amazon-product-research-seller-analytics

name Amazon Product Research & Seller Analytics version 1.1.5 description Amazon product research and seller analytics for FBA and FBM businesses. Find winning products with 14 selection strategies, track competitors, monitor BSR trends, analyze reviews, estimate monthly sales, optimize listings, and assess market opportunities. Real-time ASIN lookup with 200M+ product database. Amazon seller tools, niche research, keyword analysis, pricing strategy, and category insights powered by APIClaw API. Use when user asks about: Amazon product selection, finding products to sell, ASIN lookup, BSR analysis, competitor tracking, market opportunity, risk assessment, FBA research, review analysis, or listing optimization. Requires APICLAW_API_KEY. author SerendipityOneInc homepage https://github.com/SerendipityOneInc/Amazon-analysis-skill metadata {"openclaw":{"requires":{"env":"[Truncated]"},"primaryEnv":"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 Setup: Guide user to set the environment variable: 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 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) references/scenarios-listing.md Listing writing, optimization, content creation (8.x) 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). 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) 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 , 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: # ❌ 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) bestsellersRank bsrRank bestsellersRank is realtime/product only (array format); use bsrRank for products/competitors price (in realtime) buyboxWinner.price realtime/product nests price inside buyboxWinner object profitMargin (in realtime) ❌ N/A realtime/product does NOT return profitMargin; use products/competitors 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 sampleAvgMonthlyRevenue salesRevenue ❌ ❌ Price sampleAvgPrice price buyboxWinner.price ❌ BSR sampleAvgBsr bsrRank (integer) bestsellersRank (array) ❌ Rating sampleAvgRating rating rating avgRating Review Count sampleAvgReviewCount ratingCount ratingCount totalReviews Review Details ❌ ❌ ✅ topReviews + ratingBreakdown ❌ (no raw reviews) Sentiment Analysis ❌ ❌ ❌ ✅ sentimentDistribution Consumer Insights ❌ ❌ ❌ ✅ consumerInsights (11 dimensions) Pain Points/Issues ❌ ❌ ❌ (manual from topReviews) ✅ AI-analyzed Top Keywords ❌ ❌ ❌ ✅ topKeywords Seller ❌ 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 . Intent Routing User Says Run This Scenario File? "which category has opportunity" market + categories No