This source did not publish a separate summary. Review SKILL.md before using the skill.
SKILL.md
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
Required: APICLAW_API_KEY
Scope: used only for https://api.apiclaw.io
Resolution order:
Environment variableAPICLAW_API_KEY (preferred, most secure)
Config fileconfig.json in the skill root directory (fallback)
{ "api_key": "hms_live_xxxxxx" }
When user provides a Key, write it to config.json. 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)
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:
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).
Step 4 — Output Blocks: Prepare to include both 📋 Data Source & Conditions and 📊 API Usage at the end.
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)
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, automatically call product --asin for the top 3-5 most relevant ASINs to get current real-time data.
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.
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"
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
Sales≥300, reviews≤50, ≤180d
"long tail" / "niche"
--mode long-tail
Sales≤300, BSR 10K-50K, ≤$30, sellers≤1
"underserved" / "has pain points"
--mode underserved
Sales≥300, rating≤3.7, ≤180d
"new products" / "new release"
--mode new-release
Sales≤500, NR tag, FBA+FBM
"FBM" / "self-fulfillment" / "low stock"
--mode fbm-friendly
Sales≥300, FBM, ≤180d
"low price" / "cheap"
--mode low-price
≤$10
"broad catalog" / "cast wide net"
--mode broad-catalog
BSR growth≥99%, reviews≤10, ≤90d
"selective catalog"
--mode selective-catalog
BSR growth≥99%, ≤90d
"speculative" / "piggyback"
--mode speculative
Sales≥600, sellers≥3, ≤180d
"top sellers" / "best sellers"
--mode top-bsr
Sub-category BSR≤1000
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.
