Amazon Listing Audit Pro — 8-Dimension Health Check
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.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/apiclaw/amazon-listing-audit-proAPIClaw — 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)
- Category auto-detection: categoryPath is auto-detected from ASIN. If
category_sourcein output isinferred_from_search, confirm with user - All keyword-based endpoints MUST include
--category; ASIN-specific endpoints do NOT - Use API fields directly: revenue=
sampleAvgMonthlyRevenue(NEVER price×sales), sales=monthlySalesFloor, opportunity=sampleOpportunityIndex - reviews/analysis: needs 50+ reviews; ASIN mode first, category fallback
- Sales null fallback: Monthly sales ≈ 300,000 / BSR^0.65, tag 🔍
Execution
listing-audit --my-asin X [--keyword Y] [--category Z](composite, auto-detects category from ASIN)- 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)
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-apiclaw-amazon-listing-audit-pro": {
"enabled": true,
"auto_update": true
}
}
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