mguozhen

---
name: shopify-ab-testing
description: "Build a systematic A/B testing framework for Shopify stores to improve conversion rates through data-driven experiments. Triggers: ab testing, split testing, shopify ab test, conversion rate testing, cro testing"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/shopify-ab-testing
---

# Shopify A/B Testing Framework

A rigorous A/B testing methodology for Shopify merchants to systematically improve conversion rates. This skill generates a prioritized testing roadmap, hypothesis templates, sample size calculators, and statistical significance guidelines for ongoing CRO experiments.

## Usage

```
AB testing plan: <store niche or URL>
split testing strategy: <conversion problem>
shopify testing framework: <store type>
what to AB test: <store URL>
```

## What You Get

1. **Testing Prioritization Matrix** — ICE scoring to rank tests by impact and effort
2. **Hypothesis Templates** — Structured test hypothesis with expected outcomes
3. **Element Testing Roadmap** — Headlines, CTAs, images, layout, pricing display
4. **Sample Size & Duration** — How long to run tests for statistical validity
5. **Tool Stack Recommendations** — Native and third-party A/B testing tools for Shopify
6. **Results Analysis Framework** — How to interpret data and avoid common pitfalls
7. **Iterative Testing Calendar** — 12-week rolling test schedule and documentation

FILE:analyze.sh
#!/usr/bin/env bash
# shopify-ab-testing — A/B testing framework for Shopify stores
set -euo pipefail

INPUT="-"
if [ -z "$INPUT" ]; then
  echo "Usage: AB testing plan: <store niche or URL>"
  exit 1
fi

SESSION_ID="shopify-ab-testing-$(date +%s)"

PROMPT="You are a Shopify CRO (conversion rate optimization) expert specializing in A/B testing methodology, statistical analysis, and systematic experimentation for ecommerce stores. Build a complete A/B testing framework for: INPUT

Produce a complete A/B testing strategy report with these sections:

## 1. Test Prioritization Framework
- ICE scoring model (Impact, Confidence, Ease) for ranking test ideas
- Top 20 highest-impact A/B test ideas for this store type
- Quick wins vs long-term structural tests
- Revenue impact calculator: how 1% CR improvement affects annual revenue

## 2. Hypothesis Writing Templates
- Hypothesis structure: We believe [change] will [outcome] because [reasoning]
- 10 specific hypothesis examples for this niche
- Success metric definition for each test type
- Secondary metric tracking to detect unintended consequences

## 3. Element Testing Roadmap (Priority Order)
- Homepage: hero image, headline, featured products, navigation
- Product page: title, images, description, price display, CTA button
- Cart page: upsell placement, trust signals, checkout button
- Checkout: form fields, payment icons, progress indicators

## 4. Sample Size & Statistical Requirements
- Minimum traffic threshold for valid test results
- Sample size calculator inputs: baseline CR, MDE, confidence level
- How long to run tests: minimum 2 weeks, statistical significance at 95%
- How to handle seasonal traffic fluctuations during tests

## 5. A/B Testing Tool Stack for Shopify
- Native: Shopify built-in theme editor variants
- Third-party: Google Optimize alternatives, Shogun, Neat A/B Testing
- Heatmap and session recording: Hotjar, Microsoft Clarity (free)
- Analytics integration: GA4, Shopify Analytics, Triple Whale

## 6. Results Analysis & Decision Framework
- Statistical significance explained: p-value, confidence interval interpretation
- Common mistakes: peeking at results early, stopping tests too soon
- When to declare a winner vs when to run follow-up tests
- How to document and share learnings across the team

## 7. 12-Week Testing Calendar & Scaling Plan
- Immediate actions (week 1): baseline metrics, heatmaps, first hypothesis
- Short-term (month 1): run 2 simultaneous tests, analyze first results
- Long-term (month 3+): 10+ completed tests, compound conversion gains, personalization

Include realistic conversion rate improvement expectations (e.g., 5-15% lift per winning test), compounding effect math, and testing velocity benchmarks for stores at different traffic levels."

openclaw agent --local --message "PROMPT" --session "SESSION_ID"

---
name: ai-voc-review-insights
description: "AI-powered Voice of Customer (VoC) review intelligence agent using DeepSeek-style analysis. Deep semantic analysis of customer reviews to extract pain points, purchase motivations, unmet needs, and product improvement signals across any e-commerce platform. Triggers: voc analysis, voice of customer, review intelligence, customer sentiment, pain points, purchase motivation, review deep dive, customer insights, product feedback, ai review analysis, deepseek voc, customer voice"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/ai-voc-review-insights
---

# AI VoC Review Intelligence

Deep AI-powered Voice of Customer analysis — go beyond basic sentiment to extract purchase motivations, hidden pain points, unmet needs, and product-market fit signals from customer reviews across any platform.

## Commands

```
voc analyze <reviews>             # full VoC analysis of review set
voc pain-points <reviews>         # extract and rank customer pain points
voc motivations <reviews>         # identify purchase motivations
voc unmet-needs <reviews>         # find unserved customer needs
voc personas <reviews>            # build customer persona from reviews
voc jobs-to-be-done <reviews>     # JTBD analysis from review language
voc compare <reviews1> <reviews2> # compare VoC between two products
voc opportunity <reviews>         # identify product development opportunities
voc marketing <reviews>           # extract marketing messages from reviews
voc report <product>              # full VoC intelligence report
```

## What Data to Provide

- **Reviews** — paste 20-200 customer reviews (more = better analysis)
- **Star distribution** — 1-5 star count breakdown
- **Product category** — context for benchmarking
- **Competitor reviews** — for comparative VoC analysis
- **Your marketing copy** — to align with customer language

## VoC Analysis Framework

### Level 1: Surface Analysis (Standard Review Analysis)

**What customers say explicitly:**
```
"The product is great quality"
"Arrived quickly"
"Easy to assemble"
"A bit expensive but worth it"
```

Basic sentiment: positive/negative/neutral classification

### Level 2: Semantic Analysis (What They Really Mean)

**Reading between the lines:**
```
Review: "Exactly what I needed" → Unmet need was real, product solves it
Review: "Better than I expected" → Category has history of disappointing products
Review: "I was skeptical but..." → High purchase anxiety in this category
Review: "Bought this as a gift" → Gifting is a significant use case
Review: "Replaced my old [brand]" → Competitor switching signal
Review: "My husband/wife loves it" → Multi-person household use
Review: "Works in my [specific context]" → Niche use case validation
```

### Level 3: Jobs-to-be-Done (JTBD) Analysis

**Functional jobs** (what they hire the product to do):
- "I need to [task]"
- Extract the core functional use from review language

**Emotional jobs** (how they want to feel):
- "I feel confident/safe/proud/excited when..."
- Extract emotional outcomes from positive reviews

**Social jobs** (how they want to be perceived):
- "My [guests/family/colleagues] noticed..."
- Extract social signaling from reviews

```
JTBD template from reviews:
When I [situation], I want to [motivation], so I can [outcome].

Example from reviews of a standing desk converter:
When I work from home all day, I want to avoid back pain,
so I can stay productive without discomfort.

→ Marketing message: "Work pain-free all day. Designed for the modern home office."
```

### Pain Point Extraction Matrix

Extract all pain points and classify:

**Dimension 1: Frequency**
- Mentioned in >20% of reviews: Critical issue
- Mentioned in 10-20%: Significant issue
- Mentioned in 5-10%: Notable issue
- Mentioned in <5%: Edge case

**Dimension 2: Intensity**
- "Terrible", "awful", "destroyed", "complete waste": Severity 5
- "Disappointed", "frustrated", "annoyed": Severity 4
- "Could be better", "wished it had": Severity 3
- "Minor issue", "small complaint": Severity 2
- Implied, not stated directly: Severity 1

**Dimension 3: Resolution Potential**
- Product redesign needed: Hard (3-6 months)
- Listing/instruction update: Easy (<1 week)
- Packaging/insert improvement: Medium (2-4 weeks)
- Customer service response: Immediate

```
Pain Point Matrix:
Pain Point           Freq   Intensity  Resolution  Priority
Instructions unclear 18%    3          Easy        HIGH
Strap breaks easily  12%    5          Hard        HIGH
Bag smaller than shown 9%   4          Listing fix MEDIUM
Color slightly off    6%    2          Listing fix LOW
```

### Customer Persona Building

From review language patterns, identify buyer segments:

**Segment 1: Core buyers (most reviews)**
```
Demographics: [infer from review context]
Trigger: [what prompted purchase]
Use case: [primary use]
Success metric: [what makes them happy]
Quote: "[representative review excerpt]"
```

**Segment 2: Edge case buyers (cause most problems)**
```
Demographics: [who writes the negative reviews]
Mismatch: [how product doesn't meet their expectations]
Fix: [listing change to filter them out or meet their needs]
```

**Segment 3: Surprise buyers (unexpected use cases)**
```
Discovery: [how they found your product]
Use case: [unexpected application]
Opportunity: [new marketing angle or product variation]
```

### Purchase Motivation Analysis

Extract why people buy, beyond the obvious:

**Rational motivators** (stated reasons):
- Quality, price, functionality, specifications

**Emotional motivators** (unstated reasons):
- Status, identity, relationships, fear/risk reduction
- Safety ("my child will be safe")
- Belonging ("everyone in our community uses this")
- Achievement ("I finally solved this problem")

**Trigger events** (what caused the purchase NOW):
- "After moving to a new home"
- "Since working from home"
- "After my old one broke"
- "Doctor recommended"
- "Saw on TikTok"

### Unmet Needs Identification

Find gaps in the market from review language:

**Explicit unmet needs:**
- "I wish it came in [X]"
- "Would be perfect if it also [function]"
- "Need something like this but for [use case]"

**Implicit unmet needs** (inferred from workarounds):
- "I had to [work around]" → product doesn't do X natively
- "It would help if..." → feature request pattern
- Comparisons to competitors: what competitor does better

### Competitive Switching Signals

From reviews mentioning competitors:
```
"Switched from [Brand X]" → X is your direct competitor
"Better than [Brand X]" → X is in buyer's consideration set
"[Brand X] stopped working, got this" → X has quality issues
"Half the price of [Brand X]" → X is premium alternative
```

### Marketing Message Extraction

The best marketing copy comes directly from customer words:

```
Reviews say:                 → Marketing copy:
"Finally found one that..."  → "The [product] you've been searching for"
"Works exactly as advertised" → "What you see is what you get"
"Gift for my husband, he loves it" → "The gift he'll actually use"
"Solved my [problem]"        → "[Problem]? Problem solved."
"Worth every penny"          → "Invest in quality. Feel the difference."
```

### Sentiment Evolution Analysis

Compare early reviews vs. recent reviews:
```
Early reviews (product launch): Focus on unboxing, first impressions
Recent reviews (mature product): Focus on durability, long-term value

Declining sentiment pattern:
Early avg: 4.5 stars → Recent avg: 3.9 stars
Signal: Quality or supplier change, investigate manufacturing
```

## Workspace

Creates `~/voc-intelligence/` containing:
- `analyses/` — full VoC reports per product
- `personas/` — customer persona profiles
- `pain-points/` — pain point matrices
- `marketing/` — extracted marketing messages
- `jtbd/` — jobs-to-be-done frameworks

## Output Format

Every VoC analysis outputs:
1. **VoC Executive Summary** — 5 key findings in plain language
2. **Pain Point Matrix** — all pain points scored by frequency × intensity
3. **JTBD Framework** — functional, emotional, and social jobs identified
4. **Customer Personas** — 2-3 buyer segments with profiles
5. **Unmet Needs List** — product/feature gaps discovered
6. **Marketing Messages** — 5 ready-to-use copy lines from customer language
7. **Competitor Switching Map** — which competitors appear and in what context
8. **Product Roadmap Signals** — prioritized improvements by business impact

---
name: wildberries-warehouse-booker
description: "Wildberries smart warehouse booking and logistics management agent. Solve WB warehouse appointment scarcity with intelligent monitoring, auto-submit strategies, and fulfillment efficiency optimization for Wildberries sellers. Triggers: wildberries warehouse, wb booking, wildberries fulfillment, wb склад, wildberries logistics, wb поставка, warehouse appointment, wb fbo, wildberries supply, wildberries inventory, wb supplier, russian marketplace logistics"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/wildberries-warehouse-booker
---

# Wildberries Warehouse Booker

Solve the WB warehouse appointment challenge — intelligent monitoring strategies, auto-booking tactics, fulfillment timing optimization, and logistics management for Wildberries sellers.

## Commands

```
wb book <warehouse> <date>        # warehouse booking strategy guide
wb monitor                        # set up booking monitoring plan
wb supply plan <inventory>        # create supply delivery plan
wb warehouse choose               # compare WB warehouses by strategy
wb timing                         # optimal booking time windows
wb inventory check                # inventory level analysis and restock planning
wb coefficient <warehouse>        # analyze acceptance coefficient strategy
wb logistics plan <product>       # end-to-end logistics plan
wb emergency <situation>          # emergency restock strategy
wb report <period>                # fulfillment performance report
```

## What Data to Provide

- **Your warehouse preference** — which WB warehouse(s) you ship to
- **Inventory data** — current stock levels at each warehouse
- **Sales velocity** — daily sales per SKU
- **Supply lead time** — how long from order to WB warehouse delivery
- **Coefficient status** — current acceptance coefficients for target warehouses

## WB Warehouse Framework

### Wildberries Warehouse Network

Major WB warehouses:
```
Warehouse       Location        Characteristics
Коледино        Moscow region   Largest, most competitive to book
Подольск        Moscow region   Alternative to Коледино
Казань          Kazan           Regional, less competition
Екатеринбург    Yekaterinburg   Ural region coverage
Краснодар       Krasnodar       South Russia
Новосибирск     Novosibirsk     Siberia region
Электросталь    Moscow region   Growing capacity
```

### The Warehouse Booking Challenge

**Why booking is difficult:**
- WB warehouse capacity released in limited daily slots
- Popular warehouses (Коледино) open slots at unpredictable times
- Sellers must compete for available slots
- "Acceptance coefficient" system limits which products can be delivered
- Slot availability often announced with 1-3 days notice

**Acceptance coefficient system:**
- Coefficient 1: Normal acceptance fee
- Coefficient 2-5: Premium fee required (WB charges extra)
- Coefficient 0: Free acceptance — grab immediately!
- High coefficient: Consider alternative warehouse

### Booking Strategy

**Monitoring approach:**
```
1. Check WB Seller Dashboard: "Поставки" → "Принять поставку"
2. Best checking times: 07:00-09:00, 12:00-14:00, 20:00-22:00 Moscow time
3. New slots often released: Monday and Wednesday mornings
4. Set up Telegram notifications if WB bot available
5. Check multiple warehouses simultaneously (not just your preferred)
```

**Booking decision tree:**
```
Available slot detected?
├── Coefficient = 0: BOOK IMMEDIATELY regardless of timing
├── Coefficient 1: Book if inventory needed within 3 weeks
├── Coefficient 2-3: Book if critically low (< 7 days stock)
└── Coefficient 4-5: Decline unless emergency (cost prohibitive)
```

**Multi-warehouse diversification:**
```
Instead of: 100% to Коледино
Consider:   60% Коледино + 40% Подольск or Казань

Benefits:
- Reduces dependency on single warehouse
- Faster slots available at regional warehouses
- Geographic distribution improves delivery speed to buyers
- Regional warehouses have lower coefficient fees
```

### Supply Planning

**Reorder timeline calculation:**
```
Safety stock = Daily sales × Delivery lead time × Safety factor
Safety factor = 1.5 (recommended for WB booking uncertainty)

Example:
Daily sales: 50 units
Lead time: 21 days (production 14 + transit 7)
Safety factor: 1.5
Safety stock = 50 × 21 × 1.5 = 1,575 units

Reorder when: Inventory at WB = Safety stock + Units in transit
= 1,575 + 0 = 1,575 units trigger point
```

**Booking frequency strategy:**
```
Volume scenario       Recommended booking frequency
High volume (>200/day): Every 2 weeks — maintain 30-day supply
Medium (50-200/day):    Every 3-4 weeks — maintain 45-day supply
Low (<50/day):          Monthly — maintain 60-day supply
```

### Shipment Preparation Checklist

```
BEFORE BOOKING
[ ] Check coefficient for target warehouse (aim ≤2)
[ ] Confirm inventory ready for delivery date
[ ] Barcode all items (WB barcode requirements)
[ ] Package per WB standards (box sizes, weight limits)
[ ] Prepare shipment list in WB system

DURING BOOKING PROCESS
[ ] Book slot with buffer (at least 3 days before needed)
[ ] Complete supply order in WB system
[ ] Print supply stickers and route sheet
[ ] Schedule logistics carrier for pickup

ON DELIVERY DAY
[ ] Confirm carrier booked and confirmed
[ ] Have all documents ready (supply order, route sheet)
[ ] Arrive at warehouse at appointed time (penalties for no-show)
[ ] Get acceptance confirmation and slip number
```

### Emergency Restock Strategy

When facing stockout risk:
```
Priority 1: Check coefficient 0 warehouses immediately — book any available
Priority 2: Use FBS (seller ships to buyer) temporarily while waiting for FBO slot
Priority 3: Split shipment — send available inventory now, rest when slot opens
Priority 4: Express delivery service to WB (more expensive but fast)
Priority 5: Reduce ad spend to slow sales velocity while restocking
```

**FBS backup plan:**
- Activate FBS (seller-fulfilled) for affected SKUs
- Set processing time to 1-2 days
- Maintain small FBS stock at your location as buffer
- Deactivate FBS once FBO stock replenished

### Inventory Level Optimization

**Target inventory levels:**
```
Green zone: 30-45 days of stock at warehouse
Yellow zone: 15-30 days of stock (initiate restock)
Red zone: 7-15 days (emergency booking needed)
Critical: <7 days (out-of-stock risk, ranking damage)
```

**Out-of-stock impact on WB:**
- Ranking drops significantly after 24-48 hours out of stock
- Recovery time: 2-4 weeks to return to previous position
- Financial impact: Lost sales + ranking recovery cost in ads

### Logistics Carrier Options

For delivering to WB warehouses:
```
Carrier         Notes
ТК Деловые Линии    Reliable, major carrier
СДЭК            Good network, tracking
ПЭК             Large freight specialist
Boxberry        Good for smaller shipments
Direct delivery Own car — cheapest but limited
```

**Carrier selection by volume:**
- <500 kg per delivery: СДЭК or Boxberry
- 500-3000 kg: Деловые Линии or ПЭК
- >3000 kg: Direct negotiation with major carriers

## Workspace

Creates `~/wb-logistics/` containing:
- `bookings/` — booking history and tracking
- `inventory/` — stock level monitoring
- `supply-plans/` — delivery schedule
- `warehouses/` — warehouse comparison data
- `reports/` — fulfillment performance reports

## Output Format

Every logistics plan outputs:
1. **Inventory Status** — current days of stock by warehouse
2. **Booking Recommendation** — when and where to book next supply
3. **Coefficient Analysis** — current fees at each warehouse
4. **Supply Schedule** — next 3 delivery dates with quantities
5. **Emergency Plan** — what to do if out-of-stock imminent
6. **Checklist** — pre-delivery preparation steps
7. **Cost Estimate** — logistics cost for planned supply

---
name: jumia-opportunity-finder
description: "Jumia marketplace opportunity finder and profitable niche analyzer for African markets. Identify profitable niches, analyze competition, calculate margins, and build winning strategies for Nigeria, Kenya, Egypt, Morocco, Ghana, and other African markets. Triggers: jumia marketplace, african ecommerce, jumia seller, nigeria ecommerce, kenya marketplace, africa online shopping, jumia product, jumia nigeria, jumia kenya, jumia opportunity, africa market, profitlantern"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/jumia-opportunity-finder
---

# Jumia Opportunity Finder

Identify profitable product niches on Jumia — Africa's largest e-commerce platform. Analyze markets across Nigeria, Kenya, Egypt, Morocco, Ghana, and other African countries for entry opportunities with high ROI potential.

