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

• Setup: Guide user to set the environment variable: bash

  export APICLAW_API_KEY='hms_live_xxxxxx'
  
  

• Fallback: The script also checks config.json in the skill root directory if the env var is not set.

• Do NOT write keys to disk files. Always recommend the environment variable approach.

• New keys may need 3-5 seconds to activate — if first call returns 403, wait 3 seconds and retry (max 2 retries).

File Map#

FileWhen to LoadSKILL.md (this file)Start here — covers 80% of tasksscripts/apiclaw.pyExecute for all API calls (do NOT read into context)references/reference.mdNeed exact field names or filter parameter detailsreferences/scenarios-composite.mdComprehensive recommendations (2.10) or Chinese seller cases (3.4)references/scenarios-eval.mdProduct evaluation, risk assessment, review analysis (4.x)references/scenarios-pricing.mdPricing strategy, profit estimation, listing reference (5.x)references/scenarios-ops.mdMarket monitoring, competitor tracking, anomaly alerts (6.x)references/scenarios-expand.mdProduct expansion, trends, discontinuation decisions (7.x)references/scenarios-listing.mdListing writing, optimization, content creation (8.x)

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

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Execution Mode#

Task TypeModeBehaviorSingle ASIN lookup, simple data queryQuickExecute command, return key data. Skip evaluation criteria and output standard block.Market analysis, product selection, competitor comparison, risk assessmentFullComplete 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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⚠️ 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).

• 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.

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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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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.

ScenarioSupplement?How many ASINsSingle ASIN lookup (Quick mode)Already using realtime—Market overview (no specific ASINs)❌ No—Product selection / competitor analysis✅ YesTop 3 by salesRisk assessment✅ YesTarget ASIN + top 2 competitorsMulti-product comparison✅ YesAll compared ASINs (max 5)Listing analysisAlready using realtime—

Handling data conflicts — products/ competitors has ~T+1 delay; realtime/product is live:

FieldUse fromReasonPricerealtime ( buyboxWinner.price)Changes frequentlyBSRrealtime ( bestsellersRank)Updates hourlyRating / ratingCountrealtimeMore currentMonthly Salesproducts/competitorsRealtime doesn't have thisProfit Margin / FBA Feeproducts/competitorsRealtime doesn't have this

When realtime data differs significantly, note it: e.g. "⚡ Price updated: database $29.99 → realtime $24.99 (likely promotion)"

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Script Usage#

All commands output JSON. Progress messages go to stderr.

categories — Category tree lookup#

bash

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#

bash

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#

bash

# 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

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:

bash

# ❌ 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#

bash

python3 scripts/apiclaw.py competitors --keyword "wireless earbuds"
python3 scripts/apiclaw.py competitors --asin B09V3KXJPB

Easily confused fields (products/competitors shared):

❌ Wrong✅ CorrectNotereviewCount``ratingCountReview countbsr``bsrRankBSR ranking (integer, only in products/competitors)monthlySales / salesMonthly``atLeastMonthlySalesMonthly sales (lower bound estimate, NOT in realtime/product)bestsellersRank``bsrRank``bestsellersRank is realtime/product only (array format); use bsrRank for products/competitorsprice (in realtime)buyboxWinner.pricerealtime/product nests price inside buyboxWinner objectprofitMargin (in realtime)❌ N/Arealtime/product does NOT return profitMargin; use products/competitors

Complete field list: reference.md → Shared Product Object

product — Single ASIN real-time detail#

bash

python3 scripts/apiclaw.py product --asin B09V3KXJPB

Returns: title, brand, rating, ratingBreakdown, features, topReviews, specifications, variants, bestsellersRank, buyboxWinner

analyze — Review analysis (sentiment + consumer insights)#

bash

# 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)#

bash

python3 scripts/apiclaw.py report --keyword "pet supplies"

Runs: categories → market → products (top 50) → realtime detail (top 1).

opportunity — Product opportunity discovery (composite)#

bash

python3 scripts/apiclaw.py opportunity --keyword "pet supplies" --mode fast-movers

Runs: categories → market → products (filtered) → realtime detail (top 3).

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⚠️ Interface Data Differences#

The 4 types of interfaces return different fields. Do NOT assume they share the same structure.

