mguozhen

Amazon Supplier Sourcing

Skill

Amazon supplier sourcing and development agent. Find manufacturers on Alibaba/1688, evaluate supplier quality, generate RFQ inquiry templates, negotiate MOQ...

---
name: amazon-supplier-sourcing
description: "Amazon supplier sourcing and development agent. Find manufacturers on Alibaba/1688, evaluate supplier quality, generate RFQ inquiry templates, negotiate MOQ and pricing, and create quality control checklists for your Amazon FBA products. Triggers: supplier sourcing, alibaba sourcing, find manufacturer, rfq template, supplier evaluation, moq negotiation, alibaba supplier, product sourcing, china manufacturer, supplier inquiry, quality control, factory audit, sourcing agent, private label sourcing, fba sourcing"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amazon-supplier-sourcing
---

# Amazon Supplier Sourcing Agent

Find the right manufacturer, send professional inquiries, negotiate smart, and protect your quality. From first contact to first shipment — your sourcing co-pilot.

## Commands

```
sourcing find [product]         # generate supplier search strategy
sourcing rfq [product]          # generate professional RFQ template
sourcing evaluate [supplier]    # supplier evaluation scorecard
sourcing negotiate [details]    # MOQ and pricing negotiation script
sourcing qc [product]           # quality control checklist
sourcing questions              # 20 must-ask supplier questions
sourcing red flags               # supplier red flag checklist
sourcing compare [s1] [s2]      # compare two supplier profiles
sourcing timeline               # sourcing timeline planner
sourcing save [supplier]        # save supplier profile
```

## What Data to Provide

- **Product description** — what you want to source
- **Target price** — your maximum COGS budget
- **Target quantity** — units per order (MOQ expectations)
- **Quality requirements** — materials, certifications, specifications
- **Supplier details** — paste Alibaba/1688 profile info for evaluation

## Supplier Search Strategy

### Where to Find Suppliers

| Platform | Best For | Notes |
|----------|----------|-------|
| Alibaba.com | English-speaking factories, export experience | Higher price, more reliable |
| 1688.com | Lower price, domestic China market | Need sourcing agent or Chinese |
| Global Sources | Electronics, tech products | Trade show quality suppliers |
| Made-in-China.com | Industrial products | Less competitive than Alibaba |
| Canton Fair | Best-in-class manufacturers | Twice yearly, April & October |

### Search Keywords Strategy
- Search in Chinese (translate your product) for 1688
- Use both generic and specific terms: "yoga mat" AND "TPE yoga mat manufacturer"
- Add: OEM / ODM / private label to filter for customization capability
- Avoid: trading companies if you want factory-direct pricing

### Supplier Tiers
| Tier | Type | Pros | Cons |
|------|------|------|------|
| Tier 1 | Large factory (500+ workers) | Consistent quality, certifications | High MOQ, less flexible |
| Tier 2 | Mid factory (50–500 workers) | Balance of flexibility and quality | Variable quality control |
| Tier 3 | Small factory / workshop | Low MOQ, highly flexible | Quality consistency risk |
| Trading Co. | Middleman | Easy communication | Markup 15–30%, less control |

## RFQ Template (Professional Inquiry)

```
Subject: OEM Inquiry — [Product Name] — [Your Company Name]

Dear [Supplier Name],

I am [Name] from [Company], an Amazon FBA seller based in [Country].
I am interested in sourcing [Product] for the US/UK/EU market.

PRODUCT SPECIFICATIONS:
- Product: [detailed description]
- Material: [material requirements]
- Dimensions: [L × W × H cm] / Weight: [grams]
- Color options: [colors needed]
- Certifications required: [CE / FCC / ROHS / etc.]
- Custom branding: Logo on product / custom packaging (Yes/No)

QUANTITY & TIMELINE:
- Sample order: [X units] for quality evaluation
- Initial order: [X units] (if samples approved)
- Future orders: [X units/month] estimated
- Delivery timeline required: [X weeks from order]

QUESTIONS:
1. What is your MOQ for this product?
2. What is the unit price at [500 / 1000 / 2000 / 5000] units?
3. Can you provide OEM/private label with our logo?
4. What certifications does this product have?
5. What is the sample cost and lead time?
6. Do you offer DDP (Delivered Duty Paid) shipping to [country]?
7. What is your factory inspection policy?

Please send: product catalog, price list, and factory certification documents.

Best regards,
[Your Name]
[Company] | [Email] | [WhatsApp/WeChat]
```

## Supplier Evaluation Scorecard (100 points)

| Criteria | Weight | How to Score |
|----------|--------|-------------|
| Response speed | 10 | <24h=10, <48h=7, >48h=3 |
| Communication quality | 15 | Clear, professional, detailed |
| Factory verification | 15 | Verified badge, audit report, video tour |
| Years in business | 10 | >5yr=10, 3-5yr=7, <3yr=3 |
| Certifications | 15 | CE/FCC/ROHS/ISO as needed |
| Sample quality | 20 | Rate after receiving samples |
| Price competitiveness | 10 | Within target COGS budget |
| References/reviews | 5 | Past buyer feedback |

**Score 80+** = Preferred supplier
**Score 60–79** = Proceed with caution
**Score <60** = Find alternative

## MOQ Negotiation Scripts

**Opening ask (too high MOQ):**
> "Your MOQ is [X] units, but for our initial order we'd like to start with [lower amount] to validate the market. We can commit to [X] units per quarter if quality meets expectations. Can you accommodate a smaller first order at a slightly higher unit price?"

**Price negotiation:**
> "We've received quotes from 3 suppliers in the [price range]. To move forward with you, we need to be at [target price]. What can we do to reach that number — material adjustment, packaging simplification, or payment terms?"

**Certification leverage:**
> "We require [certification] for our market. Can you share the test report? If you don't have it, we can arrange third-party testing and split the cost."

## Quality Control Checklist

### Pre-Production
- [ ] Material specifications confirmed in writing
- [ ] Approved sample on file (golden sample)
- [ ] Packaging artwork approved
- [ ] Barcode/FNSKU placement confirmed
- [ ] Certification requirements documented

### During Production (if 3rd party inspection)
- [ ] Raw material check
- [ ] In-line production inspection (30% completion)
- [ ] Final random inspection (AQL 2.5 standard)

### Pre-Shipment
- [ ] AQL inspection passed (hire SGS/Bureau Veritas/QIMA)
- [ ] Carton drop test passed
- [ ] Weight and dimensions match spec
- [ ] Barcodes scan correctly
- [ ] All units in carton match quantity

## 20 Must-Ask Supplier Questions

1. Can I visit your factory? (Red flag if no)
2. Who else do you supply? (Reference check)
3. What is your reject rate?
4. How do you handle defective units?
5. Do you have product liability insurance?
6. What payment terms do you offer? (30/70 is standard)
7. Can I get a factory audit report?
8. What is your capacity per month?
9. How do you handle IP/design confidentiality?
10. What happens if my shipment is delayed?
11. Do you use child labor? (Compliance)
12. What raw materials do you use and where from?
13. Can you do custom packaging?
14. What shipping methods do you use?
15. Do you work with freight forwarders?
16. What is your sample lead time?
17. Can you provide references from current customers?
18. Do you have experience with Amazon FBA requirements?
19. What certifications can you provide?
20. What is your policy on NDA/confidentiality agreements?

## Red Flags — Walk Away If:

- Refuses factory visit or video call
- No verifiable business registration
- Price dramatically below market (too good to be true)
- Pushes for full payment upfront
- Cannot provide certifications for regulated categories
- Copies competitor products without hesitation
- Poor sample quality with excuses ("production will be better")
- Communication disappears after initial inquiry

## Output Format

1. **Supplier Search Plan** — where to look, what keywords to use
2. **RFQ Draft** — ready to send inquiry template
3. **Evaluation Scorecard** — fill-in assessment for each supplier
4. **Negotiation Script** — specific language for price/MOQ discussion
5. **QC Checklist** — pre-production through delivery checkpoints

ClawHub Writing Product+2

Amazon Review Management

Skill

Amazon review management and response agent. Write professional responses to negative reviews, analyze review patterns to find product improvements, build co...

---
name: amazon-review-management
description: "Amazon review management and response agent. Write professional responses to negative reviews, analyze review patterns to find product improvements, build compliant review generation strategies, and manage Vine enrollment. Triggers: review management, negative review, respond to review, amazon reviews, review response, bad review, star rating, review strategy, vine program, review generation, seller feedback, product review, amazon vine, review analysis, 1 star review, review reply"
allowed-tools: Bash
metadata:
openclaw:
homepage: https://github.com/mguozhen/amazon-review-management
---

# Amazon Review Management Agent

Handle negative reviews professionally, spot product improvement signals in review data, and build a compliant review strategy that grows your star rating over time.

## Commands

```
review respond [paste review] # write professional response to a review
review analyze [paste reviews] # find patterns and product insights
review strategy # build review generation plan
review vine # Vine enrollment guide
review request [market] # compliant review request strategy
review report [competitor ASIN] # analyze competitor review weaknesses
review crisis [situation] # crisis response for review bombing
review save [product] # save review profile and history
```

## What Data to Provide

- **Negative review text** -- paste the review to respond to
- **Your product details** -- so responses are accurate
- **Review history** -- overall star rating, total count
- **Specific complaint patterns** -- recurring issues
- **Competitor ASIN** -- for competitive review analysis

## Negative Review Response Framework

### Response Principles
1. **Respond within 24 hours** -- speed signals you care
2. **Never argue** -- even when customer is wrong
3. **Acknowledge, don't defend** -- validate their experience first
4. **Take it offline** -- move resolution to private channel
5. **Keep it short** -- 3-4 sentences max in public response

### Response Template Structure
```
[Acknowledge their experience]
[Brief, non-defensive explanation if relevant]
[Offer specific resolution]
[Invite them to contact you directly]
```

### Response Templates by Review Type

**Product didn't meet expectations:**
> "Thank you for your honest feedback. We're sorry our product didn't meet your expectations -- that's not the experience we want for our customers. Please reach out to us and we'll make it right, whether that's a replacement, refund, or troubleshooting assistance. We appreciate you giving us the chance to improve."

**Shipping/packaging complaint:**
> "We sincerely apologize for the condition your order arrived in. Damage during shipping is rare but unacceptable, and we take full responsibility. Please contact us directly and we'll send a replacement immediately at no charge. Thank you for letting us know."

**Size/fit issue:**
> "Thank you for this feedback. We're sorry the sizing didn't work for you -- we know how frustrating that is. We've noted your feedback for our size guide improvements. Please message us and we're happy to exchange for the right size or issue a full refund."

**Competitor-placed fake negative review:**
> "We appreciate all feedback. If you have specific concerns about your order, please contact our customer service team directly -- we'd love the opportunity to resolve this for you."

**Wrong product expectations:**
> "Thank you for your review. We're sorry our listing didn't clearly set the right expectations -- your feedback helps us improve. Please reach out to us directly if you'd like a refund or if there's anything we can do to help."

## Review Pattern Analysis Framework

When analyzing a set of reviews, categorize complaints:

| Category | Examples | Action |
|----------|----------|--------|
| **Product defect** | "Broke after 2 weeks", "stopped working" | Contact supplier, quality check |
| **Listing mismatch** | "Smaller than expected", "not as described" | Update listing, improve images |
| **Shipping damage** | "Arrived broken", "crushed box" | Improve packaging |
| **Missing pieces** | "Part was missing" | Review packing process |
| **Usage confusion** | "Didn't know how to..." | Add instructions, video |
| **Wrong expectations** | "Too basic for my needs" | Clarify target customer in listing |

**Signal thresholds:**
- Same complaint appearing 3+ times = systematic issue, fix the root cause
- 1-star spike in 2-week window = possible review manipulation or batch defect

## Compliant Review Generation Strategy

### What Amazon Allows
- OK: Request a review via "Request a Review" button in Seller Central (neutral, Amazon-worded)
- OK: Packaging inserts with neutral language ("We'd love to hear your feedback")
- OK: Follow-up emails via Buyer-Seller Messaging (neutral only)
- OK: Amazon Vine program (pay per unit enrolled)
- OK: Early Reviewer Program (some markets, legacy)

### What Amazon Prohibits
- NOT OK: Incentivized reviews ("Leave a review for 10% off")
- NOT OK: Asking specifically for positive reviews
- NOT OK: Family/friend reviews
- NOT OK: Review swapping with other sellers
- NOT OK: Threatening consequences for negative reviews
- NOT OK: Manipulative inserts ("Only contact us before leaving negative feedback")

### Compliant Insert Card Copy
```
Thank you for your purchase!

We're a small team that puts everything into making our products.
If you love it, we'd be grateful if you shared your experience --
your review helps other customers make the right choice.

If anything's not right, please email us first --
we'll make it right, guaranteed.
```

## Vine Program Guide

**What it is**: Amazon sends free units to vetted reviewers ("Voices") who leave honest reviews.

**Cost**: $200 per parent ASIN enrolled (US, 2024)
**Units**: 1-30 units enrolled
**Timeline**: Reviews appear within 4-8 weeks
**Best for**: New products with fewer than 30 reviews

**Eligibility**:
- Brand Registry enrolled
- Fewer than 30 reviews
- FBA listing (not FBM)
- New or relaunched products

**When NOT to use Vine**:
- Product has known quality issues (Vine reviewers are thorough)
- Category where product needs more social proof first
- If you can't afford 30 free units + $200 fee

## Star Rating Recovery Plan

If your rating drops below 4.0:

1. **Diagnose**: Find the root complaint (use Pattern Analysis)
2. **Fix the product/listing** (don't just respond to reviews)
3. **Enroll in Vine** to get fresh honest reviews
4. **Activate Request-a-Review** for all eligible orders
5. **Monitor weekly**: Track new review velocity vs. old negative reviews

Timeline: Rating typically recovers in 60-90 days with consistent action.

## Output Format

1. **Review Response Draft** -- professional, on-brand, ready to post
2. **Pattern Analysis** -- top 3 complaint categories with action items
3. **Review Strategy Plan** -- timeline and tactics for rating improvement
4. **Vine ROI Estimate** -- cost vs. projected review count
5. **Compliance Check** -- flag any current practices that risk policy violation

Amazon Global Expansion

Skill

Amazon global marketplace expansion agent. Plan your entry into UK, EU (Germany, France, Italy, Spain), Japan, Canada, and other Amazon markets. Covers VAT r...

---
name: amazon-global-expansion
description: "Amazon global marketplace expansion agent. Plan your entry into UK, EU (Germany, France, Italy, Spain), Japan, Canada, and other Amazon markets. Covers VAT registration, compliance requirements, listing localization, currency strategy, and market sizing. Triggers: amazon global, amazon uk, amazon europe, amazon germany, amazon japan, amazon canada, international expansion, vat registration, eu compliance, marketplace expansion, amazon eu, amazon fba europe, global selling, amazon international, cross-border ecommerce"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amazon-global-expansion
---

# Amazon Global Marketplace Expansion

Expand beyond Amazon US into UK, EU, Japan, Canada, and more. Get market entry plans, compliance requirements, localization strategy, and VAT guidance for each marketplace.

## Commands

```
expand analyze [market]         # full market entry analysis for a country
expand vat [country]            # VAT/tax registration requirements
expand listing [product] [lang] # localization checklist for target market
expand compare [market1] [market2]  # compare two markets for expansion
expand checklist [market]       # pre-launch compliance checklist
expand currency                 # multi-currency pricing strategy
expand roadmap                  # prioritized global expansion roadmap
expand fees [market]            # FBA fees and referral rates by market
```

## What Data to Provide

- **Current market** — where you sell now (usually US)
- **Product category** — for market-specific compliance
- **Monthly revenue** — to estimate expansion ROI
- **Target markets** — which countries interest you
- **Team capacity** — can you handle customer service in other languages?

## Market Overview

### Amazon EU Marketplaces

| Market | Language | Market Size | Entry Difficulty |
|--------|----------|------------|-----------------|
| Germany (DE) | German | Largest EU Amazon | Medium (strict compliance) |
| UK | English | 2nd largest | Low (English, Brexit compliance) |
| France (FR) | French | 3rd largest | Medium |
| Italy (IT) | Italian | Growing fast | Medium |
| Spain (ES) | Spanish | Growing | Low-Medium |
| Netherlands (NL) | Dutch | New, growing | Low |
| Sweden (SE) | Swedish | New | Low |

### Other Major Markets

| Market | Notes | Entry Difficulty |
|--------|-------|-----------------|
| Canada (CA) | English/French, close to US ops | Very Low |
| Japan (JP) | Huge market, unique consumer behavior | High |
| Australia (AU) | Growing, English | Low-Medium |
| UAE/Saudi (ME) | Emerging, strong growth | Medium |
| India (IN) | Domestic sellers preferred | High |
| Brazil (BR) | Complex tax structure | Very High |

## Market Entry Priority Framework

Score each market:
1. **Market size** (1-3): Revenue potential
2. **Competition** (1-3): How saturated is your category?
3. **Operational ease** (1-3): Language, compliance, logistics complexity
4. **Synergy with current ops** (1-3): Can you reuse existing inventory/listings?

