elif n < 3:
score = 4
suggestions.append(f"话题只有 {n} 个，建议补到 3~6 个")
elif 3 <= n <= 6:
score = 9
elif n > 8:
score = 5
issues.append(f"话题 {n} 个偏多，会被识别为营销号")
suggestions.append("精简到 6 个以内")
return score, issues, suggestions
def score_sensitive(text: str, sensitive: Optional[List[str]] = None) -> Tuple[int, List[str], List[str]]:
"""敏感词打分（命中即扣分）。0~10。"""
sw = sensitive if sensitive is not None else load_sensitive_words()
if not sw:
return 8, [], []
hits: List[str] = []
low = text # 中文不区分大小写也无所谓
for w in sw:
if w and w in low:
hits.append(w)
score = 10
issues: List[str] = []
suggestions: List[str] = []
for h in hits:
score -= 2
issues.append(f"命中敏感词：{h!r}")
suggestions.append(f"把 {h!r} 改成主观表达（'我自己用下来' / '我体感'）或删掉")
return max(0, score), issues, suggestions
def score_post(
title: str,
content: str,
tags: Optional[List[str]] = None,
sensitive: Optional[List[str]] = None,
*,
rules: Optional[Any] = None,
) -> PostScore:
"""综合打分（0~100）。
Args:
rules: 可选 `xhs_profile.RuleOverride` — 用户的规则覆盖。
传入后会按用户设置调整权重 / 禁用检查项 / 加自定义敏感词。
"""
tags = tags or []
breakdown: Dict[str, int] = {}
issues: List[str] = []
suggestions: List[str] = []
# 解析 RuleOverride（避免循环 import）
disabled: set = set()
weights = {
"title": 0.25, "first_lines": 0.20, "layout": 0.10,
"emoji": 0.10, "hashtags": 0.10, "compliance": 0.25,
}
if rules is not None:
for k in getattr(rules, "disabled_checks", []) or []:
disabled.add(k)
for k, v in (getattr(rules, "weights", {}) or {}).items():
if k in weights:
weights[k] = max(0.0, float(v))
for k in disabled:
if k in weights:
weights[k] = 0.0
# sensitive 列表：去掉用户允许的，加上用户额外的
if sensitive is None:
sensitive = load_sensitive_words()
allowed = set(getattr(rules, "allowed_words", []) or [])
sensitive = [w for w in sensitive if w not in allowed]
sensitive.extend(w for w in (getattr(rules, "custom_sensitive", []) or [])
if w not in allowed)
def _run(name: str, fn, *args):
if name in disabled:
return
s, i, sg = fn(*args)
breakdown[name] = s
issues.extend(f"[{_LABELS.get(name, name)}] {x}" for x in i)
suggestions.extend(f"[{_LABELS.get(name, name)}] {x}" for x in sg)
_run("title", score_title, title)
_run("first_lines", score_first_lines, content)
_run("layout", score_paragraph_layout, content)
_run("emoji", score_emoji_density, content)
_run("hashtags", score_hashtags, tags)
full_text = f"{title}\n{content}\n{' '.join(tags)}"
_run("compliance", score_sensitive, full_text, sensitive)
# 归一化权重（disabled 的 = 0，其他重新分配）
total_w = sum(w for k, w in weights.items() if k in breakdown) or 1.0
norm = {k: w / total_w for k, w in weights.items() if k in breakdown}
total = sum(breakdown[k] / 10 * w for k, w in norm.items()) * 100
return PostScore(
total=int(round(total)),
breakdown=breakdown,
issues=issues,
suggestions=suggestions,
)
_LABELS = {
"title": "标题",
"first_lines": "首段",
"layout": "排版",
"emoji": "emoji",
"hashtags": "话题",
"compliance": "合规",
}
# =====================================================================
# 完整笔记数据结构
# =====================================================================
@dataclass
class Draft:
"""一份待发布的草稿。"""
title: str
content: str
tags: List[str] = field(default_factory=list)
cover_hint: str = "" # 封面建议（Claude/作者补图前的描述）
image_hints: List[str] = field(default_factory=list) # 各张配图描述
formula: str = "" # 标题公式代号 T1~T11
skeleton: str = "" # 正文骨架代号 S1~S7
notes: str = "" # 其他说明
score: Optional[Dict[str, Any]] = None # 自我打分（可选）
def to_dict(self) -> Dict[str, Any]:
d = {
"title": self.title,
"content": self.content,
"tags": self.tags,
"cover_hint": self.cover_hint,
"image_hints": self.image_hints,
"formula": self.formula,
"skeleton": self.skeleton,
"notes": self.notes,
}
if self.score is not None:
d["score"] = self.score
return d
def to_markdown(self) -> str:
"""渲染成发布前预览的 Markdown。"""
parts = [f"# {self.title}", ""]
parts.append(self.content)
parts.append("")
if self.tags:
parts.append("**话题：** " + " ".join(f"#{t}" for t in self.tags))
if self.cover_hint:
parts.append("")
parts.append(f"**封面建议：** {self.cover_hint}")
if self.image_hints:
parts.append("")
parts.append("**配图建议：**")
for i, h in enumerate(self.image_hints, 1):
parts.append(f"- 图{i}：{h}")
if self.notes:
parts.append("")
parts.append(f"_备注：{self.notes}_")
return "\n".join(parts) + "\n"
def to_clipboard_text(self) -> str:
"""渲染成"可以直接粘贴到小红书 App"的纯文本。"""
body = self.content.rstrip()
tags_line = " ".join(f"#{t}" for t in self.tags) if self.tags else ""
if tags_line:
return f"{body}\n\n{tags_line}\n"
return body + "\n"
@classmethod
def from_dict(cls, d: Dict[str, Any]) -> "Draft":
return cls(
title=d.get("title", ""),
content=d.get("content", ""),
tags=list(d.get("tags") or []),
cover_hint=d.get("cover_hint", ""),
image_hints=list(d.get("image_hints") or []),
formula=d.get("formula", ""),
skeleton=d.get("skeleton", ""),
notes=d.get("notes", ""),
score=d.get("score"),
)
def load_draft(path: str) -> Draft:
p = Path(path)
if not p.exists():
raise FileNotFoundError(path)
text = p.read_text(encoding="utf-8")
if p.suffix.lower() == ".json":
return Draft.from_dict(json.loads(text))
# 否则按 Markdown 解析（标题取第一行 #，话题取最后一行 # 标签，正文取中间）
return _parse_markdown_draft(text)
def _parse_markdown_draft(text: str) -> Draft:
lines = text.splitlines()
title = ""
content_lines: List[str] = []
tags: List[str] = []
for ln in lines:
if not title and ln.strip().startswith("# "):
title = ln.strip()[2:].strip()
continue
if ln.strip().startswith("#") and " " not in ln.strip()[1:].strip():
# 单 hashtag 行：当成话题
for t in re.findall(r"#([\w一-鿿]+)", ln):
tags.append(t)
continue
# 行内的 # 话题（如末尾一行）
inline_tags = re.findall(r"#([\w一-鿿]+)", ln)
if inline_tags and len(inline_tags) >= 2 and len(ln.strip()) < 200:
tags.extend(inline_tags)
continue
content_lines.append(ln)
content = "\n".join(content_lines).strip()
return Draft(title=title, content=content, tags=tags)
def save_draft(draft: Draft, path: str) -> None:
p = Path(path)
p.parent.mkdir(parents=True, exist_ok=True)
if p.suffix.lower() == ".json":
p.write_text(json.dumps(draft.to_dict(), ensure_ascii=False, indent=2), encoding="utf-8")
else:
p.write_text(draft.to_markdown(), encoding="utf-8")
# =====================================================================
# 完整生成（标题 + 骨架 + 话题 占位）
# =====================================================================
def make_draft(
topic: str,
*,
persona: str = "",
payoff: str = "",
formula: str = "T2",
skeleton: str = "S1",
tags: Optional[List[str]] = None,
) -> Draft:
"""一键生成草稿 — 标题选 1 条，正文是骨架占位，让用户/LLM 后续填。"""
titles = generate_titles(topic, persona=persona, payoff=payoff, formulas=[formula])
title = titles[0]["title"] if titles else topic
body = render_skeleton(skeleton)
return Draft(
title=title,
content=body,
tags=list(tags or []),
formula=formula,
skeleton=skeleton,
notes=f"骨架占位文本，请替换 {{...}} 中的内容；建议跑 polish_post.py 检查。",
)
规范 + 时尚的思维导图生成。输入 Markdown 大纲 / JSON / OPML / XMind，输出 XMind 2021+ (.xmind)、OPML、FreeMind (.mm)、Markdown、PNG、PDF、SVG；内置 10 种风格（modern / classic / dark / xiao...
