🇺🇸English
APIClaw — Amazon Seller Data Analysis
AI-powered Amazon product research. From market discovery to daily operations.
Language rule : Always respond in the user's language. If the user asks in Chinese, reply in Chinese. If in English, reply in English. The language of this skill document does not affect output language. All API calls go through scripts/apiclaw.py — one script, 5 endpoints, built-in error handling.
Credentials
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Required: APICLAW_API_KEY
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Scope: used only for https://api.apiclaw.io
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Setup: Guide user to set the environment variable:
export APICLAW_API_KEY='hms_live_xxxxxx'
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Fallback: The script also checks config.json in the skill root directory if the env var is not set.
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Do NOT write keys to disk files. Always recommend the environment variable approach.
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New keys may need 3-5 seconds to activate — if first call returns 403, wait 3 seconds and retry (max 2 retries).
File Map
| File | When to Load |
|---|
SKILL.md (this file) | Start here — covers 80% of tasks |
scripts/apiclaw.py | Execute for all API calls (do NOT read into context) |
references/reference.md | Need exact field names or filter parameter details |
references/scenarios-composite.md | Comprehensive recommendations (2.10) or Chinese seller cases (3.4) |
references/scenarios-eval.md | Product evaluation, risk assessment, review analysis (4.x) |
references/scenarios-pricing.md |
Don't guess field names — if uncertain, load reference.md first.
Execution Mode
| Task Type | Mode | Behavior |
|---|
| Single ASIN lookup, simple data query | Quick | Execute command, return key data. Skip evaluation criteria and output standard block. |
| Market analysis, product selection, competitor comparison, risk assessment | Full | Complete flow: command → analysis → evaluation criteria → output standard block. |
Quick mode trigger: User asks for a single specific data point ("B09XXX monthly sales?", "how many brands in cat litter?") — no decision analysis needed.
⚠️ Pre-Execution Checklist (MANDATORY for Full Mode)
Before running any Full-mode product selection or market analysis, complete this checklist :
- Step 1 — Mode Selection: Check the Product Selection Mode Mapping table below. If ANY of the 14 preset modes matches the user's intent, USE IT (
--mode xxx). Do NOT manually piece together filters when a preset mode exists. Common mappings:
- Small/lightweight/cheap products →
--mode low-price
- New seller / beginner →
--mode beginner
- Niche / long-tail →
--mode long-tail
- Trending / rising →
--mode emerging
- Step 2 — Realtime Supplement: Plan to call
product --asin for the top 3-5 ASINs from results (see Realtime Data Supplementation below).
- Step 3 — Review Analysis: Plan to call
analyze --asins for top ASINs to get consumer insights (especially painPoints, improvements, buyingFactors).
Why this exists: In testing, AI agents repeatedly skipped preset modes, realtime supplements, and review analysis — even though the instructions below clearly describe them. This checklist forces a pause-and-verify before execution.
Execution Standards
Prioritize script execution for API calls. The script includes:
- Parameter format conversion (e.g. topN auto-converted to string)
- Retry logic (429/timeout auto-retry)
- Standardized error messages
_query metadata injection (for query traceability)
Fallback: If script fails and can't be quickly fixed, use curl directly. Note "using curl direct call" in output.
Realtime Data Supplementation
When products or competitors returns ASINs in Full-mode analysis, call product --asin for the top 3-5 most relevant ASINs to get current real-time data. For bulk lookups (>3 ASINs), confirm with the user before proceeding.
| Scenario | Supplement? | How many ASINs |
|---|
| Single ASIN lookup (Quick mode) | Already using realtime | — |
| Market overview (no specific ASINs) | ❌ No | — |
| Product selection / competitor analysis | ✅ Yes | Top 3 by sales |
| Risk assessment | ✅ Yes | Target ASIN + top 2 competitors |
| Multi-product comparison | ✅ Yes | All compared ASINs (max 5) |
| Listing analysis | Already using realtime | — |
Handling data conflicts — products/competitors has ~T+1 delay; realtime/product is live:
| Field | Use from | Reason |
|---|
| Price | realtime (buyboxWinner.price) | Changes frequently |
| BSR | realtime (bestsellersRank) | Updates hourly |
| Rating / ratingCount | realtime | More current |
| Monthly Sales | products/competitors | Realtime doesn't have this |
| Profit Margin / FBA Fee | products/competitors | Realtime doesn't have this |
When realtime data differs significantly, note it: e.g. "⚡ Price updated: database $29.99 → realtime $24.99 (likely promotion)"
Script Usage
All commands output JSON. Progress messages go to stderr.
