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name: apiclaw-analysis version: 1.1.0 description: > Amazon product research, competitor analysis, and market analysis for sellers. Use when user asks about: product selection, finding products to sell, ASIN lookup, BSR analysis, competitor lookup, market opportunity, risk assessment, category research, pricing strategy, review analysis, or any Amazon seller data needs. Powered by APIClaw API (requires APICLAW_API_KEY).

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
• Storage: config.json in the skill root directory (next to SKILL.md)

{ "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).

New users: Get API Key at apiclaw.io/api-keys.

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	Pricing strategy, profit estimation, listing reference (5.x)
references/scenarios-ops.md	Market monitoring, competitor tracking, anomaly alerts (6.x)
references/scenarios-expand.md	Product expansion, trends, discontinuation decisions (7.x)

Don't guess field names — if uncertain, load reference.md first.

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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.

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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.

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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, beginner, top-bsr

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
monthlySales / salesMonthly	atLeastMonthlySales	Monthly sales (lower bound estimate)

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

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).

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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"	product --asin XXX	scenarios-eval.md → 4.2
"compare products"	competitors or multiple product	scenarios-eval.md → 4.3
"risk assessment" / "can I do this"	product + market + competitors	scenarios-eval.md → 4.4
"monthly sales" / "estimate sales"	competitors --asin XXX	scenarios-eval.md → 4.5
"help me select products" / "find products"	products --mode XXX (see mode table)	No
"comprehensive recommendations" / "what should I sell"	products (multi-mode) + market	scenarios-composite.md → 2.10
"pricing strategy" / "how much to price"	market + products	scenarios-pricing.md → 5.1
"profit estimation"	competitors	scenarios-pricing.md → 5.2
"listing reference"	product --asin XXX	scenarios-pricing.md → 5.3
"market changes" / "recent changes"	market + products	scenarios-ops.md → 6.1
"competitor updates"	competitors --brand XXX	scenarios-ops.md → 6.2
"anomaly alerts"	market + products	scenarios-ops.md → 6.4
"what else can I sell" / "related products"	categories + market	scenarios-expand.md → 7.1
"trends"	products --growth-min 0.2	scenarios-expand.md → 7.3
"should I delist"	competitors --asin XXX + market	scenarios-expand.md → 7.4
Need exact filters or field names	—	Load reference.md

Product Selection Mode Mapping (14 types):

User Intent	Mode	Key Filters
"underserved" / "has pain points"	--mode underserved	Sales≥300, rating≤3.7
"high demand low barrier" / "easy entry"	--mode high-demand-low-barrier	Sales≥300, reviews≤50
"beginner friendly" / "new seller"	--mode beginner	Sales≥300, $15-60, FBA
"fast turnover" / "hot selling"	--mode fast-movers	Sales≥300, growth≥10%
"emerging" / "rising"	--mode emerging	Sales≤600, growth≥10%
"single variant" / "small but beautiful"	--mode single-variant	Growth≥20%, variants=1
"long tail" / "niche"	--mode long-tail	BSR 10K-50K, ≤$30
"new products" / "new release"	--mode new-release	Sales≤500, New Release tag
"low price" / "cheap"	--mode low-price	≤$10
"top sellers" / "best sellers"	--mode top-bsr	BSR≤1000
"FBM" / "self-fulfillment"	--mode fbm-friendly	Sales≥300, FBM
"broad catalog" / "cast wide net"	--mode broad-catalog	BSR growth≥99%, reviews≤10
"selective catalog"	--mode selective-catalog	BSR growth≥99%
"speculative" / "piggyback"	--mode speculative	Sales≥600, sellers≥3

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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

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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:

1. Every Full-mode analysis MUST end with this block
2. Filter conditions MUST list specific parameter values
3. If multiple interfaces used, list each one
4. If data has limitations, proactively explain

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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

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Error Handling

HTTP errors (401/402/403/404/429) are handled by the script, returning structured JSON with error.message and error.action.

Self-check: python3 scripts/apiclaw.py check — tests 4/5 endpoints, reports availability.

Error	Cause	Fix
Cannot index array with string	.data is array	Use .data[0].fieldName
Empty data: []	Keyword no match	Use categories to confirm category exists
atLeastMonthlySales: null	Missing sales data	BSR estimate: 300,000 / BSR^0.65
realtime/product slow	Real-time scraping	Normal 5-30s, be patient