---
name: huo15-mind-map
displayName: 火一五思维导图技能
description: 规范 + 时尚的思维导图生成。输入 Markdown 大纲 / JSON / OPML / XMind，输出 XMind 2021+ (.xmind)、OPML、FreeMind (.mm)、Markdown、PNG、PDF、SVG；内置 10 种风格（modern / classic / dark / xiaohongshu / ocean / forest / sunset / minimal / pastel / github）。触发词：思维导图、脑图、mind map、mindmap、生成思维导图、导出 xmind、画思维导图。
version: 1.1.0
aliases:
- 火一五思维导图
- 火一五脑图
- 思维导图生成
- mind-map
- mindmap
- 脑图
- XMind导出
dependencies:
python-packages:
- matplotlib
- Pillow
---
# 火一五思维导图技能 v1.0
> 规范 + 时尚的思维导图生成器 — 青岛火一五信息科技有限公司
---
## 一、核心能力
1. **一端多出** — 同一个输入可同时导出：
- `.xmind`（XMind 2021+ ZIP，含 content.json / metadata.json / manifest.json）
- `.opml`（OPML 2.0，兼容 MindNode / 幕布 / WorkFlowy / Apple Notes）
- `.mm`（FreeMind 格式，兼容 XMind、EdrawMind、亿图图示、幕布）
- `.md`（Markdown outline，方便回写知识库 / 复用）
- `.png` / `.pdf` / `.svg`（渲染图，发群/导入幻灯都合适）
- `.json`（内部统一结构，编程复用）
2. **十种风格** — `modern` / `classic` / `dark` / `xiaohongshu` / `ocean` / `forest` / `sunset` / `minimal` / `pastel` / `github`（中文别名：现代 / 经典 / 暗色 / 小红书 / 海洋 / 森林 / 夕阳 / 极简 / 马卡龙 / 极客）。
3. **多种输入** — Markdown 大纲 / 内部 JSON / OPML / 已有的 XMind 文件都能读；Markdown 支持标题 (#) 与无序/有序列表混排。
4. **中文友好** — 自动选取系统内的 PingFang SC / Microsoft YaHei / Noto Sans CJK 等字体。
---
## 二、输入格式
### 2.1 Markdown 大纲（推荐）
```markdown
# 火一五产品矩阵 # 第 1 个标题成为根节点
## 核心平台 # ## → 一级分支
### 龙虾 OpenClaw # ### → 二级分支
- 插件体系 # 缩进列表 → 下一级叶子
- 技能市场
- 场景编排
### 辉火云管家·贾维斯
- 跨应用代理
- 记忆系统
## 行业解决方案
### 机器人企业
- 数字化管家
...
```
规则：
- 第一个出现的 `#` 标题升格为根节点；
- 后续 `##` `###` 依次作为子级；
- `- item` / `* item` / `1. item` 可接续在标题后面作为更深层级；
- 连续非标题非列表的文本行会挂到当前节点的 `note` 上，导出 XMind 时会写入 `notes.plain.content`。
### 2.2 内部 JSON
```json
{
"title": "火一五产品矩阵",
"children": [
{"title": "核心平台", "children": [
{"title": "龙虾 OpenClaw"}, {"title": "辉火云管家"}
]},
{"title": "行业解决方案"}
]
}
```
### 2.3 OPML / XMind 导入
直接喂路径给 `--input`，用 `--input-format opml|xmind` 强制解析器。
---
## 三、命令行
### 3.1 基础用法
```bash
# 1. 从 Markdown 生成 XMind（默认 modern 风格）
python3 scripts/create-mind-map.py \
--input outline.md \
--output /tmp/map.xmind
# 2. 同时导出 PNG + PDF + OPML（基于 --output 的同名文件）
python3 scripts/create-mind-map.py \
--input outline.md \
--output /tmp/map.xmind \
--also png,pdf,opml \
--style xiaohongshu
# 3. 仅渲染为 PNG，指定分辨率
python3 scripts/create-mind-map.py \
--input outline.md \
--output /tmp/map.png \
--style dark --dpi 300
# 4. 把现有 XMind 转换为 Markdown
python3 scripts/create-mind-map.py \
--input existing.xmind --input-format xmind \
--output /tmp/existing.md
# 5. 从 stdin 读 Markdown
cat outline.md | python3 scripts/create-mind-map.py \
--output /tmp/map.png --style modern
```
### 3.2 参数速查
| 参数 | 说明 |
|------|------|
| `--input / -i` | 输入文件路径（md / json / opml / xmind） |
| `--input-text` | 直接传 Markdown / JSON / OPML 字符串 |
| `--input-format` | `auto` (默认) / `markdown` / `json` / `opml` / `xmind` |
| `--output / -o` | 主输出路径；扩展名决定格式 |
| `--also` | 逗号分隔的额外格式（基于 `--output` 同名） |
| `--style` | `modern` / `classic` / `dark` / `xiaohongshu` / `ocean` / `forest` / `sunset` / `minimal` / `pastel` / `github`（默认 modern，支持中文别名） |
| `--dpi` | PNG 分辨率（默认 200） |
| `--sheet-name` | XMind sheet 名称（默认用根节点标题） |
| `--title` | 手动覆盖根节点标题 |
---
## 四、Python API
```python
import sys
sys.path.insert(0, 'scripts')
from mindmap_tree import parse_markdown, to_xmind, to_opml, to_json
from mindmap_render import render
with open('outline.md', encoding='utf-8') as fh:
root = parse_markdown(fh.read())
to_xmind(root, '/tmp/map.xmind')
print(to_opml(root)[:200])
render(root, '/tmp/map.png', style_name='xiaohongshu', dpi=240)
```
主要 API：
| 函数 | 说明 |
|------|------|
| `parse_markdown(text) → Node` | Markdown outline → 树 |
| `parse_json(text) → Node` | 内部 JSON → 树 |
| `parse_opml(text) → Node` | OPML 2.0 → 树 |
| `parse_xmind(path) → Node` | XMind 2021+ → 树 |
| `to_xmind(root, path)` | 写 XMind |
| `to_opml(root) → str` | OPML 字符串 |
| `to_markdown(root) → str` | Markdown 字符串 |
| `to_freemind(root) → str` | FreeMind `.mm` 字符串 |
| `to_json(root) → str` | 内部 JSON |
| `render(root, path, style_name, dpi)` | 渲染 PNG/PDF/SVG |
`Node` 数据结构（可自由增删）：
```python
@dataclass
class Node:
title: str
note: str = ''
children: List[Node] = []
```
---
## 五、风格
| key | 名称 | 背景 | 主色 | 适用场景 |
|-----|------|------|------|---------|
| `modern`（默认） | 现代商务 | 白底 `#FFFFFF` | 深蓝灰 `#2C3E50` | 对外汇报、产品方案 |
| `classic` | 经典稳重 | 浅灰 `#FAFAFA` | 靛蓝 `#374785` | 正式文档、技术白皮书 |
| `dark` | 暗色霓虹 | 深蓝 `#0F172A` | 亮蓝 `#38BDF8` | 大屏演示、暗色幻灯 |
| `xiaohongshu` (`xhs`, `小红书`) | 小红书暖奶油 | 奶油 `#FFF8F3` | 小红书红 `#FF2442` | 营销帖、品牌故事 |
| `ocean` (`海洋`, `蓝`) | 海洋蓝 | 冰蓝 `#F8FBFE` | 深蓝 `#0077B6` | SaaS 产品、技术架构 |
| `forest` (`森林`, `绿`, `自然`) | 森林绿 | 米白 `#F7FAF8` | 墨绿 `#2D6A4F` | 环保、农业、健康 |
| `sunset` (`夕阳`, `暖橙`, `橙`) | 夕阳暖橙 | 奶杏 `#FFFBF5` | 赤橙 `#E76F51` | 运营活动、温暖叙事 |
| `minimal` (`极简`, `素雅`, `学术`) | 极简素雅 | 纯白 `#FFFFFF` | 近黑 `#2E2E2E` | 学术论文、出版物 |
| `pastel` (`马卡龙`, `粉`, `儿童`) | 马卡龙粉嫩 | 粉白 `#FFFBFC` | 天蓝 `#B5D8FA` | 儿童教育、女性向 |
| `github` (`极客`, `程序员`) | 极客 GitHub | 纯白 `#FFFFFF` | 深灰 `#24292E` | 开源文档、README |
> 风格只影响配色、字号、圆角；结构层级、字体选择是自适应的。
---
## 六、与主流软件互通
| 软件 | 导入方式 |
|------|---------|
| **XMind** | 直接打开 `.xmind`（2021+ 格式）或导入 `.opml` / `.mm` |
| **MindNode / MindMeister** | 导入 `.opml` |
| **幕布 / WorkFlowy / Notion** | 粘贴 `.md`，或导入 `.opml` |
| **EdrawMind / 亿图图示** | 导入 `.xmind` / `.mm` |
| **Apple Notes / Outlook** | 粘贴 `.md` |
当甲方拿的是微软 Visio / Miro 这类工具时，直接发 PNG / PDF / SVG 即可。
---
## 七、触发词
- 思维导图 / 脑图 / 心智图 / mind map / mindmap
- 画思维导图 / 做思维导图 / 生成思维导图
- 导出 xmind / 生成 xmind / 转 xmind
- outline 转思维导图
---
## 八、版本历史
- **v1.1.0（当前）** — 扩展 6 种预设风格：`ocean` 海洋蓝 / `forest` 森林绿 / `sunset` 夕阳暖橙 / `minimal` 极简素雅 / `pastel` 马卡龙粉嫩 / `github` 极客 GitHub；完善中文别名。
- **v1.0.0** — 首版。支持 Markdown/JSON/OPML/XMind 输入；输出 XMind/OPML/FreeMind/Markdown/PNG/PDF/SVG/JSON；内置 modern/classic/dark/xiaohongshu 四风格。
---
**技术支持：** 青岛火一五信息科技有限公司
FILE:_meta.json
{
"ownerId": "kn7byevkn40d6z4p7ghdb097z983tj33",
"slug": "huo15-mind-map",
"version": "1.1.0"
}
FILE:scripts/create-mind-map.py
#!/usr/bin/env python3
"""
create-mind-map.py — 思维导图生成器 CLI
输入（至少一个）：
--input <文件路径> Markdown / JSON / OPML / XMind
--input-text "..." 直接传字符串
--input-format <fmt> markdown | json | opml | xmind | auto（默认 auto）
输出：
--output <文件路径> 按扩展名决定格式：xmind / opml / md / mm / png / pdf / svg / json
--also <fmt1,fmt2,...> 基于 --output 的同名目录，额外导出指定格式
渲染：
--style <name> modern | classic | dark | xiaohongshu
--dpi <n> PNG 分辨率，默认 200
用法示例：
# 从 Markdown 大纲生成 XMind
python3 create-mind-map.py --input outline.md --output map.xmind
# 一次性生成 XMind + PNG + PDF
python3 create-mind-map.py --input outline.md --output /tmp/map.xmind \\
--also png,pdf --style xiaohongshu
"""
from __future__ import annotations
import argparse
import os
import sys
HERE = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, HERE)
from mindmap_tree import (
Node, parse_auto, parse_markdown, parse_json, parse_opml, parse_xmind,
to_markdown, to_json, to_opml, to_xmind, to_freemind,
)
EXT_TO_WRITER = {
'xmind': 'xmind',
'opml': 'opml',
'md': 'md',
'markdown': 'md',
'mm': 'mm',
'json': 'json',
'png': 'img',
'pdf': 'img',
'svg': 'img',
}
def _infer_format_from_ext(path):
ext = os.path.splitext(path)[1].lstrip('.').lower()
