Skills Amazon Review Intelligence Extractor — Consumer Insights from 1B+ Reviews

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/apiclaw/amazon-review-intelligence-extractor" ~/.claude/skills/clawdbot-skills-amazon-review-intelligence-extractor-consumer-insights-from-1b-r && rm -rf "$T"

manifest: skills/apiclaw/amazon-review-intelligence-extractor/SKILL.md

source content

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"

reviewRate	Frequency Level	Interpretation
>10%	🔴 Critical	Mentioned by 1 in 10 buyers — must address in product design 📊
5-10%	🟡 Significant	Common complaint — differentiator if solved 📊
2-5%	🟠 Notable	Worth mentioning in listing if you solve it 📊
<2%	🟢 Minor	Edge case — deprioritize unless easy fix 🔍

avgRating when mentioned	Severity
<2.5	Severe — causes returns/1-star reviews 📊
2.5-3.5	Moderate — disappoints but doesn't cause returns 🔍
>3.5	Mild — noticed but not deal-breaker 🔍

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:

Data	Endpoint	Key Params	Notes
(e.g. Market Overview)	`markets/search`	categoryPath, topN=10	📊 Top N sampling, sales are lower-bound
...	...	...	...

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)

Endpoint	Calls	Credits
(each endpoint used)	N	N
Total	N	N

Extract from

meta.creditsConsumed

per response. End with

Credits remaining: N