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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).
⚠️ Data persistence notice: When you provide an API Key, the skill saves it to config.json in the skill directory for persistent access across sessions. This file is local-only and listed in .gitignore to prevent accidental commits. If you prefer not to store the key on disk, use the environment variable method (export APICLAW_API_KEY=...) instead — no file will be created.
Quick mode trigger: User asks for a single specific data point ("B09XXX monthly sales?", "how many brands in cat litter?") — no decision analysis needed.
Execution Standards
Prioritize script execution for API calls. The script includes:
Parameter format conversion (e.g. topN auto-converted to string)
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"
Note: The realtime detail section has a different field structure than products (no sales/revenue/profitMargin). It provides review details, seller info, and listing content as qualitative supplement.
Sales≥300, growth≥3%, $15-60, FBA, ≤1yr, excl. 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 Only)
MUST include data source block after every Full-mode analysis:
---
**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
✅ Completed Example (yoga mat market analysis):
---
**Data Source & Conditions**
| Item | Value |
|----|-----|
| Data Source | APIClaw API |
| Interface | categories, markets/search, products/search |
| Category | Sports & Outdoors > Exercise & Fitness > Yoga > Yoga Mats |
| Time Range | 30d |
| Sampling | by_sale_100 |
| Top N | 10 |
| Sort | atLeastMonthlySales desc |
| Filters | monthlySalesMin: 300, reviewCountMax: 50 |
**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
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
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
AI review sentiment analysis (use topReviews + ratingBreakdown manually)
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, returning structured JSON with error.message and error.action.