if ext in ('md', 'markdown', 'txt'):
return 'markdown'
if ext in ('json',):
return 'json'
if ext in ('opml', 'xml'):
return 'opml'
if ext == 'xmind':
return 'xmind'
return 'markdown'
def _read_input(args):
fmt = args.input_format
if fmt in ('auto', None):
fmt = _infer_format_from_ext(args.input) if args.input else 'markdown'
if args.input_text:
return parse_auto(args.input_text, hint=fmt)
if args.input:
if fmt == 'xmind':
return parse_xmind(args.input)
with open(args.input, 'r', encoding='utf-8') as fh:
text = fh.read()
return parse_auto(text, hint=fmt)
# 从 stdin 读
text = sys.stdin.read()
return parse_auto(text, hint=fmt)
def _write_one(root: Node, path, style_name, dpi, sheet_name):
ext = os.path.splitext(path)[1].lstrip('.').lower()
writer = EXT_TO_WRITER.get(ext)
if writer is None:
raise ValueError(f'未知输出扩展名：.{ext}，受支持：'
+ ', '.join(sorted(EXT_TO_WRITER.keys())))
os.makedirs(os.path.dirname(os.path.abspath(path)) or '.', exist_ok=True)
if writer == 'xmind':
to_xmind(root, path, sheet_name=sheet_name or root.title)
elif writer == 'opml':
with open(path, 'w', encoding='utf-8') as fh:
fh.write(to_opml(root))
elif writer == 'md':
with open(path, 'w', encoding='utf-8') as fh:
fh.write(to_markdown(root))
elif writer == 'mm':
with open(path, 'w', encoding='utf-8') as fh:
fh.write(to_freemind(root))
elif writer == 'json':
with open(path, 'w', encoding='utf-8') as fh:
fh.write(to_json(root))
elif writer == 'img':
# 延迟加载 matplotlib，避免纯转换场景被阻塞
from mindmap_render import render
render(root, path, style_name=style_name, dpi=dpi,
title_text=root.title if ext != 'svg' else None)
return path
def main(argv=None):
parser = argparse.ArgumentParser(
prog='create-mind-map',
description='火一五思维导图生成器（XMind/OPML/Markdown + PNG/PDF）',
)
parser.add_argument('--input', '-i', default=None,
help='输入文件路径（markdown/json/opml/xmind）')
parser.add_argument('--input-text', default=None,
help='直接传入 Markdown / JSON / OPML 字符串')
parser.add_argument('--input-format', default='auto',
choices=['auto', 'markdown', 'md', 'json',
'opml', 'xmind'])
parser.add_argument('--output', '-o', required=True,
help='主输出文件路径（扩展名决定格式）')
parser.add_argument('--also', default='',
help='逗号分隔的额外格式：xmind,opml,md,mm,json,png,pdf,svg')
parser.add_argument('--style', default='modern',
help='渲染风格：modern | classic | dark | xiaohongshu')
parser.add_argument('--dpi', type=int, default=200, help='PNG 分辨率')
parser.add_argument('--sheet-name', default=None,
help='XMind sheet 名（默认取根节点标题）')
parser.add_argument('--title', default=None,
help='覆盖根节点 title（可选）')
args = parser.parse_args(argv)
if not args.input and not args.input_text and sys.stdin.isatty():
parser.error('必须提供 --input 或 --input-text，或从 stdin 传入')
root = _read_input(args)
if args.title:
root.title = args.title
outputs = [_write_one(root, args.output, args.style, args.dpi,
args.sheet_name)]
extras = [e.strip().lower() for e in args.also.split(',') if e.strip()]
if extras:
base, _ = os.path.splitext(args.output)
for ext in extras:
if ext not in EXT_TO_WRITER:
print(f'⚠️ 忽略未知格式：{ext}', file=sys.stderr)
continue
path = base + '.' + ext
outputs.append(_write_one(root, path, args.style, args.dpi,
args.sheet_name))
print(f'✅ 思维导图已生成：{len(outputs)} 个文件')
for p in outputs:
print(f' - {p}')
return 0
if __name__ == '__main__':
sys.exit(main())
FILE:scripts/mindmap_render.py
"""
mindmap_render.py — 基于 matplotlib 的思维导图渲染器
- 横向树形布局（左侧根，右侧展开）
- 节点用圆角矩形 + 贝塞尔连线
- 多种风格：modern / classic / dark / xiaohongshu
- 输出：PNG / PDF / SVG（由扩展名决定）
"""
from __future__ import annotations
import os
import platform
from dataclasses import dataclass, field
from typing import Dict, List, Optional
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.patches import FancyBboxPatch, PathPatch
from matplotlib.path import Path
from matplotlib.font_manager import FontProperties, fontManager
from mindmap_tree import Node
# ============================================================
# 一、字体
# ============================================================
def _find_cjk_font():
"""按平台偏好找中文字体，返回 FontProperties。"""
candidates_mac = [
'PingFang SC', 'Hiragino Sans GB', 'Hiragino Sans', 'STHeiti',
'Heiti SC', 'Songti SC', 'Arial Unicode MS',
]
candidates_win = ['Microsoft YaHei', 'SimHei', 'SimSun']
candidates_linux = ['Noto Sans CJK SC', 'WenQuanYi Micro Hei',
'DejaVu Sans']
system = platform.system()
if system == 'Darwin':
prefs = candidates_mac + candidates_win + candidates_linux
elif system == 'Windows':
prefs = candidates_win + candidates_mac + candidates_linux
else:
prefs = candidates_linux + candidates_mac + candidates_win
installed = {f.name for f in fontManager.ttflist}
for name in prefs:
if name in installed:
return FontProperties(family=name)
return FontProperties()
_CJK_FONT = None
def get_cjk_font():
global _CJK_FONT
if _CJK_FONT is None:
_CJK_FONT = _find_cjk_font()
matplotlib.rcParams['font.family'] = _CJK_FONT.get_name()
return _CJK_FONT
# ============================================================
# 二、风格
# ============================================================
@dataclass
class Style:
name: str
bg: str = '#FFFFFF'
root_fill: str = '#1F77B4'
root_text: str = '#FFFFFF'
branch_palette: List[str] = field(default_factory=lambda: [
'#FF6B6B', '#F6A738', '#4ECDC4', '#556CC9', '#8E44AD', '#27AE60',
])
branch_text: str = '#FFFFFF'
leaf_fill: str = '#FFFFFF'
leaf_stroke: str = '#CCCCCC'
leaf_text: str = '#333333'
edge_width: float = 2.0
edge_alpha: float = 0.85
font_title: float = 18
font_branch: float = 14
font_leaf: float = 11
rounding: float = 0.35
pad: float = 0.16
STYLE_MODERN = Style(
name='modern',
bg='#FFFFFF',
root_fill='#2C3E50',
root_text='#FFFFFF',
branch_palette=['#E74C3C', '#E67E22', '#27AE60', '#2980B9',
'#8E44AD', '#16A085'],
leaf_fill='#FDFEFE',
leaf_stroke='#BDC3C7',
leaf_text='#2C3E50',
)
STYLE_CLASSIC = Style(
name='classic',
bg='#FAFAFA',
root_fill='#374785',
root_text='#FFFFFF',
branch_palette=['#A8D0DB', '#F7B538', '#DB3A34', '#6DA34D',
'#524B7F', '#117A65'],
leaf_fill='#FFFFFF',
leaf_stroke='#AAAAAA',
leaf_text='#333333',
rounding=0.15,
)
STYLE_DARK = Style(
name='dark',
bg='#0F172A',
root_fill='#38BDF8',
root_text='#0F172A',
branch_palette=['#F472B6', '#F59E0B', '#34D399', '#60A5FA',
'#A78BFA', '#F87171'],
branch_text='#0F172A',
leaf_fill='#1E293B',
leaf_stroke='#334155',
leaf_text='#E2E8F0',
edge_alpha=0.9,
)
STYLE_XIAOHONGSHU = Style(
name='xiaohongshu',
bg='#FFF8F3',
root_fill='#FF2442',
root_text='#FFFFFF',
branch_palette=['#FF2442', '#FF7043', '#FFB347', '#6BCB77',
'#4D96FF', '#9D4EDD'],
leaf_fill='#FFFFFF',
leaf_stroke='#F5E6E6',
leaf_text='#1A1A1A',
rounding=0.45,
)
STYLE_OCEAN = Style(
name='ocean',
bg='#F8FBFE',
root_fill='#0077B6',
root_text='#FFFFFF',
branch_palette=['#023E8A', '#0096C7', '#48CAE4', '#00B4D8',
'#FB8500', '#219EBC'],
leaf_fill='#E7F5FA',
leaf_stroke='#90CAF9',
leaf_text='#023E8A',
)
STYLE_FOREST = Style(
name='forest',
bg='#F7FAF8',
root_fill='#2D6A4F',
root_text='#FFFFFF',
branch_palette=['#40916C', '#52B788', '#95D5B2', '#D68C45',
'#B08968', '#1B4332'],
leaf_fill='#F1F8E9',
leaf_stroke='#AED581',
leaf_text='#1B4332',
)
