App Store Analyzer

Mine app store reviews and ratings for actionable product insights. A consumer/mobile-specific skill that turns the firehose of user reviews into structured themes, feature requests, and bug reports.

When to Use

• "Analyze our app reviews"
• "What are users saying in the app store?"
• "Process these reviews"
• "What are the top complaints?"
• After a release to gauge reception
• Before planning to understand user pain
• To monitor competitor app reviews

Process

1. Ingest Reviews

Accept reviews in any format:

• Pasted text from app store
• Exported CSV or text file
• A description ("look at our last 50 one-star reviews")

Capture for each review:

• Rating (1-5 stars)
• Date
• Review text
• Platform (iOS / Android)
• Version (if available)

2. Classify Each Review

Tag each review with:

Sentiment: positive / neutral / negative / mixed

Category:

• Bug report — something is broken
• Feature request — user wants something new
• UX complaint — flow or design friction
• Performance — speed, battery, crashes
• Praise — something they love
• Churn signal — user says they're leaving or switching
• Onboarding — new user confusion

Severity (for negative reviews):

• Blocker — user can't complete core task
• Frustration — user can do it but hates the experience
• Annoyance — minor irritation

3. Theme Extraction

Group reviews into themes:

• Frequency — how many reviews mention this theme
• Trend — is this theme growing, stable, or declining?
• Rating correlation — do 1-star reviews cluster around specific themes?
• Platform split — is this iOS-only, Android-only, or both?

4. Prioritize Findings

Rank themes by:

1. Volume × severity — many users × blocker = top priority
2. Trend direction — growing complaints are more urgent than stable ones
3. Rating impact — themes that disproportionately drive 1-star reviews
4. Churn signal strength — themes where users explicitly mention leaving

5. Connect to Product

Map findings to:

• Existing backlog items (knowledge/products/*/roadmap.md)
• Known bugs or issues
• Recent releases that may have caused changes
• Competitor advantages mentioned in reviews

6. Store Insights

Write to memory/feedback/YYYY-MM-DD_app-store-[topic].md

Output

## App Store Analysis: [Product]

**Date:** YYYY-MM-DD
**Reviews analyzed:** [count]
**Period:** [date range]
**Average rating:** [X.X] (trend: ↑/↓/→)

### Rating Distribution
| Stars | Count | % | Trend |
|-------|-------|---|-------|
| 5 ⭐ | [n] | [%] | ↑/↓/→ |
| 4 ⭐ | [n] | [%] | ↑/↓/→ |
| 3 ⭐ | [n] | [%] | ↑/↓/→ |
| 2 ⭐ | [n] | [%] | ↑/↓/→ |
| 1 ⭐ | [n] | [%] | ↑/↓/→ |

### Top Themes
| Rank | Theme | Mentions | Avg Rating | Trend | Category |
|------|-------|----------|------------|-------|----------|
| 1 | [theme] | [n] | [X.X] | ↑/↓/→ | [category] |

### Critical Issues (Blocker + High Volume)
- **[Issue]**: [count] mentions, avg [X.X] stars — [summary]

### Feature Requests (Most Requested)
- **[Feature]**: [count] mentions — [what users want and why]

### What Users Love (Protect These)
- **[Strength]**: [count] mentions — [why they love it]

### Churn Signals
- [count] reviews mention switching or leaving
- Top reasons: [reasons]

### Recommended Actions
| Priority | Action | Theme | Expected Impact |
|----------|--------|-------|-----------------|
| P0 | [action] | [theme] | [impact on rating/retention] |

Principles

• 1-star reviews are product roadmaps — they tell you what's broken
• 5-star reviews are moats — they tell you what to protect
• Trends > snapshots — a growing problem is more urgent than a big stable one
• Cross-reference with data — reviews are qualitative; pair with analytics for the full picture