/install amazon-listing-audit-pro
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)
- 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 | \x3C100 or stuffed | Missing key terms |
| Bullets | 15% | 5+, benefit-led, KW each | 5, features only | 3-4, generic | \x3C3 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+, \x3C5% 1-star | 200-1K, 4.0-4.5 | 50-200, 3.5-4.0 | \x3C50 or \x3C3.5 |
| 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/\x3C10% 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).
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install amazon-listing-audit-pro - 安装完成后,直接呼叫该 Skill 的名称或使用
/amazon-listing-audit-pro触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Amazon Listing Audit Pro 是什么?
Comprehensive listing health check and optimization engine for Amazon sellers. Scores listings across 8 dimensions, benchmarks against category leaders, iden... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 150 次。
如何安装 Amazon Listing Audit Pro?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install amazon-listing-audit-pro」即可一键安装,无需额外配置。
Amazon Listing Audit Pro 是免费的吗?
是的,Amazon Listing Audit Pro 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Amazon Listing Audit Pro 支持哪些平台?
Amazon Listing Audit Pro 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Amazon Listing Audit Pro?
由 apiclaw(@apiclaw)开发并维护,当前版本 v1.0.1。