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review-analysis

review-analysis is a skill for Claude Code, Codex from AppKittie/aso-mcp-skills. It costs 94 tokens per session (1,359 once invoked), scanned A, original, MIT.

A workflow for studying reviews of mobile apps on Apple's App Store and Google Play. It can examine user opinions, complaints, requested features, ratings, and rating changes.

In plain words
What is it for?
Use it to summarize sentiment, find common complaints and feature requests, compare competitor reviews, or investigate rating drops.
Why use it?
It turns large numbers of app reviews into organized feedback about what users like, dislike, or want changed. This avoids having to read every review manually.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 6repo +1 yesterday A scan 94 tokens original MIT

Good fit Use it to summarize sentiment, find common complaints and feature requests, compare competitor reviews, or investigate rating drops.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/appkittie/aso-mcp-skills/review-analysis
View source ↗ AppKittie/aso-mcp-skills
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add AppKittie/aso-mcp-skills --skill review-analysis
Clone the repo
git clone --depth 1 https://github.com/AppKittie/aso-mcp-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for review-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/appkittie/aso-mcp-skills/review-analysis/github.svg)](https://agentmods.dev/skills/appkittie/aso-mcp-skills/review-analysis)
Your own site
<a href="https://agentmods.dev/skills/appkittie/aso-mcp-skills/review-analysis"><img src="https://agentmods.dev/badge/skills/appkittie/aso-mcp-skills/review-analysis/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for review-analysis

Your own site · 80×15
<a href="https://agentmods.dev/skills/appkittie/aso-mcp-skills/review-analysis"><img src="https://agentmods.dev/badge/skills/appkittie/aso-mcp-skills/review-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 94 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,359 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00094 $0.01359
Opus 5 $0.00047 $0.00679
Sonnet 5 $0.00019 $0.00272
Haiku 4.5 $0.00009 $0.00136

Measured 12d ago against content hash e5abfe585f1b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

review-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/review-analysis/SKILL.md · 144 lines

How it starts

The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Read the full file on GitHub · 144 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 144 lines · 94 tokens per session scan A e5abfe585f1b

Subscribe to this mod's changes

review-analysis is a skill published in the GitHub repository AppKittie/aso-mcp-skills (6 stars, last pushed yesterday), licensed MIT. It adds 94 tokens to every session and 1,359 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Analytics and metricsMobile
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