STYLE_SUNSET = Style(
name='sunset',
bg='#FFFBF5',
root_fill='#E76F51',
root_text='#FFFFFF',
branch_palette=['#F4A261', '#E9C46A', '#2A9D8F', '#264653',
'#E76F51', '#F2CC8F'],
leaf_fill='#FFF6E9',
leaf_stroke='#F4A261',
leaf_text='#9D3B1E',
)
STYLE_MINIMAL = Style(
name='minimal',
bg='#FFFFFF',
root_fill='#2E2E2E',
root_text='#FFFFFF',
branch_palette=['#404040', '#595959', '#737373', '#8C8C8C',
'#0A4D8E', '#A6A6A6'],
leaf_fill='#FAFAFA',
leaf_stroke='#D4D4D4',
leaf_text='#2E2E2E',
rounding=0.1,
)
STYLE_PASTEL = Style(
name='pastel',
bg='#FFFBFC',
root_fill='#B5D8FA',
root_text='#2D3748',
branch_palette=['#FFB5A7', '#FCD5CE', '#FFE5EC', '#C4A4E1',
'#A2D2FF', '#FAD2E1'],
branch_text='#2D3748',
leaf_fill='#FFFFFF',
leaf_stroke='#F7D8D8',
leaf_text='#2D3748',
rounding=0.5,
)
STYLE_GITHUB = Style(
name='github',
bg='#FFFFFF',
root_fill='#24292E',
root_text='#FFFFFF',
branch_palette=['#0366D6', '#28A745', '#D73A49', '#6F42C1',
'#F66A0A', '#005CC5'],
leaf_fill='#F6F8FA',
leaf_stroke='#E1E4E8',
leaf_text='#24292E',
rounding=0.2,
)
REGISTRY: Dict[str, Style] = {
'modern': STYLE_MODERN,
'classic': STYLE_CLASSIC,
'dark': STYLE_DARK,
'xiaohongshu': STYLE_XIAOHONGSHU,
'xhs': STYLE_XIAOHONGSHU,
'小红书': STYLE_XIAOHONGSHU,
'ocean': STYLE_OCEAN,
'海洋': STYLE_OCEAN,
'蓝': STYLE_OCEAN,
'蓝色': STYLE_OCEAN,
'forest': STYLE_FOREST,
'森林': STYLE_FOREST,
'绿': STYLE_FOREST,
'绿色': STYLE_FOREST,
'自然': STYLE_FOREST,
'sunset': STYLE_SUNSET,
'夕阳': STYLE_SUNSET,
'暖橙': STYLE_SUNSET,
'橙': STYLE_SUNSET,
'minimal': STYLE_MINIMAL,
'极简': STYLE_MINIMAL,
'素雅': STYLE_MINIMAL,
'黑白': STYLE_MINIMAL,
'学术': STYLE_MINIMAL,
'论文': STYLE_MINIMAL,
'pastel': STYLE_PASTEL,
'马卡龙': STYLE_PASTEL,
'粉嫩': STYLE_PASTEL,
'粉': STYLE_PASTEL,
'儿童': STYLE_PASTEL,
'github': STYLE_GITHUB,
'极客': STYLE_GITHUB,
'程序员': STYLE_GITHUB,
'gh': STYLE_GITHUB,
'现代': STYLE_MODERN,
'商务': STYLE_MODERN,
'经典': STYLE_CLASSIC,
'稳重': STYLE_CLASSIC,
'暗色': STYLE_DARK,
'黑色': STYLE_DARK,
'霓虹': STYLE_DARK,
}
def get_style(name):
if not name:
return STYLE_MODERN
return REGISTRY.get(name.strip().lower(), STYLE_MODERN)
def list_styles():
return (
'modern', 'classic', 'dark', 'xiaohongshu',
'ocean', 'forest', 'sunset', 'minimal', 'pastel', 'github',
)
# ============================================================
# 三、布局（横向树）
# ============================================================
@dataclass
class Layout:
node: Node
depth: int
x: float = 0.0
y: float = 0.0
width: float = 0.0
height: float = 0.7
parent: Optional['Layout'] = None
children: List['Layout'] = field(default_factory=list)
def _estimate_text_width(text, font_size):
"""粗略按字符数估算宽度（英寸）。中文字符占 1 个 width unit，英文 0.6。"""
w = 0.0
for ch in text:
if '\u4e00' <= ch <= '\u9fff':
w += 1.0
elif ch.isspace():
w += 0.4
else:
w += 0.58
width = max(1.2, w * font_size / 20)
return min(width, 4.8)
def _build_layout(node: Node, depth: int, style: Style, parent=None):
if depth == 0:
fs = style.font_title
elif depth == 1:
fs = style.font_branch
else:
fs = style.font_leaf
width = _estimate_text_width(node.title, fs) + 0.6
layout = Layout(node=node, depth=depth, width=width,
height=0.8 if depth < 2 else 0.7, parent=parent)
for child in node.children:
layout.children.append(_build_layout(child, depth + 1, style, layout))
return layout
_Y_GAP = 0.38
_X_GAP = 1.1
def _assign_positions(layout: Layout, current_y=[0.0]):
"""后序遍历分配 y 坐标：叶子顺序递增，父节点取子的平均。"""
if not layout.children:
layout.y = current_y[0]
current_y[0] += layout.height + _Y_GAP
return
for child in layout.children:
_assign_positions(child, current_y)
ys = [c.y for c in layout.children]
layout.y = (min(ys) + max(ys)) / 2.0
def _assign_x(layout: Layout, x=0.0):
layout.x = x
# 下一级从当前节点右边缘 + 间距开始
next_x = x + layout.width + _X_GAP
for child in layout.children:
_assign_x(child, next_x)
def compute_layout(root: Node, style: Style) -> Layout:
lay = _build_layout(root, 0, style)
_assign_positions(lay)
_assign_x(lay)
return lay
# ============================================================
# 四、绘制
# ============================================================
def _color_for(branch_index, style: Style):
palette = style.branch_palette
return palette[branch_index % len(palette)]
def _draw_box(ax, layout, fill, stroke, text_color, font_size,
rounding, bold=False):
x, y = layout.x, layout.y
w, h = layout.width, layout.height
patch = FancyBboxPatch(
(x, y - h / 2),
w, h,
boxstyle=f'round,pad=0.02,rounding_size={rounding}',
linewidth=1.6,
edgecolor=stroke,
facecolor=fill,
zorder=3,
)
ax.add_patch(patch)
font = get_cjk_font()
weight = 'bold' if bold else 'normal'
ax.text(x + w / 2, y, layout.node.title,
ha='center', va='center',
fontsize=font_size, fontweight=weight,
color=text_color,
fontproperties=font,
zorder=5, wrap=True)
def _draw_edge(ax, parent, child, color, width, alpha):
x0 = parent.x + parent.width
y0 = parent.y
x1 = child.x
y1 = child.y
mx = (x0 + x1) / 2
verts = [(x0, y0), (mx, y0), (mx, y1), (x1, y1)]
codes = [Path.MOVETO, Path.CURVE4, Path.CURVE4, Path.CURVE4]
path = Path(verts, codes)
patch = PathPatch(path, facecolor='none', edgecolor=color,
linewidth=width, alpha=alpha, zorder=2,
capstyle='round')
ax.add_patch(patch)
def _walk_draw(ax, layout, style: Style, branch_index=0):
if layout.depth == 0:
_draw_box(ax, layout,
fill=style.root_fill, stroke=style.root_fill,
text_color=style.root_text,
font_size=style.font_title, rounding=style.rounding + 0.15,
bold=True)
elif layout.depth == 1:
color = _color_for(branch_index, style)
_draw_box(ax, layout,
fill=color, stroke=color,
text_color=style.branch_text,
font_size=style.font_branch, rounding=style.rounding,
bold=True)
else:
_draw_box(ax, layout,
fill=style.leaf_fill, stroke=style.leaf_stroke,
text_color=style.leaf_text,
font_size=style.font_leaf, rounding=style.rounding)
for i, child in enumerate(layout.children):
branch_idx = i if layout.depth == 0 else branch_index
color = _color_for(branch_idx, style)
_draw_edge(ax, layout, child, color=color,
width=style.edge_width, alpha=style.edge_alpha)
_walk_draw(ax, child, style, branch_idx)
def render(root: Node, output_path, style_name='modern',
dpi=200, title_text=None):
style = get_style(style_name)
layout = compute_layout(root, style)
# 计算画布范围
xs, ys = [], []
for lay, _ in _iter(layout):
xs.append(lay.x)
xs.append(lay.x + lay.width)
ys.append(lay.y - lay.height / 2)
ys.append(lay.y + lay.height / 2)
if not xs:
xs = [0, 1]
ys = [0, 1]
width = max(xs) - min(xs) + 2.0
height = max(ys) - min(ys) + 2.0
width = max(width, 7)
height = max(height, 5)
fig, ax = plt.subplots(figsize=(width, height), dpi=dpi)
fig.patch.set_facecolor(style.bg)
ax.set_facecolor(style.bg)
ax.set_xlim(min(xs) - 0.8, max(xs) + 1.0)
ax.set_ylim(min(ys) - 0.8, max(ys) + 1.2)
ax.set_aspect('equal')
ax.axis('off')
if title_text:
font = get_cjk_font()
ax.text((min(xs) + max(xs)) / 2, max(ys) + 0.8, title_text,
ha='center', va='bottom', fontsize=22, fontweight='bold',
color=style.leaf_text,
fontproperties=font, zorder=6)
_walk_draw(ax, layout, style)
os.makedirs(os.path.dirname(os.path.abspath(output_path)) or '.',
exist_ok=True)
ext = os.path.splitext(output_path)[1].lower().lstrip('.')
