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AppKittieAppKittie/aso-mcp-skills/skills/review-analysis/SKILL.md

review-analysis

Skill8 starsChanged 17 days agoMITView on GitHub

When the user wants to analyze, read, or understand App Store or Google Play reviews for a mobile app. Also use when the user mentions "reviews", "user feedback", "sentiment analysis", "what users think", "app reviews", "review sentiment", "complaints", "feature requests from users", "rating analysis", or "review trends". For broader app analysis, see app-discovery. For competitor-focused work, see competitor-analysis.

6 other projects use a version of this, 1 of them edited.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

8 rules. 0 are common, 1 is distinctive, and 7 are very specific to this project.

  • 0

    Common

    Most of the 4 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 7

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Check for app-marketing-context.md — read it if available for context

    line 14
  2. Very specific:

    Use get_app_detail to get the app's metadata, rating, and review count

    line 28
  3. Very specific:

    Use get_app_reviews with maxReviews: 100 to fetch the most recent reviews.

    line 29
  4. Very specific:

    If more depth is needed, paginate with nextOffset to get additional pages

    line 30
  5. Very specific:

    Use search_apps to find competitors in the same category

    line 80
  6. Very specific:

    Use get_app_reviews for each competitor (2–3 apps)

    line 81
  7. Very specific:

    Use get_app_reviews with different country values

    line 91
  8. Distinctive:

    Look for market-specific patterns:

    line 92

“Common” is measured: kept by at least 6 in 10 of the 4 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Review Analysis

You are an expert mobile app review analyst with deep understanding of App Store and Google Play user sentiment, feedback patterns, and how reviews reflect product health. Your goal is to help the user extract actionable insights from app reviews using AppKittie's review data.

Initial Assessment

  1. Check for app-marketing-context.md — read it if available for context
  2. Determine the analysis goal:
    • Sentiment overview — "What do users think of this app?"
    • Feature request mining — "What features are users asking for?"
    • Complaint analysis — "What are users complaining about?"
    • Competitor review comparison — "How do reviews compare to competitors?"
    • Rating trend context — "Why did ratings drop recently?"

Analysis Workflows

Sentiment Overview

Understand the overall user sentiment for an app.

  1. Use get_app_detail to get the app's metadata, rating, and review count
  2. Use get_app_reviews with maxReviews: 100 to fetch the most recent reviews. The appId accepts any identifier — numeric App Store ID, Google Play package name, AppKittie app slug, or store URL. Pass source: "google_mobile" for Google Play when the identifier could be ambiguous
  3. If more depth is needed, paginate with nextOffset to get additional pages
  4. Categorize each review:
    • Positive (4–5 stars with praise)
    • Neutral (3 stars or mixed sentiment)
    • Negative (1–2 stars with complaints)
  5. Identify recurring themes across all reviews

Key questions to answer:

  • What percentage of recent reviews are positive vs negative?
  • What are the top 3 things users love?
  • What are the top 3 pain points?
  • Has sentiment shifted recently compared to the overall rating?

Feature Request Mining

Extract feature requests and improvement suggestions from reviews.

  1. Fetch 100–200 reviews using get_app_reviews (paginate if needed)
  2. Filter for reviews that contain suggestions, requests, or "wish" language
  3. Group requests by theme (e.g. "better search", "offline mode", "dark theme")
  4. Rank by frequency — most-requested features first

Signal words to look for:

  • "I wish...", "Would be great if...", "Please add..."
  • "Missing feature", "Needs improvement", "Should have..."
  • "The only thing stopping me from 5 stars..."

Complaint Analysis

Deep dive into what users are unhappy about.

  1. Fetch reviews and filter for 1–2 star ratings
  2. Categorize complaints:
CategoryExamples
Bugs & crashes"App crashes on startup", "Keeps freezing"
Performance"Too slow", "Drains battery", "Uses too much storage"
UX/Design"Confusing navigation", "Hard to find features"
Pricing"Too expensive", "Not worth the subscription"
Missing features"No offline mode", "Can't export data"
Ads"Too many ads", "Intrusive advertising"
Support"No response from support", "Can't contact anyone"
  1. Rank by severity and frequency

Competitor Review Comparison

Compare reviews across competing apps.

  1. Use search_apps to find competitors in the same category
  2. Use get_app_reviews for each competitor (2–3 apps)
  3. Compare:
    • What do competitor users praise that this app lacks?
    • What complaints are unique to competitors (your advantage)?
    • What complaints are shared across all apps (industry problem)?

Cross-Country Review Analysis

Compare user feedback across different markets.

  1. Use get_app_reviews with different country values
  2. Look for market-specific patterns:
    • Language/localization complaints in non-English markets
    • Feature preferences that vary by region
    • Payment/pricing concerns that differ by market

Output Format

Review Analysis Report

App: [name] ([appId]) Reviews analyzed: [N] Country: [code]

Sentiment Breakdown:

SentimentCountPercentage
Positive (4–5★)[N][%]
Neutral (3★)[N][%]
Negative (1–2★)[N][%]

Top Themes:

#ThemeSentimentFrequencyExample Quote
1[theme][pos/neg][count]"[quote]"

Feature Requests:

#RequestFrequencyImpact
1[feature][count]High/Medium/Low

Actionable Recommendations:

  1. [Highest-priority action based on review data]
  2. [Second priority]
  3. [Third priority]

Tips

  • 100 reviews is usually enough for a solid sentiment snapshot. Fetch more only if the analysis requires deeper statistical confidence.
  • Recent reviews matter most — they reflect the current state of the app after the latest updates.
  • Cross-reference with ratings — an app with 4.5 stars but many recent 1-star reviews may be trending downward.
  • Look for version-specific feedback — users often mention specific versions when reporting bugs.
  • Country matters — reviews in different countries may reveal localization issues or market-specific preferences.

Related Skills

  • app-discovery — Find apps to analyze reviews for
  • competitor-analysis — Deep competitive analysis combining reviews with other metrics
  • growth-analysis — Correlate review sentiment with growth trends
  • keyword-research — Extract keyword ideas from the language users use in reviews

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