## Commands

```
jumia analyze <product>           # analyze product opportunity on Jumia
jumia market <country>            # market overview for a Jumia country
jumia niche <category>            # find niches in a category
jumia compete <product>           # competitive analysis
jumia price <product> <country>   # pricing strategy for market
jumia margin <product> <price>    # margin calculation
jumia trends <country>            # trending categories in market
jumia entry <product>             # market entry assessment
jumia logistics <country>         # logistics and fulfillment options
jumia report <product>            # comprehensive market report
```

## What Data to Provide

- **Product idea or category** — what you want to sell
- **Target country** — which Jumia market to analyze
- **Competitor data** — paste competing product listings from Jumia
- **Your cost structure** — COGS, shipping to Africa
- **Budget** — investment capacity for market entry

## Jumia Market Framework

### Active Jumia Markets

| Country | Currency | Market Maturity | Key Characteristics |
|---------|----------|-----------------|---------------------|
| Nigeria (NG) | NGN | Largest (70%+ of Jumia) | Price-sensitive, mobile-first, cash on delivery |
| Kenya (KE) | KES | Growing fast | Tech-savvy, M-Pesa payments, strong middle class |
| Egypt (EG) | EGP | Second largest | Arabic-speaking, conservative categories |
| Morocco (MA) | MAD | Developed | French-Arabic bilingual, fashion-conscious |
| Ghana (GH) | GHS | Emerging | Stable economy, growing digital payment |
| Senegal (SN) | XOF | Small but growing | French-speaking West Africa |
| Ivory Coast (CI) | XOF | Stable market | French West Africa hub |
| Tanzania (TZ) | TZS | East Africa | Growing mobile money usage |
| Uganda (UG) | UGX | Small, stable | East Africa frontier |
| Algeria (DZ) | DZD | Large population | Restrictive import policies |

### Nigeria (Largest Market) Deep Dive

**Market dynamics:**
- Population: 220M (Africa's largest)
- Jumia Nigeria: ~$500M+ GMV annually
- Mobile penetration: 85%+ smartphones
- Payment preference: Cash on delivery (60%+), bank transfer, Jumia Pay
- Currency volatility: NGN fluctuates significantly — price in NGN, monitor monthly

**Top-selling categories in Nigeria:**
1. Consumer Electronics (phones, accessories)
2. Fashion (clothing, shoes, bags)
3. Computing (laptops, peripherals)
4. Phones & Tablets (mobile accessories)
5. Home & Office (appliances, furniture)
6. Beauty & Health
7. Baby Products
8. Sporting Goods

**Pricing considerations:**
- Nigerians are extremely price-conscious
- "Affordable luxury" positioning works well (look expensive but priced fairly)
- Coupons and flash sales drive 40-60% of Jumia volume
- JumiaPay incentives boost conversion (extra discounts)

### Kenya Market Deep Dive

**Market dynamics:**
- Strong middle class (30%+ of population)
- M-Pesa dominates payments (85% of transactions)
- Higher average order value than Nigeria
- Tech-forward: fintech, mobile apps popular
- East Africa hub — goods ship from Nairobi to TZ, UG, ET

**Top Kenya categories:**
1. Electronics & Accessories
2. Fashion
3. Computing
4. Beauty & Health
5. Home & Kitchen

### Product Opportunity Framework

**Opportunity Score for Jumia:**

Rate each product 1-5 on 6 dimensions:
```
1. Local demand (is this actively searched/bought in Africa?)    /5
2. Competition density (few sellers = higher score)             /5
3. Margin potential (after shipping, fees, returns)             /5
4. Sourcing feasibility (can you source for African prices?)    /5
5. Logistics ease (lightweight, non-fragile scores higher)      /5
6. Return risk (low return risk = higher score)                 /5

Total: /30
25+: Strong opportunity
20-24: Good opportunity
15-19: Moderate, requires differentiation
<15: Challenging, likely oversaturated
```

**Best product characteristics for Jumia:**
```
✓ Price point: $5-$50 equivalent in local currency
✓ Weight: <2 kg (shipping cost critical)
✓ Not perishable, not fragile
✓ Addresses a real local need (not just imported Western trend)
✓ Locally available cheaper alternative doesn't exist
✓ Brand name recognition not required (private label viable)
✓ Visual appeal (good product photo converts well)
```

### Margin Calculation for Jumia

**Fee structure:**
```
Commission: 3-15% depending on category
  Electronics: 3-5%
  Fashion: 10-15%
  General merchandise: 7-10%

Logistics fee: Jumia-managed delivery
  Local delivery: Included in Jumia logistics
  Seller ships to Jumia hub: Seller's cost

Payment fee: Jumia Pay: 1.5-2.5%
             COD: 2-3%
```

**Margin template:**
```
Selling price (local currency): NGN 15,000 (~$10)
- Commission (10%):            NGN 1,500
- Payment fee (2%):            NGN 300
- Shipping to Jumia hub:       NGN 800
- Product cost (incl. freight from China): NGN 7,000
= Net profit:                  NGN 5,400 (36% margin)

Currency risk: Hedge by pricing in USD equivalent and adjusting monthly
```

### Competitive Analysis on Jumia

Assess top 10 sellers for any category:
```
Seller type breakdown:
□ Official brand stores (hard to compete with)
□ Local distributors (may have price advantages)
□ Individual sellers (your main competition)
□ Chinese cross-border sellers (watch for price)

Market concentration:
□ <5 sellers dominating → easy entry
□ 5-20 sellers balanced → normal competition
□ 1-2 mega-sellers → only enter with strong differentiation
```

### Logistics & Fulfillment Options

**Jumia Logistics (JL) — Standard:**
- Seller ships to nearest Jumia pickup station
- Jumia handles last-mile delivery
- Coverage: Major cities and towns

**JumiaPrime / Express:**
- Faster delivery for enrolled products
- Better ranking in search results
- Requires inventory in Jumia fulfillment center

**Cross-border selling to Africa:**
```
From China:
- Sea freight: 25-45 days, cheapest for bulk
- Air freight: 5-10 days, more expensive
- E-commerce express (Yunexpress, 4PX): 12-20 days

Recommend: Air freight for first 3-6 months (test market),
           Sea freight once volume proven
```

### Top Market Entry Opportunities (2024-2025)

**Underserved niches showing promise:**
1. Smart home accessories (phone-controlled devices)
2. Solar-powered products (electricity shortages = demand)
3. Local fashion accessories (African-inspired designs)
4. Educational products (growing middle class investing in children)
5. Health monitoring devices (growing health consciousness)
6. Small agricultural tools (rural penetration growing)

## Workspace

Creates `~/jumia-research/` containing:
- `markets/` — country market analyses
- `products/` — product opportunity reports
- `competitors/` — competitor seller profiles
- `pricing/` — currency and pricing data
- `reports/` — comprehensive market entry reports

## Output Format

Every analysis outputs:
1. **Market Overview** — country profile, market size, key dynamics
2. **Product Opportunity Score** — rated on all 6 dimensions
3. **Competitive Landscape** — top sellers and market concentration
4. **Pricing Model** — local currency pricing with margin calculation
5. **Logistics Plan** — how to ship to and within the market
6. **Entry Roadmap** — 90-day plan with milestones
7. **Risk Assessment** — currency, regulatory, and operational risks

---
name: mercadolibre-return-guide
description: "Mercado Libre return management and authorization code agent. Complete returns workflow management for ML sellers — obtain return codes, manage return shipments, handle disputes, minimize return losses, and track return metrics. Triggers: mercado libre returns, mercadolibre return, ml return code, mercado pago refund, latin america ecommerce returns, ml dispute, mercadolibre seller, brazil ecommerce returns, argentina ecommerce, ml return management"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/mercadolibre-return-guide
---

# Mercado Libre Return Manager

Complete returns management for Mercado Libre sellers — from obtaining return codes to managing disputes, tracking return metrics, and minimizing return-related losses across LATAM markets.

## Commands

```
return guide <situation>          # step-by-step return handling guide
return code <order>               # how to obtain return authorization code
return dispute <case>             # handle a return dispute
return policy <market>            # ML return policy by country
return reduce <category>          # strategies to reduce return rate
return metrics <data>             # analyze your return rate data
return response <buyer-message>   # draft response to return request
return tracking <return-id>       # track return shipment status
return report <period>            # return performance report
return warehouse <setup>          # overseas warehouse return setup
```

## What Data to Provide

- **Order details** — order ID, product, purchase date, buyer message
- **Return reason** — buyer's stated reason for return
- **Country** — which ML market (BR, MX, AR, CO, CL, etc.)
- **Return rate data** — current return % by category/product
- **Dispute details** — case ID and timeline

## Mercado Libre Return Framework

### ML Markets Overview

| Market | Language | Return Window | Key Notes |
|--------|----------|---------------|-----------|
| Brazil (BR) | Portuguese | 7 days (CDC law) | Strict consumer rights |
| Mexico (MX) | Spanish | 30 days | ML policy minimum 7 days |
| Argentina (AR) | Spanish | 30 days | Economic volatility affects returns |
| Colombia (CO) | Spanish | 30 days | Growing market |
| Chile (CL) | Spanish | 10 days (SERNAC) | |
| Peru (PE) | Spanish | 7 days | |

### Brazil Return Law (Direito de Arrependimento)

**Article 49 CDC (Consumer Protection Code):**
- Any purchase made outside a physical store can be returned within 7 days
- No justification needed from buyer
- Seller MUST refund full amount including original shipping
- Return shipping: Seller bears cost if buyer exercises cooling-off right

**Practical implications for ML sellers:**
- High return rate is structurally built into Brazilian e-commerce
- Budget 5-10% returns into your Brazilian marketplace pricing
- Fast processing of returns maintains ML reputation score

### Return Authorization Process

**Step 1: Buyer requests return**
- Buyer opens return request through ML system
- Seller receives notification with deadline to respond

**Step 2: Seller review**
- Review within 3 days (or ML mediates automatically)
- Decide: Accept, offer partial refund, or dispute

**Step 3: Obtain return code (for overseas warehouse)**
If using overseas warehouse or 3PL:
```
a. Login to ML seller account
b. Go to: Sales → Active → Find Order
c. Click "View return details"
d. Download or copy the return authorization code
e. Share code with your local warehouse team
f. Warehouse uses code to accept the returned item
```

**Step 4: Inspect returned item**
- Document condition with photos (for dispute protection)
- Acceptable for resale: restock
- Damaged/used: document for partial refund dispute

**Step 5: Process refund**
- Full refund within 48 hours of receiving item
- Partial refund: requires dispute justification with evidence

### Return Dispute Process

**When to dispute:**
- Item returned in different condition than sold (used, damaged)
- Item not returned but buyer claims they sent it
- Return outside the eligible window
- Fraudulent return (different item returned)

**Evidence to gather for disputes:**
```
[ ] Original order photos (product condition when shipped)
[ ] Tracking confirmation of original delivery
[ ] Photos of returned item condition
[ ] Weight discrepancy documentation
[ ] Timeline documentation (dates and responses)
[ ] Any communication with buyer
```

**Dispute submission:**
1. Go to ML Resolution Center
2. Select case → "I disagree with the return"
3. Upload all evidence
4. Write clear, professional case narrative
5. ML mediates within 7-10 business days

### Reducing Return Rate by Category

**Clothing & Fashion (highest return category):**
```
Root causes: Wrong size, color different from photos
Solutions:
- Add accurate size chart with measurements in cm
- True-to-life product photos (no heavy filters)
- Add model measurements (height, weight, size worn)
- Include fabric composition and care instructions
Target return rate: <15%
```

**Electronics:**
```
Root causes: Doesn't work as expected, compatibility issues
Solutions:
- Detailed compatibility list (tested models)
- Clear instruction video
- Technical specs matched to product page
- Verify product works before shipping
Target return rate: <5%
```

**Home & Furniture:**
```
Root causes: Size not as expected, color mismatch
Solutions:
- Accurate dimensions with photo reference
- Color: show in natural light + artificial light
- "Assembly required" clearly stated
- Include all parts visible in listing image
Target return rate: <8%
```

**Toys & Baby:**
```
Root causes: Age-inappropriate, safety concerns
Solutions:
- Clear age range specification
- Material safety info (BPA-free, non-toxic)
- Safety certifications visible
- Accurate scale reference photo
Target return rate: <7%
```

### Return Rate Metrics

Track weekly by category:
```
Metric              Formula                    Target
Return rate         Returns / Delivered        <8%
Dispute win rate    Won disputes / Total        >70%
Refund turnaround   Avg days to refund         <3 days
Cost per return     Return costs / Returns      Minimize
Net return impact   Return value / Revenue      <5%
```

**ML reputation impact:**
- Returns under 8%: Green reputation maintained
- Returns 8-15%: Yellow warning — address immediately
- Returns >15%: Red — seller account at risk

### Return Response Templates

**Accepting legitimate return:**
```
Hola [Nombre del comprador],

Hemos recibido su solicitud de devolución. Confirmamos que
aceptamos la devolución.

Por favor use el código de autorización [CÓDIGO] para enviar
el producto a nuestra dirección:
[Dirección del almacén]

Una vez recibido e inspeccionado el producto, procesaremos
su reembolso completo en 48 horas.

Gracias por su compra.
[Tu nombre/Tienda]
```

**Requesting more information:**
```
Hola [Nombre],

Lamentamos que no esté satisfecho con su compra.

Para procesar su devolución de manera eficiente, ¿podría
por favor enviarnos fotos del producto mostrando el problema?
Esto nos ayudará a resolver su caso más rápidamente.

Estamos comprometidos a encontrar la mejor solución.
[Tu nombre/Tienda]
```

## Workspace

Creates `~/ml-returns/` containing:
- `cases/` — individual return case files
- `disputes/` — dispute evidence and outcomes
- `templates/` — response templates by language
- `metrics/` — return rate tracking
- `reports/` — return performance reports

## Output Format

Every return management output includes:
1. **Return Status** — current situation and deadline
2. **Recommended Action** — accept/dispute/negotiate with reasoning
3. **Response Draft** — ready-to-send message in Spanish/Portuguese
4. **Process Checklist** — step-by-step actions for this case
5. **Evidence List** — what to document and collect
6. **Cost Impact** — financial impact of various resolution options
7. **Prevention Note** — listing change to prevent similar returns

---
name: etsy-seo-tag-optimizer
description: "Etsy SEO tag extractor and keyword optimizer agent. Find Etsy listing tags, extract all 13 tags from any listing, research high-performing keywords, optimize your tag strategy, and improve search ranking on Etsy marketplace. Triggers: etsy tags, etsy seo, etsy keyword, etsy listing optimization, etsy tag extractor, etsy search ranking, etsy shop optimization, etsy product tags, etsy keyword research, etsy algorithm, handmade seo, etsy shop"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/etsy-seo-tag-optimizer
---

# Etsy SEO Tag Optimizer

Extract, analyze, and optimize Etsy listing tags for maximum search visibility. Find what keywords successful competitors use, build a winning tag strategy, and rank higher in Etsy search results.

## Commands

```
tag extract <listing>             # extract all tags from a pasted Etsy listing
tag analyze <tags>                # analyze tag strength and keyword quality
tag suggest <product>             # suggest 13 optimized tags for a product
tag competitor <listing>          # extract and analyze competitor tags
tag gaps <your-tags> <comp-tags>  # find missing tag opportunities
tag rank <keyword>                # estimate keyword competition on Etsy
tag seasonal <product>            # add seasonal tag opportunities
tag refresh <listing>             # refresh outdated tags with fresh keywords
tag copy <tags>                   # format tags for one-click copy
tag report <product>              # full Etsy SEO analysis report
```

## What Data to Provide

- **Etsy listing text** — paste the full listing title, description, tags
- **Product description** — what you're selling in plain terms
- **Target buyer** — who buys this (occasion, demographic, use case)
- **Competitor listings** — paste competing listing data
- **Current tags** — your existing 13 tags for optimization

## Etsy SEO Framework

### How Etsy Search Works

Etsy's algorithm ranks listings based on:
1. **Query match** — how well listing title + tags match the search
2. **Listing quality score** — views, favorites, purchases, CTR
3. **Recency** — recently added/renewed listings get temporary boost
4. **Customer experience score** — seller reviews, dispute rate
5. **Shipping price** — free shipping gets significant boost
6. **Buyer personalization** — Etsy personalizes results per buyer

**Critical insight:** Tags must exactly match what buyers search. Etsy uses EXACT match, not semantic matching like Google.

### Tag Structure Rules

**Etsy tag requirements:**
- Maximum 13 tags per listing
- Each tag: up to 20 characters
- Multi-word tags allowed (use all available space)
- Avoid single words — phrases convert better
- No punctuation except hyphens
- No trademark violations

**Tag types by function:**
```
Primary tags (5):     Core product type phrases
Attribute tags (4):   Color, material, size, style
Use case tags (2):    Occasion, recipient, purpose
Long-tail tags (2):   Specific buyer-intent phrases
```

### Tag Generation Framework

**Step 1: Identify core product terms**
```
Product: Handmade ceramic coffee mug
Core terms: coffee mug, ceramic mug, handmade mug, pottery mug, stoneware mug
```

**Step 2: Add attribute modifiers**
```
Color: blue mug, navy mug, speckled mug
Size: large coffee mug, 16oz mug, big mug
Style: rustic mug, minimalist mug, boho mug
Material: clay mug, pottery mug, stoneware mug
```

**Step 3: Identify use cases and occasions**
```
Occasions: birthday gift mug, Christmas gift, mother day gift
Recipients: gift for him, gift for her, coffee lover gift, teacher gift
Use cases: office mug, unique mug, personalized mug
```

**Step 4: Add long-tail buyer intent phrases**
```
Specific: handmade blue ceramic mug, rustic coffee cup pottery
Problem-solving: microwave safe mug, dishwasher safe pottery
Sentiment: cozy mug, unique coffee mug, artisan mug
```

**Step 5: Seasonal adjustments**
```
Q4 (Oct-Dec): Christmas gift, holiday gift, stocking stuffer
Q1 (Jan-Mar): Valentine gift, self care gift, new home gift
Q2 (Apr-Jun): Mother's Day gift, graduation gift, spring
Q3 (Jul-Sep): Birthday gift, back to school, summer
```

### Tag Optimization by Product Category

**Jewelry:**
```
Good tags: sterling silver necklace, layered necklace gift, dainty chain necklace
Poor tags: necklace, jewelry, silver (too broad, low intent)
```

**Home Decor:**
```
Good tags: boho wall art print, nursery decor girl, minimalist bedroom art
Poor tags: art, print, home (too generic)
```

**Clothing:**
```
Good tags: floral summer dress women, cottagecore midi dress, romantic dress gift
Poor tags: dress, women clothing, summer (too generic)
```

**Digital Downloads:**
```
Good tags: printable wall art instant download, budget planner digital, wedding invitation template
Poor tags: printable, download, digital (too broad)
```

### Competitor Tag Analysis

From a competitor's listing, extract all tags and classify:
```
Tag                    Type          Competition Level
coffee mug gift        Use case      High
handmade ceramic mug   Primary       Medium
rustic pottery mug     Attribute+    Low
birthday mug           Occasion      High
unique coffee cup      Modifier      Low
minimalist mug         Style         Medium
...
```

Find the low-competition tags competitors use that you don't → add to your listing.