if ext == 'pdf':
fig.savefig(output_path, format='pdf', bbox_inches='tight',
facecolor=style.bg)
elif ext == 'svg':
fig.savefig(output_path, format='svg', bbox_inches='tight',
facecolor=style.bg)
else:
fig.savefig(output_path, format='png', bbox_inches='tight',
facecolor=style.bg, dpi=dpi)
plt.close(fig)
return output_path
def _iter(layout):
yield layout, layout.depth
for child in layout.children:
yield from _iter(child)
FILE:scripts/mindmap_tree.py
"""
mindmap_tree.py — 思维导图统一数据模型 + 多格式解析 / 导出
数据模型：
Node(title, note, children) — 树形节点
输入（parse_*）：
- parse_markdown(text) : Markdown outline（#/##/### 或 ' - ' 列表）
- parse_json(text) : 内部 JSON 规约
- parse_opml(text) : OPML 2.0
- parse_xmind(path) : XMind 2021+ ZIP（content.json）
输出（to_*）：
- to_markdown(root)
- to_json(root)
- to_opml(root, title=None)
- to_xmind(root, output_path, sheet_name='思维导图')
- to_freemind(root)
"""
from __future__ import annotations
import json
import os
import re
import time
import uuid
import zipfile
from dataclasses import dataclass, field
from typing import List, Optional, Sequence
from xml.etree import ElementTree as ET
# ============================================================
# 一、数据模型
# ============================================================
@dataclass
class Node:
title: str
note: str = ''
children: List['Node'] = field(default_factory=list)
def add(self, title, note=''):
child = Node(title=title, note=note)
self.children.append(child)
return child
def depth(self):
if not self.children:
return 1
return 1 + max(c.depth() for c in self.children)
def walk(self, depth=0):
yield self, depth
for child in self.children:
yield from child.walk(depth + 1)
def count_leaves(self):
if not self.children:
return 1
return sum(c.count_leaves() for c in self.children)
def to_dict(self):
d = {'title': self.title}
if self.note:
d['note'] = self.note
if self.children:
d['children'] = [c.to_dict() for c in self.children]
return d
def _from_dict(data):
if isinstance(data, str):
return Node(title=data)
title = data.get('title') or data.get('topic') or data.get('text') or ''
note = data.get('note', '')
children_data = (data.get('children') or data.get('topics')
or data.get('nodes') or [])
children = [_from_dict(c) for c in children_data]
return Node(title=title, note=note, children=children)
# ============================================================
# 二、Markdown outline 解析
# ============================================================
_MD_HEADING_RE = re.compile(r'^(#{1,6})\s+(.+?)\s*#*\s*$')
_MD_LIST_RE = re.compile(r'^(\s*)[-*+]\s+(.+)$')
_MD_ORDERED_RE = re.compile(r'^(\s*)\d+[\.．)]\s+(.+)$')
def parse_markdown(text):
r"""把 Markdown 大纲转成 Node 树。
支持三种分层方式（按行决定层级）：
1. Markdown 标题 # / ## / ###
2. 无序列表缩进 - item （每 2 个空格算一级）
3. 有序列表 1. item
规则：
- 第一行标题（最浅的标题 / 最浅的列表）成为根节点 title；
若不存在标题级行，以 "思维导图" 为默认根。
- 同层节点平铺；深层缩进（或 `##`→`###`）成为下层。
- 连续的同一层非空行合并为节点注释（note）。
"""
lines = text.split('\n')
root = Node(title='思维导图')
stack: List[tuple[int, Node]] = [(-1, root)] # (depth, node)
root_set = False
for raw in lines:
line = raw.rstrip()
if not line.strip():
continue
heading = _MD_HEADING_RE.match(line)
if heading:
depth = len(heading.group(1)) # 1..6
title = heading.group(2).strip()
node = Node(title=title)
while stack and stack[-1][0] >= depth:
stack.pop()
parent = stack[-1][1] if stack else root
if not root_set and parent is root:
# 首个标题升格为根
root.title = title
root_set = True
stack = [(depth, root)]
continue
parent.children.append(node)
stack.append((depth, node))
continue
list_m = _MD_LIST_RE.match(line) or _MD_ORDERED_RE.match(line)
if list_m:
indent = len(list_m.group(1))
depth = 100 + indent // 2 # 超过 heading 层级
title = list_m.group(2).strip()
node = Node(title=title)
while stack and stack[-1][0] >= depth:
stack.pop()
parent = stack[-1][1] if stack else root
if not root_set and parent is root:
root.title = title
root_set = True
stack = [(depth, root)]
continue
parent.children.append(node)
stack.append((depth, node))
continue
# 纯文本行，挂到当前节点的 note 上
if stack:
current = stack[-1][1]
current.note = (current.note + ('\n' if current.note else '')
+ line.strip())
return root
def to_markdown(root: Node, max_heading=3):
"""把 Node 树还原成 Markdown outline（前 N 层用标题，其余用列表）。"""
lines = []
def _walk(node, depth):
text = node.title
if depth == 0:
lines.append(f'# {text}')
elif depth < max_heading:
lines.append('#' * (depth + 1) + ' ' + text)
else:
lines.append(' ' * (depth - max_heading) + '- ' + text)
if node.note:
lines.append('')
for ln in node.note.split('\n'):
lines.append(ln)
for child in node.children:
_walk(child, depth + 1)
_walk(root, 0)
return '\n'.join(lines) + '\n'
# ============================================================
# 三、JSON 规约
# ============================================================
def parse_json(text):
data = json.loads(text)
# 允许顶层包一层 {"title":..., "root":{...}} 或裸节点
if isinstance(data, dict) and 'root' in data:
return _from_dict(data['root'])
return _from_dict(data)
def to_json(root, indent=2):
return json.dumps(root.to_dict(), ensure_ascii=False, indent=indent)
# ============================================================
# 四、OPML 2.0 解析 / 导出
# ============================================================
def parse_opml(text):
root_node = Node(title='思维导图')
try:
tree = ET.fromstring(text)
except ET.ParseError:
return root_node
head = tree.find('head')
if head is not None:
title_el = head.find('title')
if title_el is not None and (title_el.text or '').strip():
root_node.title = title_el.text.strip()
body = tree.find('body')
if body is None:
return root_node
def _walk(el, parent):
for child_el in el.findall('outline'):
title = child_el.attrib.get('text') or child_el.attrib.get('title') or ''
note = child_el.attrib.get('_note', '')
child = Node(title=title, note=note)
parent.children.append(child)
_walk(child_el, child)
_walk(body, root_node)
return root_node
def to_opml(root: Node, title=None):
opml = ET.Element('opml', {'version': '2.0'})
head = ET.SubElement(opml, 'head')
ET.SubElement(head, 'title').text = title or root.title
ET.SubElement(head, 'dateCreated').text = time.strftime(
'%a, %d %b %Y %H:%M:%S +0000', time.gmtime())
body = ET.SubElement(opml, 'body')
def _append(parent_el, node):
attrs = {'text': node.title}
if node.note:
attrs['_note'] = node.note
outline = ET.SubElement(parent_el, 'outline', attrs)
for child in node.children:
_append(outline, child)
# root 作为第一个 outline，保留根节点信息
_append(body, root)
ET.indent(opml, space=' ')
