Amazon Review Intelligence Extractor — 11 Dimensions, 1B+ Reviews

Pre-analyzed consumer insights. Pain points, buying factors, user profiles, differentiation gaps.

Files

• Script: {skill_base_dir}/scripts/apiclaw.py — run --help for params
• Reference: {skill_base_dir}/references/reference.md (field names & response structure)

Credential

Required: APICLAW_API_KEY. Get free key at apiclaw.io/api-keys

Input (one of)

• Single ASIN: "Analyze reviews for B09V3KXJPB"
• Multi-ASIN: "Compare review pain points across these 5 competitor ASINs"
• Category-wide: keyword/category name → resolve via categories first (need ≥3-level deep path)

API Pitfalls (see apiclaw skill for full list)

• reviews/analysis needs 50+ reviews — fallback to realtime/product ratingBreakdown
• labelType is NOT an API request parameter — the API returns all 11 dimensions in one call. Filter by labelType client-side from the consumerInsights array.
• Category mode needs precise path (≥3 levels) — broad categories = diluted insights
• Field name is reviewRate (not reviewRate) for mention frequency
• ASIN-specific endpoints don't need --category; keyword-based ones do
• Category auto-detection: categoryPath is auto-detected from target ASIN. If category_source in output is inferred_from_search, confirm with user

11 Analysis Dimensions

painPoints · issues · positives · improvements · buyingFactors · keywords · userProfiles · scenarios · usageTimes · usageLocations · behaviors

Unique Logic

Analysis Modes

• Category mode: all reviews in category → market-level insights
• ASIN mode: specific products → competitive analysis
• Choose based on user intent. Category = broader, ASIN = deeper.

Pain Point Impact Ranking

Rank differentiation opportunities by: frequency × avg rating delta

"Top pain point: durability — mentioned in 27/471 reviews (5.7%), avg rating 2.4 when mentioned"

Differentiation Priority = High frequency + Low avgRating = Biggest opportunity 🔍. If top 3 pain points all have reviewRate >5% and avgRating <3.0, there is a clear product improvement opportunity 💡. If all pain points have reviewRate <2%, the category is well-served — differentiation through reviews is limited 🔍.

Consumer Profile Synthesis

Combine userProfiles + scenarios + usageTimes + usageLocations → complete buyer persona.

Listing Copy from Reviews

Quote actual customer words from positives — these are proven converting phrases. High-frequency positive elements (reviewRate >5%) should appear in title or first bullet 💡.

Competitor Comparison

Align dimensions (pain points vs pain points) across products. If competitor review data unavailable, use brand-detail sampleProducts + note limitation.

• Your pain point rate < competitor's: Advantage — highlight in listing 💡
• Your pain point rate > competitor's: Risk — address in product iteration 💡
• Both high on same pain point: Category-wide issue — solving it is a strong differentiator 🔍

Composite Command

python3 {skill_base_dir}/scripts/apiclaw.py review-deepdive --target-asin "{asin}" [--keyword "{kw}"] [--category "{path}"]

Optional: --comp-asins "{asin1},{asin2}" for comparison.

Runs: reviews × 11 dimensions + competitors + realtime + market context + price/trend.

Output

Respond in user's language.

Sections: Review Snapshot → Top 10 Pain Points (with count & %) → Top 10 Positives → Buying Factors → Improvement Wishlist → Consumer Profile → Usage Patterns → Competitor Comparison → Listing Copy Suggestions → Differentiation Roadmap (impact-ranked) → Data Provenance → API Usage

Do NOT invent insights — only report what the API returns. Omit empty dimensions.

Cross-validate: star distribution (ratingBreakdown) should match sentiment (reviews/analysis).

Language (required)

Output language MUST match the user's input language. If the user asks in Chinese, the entire report is in Chinese. If in English, output in English. Exception: API field names (e.g. monthlySalesFloor, categoryPath), endpoint names, technical terms (e.g. ASIN, BSR, CR10, FBA, credits) remain in English.

Disclaimer (required, at the top of every report)

> Data is based on APIClaw API sampling as of [date]. Monthly sales (monthlySalesFloor) are lower-bound estimates. This analysis is for reference only and should not be the sole basis for business decisions. Validate with additional sources before acting.

Confidence Labels (required, tag EVERY conclusion)

• 📊 Data-backed — direct API data (e.g. "painPoint 'durability' mentioned by 27% of reviewers 📊")
• 🔍 Inferred — logical reasoning from data (e.g. "durability is the #1 differentiation opportunity 🔍")
• 💡 Directional — suggestions, predictions, strategy (e.g. "highlight durability in bullet point #1 💡")

Rules: Strategy recommendations and listing copy suggestions are NEVER 📊. User criteria override AI judgment.

Data Provenance (required)

Include a table at the end of every report:

Extract endpoint and params from _query in JSON output. Add notes: sampling method, T+1 delay, realtime vs DB, minimum review threshold, etc.

API Usage (required)

Extract from meta.creditsConsumed per response. End with Credits remaining: N.

API Budget: ~20-30 credits