**Start with highest total score.**

**Typical recommended sequence:**
1. Canada (lowest effort, shares US FBA network via NARF)
2. UK (English, large market, EU-independent post-Brexit)
3. Germany (largest EU market)
4. France -> Italy -> Spain (bundle via Pan-European FBA)

## VAT Requirements by Market

### European Union
- **Threshold eliminated** since July 2021 -- VAT required from first sale
- Register in each country OR use OSS (One Stop Shop) for B2C sales <=10,000/market
- If using Pan-European FBA: must register in all countries where Amazon stores your inventory (typically DE, FR, IT, ES, PL, CZ)
- **Required documents**: Business registration, proof of trading

**Recommended services**: Taxjar, Avalara, Hellotax, SimplyVAT

### United Kingdom
- UK VAT separate from EU post-Brexit
- Register if sales >85,000/year OR if using Amazon FBA in UK (storage = nexus)
- HMRC registration: https://www.gov.uk/vat-registration
- **UK EORI number** required for importing goods

### Canada
- GST/HST registration required if revenue >CAD $30,000/year
- Quebec has separate QST
- Much simpler than EU -- single federal system

### Japan
- Consumption Tax (10%) registration required
- Japanese business address or local representative may be needed
- JCT registration via NTA (National Tax Agency)

## Listing Localization Requirements

### Germany (Most Important EU Market)
- **Language**: Must be in German -- Google Translate quality is NOT acceptable
- **Legal requirements**:
  - Manufacturer address in Germany/EU (or authorized rep)
  - CE marking mandatory for electronics, toys, PPE
  - WEEE registration (for electronics)
  - Extended Producer Responsibility (EPR) for packaging
- **Cultural notes**: Germans value technical precision, certifications, thoroughness

### Japan (Unique Requirements)
- Japanese listing required (not just translation -- cultural adaptation)
- PSE mark for electronics (mandatory)
- Many product-specific certifications (food contact materials, cosmetics)
- Customer service response time expectations: within 24 hours, in Japanese
- Returns policy: Japanese customers expect hassle-free returns

### UK (Post-Brexit)
- **UKCA mark** replacing CE mark for many products
- UK Responsible Person required for regulated products
- UK EPR for packaging

## Pan-European FBA vs. Multi-Country Inventory

| Strategy | Pros | Cons |
|----------|------|------|
| **Pan-EU FBA** | Amazon distributes inventory, faster delivery | VAT in 5-7 countries required |
| **EFN (European Fulfillment Network)** | Single country storage | Slower delivery, higher fees cross-border |
| **Multi-Country Inventory** | Control where stock goes | Manage inbound to each country |

**Recommendation for beginners**: Start with EFN (one country) -> upgrade to Pan-EU once VAT registered everywhere.

## Currency & Pricing Strategy

- Never just convert USD price to local currency (e.g., $29.99 -> 29.99 is wrong)
- Research local competitor pricing -- markets have different price points
- Factor in: local FBA fees (vary by market), VAT (included in displayed price in EU/UK), exchange rate fluctuation
- Price in "charm pricing" for local market: 24.99, 27.99, 2,980 yen

## Pre-Launch Checklist by Market

### Universal
- [ ] VAT/tax registration complete
- [ ] Listing translated by native speaker (not machine translation)
- [ ] Local compliance requirements researched
- [ ] Customer service coverage in local language (or English for UK/CA)
- [ ] Pricing set at local market rates
- [ ] Inbound shipping route established

### EU-Specific
- [ ] CE/UKCA/other product certification
- [ ] EPR packaging registration
- [ ] WEEE/battery registration (if applicable)
- [ ] Authorized EU Representative named on packaging

### Japan-Specific
- [ ] PSE mark (electronics)
- [ ] Japanese listing by professional translator
- [ ] Returns policy matches JP customer expectations

## Output Format

1. **Market Entry Analysis** -- opportunity size, competition, compliance overview
2. **VAT Registration Roadmap** -- step-by-step for target market
3. **Localization Checklist** -- listing, packaging, compliance
4. **Pricing Recommendation** -- local market pricing strategy
5. **Launch Sequence** -- prioritized order to enter multiple markets

ClawHub Data Analysis Product+2

Amazon Trademark Checker

Skill

Amazon intellectual property and trademark risk checker. Screen your product name, brand, keywords, and images for trademark conflicts, patent risks, and IP...

---
name: amazon-trademark-checker
description: "Amazon intellectual property and trademark risk checker. Screen your product name, brand, keywords, and images for trademark conflicts, patent risks, and IP violations before launch. Avoid account suspension and legal trouble. Triggers: trademark check, amazon ip, intellectual property, trademark search, patent check, brand registry, amazon brand, ip violation, trademark infringement, brand name check, amazon suspension, ip protection, trademark registration, product name check, amazon brand registry"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amazon-trademark-checker
---

# Amazon Trademark & IP Risk Checker

Screen your product, brand name, and keywords for intellectual property risks before you launch. Catch trademark conflicts, patent issues, and image copyright problems early — before they cost you your account.

## Commands

```
ip check [brand name]           # full IP risk screening for brand name
ip trademark [term]             # check trademark conflicts for a term
ip patent [product type]        # identify common patents in product category
ip keywords [keyword list]      # flag trademarked terms in keyword list
ip images                       # guide for image IP risk assessment
ip brand registry               # Amazon Brand Registry eligibility check
ip report [product]             # generate full IP risk report
ip monitor [brand]              # set up ongoing monitoring checklist
```

## What Data to Provide

- **Brand name** — the name you plan to register/use
- **Product type** — category helps identify relevant patents
- **Keywords** — your title and PPC keyword list
- **Product images** — describe them (logos, characters, designs)
- **Market** — US, EU, UK, JP (trademark jurisdiction varies)

## IP Risk Categories

### 1. Trademark Infringement
**Risk**: Using a name, logo, or slogan that another company owns.

**Where to check:**
- USPTO TESS: https://www.uspto.gov/trademarks/search
- EUIPO (EU): https://euipo.europa.eu/eSearch
- Amazon Brand Registry conflict lookup

**Red flags:**
- Your brand name contains another registered trademark
- Similar-sounding name in the same product category
- Using competitor brand names in your title or bullets
- Keyword stuffing with brand names (e.g., "compatible with Nike")

**Safe practices:**
- Use "compatible with [Brand]" only if truthful and not misleading
- Never use brand names in your product title
- Register your own brand before launch

### 2. Patent Infringement
**Risk**: Your product design or mechanism is protected by an existing patent.

**Types of patents:**
| Type | Covers | How to Check |
|------|--------|-------------|
| Utility Patent | How something works | USPTO Patent Full-Text Search |
| Design Patent | How something looks | Google Patents, USPTO |
| Trade Dress | Overall commercial image | Harder to search — consult lawyer |

**High-risk categories** (frequent patent enforcement):
- Phone accessories (cases, mounts, chargers)
- Sports equipment (yoga, fitness)
- Kitchen gadgets
- Baby products
- Medical devices

**Patent search steps:**
1. Search USPTO at https://patents.google.com
2. Use product function keywords: "yoga mat alignment" not product name
3. Check filing date — patents expire after 20 years (utility) / 15 years (design)
4. Look for "continuation" patents — related to expired ones

### 3. Image Copyright
**Risk**: Using copyrighted images, characters, logos, or artwork.

**Automatic red flags:**
- Disney, Marvel, DC characters
- Sports team logos (NFL, NBA, MLB)
- Any cartoon character you didn't create
- Stock photos without commercial license
- Celebrity photos or names
- Song lyrics, book excerpts

**Safe image sources:**
- Photos you took yourself
- Canva Pro (commercial license included)
- Shutterstock, Getty (with commercial license)
- Public domain images (check carefully)

### 4. Amazon Policy Violations
Beyond legal IP, Amazon has additional restrictions:

| Violation | Risk |
|-----------|------|
| Brand name keyword stuffing | Listing suppression |
| Fake "brand" (no actual brand) | Account warning |
| Counterfeit products | Permanent ban |
| False "Amazon's Choice" claims | Immediate takedown |
| Misleading compatibility claims | ASIN removal |

## Brand Registry Eligibility Checklist

To enroll in Amazon Brand Registry you need:
- [ ] Active registered trademark (word mark or design mark)
- [ ] Trademark in one of: US, CA, MX, BR, EU, UK, JP, AU, IN
- [ ] Trademark status: Registered (not just "pending" for most markets)
- [ ] Brand name matches trademark exactly
- [ ] Products match trademark class

**Trademark classes for common Amazon products:**
- Class 9: Electronics, software, apps
- Class 14: Jewelry, watches
- Class 18: Bags, leather goods
- Class 20: Furniture
- Class 21: Kitchen tools, cookware
- Class 25: Clothing, shoes
- Class 28: Toys, games, sporting goods

## Risk Scoring

Score your IP risk before launch:

| Factor | Low Risk | Medium Risk | High Risk |
|--------|----------|-------------|-----------|
| Brand name | Unique/invented | Generic word | Similar to known brand |
| Product type | New category | Common product | Patent-heavy category |
| Keywords | Generic terms | Some brand names | Heavy brand keyword use |
| Images | Original photos | Licensed stock | Characters/logos |
| Market | Single country | 2–3 countries | Global launch |

**Overall Risk**: Low = proceed; Medium = get legal review; High = stop and consult IP attorney

## Output Format

1. **Risk Summary** — overall IP risk rating (Low/Medium/High)
2. **Trademark Conflicts** — specific conflicts found with search links
3. **Patent Watch List** — categories/functions to investigate further
4. **Keyword Cleanup** — remove these terms from your listing
5. **Recommended Actions** — concrete next steps before launch
6. **Brand Registry Roadmap** — steps to get protected

## Rules

1. Always recommend professional legal review for High risk assessments
2. This tool provides guidance, not legal advice
3. Flag when product category has historically high IP enforcement
4. Check BOTH US and target market jurisdictions
5. Remind: Amazon can act on complaints even before legal ruling

ClawHub Coding Data Analysis+2

Amazon Listing Writer

Skill

Write high-converting, SEO-optimized Amazon listings including title, bullets, description, A+ content, and backend search terms. Triggers: listing write, li...

---
name: amazon-listing-writer
description: "Write high-converting, SEO-optimized Amazon listings including title, bullets, description, A+ content, and backend search terms. Triggers: listing write, listing title, listing bullets, listing description, listing a+, listing backend, listing rewrite, listing score, listing translate"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amazon-listing-writer
---

# Amazon Listing Copywriter

Write high-converting, SEO-optimized Amazon listings. Input your product details and target keywords — get a complete, ready-to-publish listing: title, 5 bullets, description, A+ content outline, and backend search terms.

## Commands

```
listing write                   # write complete listing (interactive)
listing title [product]         # write optimized title only
listing bullets [product]       # write 5 bullet points
listing description [product]   # write product description
listing a+ [product]            # outline A+ content modules
listing backend [product]       # generate backend search terms
listing rewrite [paste text]    # rewrite existing weak listing
listing score [paste text]      # score existing listing (1–100)
listing translate [lang]        # adapt listing for UK/DE/JP market
```

## What Data to Provide

- **Product name & category** — what it is
- **Key features** — materials, dimensions, what makes it special
- **Target customer** — who buys it and why
- **Top keywords** — your primary and secondary keywords
- **Competitor listings** — paste 1–2 competitor titles/bullets to differentiate
- **Use cases** — how/when customers use it

## Listing Structure Guide

### Title Formula (200 bytes max)
```
[Brand] + [Primary Keyword] + [Key Feature] + [Size/Color/Qty] + [Use Case/Benefit]

Example:
"BrandX Yoga Mat Non-Slip — Extra Thick 6mm, 72" × 24" — Eco-Friendly TPE,
Alignment Lines — For Hot Yoga, Pilates, Home Gym"
```

**Title Rules:**
- Lead with primary keyword (highest search volume)
- Include 2–3 secondary keywords naturally
- No promotional phrases (Best, #1, Amazing)
- No subjective claims without proof
- Capitalize each major word

### Bullet Point Formula (5 bullets, 200 chars each)
Structure each bullet:
```
🔑 [FEATURE IN CAPS] — [Benefit explanation] [Social proof/spec if available]
```

**Bullet 1:** Primary differentiator (why choose this over competitors)
**Bullet 2:** Key material/quality feature
**Bullet 3:** Dimensions/compatibility/what's included
**Bullet 4:** Use case / who it's for
**Bullet 5:** Warranty / customer satisfaction guarantee

### Backend Search Terms (250 bytes)
- Synonyms not in title/bullets
- Common misspellings
- Spanish terms (for US market)
- Long-tail phrases
- Competitor brand names (careful — no trademark infringement)

## Copywriting Principles

### Conversion-First Language
| Weak | Strong |
|------|--------|
| "Good quality" | "Aircraft-grade aluminum, tested to 500 lb load" |
| "Easy to use" | "Set up in 3 minutes — no tools required" |
| "Great for families" | "Safe for kids 3+ — BPA-free, no sharp edges" |
| "Long lasting" | "18-month warranty, 50,000+ units sold" |

### Emotional Triggers by Category
- **Health/Fitness**: Transformation, confidence, results
- **Home/Kitchen**: Simplicity, time-saving, pride of home
- **Baby/Kids**: Safety, development, peace of mind
- **Electronics**: Performance, reliability, seamless experience
- **Outdoor**: Adventure, freedom, durability

## Listing Score Rubric (100 points)

| Element | Max Points | Criteria |
|---------|-----------|---------|
| Title keyword placement | 20 | Primary keyword in first 80 chars |
| Title completeness | 10 | Brand + feature + benefit present |
| Bullet differentiation | 20 | Each bullet = unique selling point |
| Bullet readability | 10 | Scannable, no walls of text |
| Keyword coverage | 15 | Top 10 keywords placed naturally |
| Social proof signals | 10 | Specs, certifications, stats |
| Mobile optimization | 10 | First 2 bullets visible without scroll |
| Backend terms | 5 | 250 bytes used efficiently |

**Score 85+** = Publish ready
**Score 65–84** = Minor revisions needed
**Score <65** = Major rewrite required

## Output Format

1. **Complete Listing** — title + 5 bullets + description ready to paste
2. **Keyword Placement Map** — which keywords appear where
3. **Listing Score** — 100-point breakdown with specific improvements
4. **A+ Content Outline** — module suggestions with copy direction
5. **Split Test Suggestions** — 2 title variants to A/B test

## Rules

1. Never use restricted phrases: "FDA approved" (unless true), "#1 Best Seller", "guaranteed cure"
2. All claims must be supportable — no invented statistics
3. Front-load benefits in each bullet (customer scans first 50 chars)
4. Match reading level to target audience
5. Always include primary keyword in title within first 5 words
6. Check competitor listings before writing — differentiate, don't copy

ClawHub Backend Writing+2

Amazon Hot Products

Skill

Scout Amazon trending products, hot searches, new releases, and rising categories to find blue ocean opportunities early. Triggers: hot products, hot search,...

---
name: amazon-hot-products
description: "Scout Amazon trending products, hot searches, new releases, and rising categories to find blue ocean opportunities early. Triggers: hot products, hot search, hot new releases, hot movers, hot seasonal, hot compare, hot report, hot save"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amazon-hot-products
---

# Amazon Hot Products & Trending Scout

Track Amazon's real-time hot searches, new releases, and rising categories. Spot trending products before they become saturated — find blue ocean opportunities early.

## Commands

```
hot products                    # scan trending products across categories
hot search [category]           # analyze hot search terms in category
hot new releases [category]     # find new releases with early traction
hot movers [category]           # find products with rapid BSR improvement
hot seasonal                    # identify upcoming seasonal trends
hot compare [cat1] [cat2]       # compare trend momentum between categories
hot report                      # generate weekly trend report
hot save [opportunity]          # save a trend opportunity to memory
```

## What Data to Provide

- **Category** — broad (Electronics) or specific (Wireless Earbuds)
- **BSR data** — paste BSR rankings if you have them
- **Search term data** — trending search terms from Seller Central
- **Time period** — last 7/30/90 days
- **Market** — US, UK, DE, JP, etc.

No API key needed. Provide data verbally or paste raw numbers.

## Trend Identification Framework

### Signal 1: Search Volume Surge
- Search term appears in Amazon's "Hot New Keywords" (from Seller Central Brand Analytics)
- Week-over-week search volume growth >20%
- Low current competition (fewer than 1,000 results for exact match)

### Signal 2: BSR Velocity
| BSR Movement | Signal Strength |
|---|---|
| BSR improved >50% in 30 days | 🔥 Strong |
| BSR improved 20–50% in 30 days | ✅ Moderate |
| BSR stable | ⚪ Neutral |
| BSR declining | ❌ Avoid |

### Signal 3: Review Accumulation Rate
- New products getting 50+ reviews in first 60 days = high demand signal
- Multiple competitors launching simultaneously = category heating up

### Signal 4: Seasonal Calendar
| Month | Trending Categories |
|---|---|
| Jan–Feb | Fitness, Organization, New Year |
| Mar–Apr | Outdoor, Garden, Spring Cleaning |
| May–Jun | Graduation, Father's Day, Summer |
| Jul–Aug | Back to School, Pool/Beach |
| Sep–Oct | Halloween, Fall Home |
| Nov–Dec | Holiday Gifts, Holiday Decor |

## Blue Ocean Score (1–10)

Score each trending product opportunity:
- **Demand** (1–3): Search volume trend direction
- **Competition** (1–3): # of sellers, review counts, listing quality
- **Margin** (1–2): Estimated price point vs. likely COGS
- **Differentiation** (1–2): Can you improve on existing products?

**Score 7+** = Enter aggressively
**Score 5–6** = Enter cautiously with differentiation
**Score <5** = Skip or monitor

## Output Format

1. **Trending Opportunities** — ranked list with Blue Ocean Score
2. **Category Heat Map** — which categories are rising vs. cooling
3. **Early Entry Windows** — products with <200 reviews but rising BSR
4. **Avoid List** — saturated trends (too late to enter profitably)
5. **30-Day Watch List** — opportunities to monitor for next scan

## Rules

1. Always check review count before calling a trend "early" — >500 reviews = not early
2. Flag categories with known high return rates (electronics, clothing)
3. Distinguish between fad (short spike) and trend (sustained growth)
4. Note when seasonal peaks are approaching — timing matters
5. Always pair trend data with estimated margin — demand means nothing if margins are thin

Amazon Keyword Research

Skill

Amazon keyword research and strategy agent. Input a seed keyword, product idea, or competitor ASIN — get keyword clusters, search volume estimates, competiti...