return '<?xml version="1.0" encoding="UTF-8"?>\n' + ET.tostring(
opml, encoding='unicode')
# ============================================================
# 五、XMind 2021+ 解析 / 导出
# ============================================================
def _node_to_xmind_topic(node: Node):
topic = {
'id': uuid.uuid4().hex,
'title': node.title,
}
if node.note:
topic['notes'] = {'plain': {'content': node.note}}
if node.children:
topic['children'] = {
'attached': [_node_to_xmind_topic(c) for c in node.children]
}
return topic
def _xmind_topic_to_node(topic):
title = topic.get('title', '')
note = ''
notes = topic.get('notes') or {}
plain = notes.get('plain') or {}
if isinstance(plain, dict):
note = plain.get('content') or ''
node = Node(title=title, note=note)
children = topic.get('children') or {}
attached = children.get('attached') if isinstance(children, dict) else []
for c in attached or []:
node.children.append(_xmind_topic_to_node(c))
return node
def to_xmind(root: Node, output_path, sheet_name='思维导图'):
"""写 XMind 2021+ 压缩包（content.json）。"""
root_topic = _node_to_xmind_topic(root)
content = [{
'id': uuid.uuid4().hex,
'class': 'sheet',
'title': sheet_name,
'rootTopic': root_topic,
}]
metadata = {
'creator': {
'name': 'huo15-mind-map',
'version': '1.0.0',
},
'dataStructureVersion': '2',
}
manifest = {
'file-entries': {
'content.json': {},
'metadata.json': {},
},
}
os.makedirs(os.path.dirname(os.path.abspath(output_path)) or '.',
exist_ok=True)
with zipfile.ZipFile(output_path, 'w', zipfile.ZIP_DEFLATED) as zf:
zf.writestr('content.json', json.dumps(content, ensure_ascii=False))
zf.writestr('metadata.json', json.dumps(metadata, ensure_ascii=False))
zf.writestr('manifest.json', json.dumps(manifest, ensure_ascii=False))
return output_path
def parse_xmind(path):
"""读 XMind 2021+ 文件。旧版 (content.xml) 暂不支持。"""
with zipfile.ZipFile(path, 'r') as zf:
names = zf.namelist()
if 'content.json' in names:
data = json.loads(zf.read('content.json').decode('utf-8'))
sheets = data if isinstance(data, list) else [data]
if not sheets:
return Node(title='思维导图')
root_topic = sheets[0].get('rootTopic') or {}
return _xmind_topic_to_node(root_topic)
return Node(title='思维导图')
# ============================================================
# 六、FreeMind .mm 导出
# ============================================================
def to_freemind(root: Node):
"""FreeMind .mm 格式（很多国产思维导图也支持导入）。"""
doc = ET.Element('map', {'version': '1.0.1'})
def _append(parent_el, node):
el = ET.SubElement(parent_el, 'node', {'TEXT': node.title})
if node.note:
richcontent = ET.SubElement(el, 'richcontent',
{'TYPE': 'NOTE'})
body = ET.SubElement(richcontent, 'html')
body = ET.SubElement(body, 'body')
p = ET.SubElement(body, 'p')
p.text = node.note
for child in node.children:
_append(el, child)
_append(doc, root)
ET.indent(doc, space=' ')
return '<?xml version="1.0" encoding="UTF-8"?>\n' + ET.tostring(
doc, encoding='unicode')
# ============================================================
# 七、格式派发
# ============================================================
def parse_auto(path_or_text, hint=None):
"""根据 hint (扩展名或 'markdown'/'json'/'opml'/'xmind') 解析。"""
if hint is None and os.path.exists(path_or_text):
hint = os.path.splitext(path_or_text)[1].lstrip('.').lower()
with open(path_or_text, 'rb') as fh:
raw = fh.read()
if hint == 'xmind':
return parse_xmind(path_or_text)
text = raw.decode('utf-8', errors='replace')
else:
text = path_or_text
hint = (hint or '').lower()
if hint in ('md', 'markdown', 'txt', ''):
if text.lstrip().startswith('{'):
return parse_json(text)
if text.lstrip().startswith('<opml'):
return parse_opml(text)
return parse_markdown(text)
if hint in ('json',):
return parse_json(text)
if hint in ('opml', 'xml'):
return parse_opml(text)
return parse_markdown(text)
从想法到论文的全自主研究管道，6阶段，含HITL，输出到Obsidian
---
name: huo15-research-pipeline
version: 1.0.2
aliases:
- 火一五研究管道
- 火一五论文管道
- 自动研究
description: 从想法到论文的全自主研究管道，6阶段，含HITL，输出到Obsidian
---
# huo15-research-pipeline
> 从想法到论文的全自主研究管道，灵感来自 AutoResearchClaw
## 元信息
- **version**: 1.0.0
- **trigger**: 研究管道、research pipeline、自动研究、想法到论文、研究 [课题]
- **author**: huo15
- **compatibility**: OpenClaw >= 1.0
## 功能概述
将一个研究课题全自动推进至论文产出，涵盖范围定义、文献发现、知识综合、实验设计、结果分析与论文撰写。全程人类在环（HITL），每个 Phase 完成后等待用户确认。
## 核心流程
```
用户: "研究 [课题]"
│
▼
┌─────────────────┐
│ Phase A: 范围定义 │ → 用户确认
└────────┬────────┘
▼
┌─────────────────┐
│ Phase B: 文献发现 │ → 用户确认
└────────┬────────┘
▼
┌─────────────────┐
│ Phase C: 知识综合 │ → 用户确认
└────────┬────────┘
▼
┌─────────────────┐
│ Phase D: 实验设计 │ → 用户确认
└────────┬────────┘
▼
┌─────────────────┐
│ Phase E: 结果分析 │ → 用户确认
└────────┬────────┘
▼
┌─────────────────┐
│ Phase F: 论文撰写 │ → 完成
└────────┬────────┘
▼
输出到 Obsidian
```
## Phase 详细说明
### Phase A: 研究范围定义
定义研究问题、目标、范围边界和成功标准。
**输出：**
- 研究问题（1-3 个核心问题）
- 研究目标（ SMART 格式）
- 范围边界（ in-scope / out-of-scope）
- 成功标准
### Phase B: 文献发现与筛选
搜索相关论文、博客、技术报告，筛选高质量来源。
**输出：**
- 关键词列表
- 筛选出的文献列表（含标题、摘要、链接）
- 文献质量评分
### Phase C: 知识综合与假设生成
整合文献发现，生成假设或研究问题。
**输出：**
- 知识图谱摘要
- 核心发现列表
- 假设 / 待验证命题
### Phase D: 实验设计与执行
设计验证假设的实验方案，包括数据、方法和评估指标。
**输出：**
- 实验设计方案
- 数据需求
- 评估指标
### Phase E: 结果分析
分析实验结果，生成洞察。
**输出：**
- 结果摘要
- 统计显著性（如适用）
- 洞察列表
### Phase F: 论文撰写
按学术论文结构输出完整内容。
**输出：**
- 标题 + 摘要
- 引言
- 相关工作
- 方法
- 结果
- 讨论
- 结论
- 参考文献
## 使用方式
```
用户: 研究 大语言模型在代码补全任务中的性能评估
```
或直接说：
```
用户: 自动研究 基于强化学习的机器人抓取策略
```
## 输出位置
所有研究产物保存至：
```
$HOME/.openclaw/agents/main/agent/kb/raw/research-{课题名}-{日期}/
```
结构：
```
research-{课题}-{日期}/
├── 00_scope.md # Phase A 输出
├── 01_discovery.md # Phase B 输出
├── 02_synthesis.md # Phase C 输出
├── 03_experiment.md # Phase D 输出
├── 04_analysis.md # Phase E 输出
├── 05_paper.md # Phase F 输出（完整论文）
└── log.txt # 执行日志
```
## 人类在环（HITL）
每个 Phase 完成后，脚本会：
1. 显示该阶段产出摘要
2. 询问用户：`继续下一步 / 调整参数 / 终止`
3. 等待用户输入后执行下一阶段
## 调用示例
```bash
cd ~/.openclaw/workspace/skills/huo15-research-pipeline
./scripts/research.sh "大语言模型在代码补全任务中的性能评估"
```
## 依赖
- `openclaw` CLI（用于 LLM 调用）
- `curl`（用于 Web 搜索）
- `jq`（用于 JSON 解析）
- `obsidian-cli`（可选，用于直接写入 Obsidian）
## 注意事项
- 研究过程可能耗时较长，建议在空闲时运行
- 每个 Phase 的产出都会保存，中断后可从断点继续
- 论文撰写 Phase 会尽量保持学术规范，可直接提交或进一步编辑
FILE:_meta.json
{
"ownerId": "kn7byevkn40d6z4p7ghdb097z983tj33",
"slug": "huo15-research-pipeline",
"version": "1.0.2"
}
FILE:scripts/research.sh
#!/bin/bash
#===============================================
# huo15-research-pipeline
# 从想法到论文的全自主研究管道
#===============================================
set -e
# 参数
TOPIC="$1"
DATE=$(date +%Y-%m-%d)
SLUG=$(echo "$TOPIC" | tr ' ' '-' | tr -d ':/?&' | cut -c1-50)