categories — Category tree lookup
python3 scripts/apiclaw.py categories --keyword "pet supplies"
python3 scripts/apiclaw.py categories --parent "Pet Supplies"
Common fields: categoryName (not name), categoryPath, productCount, hasChildren
market — Market-level aggregate data
python3 scripts/apiclaw.py market --category "Pet Supplies,Dogs" --topn 10
Key output fields: sampleAvgMonthlySales, sampleAvgPrice, topSalesRate (concentration), topBrandSalesRate, sampleNewSkuRate, sampleFbaRate, sampleBrandCount
products — Product selection with filters
# Preset mode (14 built-in)
python3 scripts/apiclaw.py products --keyword "yoga mat" --mode beginner
# Explicit filters
python3 scripts/apiclaw.py products --keyword "yoga mat" --sales-min 300 --reviews-max 50
# Mode + overrides (overrides win)
python3 scripts/apiclaw.py products --keyword "yoga mat" --mode beginner --price-max 30
Available modes: fast-movers, emerging, single-variant, high-demand-low-barrier, long-tail, underserved, new-release, fbm-friendly, low-price, broad-catalog, selective-catalog, speculative, ,
Keyword matching: Default is fuzzy (matches brand names too — e.g. "smart ring" matches "Smart Color Art" pens). Use --keyword-match-type exact or phrase for precise results. Always combine with --category when possible to reduce noise.
Category path with commas: Some category names contain commas (e.g. "Pacifiers, Teethers & Teething Relief"). Use > separator instead of , to avoid parsing errors:
# ❌ Wrong — comma in name breaks parsing
--category "Baby Products,Baby Care,Pacifiers, Teethers & Teething Relief"
# ✅ Correct — use ' > ' separator
--category "Baby Products > Baby Care > Pacifiers, Teethers & Teething Relief"
competitors — Competitor lookup
python3 scripts/apiclaw.py competitors --keyword "wireless earbuds"
python3 scripts/apiclaw.py competitors --asin B09V3KXJPB
Easily confused fields (products/competitors shared) :
| ❌ Wrong | ✅ Correct | Note |
|---|
reviewCount | ratingCount | Review count |
bsr | bsrRank | BSR ranking (integer, only in products/competitors) |
monthlySales / salesMonthly | atLeastMonthlySales | Monthly sales (lower bound estimate, NOT in realtime/product) |
Complete field list: reference.md → Shared Product Object
product — Single ASIN real-time detail
python3 scripts/apiclaw.py product --asin B09V3KXJPB
Returns: title, brand, rating, ratingBreakdown, features, topReviews, specifications, variants, bestsellersRank, buyboxWinner
analyze — Review analysis (sentiment + consumer insights)
# Single ASIN
python3 scripts/apiclaw.py analyze --asin B09V3KXJPB
# Multiple ASINs (competitive review comparison)
python3 scripts/apiclaw.py analyze --asins B09V3KXJPB,B08YYYYY,B07ZZZZZ
# Category-level insights
python3 scripts/apiclaw.py analyze --category "Pet Supplies,Dogs,Toys" --period 90d
# Specific insight dimension
python3 scripts/apiclaw.py analyze --asin B09V3KXJPB --label-type painPoints,buyingFactors
Returns: totalReviews, avgRating, sentimentDistribution, ratingDistribution, consumerInsights (by labelType), topKeywords, verifiedRatio
Available labelType: scenarios, issues, positives, improvements, buyingFactors, painPoints, keywords, userProfiles, usageTimes, usageLocations, behaviors
report — Full market analysis (composite)
python3 scripts/apiclaw.py report --keyword "pet supplies"
Runs: categories → market → products (top 50) → realtime detail (top 1).
opportunity — Product opportunity discovery (composite)
python3 scripts/apiclaw.py opportunity --keyword "pet supplies" --mode fast-movers
Runs: categories → market → products (filtered) → realtime detail (top 3).
⚠️ Interface Data Differences
The 4 types of interfaces return different fields. Do NOT assume they share the same structure.
| Data | market | products/competitors | realtime/product | reviews/analyze |
|---|
| Monthly Sales | sampleAvgMonthlySales | ✅ atLeastMonthlySales | ❌ | ❌ |
| Revenue | |
Usage rule:
- Use
products / competitors for sales, pricing, and competition data
- Use
realtime/product for review details, listing content, and seller info
- Use
market for category-level aggregate metrics
- Use
reviews/analyze for AI-powered review insights (sentiment, pain points, buying factors — covers all reviews, not just topReviews)
- For reports: combine
products/competitors (quantitative) + realtime/product (qualitative) + reviews/analyze (consumer insights) as evidence
Data Structure Reminder
All interfaces return .data as an array. Use .data[0] to get the first record, NOT .data.fieldName.