### Tag Gap Analysis

```
Your tags: [A, B, C, D, E, F, G, H, I, J, K, L, M]
Comp tags: [B, C, D, E, F, N, O, P, Q, R, S, T, U]

Gap (in theirs, not yours): N, O, P, Q, R, S, T, U
Your unique tags:            A, G, H, I, J, K, L, M

Recommendation:
- Drop lowest-performing unique tags
- Add gap tags with good potential
```

### Etsy Title Optimization

Title works with tags for search matching. Rules:
- First 40 characters most important (show in search results)
- Include top 2-3 tag phrases naturally in title
- Keep it readable — not keyword-stuffed
- Format: `[Primary Keyword] [Key Feature/Attribute] [Use Case/Gift]`

```
Bad title: "Mug Handmade Ceramic Blue Coffee Gift Pottery Cup"
Good title: "Handmade Ceramic Coffee Mug | Blue Pottery Cup | Birthday Gift for Coffee Lover"
```

### Keyword Competition Estimation

Estimate competition level:
```
Low competition (good target):    <5,000 Etsy results, meaningful buyer intent
Medium competition:               5,000-50,000 results
High competition (hard to win):   >50,000 results
Very high (avoid as main tag):    >200,000 results

Strategy: Mix 3-4 low competition tags with 5-6 medium, 2-3 high
```

### Tag Refresh Strategy

Refresh tags when:
- Listing has been active >90 days with declining traffic
- Season changes (swap seasonal tags)
- New trends emerge in your category
- After analyzing competitor tags of new top sellers

**Refresh cadence:**
- Seasonal tags: Every 3 months
- Trend tags: Monthly (monitor Etsy Trend Reports)
- Core product tags: Only if not performing after 90 days

## Workspace

Creates `~/etsy-seo/` containing:
- `tags/` — optimized tag sets per product
- `competitors/` — competitor tag extractions
- `seasonal/` — seasonal tag calendars
- `research/` — keyword competition data
- `reports/` — full SEO audit reports

## Output Format

Every tag optimization outputs:
1. **Optimized 13 Tags** — ready-to-copy tag set with type classification
2. **One-Click Copy Format** — tags formatted for easy pasting into Etsy
3. **Tag Analysis** — competition estimate and rationale for each tag
4. **Title Recommendation** — optimized title incorporating top tags
5. **Competitor Gap Analysis** — tags from top competitors you're missing
6. **Seasonal Tags** — upcoming seasonal additions with optimal timing
7. **Monthly Refresh Calendar** — when to swap which tags

---
name: tiktok-influencer-tracker
description: "TikTok creator/influencer management, sample tracking, and fulfillment analytics agent. Manage creator relationships, track sample shipments, analyze influencer performance, and optimize your TikTok Shop creator program. Triggers: tiktok influencer, creator management, tiktok creator, sample tracking, influencer outreach, tiktok affiliate, creator program, ugc management, influencer analytics, tiktok shop creator, creator collaboration, kol management"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/tiktok-influencer-tracker
---

# TikTok Creator & Influencer Manager

Manage your TikTok creator program end-to-end — from outreach and collaboration to sample tracking, content performance, and affiliate commission analysis. Build and scale a creator network that drives consistent TikTok Shop revenue.

## Commands

```
creator add <username>            # add creator to management database
creator outreach <creator>        # generate outreach message for creator
creator profile <username>        # analyze creator profile and fit score
creator sample <creator> <product> # log sample shipment to creator
creator track                     # track all pending sample shipments
creator content <creator>         # log and analyze creator's content performance
creator commission <creator>      # calculate affiliate commission earned
creator rank                      # rank all creators by performance
creator report <period>           # full creator program performance report
creator recruit <niche>           # find creator recruitment criteria for a niche
```

## What Data to Provide

- **Creator username/handle** — TikTok username
- **Creator profile data** — follower count, engagement rate, niche
- **Sample shipment info** — product sent, date shipped, tracking number
- **Content performance** — views, likes, GMV driven per video
- **Commission data** — sales attributed to each creator

## Creator Management Framework

### Creator Tier Classification

**Mega influencers** (1M+ followers):
- Reach: Massive
- Cost: High ($1,000-10,000+ per post)
- ROI: Unpredictable, good for brand awareness
- Sample value: Free + commission (10-15%)
- Best for: Brand launches, viral moments

**Macro influencers** (100K-1M followers):
- Reach: Large
- Cost: $200-2,000 per post
- ROI: Moderate, trackable via affiliate
- Sample value: Free + 15-20% commission
- Best for: Product launches, key market segments

**Mid-tier creators** (10K-100K followers):
- Reach: Meaningful niche audiences
- Cost: $50-500 per post or free samples only
- ROI: Good, highly measurable
- Sample value: Free sample + 20-25% commission
- Best for: Most brands, consistent sales engine

**Micro influencers** (<10K followers):
- Reach: Small but highly engaged
- Cost: Free samples or $0-50
- ROI: Excellent per dollar
- Sample value: Product only (no fee)
- Best for: Product validation, review generation

**Recommendation**: Build 80% of program on mid-tier and micro creators (volume + ROI), 20% on macro for reach.

### Creator Fit Score

Rate each creator 1-5 on 4 dimensions:

1. **Niche alignment** — does their content match your product category?
2. **Audience match** — does their audience match your buyer profile?
3. **Engagement rate** — likes+comments / followers (benchmark: >3%)
4. **Content quality** — production value and authenticity

**Fit score formula:**
```
Total = Niche × 3 + Audience × 3 + Engagement × 2 + Quality × 2
Max score = 50

35+: Strong fit — prioritize outreach
25-34: Good fit — worth pursuing
15-24: Moderate fit — lower priority
<15: Poor fit — skip
```

**Engagement rate benchmarks:**
```
Nano (<10K):    >6% engagement is strong
Micro (10-50K): >4% engagement is strong
Mid (50-500K):  >2.5% engagement is strong
Macro (500K+):  >1.5% engagement is strong
```

### Outreach Message Templates

**Initial outreach (product gifting):**
```
Hi [Creator Name],

I love your content about [specific topic/video reference] —
your audience's reaction to [specific thing] was amazing.

I'm with [Brand], and we have a [product description] that I
think your audience would genuinely love.

Would you be interested in trying it? No strings attached —
we'd love to gift you one. If you like it and want to share
your honest review, we also have an affiliate program that
pays [X]% commission on all sales you drive.

Happy to send one your way. Interested?

[Your name]
[Brand]
```

**Affiliate program invitation:**
```
Hi [Creator Name],

Your TikTok content is exactly the type we love partnering with.

We'd like to invite you to our affiliate program for [Brand]:
• [X]% commission on every sale you drive
• Free products to review
• Exclusive discount code for your audience
• Performance bonuses for top creators

Here's how it works:
1. We send you our best-selling [product]
2. You create your authentic take
3. We track sales via your affiliate link
4. You earn [X]% on every purchase

Interested? Just reply and I'll send our collaboration brief.

[Your name]
```

### Sample Tracking System

Maintain a sample tracker:
```
Creator | Product | Sent Date | Expected Receive | Status | Follow-up | Content Posted | GMV
@user1  | Prod A  | Jan 5     | Jan 12           | ✓ Rcvd | Done      | Jan 20         | $450
@user2  | Prod B  | Jan 8     | Jan 15           | Shipped| Needed    | —              | $0
@user3  | Prod A  | Jan 10    | Jan 17           | Pending| —         | —              | —
```

**Follow-up timeline:**
- Day 0: Sample shipped, confirm tracking sent to creator
- Day +3: Check tracking, confirm delivered
- Day +5 post-delivery: First follow-up (how did you like it?)
- Day +10 post-delivery: Content reminder if not yet posted
- Day +14 post-delivery: Final follow-up, offer help or extension
- Day +21 post-delivery: Close out (content posted or write off)

### Content Performance Tracking

For each creator's content piece:
```
Creator   | Video Date | Views   | Likes  | Comments | GMV    | Conv%
@creator1 | Jan 20     | 45,000  | 2,300  | 180      | $890   | 2.0%
@creator2 | Jan 22     | 12,000  | 450    | 35       | $120   | 1.0%
@creator3 | Jan 25     | 280,000 | 18,000 | 1,200    | $5,400 | 1.9%
```

**Key performance formulas:**
```
GMV per view = Total GMV / Total views
Cost per sale = Sample cost / Units sold
ROAS = GMV / (Sample cost + any paid fee)
Creator ROI = (GMV - Cost) / Cost × 100
```

### Creator Commission Calculator

```
Example: Creator drives 50 sales at $30 each
Gross GMV:           $1,500
Commission rate:     20%
Creator earned:      $300
Your net:           $1,200
Product COGS (50×$8): $400
Profit from creator: $800

Creator ROI: ($800) / (sample $30 + commission $300) = 2.4x ROAS
```

### Scaling the Creator Program

**Funnel structure:**
```
Top of funnel:     100 creators contacted
↓
Agreed to try:      40 creators (40% acceptance)
↓
Sample sent:        35 creators
↓
Content posted:     20 creators (57% post rate)
↓
Sales generated:    15 creators (75% drive at least some GMV)
↓
Repeat creators:    8 creators (ongoing relationship)
```

**Monthly scaling cadence:**
- Week 1: Recruit 20-30 new creators
- Week 2: Ship samples, confirm receipt
- Week 3: Follow up, collect content
- Week 4: Analyze performance, tier creators for next month

## Workspace

Creates `~/creator-management/` containing:
- `database/` — creator profiles and status
- `samples/` — sample shipment tracking
- `content/` — content performance logs
- `outreach/` — message templates and history
- `reports/` — creator program performance reports

## Output Format

Every report outputs:
1. **Program Dashboard** — total GMV, active creators, ROAS summary
2. **Creator Leaderboard** — top 10 creators ranked by GMV and ROI
3. **Sample Pipeline** — status of all outstanding samples
4. **Content Calendar** — expected content from each creator
5. **Commission Report** — payouts owed to each creator
6. **Recruitment Targets** — criteria for next batch of creators to recruit
7. **Optimization Actions** — 3 specific changes to improve program performance

---
name: aliexpress-shipping-optimizer
description: "AliExpress shipping template optimizer and pricing calculator agent. Batch configure shipping templates, calculate regional pricing, set up overseas warehouse templates, and optimize freight strategies for AliExpress sellers. Triggers: aliexpress shipping, freight template, aliexpress pricing, shipping cost calculator, overseas warehouse, aliexpress logistics, freight optimization, shipping template, regional pricing, aliexpress freight, cross-border logistics"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/aliexpress-shipping-optimizer
---

# AliExpress Shipping Template Optimizer

One-click batch shipping template configuration, regional pricing calculator, overseas warehouse setup, and freight strategy optimization for AliExpress cross-border sellers.

## Commands

```
shipping template <product>       # create optimal shipping template
shipping batch <products>         # batch configure templates for multiple products
shipping price <product> <region> # calculate shipping cost for region
shipping warehouse <product>      # overseas warehouse strategy
shipping zone <country>           # shipping options and costs for a country
shipping compare <carriers>       # compare carrier options
shipping calculator <weight> <dims> <dest> # freight cost estimate
shipping optimize <current>       # optimize existing shipping template
shipping seasonal <period>        # peak season shipping adjustments
shipping report <product>         # full logistics strategy report
```

## What Data to Provide

- **Product specs** — weight, dimensions, category
- **Origin country** — where you ship from (China, typically)
- **Target markets** — which countries/regions to sell to
- **Current carriers** — what shipping methods you're using
- **Cost targets** — maximum logistics cost you can afford

## Shipping Strategy Framework

### AliExpress Shipping Template Types

**Standard Template** (most common):
- Fixed shipping cost by region
- Customer pays shipping fee
- Use when: product price <$20, margins tight

**Free Shipping Template**:
- Shipping cost built into product price
- Removes buyer hesitation → higher conversion
- Use when: product price >$20, margins allow

**Dynamic Pricing Template**:
- Different prices per country/region
- Optimize based on actual shipping costs
- Use when: selling globally with varied logistics costs

### Major Shipping Carriers for AliExpress

**ePacket** (China → US/EU):
```
Delivery time: 12-20 days
Tracking: Full end-to-end tracking
Cost: $3-8 for 0-2kg packages
Best for: Small items <2kg to US/EU/AU markets
```

**AliExpress Standard Shipping**:
```
Delivery time: 15-35 days
Tracking: Yes
Cost: $2-6 competitive pricing
Best for: General goods, most destinations
```

**AliExpress Premium Shipping**:
```
Delivery time: 7-15 days
Cost: $8-20
Best for: Buyers wanting faster delivery, higher-value items
```

**China Post Ordinary Small Packet**:
```
Delivery time: 20-45 days
Cost: $1-3 (cheapest option)
Best for: Very low-cost items where speed isn't priority
```

**DHL Express / FedEx / UPS**:
```
Delivery time: 3-7 days
Cost: $15-60+ depending on weight
Best for: High-value items, time-sensitive orders
```

**SF Express / Yanwen / 4PX**:
```
Delivery time: 10-20 days
Cost: $4-12
Best for: Alternative to ePacket for some routes
```

### Regional Shipping Pricing Guide

**North America (US, CA):**
```
0-100g:  $2-4   (AliExpress Standard)
100-500g: $4-8  (ePacket preferred)
500g-2kg: $8-15 (AliExpress Premium or AliExpress Standard)
2kg+:     $20+  (DHL or sea freight for FBA)
```

**Western Europe (DE, FR, UK, ES, IT):**
```
0-100g:  $3-5
100-500g: $5-10
500g-2kg: $10-18
UK-specific: Add customs declaration since Brexit
```

**Southeast Asia (TH, VN, MY, PH, ID):**
```
0-100g:  $1-3   (very competitive rates)
100-500g: $3-6
500g-2kg: $6-12
Best carriers: AliExpress Standard, Yanwen
```

**Australia & NZ:**
```
0-500g:  $4-8
500g-2kg: $8-15
Alternative: ePacket if available
```

**Brazil (complex):**
```
All weights: Add 20-40% for customs clearance
ePacket NOT available → use AliExpress Standard
Expect 30-60 day delivery due to customs
```

### Overseas Warehouse Strategy

**When to use overseas warehouse:**
- Selling in US/EU with high volume (>100 units/month per SKU)
- Customers complain about long delivery times
- Category where 1-2 week delivery is competitive expectation
- Products with high repeat purchase rate

**Overseas warehouse options:**
```
US warehouse (dropshipping warehouse):
- Pre-ship inventory to US warehouse
- Ship to buyer in 3-7 days from US
- Cost: $50-150/pallet/month storage + pick & pack
- Break-even: typically >200 units/month/SKU

EU warehouse (Germany most common):
- Covers most EU with 3-7 day shipping
- VAT registration may be required
- Cost: similar to US, €50-120/pallet/month

AliExpress Local Warehouse:
- AliExpress-managed overseas fulfillment
- Apply through Seller Center
- Simplest setup, less control over costs
```

### Shipping Template Configuration

**Standard US-focused template:**
```
Region          Method                  Cost        Processing Time
United States   ePacket                 Free        2-3 days
Canada          AliExpress Standard     $2.99       2-3 days
UK              AliExpress Standard     $1.99       2-3 days
Germany         AliExpress Standard     $1.99       2-3 days
France          AliExpress Standard     $1.99       2-3 days
Australia       AliExpress Standard     $3.99       2-3 days
Brazil          AliExpress Standard     $4.99       2-3 days
Rest of World   China Post              $3.99       3-5 days
```

**Premium global template (higher margin products):**
```
US/CA/AU:       AliExpress Premium      Free        1-2 days
EU (Top 5):     AliExpress Premium      Free        1-2 days
Rest of EU:     AliExpress Standard     Free        2-3 days
Asia:           AliExpress Standard     Free        2-3 days
LATAM:          AliExpress Standard     $2.99       3-5 days
Rest of World:  AliExpress Standard     $4.99       3-5 days
```

### Product Pricing Calculator

```
Selling price calculation:
1. Product cost (from supplier): $X
2. AliExpress commission (5-10%): $Y
3. Shipping cost (if free shipping): $Z
4. Target profit margin: $M

Formula: Price = (X + Z + M) / (1 - Commission%)
Example: ($5 + $4 + $3) / (1 - 0.08) = $13 / 0.92 = $14.13
Recommended list price: $14.99 (round up for psychological pricing)
```

### Seasonal Shipping Adjustments

**Chinese New Year (Jan-Feb):**
- Factories close 2-4 weeks → pre-stock 4-6 weeks of inventory
- Announce extended processing time (5-7 days)
- Consider overseas warehouse for continuity

**Peak season (Oct-Dec)**:
- Shipping times increase by 30-50%
- Add 7-10 days to estimated delivery in templates
- Use premium carriers to differentiate
- Raise prices 10-15% to cover logistics surge

**Covid-like disruptions / force majeure:**
- Keep buyers updated on delays proactively
- Offer partial refund for extreme delays
- Document all shipping exceptions

## Workspace

Creates `~/aliexpress-logistics/` containing:
- `templates/` — shipping templates per product category
- `pricing/` — price calculations by region
- `warehouses/` — overseas warehouse research
- `carriers/` — carrier comparison data
- `reports/` — logistics strategy reports

## Output Format

Every shipping optimization outputs:
1. **Recommended Shipping Template** — region-by-region carrier and cost table
2. **Pricing Recommendation** — product price by market with margin calculation
3. **Carrier Comparison** — top 3 carrier options for your main markets
4. **Overseas Warehouse Assessment** — ROI analysis for warehousing decision
5. **Delivery Time Estimate** — realistic delivery ranges by region
6. **Seasonal Calendar** — upcoming peaks requiring template adjustments
7. **Cost Savings Opportunities** — specific ways to reduce logistics costs

---
name: shopee-one-click-listing
description: "Shopee one-click product listing and cross-platform publishing agent. Efficiently create, optimize, and publish Shopee listings, enable one-click cross-listing across multiple platforms, and manage product catalog operations. Triggers: shopee listing, shopee publish, shopee product upload, one-click listing, shopee cross-listing, shopee catalog, product listing creation, shopee image, shopee description, multi-platform listing, shopee store management"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/shopee-one-click-listing
---

# Shopee One-Click Listing Agent

Create optimized Shopee product listings in one step — from raw product data to publish-ready listing with SEO-optimized title, compelling description, strategic pricing, and category configuration. Cross-list to multiple Shopee markets effortlessly.

