Agent skill · Code Review & Quality

app-store-review-arbitrage

Fetches low-star App Store and Google Play reviews, clusters them into broken-promise patterns, and generates a ranked copy brief with positioning opportunities.

Varnan-Techgithub.com/Varnan-TechGitHub ↗
claude-codecopilotships scriptsMIT
Install
npx skills add Varnan-Tech/opendirectory --skill app-store-review-arbitrage --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 11
SKILL.md size: 11 KB
Bundled scripts: yes
Version: 1.0.0
Requires: [claude-code, gemini-cli, github-copilot]
Path: skills/app-store-review-arbitrage/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 571
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# app-store-review-arbitrage Convert a competitor's App Store or Google Play URL into a one-session GTM brief: ranked complaint clusters, a broken promise map, landing page headlines, and ad copy directions — all sourced from verbatim reviews. --- ## Critical Rules (read before Step 1) These rules apply throughout all steps. Violating any of them fails Self-QA (Step 6). 1. **Every quote must be verbatim.** No paraphrase, no grammar correction, no cleaning. Exact reviewer words only. 2. **No fabricated statistics.** Do not write "40% faster" or "2× more reliable" unless a reviewer explicitly used similar language. The Self-QA step checks for uncited percentages. 3. **Cluster names must use reviewer language.** Study the anti-pattern table in Step 3. 4. **Every headline and ad copy direction must cite its source cluster.** Format: `[cluster: "cluster-name"]`. 5. **Section 2 is always present** in the output — even when degraded. Never skip or omit it. 6. **No banned words** in any generated copy: powerful, robust, seamless, innovative, game-changing, streamline, leverage, revolutionize, transform. --- ## Step 1 — Parse Input and Detect Platform Accept a natural language prompt contai

What's inside
Steps it walks through
  1. Critical Rules (read before Step 1)
  2. Step 1 — Parse Input and Detect Platform
  3. Step 2 — Collect Reviews & Metadata
  4. Step 3 — Complaint Clustering
  5. Step 4 — Broken Promise Detection
  6. Step 5 — Generate Copy
  7. Step 6 — Self-QA
  8. Step 7 — Save Output
Ships with 10 files
  • .env.example
  • README.md
  • evals/README.md
  • evals/evals.json
  • evals/fixtures/eval3-mock.json
  • package.json
  • references/brief-format.md
  • references/broken-promise.md
  • references/scoring.md
  • scripts/fetch_reviews.py
Commands it runs
python3 scripts/fetch_reviews.py "{app_url}" --output {tmpdir}/asr-raw.json
More from opendirectory
All skills →
About this skill
What does the app-store-review-arbitrage skill do?

Fetches low-star App Store and Google Play reviews, clusters them into broken-promise patterns, and generates a ranked copy brief with positioning opportunities.

How do I install it?

Run `npx skills add Varnan-Tech/opendirectory --skill app-store-review-arbitrage --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From Varnan-Tech/opendirectory, a repository with 571 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going