---
name: amazon-keyword-research
description: "Amazon keyword research and strategy agent. Input a seed keyword, product idea, or competitor ASIN — get keyword clusters, search volume estimates, competition level, and a prioritized keyword strategy for your listing and PPC campaigns. Triggers: keyword research, amazon keywords, keyword strategy, search volume, amazon seo, listing keywords, ppc keywords, keyword discovery, long tail keywords, keyword clusters, amazon keyword tool, keyword difficulty, search term research, asin keywords, amazon search ranking"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amazon-keyword-research
---

# Amazon Keyword Research Agent

Build a complete keyword strategy for any Amazon product. From a seed keyword or competitor ASIN, discover high-converting terms, estimate volumes, and prioritize for listing SEO and PPC.

## Commands

```
kw research [keyword]           # full keyword research from seed term
kw asin [ASIN]                  # reverse ASIN keyword extraction
kw cluster [keyword]            # build keyword clusters/themes
kw difficulty [keyword]         # assess competition level
kw trending                     # find trending search terms
kw negative [keyword]           # generate negative keyword list
kw listing [keyword]            # extract keywords for listing placement
kw save [name]                  # save keyword set to memory
kw history                      # show saved keyword sets
```

## What Data to Provide

- **Seed keyword** — your main product keyword (e.g., "yoga mat")
- **Product description** — helps discover related terms
- **Competitor ASINs** — reverse-engineer their keyword strategy
- **Target market** — US, UK, DE, etc.
- **Budget/focus** — broad discovery vs. focused exact-match targeting

## Keyword Research Framework

### Phase 1: Seed Expansion
- Head terms → broad variations → long-tail
- Synonym discovery (material, use case, audience, benefit)
- Negative intent filtering (irrelevant, competitor brand)

### Phase 2: Classification
| Type | Example | Best Use |
|------|---------|----------|
| **Head term** | yoga mat | Title, high competition |
| **Long-tail** | non-slip yoga mat 6mm thick | Bullet points, backend |
| **Use-case** | yoga mat for hot yoga | Backend, A+ content |
| **Audience** | yoga mat for beginners | PPC exact match |
| **Attribute** | extra thick yoga mat purple | Long-tail PPC |
| **Problem** | yoga mat without smell | FAQ, backend |

### Phase 3: Priority Scoring
Score each keyword 1–10 based on:
- Search volume (estimated from category context)
- Relevance to product (1–5 scale)
- Competition level (# of sponsored results)
- Conversion intent (informational vs. transactional)

**Priority formula**: (Volume × Relevance × Intent) / Competition

### Phase 4: Placement Strategy
| Location | Keyword Type | Character Limit |
|----------|-------------|-----------------|
| Title | Top 3–5 keywords | 200 bytes |
| Bullet 1 | Feature keywords | 500 bytes each |
| Search Terms (backend) | Long-tail, synonyms | 250 bytes |
| A+ Content | Audience/use-case | No limit |
| PPC Broad | Discovery terms | — |
| PPC Exact | Proven converters | — |

## Output Format

1. **Keyword Universe** — 30–50 keywords organized by cluster
2. **Priority Matrix** — top 10 keywords to focus on first
3. **Placement Map** — where each keyword goes (title/bullets/backend/PPC)
4. **Negative List** — irrelevant terms to exclude from PPC
5. **Content Gaps** — search intents your listing currently misses

## Rules

1. Never recommend stuffing keywords unnaturally into listing copy
2. Flag brand name keywords (trademark risk)
3. Prioritize buyer-intent keywords over informational
4. Always separate PPC keywords from organic/listing keywords
5. Recommend testing 2–3 title variations for new products

ClawHub Backend Testing+2

Amazon Fba Calculator

Skill

Amazon FBA profit calculator. Input product price, COGS, dimensions, weight, and ad spend — get true net profit, margin, ROI, and break-even ACoS. Triggers:...

---
name: amazon-fba-calculator
description: "Amazon FBA profit calculator. Input product price, COGS, dimensions, weight, and ad spend — get true net profit, margin, ROI, and break-even ACoS. Triggers: fba calculator, fba fees, amazon profit calculator, fba profit, amazon margin, fba fee calculator, amazon net profit, fba cost, product profitability, amazon roi, break-even acos, fba revenue calculator, amazon seller profit, cogs calculator, fba fulfillment fee"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amazon-fba-calculator
---

# Amazon FBA Profit Calculator

Calculate true net profit for any Amazon product — accounting for all fees, costs, and ad spend. Know your real margin before you source.

## Commands

```
fba calc                        # interactive profit calculation
fba calc [price] [cogs] [weight]  # quick calculation with known values
fba fees                        # show current FBA fee schedule
fba break-even                  # calculate break-even ACoS
fba compare [price1] [price2]   # compare two price points
fba save [product-name]         # save product profile
fba history                     # show saved products
```

## What Data to Provide

- **Sale price** — your selling price on Amazon
- **COGS** — product cost + freight to Amazon warehouse
- **Dimensions & weight** — for FBA fulfillment fee calculation
- **Category** — for referral fee %
- **Ad spend / ACoS** — optional, for true profit with PPC
- **Other costs** — prep fees, photography, samples

No API keys needed. Works from verbal descriptions or exact numbers.

## Fee Structure (2024–2025)

### Amazon Referral Fees by Category
| Category | Referral Fee |
|----------|-------------|
| Most categories | 15% |
| Electronics | 8% |
| Clothing & Accessories | 17% |
| Jewelry | 20% (up to $250) |
| Books, Music, Video | 15% |
| Computers | 8% |
| Furniture | 15% (+ $30 min) |
| Automotive | 12% |

### FBA Fulfillment Fees (2025 US)
| Size Tier | Weight | Fee |
|-----------|--------|-----|
| Small Standard | ≤4 oz | $3.06 |
| Small Standard | 4–8 oz | $3.15 |
| Small Standard | 8–12 oz | $3.24 |
| Large Standard | ≤4 oz | $3.68 |
| Large Standard | 1 lb | $4.51 |
| Large Standard | 2 lb | $5.22 |
| Large Standard | +1 lb | +$0.38/lb |
| Large Bulky | up to 50 lb | $9.73 + $0.42/lb over 1lb |

### Additional Fees to Include
- **FBA Storage**: $0.78/cubic foot/month (Jan–Sep), $2.40 (Oct–Dec)
- **Monthly Storage**: estimate based on sell-through rate
- **Prep/Labeling**: $0.55/unit (Amazon prep service)
- **Returns**: ~2–5% of revenue depending on category

## Calculation Formula

```
Gross Revenue       = Sale Price
- Referral Fee      = Sale Price × Referral %
- FBA Fee           = Based on size/weight tier
- COGS              = Product cost + inbound freight
- PPC Cost          = Sale Price × ACoS
- Storage           = (Units/month × cubic feet × rate) / units sold
- Other Costs       = Prep + photography amortized
─────────────────────────────────────
Net Profit          = Revenue - All costs above
Net Margin %        = Net Profit / Sale Price × 100
ROI                 = Net Profit / COGS × 100
Break-even ACoS     = Net Margin % (before PPC)
```

## Output Format

Every calculation outputs:
1. **P&L Table** — line-by-line cost breakdown
2. **Key Metrics** — Net Profit / Margin / ROI / Break-even ACoS
3. **Sensitivity Analysis** — profit at ±10%, ±20% price changes
4. **Risk Flags** — margin below 15%, storage risk, high referral fee categories
5. **Optimization Tips** — specific suggestions to improve margin

## Rules

1. Always show full fee breakdown — never hide line items
2. Flag when margin is below 15% (risky for Amazon sellers)
3. Include seasonal storage surcharge warning for Q4
4. Show both pre-PPC and post-PPC margins
5. Ask for category if not provided — referral fee varies significantly
6. Note when estimated values are used vs. exact inputs

## Benchmarks

| Metric | Healthy | Warning | Danger |
|--------|---------|---------|--------|
| Net Margin | >25% | 15–25% | <15% |
| ROI | >50% | 30–50% | <30% |
| Break-even ACoS | >25% | 15–25% | <15% |
| Storage/Revenue | <3% | 3–8% | >8% |

Shopify Seo Audit

Skill

Shopify SEO audit agent. Audits store pages for missing meta titles and descriptions, duplicate content, thin product pages, broken internal links, image alt...

---
name: shopify-seo-audit
description: "Shopify SEO audit agent. Audits store pages for missing meta titles and descriptions, duplicate content, thin product pages, broken internal links, image alt tags, page speed signals, and collection page optimization — with a prioritized fix list. Triggers: shopify seo, shopify audit, seo audit, product page seo, shopify meta, collection seo, shopify speed, shopify structured data, ecommerce seo, shopify optimization, shopify ranking, shopify google"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/shopify-seo-audit
---

# Shopify SEO Audit

AI-powered Shopify SEO audit agent — identifies on-page issues, Shopify-specific technical problems, thin content, and structured data gaps, then delivers a prioritized fix list organized by impact.

Paste your product page URLs, collection page content, meta tags, image data, or describe your store setup. The agent runs through a comprehensive checklist and returns issues ranked by SEO impact — not just a list of problems, but a clear action queue.

## Commands

```
seo audit                          # full store SEO audit (paste URLs, content, or describe setup)
seo product pages                  # audit product pages for content depth and on-page signals
seo collections                    # audit collection pages for SEO structure and duplicate issues
seo meta check                     # check meta titles and descriptions across provided pages
seo images                         # audit image alt text, file names, and size signals
seo speed                          # assess page speed signals and Core Web Vitals impact factors
seo internal links                 # check internal link structure and anchor text patterns
seo score                          # generate overall SEO health score with per-category breakdown
seo fix list                       # output prioritized fix list sorted by estimated impact
seo save                           # save audit results to ~/shopify-seo/
```

## What Data to Provide

The agent works with:
- **URLs** — paste product page, collection page, or home page URLs
- **Source HTML or content** — paste page source, meta tags, or product descriptions
- **GA4 / Search Console data** — paste crawl errors, page performance, or coverage reports
- **Product descriptions** — paste text for depth and uniqueness analysis
- **Image data** — file names, alt text, sizes if known
- **Store description** — "I have 120 products in 8 collections, using Dawn theme, 12 apps installed"

No Shopify API access required. Works from pasted data and verbal descriptions.

## Workspace

Creates `~/shopify-seo/` containing:
- `memory.md` — saved store profile, theme, and past audit findings
- `audits/` — audit reports saved as markdown (audit-YYYY-MM-DD.md)
- `fix-queue.md` — running prioritized fix list updated across sessions

## Analysis Framework

### 1. On-Page Checklist

**Meta Title**
- Optimal length: 50-60 characters (truncated in Google SERPs beyond 60)
- Must contain primary keyword, ideally near the start
- Unique across all pages — no duplicate titles
- Format recommendation: Primary Keyword - Product Name | Brand Name

**Meta Description**
- Optimal length: 150-160 characters
- Must include primary keyword and a call to action
- Not a ranking factor but directly impacts click-through rate from SERPs
- Unique per page — duplicate descriptions signal thin content

**H1 Tag**
- One H1 per page only — multiple H1s dilute signal
- Must match or closely match the meta title keyword
- Shopify default: product title becomes H1 — verify theme is not overriding this

**Keyword in Title, Description, and Body**
- Primary keyword should appear: in title, in first 100 words, in at least one H2, and naturally throughout body
- Keyword stuffing (more than 3-4% density) is a negative signal — flag if present

### 2. Shopify-Specific Technical Issues

**Duplicate URL Patterns**
- Shopify generates both `/products/product-slug` and `/collections/collection-name/products/product-slug`
- The collection-scoped URL is a duplicate unless properly canonicalized
- Verify canonical tags point to `/products/` URL as the canonical version

**Pagination Canonicals**
- Collection pages with pagination (?page=2, ?page=3) must use canonical tags pointing to page 1
- Or use rel="next" / rel="prev" pagination signals (older approach, still valid)
- Missing canonicals on paginated collection pages causes duplicate content indexing

**Theme Bloat from Unused Apps**
- Each installed Shopify app may inject CSS and JavaScript on every page load
- Unused apps that still load scripts are a Core Web Vitals liability
- Flag when more than 8 apps are installed — audit which apps load globally vs. on specific pages

**Default Shopify URLs that Cannot Be Changed**
- `/collections/`, `/products/`, `/pages/`, `/blogs/` prefixes are fixed — do not attempt to remove
- Focus SEO effort on the slug portion (after the prefix) — this is controllable

### 3. Product Page Content Depth

**Word Count**
- Minimum: 300 words of unique body content per product page
- Under 300 words is classified as thin content by most SEO tools
- Manufacturer copy (identical text across multiple sellers) is treated as duplicate content

**Unique Descriptions**
- Never use manufacturer-provided product descriptions verbatim
- Rewrite descriptions with buyer-intent language, use-case details, and category-specific keywords
- Add FAQs, size guides, care instructions, or comparison tables to boost depth

**Variant Pages**
- Shopify creates separate URLs for product variants by default in some themes
- Audit whether variant URLs are indexable and whether they have unique content
- If variants lack unique content, canonicalize to the main product URL

### 4. Image Optimization

**Alt Text**
- Every product image must have descriptive alt text containing the primary keyword naturally
- Format: "[Keyword] - [Product Name] - [Key Feature or Color]"
- Empty alt text is an indexability miss; decorative images should use alt=""

**File Names**
- Rename before uploading: `red-ceramic-coffee-mug-12oz.jpg` not `IMG_4821.jpg`
- Shopify preserves the original filename in the CDN URL — this is a minor ranking signal

**File Size**
- Target: under 500KB per image; under 200KB is ideal for Core Web Vitals
- Use WebP format when possible (Shopify supports WebP delivery via CDN)
- Large images are the most common cause of poor Largest Contentful Paint (LCP) scores

### 5. Core Web Vitals Impact on Rankings

Google uses Core Web Vitals as a ranking signal. Shopify-specific factors that affect scores:

**Largest Contentful Paint (LCP) — target under 2.5 seconds**
- Hero image size is the primary LCP element on most product pages
- Third-party app scripts that block rendering delay LCP

**Cumulative Layout Shift (CLS) — target under 0.1**
- Images without explicit width/height attributes cause layout shift
- Shopify review apps loading asynchronously commonly cause CLS

**Interaction to Next Paint (INP) — target under 200ms**
- Excessive JavaScript from apps increases INP
- Audit app script load with Google PageSpeed Insights on key pages

### 6. Structured Data (Schema Markup)

**Product Schema**
- Must include: name, description, image, price, currency, availability, brand
- Strongly recommended: aggregateRating (average rating + review count), sku, gtin
- Shopify themes include basic Product schema by default — verify it is populated correctly

**BreadcrumbList Schema**
- Helps Google understand collection hierarchy
- Verify breadcrumb schema matches visible breadcrumb navigation on page

**Organization Schema**
- Include on homepage: name, url, logo, contactPoint, sameAs (social profiles)
- Not a direct ranking factor but improves Knowledge Panel eligibility

## Output Format

Every `seo audit` outputs:
1. **SEO Health Score** — overall score /100 with per-category breakdown (on-page, technical, content, images, structured data)
2. **Critical Issues** — items causing active ranking suppression (fix immediately)
3. **High-Impact Fixes** — issues with significant ranking upside (fix within 30 days)
4. **Quick Wins** — low-effort, moderate-impact improvements (fix this week)
5. **Shopify-Specific Issues** — duplicate URL, canonical, and theme issues
6. **Prioritized Fix List** — numbered action queue ordered by estimated impact
7. **Page-by-Page Notes** — per-URL findings when multiple URLs are provided

## Rules

1. Always distinguish between Shopify platform constraints (cannot change) and configurable issues (can fix) before recommending changes
2. Never flag the `/products/` or `/collections/` URL prefix as an SEO issue — these are Shopify defaults and cannot be removed
3. Score each audit category independently — do not let one strong area mask weaknesses in another
4. When word count is under 300 words, classify as thin content and flag as high priority regardless of other signals
5. Always check for canonical tag correctness on collection-scoped product URLs before recommending other duplicate content fixes
6. Flag any page with a duplicate meta title or description as a critical issue — uniqueness is non-negotiable
7. Save all audit reports to `~/shopify-seo/audits/` and update `~/shopify-seo/fix-queue.md` when the user requests `seo save`

ClawHub Frontend Data Analysis+2

Amazon Review Request Optimizer

Skill

Amazon review request optimization agent. Identifies underperforming ASINs by review velocity, calculates optimal timing windows, checks Amazon ToS complianc...

---
name: amazon-review-request-optimizer
description: "Amazon review request optimization agent. Identifies underperforming ASINs by review velocity, calculates optimal timing windows, checks Amazon ToS compliance, and generates compliant follow-up messaging that maximizes response rates without risking account health. Triggers: review request, amazon review, review velocity, review timing, review rate, review optimization, request review, review strategy, seller feedback, amazon feedback, review follow up, review message"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amazon-review-request-optimizer
---

# Amazon Review Request Optimizer

AI-powered Amazon review request agent — identifies which ASINs need review attention, calculates optimal timing windows, and generates ToS-compliant messaging that maximizes response rates.

Provide your ASIN list, order volume, and current review counts. The agent benchmarks your velocity against category averages, identifies review gaps, and produces compliant follow-up messaging templates ready to use in Seller Central's Request a Review tool or approved third-party tools.

