Amazon Listing Audit Pro

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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"}}

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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.

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).