## Commands

```
listing create <product>          # create full Shopee listing from product info
listing optimize <listing>        # optimize existing Shopee listing
listing localize <listing> <market> # adapt listing for specific SEA market
listing cross-publish <listing>   # generate listings for multiple markets
listing category <product>        # find correct Shopee category path
listing attributes <category>     # list required attributes for a category
listing image guide <product>     # image requirements and optimization guide
listing price <product> <market>  # pricing recommendation for market
listing checklist <listing>       # pre-publish quality checklist
listing report <product>          # full listing optimization report
```

## What Data to Provide

- **Product information** — name, description, specs, materials, dimensions
- **Images** — number and type of images you have
- **Cost price** — for pricing recommendation
- **Target market** — which Shopee market (SG, MY, TH, ID, PH, VN, TW)
- **Competitor listings** — similar products for benchmarking

## Shopee Listing Framework

### Listing Elements Checklist

```
Required:
[ ] Product title (max 120 chars)
[ ] Category path (accurate)
[ ] Price (in local currency)
[ ] Stock quantity
[ ] Shipping weight and dimensions
[ ] At least 1 image (recommend 9 images)

Strongly Recommended:
[ ] 6-9 images including lifestyle shots
[ ] Short description (highlights/bullets)
[ ] Long description (detailed info)
[ ] All product attributes filled
[ ] Variations set up (if applicable)
[ ] Wholesale pricing configured
```

### Title Optimization

**Shopee title formula:**
`[Brand (if applicable)] [Product Type] [Key Feature] [Attribute: Size/Color/Material] [Use Case/Occasion]`

**Title rules:**
- 100-120 characters (use close to max)
- Lead with highest-search keyword
- Include 2-3 key attributes buyers filter by
- Avoid ALL CAPS (poor readability)
- Include local language terms for SEA markets

**Title examples:**
```
Bad:  "Water Bottle"
Good: "Stainless Steel Water Bottle 500ml Insulated Thermos Coffee Tea Hot Cold Gym Sport"

Bad:  "Phone Case iPhone"
Good: "iPhone 15 Pro Max Case Transparent MagSafe Compatible Shockproof Anti-Yellow TPU Cover"
```

**Market-specific title adaptations:**
```
SG/MY: English-first, include product specs precisely
ID:    Bahasa Indonesia terms, include "premium" or "import" for trust
TH:    Thai keywords in description minimum, English OK in title
PH:    English with Filipino lifestyle context
VN:    Vietnamese-friendly terms, quality signals important
TW:    Traditional Chinese preferred, quality and certification focus
```

### Image Requirements and Optimization

**Image specifications:**
- Minimum: 500×500 pixels
- Recommended: 2000×2000 pixels (allows zoom)
- Format: JPG, PNG, or BMP
- Main image: Product on white or clean background
- Maximum 9 images per listing

**Image sequence strategy:**
```
Image 1: Main product (white background, hero shot)
Image 2: Product from different angle / multiple pieces
Image 3: Lifestyle shot (product in use context)
Image 4: Key feature close-up / infographic
Image 5: Size comparison or dimension callout
Image 6: Material / quality detail shot
Image 7: Packaging / what's in the box
Image 8: Use case / occasion shot
Image 9: Brand story / certificate / guarantee
```

### Description Framework

**Short description (bullet points — visible above fold):**
```
✓ [Primary benefit] — [supporting detail]
✓ [Material/quality spec]
✓ [Key feature + why it matters]
✓ [Compatibility / sizing info]
✓ [What's included in package]
```

**Long description structure:**
```
PRODUCT OVERVIEW
[2-3 sentences explaining what the product is and who it's for]

KEY FEATURES
✓ Feature 1: [Detail]
✓ Feature 2: [Detail]
✓ Feature 3: [Detail]

SPECIFICATIONS
Material: [spec]
Dimensions: [L×W×H] cm / [weight] g
Color options: [list]
Package contents: [list]

HOW TO USE / CARE INSTRUCTIONS
[Simple numbered steps if relevant]

SHIPPING & RETURNS
Ships within [X] business days
[Return policy statement]

[BRAND STORY — 1-2 sentences about brand quality commitment]
```

### Pricing Strategy for Shopee

**Pricing framework:**
1. Calculate floor price (cost + fees + minimum margin)
2. Research competitor price range on Shopee
3. Set strategic price position:

```
Penetration (new listing): Set at -10% vs. median competitor
Growth (50+ reviews): Set at market median
Premium (100+ reviews, top-rated): Set at +10-20% with strong differentiation

Shopee fees to account for:
Commission: 2-5% (varies by category and seller tier)
Transaction fee: 2%
```

**Flash sale pricing:**
- Platform flash deals require min 20% discount from regular price
- Set regular price accounting for frequent promotions
- Formula: Regular price = Target sell price / (1 - max discount %)

### Variation Setup

**Shopee variation types:**
```
Single variation: Size (S, M, L, XL, XXL)
                  Color (Red, Blue, Black, White)
                  Material (Cotton, Polyester, Silk)

Double variation: Size × Color (Red S, Red M, Blue S, Blue M...)
                  Volume × Flavor (100ml Vanilla, 100ml Chocolate...)
```

**Variation naming conventions:**
- Use clear, customer-facing names (not internal SKU codes)
- Keep consistent naming across all products
- Add "Free Size" or "One Size" for non-varying dimension

### Cross-Market Publishing

When listing in multiple Shopee markets:
```
Base: Create listing in primary market (e.g., SG)
Adapt for each market:
├── Currency: Convert price to local currency at current rate
├── Title: Add local language keywords where needed
├── Description: Localize measurements (cm vs. inches), terminology
├── Shipping: Set correct shipping from your warehouse to each country
└── Compliance: Check category restrictions per country
```

**Price conversion guide:**
```
If SG base price = SGD $25:
MY:  MYR 85   (×3.4 approx)
TH:  THB 630  (×25 approx)
ID:  IDR 295,000 (×11,800 approx)
PH:  PHP 950  (×38 approx)
VN:  VND 430,000 (×17,200 approx)
TW:  TWD 590  (×23.6 approx)
```

### Pre-Publish Checklist

```
CONTENT
[ ] Title: 100-120 chars, keyword-rich, readable
[ ] Description: short + long both filled
[ ] All product attributes completed
[ ] Correct category path selected

IMAGES
[ ] 9 images uploaded (minimum 4)
[ ] Main image: white background, product centered
[ ] At least 1 lifestyle image
[ ] At least 1 infographic/spec callout

PRICING & STOCK
[ ] Price competitive with top 10 listings
[ ] Stock quantity set accurately
[ ] Shipping weight/dimensions filled
[ ] Variations configured correctly (if applicable)

LOGISTICS
[ ] Shipping options enabled
[ ] Processing time set (1-2 days standard)
[ ] Shipping fee correctly set or free shipping enabled
```

## Workspace

Creates `~/shopee-listings/` containing:
- `drafts/` — listing drafts before publishing
- `published/` — records of published listings by market
- `images/` — image requirements and optimization notes
- `pricing/` — price sheets by market
- `reports/` — listing performance reports

## Output Format

Every listing creation outputs:
1. **Optimized Title** — final title with character count for each market
2. **Short Description** — 5 bullet points, ready to paste
3. **Long Description** — full HTML-compatible description
4. **Attribute Checklist** — required attributes for the selected category
5. **Pricing Recommendations** — suggested price per market with margin calculation
6. **Image Guide** — specific guidance on what images to prepare
7. **Pre-Publish Checklist** — complete verification before going live
8. **Cross-Market Variants** — adapted listings for each target market

---
name: amazon-review-export
description: "Amazon product review export and analysis agent. Extract, organize, and analyze Amazon reviews — export to structured format, identify sentiment patterns, surface product insights, and generate competitive intelligence from review data. Triggers: amazon review export, review analysis, export reviews, review data, review csv, sentiment analysis, review insights, customer feedback analysis, review scraper, product reviews, review patterns, voc amazon"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amazon-review-export
---

# Amazon Review Export & Analyzer

Extract intelligence from Amazon product reviews — organize into structured data, analyze sentiment patterns, identify product improvement opportunities, and generate competitive insights from customer voice data.

## Commands

```
review export <asin>              # structure reviews into exportable format
review analyze <reviews>          # full sentiment and pattern analysis
review sentiment <reviews>        # sentiment scoring breakdown
review patterns <reviews>         # find recurring themes and pain points
review compare <asin1> <asin2>    # compare review profiles between products
review insights <reviews>         # extract product improvement opportunities
review competitive <comp-reviews> # analyze competitor review weaknesses
review summary <reviews>          # executive summary of review data
review csv <reviews>              # format reviews as CSV-ready data
review report <asin>              # comprehensive review intelligence report
```

## What Data to Provide

- **Review text** — paste reviews directly (as many as possible)
- **Star rating distribution** — number of reviews at each star level
- **ASIN** — product identifier
- **Competitor reviews** — for competitive analysis
- **Time period** — recent reviews vs. older reviews for trend analysis

## Review Analysis Framework

### Review Export Format

Structure raw reviews into:
```csv
Date,Rating,Title,Review Text,Verified,Helpful Votes,Reviewer
2024-01-15,5,"Great product","Very satisfied with...",Yes,12,Customer123
2024-01-10,2,"Disappointing","Expected better...",Yes,3,Customer456
```

### Sentiment Analysis Framework

**5-star rating interpretation:**
```
⭐⭐⭐⭐⭐ (5-star): Delighted — read for what exceeds expectations
⭐⭐⭐⭐   (4-star): Satisfied — note any "but" qualifiers
⭐⭐⭐     (3-star): Neutral — mixed feelings, often most useful insights
⭐⭐       (2-star): Dissatisfied — specific complaints, high value for improvement
⭐         (1-star): Angry — often extreme cases, filter for systemic vs. one-off
```

**Sentiment scoring:**
```
Positive signals (+): "love", "perfect", "great", "amazing", "exactly what I needed"
Negative signals (-): "disappointed", "broke", "doesn't work", "waste", "returned"
Neutral signals (=): "okay", "fine", "average", "as expected", "decent"

Net Sentiment Score = (Positive reviews - Negative reviews) / Total reviews × 100
Target: Score > 60 = healthy product sentiment
```

### Theme Identification (Qualitative Coding)

Categorize all reviews into themes:

**Product quality themes:**
```
□ Build quality / durability
□ Materials / finish quality
□ Sizing / dimensions (accurate vs. listing)
□ Performance (does it work as claimed?)
□ Longevity (how long does it last?)
```

**Customer experience themes:**
```
□ Packaging / unboxing experience
□ Instructions / ease of setup
□ Customer service experience
□ Shipping / delivery condition
□ Value for money perception
```

**Use case themes:**
```
□ Intended use (matches expected use case)
□ Alternative uses (how customers use it unexpectedly)
□ Gifting (bought as a gift)
□ Replacement (replacing specific previous product)
□ Professional vs. personal use
```

### Frequency Analysis

Count mentions of each theme:
```
Theme                    Mentions    % of Reviews    Sentiment
Durable/sturdy           45          42%             Positive
Easy to assemble         38          35%             Positive
Instructions unclear     22          20%             Negative
Size smaller than shown  15          14%             Negative
Great value for money    52          48%             Positive
```

**Priority fix threshold**: Any negative theme appearing in >10% of reviews requires action.

### Pain Point Extraction

From negative reviews, extract specific pain points:
```
Pain Point              Frequency   Severity    Fix Category
Product breaks quickly  23 mentions High        Product quality
Wrong size/dimensions   15 mentions Medium      Listing accuracy
No instructions         12 mentions Low         Packaging insert
Hard to clean           8 mentions  Low         Product design
```

**Severity classification:**
- High: Safety, complete product failure, cannot use product
- Medium: Significant disappointment, reduced usefulness
- Low: Minor inconvenience, still satisfied overall

### Competitive Review Intelligence

From competitor reviews, extract:

**Competitor weaknesses** (from their negative reviews):
→ These are your differentiation opportunities

**Competitor strengths** (from their positive reviews):
→ Baseline expectations you must meet or exceed

```
Competitor Pain Points → Your Product Claims
"Instructions are confusing" → "Clear 10-step illustrated guide included"
"Flimsy material" → "Reinforced with aircraft-grade aluminum"
"Customer service ignores" → "24/7 support with 1-hour response guarantee"
```

### Review Trend Analysis

Compare recent vs. older reviews:
```
Period          Avg Rating    Top Complaint        Top Praise
Last 90 days:   4.1           Size issues (18%)    Easy use (42%)
6-12 months:    4.4           No issues dominant   Quality (55%)
12+ months:     4.6           Rare complaints      Durability (60%)

Trend: Rating declining → investigate recent product/supplier change
```

### VOC (Voice of Customer) Summary

Generate a customer perspective summary:
```
WHAT CUSTOMERS LOVE (keep and amplify in marketing):
1. [Most praised attribute + quote]
2. [Second most praised + quote]
3. [Third most praised + quote]

WHAT CUSTOMERS WANT IMPROVED (product/listing fixes):
1. [Top pain point + specific ask]
2. [Second pain point + ask]
3. [Third pain point + ask]

WHAT SURPRISES CUSTOMERS (unintended uses or unexpected positives):
1. [Unexpected use case]
2. [Unexpected benefit]
```

### Review-to-Listing Optimization

Map review insights directly to listing improvements:
```
Review insight → Listing change
"Sturdy, holds 50lbs easily" → Add to bullets: "HEAVY-DUTY CONSTRUCTION — tested to hold up to 50 lbs"
"Works great as a gift" → Title: add "Perfect Gift" / create gift-focused image
"Instructions confusing" → Add instruction image to image gallery
"Looks exactly as shown" → Emphasize "true-to-photo" in listing
```

## Workspace

Creates `~/review-data/` containing:
- `exports/` — structured CSV exports per ASIN
- `analyses/` — full review analysis reports
- `themes/` — coded theme frequency data
- `competitive/` — competitor review intelligence
- `voc/` — voice of customer summaries

## Output Format

Every review analysis outputs:
1. **Rating Distribution** — star breakdown with percentage for each level
2. **Net Sentiment Score** — overall sentiment health (0-100)
3. **Top 5 Positive Themes** — what customers love most (with frequency)
4. **Top 5 Negative Themes** — main pain points (with frequency + severity)
5. **VOC Summary** — customer voice in plain language
6. **Listing Optimization Map** — review insights → specific listing improvements
7. **Product Development Signals** — engineering/sourcing changes implied by feedback
8. **CSV Export** — structured data ready to paste into spreadsheet

---
name: amazon-backend-report
description: "Amazon Seller Central backend data report auto-analysis agent. Automatically analyze business reports, inventory reports, advertising reports, and financial reports from Amazon Seller Central to surface actionable insights. Triggers: amazon backend report, seller central report, business report, inventory report, amazon data analysis, seller central analytics, amazon performance report, sales report analysis, fba report, advertising report analysis, financial report"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amazon-backend-report
---

# Amazon Backend Report Analyzer

Automatically analyze Amazon Seller Central reports — business performance, inventory health, advertising, and financial data — and surface the insights that matter most for your business.

Paste your exported report data. The agent processes it, identifies anomalies, highlights opportunities, and outputs a clear performance summary with prioritized action items.

## Commands

```
report business <data>            # analyze business report (traffic + conversion)
report inventory <data>           # analyze inventory health report
report advertising <data>         # analyze advertising performance report
report financial <data>           # analyze financial/payment report
report fba <data>                 # analyze FBA performance and fees
report returns <data>             # analyze returns and refunds report
report search-terms <data>        # analyze search term report from ads
report compare <period1> <period2> # compare two time periods
report digest                     # generate weekly management digest
report save <period>              # save analysis to workspace
```

## What Data to Provide

- **Report data** — paste CSV/text data from any Seller Central report
- **Time period** — which date range the report covers
- **Historical baseline** — prior period data for comparison (if available)
- **Goals** — your targets (revenue, ACOS, margin, etc.)
- **ASIN list** — specific products to focus on

## Report Analysis Framework

### Business Report Analysis

**Key metrics in Business Report:**
```
Column              What to track               Benchmark
Sessions            Unique visitors             Growing week-over-week
Page Views          Total product page views     Ratio to sessions
Buy Box %           % of time you own Buy Box   >90%
Units Ordered       Units sold per period       Week-over-week trend
Unit Session %      Conversion rate             >10% (varies by category)
Ordered Product Sales Revenue by ASIN           Growing trend
```

**Conversion rate diagnostic:**
```
CVR < 5%:    Listing issue — images, bullets, or price problem
CVR 5-10%:   Below average — improve weakest listing element
CVR 10-15%:  Good — optimize further for incremental gains
CVR > 15%:   Excellent — scale traffic with ads
```

**Sessions dropping:**
- External algorithm change
- Out-of-stock event caused ranking loss
- Competitor improved listing quality
- Seasonal demand shift
- Ad spend reduced

**Sessions growing, revenue not growing:**
- Price decreased (more traffic, same revenue)
- Conversion rate dropped (traffic quality changed)
- Returns increased (inflating gross revenue, but refunds eating margin)

### Inventory Health Report Analysis

**Key inventory metrics:**
```
Metric                  Target              Action if violated
Days of Supply          30-60 days          Reorder if <30 days
Inventory Age (0-90d)   >90% of stock       Monitor aging inventory
Inventory Age (91-180d) <10% of stock       Run promotions to clear
Inventory Age (180d+)   <2% of stock        Deep discount or removal
Stranded Inventory      0 units             Fix listing issues immediately
Unfulfillable Units     0 units             Remove or dispose
Excess Inventory        <30 days excess     Reduce reorder quantity
```

**Long-term storage fee risk:**
- Units stored >181 days incur additional fees ($1.50/cubic foot/month)
- Units stored >365 days: $6.90/cubic foot/month
- Action: identify aging SKUs and run promotions before 181-day mark

**Inventory planning dashboard:**
```
ASIN | Units | Daily Sales | Days Cover | Reorder Point | Order Now?
BXXX |  450  |    15/day   |   30 days  |  150 units    |  ⚠ Yes
BYYY |  800  |    10/day   |   80 days  |  100 units    |  ✓ No
BZZZ |   50  |     5/day   |   10 days  |   75 units    |  🚨 Urgent
```

### Advertising Report Analysis

**Search Term Report (most important ad report):**
```
Analysis steps:
1. Sort by Spend descending — find where budget is going
2. Find terms with high spend, zero conversions → Negative match
3. Find terms with conversions, good ACOS → Add to Exact campaign
4. Find terms with high impressions, 0 clicks → Review bid or relevance
5. Compute true ACOS per term: Ad Spend / Ad Sales × 100
```

**ACOS benchmarks by action:**
```
ACOS < 15%: Raise bids — underinvesting in profitable keywords
ACOS 15-25%: Maintain — at break-even or profitable range
ACOS 25-35%: Review — marginal, may be justified for ranking
ACOS > 35%: Reduce bids or pause — unprofitable
```

**Campaign performance buckets:**
```
Bucket                  ACOS    Impressions  Conversions
Stars:                  <20%    High         High        → Scale budget
Cash Cows:              <20%    Low          Moderate    → Increase bids
Potential:              20-35%  High         Low         → Improve listing
Underperformers:        >35%    Any          Low         → Reduce or pause
```

### Financial Report Analysis

**Key financial metrics:**
```
Metric                  Formula                           Target
Gross Revenue           Sum of product sales              —
Amazon Fees             Referral + FBA + storage + ads    <40% of revenue
Returns/Refunds         Refund amount / gross revenue     <5%
Net Revenue             Gross - Returns - Fees            —
Net Margin              Net Revenue / Gross Revenue       >20%
```

**Fee percentage benchmarks by category:**
```
Category        Referral    FBA        Total Fees  Net Margin Target
Electronics     8%          $5-8       18-22%      >25%
Home & Kitchen  15%         $4-6       22-26%      >20%
Clothing        17%         $3-5       22-26%      >20%
Beauty          15%         $3-5       21-25%      >22%
```

**Cash flow planning:**
```
Disbursement cycle: Every 14 days (typically)
Reserved amount: Amazon holds ~7 days of sales as reserve
Working capital needed: 45-60 days of COGS (production + freight + reserve)
```

### Weekly Management Digest Format

```
WEEK OF [DATE] — PERFORMANCE DIGEST

📊 REVENUE
  This week: $XX,XXX | Last week: $XX,XXX | Change: +/-X%
  YTD: $XXX,XXX | YTD goal: $XXX,XXX | On track: Yes/No

📦 INVENTORY
  Total units: X,XXX | Days of supply: XX days
  ⚠ Low stock: [ASIN list]
  🚨 Urgent reorder: [ASIN list]

📢 ADVERTISING
  Ad spend: $X,XXX | ACOS: XX% | ROAS: X.Xx
  Best performer: [term] at X% ACOS
  Worst performer: [term] at X% ACOS — ACTION NEEDED

⚠ ALERTS
  [List of any anomalies detected]

✅ TOP 3 ACTIONS THIS WEEK
  1. [Highest priority action]
  2. [Second priority]
  3. [Third priority]
```

## Workspace

Creates `~/backend-reports/` containing:
- `business/` — business report analyses by period
- `inventory/` — inventory health tracking
- `advertising/` — ad performance archives
- `financial/` — financial summaries
- `digests/` — weekly management digests

## Output Format

Every report analysis outputs:
1. **Performance Summary** — key metrics vs. prior period with trend indicators
2. **Anomaly Detection** — unusual patterns that require attention
3. **Inventory Status** — stock health with specific reorder actions
4. **Ad Performance** — ACOS summary with specific optimization actions
5. **Financial Health** — margin and fee analysis
6. **Priority Action List** — top 5 actions ranked by expected business impact
7. **Next Week Forecast** — projected performance based on current trends

---
name: shopee-big-data-analytics
description: "Shopee big data analytics and product selection agent. One-stop data service for Shopee sellers — product selection, competitive analysis, sales trend tracking, marketing intelligence, and store performance optimization across Southeast Asian markets. Triggers: shopee analytics, shopee product selection, shopee big data, shopee seller tool, shopee marketing, shopee trends, shopee competition, shopee store, southeast asia ecommerce, shopee data, shopee insights, shopdora"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/shopee-big-data-analytics
---

# Shopee Big Data Analytics

One-stop analytics for Shopee sellers — product selection intelligence, competitive analysis, sales trend tracking, and marketing insights across Southeast Asian markets (SG, MY, TH, ID, PH, VN, TW).