## Commands

```
review audit                       # full review health audit across all ASINs you provide
review velocity <asin>             # calculate current review velocity vs. category benchmark
review timing                      # identify optimal post-delivery timing windows by category
review message                     # generate compliant review request message templates
review benchmark                   # show category-level review rate benchmarks
review request rate                # calculate your current request-to-review conversion rate
review risk check                  # scan messaging for Amazon ToS compliance issues
review save                        # save audit and templates to ~/amazon-reviews/
```

## What Data to Provide

The agent works with:
- **ASIN list** — paste ASINs with current star rating and review count
- **Order volume** — monthly units sold per ASIN (or estimate)
- **Delivery timeframes** — average days to delivery for your products
- **Category** — product category (electronics, kitchenware, apparel, etc.)
- **Existing messaging** — paste your current follow-up emails for compliance review
- **Buyer segments** — if available, distinguish new vs. repeat buyers

No API keys required. Works from pasted data and verbal descriptions.

## Workspace

Creates `~/amazon-reviews/` containing:
- `memory.md` — saved ASIN profiles, category baselines, and velocity history
- `templates/` — approved message templates saved as markdown
- `audits/` — past audit reports (audit-YYYY-MM-DD.md)

## Analysis Framework

### 1. Review Velocity Benchmarks by Category

Review velocity is measured as reviews earned per orders shipped:

| Category | Expected Velocity | Strong Velocity |
|----------|------------------|-----------------|
| Electronics | 1 review per 50 orders | 1 per 30 orders |
| Kitchenware / Home | 1 review per 30 orders | 1 per 20 orders |
| Apparel / Fashion | 1 review per 40 orders | 1 per 25 orders |
| Beauty / Personal Care | 1 review per 25 orders | 1 per 15 orders |
| Books | 1 review per 100 orders | 1 per 60 orders |
| Toys / Games | 1 review per 35 orders | 1 per 20 orders |
| Sports / Outdoors | 1 review per 40 orders | 1 per 25 orders |

Flag ASINs performing below expected velocity as underperforming — prioritize these for review request focus.

### 2. Optimal Timing Windows

Timing of the review request relative to delivery is the single highest-impact variable:

- **Day 4-7 post-delivery** — optimal window for most categories; buyer has used the product enough to have an opinion but the experience is still fresh
- **Day 2-3** — too early for most physical goods; buyer may not have opened the package
- **Day 8-14** — acceptable for complex products (electronics, assembly-required) requiring more setup time
- **Day 14+** — diminishing response rate; buyer memory of experience fades
- **Adjust for category**: consumables (day 7-10, after first use), apparel (day 4-6, after wearing), electronics (day 7-10, after setup)

One request per order is the Amazon-permitted maximum. Do not send multiple follow-ups.

### 3. Amazon ToS Compliance Checklist

Every message template must pass all compliance checks before use:

**Prohibited (account suspension risk)**
- Offering incentives for reviews (discounts, refunds, free products, gift cards)
- Asking only for positive reviews or filtering by satisfaction ("if you're happy, please review")
- Asking buyers to change or remove an existing review
- Including marketing content or promotions in the review request message
- Sending to buyers who opted out of Seller Messaging

**Required Elements**
- Reference to the specific order (order number or product name)
- Genuine, unconditional request for honest feedback
- No pressure language or urgency around leaving a review
- If using third-party tools: must be Amazon-approved tools only

**Compliant Language Patterns**
- "We'd love to hear your honest feedback about your [Product Name]"
- "If you have a moment, a review would help other customers make informed decisions"
- "Your experience with this product — good or bad — is valuable"

**Non-Compliant Language Patterns (never use)**
- "If you loved your purchase, please leave a 5-star review"
- "Leave a review and get 10% off your next order"
- "Please update your review if we've resolved your issue"

### 4. Buyer Segment Analysis

Buyer segment dramatically affects review response rates:

- **Repeat buyers** — 3x higher review response rate than first-time buyers; prioritize in request queue
- **High-value orders** — buyers who spent more tend to engage more with feedback requests
- **First-time buyers** — lower response rate but higher lifetime value from the review relationship; still worth requesting
- **Returns / refunds** — do not send review requests to buyers who have requested a return; Amazon flags this
- **Prime vs. non-Prime** — Prime buyers tend to review more; no action needed, just context

### 5. Message Template Framework

Every compliant review request message follows this structure:

1. **Greeting** — acknowledge the order by product name (not order number in email body)
2. **Value acknowledgment** — brief genuine statement about the product's purpose
3. **Honest ask** — single, unconditional request for a review
4. **No pressure close** — make clear a response is optional and any feedback is welcome
5. **No promotional content** — zero marketing language, no upsells, no discount codes

Keep total message length under 150 words. Shorter messages have higher open and response rates.

### 6. Suppressed Review Detection Signals

Reviews may be suppressed by Amazon without notification. Watch for:
- Review count not increasing despite high order volume and confirmed requests sent
- Star rating changing without visible new reviews appearing
- Review total on the product page not matching total reviews shown in Seller Central
- Sudden drop in review velocity with no change in product quality or request strategy

When suppression is suspected: check Seller Central Account Health, verify no policy warnings are active, and contact Seller Support with specific ASIN and date range data.

## Output Format

Every `review audit` outputs:
1. **Velocity Report** — table of each ASIN with current velocity vs. category benchmark
2. **Priority Queue** — ASINs ranked by review gap (furthest below benchmark first)
3. **Timing Recommendations** — optimal send window per ASIN based on category and delivery time
4. **Compliant Message Templates** — 2-3 ready-to-use templates per ASIN or category
5. **Compliance Check** — pass/fail on all ToS criteria for provided or generated messages
6. **Risk Flags** — any suppression signals or account health considerations

## Rules

1. Never generate message templates that offer incentives for reviews — this is an immediate suspension risk
2. Always run compliance check before presenting any message template as ready to use
3. Flag any ASIN with zero reviews after 100+ orders as a potential suppression case, not just a velocity issue
4. Distinguish between "request not sent" (operational problem) and "request sent but no response" (messaging or timing problem) before recommending fixes
5. Never recommend sending more than one review request per order — Amazon permits exactly one
6. When auditing buyer-provided messages, highlight every non-compliant phrase individually and suggest a compliant replacement
7. Save all templates and audit reports to `~/amazon-reviews/` when the user requests `review save`

ClawHub Data Analysis Product+2

Email Subject Line Tester

Skill

Email subject line optimization agent. Generates 10 subject line variants for any email, scores each by open rate predictors (urgency, personalization, curio...

---
name: email-subject-line-tester
description: "Email subject line optimization agent. Generates 10 subject line variants for any email, scores each by open rate predictors (urgency, personalization, curiosity, length, emoji use), and recommends the top 3 for A/B testing. Triggers: subject line, email subject, subject line tester, subject line generator, email open rate, ab test email, subject line optimization, email copywriting, subject line ideas, newsletter subject, email marketing subject"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/email-subject-line-tester
---

# Email Subject Line Tester

AI-powered email subject line optimization agent — generates 10 variants, scores each on 7 open-rate predictor signals, and selects the top 3 for A/B testing.

Describe your email's topic, audience, and goal. The agent produces scored variants across multiple psychological angles (urgency, curiosity, personalization, social proof) and gives you a ready-to-launch A/B test plan with sample size guidance.

## Commands

```
subject test <topic>               # generate and score 10 subject line variants for a topic
subject generate                   # generate variants with more context (audience, goal, tone)
subject score <line>               # score a specific subject line you already have
subject ab test                    # structure a full A/B test plan with sample size formula
subject analyze competitors        # analyze subject lines from competitor emails you paste
subject by industry                # get industry-specific benchmarks and top-performing patterns
subject history                    # show previously tested subject lines and their scores
subject save                       # save current session results to ~/email-subjects/
```

## What Data to Provide

The agent works with:
- **Topic description** — "promotional email for 30% off summer sale, audience is past buyers"
- **Draft subject lines** — paste your own for scoring and improvement suggestions
- **Competitor examples** — paste subject lines from competitor emails for pattern analysis
- **Audience details** — industry, demographic, relationship (subscriber, buyer, lead), engagement tier
- **Email goal** — promotional, transactional, re-engagement, newsletter, event invite

No integrations required. Works entirely from your descriptions.

## Workspace

Creates `~/email-subjects/` containing:
- `memory.md` — saved audience profiles, brand voice notes, and past A/B test results
- `history/` — past testing sessions saved as markdown (session-YYYY-MM-DD.md)
- `benchmarks.md` — industry benchmark reference updated during sessions

## Analysis Framework

### 1. The 7 Open-Rate Predictor Signals

Each subject line is scored 0-10 on each signal; total score is out of 70:

**Signal 1 — Urgency Words**
- Time-limited language: "today only", "ends tonight", "last chance", "24 hours left"
- Quantity scarcity: "only 3 left", "limited spots", "while supplies last"
- Diminishes with overuse — flag if brand history shows urgency fatigue

**Signal 2 — Personalization Tokens**
- Name token {{first_name}} adds 2-5% open rate lift on average
- Behavioral personalization: "You left something behind", "Based on your last order"
- Segment-specific language (buyer vs. subscriber vs. VIP)

**Signal 3 — Question Format**
- Open questions create curiosity loops: "What's your biggest email mistake?"
- Yes/No questions drive agreement priming: "Ready to double your open rates?"
- Rhetorical questions require no answer — lower friction than calls to action

**Signal 4 — Number Inclusion**
- Specific numbers outperform vague claims: "Save $47" beats "Save money"
- Odd numbers slightly outperform round numbers in most studies
- List-format subject lines: "5 mistakes killing your open rates"

**Signal 5 — Emoji Presence**
- Single relevant emoji adds novelty in crowded inboxes; more than 2 reduces credibility
- Emoji at start of subject performs differently than at end (test both)
- Inappropriate for B2B enterprise, legal, financial contexts — flag by industry

**Signal 6 — Character Length**
- Optimal range: 30-50 characters for desktop and mobile rendering
- Under 20 characters: too vague, loses context
- Over 60 characters: truncated on mobile (58% of opens are mobile)
- Preheader pairing: subject + preheader combined should tell the full story in 90 characters

**Signal 7 — Power Words**
- High-engagement triggers: "exclusive", "secret", "proven", "free", "new", "you"
- Spam-trigger words to avoid: "100% free", "act now", "cash bonus", "no cost", "winner"
- Run spam filter check on every generated variant

### 2. Industry Benchmark Reference

| Industry | Average Open Rate | Top Quartile |
|----------|------------------|--------------|
| Ecommerce | 15-20% | >25% |
| SaaS / Software | 20-25% | >32% |
| Newsletter / Media | 25-35% | >45% |
| B2B Services | 20-28% | >35% |
| Nonprofit | 26-30% | >40% |
| Healthcare | 22-27% | >35% |

### 3. Spam Trigger Detection
- Scan each variant against known spam trigger word list
- Flag phrases that increase spam folder placement risk
- Check for ALL CAPS usage (more than 2 consecutive caps words triggers filters)
- Check for excessive punctuation (!!!  or ???)

### 4. Mobile Preview Check
- Simulate rendering at 40 characters (iPhone lock screen) and 58 characters (Gmail mobile)
- Flag subject lines that truncate at an awkward word break
- Suggest preheader text that completes the message naturally when subject is truncated

### 5. A/B Test Setup Guidance
- Minimum sample size formula: n = (Z^2 × p × (1-p)) / E^2
  - Z = 1.96 for 95% confidence, p = baseline open rate, E = minimum detectable effect (typically 0.02)
  - Example: 25% baseline, detect 2pp lift → n = 2,401 per variant
- Test only one variable per test (subject line only, never combine with send time changes)
- Recommended test split: 20% / 20% test, 60% winner send
- Minimum test duration: 4 hours before declaring winner (allow for time-zone spread)

## Output Format

Every `subject test` run outputs:
1. **10 Scored Variants** — each with total score /70, per-signal breakdown, and character count
2. **Top 3 Picks** — recommended for A/B testing, with rationale for each selection
3. **Spam Flag Report** — any variants with trigger words highlighted
4. **Mobile Preview Simulation** — truncated rendering at 40 and 58 characters
5. **A/B Test Plan** — test setup instructions with sample size recommendation
6. **Preheader Suggestions** — paired preheader for each top-3 variant

## Rules

1. Always generate exactly 10 variants before scoring — never fewer
2. Never recommend a variant containing known spam trigger words without flagging the risk
3. Score every variant on all 7 signals — no signal may be skipped
4. Flag when the audience or industry context makes certain signals inappropriate (e.g., emoji in B2B financial services)
5. Always include character count and mobile truncation preview for every variant
6. When scoring a user-provided subject line, explain each signal score individually — not just the total
7. Save session results to `~/email-subjects/history/` when the user requests `subject save`

ClawHub Testing Writing+2

Campaign Performance Report

Skill

Multi-channel marketing performance agent. Pulls together Meta, Google, email, and organic data into a unified weekly report with AI executive commentary — e...

---
name: campaign-performance-report
description: "Multi-channel marketing performance agent. Pulls together Meta, Google, email, and organic data into a unified weekly report with AI executive commentary — eliminating manual cross-platform data assembly. Triggers: campaign report, marketing report, multi channel report, weekly marketing report, meta report, google ads report, performance report, marketing performance, channel report, ad performance, weekly report marketing, paid media report"
allowed-tools: Bash
metadata:
openclaw:
homepage: https://github.com/mguozhen/campaign-performance-report
---

# Campaign Performance Report

AI-powered multi-channel marketing performance agent — unifies Meta, Google, email, and organic data into a single weekly report with executive-level AI commentary.

Paste your numbers from each platform dashboard, describe your results verbally, or upload CSV exports. The agent normalizes metrics across channels, surfaces anomalies, compares week-over-week, and delivers a 3-bullet executive summary: win, miss, and action.

## Commands

```
report setup # configure channels, KPIs, and report preferences
report weekly # generate full unified weekly performance report
report by channel # break down performance per channel side-by-side
report compare weeks # compare current week vs. prior week or custom date range
report meta # focus report on Meta (Facebook/Instagram) data only
report google # focus report on Google Ads data only
report email # focus report on email campaign metrics only
report organic # focus report on organic search and social metrics only
report save # save this week's report to ~/campaign-reports/
```

## What Data to Provide

The agent works with:
- **Manual paste** — copy numbers directly from Meta Ads Manager, Google Ads, Klaviyo, GA4, etc.
- **CSV export** — paste rows from platform exports (impressions, clicks, spend, conversions)
- **Verbal description** — "Meta spend $4,200, 180 conversions, ROAS 3.2; Google $3,100, 210 conversions, ROAS 4.1"
- **Partial data** — report on whichever channels you have; agent flags missing channels

No API keys needed. No integrations required.

## Workspace

Creates `~/campaign-reports/` containing:
- `memory.md` — saved channel configs, KPI targets, and account baselines
- `reports/` — weekly reports saved as markdown (weekly-YYYY-MM-DD.md)
- `data/` — raw channel data snapshots for trend tracking

## Analysis Framework

### 1. Channel Data Input and Normalization
- Accept raw numbers from Meta Ads Manager, Google Ads, email platform (Klaviyo/Mailchimp), GA4 organic
- Normalize to unified metric set: impressions, clicks, spend, conversions, revenue, CTR, CVR, CPC, ROAS
- Flag mismatched attribution windows across platforms (Meta 7-day click vs. Google last-click)
- Handle zero-spend channels (organic, email) using CPM-equivalent cost estimates when available

### 2. Cross-Channel ROAS Comparison
- Calculate blended ROAS across all paid channels: Total Revenue / Total Paid Spend
- Rank channels by ROAS, CVR, and CPA
- Identify highest and lowest efficiency channels
- Flag channels where ROAS is below break-even threshold (1.0 for revenue, varies by margin)

### 3. Spend Allocation Efficiency
- Show spend distribution across channels as percentages
- Compare spend share vs. conversion share per channel (over/under-indexed channels)
- Flag channels absorbing budget without proportional returns
- Suggest reallocation direction (not specific amounts — flag for human review)

### 4. Week-over-Week Trend Analysis
- Calculate delta for every key metric vs. prior week
- Display direction arrows (up/down) and percentage change
- Compute 4-week rolling average as baseline for trend context
- Flag metrics moving in opposite direction from spend (spend up, conversions down = efficiency drop)

### 5. Anomaly Flagging
- Flag any metric with greater than 20% week-over-week delta (positive or negative)
- CPM spikes greater than 30% may signal audience saturation or auction pressure
- CTR drops greater than 20% with stable spend may indicate creative fatigue
- Conversion rate drops greater than 15% with stable traffic may indicate landing page issues

### 6. Budget Pacing Check
- Compare actual spend-to-date vs. expected pacing for the month
- Flag overpace (greater than 110% of expected) and underpace (less than 90% of expected)
- Estimate end-of-month projected spend at current run rate

### 7. AI Executive Summary
- 3-bullet format always: Win (best performance signal this week), Miss (biggest underperformance), Action (single highest-priority recommendation)
- Keep each bullet to one sentence — built for executive skimming
- Cite specific numbers in each bullet (no vague language)

## Output Format

Every weekly report outputs:
1. **Executive Summary** — Win / Miss / Action (3 bullets, one sentence each)
2. **Channel Scorecard** — table with all channels, all key metrics, week-over-week delta
3. **Anomalies** — flagged metrics exceeding 20% delta with likely cause
4. **Budget Pacing** — spend status vs. monthly plan
5. **Top Performers** — best-performing campaigns or content across all channels
6. **Actions Queue** — prioritized list of items requiring human decisions
7. **Next Week Focus** — 2-3 optimization priorities for the coming week

## Rules

1. Never fabricate platform data — if a channel's data is missing, mark it as "not provided" rather than estimating
2. Always note attribution window differences between platforms when comparing ROAS across channels
3. Flag anomalies with a likely cause hypothesis, not just the raw number
4. Distinguish between spend-driven metric changes (more budget = more impressions) vs. efficiency changes (same spend, fewer results)
5. Save reports to `~/campaign-reports/reports/` using the filename format `weekly-YYYY-MM-DD.md`
6. When comparing weeks, require at least 7 full days of data per period before drawing trend conclusions
7. Never recommend pausing a channel based on a single week of data — flag for review instead

Amazon Launch Checklist

Skill

Amazon product launch audit agent. Scores listing completeness, keyword coverage, image quality requirements, pricing competitiveness, initial PPC structure,...