# 目录
RESEARCH_DIR="$HOME/.openclaw/agents/main/agent/kb/raw/research-SLUG-DATE"
mkdir -p "$RESEARCH_DIR"
LOG="$RESEARCH_DIR/log.txt"
echo "========================================" | tee -a "$LOG"
echo "研究管道启动: $TOPIC" | tee -a "$LOG"
echo "时间: $(date)" | tee -a "$LOG"
echo "输出目录: $RESEARCH_DIR" | tee -a "$LOG"
echo "========================================" | tee -a "$LOG"
#---------------------------------------
# 通用：调用 LLM 生成内容
#---------------------------------------
call_llm() {
local prompt="$1"
local output_file="$2"
local model="-minimax/MiniMax-M2.7-highspeed"
echo "[LLM] 生成中..." | tee -a "$LOG"
local response
response=$(openclaw llm generate \
--model "$model" \
--prompt "$prompt" 2>&1) || true
if [ -n "$response" ]; then
echo "$response" > "$output_file"
echo "[LLM] 已保存至 $output_file ($(wc -c < "$output_file") bytes)" | tee -a "$LOG"
else
echo "[LLM] 警告: 未获取到响应" | tee -a "$LOG"
echo "# 无内容" > "$output_file"
fi
}
#---------------------------------------
# 通用：Web 搜索
#---------------------------------------
web_search() {
local query="$1"
echo "[搜索] $query" | tee -a "$LOG"
openclaw web search --query "$query" --count 5 2>&1 || true
}
#---------------------------------------
# HITL：等待用户确认
#---------------------------------------
hitl() {
local phase_name="$1"
local output_file="$2"
echo ""
echo "========================================" | tee -a "$LOG"
echo " Phase $phase_name 完成" | tee -a "$LOG"
echo "========================================" | tee -a "$LOG"
echo "产出预览:" | tee -a "$LOG"
head -20 "$output_file" | tee -a "$LOG"
echo "...(已保存至 $output_file)" | tee -a "$LOG"
echo ""
read -p "→ 输入 [c]继续 [a]调整 [q]退出: " user_input
case "$user_input" in
q|Q) echo "[退出] 研究管道终止"; exit 0 ;;
a|A) echo "[调整] 请说明要调整的内容..."; read -r adjustment; return 1 ;;
*) echo "[继续] 进入下一阶段..." ;;
esac
return 0
}
#---------------------------------------
# Phase A: 研究范围定义
#---------------------------------------
phase_a() {
echo ""
echo "=== Phase A: 研究范围定义 ===" | tee -a "$LOG"
local prompt="你是一个研究顾问。用户要研究以下课题：
课题: $TOPIC
请生成一份研究范围定义文档，包含：
1. **研究问题**：列出 1-3 个核心研究问题（精确、可验证）
2. **研究目标**：用 SMART 格式（具体、可衡量、可实现、相关、有时限）描述研究目标
3. **范围边界**：
- In-scope（包含的内容）
- Out-of-scope（不包含的内容）
4. **成功标准**：如何判断研究成功了？列出 3-5 个具体指标
5. **研究类型**：实证研究 / 理论研究 / 案例研究 / 文献综述
请用中文输出，Markdown 格式。"
call_llm "$prompt" "$RESEARCH_DIR/00_scope.md"
local retry=0
while true; do
if hitl "A" "$RESEARCH_DIR/00_scope.md"; then
break
fi
retry=$((retry + 1))
if [ $retry -gt 3 ]; then
echo "[重试上限] 继续进入下一阶段"
break
fi
done
}
#---------------------------------------
# Phase B: 文献发现
#---------------------------------------
phase_b() {
echo ""
echo "=== Phase B: 文献发现与筛选 ===" | tee -a "$LOG"
# 读取 Phase A 的研究问题用于指导搜索
local research_questions=""
if [ -f "$RESEARCH_DIR/00_scope.md" ]; then
research_questions=$(grep -A5 "研究问题" "$RESEARCH_DIR/00_scope.md" | head -20 || true)
fi
local prompt="你是一个文献检索专家。用户正在研究以下课题：
课题: $TOPIC
Phase A 产出的研究问题摘要:
$research_questions
请执行以下任务：
1. **生成关键词列表**：列出 10-15 个用于搜索的关键词（中英文），包括同义词和下位词
2. **文献搜索**：基于上述关键词，搜索相关学术论文、技术报告、博客文章
3. **文献筛选**：从搜索结果中筛选出 8-15 篇高质量文献，说明筛选标准
4. **文献列表**：每篇文献包含：标题、作者/来源、年份、URL、摘要要点（3-5句）、相关性评分（1-10）
请用中文输出，Markdown 格式。如果无法访问学术数据库，请使用网络搜索补充。"
call_llm "$prompt" "$RESEARCH_DIR/01_discovery.md"
local retry=0
while true; do
if hitl "B" "$RESEARCH_DIR/01_discovery.md"; then
break
fi
retry=$((retry + 1))
if [ $retry -gt 3 ]; then
echo "[重试上限] 继续进入下一阶段"
break
fi
done
}
#---------------------------------------
# Phase C: 知识综合
#---------------------------------------
phase_c() {
echo ""
echo "=== Phase C: 知识综合与假设生成 ===" | tee -a "$LOG"
local scope_content=""
local discovery_content=""
[ -f "$RESEARCH_DIR/00_scope.md" ] && scope_content=$(cat "$RESEARCH_DIR/00_scope.md")
[ -f "$RESEARCH_DIR/01_discovery.md" ] && discovery_content=$(cat "$RESEARCH_DIR/01_discovery.md")
local prompt="你是一个研究分析师。请综合以下研究范围和文献发现，生成假设和知识综合报告。
## 研究课题
$TOPIC
## 研究范围定义
$scope_content
## 文献发现
$discovery_content
请执行以下任务：
1. **知识图谱摘要**（200-300字）：总结该领域当前研究状态和主要发现
2. **核心发现列表**：列出 5-8 个经过文献验证的核心发现
3. **研究缺口**：找出当前研究中的空白或争议点
4. **假设 / 待验证命题**：基于核心发现，生成 3-5 个可验证的假设或待验证命题
5. **理论框架**：提出支撑假设的理论框架或概念模型
6. **方法论建议**：适合验证假设的研究方法
请用中文输出，Markdown 格式。"
call_llm "$prompt" "$RESEARCH_DIR/02_synthesis.md"
local retry=0
while true; do
if hitl "C" "$RESEARCH_DIR/02_synthesis.md"; then
break
fi
retry=$((retry + 1))
if [ $retry -gt 3 ]; then
echo "[重试上限] 继续进入下一阶段"
break
fi
done
}
#---------------------------------------
# Phase D: 实验设计
#---------------------------------------
phase_d() {
echo ""
echo "=== Phase D: 实验设计与执行 ===" | tee -a "$LOG"
local synthesis_content=""
[ -f "$RESEARCH_DIR/02_synthesis.md" ] && synthesis_content=$(cat "$RESEARCH_DIR/02_synthesis.md")
local prompt="你是一个实验设计专家。请为以下研究假设设计实验方案。
## 研究课题
$TOPIC
## Phase C 知识综合
$synthesis_content
请执行以下任务：
1. **实验设计方案**：为每个假设设计具体的实验方案（描述实验组/对照组、变量设置）
2. **数据需求**：
- 需要什么类型的数据？
- 数据来源在哪里？
- 数据量预估
3. **实验步骤**：详细的实验执行步骤（分步说明）
4. **评估指标**：定义如何衡量实验成功（准确率、提升幅度、A/B测试指标等）
5. **潜在偏差与控制**：识别可能的混杂变量和消除方法
6. **伦理考量**：实验是否涉及伦理问题？如何处理？
7. **资源需求**：计算资源、数据存储、工具依赖
如果该研究不适合实验方法（如纯理论研究或文献综述），请说明并提供替代验证方案。
请用中文输出，Markdown 格式。"
call_llm "$prompt" "$RESEARCH_DIR/03_experiment.md"
local retry=0
while true; do
if hitl "D" "$RESEARCH_DIR/03_experiment.md"; then
break
fi
retry=$((retry + 1))
if [ $retry -gt 3 ]; then
echo "[重试上限] 继续进入下一阶段"
break
fi
done
}
#---------------------------------------
# Phase E: 结果分析
#---------------------------------------
phase_e() {
echo ""
echo "=== Phase E: 结果分析 ===" | tee -a "$LOG"
local experiment_content=""
[ -f "$RESEARCH_DIR/03_experiment.md" ] && experiment_content=$(cat "$RESEARCH_DIR/03_experiment.md")
local prompt="你是一个数据分析专家。请基于实验设计方案，提供结果分析框架。
## 研究课题
$TOPIC
## Phase D 实验设计
$experiment_content
请执行以下任务：
1. **结果分析框架**：描述如何分析实验/研究结果（统计分析方法、可视化方案）
2. **预期结果模式**：描述可能的几种结果模式及其解读方式
3. **统计显著性判断**：如何判断结果是否具有统计显著性（置信区间、p值等）
4. **结果解读指南**：不同类型的结果应如何解读
5. **敏感性分析**：如何检验结果对假设条件的敏感性
6. **局限性讨论**：本研究的潜在局限性
如果用户已有实验数据，请替换上述内容并提供实际分析。
请用中文输出，Markdown 格式。"
call_llm "$prompt" "$RESEARCH_DIR/04_analysis.md"
local retry=0
while true; do
if hitl "E" "$RESEARCH_DIR/04_analysis.md"; then
break
fi
retry=$((retry + 1))
if [ $retry -gt 3 ]; then
echo "[重试上限] 继续进入下一阶段"
break
fi
done
}
#---------------------------------------
# Phase F: 论文撰写
#---------------------------------------
phase_f() {
echo ""
echo "=== Phase F: 论文撰写 ===" | tee -a "$LOG"
local scope_content=""
local discovery_content=""
local synthesis_content=""
local experiment_content=""
local analysis_content=""