Datamarket``products/ competitors``realtime/product``reviews/analyzeMonthly SalessampleAvgMonthlySales✅ atLeastMonthlySales❌❌RevenuesampleAvgMonthlyRevenue``salesRevenue❌❌PricesampleAvgPrice``price``buyboxWinner.price❌BSRsampleAvgBsr``bsrRank (integer)bestsellersRank (array)❌RatingsampleAvgRating``rating``rating``avgRatingReview CountsampleAvgReviewCount``ratingCount``ratingCount``totalReviewsReview Details❌❌✅ topReviews + ratingBreakdown❌ (no raw reviews)Sentiment Analysis❌❌❌✅ sentimentDistributionConsumer Insights❌❌❌✅ consumerInsights (11 dimensions)Pain Points/Issues❌❌❌ (manual from topReviews)✅ AI-analyzedTop Keywords❌❌❌✅ topKeywordsSeller❌buyboxSeller (string)buyboxWinner (object)❌Profit Margin❌profitMargin❌❌FBA Fee❌fbaFee❌❌Seller Count❌sellerCount❌❌Features/Bullets❌❌✅ features❌Variants❌variantCount (integer)variants (full list)❌

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.

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Intent Routing#

User SaysRun ThisScenario File?"which category has opportunity"market + categoriesNo"check B09XXX" / "analyze ASIN"product --asin XXXNo"Chinese seller cases"competitors --keyword XXX --page-size 50``scenarios-composite.md → 3.4"pain points" / "negative reviews" / "consumer insights"analyze --asin XXX + product --asin XXX``scenarios-eval.md → 4.2"category pain points" / "category user portrait"analyze --category XXX``scenarios-eval.md → 4.6"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"write listing" / "generate bullet points" / "write title"product --asin XXX (competitors)scenarios-listing.md → 8.2"analyze competitor listing" / "their selling points"product --asin XXX (multiple)scenarios-listing.md → 8.1"optimize my listing" / "listing diagnosis"product --asin XXX + competitors``scenarios-listing.md → 8.3Need exact filters or field names—Load reference.md

Product Selection Mode Mapping (14 types):

User IntentModeKey Filters"beginner friendly" / "new seller"--mode beginnerSales≥300, growth≥3%, $15-60, FBA, ≤1yr, auto-excludes 150+ red ocean keywords"fast turnover" / "hot selling"--mode fast-moversSales≥300, growth≥10%"emerging" / "rising"--mode emergingSales≤600, growth≥10%, ≤180d"single variant" / "small but beautiful"--mode single-variantGrowth≥20%, variants=1, ≤180d"high demand low barrier" / "easy entry"--mode high-demand-low-barrierSales≥300, reviews≤50, ≤180d"long tail" / "niche"--mode long-tailSales≤300, BSR 10K-50K, ≤$30, sellers≤1"underserved" / "has pain points"--mode underservedSales≥300, rating≤3.7, ≤180d"new products" / "new release"--mode new-releaseSales≤500, NR tag, FBA+FBM"FBM" / "self-fulfillment" / "low stock"--mode fbm-friendlySales≥300, FBM, ≤180d"low price" / "cheap"--mode low-price≤$10"broad catalog" / "cast wide net"--mode broad-catalogBSR growth≥99%, reviews≤10, ≤90d"selective catalog"--mode selective-catalogBSR growth≥99%, ≤90d"speculative" / "piggyback"--mode speculativeSales≥600, sellers≥3, ≤180d"top sellers" / "best sellers"--mode top-bsrSub-category BSR≤1000

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Quick Evaluation Criteria#

Market Viability (from market output)#

MetricGoodMediumWarningMarket value (avgRevenue × skuCount)> $10M$5–10M< $5MConcentration (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)> 5020–50< 20

Product Potential (from product output)#

MetricHighMediumLowBSRTop 10001000–5000> 5000Reviews< 200200–1000> 1000Rating> 4.34.0–4.3< 4.0Negative 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 — 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)#

markdown

---
📋 **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:

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
5. ⚠️ 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.

markdown

📊 **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:

1. Count each apiclaw.py execution as 1 call to the corresponding interface
2. Sum _credits.consumed from every API response for total consumed
3. Use _credits.remaining from the last API response as remaining balance
4. If _credits fields are null, show "N/A"
5. ⚠️ Self-check before sending: scan your response — if you don't see 📊 **API Usage** at the bottom, ADD IT before replying

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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
• Raw individual review text export (use realtime/product topReviews for specific review quotes)

API Coverage Boundaries#

ScenarioCoverageSuggestionMarket data: Popular keywords✅ Has dataUse --keyword directlyMarket data: Niche/long-tail keywords⚠️ May be emptyUse --category insteadProduct data: Active ASIN✅ Has data—Product data: Delisted/variant ASIN❌ No dataTry parent ASIN or realtimeReal-time data: US site✅ Full support—Real-time data: Non-US sites⚠️ PartialCore fields OK, sales may be null

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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

ErrorFixCannot index array with stringUse .data[0].fieldName ( .data is array)Empty data: []Use categories to confirm category existsatLeastMonthlySales: nullBSR estimate: 300,000 / BSR^0.65