---
name: amazon-launch-checklist
description: "Amazon product launch audit agent. Scores listing completeness, keyword coverage, image quality requirements, pricing competitiveness, initial PPC structure, and launch sequence timing for new Amazon listings. Triggers: amazon launch, product launch, listing launch, launch checklist, launch audit, new listing, fba launch, launch strategy, listing score, launch ready, amazon launch plan"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amazon-launch-checklist
---

# Amazon Launch Checklist

Pre-launch audit for new Amazon listings — score your readiness before you go live and waste ad spend on an incomplete listing.

Paste your listing draft, ASIN, or describe your product. The agent scores every launch component and gives you a prioritized fix list before day one.

## Commands

```
launch audit                       # full pre-launch audit across all components
launch score                       # overall launch readiness score (0–100)
launch keywords                    # keyword coverage audit (title, bullets, backend)
launch images check                # image requirements checklist
launch pricing check               # competitive pricing analysis
launch ppc plan                    # initial PPC campaign structure recommendation
launch sequence                    # day-by-day launch timeline (week 1–4)
launch ready                       # go/no-go decision with blocking issues listed
launch save                        # save audit results to workspace
```

## What Data to Provide

The agent works with:
- **Listing draft** — paste title, bullet points, description, backend keywords
- **Product details** — category, price, ASIN (if live), brand
- **Images list** — describe images you have (main, lifestyle, infographic, etc.)
- **Competitor data** — "top 3 competitors price at $22–$28, all have 500+ reviews"
- **Budget** — daily PPC budget for launch phase
- **Target keywords** — primary keyword you want to rank for

No Seller Central access needed. Works from pasted data.

## Workspace

Creates `~/amazon-launch/` containing:
- `listings/` — saved listing drafts and audit results per ASIN/product
- `launch-plans/` — generated launch sequence plans
- `templates/` — reusable PPC campaign structures
- `memory.md` — brand notes, category benchmarks, previous launches

## Analysis Framework

### 1. Listing Completeness Score (30 points)
Award points for each completed element:
- Title: present and ≥150 characters (5 pts) / includes primary keyword (5 pts)
- Bullet points: all 5 filled (5 pts) / each ≥100 characters with benefits (5 pts)
- Product description / A+ content: present (5 pts)
- Backend search terms: filled (5 pts)

Flag: missing elements are blocking — do not launch without a complete listing.

### 2. Keyword Audit (25 points)
- Primary keyword: must appear in title, ideally in first 80 characters (10 pts)
- Secondary keywords: distributed across bullet points 1–3 (5 pts)
- Long-tail keywords: at least 5 in backend search terms (5 pts)
- Competitor keyword gap: compare your keywords vs. top 3 competitors' titles/bullets (5 pts)
- Forbidden in backend: no repeated keywords already in title, no ASINs, no competitor brand names
- Backend field: 250 bytes maximum — use all available space

### 3. Image Requirements (20 points)
Award points for each image:
- Main image: white background, product fills 85% of frame, no text/logos (5 pts)
- Lifestyle image: product in use, shows scale and context (3 pts)
- Infographic: 3–5 key features called out with icons/text overlays (4 pts)
- Sizing/dimension chart: critical for apparel, bags, accessories, furniture (3 pts)
- Comparison chart: your product vs. generic competitor on key features (3 pts)
- Video: product demo or brand video (2 pts — bonus)

Minimum to launch: main + lifestyle + infographic. Missing any of these = do not launch.

### 4. Pricing Strategy (15 points)
- Competitive range: price within ±20% of top 3 competitors at similar review count (5 pts)
- Penetration pricing: for <25 reviews, consider pricing 10–15% below category average to drive velocity (5 pts)
- Price ceiling check: does your price allow for a launch coupon (10–20%) and still be profitable? (5 pts)
- Common launch mistake: pricing too high before reviews establish trust — new listings need conversion velocity, not margin
- Recommended: launch at penetration price for first 60–90 days, raise after reaching 50+ reviews

### 5. Launch PPC Structure
Recommended initial campaign architecture:

**Campaign 1: Auto — Discovery**
- Budget: 30% of daily PPC budget
- Bid strategy: Dynamic bids — down only
- Purpose: let Amazon find converting search terms; harvest data for manual campaigns
- Set negative: obvious irrelevant terms from day 1

**Campaign 2: Manual Broad — Scale**
- Budget: 30% of daily PPC budget
- Keywords: 5–10 most important head terms in broad match
- Bid: start at suggested bid, adjust weekly based on ACoS

**Campaign 3: Manual Exact — Defend**
- Budget: 40% of daily PPC budget
- Keywords: your exact primary keyword + 3–5 high-intent exact match terms
- Bid: 20–30% above broad match bids to win impressions on core terms

Week 3+ action: promote converting auto/broad search terms to exact match manual campaign.

### 6. External Traffic Plan
- Amazon Vine enrollment: eligible at launch if enrolled in Brand Registry — submit 2–8 units for Vine reviews
- External traffic sources: social media posts, email list, micro-influencers with trackable links
- Rebate/launch services: use cautiously — Amazon monitors unnatural review velocity
- Launch week goal: 5–10 initial reviews before scaling PPC spend

### 7. Review Velocity Targets
| Week | Target Reviews | PPC ACoS Budget |
|------|---------------|-----------------|
| 1 | 1–3 (Vine) | High (50–80% ACoS OK) |
| 2 | 5–10 | High (40–60% ACoS OK) |
| 3–4 | 15–25 | Medium (30–40% ACoS) |
| 5–8 | 30–50 | Optimize toward target |
| 8+ | 50+ | Target ACoS mode |

## Launch Readiness Score

Score bands:
- **85–100**: Launch ready — go live and start PPC
- **70–84**: Almost ready — fix flagged items within 48 hours of launch
- **50–69**: Not ready — complete blockers before spending any ad budget
- **0–49**: Major gaps — listing will not convert; fix fundamentals first

## Rules

1. Never give a launch-ready verdict on a listing with fewer than 3 bullet points or a missing main image — these are hard blockers
2. Always ask for the target primary keyword before running the keyword audit — every other keyword decision depends on it
3. Do not recommend a launch price without first establishing the seller's landed cost and minimum acceptable margin
4. Flag Vine enrollment eligibility at the start — Vine reviews are the highest-leverage early review strategy and have a lead time
5. Distinguish between blocking issues (must fix before launch) and optimization issues (fix in first 30 days) — priority matters
6. The PPC plan is a starting structure only — advise the seller to review and adjust weekly for the first 4 weeks
7. Save audit results to `~/amazon-launch/listings/` with the product name and date when `launch save` is called

Shopify Ad Attribution

Skill

Shopify ad attribution agent. Calculates true ROAS per channel by correlating Shopify order UTM data with ad spend — reveals which channels actually drive pr...

---
name: shopify-ad-attribution
description: "Shopify ad attribution agent. Calculates true ROAS per channel by correlating Shopify order UTM data with ad spend — reveals which channels actually drive profit vs. which ones just get credit. Triggers: ad attribution, shopify attribution, roas by channel, true roas, marketing attribution, utm analysis, ad spend analysis, channel performance, meta attribution, google attribution, shopify ads"
allowed-tools: Bash
metadata:
openclaw:
homepage: https://github.com/mguozhen/shopify-ad-attribution
---

# Shopify Ad Attribution

Cut through attribution lies — find out which channels actually drive profit, not just which ones take credit.

Paste your Shopify order UTM data and ad spend by channel. The agent calculates true ROAS, profit-adjusted ROAS, and surfaces channels that over- or under-claim credit.

## Commands

```
attribution setup # configure store, COGS%, channels, and spend data
attribution report # full attribution analysis across all channels
attribution by channel # per-channel revenue, spend, and ROAS breakdown
attribution roas # ROAS and profit-adjusted ROAS per channel
attribution ltv # LTV-adjusted attribution (repeat purchase value)
attribution last click vs multi touch # compare last-click vs. linear vs. time-decay models
attribution anomaly # flag channels with unusual credit patterns
attribution save # save setup and latest report to workspace
```

## What Data to Provide

The agent works with:
- **Shopify orders export** — paste UTM source/medium/campaign columns from order export CSV
- **Ad spend by channel** — "Meta: $3,200 | Google: $1,800 | TikTok: $900 this month"
- **COGS and margin** — "product cost is 30% of revenue, Shopify fees ~3%"
- **Channel setup** — list of active ad channels and their primary UTM source values
- **LTV data** — if available: average repeat purchase rate and second-order value

No integrations needed. Paste exported data directly.

## Workspace

Creates `~/shopify-attribution/` containing:
- `setup.md` — store configuration, COGS%, channel mapping, UTM conventions
- `reports/` — monthly attribution reports
- `spend-log.md` — historical ad spend by channel
- `anomalies.md` — flagged attribution anomalies

## Analysis Framework

### 1. UTM Parameter Mapping
- Map UTM source to channel: facebook/instagram → Meta, google/cpc → Google, tiktok → TikTok, email → Email, organic → Organic, (none)/(direct) → Direct
- Clean UTM data: normalize case, strip typos, consolidate variants (e.g., "FB" and "facebook" → Meta)
- Flag orders with missing UTM data — these are attribution dark zones (often direct/email/organic)
- Compute UTM coverage rate: % of orders with valid UTM source attribution
- Group by: source, medium, campaign for granular analysis

### 2. Last-Click Attribution Model
- Assign 100% of order revenue to the last UTM source before purchase
- Compute per-channel: total revenue, order count, average order value
- Match against ad spend to get last-click ROAS: Revenue / Spend
- Flag: channels with very high last-click ROAS — may be capturing credit from upper-funnel channels
- Flag: direct/(none) volume — if >30% of revenue is unattributed, attribution picture is incomplete

### 3. Linear Attribution Model
- Distribute revenue equally across all touchpoints in a customer journey
- Requires multi-session UTM data — if not available, estimate using channel mix ratios
- Compare linear attribution revenue vs. last-click revenue per channel
- Channels that gain credit under linear: typically top-of-funnel (Meta, TikTok, YouTube)
- Channels that lose credit under linear: typically bottom-of-funnel (Google Brand, Email)

### 4. Time-Decay Attribution Model
- Weight touchpoints more heavily the closer they are to the purchase
- Decay formula: weight = e^(−λ × days_before_purchase), λ = 0.1 for 7-day half-life
- Useful for longer purchase cycles (furniture, high-ticket items)
- Compare time-decay vs. last-click — large differences indicate assisted conversion patterns

### 5. ROAS Calculation
- **Reported ROAS** = Total Revenue Attributed / Ad Spend
- **Gross Profit ROAS** = (Revenue × Gross Margin%) / Ad Spend
- **Net Profit ROAS** = (Revenue × Net Margin% after fees) / Ad Spend
- Profitability threshold: Net Profit ROAS must exceed 1.0 to be contribution-positive
- True break-even ROAS = 1 / (Gross Margin% − Platform Fee%)
- Example: 60% margin, 3% Shopify fee → Break-even ROAS = 1 / 0.57 = 1.75

### 6. Channel Overlap and LTV Adjustment
- Identify customers who converted via multiple channels in a 30-day window
- Flag: Meta + Google overlap — common pattern where Meta drives discovery, Google captures conversion
- LTV adjustment: multiply first-order ROAS by repeat purchase multiplier
- If avg customer makes 1.4 purchases in first year, LTV ROAS = Reported ROAS × 1.4
- Cohort LTV by acquisition channel — some channels acquire better long-term customers

### 7. Attribution Anomaly Detection
- Flag: channel spend increased but attributed revenue flat → ad performance degrading or UTM broken
- Flag: direct/(none) revenue spike without organic traffic explanation → UTM tags broken in campaign
- Flag: single campaign taking disproportionate credit (>40% of revenue) → potential tracking issue
- Flag: ROAS dramatically higher than industry benchmark → verify UTM data quality

## Output Format

`attribution report` delivers:

### Channel Summary Table
| Channel | Spend | Revenue (LC) | ROAS (LC) | Profit ROAS | Orders |
|---------|-------|-------------|-----------|-------------|--------|
| Meta | $X | $X | X.Xx | X.Xx | N |
| Google | ... | ... | ... | ... | ... |

### Attribution Model Comparison
| Channel | Last-Click | Linear | Time-Decay | Difference |
|---------|-----------|--------|------------|------------|

### Key Findings
1. Best true-ROAS channel (profit-adjusted)
2. Most over-credited channel (last-click vs. linear gap)
3. Attribution coverage rate and dark zone estimate
4. Recommended budget reallocation

## Rules

1. Always establish COGS and margin before computing profit-adjusted ROAS — reported ROAS without margin context is misleading
2. Never declare a channel unprofitable based on last-click attribution alone — always show multi-touch comparison
3. Flag UTM coverage rate prominently — if >25% of orders lack UTM data, all channel numbers are understated
4. Apply the correct break-even ROAS threshold for the store's margin — not a generic benchmark
5. Distinguish between revenue attribution and profit attribution — high-AOV channels may look great on revenue but poor on profit
6. Identify the Meta vs. Google credit-stealing dynamic by default — it is the most common misattribution pattern in Shopify stores
7. Save reports to `~/shopify-attribution/reports/` with month-year filename on every `attribution save` call

ClawHub Data Analysis Marketing+2

Content Repurposer

Skill

Content repurposing agent. Transforms long-form content (blog posts, video transcripts, podcast notes) into platform-optimized formats: LinkedIn post, X/Twit...

---
name: ai-content-repurposer
description: "Content repurposing agent. Transforms long-form content (blog posts, video transcripts, podcast notes) into platform-optimized formats: LinkedIn post, X/Twitter thread, email newsletter, Instagram caption, YouTube description, TikTok script. Triggers: repurpose content, content repurposing, repurpose blog post, linkedin post from blog, twitter thread from article, email from content, content distribution, content recycling, multi-platform content, content atomization"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/content-repurposer
---

# Content Repurposer

Turn one piece of long-form content into six platform-ready formats — without losing the core message or your voice.

Paste a blog post URL, drop in a transcript, or summarize your content. The agent extracts key insights and adapts them for each platform's unique format, audience expectation, and algorithm behavior.

## Commands

```
repurpose <url or paste>           # extract and analyze source content
repurpose to linkedin              # generate LinkedIn post from loaded content
repurpose to twitter thread        # generate X/Twitter thread
repurpose to email                 # generate email newsletter version
repurpose to instagram             # generate Instagram caption with hashtags
repurpose to youtube               # generate YouTube description (SEO-optimized)
repurpose to tiktok                # generate TikTok script (hook-first, 60s)
repurpose all                      # generate all 6 platform formats at once
repurpose save                     # save source content and all outputs to workspace
```

## What Data to Provide

The agent works with:
- **Blog post URL** — paste the URL and describe the content (agent cannot fetch URLs directly)
- **Pasted text** — copy-paste the full article, transcript, or notes
- **Summary brief** — "my podcast episode covered X, Y, Z main points, audience is [niche]"
- **Tone notes** — "keep it professional" / "casual and punchy" / "educational"
- **Audience notes** — "B2B SaaS founders" / "fitness enthusiasts" / "Amazon sellers"

The more context you provide, the more on-brand the output.