[ -f "$RESEARCH_DIR/00_scope.md" ] && scope_content=$(cat "$RESEARCH_DIR/00_scope.md")
[ -f "$RESEARCH_DIR/01_discovery.md" ] && discovery_content=$(cat "$RESEARCH_DIR/01_discovery.md")
[ -f "$RESEARCH_DIR/02_synthesis.md" ] && synthesis_content=$(cat "$RESEARCH_DIR/02_synthesis.md")
[ -f "$RESEARCH_DIR/03_experiment.md" ] && experiment_content=$(cat "$RESEARCH_DIR/03_experiment.md")
[ -f "$RESEARCH_DIR/04_analysis.md" ] && analysis_content=$(cat "$RESEARCH_DIR/04_analysis.md")
local prompt="你是一个学术论文撰写专家。请基于以下所有研究阶段产出，撰写一篇完整的学术论文。
## 研究课题
$TOPIC
## Phase A - 研究范围
$scope_content
## Phase B - 文献发现
$discovery_content
## Phase C - 知识综合
$synthesis_content
## Phase D - 实验设计
$experiment_content
## Phase E - 结果分析框架
$analysis_content
请撰写一篇结构完整的学术论文，包含：
1. **标题**（中英文）
2. **摘要**（200-300字，中英文）
3. **关键词**（5-8个，中英文）
4. **引言**（研究背景、动机、贡献）
5. **相关工作**（文献综述）
6. **方法**（详细方法论）
7. **结果**（基于分析框架的预期/实际结果）
8. **讨论**（结果解读、局限性、未来工作）
9. **结论**
10. **参考文献**（列出引用的文献）
请用中文撰写主要部分，英文标题和关键词。Markdown 格式，便于后续编辑转换。
注意：
- 论点要清晰，论据要充分
- 保持学术写作规范
- 字数目标：3000-6000字（正文）
- 图表可用 Markdown 表格占位"
call_llm "$prompt" "$RESEARCH_DIR/05_paper.md"
echo ""
echo "========================================" | tee -a "$LOG"
echo " Phase F: 论文撰写 完成" | tee -a "$LOG"
echo "========================================" | tee -a "$LOG"
echo "产出: $RESEARCH_DIR/05_paper.md" | tee -a "$LOG"
}
#---------------------------------------
# 输出到 Obsidian（可选）
#---------------------------------------
output_to_obsidian() {
echo ""
echo "=== 同步到 Obsidian ===" | tee -a "$LOG"
if command -v obsidian &>/dev/null; then
obsidian new research --title "$TOPIC" --folder "research" 2>&1 || true
fi
echo "[Obsidian] 可手动导入以下文件:"
echo " $RESEARCH_DIR/05_paper.md"
}
#---------------------------------------
# 主流程
#---------------------------------------
main() {
if [ -z "$TOPIC" ]; then
echo "用法: $0 <研究课题>"
echo "示例: $0 \"大语言模型在代码补全任务中的性能评估\""
exit 1
fi
echo "[研究管道] 启动: $TOPIC"
echo "[输出目录] $RESEARCH_DIR"
phase_a
phase_b
phase_c
phase_d
phase_e
phase_f
echo ""
echo "========================================"
echo "研究管道完成！"
echo "========================================"
echo "所有产出保存在: $RESEARCH_DIR"
echo ""
echo "文件列表:"
ls -lh "$RESEARCH_DIR"/*.md 2>/dev/null || echo " (无文件)"
echo ""
output_to_obsidian
echo ""
echo "如需继续编辑论文，请查看: $RESEARCH_DIR/05_paper.md"
}
main "$@"
FILE:scripts/templates/phase_a_scope.sh
#!/bin/bash
#===============================================
# Phase A: 研究范围定义 Prompt 模板
#===============================================
TOPIC="$1"
OUTPUT_FILE="$2"
SCOPE_PROMPT="你是一个研究顾问。用户要研究以下课题：
课题: TOPIC
请生成一份研究范围定义文档，包含：
1. **研究问题**：列出 1-3 个核心研究问题（精确、可验证）
2. **研究目标**：用 SMART 格式（具体、可衡量、可实现、相关、有时限）描述研究目标
3. **范围边界**：
- In-scope（包含的内容）
- Out-of-scope（不包含的内容）
4. **成功标准**：如何判断研究成功了？列出 3-5 个具体指标
5. **研究类型**：实证研究 / 理论研究 / 案例研究 / 文献综述
请用中文输出，Markdown 格式。"
echo "$SCOPE_PROMPT" > "$OUTPUT_FILE"
echo "[Phase A] Prompt 已保存至 $OUTPUT_FILE"
FILE:scripts/templates/phase_b_discovery.sh
#!/bin/bash
#===============================================
# Phase B: 文献发现与筛选 Prompt 模板
#===============================================
TOPIC="$1"
SCOPE_FILE="$2"
OUTPUT_FILE="$3"
SCOPE_CONTENT=$(cat "$SCOPE_FILE" 2>/dev/null || echo "无")
DISCOVERY_PROMPT="你是一个文献检索专家。用户正在研究以下课题：
课题: TOPIC
Phase A 产出的研究问题摘要:
SCOPE_CONTENT
请执行以下任务：
1. **生成关键词列表**：列出 10-15 个用于搜索的关键词（中英文），包括同义词和下位词
2. **文献搜索**：基于上述关键词，搜索相关学术论文、技术报告、博客文章
3. **文献筛选**：从搜索结果中筛选出 8-15 篇高质量文献，说明筛选标准
4. **文献列表**：每篇文献包含：标题、作者/来源、年份、URL、摘要要点（3-5句）、相关性评分（1-10）
请用中文输出，Markdown 格式。如果无法访问学术数据库，请使用网络搜索补充。"
echo "$DISCOVERY_PROMPT" > "$OUTPUT_FILE"
echo "[Phase B] Prompt 已保存至 $OUTPUT_FILE"
FILE:scripts/templates/phase_c_synthesis.sh
#!/bin/bash
#===============================================
# Phase C: 知识综合与假设生成 Prompt 模板
#===============================================
TOPIC="$1"
SCOPE_FILE="$2"
DISCOVERY_FILE="$3"
OUTPUT_FILE="$4"
SCOPE_CONTENT=$(cat "$SCOPE_FILE" 2>/dev/null || echo "无")
DISCOVERY_CONTENT=$(cat "$DISCOVERY_FILE" 2>/dev/null || echo "无")
SYNTHESIS_PROMPT="你是一个研究分析师。请综合以下研究范围和文献发现，生成假设和知识综合报告。
## 研究课题
TOPIC
## 研究范围定义
SCOPE_CONTENT
## 文献发现
DISCOVERY_CONTENT
请执行以下任务：
1. **知识图谱摘要**（200-300字）：总结该领域当前研究状态和主要发现
2. **核心发现列表**：列出 5-8 个经过文献验证的核心发现
3. **研究缺口**：找出当前研究中的空白或争议点
4. **假设 / 待验证命题**：基于核心发现，生成 3-5 个可验证的假设或待验证命题
5. **理论框架**：提出支撑假设的理论框架或概念模型
6. **方法论建议**：适合验证假设的研究方法
请用中文输出，Markdown 格式。"
echo "$SYNTHESIS_PROMPT" > "$OUTPUT_FILE"
echo "[Phase C] Prompt 已保存至 $OUTPUT_FILE"
FILE:scripts/templates/phase_d_experiment.sh
#!/bin/bash
#===============================================
# Phase D: 实验设计与执行 Prompt 模板
#===============================================
TOPIC="$1"
SYNTHESIS_FILE="$2"
OUTPUT_FILE="$3"
SYNTHESIS_CONTENT=$(cat "$SYNTHESIS_FILE" 2>/dev/null || echo "无")
EXPERIMENT_PROMPT="你是一个实验设计专家。请为以下研究假设设计实验方案。
## 研究课题
TOPIC
## Phase C 知识综合
SYNTHESIS_CONTENT
请执行以下任务：
1. **实验设计方案**：为每个假设设计具体的实验方案（描述实验组/对照组、变量设置）
2. **数据需求**：
- 需要什么类型的数据？
- 数据来源在哪里？
- 数据量预估
3. **实验步骤**：详细的实验执行步骤（分步说明）
4. **评估指标**：定义如何衡量实验成功（准确率、提升幅度、A/B测试指标等）
5. **潜在偏差与控制**：识别可能的混杂变量和消除方法
6. **伦理考量**：实验是否涉及伦理问题？如何处理？
7. **资源需求**：计算资源、数据存储、工具依赖
如果该研究不适合实验方法（如纯理论研究或文献综述），请说明并提供替代验证方案。
请用中文输出，Markdown 格式。"
echo "$EXPERIMENT_PROMPT" > "$OUTPUT_FILE"
echo "[Phase D] Prompt 已保存至 $OUTPUT_FILE"
FILE:scripts/templates/phase_g_write.sh
#!/bin/bash
#===============================================
# Phase G: 论文撰写 Prompt 模板
#===============================================
TOPIC="$1"
SCOPE_FILE="$2"
DISCOVERY_FILE="$3"
SYNTHESIS_FILE="$4"
EXPERIMENT_FILE="$5"
ANALYSIS_FILE="$6"
OUTPUT_FILE="$7"
SCOPE_CONTENT=$(cat "$SCOPE_FILE" 2>/dev/null || echo "无")
DISCOVERY_CONTENT=$(cat "$DISCOVERY_FILE" 2>/dev/null || echo "无")
SYNTHESIS_CONTENT=$(cat "$SYNTHESIS_FILE" 2>/dev/null || echo "无")
EXPERIMENT_CONTENT=$(cat "$EXPERIMENT_FILE" 2>/dev/null || echo "无")
ANALYSIS_CONTENT=$(cat "$ANALYSIS_FILE" 2>/dev/null || echo "无")
PAPER_PROMPT="你是一个学术论文撰写专家。请基于以下所有研究阶段产出，撰写一篇完整的学术论文。
## 研究课题
TOPIC
## Phase A - 研究范围
SCOPE_CONTENT
## Phase B - 文献发现
DISCOVERY_CONTENT
## Phase C - 知识综合
SYNTHESIS_CONTENT
## Phase D - 实验设计
EXPERIMENT_CONTENT
## Phase E - 结果分析框架
ANALYSIS_CONTENT
请撰写一篇结构完整的学术论文，包含：
1. **标题**（中英文）
2. **摘要**（200-300字，中英文）
3. **关键词**（5-8个，中英文）
4. **引言**（研究背景、动机、贡献）
5. **相关工作**（文献综述）
6. **方法**（详细方法论）
7. **结果**（基于分析框架的预期/实际结果）
8. **讨论**（结果解读、局限性、未来工作）
9. **结论**
10. **参考文献**（列出引用的文献）
请用中文撰写主要部分，英文标题和关键词。Markdown 格式，便于后续编辑转换。
注意：
- 论点要清晰，论据要充分
- 保持学术写作规范
- 字数目标：3000-6000字（正文）
- 图表可用 Markdown 表格占位"
echo "$PAPER_PROMPT" > "$OUTPUT_FILE"
echo "[Phase G] Prompt 已保存至 $OUTPUT_FILE"