## Commands

```
shopee product <keyword>          # research product opportunity on Shopee
shopee trends <category>          # trending products and categories
shopee competition <product>      # competitive analysis for a product
shopee store analyze <data>       # analyze your store performance data
shopee keyword <term>             # Shopee keyword research and search volume
shopee marketing <product>        # marketing strategy recommendations
shopee price <product>            # pricing intelligence for a product
shopee market <country>           # market overview for a SEA country
shopee forecast <product>         # sales forecast for a product
shopee report <product>           # comprehensive product/market report
```

## What Data to Provide

- **Product or keyword** — what you want to research
- **Target market** — which SEA country (SG, MY, TH, ID, PH, VN, TW)
- **Competitor data** — paste competing shop/product data from Shopee
- **Your store data** — sales, traffic, conversion metrics
- **Price information** — current pricing in local currency

## Shopee Market Framework

### Southeast Asia Market Overview

| Market | Currency | Market Size | Key Characteristics |
|--------|----------|-------------|---------------------|
| Indonesia (ID) | IDR | Largest SEA | Price-sensitive, mobile-first, COD dominant |
| Thailand (TH) | THB | Fast-growing | Fashion-forward, livestream shopping |
| Philippines (PH) | PHP | Social commerce | Strong community, value-oriented |
| Malaysia (MY) | MYR | Developed | Brand-conscious, bilingual |
| Singapore (SG) | SGD | Premium | High spending power, quality focus |
| Vietnam (VN) | VND | Emerging | Young population, trend-driven |
| Taiwan (TW) | TWD | Mature | Tech-savvy, high standards |

### Shopee Algorithm Ranking Factors

1. **Relevance** — keyword match in title, description
2. **Sales velocity** — recent orders per unit time
3. **Conversion rate** — view → purchase ratio
4. **Rating & reviews** — average rating, review count
5. **Response rate** — seller response to queries
6. **Shop rating** — overall seller performance score
7. **Listing completeness** — category, attributes filled
8. **Price competitiveness** — vs. similar items
9. **Shipping speed** — faster = ranking boost
10. **Shopee Preferred** — preferred seller status

### Product Opportunity Assessment

**Shopee product selection criteria:**
```
Signal              Threshold       Source
Monthly orders      >100/month      Category leaders
Average price       SGD $5-$50      Local currency equivalent
Review count top 5  100-2000        Entry possible
Shops selling       3-20            Not oversaturated
Price margin        >40%            After platform fees
```

**Shopee fee structure:**
```
Commission:         0-5% by category and seller tier
Transaction fee:    2% standard / 1.5% preferred sellers
Payment fee:        Included in transaction fee
Shipping subsidy:   Platform-subsidized (check current promotion)
```

**Net margin calculation:**
```
Selling price:       $XX
- Commission (3%):   -$XX
- Transaction (2%):  -$XX
- COGS + shipping:   -$XX
= Net profit:        $XX
Target margin:       >25%
```

### Shopee Keyword Research

**Search behavior by market:**
- ID: Search in Bahasa Indonesia, include brand names
- TH: Thai script essential, transliteration helps
- PH: English + Filipino, brand names important
- MY: English + Malay, bilingual approach
- SG: English, quality keywords matter
- VN: Vietnamese, brand names English OK

**Keyword strategy:**
```
Primary keywords: Main product category in local language
Long-tail: Product + attribute (color, size, material)
Use case: Product + occasion/purpose
Brand keywords: If stocking branded items
```

### Competitive Analysis Framework

For any product on Shopee:

**Top seller analysis (assess top 10 shops):**
```
| # | Shop | Monthly Sales | Price | Rating | Reviews | Response | Badge |
| 1 | xxx  | ~500 units    | $15   | 4.8    | 1,200   | 99%      | Preferred |
```

**Market concentration:**
- Top 3 shops >70% of sales → concentrated, hard to enter
- Distributed among 10+ shops → fragmented, opportunity exists
- No dominant seller → blue ocean, move fast

**Competitive advantage opportunities:**
- Lower price with same quality
- Better product photos
- Faster shipping
- Better customer service (response rate)
- Bundle offers
- Shopee Live presence
- More reviews (aggressive review collection)

### Marketing Intelligence

**Shopee marketing channels:**
1. **Shopee Ads** — sponsored search + discovery ads
   - Target: high-converting keywords first
   - Budget: start $5-10/day per keyword cluster
   - Metric: target ROAS >3x

2. **Shopee Live** — livestream selling
   - High conversion for visual products
   - Schedule during peak hours (8-10 PM local time)
   - Prepare product demos and exclusive live deals

3. **Flash Deals** — platform-curated time-limited sales
   - Apply through seller center for inclusion
   - Requires competitive pricing (typically -30%)
   - High traffic, low margin — useful for ranking boost

4. **Voucher Codes** — shop or item-level discounts
   - Create shop vouchers to increase basket size
   - Minimum spend vouchers improve order value
   - Follow buyer vouchers drive acquisition

5. **Shopee Affiliates** — creator partnership
   - List products in Shopee affiliate program
   - Pay commission to creators who drive sales
   - 5-10% commission typical

### Sales Trend Analysis

Track these Shopee metrics weekly:
```
Metric              Formula                     Target
View rate change    (This week - Last week)/Last week  Positive
Conversion rate     Orders / Product views       >2%
Response rate       Replies < 12hrs / Total     >95%
Cancellation rate   Cancelled / Total orders    <1%
Return rate         Returns / Delivered         <5%
Shop rating         Platform calculated         >4.7
```

**Seasonal peaks by market:**
- 9.9, 10.10, 11.11, 12.12 — Shopee's major sale events (all markets)
- Chinese New Year (Jan-Feb) — SG, MY, TW, VN
- Ramadan/Eid (varies) — ID, MY, PH
- Thai New Year Songkran (April) — TH
- Harbolnas 12.12 — ID national shopping day

## Workspace

Creates `~/shopee-analytics/` containing:
- `products/` — product research reports
- `competitors/` — competitor shop profiles
- `keywords/` — localized keyword research
- `marketing/` — campaign performance data
- `reports/` — comprehensive market reports

## Output Format

Every Shopee analysis outputs:
1. **Market Overview** — category size, growth trend, competition level
2. **Product Opportunity Score** — 0-100 with factor breakdown
3. **Top 10 Competitive Landscape** — shops ranked by estimated sales
4. **Pricing Intelligence** — recommended price with margin calculation
5. **Keyword Blueprint** — top 10 search terms with local language variants
6. **Marketing Calendar** — key sales events and recommended promotions
7. **Entry Roadmap** — 90-day launch plan with milestones and KPIs

---
name: amazon-sif-optimizer
description: "Amazon Listing, keyword, and advertising deep optimization agent. Comprehensive SIF (Search-Index-Funnel) optimization — audit and improve listings for search indexing, keyword relevance, and conversion funnel performance. Triggers: amazon listing optimizer, sif optimizer, listing optimization, keyword optimization, ad optimization, search indexing, listing audit, listing score, conversion optimization, ppc optimization, listing quality, amazon seo optimization"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amazon-sif-optimizer
---

# Amazon SIF Optimizer

Search-Index-Funnel (SIF) — the complete framework for Amazon listing optimization. Audit your listing's search discoverability, keyword indexing, and conversion funnel, then get specific improvements to climb rankings and boost sales.

## Commands

```
sif audit <listing>               # full SIF audit of a listing
sif search <listing>              # search discovery audit (S)
sif index <listing> <keywords>    # keyword indexing check (I)
sif funnel <listing>              # conversion funnel audit (F)
sif title <product> <keywords>    # optimize product title
sif bullets <product>             # rewrite bullet points
sif backend <listing>             # optimize backend search terms
sif ads <campaign-data>           # optimize PPC campaign structure
sif score <listing>               # compute SIF score (0-100)
sif compare <listing1> <listing2> # SIF comparison with competitor
sif improve <listing>             # generate full improved listing draft
sif report <listing>              # comprehensive SIF optimization report
```

## What Data to Provide

- **Current listing** — full title, 5 bullets, description, current price
- **Backend search terms** — what you currently have in backend
- **Target keywords** — keywords you want to rank for
- **Competitor ASINs** — top competitors to benchmark against
- **Ad campaign data** — current PPC campaigns and performance
- **Metrics** — click-through rate, conversion rate, sales/day

## SIF Framework

### S — Search Discovery

How easily can buyers find your listing?

**Search discovery factors:**
1. **Primary keyword in title** — must appear in first 60 characters
2. **Keyword indexing** — is Amazon actually indexing your keywords?
3. **Search rank position** — where do you appear for key terms?
4. **Ad coverage** — are you bidding on all critical terms?

**Search audit checklist:**
```
[ ] Primary keyword in title (first 60 chars)?
[ ] Top 3 keywords appear in title?
[ ] All bullet points contain at least 1 target keyword?
[ ] Description is keyword-rich?
[ ] Backend search terms filled (249 chars)?
[ ] Backend has no word repetition from title/bullets?
[ ] Backend includes misspellings, synonyms, long-tails?
[ ] Running Sponsored Products on exact match for top 10 keywords?
[ ] Auto campaign running to discover new keywords?
```

**Search score (0-25):**
- Title keyword presence: 0-8 pts
- Backend completeness: 0-7 pts
- Ad coverage: 0-5 pts
- Search rank on key terms: 0-5 pts

### I — Index Verification

Is Amazon indexing your keywords?

**Indexing check method:**
- Search: `site:amazon.com/dp/[ASIN] keyword`
- Or search the keyword on Amazon and check if your ASIN appears
- Note: Amazon drops keywords that don't drive conversions

**Common indexing failures:**
```
Reason                          Fix
Too many keywords in title      Prioritize top 5 only
Duplicate keywords              Remove repeats from backend
Restricted/prohibited terms     Replace with compliant alternatives
Non-English characters          Use ASCII equivalents
Keyword not in any field        Add to backend search terms
```

**Index score (0-25):**
- Primary keyword indexed: 0-10 pts
- All Tier 1-2 keywords indexed: 0-10 pts
- No prohibited terms detected: 0-5 pts

### F — Conversion Funnel

Once a buyer lands on your listing, does it convert?

**Conversion funnel stages:**
```
Search results → Click (driven by main image + title + price)
                    ↓
Product page → Add to Cart (driven by images, bullets, price, reviews)
                    ↓
Cart → Purchase (driven by trust signals, price, delivery)
```

**Conversion funnel audit:**

**Stage 1 — Click-through (CTR):**
```
[ ] Main image: white background, product fills 85%, no text overlays
[ ] Title: keyword-rich AND human-readable, not stuffed
[ ] Price: competitive within ±10% of top 3 competitors
[ ] Review count: minimum 15+ reviews to compete
[ ] Prime badge: FBA/FBM with Prime eligibility
[ ] CTR benchmark: >0.5% for search results
```

**Stage 2 — Page conversion (CVR):**
```
[ ] 7+ high-quality images (main + lifestyle + infographic + detail)
[ ] Video present (boosts CVR by 20-30%)
[ ] All 5 bullet points used, benefit-led format
[ ] A+ content present (Brand Registry required)
[ ] Price anchored appropriately (MSRP shown if discounted)
[ ] Review rating ≥4.0 stars
[ ] FAQ section answered (reduces purchase hesitation)
[ ] CVR benchmark: >10% for category (varies widely)
```

**Stage 3 — Cart to purchase:**
```
[ ] Fast delivery promise (2-day Prime preferred)
[ ] Return policy visible (30-day free returns = trust signal)
[ ] Secure transaction badge
[ ] No negative red flags in Q&A section
[ ] Seller feedback rating >95%
```

**Funnel score (0-50):**
- CTR optimization: 0-15 pts
- Page CVR optimization: 0-25 pts
- Trust signals: 0-10 pts

### SIF Score Interpretation

```
Total SIF Score (0-100):
90-100: Elite listing — maximize ad spend, listing is a machine
75-89:  Strong listing — minor improvements for incremental gains
60-74:  Good listing — targeted improvements will boost performance significantly
45-59:  Average listing — significant work needed on multiple fronts
<45:    Poor listing — comprehensive rewrite required before scaling ads
```

## Title Optimization Framework

**Title formula:**
`[Brand] + [Primary Keyword] + [Top Feature] + [Secondary Keyword] + [Benefit/Use Case]`

**Title scoring:**
- Character count: 150-200 chars (not <80, not >200)
- Primary keyword in first 40 chars: essential
- 2-3 key selling features: yes
- Mobile truncation (first 80 chars): must be compelling on mobile
- Readability: must make sense as a sentence

**Example:**
```
Before: "Water Bottle Stainless Steel Insulated 32oz BPA Free Lid Straw Sport School Office"
After: "HydroMax Insulated Water Bottle 32oz | Stainless Steel, Leak-Proof Lid, Stays Cold 24hrs | BPA-Free Flask for Gym, Hiking, School"
```

## Bullet Point Framework

**5-bullet structure:**
```
Bullet 1: [Primary benefit + primary keyword] — your biggest selling point
Bullet 2: [Key feature + secondary keyword] — technical proof of benefit 1
Bullet 3: [Use case/versatility + long-tail keyword] — broadens appeal
Bullet 4: [Trust signal + quality claim] — certifications, compatibility, warranty
Bullet 5: [Customer promise + brand keyword] — satisfaction guarantee, service
```

**Format rules:**
- Start with ALL CAPS benefit statement: "LEAK-PROOF LID GUARANTEED —"
- Follow with specific evidence: "triple-seal technology prevents spills..."
- End with use case or emotional benefit

## PPC Campaign Optimization

**3-campaign structure:**
```
Campaign 1: Exact Match — top 20 keywords, aggressive bids
Campaign 2: Phrase Match — broad keyword variations
Campaign 3: Auto — discovery, harvest new keywords weekly

Weekly tasks:
1. Harvest converting keywords from auto → add to exact/phrase
2. Negate non-converting terms from all campaigns
3. Adjust bids: raise for ACOS <target, lower for ACOS >target
4. Check new keyword opportunities from search term reports
```

**Bid optimization matrix:**
```
ACOS < 15%:    Raise bid 20% — underinvesting in a winner
ACOS 15-25%:   Maintain — at target range
ACOS 25-40%:   Lower bid 15% — marginal profitability
ACOS > 40%:    Lower bid 30% or pause — burning cash
0 impressions: Raise bid significantly or check keyword match
0 conversions >50 clicks: Negative match + listing review
```

## Workspace

Creates `~/sif-optimizer/` containing:
- `audits/` — SIF audit reports per ASIN
- `listings/` — optimized listing drafts
- `keywords/` — keyword tracking and indexing status
- `campaigns/` — PPC campaign structures and notes
- `scores/` — historical SIF scores to track improvement

## Output Format

Every SIF audit outputs:
1. **SIF Score Summary** — S/I/F scores and total with grade (A/B/C/D/F)
2. **Search Audit** — keyword presence check with specific gaps
3. **Index Report** — confirmed indexed keywords vs. missing
4. **Funnel Diagnosis** — stage-by-stage conversion blockers
5. **Improved Title Draft** — optimized title ready to use
6. **Bullet Rewrites** — all 5 bullets rewritten with improvements
7. **Backend Optimization** — updated 249-char backend search terms
8. **PPC Recommendation** — campaign structure changes needed
9. **Priority Fix List** — top 5 changes ranked by expected impact

---
name: temu-shein-selector
description: "Temu, SHEIN, AliExpress, and TikTok product selection big data analysis agent. Identify trending products, analyze competition, estimate demand, and find sourcing opportunities across fast-fashion and marketplace platforms. Triggers: temu product selection, shein product research, aliexpress trending, tiktok shop products, temu selector, fast fashion products, temu trending, shein trends, product sourcing, dropshipping products, temu winning products"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/temu-shein-selector
---

# Temu / SHEIN / AliExpress / TikTok Product Selector

Big data-driven product selection for the world's fastest-growing e-commerce platforms. Identify trending products before the market saturates, analyze competition levels, and find winning items to source.