## Workspace

Creates `~/content-repurposer/` containing:
- `memory.md` — saved brand voice notes, tone preferences, recurring topics
- `projects/` — source content and all platform outputs per repurpose session
- `templates/` — custom platform templates if you want to override defaults

## Analysis Framework

### 1. Content Extraction and Summarization
- Identify the core thesis: what is the single most important claim or insight?
- Extract the top 3–5 supporting points or examples
- Note the strongest quote, statistic, or story moment
- Identify the call-to-action or transformation promised by the content
- Tag the content type: how-to, opinion, case study, list, story, interview

### 2. Key Insight Identification
- Rank insights by: surprising > actionable > validating > educational
- Lead with the most surprising or counterintuitive point across all platforms
- Identify which insights work best as standalone hooks vs. supporting evidence
- Note any data points, specific numbers, or named frameworks — these anchor credibility

### 3. Platform Adaptation Rules

**LinkedIn** (professional, story-led, 1,300–3,000 characters)
- Open with a single-sentence hook on its own line
- Use short paragraphs (1–3 lines max) with line breaks between each
- Build toward the key insight with a story arc: situation → problem → solution → result
- End with a question or soft CTA to drive comments
- No hashtag spam — 3–5 relevant hashtags maximum, placed at end
- Optimal length: 150–300 words for most posts; long-form (700+ words) for thought leadership

**X / Twitter Thread** (punchy, hook-first, 270 characters per tweet)
- Tweet 1: the hook — most surprising claim or bold take, standalone value
- Tweets 2–7: one insight or example per tweet, numbered (2/8, 3/8, etc.)
- Tweet N-1: synthesis or "here's what this means for you" framing
- Final tweet: CTA (follow, reply, bookmark) + link back to source
- No filler tweets — every tweet must deliver value if read standalone
- Use line breaks within tweets for scannability

**Email Newsletter** (personal, direct, subject line drives opens)
- Subject line formula: [specific number or outcome] + [who it's for] + [timeframe or qualifier]
- Preview text: first sentence of body, no emoji, under 90 characters
- Opening: address reader directly, no "I wanted to reach out" — get to the point
- Body: 3-section structure — context, key insight, actionable takeaway
- CTA: one clear action only (read, reply, click, try)
- P.S. line: restate the best insight in one sentence for skimmers

**Instagram Caption** (visual-first, emotional, hashtag-rich)
- First line: hook that works without the image context (reader sees first 125 chars)
- Use line breaks and spacing for readability in the caption
- Inject 2–3 relevant emojis naturally — not as decoration but as emphasis
- End with a question to prompt comments
- Hashtags: 10–20, mix of large (1M+), medium (100k–1M), and niche (<100k)
- Separate hashtags from caption body with a line break or dots

**YouTube Description** (SEO-optimized, first 150 chars critical)
- First 150 characters: include primary keyword + clear value statement (shows before "more")
- Timestamps: include if content has chapters (00:00 Intro, 01:30 Main point, etc.)
- Links: website, social, related videos in body
- Keyword paragraph: 2–3 sentences naturally incorporating 3–5 target search terms
- End with subscribe CTA and community links

**TikTok Script** (hook-first, 30–60 seconds, pattern interrupt)
- Second 1–3: pattern interrupt hook — say the most surprising thing first
- Second 4–15: problem or tension setup — "most people think X, but actually..."
- Second 16–45: the insight, delivered fast with examples (no padding)
- Second 46–60: payoff + CTA ("follow for more" or "comment if you agree")
- Write in spoken word cadence — short sentences, punchy, as if talking to a friend
- Flag: hook must make viewer stop scrolling; test 2–3 hook variants

### 4. Tone Calibration Per Platform
- LinkedIn: professional but human — expertise without jargon
- Twitter: direct, confident, slightly edgy — opinions welcome
- Email: warm and personal — writing to one person, not a broadcast
- Instagram: authentic, aspirational, community-feeling
- YouTube: informative, enthusiastic, searchable
- TikTok: energetic, fast, relatable — no corporate speak

## Output Format

For `repurpose all`, outputs are delivered in labeled sections:

```
## LinkedIn Post
[output]

## Twitter Thread
[Tweet 1/N]
[Tweet 2/N]
...

## Email Newsletter
Subject: [subject line]
Preview: [preview text]
[body]

## Instagram Caption
[caption]
[hashtags]

## YouTube Description
[description]

## TikTok Script
[0:00–0:03] Hook:
[0:04–0:15] Setup:
[0:16–0:45] Insight:
[0:46–0:60] Payoff + CTA:
```

## Rules

1. Never change the core facts, statistics, or claims from the source content — adapt the format, not the substance
2. Always ask for target audience and tone before generating if not provided — generic output is low value
3. Each platform format must work independently — do not reference "as mentioned above" or cross-platform
4. Flag when source content is too thin to fill all 6 formats — recommend which platforms to prioritize
5. Do not pad outputs to hit length targets — shorter and sharper beats longer and diluted on every platform
6. Save all outputs to `~/content-repurposer/projects/` with the session date when `repurpose save` is called
7. If the user provides brand voice notes, apply them consistently across all platform outputs

Amazon Inventory Forecast

Skill

Amazon inventory forecasting agent. Calculates optimal reorder points and quantities from sales velocity, lead time, and storage costs — tells sellers exactl...

---
name: amazon-inventory-forecast
description: "Amazon inventory forecasting agent. Calculates optimal reorder points and quantities from sales velocity, lead time, and storage costs — tells sellers exactly when to reorder and how much to order. Triggers: inventory forecast, reorder point, stock forecast, amazon inventory, fba inventory, stockout prevention, overstock, safety stock, eoq, inventory management, when to reorder, inventory calculator"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amazon-inventory-forecast
---

# Amazon Inventory Forecast

FBA inventory intelligence — know exactly when to reorder and how much to order before you stock out or overstock.

Provide your sales data, lead time, and current stock levels. The agent calculates reorder points, EOQ, stockout risk windows, and storage fee exposure.

## Commands

```
forecast add <sku>                 # add a SKU to track with sales and lead time data
forecast check                     # run forecast update on all tracked SKUs
forecast reorder point             # calculate reorder point for each SKU
forecast eoq                       # calculate economic order quantity
forecast stockout risk             # identify SKUs at risk of stocking out
forecast overstock risk            # identify SKUs at risk of long-term storage fees
forecast report                    # full inventory health report
forecast save                      # save all SKU data and forecasts to workspace
```

## What Data to Provide

The agent works with:
- **SKU + sales data** — "SKU: B-RED-LG, sold 240 units last 30 days, currently 180 units on hand"
- **Lead time** — "supplier takes 25 days to ship, FBA check-in adds 5 days"
- **Storage cost** — monthly FBA storage fee rate (standard or oversized)
- **Unit cost** — your landed cost per unit (for EOQ calculation)
- **Seasonal notes** — "Q4 demand doubles, Prime Day adds ~3x spike"

No integrations needed. Paste your data directly.

## Workspace

Creates `~/amazon-inventory/` containing:
- `skus.md` — tracked SKUs with sales history and parameters
- `forecasts/` — generated forecast reports per SKU
- `alerts.md` — stockout and overstock alert log
- `reorder-log.md` — history of reorder recommendations made

## Analysis Framework

### 1. Sales Velocity Calculation
- Compute average daily sales from 30-day, 60-day, and 90-day windows
- Weight recent data more heavily: 30-day gets 50%, 60-day gets 30%, 90-day gets 20%
- Weighted daily sales = (30d avg × 0.5) + (60d avg × 0.3) + (90d avg × 0.2)
- Flag: high variance between windows — demand is trending up or down
- Flag: 30d velocity >20% above 90d average — demand acceleration detected

### 2. Lead Time Buffer Calculation
- Total lead time = supplier processing + shipping transit + FBA check-in buffer
- Default FBA check-in buffer: 7 days (use 10 days during Oct–Dec peak season)
- Safety stock formula: Safety Stock = Z-score × σ(daily demand) × √(lead time)
- Conservative Z-score = 1.65 (95% service level); aggressive = 1.28 (90%)
- Minimum safety stock: 14 days of average daily sales

### 3. Reorder Point Formula
- Reorder Point = (Average Daily Sales × Total Lead Time) + Safety Stock
- Example: 8 units/day × 30 days lead time + 56 units safety stock = 296 units
- Express reorder point both in units and in days-of-stock-remaining
- Show the calculation explicitly so sellers can verify inputs

### 4. EOQ Formula (Economic Order Quantity)
- EOQ = √(2DS / H)
  - D = annual demand (units/year)
  - S = order cost per purchase order (shipping + prep + admin, typically $50–$200)
  - H = annual holding cost per unit (FBA storage fee × 12 + opportunity cost)
- Round EOQ up to nearest full case pack quantity
- Show sensitivity analysis: EOQ at ±20% demand change

### 5. Storage Fee Avoidance
- FBA long-term storage fee triggers: units stored >365 days
- Q4 surcharge period: Oct 1 – Dec 31 (higher monthly rates)
- Q4 inventory removal deadline: recommend sending final Q4 shipment no later than Sept 15
- Overstock flag: current inventory > 180 days of supply at current velocity
- Compute projected months-of-supply: Current Stock / (Daily Sales × 30)

### 6. Demand Seasonality Adjustments
- Apply seasonal multipliers when user provides them
- Common multipliers: Q4 holiday = 1.5–3x, Prime Day = 2–4x (48-hour window), Back-to-school = 1.2–1.5x
- Adjusted forecast = base velocity × seasonal multiplier
- For Q4 planning: build to cover Oct 1 – Dec 31 + post-holiday return buffer
- Flag: if current stock will not cover a known seasonal spike, surface reorder urgency

## Reorder Decision Output

Every `forecast check` shows per SKU:
| SKU | Daily Sales | Days of Stock | Reorder Point | EOQ | Status |
|-----|------------|---------------|---------------|-----|--------|
| ... | ... | ... | ... | ... | OK / REORDER NOW / URGENT |

Status levels:
- **OK** — days of stock > reorder point days
- **REORDER SOON** — within 14 days of reorder point
- **REORDER NOW** — at or below reorder point
- **URGENT** — less than lead time days of stock remaining (stockout imminent)

## Rules

1. Always collect lead time before computing reorder points — the formula is useless without it
2. Never recommend a reorder quantity below one full case pack — partial cases create receiving complications at FBA
3. Flag all assumptions explicitly — if the user has not provided 90-day sales data, state which averages were used
4. Apply Q4 seasonality adjustments automatically for any forecast that spans October–December
5. Show the full math for every EOQ and reorder point calculation — sellers need to verify with their own numbers
6. Distinguish between units currently at FBA and units in transit — both count toward days-of-supply
7. Save updated forecasts to `~/amazon-inventory/forecasts/` on every `forecast save` call

Amazon Competitor Spy

Skill

Amazon competitor intelligence agent. Tracks competitor ASINs over time — price changes, BSR movements, review count velocity, listing changes, new images/va...

---
name: amazon-competitor-spy
description: "Amazon competitor intelligence agent. Tracks competitor ASINs over time — price changes, BSR movements, review count velocity, listing changes, new images/variations. Outputs trend tables and competitive alerts. Triggers: competitor spy, amazon competitor, track competitor, asin tracker, competitor analysis, bsr tracking, price tracking, amazon spy, competitor monitoring, listing changes, review velocity, competitive intelligence"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amazon-competitor-spy
---

# Amazon Competitor Spy

Track competitor ASINs over time and surface competitive intelligence — price shifts, BSR trends, review velocity, and listing changes.

Paste an ASIN or describe a competitor product. The agent tracks changes, computes trends, and alerts you when competitors make significant moves.

## Commands

```
spy add <asin>                     # add an ASIN to your watchlist
spy check                          # run check on all tracked ASINs
spy compare                        # side-by-side competitor comparison matrix
spy price history                  # show price change timeline for tracked ASINs
spy bsr trend                      # 30-day BSR movement analysis
spy review velocity                # reviews/week rate for each tracked ASIN
spy listing diff                   # detect title/bullet/image changes vs. last snapshot
spy report                         # full competitive intelligence report
spy save                           # save current watchlist and snapshots to workspace
```

## What Data to Provide

The agent works with:
- **ASIN list** — paste ASINs directly, one per line or comma-separated
- **Verbal description** — "my competitor is B08XYZ123, they just dropped price to $24.99"
- **Listing data** — paste competitor title, bullets, price, BSR rank from Amazon
- **Historical notes** — "last month their BSR was 3,200, now it's 980"

No API keys needed. No scraping tools required.

## Workspace

Creates `~/amazon-spy/` containing:
- `watchlist.md` — tracked ASINs with metadata and notes
- `snapshots/` — listing snapshots per ASIN (ASIN-YYYY-MM-DD.md)
- `reports/` — generated competitive intelligence reports
- `alerts.md` — triggered alerts log

## Analysis Framework

### 1. ASIN Tracking Setup
- Store ASIN, product title, brand, category, initial price, initial BSR, initial review count
- Record date added and last-checked timestamp
- Tag each ASIN with a competitive tier (direct / indirect / aspirational)
- Link your own ASIN to each competitor for direct comparison

### 2. Price Monitoring (Keepa-Style Logic)
- Track price changes over time with date-stamped entries
- Compute price volatility: (max price − min price) / avg price
- Flag: sustained price drops >10% — potential margin squeeze or launch push
- Flag: price increases >15% — possible supply issues or repositioning
- Identify coupon/promo patterns if mentioned (e.g., "20% off coupon visible")
- Compare competitor price vs. your price: gap analysis (you're $X cheaper/more expensive)

### 3. BSR Trend Analysis (30-Day Window)
- Log BSR data points at each check with timestamps
- Compute BSR velocity: (BSR start − BSR end) / days = ranks gained per day
- Positive velocity = ranking improving (lower BSR number)
- Flag: BSR improvement >500 ranks/week — aggressive launch or promotion detected
- Flag: BSR deterioration >1,000 ranks/week — possible listing suppression or inventory out
- Output sparkline-style trend table with direction indicators (↑↓→)

### 4. Review Velocity Analysis
- Track total review count at each snapshot date
- Compute weekly velocity: (reviews now − reviews at start) / weeks elapsed
- Industry benchmark: healthy launch pace = 10–30 reviews/week
- Flag: velocity >50 reviews/week — possible incentivized review campaign (watch for TOS risk)
- Flag: sudden review drop — potential review removals (negative signal)
- Compute projected total reviews in 30/60/90 days at current velocity

### 5. Listing Change Detection
- Snapshot: title, bullet points (all 5), A+ content presence, image count, video presence
- On `spy listing diff`, compare current snapshot vs. previous snapshot
- Highlight: title keyword changes (competitive repositioning signal)
- Highlight: bullet restructuring (conversion optimization attempt)
- Highlight: new images or video added (brand investment signal)
- Highlight: variation count changes (new sizes/colors = market expansion)

### 6. Competitive Positioning Matrix
- Build a comparison table: Your ASIN vs. each competitor
- Columns: Price | BSR | Reviews | Rating | Images | A+ | Video | Prime
- Score each competitor 1–5 on listing quality
- Identify your advantages and gaps
- Output recommended actions based on gaps found

## Output Format

Every report outputs:
1. **Competitive Snapshot** — current standings table for all tracked ASINs
2. **Movement Alerts** — any significant changes since last check
3. **Trend Charts** — BSR and price trends (text-based sparklines)
4. **Opportunity Flags** — competitor weaknesses you can exploit
5. **Recommended Actions** — prioritized list of competitive responses

## Rules

1. Always ask for the user's own ASIN when adding competitors — context requires knowing your position in the market
2. Never draw conclusions from a single data point — require at least 2 snapshots before declaring a trend
3. Flag data gaps explicitly — if a metric is missing, say so rather than estimating silently
4. Distinguish between correlation and causation — a BSR improvement could be organic or promotional
5. Never recommend matching a competitor's price cut without first checking your own margin floor
6. Save all snapshots to `~/amazon-spy/snapshots/` when `spy save` is called — tracking is only useful if historical data is preserved
7. Alert on anomalies proactively — if velocity data suggests a launch push or suppression event, surface it immediately

ClawHub Data Analysis Marketing+2

Shopify Email Flow Builder

Skill

Klaviyo and Shopify Email automation flow designer. Builds complete email flow blueprints for abandoned cart, post-purchase, win-back, welcome series, and br...

---
name: shopify-email-flow-builder
description: "Klaviyo and Shopify Email automation flow designer. Builds complete email flow blueprints for abandoned cart, post-purchase, win-back, welcome series, and browse abandonment — with timing, subject lines, copy angles, segmentation logic, and A/B test ideas. Triggers: email flow, klaviyo flow, email automation, abandoned cart email, post purchase email, winback email, welcome series, email sequence, shopify email, email marketing automation, drip campaign, email funnel"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/shopify-email-flow-builder
---

# Shopify Email Flow Builder

AI-powered email automation design agent — creates complete Klaviyo or Shopify Email flow blueprints with every email mapped out: timing, subject lines, copy angles, segmentation, and A/B test suggestions.

Describe your store, your product, or the flow you need. The agent builds the full sequence from trigger to exit, ready to hand to a copywriter or implement directly.

## Commands

```
build flow <type>                  # build a complete flow blueprint (e.g. build flow abandoned cart)
abandoned cart flow                # abandoned cart recovery sequence (3-email default)
post purchase flow                 # post-purchase onboarding and upsell sequence
winback flow                       # win-back lapsed customers sequence
welcome flow                       # welcome series for new subscribers
browse abandonment flow            # browse abandonment trigger sequence
flow audit                         # audit an existing flow for gaps and optimizations
email copy angle                   # generate subject line + copy angle options for a specific email
flow save <flow-name>              # save flow blueprint to workspace
```

## What Data to Provide

The agent works with:
- **Flow type** — "build abandoned cart flow for a skincare brand"
- **Store context** — product type, price point, brand voice, target customer
- **Existing setup** — "I have a 2-email abandoned cart but it's underperforming"
- **Performance data** — open rates, click rates, revenue per recipient if available
- **Platform** — Klaviyo, Shopify Email, Mailchimp, or other ESP

No API keys needed. No platform integration required.

