Skill: apiclaw/amazon-listing-audit-pro (version 1.0.1)

--- name: Amazon Listing Audit Pro — 8-Dimension Health Check version: 1.0.1 description: > Comprehensive listing health check and optimization engine for Amazon sellers. Scores listings across 8 dimensions, benchmarks against category leaders, identifies keyword gaps, and generates data-backed improvement recommendations. Supports single ASIN or bulk audit (10-100+ ASINs for agencies). Uses all 11 APIClaw API endpoints with cross-validation. Use when user asks about: listing audit, listing optimization, listing score, listing quality, improve my listing, listing review, listing diagnosis, title optimization, bullet point optimization, keyword gaps, listing benchmark, A+ content, listing health check, listing comparison. Requires APICLAW_API_KEY. author: SerendipityOneInc homepage: https://github.com/SerendipityOneInc/APIClaw-Skills metadata: {"openclaw": {"requires": {"env": ["APICLAW_API_KEY"]}, "primaryEnv": "APICLAW_API_KEY"}} --- # APIClaw — Amazon Listing Audit Pro > 8-dimension health check. Benchmark against leaders. Fix what matters most. Respond in user's language. ## Files | File | Purpose | |------|---------| | `{skill_base_dir}/scripts/apiclaw.py` | **Execute** for all API calls (run `--help` for params) | | `{skill_base_dir}/references/reference.md` | Load for exact field names or response structure | ## Credential Required: `APICLAW_API_KEY`. Get free key at [apiclaw.io/api-keys](https://apiclaw.io/en/api-keys). ## Input Required: my_asin. Optional: keyword, category. Category is auto-detected from ASIN via `realtime/product` if not provided. If `category_source` is `inferred_from_search`, confirm with user before proceeding. ## API Pitfalls (CRITICAL) 1. **Category auto-detection**: categoryPath is auto-detected from ASIN. If `category_source` in output is `inferred_from_search`, confirm with user 2. **All keyword-based endpoints MUST include `--category`**; ASIN-specific endpoints do NOT 3. **Use API fields directly**: revenue=`sampleAvgMonthlyRevenue` (NEVER price×sales), sales=`monthlySalesFloor`, opportunity=`sampleOpportunityIndex` 4. **reviews/analysis**: needs 50+ reviews; ASIN mode first, category fallback 5. **Sales null fallback**: Monthly sales ≈ 300,000 / BSR^0.65, tag 🔍 ## Execution 1. `listing-audit --my-asin X [--keyword Y] [--category Z]` (composite, auto-detects category from ASIN) 3. Score 8 dimensions → generate report with improvements ## 8 Scoring Dimensions | Dimension | Weight | 90-100 | 60-89 | 30-59 | 0-29 | |-----------|--------|--------|-------|-------|------| | Title | 15% | 150+ chars, top 3 KW, brand first | 100-150, 2 KW | <100 or stuffed | Missing key terms | | Bullets | 15% | 5+, benefit-led, KW each | 5, features only | 3-4, generic | <3 bullets | | Images | 15% | 7+, infographic+lifestyle | 5-6, decent | 3-4, basic | 1-2 images | | A+ Content | 10% | Rich A+, comparison, brand story | Basic A+ | No A+ w/ description | Nothing | | Reviews | 15% | 1000+, 4.5+, <5% 1-star | 200-1K, 4.0-4.5 | 50-200, 3.5-4.0 | <50 or <3.5 | | Keywords | 10% | Top 5 competitor KW covered | 3-4 covered | 1-2 covered | None matched | | Category Fit | 10% | Optimal category, top 1% BSR | Top 5% | Suboptimal | Wrong category | | Pricing | 10% | In opportunity band, margin >25% | Hottest band | Outside top bands | Overpriced/<10% margin | Score each 0-100, calculate weighted total. Include "Basis" column explaining each score. ## Output Spec Sections: Overall Score (X/100, A-F grade) → 8-Dimension Scorecard → Title Audit (analysis + suggested rewrite) → Bullets Audit (vs leaders, missing points, rewrites) → Image Audit → Review Health → Keyword Gap Analysis (vs Top 5 leader titles/bullets) → vs Category Leaders (side-by-side Top 3) → Priority Fix List (lowest scores first) → Data Provenance → API Usage. Suggested rewrites should incorporate high-frequency positive review language. ### Language (required) Output language MUST match the user's input language. If the user asks in Chinese, the entire report is in Chinese. If in English, output in English. Exception: API field names (e.g. `monthlySalesFloor`, `categoryPath`), endpoint names, technical terms (e.g. ASIN, BSR, CR10, FBA, credits) remain in English. ### Disclaimer (required, at the top of every report) > Data is based on APIClaw API sampling as of [date]. Monthly sales (`monthlySalesFloor`) are lower-bound estimates. This analysis is for reference only and should not be the sole basis for business decisions. Validate with additional sources before acting. ### Confidence Labels (required, tag EVERY conclusion) - 📊 **Data-backed** — direct API data (e.g. "CR10 = 54.8% 📊") - 🔍 **Inferred** — logical reasoning from data (e.g. "brand concentration is moderate 🔍") - 💡 **Directional** — suggestions, predictions, strategy (e.g. "consider entering $10-15 band 💡") Rules: Strategy recommendations are NEVER 📊. User criteria override AI judgment. Bulk audit: share market data across ASINs, run audit per ASIN. ### Data Provenance (required) Include a table at the end of every report: | Data | Endpoint | Key Params | Notes | |------|----------|------------|-------| | (e.g. Market Overview) | `markets/search` | categoryPath, topN=10 | 📊 Top N sampling, sales are lower-bound | | ... | ... | ... | ... | Extract endpoint and params from `_query` in JSON output. Add notes: sampling method, T+1 delay, realtime vs DB, minimum review threshold, etc. ### API Usage (required) | Endpoint | Calls | Credits | |----------|-------|---------| | (each endpoint used) | N | N | | **Total** | **N** | **N** | Extract from `meta.creditsConsumed` per response. End with `Credits remaining: N`. ## API Budget: ~20-25 credits Audit target(1) + Categories/Products/Competitors(3) + Realtime×5(5) + Market/Brand(3) + Price(2) + Reviews(2) + History(1) + Buffer(3-8).


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)