aso-reviews

name	aso-reviews
description	Review and rating analysis for app store listings. Sentiment classification, keyword extraction from reviews, theme clustering, rating distribution, developer response strategy. Triggers on: "reviews", "ratings", "sentiment", "review response".
user-invokable	true
argument-hint	<app-id> [--count 50]

ASO Reviews — Review & Rating Management

Capabilities

1. Rating distribution and trend analysis
2. Sentiment classification (positive/neutral/negative)
3. Theme clustering (performance, UI, features, pricing, bugs, support)
4. Keyword extraction from review text (Android: reviews are indexed!)
5. Developer response strategy and templates
6. Review velocity assessment

Process

1. Fetch reviews (from listing data or user-provided JSON)
2. Run sentiment analysis: uv run python scripts/review_analyzer.py --file reviews.json --json
3. Extract keyword themes
4. Analyze rating distribution
5. Generate response templates for negative reviews

Scoring (0-100)

Factor	Weight	Thresholds
Average rating	30%	<3.5=Critical, 3.5-3.9=Poor, 4.0-4.4=Good, 4.5+=Excellent
Rating volume	20%	<100=Low credibility, 100-1000=Moderate, >1000=Strong
Sentiment ratio	20%	>70% positive=Good, 50-70%=Fair, <50%=Poor
Rating velocity	15%	Improving trend=bonus, declining=penalty
Response rate	15%	>80%=Excellent, 50-80%=Good, <50%=Needs work

Platform-Specific Impact

iOS

• Reviews affect ranking via rating signals only
• Review TEXT is NOT indexed for search
• Developer responses shown prominently — build trust
• Rating resets possible with major version updates

Android

• Review TEXT IS INDEXED — keywords in reviews impact search visibility
• Keywords from reviews can boost rankings for mentioned features
• Encourage reviews that mention key use cases
• No rating reset option

Response Strategy: HEAR Framework

1. Hear — Acknowledge the specific concern
2. Empathize — Show understanding
3. Act — Describe what you're doing about it
4. Resolve — Provide next steps or solution

Response Templates

1-2 star (bug/crash):

Thank you for reporting this. We've identified the [specific issue] and our team is working on a fix for the next update. Please contact us at [email] if you need immediate help.

1-2 star (feature request):

We appreciate your feedback about [feature]. This is on our roadmap and we're actively working on it. We'll keep you updated.

3 star (mixed):

Thanks for the detailed feedback! We're glad [positive aspect] is working well. We hear you on [negative aspect] and are improving it in upcoming updates.

4-5 star:

Thank you for the kind words! We're thrilled you enjoy [specific feature]. Your support means a lot to our team.

Output Format

# Review Analysis: [App Name]

## Rating Health
- Average: X.X/5.0
- Distribution: [histogram]
- Volume: X total reviews
- Velocity: X reviews/week

## Sentiment Analysis
- Positive: X%
- Neutral: X%
- Negative: X%

## Top Themes
| Theme | Mentions | Sentiment | Key Quotes |
|-------|----------|-----------|------------|

## Keyword Opportunities (from reviews)
[Keywords frequently mentioned that could inform metadata]

## Recommended Response Actions
[Prioritized list of reviews to respond to]

Available Tools

Read, Bash, Write, Glob, Grep