## Workspace

Creates `~/email-flows/` containing:
- `memory.md` — saved store profiles, brand voice notes, past flows
- `flows/` — saved flow blueprints (markdown)
- `copy-angles.md` — library of subject lines and copy angles by flow type

## The 6 Core Flows

### Flow 1: Welcome Series
**Trigger**: New subscriber (email capture, not yet a customer)
**Goal**: Build relationship, establish brand, drive first purchase

| Email | Timing | Purpose |
|-------|--------|---------|
| Email 1 | Immediately | Welcome + deliver lead magnet (if any), brand story |
| Email 2 | Day 2 | Social proof — bestsellers, reviews, UGC |
| Email 3 | Day 4 | Education — how to use / why you'll love the product |
| Email 4 | Day 7 | Urgency offer — first-purchase discount or free shipping |
| Email 5 | Day 10 | Last chance — expiring offer or simple "did we lose you?" |

### Flow 2: Abandoned Cart
**Trigger**: Cart created, checkout not completed (1 hour minimum delay)
**Goal**: Recover the sale without over-discounting

| Email | Timing | Purpose |
|-------|--------|---------|
| Email 1 | 1 hour after abandonment | Reminder — "You left something behind", no discount |
| Email 2 | 24 hours after Email 1 | Objection handling — reviews, guarantee, FAQ |
| Email 3 | 48 hours after Email 2 | Incentive — offer discount only if previous emails failed |

**Segmentation split**: suppress Email 3 discount for VIP customers (they convert without it).

### Flow 3: Post-Purchase
**Trigger**: Order placed (first purchase)
**Goal**: Confirm, delight, cross-sell, generate reviews

| Email | Timing | Purpose |
|-------|--------|---------|
| Email 1 | Immediately | Order confirmation + what to expect (shipping timeline) |
| Email 2 | Day 3 | Product education — how to get the most from purchase |
| Email 3 | Day 7 (or at delivery) | Check-in — "Did it arrive? How are you liking it?" |
| Email 4 | Day 14 | Review request (post-usage, not at delivery) |
| Email 5 | Day 21 | Cross-sell / complementary product recommendation |
| Email 6 | Day 45 | Replenishment prompt (for consumable products only) |

### Flow 4: Win-Back (Lapsed Customer)
**Trigger**: X days since last purchase (typically 90–180 days, adjust by product purchase cycle)
**Goal**: Re-engage before customer churns permanently

| Email | Timing | Purpose |
|-------|--------|---------|
| Email 1 | Day 0 (trigger) | "We miss you" — no offer, just re-engagement |
| Email 2 | Day 7 | What's new — new products, improvements, social proof |
| Email 3 | Day 14 | Win-back offer — discount or bonus |
| Email 4 | Day 21 | Final attempt — "Is this goodbye?" (sunset path begins) |

**Sunset**: if no engagement after Email 4, move to suppression list or low-frequency list.

### Flow 5: Browse Abandonment
**Trigger**: Viewed product page, did NOT add to cart (session ended)
**Goal**: Return visitor to the product while interest is warm

| Email | Timing | Purpose |
|-------|--------|---------|
| Email 1 | 4–6 hours after browse | Product spotlight — features, benefits, reviews |
| Email 2 | 48 hours after Email 1 | Social proof focus — reviews, UGC, bestseller badge |

**Note**: only trigger if subscriber, not an existing customer in an active abandoned cart flow.

### Flow 6: VIP / High-Value Customer
**Trigger**: Customer reaches spend threshold (e.g., 3+ orders or $500+ LTV)
**Goal**: Reward loyalty, prevent churn, increase AOV

| Email | Timing | Purpose |
|-------|--------|---------|
| Email 1 | At threshold | VIP welcome — exclusive status, thank you |
| Email 2 | Monthly | Early access to new products / sales |
| Email 3 | Birthday or anniversary | Personal recognition + exclusive offer |

## Subject Line Formulas

| Formula | Example |
|---------|---------|
| Curiosity gap | "This is why your [product] isn't working" |
| Direct benefit | "Get [outcome] in [timeframe]" |
| Social proof | "[X] customers swear by this" |
| Urgency | "Last chance — [offer] ends tonight" |
| Personalization | "[First name], you left something behind" |
| Question | "Did something go wrong?" |
| Number list | "3 things you didn't know about [product]" |

## Segmentation Logic

Key segments to build in Klaviyo:
- **Engaged subscribers**: opened email in last 90 days
- **Unengaged subscribers**: no opens in 90–180 days (reduce send frequency, sunset after 180)
- **First-time buyers**: 1 order placed
- **Repeat buyers**: 2+ orders (exclude heavy discounters)
- **VIP**: top 10% by LTV or order count threshold
- **Discount buyers**: purchased only during sales (suppress from non-sale flows, protect margin)

## A/B Test Suggestions by Flow

| Flow | Test Idea |
|------|-----------|
| Abandoned cart Email 1 | Timing: 30 min vs. 1 hour delay |
| Abandoned cart Email 3 | Discount type: % off vs. $ off vs. free shipping |
| Welcome Email 1 | With vs. without founder video/story |
| Post-purchase Email 4 | Review request timing: Day 7 vs. Day 14 |
| Win-back Email 1 | Emotional tone vs. product-led subject line |

## Output Format

Every flow blueprint outputs:
1. **Flow Overview** — trigger, goal, total emails, estimated duration
2. **Email-by-Email Blueprint** — for each email: timing, subject line (3 options), preview text, copy angle, CTA, segmentation notes
3. **Flow Logic Map** — trigger conditions, exit conditions, branch logic (e.g., "if purchased, exit flow")
4. **Segmentation Rules** — who enters, who is excluded, branch splits
5. **A/B Test Recommendations** — 2–3 specific tests to run first
6. **Performance Benchmarks** — expected open rate, click rate, revenue per recipient by flow type

## Benchmarks

| Flow | Avg Open Rate | Avg Revenue/Recipient |
|------|--------------|----------------------|
| Welcome | 40–60% | $3–8 |
| Abandoned Cart | 35–50% | $5–15 |
| Post-Purchase | 50–70% | $2–6 |
| Win-Back | 10–20% | $1–4 |
| Browse Abandon | 25–40% | $2–8 |

## Rules

1. Always ask for product type and price point before designing flows — a $15 impulse product needs different timing than a $300 considered purchase
2. Never add a discount to abandoned cart Email 1 — train customers to wait for the offer; use reminder tone first
3. Suppress existing customers from welcome flows and new subscribers from win-back flows — overlap causes brand confusion
4. Flag when a store is on Shopify Email vs. Klaviyo — Klaviyo supports conditional splits and predictive analytics; Shopify Email does not
5. Recommend sunset logic for every win-back flow — unengaged subscribers hurt deliverability
6. Keep subject lines under 50 characters for mobile optimization (label when testing longer variants)
7. Save flow blueprints to `~/email-flows/flows/` when flow save command is used

ClawHub Writing Marketing+2

Seo Content Brief Generator

Skill

SEO content brief generator agent. Produces complete content briefs from a target keyword: search intent classification, content outline, target word count,...

---
name: seo-content-brief-generator
description: "SEO content brief generator agent. Produces complete content briefs from a target keyword: search intent classification, content outline, target word count, LSI and semantic terms, competitor content gaps, E-E-A-T signals, and internal link suggestions. Triggers: seo brief, content brief, keyword brief, seo content, content outline, content strategy, keyword research brief, content plan, seo writing, article brief, blog brief, content gap"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/seo-content-brief-generator
---

# SEO Content Brief Generator

AI-powered SEO brief agent — turns a single keyword into a complete, writer-ready content brief with structure, targets, and competitive intelligence baked in.

Provide a keyword, describe your site or niche, or paste competitor URLs and content. The agent classifies search intent, builds a full outline, sets word count targets, identifies semantic terms, and surfaces the gaps your content must fill to rank.

## Commands

```
brief for <keyword>                # generate complete SEO content brief for a keyword
content brief                      # start interactive brief builder (prompts for details)
competitor gaps                    # analyze what top-ranking content covers that yours doesn't
outline only                       # get just the H2/H3 outline without full brief
brief save <keyword>               # save brief to workspace for future reference
brief history                      # list all saved briefs in workspace
```

## What Data to Provide

The agent works with:
- **Target keyword** — "brief for best standing desk under $500"
- **Site context** — niche, domain authority, existing content, target audience
- **Competitor content** — paste URLs or copy text from top-ranking articles
- **Content goals** — rank for keyword, drive conversions, support existing pillar page
- **Existing content** — "I already have an article about standing desks, this is a supporting piece"

No API keys needed. No SEO tool subscription required.

## Workspace

Creates `~/seo-briefs/` containing:
- `memory.md` — site context, niche profile, past keyword research
- `briefs/` — saved content briefs organized by keyword (markdown)
- `clusters.md` — topic cluster maps and internal link architecture

## Analysis Framework

### 1. Search Intent Classification

Every keyword falls into one of four intents:

| Intent | Definition | Content Type |
|--------|-----------|--------------|
| Informational | User wants to learn | How-to guide, explainer, listicle |
| Commercial | User is comparing options | Best-of list, comparison, review |
| Transactional | User is ready to buy | Product page, landing page, pricing |
| Navigational | User wants a specific site | Brand/site-specific page |

**Mixed intent detection**: some keywords have blended signals (e.g., "best CRM for small business" = commercial + transactional). Brief format adapts accordingly.

### 2. SERP Analysis Approach
Without live SERP access, analyze based on keyword signals:
- Question words (what, how, why, when) → informational
- Modifiers (best, top, review, vs., alternative) → commercial
- Action words (buy, get, download, sign up) → transactional
- Brand names in keyword → navigational
- Ask user to describe top 3 ranking results for calibration

### 3. Outline Structure by Intent

#### Informational
- H1: Target keyword (conversational, long-tail phrasing)
- Introduction: Define the problem/question (150 words)
- H2: Background / Why This Matters
- H2: [Core Concept 1] — H3 subtopics
- H2: [Core Concept 2] — H3 subtopics
- H2: [Core Concept 3] — H3 subtopics
- H2: Step-by-Step Guide or Practical Application
- H2: Common Mistakes / FAQs
- Conclusion + CTA

#### Commercial Investigation
- H1: Best [Product Category] for [Use Case] — [Year]
- Introduction: Who this guide is for + selection criteria (200 words)
- H2: Quick Picks (summary table)
- H2: [Product 1] Review — H3: Pros, Cons, Best For
- H2: [Product 2] Review (repeat pattern)
- H2: Buyer's Guide — H3: Key factors to consider
- H2: FAQ
- Conclusion + CTA

#### Transactional
- H1: [Primary Keyword] — [Value Prop]
- Above-fold: clear CTA, key benefits, trust signals
- H2: Features / What You Get
- H2: How It Works
- H2: Pricing
- H2: Social Proof / Case Studies
- H2: FAQ / Objection Handling
- Final CTA

### 4. Word Count Benchmarks by Intent
| Intent | Recommended Word Count |
|--------|----------------------|
| Informational (simple) | 800–1,200 |
| Informational (complex) | 1,500–2,500 |
| Commercial list (5–10 products) | 2,000–3,500 |
| Commercial comparison | 1,500–2,500 |
| Transactional / Landing page | 600–1,200 |
| Pillar / Hub page | 3,000–5,000+ |

### 5. Semantic Keyword Clusters
For each brief, identify 3 types of supplementary terms:
- **LSI (Latent Semantic Indexing) terms**: conceptually related words (e.g., for "standing desk": ergonomics, posture, height-adjustable, sit-stand)
- **Entity terms**: proper nouns, brands, people Google associates with the topic
- **Question variations**: PAA-style questions to answer (People Also Ask)

Target natural density: primary keyword 1–2%, semantic terms woven throughout, zero stuffing.

### 6. Competitor Content Gap Analysis
Gaps to identify and fill:
- Topics covered by top-ranking content that a draft would miss
- Questions left unanswered in competitor articles
- Outdated information that can be replaced with current data
- Missing content types (no video, no table, no comparison chart)
- Thin sections where competitors spend only 1 paragraph on a subtopic that deserves depth

### 7. E-E-A-T Signals (Experience, Expertise, Authoritativeness, Trustworthiness)
Checklist for brief:
- [ ] Author bio with credentials relevant to topic
- [ ] First-hand experience signals ("in my testing", "I used this for 30 days")
- [ ] Cite original research or primary sources
- [ ] Last updated date visible on page
- [ ] Expert quotes or external validation
- [ ] Clear editorial process or review policy (especially for YMYL topics)
- [ ] Transparent affiliate disclosure if applicable

### 8. Internal Link Suggestions
- **Link from**: existing high-authority pages on your site that should pass equity to this article
- **Link to**: supporting pages this article should reference (product pages, related guides)
- **Anchor text**: descriptive, keyword-relevant (not "click here")
- **Cluster logic**: this article should sit within a topic cluster with a defined pillar page

## Output Format

Every brief outputs:
1. **Brief Header** — keyword, intent classification, target audience, content goal
2. **Word Count Target** — with rationale based on intent and competition
3. **Full Outline** — H1, H2s, H3s with brief description of each section's purpose
4. **Semantic Terms List** — LSI terms, entities, and question variations
5. **E-E-A-T Checklist** — items to include for trust and authority signals
6. **Competitor Gap List** — 3–5 specific gaps to fill vs. top-ranking content
7. **Internal Link Map** — pages to link from and to, with anchor text suggestions
8. **Writer Notes** — tone, format preferences, any avoid list

## Rules

1. Always classify search intent before building any outline — wrong intent = wrong content type = no rank
2. Never recommend targeting a keyword without understanding the site's domain authority context — realistic rank assessment matters
3. Provide word count as a range, not a single number — "aim for 1800–2200" not "write 2000 words"
4. Flag YMYL (Your Money Your Life) topics — health, finance, legal — these require stricter E-E-A-T treatment
5. Separate primary keyword from semantic terms clearly — brief must not confuse writers about what the main target is
6. Internal link suggestions are only useful if the site has existing relevant content — ask before suggesting
7. Save briefs to `~/seo-briefs/briefs/` when brief save command is used

ClawHub Data Analysis Research+2

Amazon Listing Health Monitor

Skill

Amazon listing health audit agent. Checks title and bullet completeness, keyword coverage, image count, BSR trends, Buy Box eligibility, suppression risk, an...

---
name: amazon-listing-health-monitor
description: "Amazon listing health audit agent. Checks title and bullet completeness, keyword coverage, image count, BSR trends, Buy Box eligibility, suppression risk, and hijacker signals. Scores each listing and delivers a prioritized fix list. Triggers: listing audit, amazon listing, listing health, listing score, buy box, listing optimization, listing checker, product listing, amazon seo, backend keywords, listing suppression, hijacker, listing quality"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/amazon-listing-health-monitor
---

# Amazon Listing Health Monitor

AI-powered Amazon listing audit agent — scores your listing across content, keywords, images, and account health signals, then tells you exactly what to fix first.

Paste your listing details, describe your ASIN, or provide a bulk export. The agent audits every dimension and returns a prioritized action plan.

## Commands

```
listing audit                      # full health audit (paste listing content or describe ASIN)
listing score                      # get numeric health score with dimension breakdown
bsr track                          # log and analyze BSR trend for a product
buy box check                      # assess Buy Box eligibility and loss signals
keyword audit                      # evaluate keyword coverage in title, bullets, description
image audit                        # check image count, types, and compliance
listing compare                    # compare two listing versions side by side
listing save <asin>                # save listing snapshot to workspace
```

## What Data to Provide

The agent works with:
- **Listing content** — paste title, bullet points, description, backend keywords
- **ASIN description** — "ASIN B08XYZ, baby monitor, 3.8 stars, 1200 reviews, BSR 450 in Baby"
- **Seller Central export** — paste Flat File or Inventory report rows
- **Screenshots** — listing page, Seller Central listing quality dashboard
- **Metrics** — "title is 95 chars, 4 bullets, 5 images, lost Buy Box 3 days ago"

No API keys needed. No tools required.

## Workspace

Creates `~/amazon-listings/` containing:
- `memory.md` — saved ASIN profiles and audit history
- `reports/` — past audit reports (markdown)
- `bsr-log.md` — BSR snapshots for trend tracking

## Analysis Framework

### 1. Content Completeness Audit

#### Title
| Signal | Benchmark |
|--------|-----------|
| Character length | 150–200 chars (category-dependent) |
| Primary keyword | In first 80 characters |
| Brand name | Present |
| Key attributes | Size, color, quantity, material |
| Forbidden elements | No promotional claims, no symbols like !, $ |

#### Bullet Points
- Count: 5 bullets (maximum allowed, all should be used)
- Each bullet: 150–250 characters (enough detail, not truncated in mobile)
- Top-3 bullets: lead with benefit, not feature
- Keyword integration: secondary and LSI keywords distributed across bullets
- Forbidden: shipping claims, seller-specific info, subjective claims without context

#### Product Description / A+ Content
- A+ Content preferred over plain description for brand-registered sellers
- Plain description: 2000 character limit, HTML formatting supported
- A+ Content: check for comparison chart module (drives conversion)

### 2. Keyword Coverage Check
- **Title keywords**: primary high-volume keyword must appear in title
- **Bullet keywords**: secondary keywords distributed (not keyword stuffed)
- **Backend search terms**: 250 bytes (not characters) total, no repetition, no brand names of competitors
- **Subject Matter fields**: used for additional indexing (intended use, target audience, material)
- Red flag: primary keyword absent from both title and backend = indexing gap

### 3. Image Audit
| Requirement | Standard |
|-------------|----------|
| Minimum images | 4 (7+ strongly recommended) |
| Main image | Pure white background, product fills 85%+ of frame |
| Lifestyle images | At least 2 showing product in use |
| Infographic | At least 1 with key specs/benefits called out |
| Size chart | Required for apparel, recommended for any sized product |
| Video | Strongly recommended — increases conversion rate |

### 4. BSR Benchmarks by Category
| BSR Range | Category Signal |
|-----------|----------------|
| Top 100 | Bestseller in category |
| Top 1,000 | Strong seller |
| Top 10,000 | Established, moderate velocity |
| Top 100,000 | Low velocity, optimization needed |
| > 100,000 | Low sales, investigate listing issues |

BSR drops > 20% in 7 days = investigate: stock-out, price change, competitor surge, listing suppression.

### 5. Buy Box Eligibility Signals
Factors Amazon weighs for Buy Box:
- **Price competitiveness**: within ~2% of lowest FBA price
- **Fulfillment method**: FBA preferred over FBM
- **Order defect rate (ODR)**: must be < 1%
- **Late shipment rate**: < 4%
- **Seller feedback score**: > 95% positive (trailing 12 months)
- **In-stock rate**: consistent stock, no stockouts
- **Account health**: no active policy violations

Buy Box loss signals: price undercut by competitor, new FBA seller entered, account metric dip.

### 6. Suppression Trigger Checklist
Common suppression causes:
- Main image does not meet white background requirement
- Title exceeds character limit for category
- Missing required attributes (e.g., material, size for apparel)
- Listing flagged for prohibited content (health claims, offensive language)
- Pricing error (price too high or too low vs. reference price)
- ASIN merged/split issue creating content conflict

### 7. Hijacker Detection Signals
- Buy Box seller is not your brand
- Product reviews mention a different product than yours
- Main image has changed without your action
- Listing title or bullets contain unfamiliar text
- Sudden BSR drop with no change in your ad spend or pricing

## Listing Health Score

Score each dimension 1–10. Overall Health Score = weighted average:

| Dimension | Weight |
|-----------|--------|
| Title quality | 20% |
| Bullet quality | 20% |
| Image completeness | 20% |
| Keyword coverage | 20% |
| Buy Box status | 10% |
| Suppression risk | 10% |

Score interpretation: 8–10 = Healthy, 6–7 = Needs work, < 6 = Critical issues present.