Intent Routing
| User Says | Run This | Scenario File? |
|---|
| "which category has opportunity" | market + categories | No |
| "check B09XXX" / "analyze ASIN" | product --asin XXX | No |
| "Chinese seller cases" | competitors --keyword XXX --page-size 50 | scenarios-composite.md → 3.4 |
| "pain points" / "negative reviews" / "consumer insights" | analyze --asin XXX + |
Product Selection Mode Mapping (14 types) :
| User Intent | Mode | Key Filters |
|---|
| "beginner friendly" / "new seller" | --mode beginner | Sales≥300, growth≥3%, $15-60, FBA, ≤1yr, auto-excludes 150+ red ocean keywords |
| "fast turnover" / "hot selling" | --mode fast-movers | Sales≥300, growth≥10% |
| "emerging" / "rising" | --mode emerging | Sales≤600, growth≥10%, ≤180d |
| "single variant" / "small but beautiful" | --mode single-variant | Growth≥20%, variants=1, ≤180d |
| "high demand low barrier" / "easy entry" | --mode high-demand-low-barrier |
Quick Evaluation Criteria
Market Viability (from market output)
| Metric | Good | Medium | Warning |
|---|
| Market value (avgRevenue × skuCount) | > $10M | $5–10M | < $5M |
| Concentration (topSalesRate, topN=10) | < 40% | 40–60% | > 60% |
| New SKU rate (sampleNewSkuRate) | > 15% | 5–15% | < 5% |
| FBA rate (sampleFbaRate) | > 50% | 30–50% | < 30% |
| Brand count (sampleBrandCount) | > 50 | 20–50 | < 20 |
Product Potential (from product output)
| Metric | High | Medium | Low |
|---|
| BSR | Top 1000 | 1000–5000 | > 5000 |
| Reviews | < 200 | 200–1000 | > 1000 |
| Rating | > 4.3 | 4.0–4.3 | < 4.0 |
| Negative reviews (1-2★ %) | < 10% | 10–20% | > 20% |
Sales Estimation Fallback
When atLeastMonthlySales is null: Monthly sales ≈ 300,000 / BSR^0.65
⚠️ Output Standards (Full Mode — MANDATORY, DO NOT SKIP)
Two blocks are REQUIRED at the end of every Full-mode analysis: ① Data Source & Conditions, ② API Usage. Missing either one = violating the skill contract.
① Data Source & Conditions (Full Mode Only)
---
📋 **Data Source & Conditions**
| Item | Value |
|----|-----|
| Data Source | APIClaw API |
| Interface | [interfaces used] |
| Category | [category path] |
| Time Range | [dateRange] |
| Sampling | [sampleType] |
| Top N | [topN value] |
| Sort | [sortBy + sortOrder] |
| Filters | [specific parameter values] |
**Data Notes**
- Monthly sales are **lower bound estimates** (Amazon displays "10,000+ bought"), actual may be higher
- Database data has ~T+1 delay; realtime/product is current real-time data
- Concentration metrics based on Top N sample; different topN → different results
Rules :
- Every Full-mode analysis MUST end with this block
- Filter conditions MUST list specific parameter values
- If multiple interfaces used, list each one
- If data has limitations, proactively explain
- ⚠️ Self-check: scan your response — if you don't see
📋 **Data Source & Conditions**, ADD IT before replying
⚠️ API Usage Summary (All Modes — MANDATORY, DO NOT SKIP)
This block is NON-NEGOTIABLE. Every single response — Quick or Full mode — MUST end with this table. No exceptions. If you forget, you are violating the skill contract.
📊 **API Usage**
| Interface | Calls |
|-----------|-------|
| categories | 1 |
| markets/search | 1 |
| products/search | 2 |
| realtime/product | 3 |
| reviews/analyze | 1 |
| **Total** | **8** |
| **Credits consumed** | **8** |
| **Credits remaining** | **492** |
Tracking rules:
- Count each
apiclaw.py execution as 1 call to the corresponding interface
- Sum
_credits.consumed from every API response for total consumed
- Use
_credits.remaining from the last API response as remaining balance
- If
_credits fields are null, show "N/A"
- ⚠️ Self-check before sending: scan your response — if you don't see
📊 **API Usage** at the bottom, ADD IT before replying
Limitations
What This Skill Cannot Do
- Keyword research / reverse ASIN / ABA data
- Traffic source analysis
- Historical sales trends (14-month curves)
- Historical price / BSR charts
- Raw individual review text export (use
realtime/product topReviews for specific review quotes)
API Coverage Boundaries
| Scenario | Coverage | Suggestion |
|---|
| Market data: Popular keywords | ✅ Has data | Use --keyword directly |
| Market data: Niche/long-tail keywords | ⚠️ May be empty | Use --category instead |
| Product data: Active ASIN | ✅ Has data | — |
| Product data: Delisted/variant ASIN | ❌ No data | Try parent ASIN or realtime |
| Real-time data: US site | ✅ Full support | — |
| Real-time data: Non-US sites | ⚠️ Partial | Core fields OK, sales may be null |
Error Handling
HTTP errors (401/402/403/404/429) are handled by the script with structured JSON output. Self-check: python3 scripts/apiclaw.py check
| Error | Fix |
|---|
Cannot index array with string | Use .data[0].fieldName (.data is array) |
Empty data: [] | Use categories to confirm category exists |
atLeastMonthlySales: null | BSR estimate: 300,000 / BSR^0.65 |
Weekly Installs
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Repository
serendipityonei…is-skill
GitHub Stars
27
First Seen
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