## Commands

```
select temu <category>            # find trending products on Temu
select shein <category>           # identify SHEIN trending items
select ali <keyword>              # AliExpress trending product analysis
select tiktok <niche>             # TikTok Shop viral product identification
select score <product>            # score a product for multi-platform potential
select compare <platform1> <platform2> # compare same product across platforms
select trending                   # daily trending products across all platforms
select source <product>           # sourcing recommendations for a product
select entry <product>            # market entry analysis for new product
select report <product>           # full cross-platform product report
```

## What Data to Provide

- **Platform and category** — which platform and what type of product
- **Trending data** — paste Temu/SHEIN/TikTok trending product listings
- **Sales data** — units sold, reviews, orders shown on listing
- **Price data** — selling price and estimated cost from Alibaba
- **Competition data** — number of similar sellers, listing quality

## Product Selection Framework

### Temu Product Selection

**Temu business model specifics:**
- Seller provides cost price; Temu sets final price
- Margin = (Temu settlement price) - (your cost) - (shipping)
- Temu promotes heavily discounted items → need low COGS
- Quality standard enforcement — defect rate must stay <2%

**Temu winning product criteria:**
```
[ ] Alibaba cost price <$2 for sub-$10 Temu item (>50% margin)
[ ] Product weight <200g (shipping cost critical)
[ ] Not restricted category (no food, meds, hazmat)
[ ] Strong visual appeal (drives Temu browse behavior)
[ ] Unique or trending design (not commodity)
[ ] High order count on similar items (>500 orders = validated)
[ ] Manageable defect risk (simple product = low defect rate)
```

**Temu category opportunities:**
```
High opportunity: Home decor, phone accessories, jewelry, pet products
Medium opportunity: Clothing basics, kitchen gadgets, toy novelties
Avoid: Electronics (high defect/return), perishables, fragile items
```

### SHEIN Product Selection

**SHEIN model**: Fast fashion, 2-week trend cycles
- New styles added daily — trend speed is extreme
- Small batch production (50-100 units) acceptable
- Quality threshold: acceptable for fast fashion price point

**SHEIN winning product signals:**
```
[ ] Fashion-forward design (1-2 weeks ahead of mainstream)
[ ] Price point: $5-$25 sweet spot
[ ] Minimum 4 sizes (S/M/L/XL)
[ ] Photogenic — strong visual on model
[ ] Trending color/print for the season
[ ] Similar items already selling on SHEIN (validation)
[ ] Sourceable quickly (2-4 week turnaround)
```

**Trend identification for SHEIN:**
- Monitor TikTok fashion hashtags (#OOTD, #fashiontrend)
- Check Pinterest trending boards weekly
- Analyze SHEIN "New In" and "Trending" sections
- Use Google Trends for fashion terms

### AliExpress Product Selection

**AliExpress modes:**
- Dropshipping: sell first, ship directly from supplier
- Wholesale: buy inventory, ship yourself
- Hybrid: test with dropship, transition to stock

**AliExpress opportunity signals:**
```
Monthly orders >500:  Validated demand
Price margin >60%:    Viable for dropshipping
Seller rating >95%:   Reliable supplier
Shipping time <15d:   Acceptable for most buyers
Reviews >100:         Product quality validated
Rising trend:         Look for 20%+ order growth over 3 months
```

**Finding trending AliExpress products:**
1. Sort by "Orders" in category — find high-velocity items
2. Filter by "Ship from overseas" for faster delivery
3. Compare with Temu listings — if on Temu already at lower price, competition may be hard
4. Check launch date — recent listings with high orders = catching a trend

### TikTok Shop Product Selection

**TikTok virality requirements:**
- Product must be demonstrable in 15-60 seconds
- "Before/after" or "transformation" products perform best
- Visual impact critical (color, size reveal, satisfying mechanism)
- Relatable problem → satisfying solution format

**TikTok winning product formula:**
```
TREND × DEMO POTENTIAL × PRICE POINT = Viral Score

Trend:           Is it already viral? Google Trends rising?
Demo Potential:  Can you show compelling results in <30 seconds?
Price Point:     $10-$40 impulse buy range
```

**TikTok product categories with high viral rates:**
- Beauty gadgets (face massagers, skin tools)
- Kitchen novelties (unique tools, organizers)
- Cleaning products (satisfying demo)
- Fitness accessories (visible results)
- Home organization (before/after)
- Fashion accessories (quick styling tips)

### Cross-Platform Scoring Matrix

Score any product 1-5 on 6 dimensions:

```
1. Trend momentum (growing fast = 5, declining = 1)
2. Competition density (low competition = 5, saturated = 1)
3. Margin potential (>60% margin = 5, <20% = 1)
4. Sourcing ease (readily available = 5, custom only = 1)
5. Shipping friendliness (light/compact = 5, heavy/fragile = 1)
6. Demo/visual appeal (very photogenic = 5, boring = 1)

Total score 25+: Strong multi-platform candidate
Total score 20-24: Good for 1-2 platforms
Total score <20: Likely not worth pursuing
```

### Sourcing Intelligence

For any trending product, sourcing approach:
```
Step 1: Search Alibaba with product category terms
Step 2: Filter: Trade Assurance + >2 years in business + >4.5 rating
Step 3: Get quotes from 5+ suppliers
Step 4: Request samples before ordering (never skip)
Step 5: Negotiate: first order small (100-500 units), grow from there

Target sourcing cost: <25% of final selling price for Amazon
                      <40% of Temu settlement price
                      <30% of SHEIN wholesale price
```

## Workspace

Creates `~/product-selection/` containing:
- `trending/` — daily trending product snapshots by platform
- `evaluated/` — scored product evaluations
- `sourcing/` — supplier notes and quotes
- `pipeline/` — products under active consideration
- `reports/` — platform-specific product reports

## Output Format

Every product selection report outputs:
1. **Product Score Card** — scores on all 6 dimensions with total
2. **Platform Fit Analysis** — which platform(s) best suited and why
3. **Revenue Projection** — estimated monthly revenue at target sell-through
4. **Margin Model** — cost, fees, and net profit per unit
5. **Sourcing Guide** — recommended supplier type, MOQ, target cost
6. **Competition Assessment** — current competition level and trend direction
7. **Go/No-Go Verdict** — clear recommendation with top 3 reasons

---
name: amazon-brand-analytics
description: "Amazon brand analytics and market opportunity mining agent. Analyze brand-level data, identify market opportunities, track brand performance trends, and make data-driven decisions for product selection, operations, and advertising. Triggers: amazon brand analytics, brand analysis, market opportunity, brand performance, amazon trends, brand registry analytics, search query performance, repeat purchase, market basket, demographics analytics, brand intelligence"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amazon-brand-analytics
---

# Amazon Brand Analytics

Mine Amazon's brand analytics data for market opportunities, competitor intelligence, and product selection signals. Turn search query data, purchase patterns, and demographic insights into strategic decisions.

## Commands

```
brand analyze <brand>             # full brand performance analysis
brand opportunity <category>      # identify market opportunities in category
brand search-queries <data>       # analyze search query performance report
brand repeat-purchase <data>      # analyze customer loyalty metrics
brand market-basket <asin>        # find what customers buy together
brand demographics <data>         # analyze buyer demographics
brand compare <brand1> <brand2>   # head-to-head brand comparison
brand trends <category>           # identify trending brands/products
brand whitespace <market>         # find unserved market segments
brand report <brand>              # comprehensive brand intelligence report
```

## What Data to Provide

- **Brand Analytics report data** — paste exported data from Brand Analytics dashboard
- **Search Query Performance** — top queries, click share, conversion share
- **Repeat Purchase data** — repurchase rates by ASIN
- **Market Basket data** — what customers also buy
- **Competitor brand names** — for competitive brand analysis
- **Category context** — which category to analyze

## Brand Analytics Framework

### Search Query Performance Analysis

Amazon Brand Analytics shows top search terms that lead to your brand's page views.

**Key metrics:**
```
Metric              What it means               Benchmark
Search Query Volume Top searches in category     Track changes monthly
Click Share         Your clicks / total clicks   Target >5% on key terms
Conversion Share    Your conversions / total      Should exceed click share
```

**Click Share vs Conversion Share analysis:**
- Click Share > Conversion Share → listing or pricing issue (traffic not converting)
- Conversion Share > Click Share → strong listing, expand visibility
- Both low → ranking issue, invest in ads or SEO to appear more

**Opportunity identification:**
- High volume terms where your brand has 0% click share → keyword target
- Terms where competitor has >30% click share → competitive threat
- Terms with high search volume but low competition → blue ocean

### Market Opportunity Mining

Use search query data to find opportunities:

```
Step 1: Find terms with high search volume (top 1000 in category)
Step 2: Check which brands dominate each term (click share)
Step 3: Identify terms with fragmented click share (<10% for any brand)
Step 4: Cross-reference with your product ability to serve that need
Step 5: Score opportunity: Volume × (1 - Max click share) = Opportunity Score
```

**Blue Ocean Criteria:**
- Top brand has <15% click share
- Search volume rank in top 500
- Your product can serve the need
- Estimated conversion opportunity exists

### Repeat Purchase Rate Analysis

Brand loyalty signals by product type:
```
Product Type           Expected Repeat Rate    Your Target
Consumables (30-day):  30-50%                  >40%
Consumables (90-day):  20-40%                  >30%
Durable goods:         5-15%                   >10%
Seasonal items:        15-25%                  >20%
```

**Low repeat purchase rate causes:**
1. Product quality disappointment
2. Better alternative found (competitor won)
3. Category not naturally repurchasable
4. Customer service issue
5. Pricing increased after first purchase

**High repeat purchase signals:**
- Strong product-market fit
- Subscription candidate
- Invest in Subscribe & Save

### Market Basket Intelligence

What customers buy with your product reveals:
- **Bundle opportunities** — sell complementary products together
- **Competitive threats** — if customers buy your product WITH a competitor's, they may switch
- **Category adjacency** — expand product line to items customers already buy

**Bundle strategy from market basket:**
```
If your item frequently bought with Item X:
→ Create bundle of Your Item + Item X (15-20% discount vs. buying separately)
→ Use Item X keywords in your backend search terms
→ Target buyers of Item X with Sponsored Products
```

### Demographic Analysis

Amazon Brand Analytics shows buyer demographics (age, income, education, gender):

**Using demographic data:**
```
If buyers skew 45-64 female, high income:
→ Adjust imagery to show aspirational, quality-focused lifestyle
→ Price premium positioning viable
→ Focus on quality and trustworthiness in copy
→ Facebook/Pinterest ads may supplement Amazon ads

If buyers skew 25-34 male, medium income:
→ Feature-focused copy and specs
→ Value proposition important
→ Reddit and YouTube integration
```

### Competitor Brand Intelligence

For a competitor brand, estimate using Brand Analytics signals:

**Proxy metrics:**
- Search terms they dominate (high click share) = their strength
- Terms they don't appear on = their weakness / your opportunity
- Categories where they appear = their product range
- Review volume growth rate = their investment level

### Brand Performance Trending

Track monthly to identify patterns:
```
Month | Click Share (main kw) | Conversion Share | Top New Keywords
Jan   | 8.2%                  | 10.1%            | [term A, term B]
Feb   | 9.1%                  | 11.3%            | [term C]
Mar   | 7.8%                  | 9.2%             | [none]
Apr   | 6.2%                  | 7.8%             | [none]

Trend: Declining → investigate competitor gains or listing issues
```

### Whitespace Identification

Finding underserved market segments:
```
Segment example: "organic dog treats for senior dogs"
Step 1: Search volume meaningful (>1,000/month estimate)
Step 2: Existing results: generic dog treats not optimized for seniors
Step 3: Conversion data: broad terms converting but specific terms underserved
Step 4: Product feasibility: can create differentiated product
Result: WHITESPACE OPPORTUNITY
```

## Workspace

Creates `~/brand-analytics/` containing:
- `search-queries/` — monthly search query performance snapshots
- `opportunities/` — identified market opportunities
- `competitors/` — competitor brand profiles
- `demographics/` — buyer profile reports
- `reports/` — full brand intelligence reports

## Output Format

Every brand analysis outputs:
1. **Search Query Performance Summary** — top 10 opportunity terms with click/conversion gap
2. **Market Opportunity Ranking** — top 5 whitespace opportunities with scoring
3. **Competitive Threat Map** — which brands are gaining in your key terms
4. **Customer Loyalty Metrics** — repeat purchase rate vs. benchmark
5. **Bundle Opportunities** — top 3 market-basket-driven bundle ideas
6. **Demographic Profile** — buyer profile summary with marketing implications
7. **Monthly Action Plan** — 5 prioritized brand growth actions

---
name: walmart-store-analyzer
description: "Walmart marketplace store traffic and performance analysis agent. Auto-analyze Walmart seller traffic reports, item performance, search analytics, and competitive positioning to grow your Walmart business. Triggers: walmart analyzer, walmart traffic, walmart seller, walmart marketplace, walmart store analysis, walmart performance, walmart search ranking, walmart item rank, walmart analytics, walmart seo, walmart competitor, walmart listing"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/walmart-store-analyzer
---

# Walmart Store Analyzer

Deep analysis of your Walmart marketplace performance — traffic sources, item rankings, conversion rates, and competitive positioning. Turn your Walmart seller data into growth actions.

Paste your Walmart seller data or traffic reports. The agent analyzes performance, identifies opportunities, and outputs a prioritized action plan.

## Commands

```
walmart traffic <data>            # analyze traffic report data
walmart rank <item-id>            # analyze item search ranking
walmart compete <query>           # competitive analysis for a search term
walmart listing grade <item>      # score and improve a Walmart listing
walmart keyword <term>            # Walmart-specific keyword research
walmart buybox <item>             # Buy Box win rate analysis
walmart ads <data>                # Walmart Connect ad performance analysis
walmart report <period>           # full store performance report
walmart compare <item1> <item2>   # compare two item performances
walmart optimize <item>           # listing optimization recommendations
```

## What Data to Provide

- **Traffic reports** — views, clicks, add-to-carts, purchases by item
- **Item IDs / WMT IDs** — specific items to analyze
- **Search terms** — keywords you want to rank for
- **Competitor data** — competing items on same search results
- **Ad data** — Walmart Connect campaign metrics
- **Sales data** — units sold, revenue, return rates

## Walmart Marketplace Framework

### Walmart vs Amazon Key Differences

| Factor | Walmart | Amazon |
|--------|---------|--------|
| Commission | 6-15% (category) | 6-20% (category) |
| Monthly fee | None | $39.99 |
| Fulfillment | WFS or seller | FBA or FBM |
| Traffic | Growing, less saturated | Very high, very competitive |
| Review barrier | Lower (fewer sellers) | High in most categories |
| Ads | Walmart Connect | Amazon Ads |
| Algorithm | Relevance + conversion | A9 (similar) |

### Walmart Search Ranking Algorithm

Key ranking factors:
1. **Relevance** — title, description keyword match
2. **Performance signals** — click-through rate, conversion rate
3. **Price** — competitive pricing vs. category
4. **In-stock status** — out-of-stock = ranking penalty
5. **Customer ratings** — star rating and review count
6. **Fulfillment method** — WFS items get priority (similar to FBA)
7. **Listing completeness** — content score from Walmart

### Traffic Report Analysis

When given Walmart traffic data, analyze:

```
Metric                 Benchmark      Action if below
Views/week             >100           SEO optimization needed
CTR (views→clicks)     >3%            Improve title/image
Add-to-cart rate       >5% of views   Improve images, price
Purchase rate          >2% of views   Address price/reviews
Return rate            <5%            Product quality issue
```

**Traffic source breakdown:**
- Search traffic: Main driver — keyword optimization focus
- Browse traffic: Category placement — content score matters
- Paid traffic: Walmart Connect ads
- External traffic: Affiliate and off-site

### Walmart Listing Optimization

**Content Score** (Walmart grades your listing):
- Items with 100% content score rank higher
- Check: title, short description, long description, key features, images, specs

**Title Best Practices:**
- Format: `[Brand] [Product Type] [Key Feature] [Size/Color] [Use Case]`
- Character limit: 75 characters recommended (200 max)
- Lead with strongest keyword
- Include size, material, or key differentiator

**Image Requirements:**
- Main image: white background, product fills 85% of frame
- Minimum 4 images (aim for 8+)
- Lifestyle shots: product in use context
- Infographic: key features callouts
- Minimum 2000×2000 pixels

**Short Description** (Key Features):
- 6 bullet points, 80 chars each
- Lead each bullet with the benefit, support with feature
- Include primary keywords naturally

### Walmart Buy Box Analysis

Walmart Buy Box rules:
- Lowest price (among fulfilled, available sellers) typically wins
- WFS sellers get advantage at similar prices
- Seller rating impacts eligibility
- In-stock required

**Buy Box win rate formula:**
```
If you're winning:      Item price ≤ market price, good metrics
If you're losing:       Check price competitiveness first
                        Then check seller metrics (>98% on-time ship, <2% cancel)
                        Then check fulfillment method (WFS advantage)
```

### Walmart Connect (Ad) Metrics

Key ad performance metrics:
```
ROAS:          Revenue / Ad spend (target >4x)
CPC:           Cost per click (benchmark $0.30-$1.50)
CTR:           Ad clicks / impressions (target >0.5%)
CVR:           Purchases / clicks (target >2%)
ACOS:          Ad cost / revenue × 100 (target <25%)
```

**Ad campaign types:**
- Sponsored Products: Item-level targeting, most common
- Sponsored Brands: Brand banner + 3 products
- Video Ads: High impact, higher CPM

### Competitive Positioning on Walmart

Search for your category on Walmart:
```
Position 1-3: Premium placement, highest traffic share
Position 4-8: Good visibility, competitive zone
Position 9-16: Second page (mobile), less visible
Position 17+: Low visibility, need ranking improvement
```

**Competitive gaps on Walmart vs Amazon:**
- Many Amazon sellers haven't expanded to Walmart
- Lower review counts needed to rank
- Less sophisticated ads competition
- WFS (Walmart Fulfillment Services) gives strong advantage

## Workspace

Creates `~/walmart-tracker/` containing:
- `traffic/` — traffic report analyses
- `keywords/` — Walmart keyword research
- `listings/` — listing scores and optimization notes
- `ads/` — Walmart Connect performance data
- `reports/` — full store performance reports

## Output Format

Every analysis outputs:
1. **Store Performance Summary** — top metrics vs. benchmarks with trend arrows
2. **Traffic Source Breakdown** — where traffic comes from and conversion by source
3. **Item Ranking Report** — search position for key terms
4. **Listing Score Card** — content score with specific improvement actions
5. **Ad Performance** — ROAS, ACOS, top performing keywords
6. **Opportunity Items** — items with traffic but low conversion (quick wins)
7. **Weekly Action Plan** — prioritized optimization tasks

---
name: amazon-fba-product-finder
description: "Amazon FBA product research agent. Find profitable FBA products — extract rank, sales volume, estimated revenue, competition score, and profit potential without entering every product page. Inspired by Jungle Scout methodology. Triggers: fba product research, jungle scout, amazon product finder, fba opportunity, product research, amazon niche finder, product validation, fba profit, sales estimate, revenue estimate, product opportunity score, amazon sourcing"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amazon-fba-product-finder
---

# Amazon FBA Product Finder

Research Amazon FBA product opportunities like a pro. Analyze rank, sales volume, estimated revenue, and competition score to validate products before sourcing — no tool subscription required.

Paste product data from Amazon search results or product pages. The agent validates opportunity and outputs a go/no-go recommendation with full reasoning.

## Commands

```
find research <product-idea>      # research a product idea end-to-end
find validate <data>              # validate product with pasted Amazon data
find score <product>              # compute opportunity score for a product
find market <keyword>             # analyze top 10 results for a search term
find revenue <asin> <bsr>         # estimate monthly revenue from BSR
find compare <product1> <product2># compare two product opportunities
find niche <category>             # identify niches within a category
find checklist <product>          # run 10-point FBA viability checklist
find report <product>             # full product research report
find save <product>               # save research to workspace
```

## What Data to Provide

- **Product idea / keyword** — what you want to research
- **Amazon search results data** — titles, prices, BSRs, review counts from the page
- **Category** — main category (used for BSR-to-sales conversion)
- **Your target price point** — expected selling price
- **Sourcing context** — where you plan to source (Alibaba, domestic, etc.)