## Output Format

Every audit outputs:
1. **Health Score** — numeric score with per-dimension breakdown
2. **Critical Issues** — suppression risks, Buy Box loss signals, hijacker alerts (fix immediately)
3. **Content Gaps** — missing images, thin bullets, title issues (fix this week)
4. **Keyword Opportunities** — indexing gaps and backend keyword improvements
5. **Prioritized Action List** — ranked by impact, with specific copy suggestions where possible

## Rules

1. Always ask for the product category before auditing — title length limits and required attributes vary by category
2. Flag suppression risks before any other finding — suppression means zero sales
3. Never suggest keyword stuffing — penalization risk outweighs any indexing gain
4. Distinguish between brand-registered and non-registered listings — A+ Content and Brand Story only available to registered brands
5. When Buy Box is lost, investigate pricing and fulfillment method first — these are the two most common causes
6. Note when BSR data is a single snapshot vs. trend — single point is insufficient for meaningful analysis
7. Save listing snapshots to `~/amazon-listings/` when listing save command is used

Reddit Pain Point Scanner

Skill

Reddit market research agent. Scans subreddits and keywords for recurring user complaints, unmet needs, and product gaps. Clusters pain points by frequency a...

---
name: reddit-pain-point-scanner
description: "Reddit market research agent. Scans subreddits and keywords for recurring user complaints, unmet needs, and product gaps. Clusters pain points by frequency and urgency, and outputs structured opportunity reports for product development or positioning. Triggers: reddit pain points, subreddit research, market research reddit, customer complaints reddit, product opportunity, reddit scanner, reddit insights, pain point analysis, reddit market research, voice of customer"
allowed-tools: Bash
metadata:
openclaw:
homepage: https://github.com/mguozhen/reddit-pain-point-scanner
---

# Reddit Pain Point Scanner

AI-powered Reddit research agent — surfaces recurring complaints, unmet needs, and product gaps hiding in subreddit discussions.

Describe a subreddit, paste post/comment text, or name a market vertical. The agent clusters pain points, scores frequency and urgency, and delivers a structured opportunity report you can act on immediately.

## Commands

```
scan r/<subreddit> # analyze pain points in a target subreddit
scan keyword <term> # find complaints around a specific keyword or product
pain points report # generate ranked pain point summary with frequency scores
trending complaints # surface newest or fastest-rising complaint themes
opportunity gaps # identify unmet needs with low existing solution density
save scan <topic-name> # save current scan results to workspace
```

## What Data to Provide

The agent works with:
- **Subreddit name** — "scan r/mealprep" or "analyze r/solotravel complaints"
- **Pasted posts/comments** — copy Reddit thread text directly into chat
- **Keywords** — "find pain points around standing desks" or "complaints about project management tools"
- **Niche description** — "I'm targeting home gym owners, find their biggest frustrations"
- **Multiple subreddits** — "compare pain points across r/personalfinance and r/financialindependence"

No API keys needed. No Reddit scraping required — paste content or describe what you've found.

## Workspace

Creates `~/reddit-scanner/` containing:
- `memory.md` — saved scan history, tracked subreddits, and recurring themes
- `reports/` — past pain point reports (markdown)
- `opportunities.md` — curated product/positioning opportunity log

## Analysis Framework

### 1. Post Collection Strategy
- Focus posts: titles containing complaint signals ("why is", "anyone else", "frustrated", "why can't", "hate that", "wish there was", "does anyone know how to fix")
- High-signal comment threads: replies with high upvotes agreeing with a pain point
- Recurring threads: same question asked multiple times = unmet need
- Rant/vent posts: explicit frustration with existing products or workflows

### 2. Complaint Clustering
Group similar complaints into named themes:
- Parse for shared nouns (product category, feature, situation)
- Merge semantically similar complaints ("too expensive" + "price is insane" + "can't afford" = **Pricing Barrier**)
- Label each cluster with a clear theme name and representative quote
- Minimum cluster size: 2+ mentions to qualify (single mentions logged separately as weak signals)

### 3. Frequency Scoring
| Score | Criteria |
|-------|----------|
| 5 — Very High | 10+ distinct mentions, multiple threads, ongoing |
| 4 — High | 5–9 mentions across threads |
| 3 — Medium | 3–4 mentions |
| 2 — Low | 2 mentions |
| 1 — Weak Signal | 1 mention, notable quality |

### 4. Urgency Signal Detection
High-urgency pain points contain language like:
- "please", "desperately need", "so frustrated", "I give up", "why can't anyone"
- Active workarounds described (problem is real and unsolved)
- Monetary loss or time loss mentioned ("wasted 3 hours", "cost me $200")
- Multiple commenters validating with "same here", "this exactly", "+1"

### 5. Existing Solution Density
- Are existing products being mentioned as partial solutions?
- Are users recommending workarounds (DIY, duct-tape fixes)?
- Zero mentions of solutions = white space opportunity
- Many solutions mentioned but still frustrated = execution gap (better UX, price, support)

### 6. Opportunity Assessment Matrix
| Dimension | Signal |
|-----------|--------|
| Frequency | How often is it mentioned? |
| Urgency | How strong is the emotional signal? |
| Solution Gap | How poorly is the current solution meeting needs? |
| Addressability | Can a product/service realistically solve this? |
| Market Size | Does the subreddit represent a large audience? |

Score each dimension 1–5. Opportunity Score = average across all 5. Scores above 3.5 = high-priority opportunity.

## Output Format

Every scan outputs:
1. **Top Pain Points** — ranked list with theme name, frequency score, urgency level, and representative quote
2. **Opportunity Gaps** — pain points with low solution density, sorted by opportunity score
3. **Verbatim Evidence** — 2–3 direct quotes per major pain point
4. **Workaround Patterns** — what users are doing today to cope (indicates willingness to pay)
5. **Recommended Action** — product idea, positioning angle, or content opportunity for each gap

## Rules

1. Always cluster complaints before scoring — raw mention counts without grouping mislead
2. Distinguish between complaints about an existing product vs. complaints about the absence of a solution
3. Never conflate feature requests with pain points — they are different signal types (both valuable, labeled separately)
4. Flag when a subreddit is too small (fewer than 10k members) — data may not generalize
5. Note recency: a pain point from 3 years ago may be solved today — ask user to confirm current relevance
6. Save all scans to `~/reddit-scanner/reports/` when save scan command is used
7. Always quote verbatim Reddit language in the report — user's own words are the most powerful positioning fuel

ClawHub Data Analysis Product+2

Shopify Profit Calculator

Skill

Shopify profit calculation agent. Computes true net profit per product or order by subtracting COGS, Shopify fees, Stripe/payment fees, ad spend, and shippin...

---
name: shopify-profit-calculator
description: "Shopify profit calculation agent. Computes true net profit per product or order by subtracting COGS, Shopify fees, Stripe/payment fees, ad spend, and shipping from revenue. Tracks margin trends and flags unprofitable SKUs. Triggers: shopify profit, profit calculator, shopify margin, true profit, net profit, cogs, shopify fees, profit per product, ecommerce profit, shopify p&l, gross margin, contribution margin"
allowed-tools: Bash
metadata:
  openclaw:
    homepage: https://github.com/mguozhen/shopify-profit-calculator
---

# Shopify Profit Calculator

AI-powered profit analysis agent for Shopify stores — calculates your true net profit after every fee, cost, and ad dollar is accounted for.

Paste your order data, describe your cost structure verbally, or ask about specific products. The agent computes real margins, flags danger zones, and shows exactly where your money is going.

## Commands

```
profit calc                        # calculate net profit (paste order/product data or describe)
profit by product                  # break down profit margin for each SKU
profit by order                    # compute net profit for a specific order
profit trend                       # show margin trend over time (requires saved data)
set cogs <product> <cost>          # save COGS for a product to memory
profit report                      # generate full P&L summary report
profit save <store-name>           # save store profile and COGS table to workspace
```

## What Data to Provide

The agent works with:
- **Order exports** — paste Shopify order CSV rows (revenue, product, shipping collected)
- **Verbal description** — "I sell a $49 product, COGS $12, Shopify Basic plan, running $800/mo Facebook ads, average shipping $6"
- **Product details** — price, COGS, ad spend per unit, shipping cost, return rate
- **Screenshots** — Shopify analytics, ad dashboards, payment processor summaries

No API keys needed. No setup required.

## Workspace

Creates `~/shopify-profit/` containing:
- `memory.md` — saved store profiles, COGS tables, fee structures
- `reports/` — past profit reports (markdown)
- `products.md` — per-SKU cost and margin records

## Fee Structure Reference

### Shopify Platform Fees (transaction fees on top of plan cost)
| Plan | Transaction Fee (non-Shopify Payments) |
|------|----------------------------------------|
| Basic | 2.0% |
| Shopify | 1.0% |
| Advanced | 0.5% |
| Plus | 0.15% |
| Shopify Payments | 0% transaction fee |

### Payment Processing Fees (Shopify Payments)
| Plan | Online Rate |
|------|-------------|
| Basic | 2.9% + 30¢ |
| Shopify | 2.6% + 30¢ |
| Advanced | 2.4% + 30¢ |

### Stripe (if used instead of Shopify Payments)
- Standard: 2.9% + 30¢ per transaction
- Plus Shopify transaction fee on top

## Analysis Framework

### 1. Revenue Breakdown
- Gross revenue (order total)
- Refunds and chargebacks deducted
- Net revenue = Gross - Refunds

### 2. Cost Stack (deducted from net revenue)
1. **COGS** — product cost, packaging, manufacturing
2. **Shopify platform fee** — transaction % based on plan
3. **Payment processing fee** — Stripe/Shopify Payments % + fixed
4. **Shipping cost** — label cost minus any shipping collected from customer
5. **Ad spend attribution** — ad spend / units sold (blended) or per-order if tracked
6. **Returns/refunds reserve** — estimated return rate × avg order value
7. **Other variable costs** — fulfillment center fees, insert cards, etc.

### 3. Profit Margin Benchmarks
| Status | Net Margin | Action |
|--------|-----------|--------|
| Healthy | > 20% | Maintain, scale ad spend |
| Warning | 10–20% | Audit fees and COGS, reduce waste |
| Danger | < 10% | Stop scaling, fix cost structure immediately |
| Loss | < 0% | Pause ads, renegotiate COGS or raise price |

### 4. Contribution Margin Analysis
- Contribution Margin = Revenue - Variable Costs (COGS + fees + shipping + ads)
- Fixed costs (Shopify plan, apps, staff) allocated across unit volume
- Break-even units = Fixed Costs / Contribution Margin per Unit

### 5. Ad Attribution Methods
- **Blended ROAS**: Total Revenue / Total Ad Spend (store-wide view)
- **Per-product**: Ad spend tagged to product × units sold
- **MER (Marketing Efficiency Ratio)**: Total Revenue / Total Marketing Spend (most reliable for multi-channel)

### 6. Trend Analysis
- Month-over-month margin movement
- Margin erosion detection (fees crept up, COGS increased, shipping surcharge added)
- Best and worst performing SKUs by net margin

## Output Format

Every profit calculation outputs:
1. **Net Profit Summary** — revenue, total costs, net profit, net margin %
2. **Cost Waterfall** — each deduction line-item with $ amount and % of revenue
3. **Margin Status** — Healthy / Warning / Danger / Loss with color signal
4. **Highest Impact Fix** — single biggest lever to improve margin
5. **Comparison** — vs. industry benchmark and vs. prior period if data available

## Rules

1. Always ask for COGS and Shopify plan before calculating — these are the two biggest variables
2. Never assume payment processor; ask if Shopify Payments or Stripe/other is in use
3. Show every deduction line by line — no black-box totals
4. Flag if ad spend attribution is blended vs. per-order (blended understates product-level margin)
5. Warn when a product's contribution margin is positive but net margin is negative (fixed cost allocation issue)
6. Save COGS data to `~/shopify-profit/products.md` when set cogs command is used
7. Always express margin as both $ per unit and % of revenue

Amazon Ppc Analyzer

Skill

Amazon PPC specialist agent. Audits Sponsored Products campaigns, finds wasted spend, surfaces high-converting search terms, suggests bid adjustments, and bu...

---
name: amazon-ppc-analyzer
description: "Amazon PPC specialist agent. Audits Sponsored Products campaigns, finds wasted spend, surfaces high-converting search terms, suggests bid adjustments, and builds negative keyword lists — all from your bulk report data or verbal description. Triggers: amazon ppc, ppc audit, sponsored products, ppc analyzer, bid optimization, search term report, wasted spend, negative keywords, acos, roas, ppc strategy, amazon advertising, amazon ads, ppc campaign, keyword harvesting, ppc review, ad spend analysis, amazon ppc optimization"
allowed-tools: Bash
metadata:
openclaw:
homepage: https://github.com/mguozhen/amazon-ppc-analyzer
---

# Amazon PPC Analyzer

AI-powered Amazon PPC audit agent — turns your campaign data into actionable optimizations.

Paste your bulk report, describe your campaigns verbally, or ask specific questions. The agent audits structure, finds waste, surfaces winners, and tells you exactly what to do next.

## Commands

```
ppc audit # full campaign audit (paste data or describe)
ppc wasted spend # find keywords draining budget with no conversions
ppc bid suggestions # get bid adjustment recommendations
ppc search terms # harvest converting search terms for exact match
ppc negatives # build negative keyword list from irrelevant terms
ppc structure check # evaluate campaign/ad group architecture
ppc budget allocation # identify over/under-funded campaigns
ppc weekly report # generate weekly performance summary
ppc save <campaign-name> # save campaign profile to memory
ppc history # show saved campaigns and past audits
```

## What Data to Provide

The agent works with:
- **Bulk report CSV** — paste rows directly into chat (Search Term Report, Campaign Performance Report, Keyword Report)
- **Verbal description** — "I have 3 SP campaigns, $150/day budget, 45% ACoS, mainly broad match"
- **Screenshots** — paste Seller Central campaign manager data
- **Metrics only** — "keyword X spent $200, 0 orders, 12 clicks"

No API keys needed. No setup required.

## Workspace

Creates `~/amazon-ppc/` containing:
- `memory.md` — saved campaign profiles and account history
- `reports/` — past audit reports (markdown)
- `data/` — raw data snapshots for trend tracking

## Analysis Framework

### 1. Campaign Structure Audit
- SP / SB / SD separation
- Match type distribution (broad/phrase/exact ratio)
- Ad group granularity (1 product per ad group vs. lumped)
- Auto vs. manual campaign relationship

### 2. Wasted Spend Detection
- Keywords with spend > $X and 0 orders (X = your break-even CPC threshold)
- Irrelevant search terms triggering ads (auto campaign bleed)
- Duplicate keywords across campaigns causing internal competition

### 3. Bid Optimization
- Keywords with ACoS > target → bid down formula: New Bid = (Current Bid × Target ACoS) / Current ACoS
- Keywords with ACoS < target and low impressions → bid up to capture more volume
- Keywords converting well but losing impressions → identify bid floor vs. auction pressure
- ROAS equivalent: Target ROAS = 1 / Target ACoS (e.g. 25% ACoS = 4x ROAS target); same bid formula applies

### 4. Search Term Harvesting
- Converting search terms in auto/broad → promote to exact match manual campaigns (minimum 2 clicks + 1 order before harvesting to avoid single-click noise)
- High-impression, zero-click search terms → add as phrase negatives
- Competitor ASINs appearing as search terms → ASIN targeting opportunities

### 5. Negative Keyword Mining
- Irrelevant terms from auto campaign search term report
- Shared negatives across campaign portfolio
- Campaign-level vs. ad group-level negative placement logic

### 6. Budget & Dayparting Analysis
- Campaigns hitting budget cap before end of day → lost impression share
- Budget reallocation from low-ROAS to high-ROAS campaigns
- Day/hour performance patterns (if data available)

## Benchmarks Used

| Metric | Aggressive | Balanced | Conservative |
|--------|-----------|----------|--------------|
| ACoS target | 15–20% | 25–30% | 35–40% |
| CTR (good) | >0.5% | 0.3–0.5% | <0.3% |
| CVR (good) | >10% | 5–10% | <5% |
| Impression share | >60% | 40–60% | <40% |

## Weekly Report Format

`ppc weekly report` generates a consistent 7-day performance summary:
1. **Spend vs. Last Week** — total spend delta and budget utilization %
2. **ACoS Trend** — overall ACoS this week vs. last week, direction arrow
3. **Top 5 Performing Keywords** — ranked by orders, with ACoS each
4. **Top 5 Wasted Spend Keywords** — spend with 0 orders, sorted by $ wasted
5. **Search Term Wins** — new converting search terms worth harvesting
6. **Actions Taken / Pending** — changes made and outstanding items
7. **Next Week Focus** — 2–3 priority optimizations for the coming week

Saves to `~/amazon-ppc/reports/weekly-YYYY-MM-DD.md` automatically.

## Output Format

Every audit outputs:
1. **Executive Summary** — 3-bullet account health snapshot
2. **Quick Wins** — actions you can take in the next 30 minutes
3. **Structural Issues** — longer-term fixes
4. **Data Table** — keywords sorted by priority (waste / opportunity)
5. **Next Audit Checklist** — what to check again in 7 days

## Rules

1. Always ask for target ACoS or profit margin before making bid recommendations
2. Never recommend pausing or making aggressive bid cuts on keywords with fewer than 10 clicks — insufficient data
3. Flag when data sample is too small for reliable conclusions
4. Distinguish between launch phase (high ACoS acceptable) vs. optimization phase
5. Show math behind every bid recommendation
6. Save audit findings to `~/amazon-ppc/reports/` when asked