> ## Documentation Index > Fetch the complete documentation index at: https://www.opendirectory.dev/docs/llms.txt > Use this file to discover all available pages before exploring further. # app-store-review-arbitrage > Give this skill a competitor's App Store or Google Play URL. It collects their public low-star reviews, detects where their store description's claims break... **Category:** GTM Intelligence · **Version:** `1.0.0` · **Author:** `opendirectory` · **Compatibility:** All targets (claude, codex, opencode, gemini, anti-gravity, openclaw, hermes) Give this skill a competitor's App Store or Google Play URL. It collects their public low-star reviews, detects where their store description's claims break against real complaints, and outputs a one-session brief: ranked complaint clusters, a broken promise map, landing page headline variants, and ad copy you can use tomorrow. No API key. No paid tools. One URL in, one ready-to-use brief out. *** ## Install ```bash theme={null} npx "@opendirectory.dev/skills" install app-store-review-arbitrage --target claude ``` ```bash theme={null} npx skills add Varnan-Tech/opendirectory --skill app-store-review-arbitrage ``` ```bash theme={null} /plugin marketplace add Varnan-Tech/opendirectory /plugin install opendirectory-gtm-skills@opendirectory-marketplace ``` ## What It Does 1. Collects up to 200 public reviews from App Store or Google Play — free, no API key, no login required. *(Note: App Store apps may sometimes return 0 reviews due to Apple RSS API limitations; Google Play is the primary supported path.)* 2. Filters to 1–3 star reviews and scores each by severity (star rating) and recency — the most recent failures get the highest weight 3. Clusters complaints into 4–6 named themes using the reviewers' own language — no abstracted labels like "stability issues" 4. Detects broken promises: cross-references the competitor's own store description against the complaint clusters and flags where their marketing overclaims reality 5. Outputs a copy-ready brief: complaint leaderboard with verbatim quotes, broken promise map, landing page H1 variants, ad copy directions, and anti-claim warnings *** ## Prerequisites * Python 3.9+ * One pip package (free, open source, no API key): ```bash theme={null} pip install google-play-scraper ``` * An AI agent: Claude Code, Gemini CLI, GitHub Copilot, or any compatible tool *** ## Usage Pass any of these prompts to your agent: ``` Analyze competitor reviews: https://apps.apple.com/us/app/notion-notes-tasks-ai/id1232780281 ``` ``` Run app-store-review-arbitrage on https://play.google.com/store/apps/details?id=com.robinhood.android ``` ``` Scan this competitor's reviews and give me ad copy angles: https://apps.apple.com/us/app/todoist-to-do-list-tasks/id572688855 My product does: offline-first task management that syncs without a subscription. ``` The `product_context` line (what your product does) is optional. If provided, ad copy directions will reference your specific product features. If absent, directions are phrased as positioning templates. *** ## Output One markdown file saved to `docs/review-briefs/[app-slug]-[YYYY-MM-DD].md`. Five sections, always present: | Section | Contents | | - | - | | 1. Complaint Cluster Leaderboard | Ranked complaint themes with scores, review counts, and verbatim quotes | | 2. Broken Promise Map | Competitor's store claims cross-referenced against complaint clusters | | 3. Landing Page H1 Bank | 3–5 headline variants derived from reviewer language | | 4. Ad Copy Directions | 3 "say this / not that" pairs grounded in complaint evidence | | 5. Anti-Claim Warnings | What not to claim in your own marketing, and why | *** ## When NOT to Use * **Need to map a whole market category?** Use [`map-your-market`](/docs/skills/gtm-intelligence/map-your-market) instead * **Need to find what features users want built next?** Use [`gh-issue-to-demand-signal`](/docs/skills/gtm-intelligence/gh-issue-to-demand-signal) instead * **Need to write outreach emails after reading the brief?** Use [`outreach-sequence-builder`](/docs/skills/outreach/outreach-sequence-builder) next * **Need to analyze your own app's reviews?** This skill is for competitor analysis only — the workflow is different * **App has fewer than 10 low-star reviews?** The skill will stop and tell you — that's by design *** ## Plays Well With | Skill | How | | - | - | | [`map-your-market`](/docs/skills/gtm-intelligence/map-your-market) | Run first to understand the category; run this skill to extract copy from the top competitor | | [`outreach-sequence-builder`](/docs/skills/outreach/outreach-sequence-builder) | Feed complaint cluster themes as "pain points your product solves" context when building cold outreach angles | | [`noise-to-linkedin-carousel`](/docs/skills/content/noise-to-linkedin-carousel) | Use the complaint cluster themes as slide content for a "why we built this" carousel | *** ## Cost per Run | Component | Cost | | - | - | | App Store collection | Free — uses direct iTunes RSS JSON API (no package, no API key) | | Google Play collection | Free — uses `google-play-scraper` (pip, open source, no API key) | | LLM analysis | Uses the model already running the skill — no additional cost | | **Total** | **Free** | *** ## Data Quality Notes * All quotes in the output are verbatim from collected reviews — no paraphrase, no editing * Broken promise detection is only as good as the competitor's store description. If the description is sparse, Section 2 will note this rather than fabricating comparisons * Collection is capped at 200 reviews. For apps with very high review volumes, this is a sample — the most recent reviews by default * The scoring formula weights recent 1-star reviews most heavily: `complaint_weight = (4 - rating) × recency_factor` * See [`references/scoring.md`](/docs/skills/gtm-intelligence/app-store-review-arbitrage/references/scoring) for the full formula and tier thresholds This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.