## Product Research Framework

### Step 1: Market Size Estimation

Estimate monthly revenue for the top 10 results:
- Total market revenue = sum of estimated monthly revenue for top 10 listings
- Healthy market: Top 10 generate $50,000-$500,000/month combined
- Too small (<$30k): Limited opportunity
- Too large (>$1M): Possibly too competitive for new entrant

### Step 2: BSR to Sales Volume Conversion

Estimated monthly sales by category and BSR:
```
Category: Kitchen & Home
BSR 1-100:      8,000-50,000 units/month
BSR 100-500:    2,000-8,000 units/month
BSR 500-1000:   800-2,000 units/month
BSR 1000-3000:  300-800 units/month
BSR 3000-10000: 100-300 units/month
BSR 10000+:     <100 units/month

Category: Sports & Outdoors
BSR 1-100:      5,000-30,000 units/month
BSR 100-500:    1,500-5,000 units/month
BSR 500-2000:   500-1,500 units/month
BSR 2000-5000:  150-500 units/month
BSR 5000-15000: 50-150 units/month

Adjust by ±30% based on seasonality and listing quality.
```

### Step 3: Competition Analysis

Assess the top 10 listings:

| Signal | Green | Yellow | Red |
|--------|-------|--------|-----|
| Avg reviews | <200 | 200-500 | >500 |
| Review gap (1st vs 10th) | <5x | 5-20x | >20x |
| Listing quality | Poor-Medium | Medium | Excellent |
| Brand dominance | 0 brands in top 5 | 1-2 brands | 3+ same brands |
| Price range | $10-$50 | $5-$10 or $50-$100 | <$5 or >$100 |

### Step 4: Product Opportunity Score (POS)

Score 0-100 based on 10 factors:

```
1. Search volume (keyword demand)        0-10 pts
2. Revenue potential (top 10 combined)   0-10 pts
3. Competition gap (review accessibility) 0-10 pts
4. Margin potential (price vs. cost)     0-10 pts
5. Product simplicity (risk of defects)  0-10 pts
6. Differentiation potential             0-10 pts
7. Seasonal stability                    0-10 pts
8. Sourcing accessibility               0-10 pts
9. Regulatory risk (no hazmat/IP issues) 0-10 pts
10. Growth trajectory                   0-10 pts

POS 75+:  Strong opportunity — proceed to sourcing
POS 60-74: Moderate opportunity — validate further
POS 45-59: Weak opportunity — significant risks
POS <45:  Pass — not worth pursuing
```

### Step 5: 10-Point FBA Viability Checklist

```
[ ] 1. Price $15-$70 (sweet spot for FBA margins)
[ ] 2. Lightweight <2 lbs (keeps FBA fees manageable)
[ ] 3. Small dimensions (standard-size, not oversize)
[ ] 4. No seasonal dependency (sells year-round)
[ ] 5. No brand dominance in top 10 (room for new sellers)
[ ] 6. Top seller has <500 reviews (achievable competition)
[ ] 7. Estimated monthly revenue $5,000+ (viable market)
[ ] 8. Clear differentiation opportunity (can improve on top listings)
[ ] 9. No dangerous goods / fragile items
[ ] 10. Sourceable on Alibaba for <30% of selling price
```

### FBA Fee Structure

```
Referral fee:     8-15% of selling price (varies by category)
FBA fulfillment:
  Small standard: $3.22 (≤16oz) to $4.37 (≤1lb)
  Large standard: $5.42 (≤1lb) to $9.73 (≤20lb)
Storage fee:
  Standard:       $0.87/cubic foot (Jan-Sep)
                  $2.40/cubic foot (Oct-Dec)

Quick check: Target 25-35% net margin after all fees
```

### Margin Calculation Template

```
Selling price:        $XX.XX
- Referral fee (15%): $XX.XX
- FBA fee:            $XX.XX
- COGS (incl. ship):  $XX.XX
- PPC cost (est. 15%): $XX.XX
= Net profit per unit: $XX.XX
= Net margin:          XX%

Minimum viable margin: 20% net
```

## Workspace

Creates `~/fba-research/` containing:
- `opportunities/` — validated product research reports
- `rejected/` — products considered but passed
- `pipeline/` — products under active consideration
- `sourcing/` — supplier notes linked to products

## Output Format

Every product research outputs:
1. **Product Overview** — name, category, price range, estimated market size
2. **Opportunity Score** — POS out of 100 with factor breakdown
3. **Top 10 Competitive Landscape** — table of top listings with key metrics
4. **Revenue Estimate** — range of monthly revenue at various BSR positions
5. **Margin Analysis** — expected net profit per unit at target price
6. **Viability Checklist** — pass/fail on all 10 criteria
7. **Go/No-Go Recommendation** — clear verdict with reasoning
8. **Next Steps** — if go: sourcing plan; if no-go: why and what to look for instead

---
name: amazon-keyword-reverse-lookup
description: "Amazon keyword reverse lookup engine. Find all keywords driving traffic to any ASIN, uncover hidden long-tail opportunities, build CPC ad keyword lists, and optimize listings with data-driven keyword intelligence. Triggers: keyword reverse lookup, asin keyword, amazon keyword research, reverse asin, listing keywords, cpc keyword, ppc keyword, amazon seo keyword, keyword spy, traffic keywords, search terms, backend keywords, keyword extraction"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amazon-keyword-reverse-lookup
---

# Amazon Keyword Reverse Lookup Engine

The keyword intelligence engine behind your Amazon Listing optimization and CPC ad targeting. Given an ASIN or product description, uncover every keyword driving traffic — from high-volume head terms to long-tail conversion gold.

## Commands

```
reverse <asin>                    # extract keyword profile for an ASIN
reverse bulk <asin1,asin2,...>    # batch keyword extraction for multiple ASINs
keyword gap <your-asin> <comp>    # find keywords competitor ranks for but you don't
keyword rank check <asin> <kw>    # estimate ranking for specific keyword
keyword cluster <list>            # group keywords by semantic theme
keyword priority                  # score and rank keywords by opportunity
cpc suggest <keyword>             # CPC bid suggestions based on competition
backend generate <product>        # generate backend search terms (249 chars)
listing inject <title> <kws>      # naturally inject keywords into listing copy
keyword save <product>            # save keyword research to workspace
```

## What Data to Provide

- **ASIN** — the product to reverse-lookup
- **Competitor ASINs** — to find keyword gaps
- **Product title/bullets** — for keyword extraction from listing
- **Category** — for benchmark search volumes
- **Seed keywords** — terms you already know (to expand from)
- **Budget context** — CPC budget range for bid suggestions

## Keyword Research Framework

### Keyword Tier Classification

**Tier 1 — Head Terms** (Primary keywords):
- Monthly searches: 10,000+
- Competition: Very High
- Use in: Title (first 60 chars), backend
- Strategy: Must rank here eventually, but may need 6-12 months

**Tier 2 — Core Keywords** (Secondary keywords):
- Monthly searches: 1,000-10,000
- Competition: Medium-High
- Use in: Title, bullet points, backend
- Strategy: Primary target for new listings — achievable with 50+ reviews

**Tier 3 — Long-tail Keywords** (Conversion keywords):
- Monthly searches: 100-1,000
- Competition: Low-Medium
- Use in: Description, backend, PPC exact match
- Strategy: Launch focus — win these first to build velocity

**Tier 4 — Niche Keywords** (Discovery keywords):
- Monthly searches: <100
- Competition: Very Low
- Use in: Backend search terms, PPC broad match
- Strategy: Passive traffic, zero cost to rank for

### Keyword Extraction from Listing Text

From a product title/bullets, extract keywords by:
1. Identify product type (noun phrase)
2. Extract all modifiers (size, color, material, use case)
3. Generate all meaningful combinations
4. Add related synonyms and alternative terms
5. Add use-case and problem-based phrases

Example for "Stainless Steel Water Bottle 32oz":
```
Primary: water bottle, stainless steel water bottle
Modifiers: 32oz, large, insulated, vacuum, leak-proof, BPA free
Use cases: hiking, gym, sports, outdoor, camping, travel
Problem-based: keeps water cold, hot coffee thermos
Synonyms: tumbler, flask, canteen, hydration bottle
Long-tail: 32oz insulated water bottle, stainless steel gym bottle leak proof
```

### Reverse Lookup Logic (Manual ASIN Analysis)

When given an ASIN, analyze by:
1. Extract all words from title, bullets, description
2. Generate keyword permutations
3. Identify brand terms vs. generic terms
4. Cross-reference with category common keywords
5. Score by estimated commercial intent

### Keyword Gap Analysis

Given your ASIN vs. competitor ASIN:
```
Your keywords:       [A, B, C, D, E]
Competitor keywords: [B, C, D, F, G, H]
Gap keywords:        [F, G, H] — competitor has these, you don't
Unique to you:       [A, E] — your advantage, protect these
```

### CPC Bid Intelligence

**Bid estimation by competition level:**
```
Very High competition (Tier 1): $1.50 - $3.00+ CPC
High competition (Tier 2):      $0.80 - $1.50 CPC
Medium competition (Tier 3):    $0.30 - $0.80 CPC
Low competition (Tier 4):       $0.10 - $0.30 CPC
```

**Bid strategy by campaign type:**
- Exact match: Bid at estimated CPC
- Phrase match: Bid at 80% of exact
- Broad match: Bid at 60% of exact
- Auto campaign: Start at $0.50, scale based on ACOS

### Backend Search Terms (249 Characters)

Rules for backend keyword field:
- No commas needed — spaces work
- No repeat words — every character counts
- Include misspellings of your product name
- Include complementary product terms
- No ASINs, brand names of competitors, or prohibited terms
- Use singular forms only (Amazon pluralizes automatically)

**Template:**
```
[synonyms] [materials] [use cases] [compatible items] [problem solved] [occasion] [demographic]
```

### Listing Keyword Injection

**Title formula:**
`[Brand] [Primary Keyword] [Key Feature] [Size/Variant] [Secondary Keyword]`

**Bullet point structure:**
- Bullet 1: Primary keyword + biggest benefit
- Bullet 2: Secondary keyword + feature proof
- Bullet 3: Long-tail keyword + use case
- Bullet 4: Trust signal (certifications, compatibility)
- Bullet 5: Guarantee / customer promise + brand keyword

## Keyword Scoring Matrix

Score each keyword on 4 factors (1-5):
1. **Search volume** — how many searches per month
2. **Relevance** — how closely it matches your product
3. **Competition** — how hard to rank (5=very easy)
4. **Intent** — how likely searcher is to buy (5=high purchase intent)

**Priority Score = (Volume + Relevance + Competition + Intent) / 4**
Focus first on keywords scoring 4.0+

## Workspace

Creates `~/keyword-research/` containing:
- `by-asin/` — keyword profiles per ASIN
- `campaigns/` — PPC keyword lists by match type
- `gaps/` — competitor gap analysis files
- `backend/` — generated backend search term strings

## Output Format

Every keyword research outputs:
1. **Master Keyword Table** — full keyword list with tier, volume estimate, competition, priority score
2. **Top 10 Priority Keywords** — highest opportunity, recommended to target first
3. **Backend Search Terms** — ready-to-paste 249-character string
4. **PPC Campaign Structure** — exact/phrase/broad keyword groups with bid suggestions
5. **Listing Optimization Notes** — which keywords are missing from current listing

---
name: amazon-price-history
description: "Amazon product price history tracker and drop alert agent. Track price trends over time, identify the best time to buy or sell, analyze competitor pricing patterns, and set smart price alerts. Triggers: amazon price history, price tracker, price drop alert, keepa alternative, amazon price trend, historical pricing, price chart, best time to buy, price monitoring, amazon deal finder, price volatility, buy box price"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amazon-price-history
---

# Amazon Price History Tracker

Track Amazon product price history, identify pricing patterns, and time your pricing decisions with precision. Alternative to Keepa for sellers who want AI-assisted price analysis.

Paste product data, price snapshots, or competitor pricing. The agent builds price timelines, detects patterns, and recommends optimal pricing windows.

## Commands

```
price add <asin> <price> [date]   # log a price data point
price trend <asin>                # analyze price trend direction
price pattern <asin>              # detect seasonal and cyclical patterns
price alert set <asin> <target>   # set alert for target price threshold
price compare <asin1> <asin2>     # compare pricing histories side-by-side
price best-time <asin>            # identify best time window to raise/lower price
price volatility <asin>           # compute price stability score
price chart <asin>                # render text-based price chart
price report <asin>               # full pricing analysis report
price save                        # save all price history to workspace
```

## What Data to Provide

- **ASIN + price data** — current or historical prices with dates
- **Buy Box price** — the winning price you see on the listing
- **Competitor prices** — other sellers on the same listing
- **Your cost structure** — COGS, FBA fees, target margin
- **Seasonal context** — any known promotions or peak periods

## Price Analysis Framework

### Price Data Points to Track

For each ASIN, capture:
```
Date | Price | Seller | Buy Box? | Coupon | Sale Event | Notes
2024-01-15 | $29.99 | Brand | Yes | 10% off | None | Normal
2024-01-20 | $24.99 | Brand | Yes | None | Flash sale | -17%
2024-02-01 | $32.99 | Brand | Yes | None | None | Post-sale
```

### Price Trend Classification

**Uptrend** (Price rising):
- Average price this month > last month by >5%
- Signal: possible demand increase, supply constraints, or brand repositioning
- Seller action: monitor for opportunity to raise your price

**Downtrend** (Price falling):
- Average price this month < last month by >5%
- Signal: increased competition, excess inventory, or market correction
- Seller action: evaluate your cost floor, avoid race-to-bottom

**Stable** (±5% range):
- Price within 5% of 90-day average
- Signal: mature market equilibrium
- Seller action: compete on other factors (listing quality, reviews, ads)

**Volatile** (Frequent swings >10%):
- Price swings more than 10% within 30 days
- Signal: heavy promotional activity or multiple sellers competing
- Seller action: dynamic repricing is essential

### Price Volatility Score

```
Volatility = (Max Price - Min Price) / Average Price × 100

Score 0-10:   Very stable — predictable market
Score 10-25:  Moderate — some promotional activity
Score 25-50:  Volatile — heavy competition or frequent sales
Score 50+:    Highly volatile — use dynamic repricing or avoid
```

### Seasonal Pattern Detection

Map price history against calendar events:
- **Q4 (Oct-Dec)**: Holiday surge — premium pricing possible
- **Prime Day (July)**: Deep discounts expected, plan inventory
- **Back to School (Aug-Sep)**: Category-specific peaks
- **Valentine's/Mother's Day**: Gift category spikes
- **Post-holiday (Jan-Feb)**: Price depression, clear inventory

### Buy Box Price Analysis

The Buy Box price matters more than list price:
- Track who holds the Buy Box at each snapshot
- Compute your price gap vs. current Buy Box winner
- Identify patterns in Buy Box rotation (if multiple sellers)

**Buy Box pricing rules:**
- New seller entering: typically prices 5-10% below current winner
- FBA advantage: can price slightly higher than FBM and still win
- Low inventory: Buy Box may shift to higher-priced seller

### Price Alert Thresholds

```
PRICE DROP ALERT:   Current price falls >10% below 30-day average
PRICE SPIKE ALERT:  Current price rises >20% above 30-day average
BUY BOX LOST:       Your price is no longer the Buy Box winner
COMPETITOR ENTRY:   New seller appears at significantly lower price
FLOOR ALERT:        Price drops below your calculated margin floor
```

### Optimal Repricing Windows

Based on historical patterns:
- **Best time to raise price**: After a competitor goes out of stock
- **Best time to lower price**: 2 weeks before Prime Day to boost velocity
- **Avoid repricing**: During major sale events (prices stabilize after)
- **Post-holiday**: Expect 15-25% price compression, plan ahead

## Margin Floor Calculation

```
Margin Floor = COGS + FBA Fees + Referral Fee + PPC Cost + Minimum Profit
Example:
  COGS:          $8.00
  FBA fees:      $4.50
  Referral (15%): price × 0.15
  PPC cost:      $2.00
  Min profit:    $2.00
  Floor = $8 + $4.50 + (price × 0.15) + $2 + $2 = solve for price
  Minimum price = ($16.50) / (1 - 0.15) = $19.41
```

## Workspace

Creates `~/price-tracker/` containing:
- `history/` — price logs per ASIN (ASIN.md files)
- `alerts/` — triggered price alerts
- `patterns/` — seasonal pattern analysis
- `reports/` — full pricing reports

## Output Format

Every price analysis outputs:
1. **Price Timeline Table** — chronological price history with key events marked
2. **Trend Direction** — current trend with confidence level
3. **Volatility Score** — 0-100 stability rating with interpretation
4. **Pattern Summary** — detected seasonal or cyclical patterns
5. **Optimal Price Window** — recommended price and timing
6. **Margin Check** — current price vs. your floor, profit per unit

---
name: ozon-wildberries-analyzer
description: "OZON and Wildberries marketplace deep analysis agent. Market intelligence for Russian e-commerce — category trends, competitor analysis, pricing strategy, keyword research, and seller performance insights. Triggers: ozon analyzer, wildberries analysis, wb marketplace, ozon seller, russian ecommerce, wildberries trends, ozon product research, wb competitor, ozon keyword, wildberries pricing, wb seller, eastern europe ecommerce"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/ozon-wildberries-analyzer
---

# OZON & Wildberries Marketplace Analyzer

Deep market intelligence for Russia's top two e-commerce platforms — OZON and Wildberries (WB). Analyze categories, track competitors, research keywords, and build winning pricing strategies.

Paste product data, category names, or competitor URLs. The agent analyzes market dynamics and outputs actionable seller intelligence.

## Commands

```
wb analyze <product>          # analyze product opportunity on Wildberries
ozon analyze <product>        # analyze product opportunity on OZON
market trends <category>      # identify trending categories and products
competitor spy <seller-name>  # analyze competitor seller performance
pricing strategy <product>    # build competitive pricing approach
keyword research <term>       # find search keywords for both platforms
category map                  # map category structure and competition levels
entry assessment <product>    # assess market entry difficulty and opportunity
report <product>              # full cross-platform analysis report
```

## What Data to Provide

- **Product name or category** — in Russian or English
- **Competitor product data** — price, rating, review count, sales estimate
- **Your product specs** — dimensions, weight, cost price
- **Target market** — Russia, Kazakhstan, Belarus, or broader CIS
- **Budget context** — launch budget, margin requirements

## Market Analysis Framework

### OZON vs Wildberries Comparison

| Factor | OZON | Wildberries |
|--------|------|-------------|
| Market share | ~35% | ~55% |
| Commission | 4-25% by category | 5-25% by category |
| Logistics | FBO/FBS/realFBS | FBO/FBS |
| Buyer demographics | Urban, tech-savvy | Mass market |
| Return rate | Lower | Higher (fashion) |
| Best for | Electronics, home | Fashion, FMCG |

### Category Opportunity Scoring

Rate each category on 5 factors (1-5 scale):

1. **Search volume** — estimated monthly searches for main keyword
2. **Competition density** — number of active sellers in top 100
3. **Average margin** — (avg selling price - avg cost) / avg selling price
4. **Review barrier** — minimum reviews needed to appear in top 20
5. **Growth trend** — YoY category GMV growth (declining=1, fast-growing=5)

**Score 20+** = Strong opportunity, prioritize
**Score 15-19** = Moderate opportunity, consider entering
**Score <15** = Saturated or declining, avoid unless strong advantage

### Pricing Strategy Models

**Penetration pricing** (new entrant):
- Set price 15-20% below market average
- Accept low/no profit for first 60 days
- Goal: accumulate reviews and BSR quickly
- Exit: raise price by 5% every 2 weeks once ranked

**Market rate pricing** (established):
- Price within ±5% of top 10 average
- Differentiate on review count and listing quality
- Use coupons (скидка) for promotional periods

**Premium positioning**:
- Price 20-30% above market average
- Requires A+ listing, brand registered, 100+ reviews
- Focus on brand storytelling and quality signals

### Keyword Research (WB & OZON)

**Primary keywords**: High volume, direct match (e.g., "чехол для iPhone 15")
**Long-tail keywords**: Lower volume, higher conversion (e.g., "чехол для iPhone 15 pro прозрачный")
**Seasonal keywords**: Track peak months (e.g., "подарки на Новый год" — November-December)

Keyword integration points:
- Product title (первые 60 символов most important)
- Rich content / description
- Search tags (теги) — use all available slots
- Answer questions section

### Wildberries-Specific Intelligence

**WB ranking factors:**
1. Sales velocity (главный фактор)
2. Conversion rate (клики → заказы)
3. Review count and rating
4. Redemption rate (выкуп) — aim for >80%
5. Logistics speed (FBO gets priority)

**WB Buy Box signals:**
- FBO sellers get priority in search
- Price with applied coupon (цена со скидкой) is displayed
- Seller rating affects placement

**WB Return Rate benchmarks:**
- Fashion: 40-60% returns normal
- Electronics: <10% target
- Home goods: <15% target

### OZON-Specific Intelligence

**OZON Premium** program benefits:
- Search boost for premium items
- Trust badge improves conversion
- Cost: subscription fee

**OZON Rich Content** types:
- Video (highest conversion lift: +30%)
- 360° images
- Infographics
- Comparison tables

**OZON advertising formats:**
- Search promotion (контекстная реклама)
- Product placement boost (продвижение)
- Banner ads (медийная реклама)

## Workspace

Creates `~/wb-ozon-tracker/` containing:
- `categories/` — category analysis reports
- `competitors/` — seller profile snapshots
- `keywords/` — keyword research files
- `pricing/` — price strategy documents
- `reports/` — full analysis reports

## Output Format

Every analysis outputs:
1. **Market Overview** — platform comparison, category size estimate, competition level
2. **Top Competitors Table** — top 5-10 sellers with key metrics
3. **Keyword Blueprint** — primary + long-tail keyword list with priority ranking
4. **Pricing Recommendation** — suggested entry price, target price, minimum margin
5. **Entry Roadmap** — 90-day launch plan with milestones
6. **Risk Flags** — market saturation signals, seasonal risks, regulatory notes

---
name: amz-asin-data-tracker
description: "Amazon ASIN visual data collection and monitoring agent. Zero-code collection of Amazon ASIN and competitor data, supports scheduled tasks, real-time alerts, and multi-format export. Triggers: asin data tracker, amazon data collection, asin monitor, competitor data, amazon scraper, asin tracking, real-time alert, amazon export, asin collector, product monitoring, amazon surveillance, data export"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amz-asin-data-tracker
---

# Amazon ASIN Data Tracker

Zero-code Amazon ASIN and competitor data collection with scheduled monitoring, real-time alerts, and multi-format export — the essential tool for Amazon sellers.

Paste an ASIN or product URL. The agent collects key metrics, sets up monitoring schedules, and alerts you to significant changes.

## Commands

```
track add <asin>              # add ASIN to monitoring list
track snapshot                # capture current data for all tracked ASINs
track alert <threshold>       # set alert conditions (price drop %, BSR change)
track schedule <interval>     # configure monitoring schedule (daily/weekly)
track export <format>         # export data as CSV/JSON/Markdown table
track compare <asin1> <asin2> # side-by-side ASIN comparison
track history <asin>          # show full history for an ASIN
track report                  # generate comprehensive monitoring report
track save                    # save all tracking data to workspace
```

## What Data to Provide

- **ASIN** — Amazon Standard Identification Number (e.g., B08XYZ1234)
- **Product page data** — paste title, price, BSR, review count, rating
- **Historical data** — any prior snapshots you have
- **Alert preferences** — what changes matter most to you
- **Category context** — niche, main keywords, target market

## Data Collection Framework

### Core Metrics Tracked Per ASIN

| Metric | Collection Method | Alert Threshold |
|--------|------------------|-----------------|
| Price | Page data / paste | ±10% change |
| BSR | Page data / paste | ±500 ranks |
| Review count | Page data / paste | +20 new reviews |
| Star rating | Page data / paste | Drop below 4.0 |
| Seller count | Page data / paste | New sellers entering |
| Image count | Manual observation | New images added |
| Variation count | Manual observation | New variations |

### Scheduled Monitoring Cadences

**Daily monitoring** (high-priority ASINs):
- Price and BSR check
- New review detection
- Buy Box status

**Weekly monitoring** (standard watchlist):
- Full metric snapshot
- Listing change detection
- Competitor movement summary

**Monthly monitoring** (market overview):
- Trend analysis report
- Market share estimation
- Seasonal pattern identification

### Alert Conditions

```
PRICE DROP ALERT:     Price falls >10% from baseline
BSR SPIKE ALERT:      BSR improves >500 ranks in 7 days
REVIEW BOMB ALERT:    >10 new 1-star reviews in 48 hours
REVIEW SURGE ALERT:   >30 new reviews in 7 days (possible manipulation)
LISTING CHANGE ALERT: Title or main image changed
SELLER ALERT:         New seller with <100 feedback enters Buy Box
STOCK ALERT:          "Only X left in stock" message appears
```

## Export Formats

### CSV Export
```
ASIN, Title, Price, BSR, Reviews, Rating, Date, Category
B08XYZ123, "Product Name", $29.99, 1234, 456, 4.3, 2024-01-15, Kitchen
```

### Markdown Table
```
| ASIN | Price | BSR | Reviews | Rating | Updated |
|------|-------|-----|---------|--------|---------|
| B08X | $29.99 | 1,234 | 456 | ⭐4.3 | Jan 15 |
```

### JSON Export
```json
{
  "asin": "B08XYZ123",
  "snapshots": [
    {"date": "2024-01-15", "price": 29.99, "bsr": 1234, "reviews": 456}
  ]
}
```

## Workspace Structure

Creates `~/asin-tracker/` containing:
- `watchlist.md` — all tracked ASINs with settings
- `snapshots/` — date-stamped metric files per ASIN
- `alerts/` — triggered alert log
- `exports/` — generated export files
- `reports/` — monitoring summary reports

## Analysis Rules

1. Require at least 2 data points before declaring a trend
2. Distinguish between organic and promotional BSR movements
3. Flag BSR improvements during Prime Day, Black Friday, holiday seasons as potentially seasonal
4. Never declare a price drop permanent until it holds for 7+ days
5. Cross-reference review velocity with known promotion dates before flagging manipulation
6. Always timestamp every data point — tracking is only useful with temporal context

## Output Format

Every report outputs:
1. **Current Snapshot Table** — all tracked ASINs with latest metrics
2. **Changes Since Last Check** — delta table showing what moved and by how much
3. **Active Alerts** — list of triggered conditions with severity (High/Medium/Low)
4. **Trend Summary** — 30-day direction for each key metric
5. **Recommended Actions** — prioritized response list based on detected changes

---
name: social-growth-hacker
description: "Platform-specific social media growth strategy for creators and brands. Get a personalized 90-day growth plan with tactics to gain real followers, increase reach, and build authority on Instagram, TikTok, LinkedIn, X/Twitter, or YouTube. Triggers: social media growth, grow followers, growth strategy, grow Instagram, grow TikTok, grow LinkedIn, YouTube growth, follower growth"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/social-growth-hacker
---

# Social Growth Hacker

A 90-day battle plan to grow your social media account from [X] to [Y] followers.

## Usage

```
growth: grow Instagram from 500 to 5000 followers, fitness niche
TikTok growth: 0 to 10K followers strategy for cooking content
LinkedIn growth: B2B founder 1K to 10K connections in 90 days
grow followers: YouTube channel tech tutorials 100 to 1000 subscribers
```

## What You Get

1. **Growth Rate Analysis** — realistic targets based on niche + platform
2. **Top 5 Growth Levers** — ranked by impact and effort
3. **90-Day Week-by-Week Plan** — specific actions every week
4. **Viral Content Formula** — post structures that attract new followers
5. **Collaboration Strategy** — how to grow through others' audiences
6. **Algorithm Hacks** — platform-specific tricks that work right now
7. **Tracking Dashboard** — metrics to monitor weekly

FILE:analyze.sh
#!/usr/bin/env bash
set -euo pipefail
INPUT="-"
[ -z "$INPUT" ] && echo "Usage: growth: <platform, niche, current followers, follower goal>" && exit 1
SESSION_ID="growth-$(date +%s)"
PROMPT="You are a social media growth hacker with proven results across platforms. Build a 90-day growth strategy for: INPUT

## Growth Reality Check
Realistic growth projections for this platform + niche:
- Conservative (low effort): +X followers/month
- Realistic (consistent effort): +X followers/month
- Aggressive (high effort + luck): +X followers/month
- What separates top 10% growers in this niche: [key differentiator]

## Top 5 Growth Levers
Ranked by follower growth impact:

**Lever #1: [Name]** — Impact: HIGH / Effort: MEDIUM
- What it is: [explanation]
- Exactly how to do it: [step by step]
- Time investment: X hours/week
- Expected result: +X followers/month

**Lever #2: [Name]**
[Same format]

**Lever #3: [Name]**
[Same format]

**Lever #4: [Name]**
[Same format]

**Lever #5: [Name]**
[Same format]

## 90-Day Growth Plan

### Month 1 — Foundation (Weeks 1-4)
Goal: [specific follower target + engagement rate target]

Week 1: [5 specific daily actions]
Week 2: [5 specific daily actions]
Week 3: [5 specific daily actions]
Week 4: [Review + what to double down on]

### Month 2 — Acceleration (Weeks 5-8)
Goal: [specific target]
Focus: [main tactic to push hard]
[Weekly breakdown]

### Month 3 — Compound (Weeks 9-12)
Goal: [specific target]
Focus: [what's working — scale it]
[Weekly breakdown]

## Viral Content Formula
The post structure that attracts NEW followers (not just engagement from existing):
- Format: [specific format — reel / thread / carousel / etc.]
- Hook pattern: [exact hook formula]
- Content structure: [outline]
- Distribution hack: [how to get it seen beyond your followers]
- Frequency: [how often to post this type]

## Collaboration & Cross-Pollination
How to borrow audiences from bigger accounts:
- Collab post strategy (for this platform)
- Comment farming: how to leave comments that drive profile visits
- Duet / Stitch / Quote Tweet strategy
- Shoutout exchange framework (how to pitch accounts)
- Who to target: [follower size range that makes sense]

## Algorithm Hacks (Platform-Specific)
What's working RIGHT NOW on this platform:
1. [Specific tactic]
2. [Specific tactic]
3. [Specific tactic]
4. [Specific tactic]
5. [Specific tactic]

## Weekly Tracking Dashboard
Metrics to check every Monday:
| Metric | Week 1 Target | Week 4 Target | Week 12 Target |
|--------|-------------|--------------|----------------|
| Followers | | | |
| Avg reach per post | | | |
| Engagement rate | | | |
| Profile visits | | | |
| Follower conversion rate | | | |

## Common Growth Mistakes to Avoid
Top 5 things that stall growth in this niche — with fixes."

RESULT=$(openclaw agent --local --session-id "$SESSION_ID" --json -m "$PROMPT" 2>/dev/null)
REPORT=$(echo "$RESULT" | python3 -c "
import json,sys
data=json.load(sys.stdin)
texts=[p.get('text','') for p in data.get('payloads',[]) if p.get('text')]
print('\n'.join(texts))
" 2>/dev/null)
[ -z "$REPORT" ] && echo "Error: Could not generate growth strategy." && exit 1
echo ""
echo "=== SOCIAL GROWTH STRATEGY === INPUT ==="
echo ""
echo "$REPORT"

---
name: social-bio-optimizer
description: "Social media bio optimizer for all platforms. Write a high-converting bio for Instagram, LinkedIn, TikTok, X/Twitter, or Threads that attracts followers, communicates value, and drives action. Triggers: bio optimizer, social media bio, Instagram bio, LinkedIn bio, TikTok bio, Twitter bio, profile bio, bio writer, write bio"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/social-bio-optimizer
---

# Social Bio Optimizer

Write a profile bio that converts visitors into followers — for every platform.

## Usage

```
bio: LinkedIn bio for product designer at a startup
Instagram bio: fitness coach helping moms lose weight without gym
TikTok bio: personal finance creator for millennials
bio optimizer: X/Twitter bio for indie hacker and developer
```

## What You Get

1. **5 Bio Variations** — different styles and lengths
2. **Platform-Specific Version** — optimized for each platform's format
3. **Keyword Integration** — searchable terms baked in
4. **CTA Optimization** — the best link-in-bio CTA for your goal
5. **Character Budget** — maximizing every character allowed
6. **A/B Test Pairs** — top 2 bios to rotate and test

FILE:analyze.sh
#!/usr/bin/env bash
set -euo pipefail
INPUT="-"
[ -z "$INPUT" ] && echo "Usage: bio: <platform, role/niche, target audience, and goal>" && exit 1
SESSION_ID="bio-$(date +%s)"
PROMPT="You are a social media profile optimization expert. Write optimized bios for: INPUT

## Platform Character Limits
- Instagram: 150 chars
- TikTok: 80 chars
- X/Twitter: 160 chars
- LinkedIn Summary: 2,600 chars / Headline: 220 chars
- Threads: 150 chars

## Bio Formula Analysis
Best formula for this person/brand:
[WHO you help] + [WHAT result they get] + [HOW/unique angle] + [CTA]

## 5 Bio Variations

**Variation 1 — Value-First**
[Bio leading with the benefit the audience gets]
Character count: [X]
Best for: [platform]

**Variation 2 — Identity-Led**
[Bio leading with who you are and who you help]
Character count: [X]
Best for: [platform]

**Variation 3 — Results/Proof**
[Bio leading with a credibility signal or result]
Character count: [X]
Best for: [platform]

**Variation 4 — Personality/Vibe**
[Bio with more personality, humor, or emoji]
Character count: [X]
Best for: [platform]

**Variation 5 — Ultra-Short Punchy**
[One-liner bio — max 80 chars]
Character count: [X]
Best for: TikTok, Twitter

## Platform-Specific Versions

**Instagram Bio (max 150 chars):**
[Optimized version with line breaks — Instagram renders each line separately]
Line 1: [Role / who you help]
Line 2: [What result / unique angle]
Line 3: [Social proof or personality]
Line 4: [CTA + emoji]

**LinkedIn Headline (max 220 chars):**
[Professional headline with keywords]
Keyword audit: [3 keywords integrated]

**LinkedIn About Section (first 300 chars that show before 'See more'):**
[Opening paragraph that hooks readers]

**TikTok Bio (max 80 chars):**
[Ultra-compressed version]

**X/Twitter Bio (max 160 chars):**
[Version with personality + keywords]

**Threads Bio (max 150 chars):**
[Version]

## Keyword Integration
Top 5 searchable keywords to include in bio for discoverability:
1. [keyword] — search volume estimate
2. [keyword]
3. [keyword]
4. [keyword]
5. [keyword]

## CTA Optimization
Best link-in-bio CTA options based on goal:
- If goal = grow email list: '[CTA text]'
- If goal = sell product: '[CTA text]'
- If goal = drive DMs: '[CTA text]'
- If goal = grow following: '[CTA text]'

## A/B Test Recommendation
Test these two against each other:
- **Bio A:** [Best overall variation]
- **Bio B:** [Second best — different angle]
Change only the bio, keep everything else constant. Run for 2 weeks."

RESULT=$(openclaw agent --local --session-id "$SESSION_ID" --json -m "$PROMPT" 2>/dev/null)
REPORT=$(echo "$RESULT" | python3 -c "
import json,sys
data=json.load(sys.stdin)
texts=[p.get('text','') for p in data.get('payloads',[]) if p.get('text')]
print('\n'.join(texts))
" 2>/dev/null)
[ -z "$REPORT" ] && echo "Error: Could not generate bio." && exit 1
echo ""
echo "=== BIO OPTIMIZER === INPUT ==="
echo ""
echo "$REPORT"

---
name: social-engagement-audit
description: "Social media engagement audit and optimization plan. Analyze your engagement rate, identify what's working and what's not, and get a specific action plan to improve reach, saves, comments, and shares. Triggers: engagement audit, social media audit, engagement rate, low engagement, improve engagement, social audit, account audit"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/social-engagement-audit
---

# Social Engagement Audit

Diagnose why your engagement is low and get a concrete plan to fix it.

## Usage

```
engagement audit: Instagram account fitness niche 2.1% engagement rate
social audit: LinkedIn page B2B software, 500 followers, 10 likes per post
account audit: TikTok creator food niche, 10K followers, 200 average views
engagement rate: analyze my Twitter account tech content, 1K followers
```

## What You Get

1. **Engagement Rate Diagnosis** — how you compare to benchmarks
2. **Top 5 Engagement Killers** — specific things dragging you down
3. **Content Autopsy** — what type of posts are underperforming and why
4. **Algorithm Alignment Check** — are you fighting or working with the algorithm
5. **30-Day Fix Plan** — specific weekly actions to double engagement
6. **Quick Wins** — 3 things to change today for immediate impact

FILE:analyze.sh
#!/usr/bin/env bash
set -euo pipefail
INPUT="-"
[ -z "$INPUT" ] && echo "Usage: engagement audit: <platform, niche, followers, current engagement rate>" && exit 1
SESSION_ID="audit-$(date +%s)"
PROMPT="You are a social media analytics expert specializing in engagement optimization. Run a full engagement audit for: INPUT

## Engagement Rate Benchmark
Industry benchmarks for this platform + niche:
| Metric | Below Average | Average | Good | Excellent |
|--------|-------------|---------|------|-----------|
| Engagement Rate | <X% | X-X% | X-X% | >X% |
| Reach Rate | | | | |
| Save Rate | | | | |
| Share Rate | | | | |
| Comment Rate | | | | |

Current status based on input: [Rating + comparison to benchmark]

## Top 5 Engagement Killers
Diagnose the most likely causes based on the account description:

**Killer #1: [Issue Name]**
- What it looks like: [specific symptom]
- Why it happens: [root cause]
- How to fix it: [specific action]

**Killer #2: [Issue Name]**
[Same format]

**Killer #3: [Issue Name]**
[Same format]

**Killer #4: [Issue Name]**
[Same format]

**Killer #5: [Issue Name]**
[Same format]

## Content Autopsy
Most common reasons posts fail in this niche:
- Hook failure: [what bad hooks look like + fix]
- Caption failure: [common mistake + fix]
- Format mismatch: [format vs what algorithm rewards right now]
- Posting time failure: [when not to post + best times]
- CTA failure: [weak vs strong CTAs for this platform]

## Algorithm Alignment Check
What this platform's algorithm rewards right now:
| Signal | What Algorithm Wants | Are You Doing This? | Fix |
|--------|---------------------|---------------------|-----|
| Watch time / dwell time | | | |
| Saves | | | |
| Shares / Sends | | | |
| Comments | | | |
| Profile visits | | | |
| Follower ratio | | | |

## 30-Day Engagement Fix Plan
**Week 1 — Foundation:**
- Day 1-2: [specific action]
- Day 3-4: [specific action]
- Day 5-7: [specific action]

**Week 2 — Content Overhaul:**
- [Specific changes to make]

**Week 3 — Engagement Farming:**
- [Specific tactics]

**Week 4 — Analyze & Double Down:**
- [What to measure and what to scale]

## 3 Quick Wins (Do Today)
1. [Action you can do in 10 minutes that will improve engagement]
2. [Action you can do in 30 minutes]
3. [Action you can do this week]

## Engagement Rate Calculator
If you implement all recommendations:
- Current rate: X%
- Expected rate in 30 days: X%
- Expected rate in 90 days: X%
- Key metric to watch as leading indicator: [metric]"

RESULT=$(openclaw agent --local --session-id "$SESSION_ID" --json -m "$PROMPT" 2>/dev/null)
REPORT=$(echo "$RESULT" | python3 -c "
import json,sys
data=json.load(sys.stdin)
texts=[p.get('text','') for p in data.get('payloads',[]) if p.get('text')]
print('\n'.join(texts))
" 2>/dev/null)
[ -z "$REPORT" ] && echo "Error: Could not generate audit." && exit 1
echo ""
echo "=== ENGAGEMENT AUDIT === INPUT ==="
echo ""
echo "$REPORT"