android-reverse-engineering.../plugins/market-research
fatih.bulut fbfa3aece7 feat(market-research): add play.google.com charts scraper
Replaces the AppBrain scraper (Cloudflare-blocked 403, confirmed dead).
play.google.com server-rendered chart HTML works: live-validated, real
Android packages + names + ratings from both /store/apps/top and
/store/apps/category/<CAT>. CLI: <top|category> [--category CAT]
[--region R] [--limit N] [--html-file F]; output source:"google-play",
entries {rank,name,package,rating}. Package link is the stable field;
title (Epkrse class) and rating are best-effort.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-06-23 22:42:20 +03:00
..
.claude-plugin feat(market-research): scaffold plugin, command, README, marketplace entry 2026-06-22 00:46:09 +03:00
commands feat(market-research): scaffold plugin, command, README, marketplace entry 2026-06-22 00:46:09 +03:00
skills/market-research feat(market-research): add play.google.com charts scraper 2026-06-23 22:42:20 +03:00
tests feat(market-research): add play.google.com charts scraper 2026-06-23 22:42:20 +03:00
README.md feat(market-research): scaffold plugin, command, README, marketplace entry 2026-06-22 00:46:09 +03:00

README.md

market-research

A Claude Code plugin that autonomously researches the app/game market and surfaces scored, non-repeating clone candidates, then hands user-chosen candidates to the clone-app plugin for feasibility analysis.

What it does

/market-research runs a 6-phase workflow:

  1. Seed rotation — pick varied search angles (category × region × niche) so runs differ.
  2. Gather — App Store RSS chart data (fetch-charts.py) + free web search for trends.
  3. Synthesize — cluster findings into ≥10 distinct app/game ideas.
  4. Score — composite score: cloneability + market opportunity + monetization fit.
  5. Dedup — exclude anything already in ./work/market-research/history.json.
  6. Present + handoff — show the ranked table; chosen candidates flow into clone-app.

State

Written under ./work/market-research/ in your current directory:

  • history.json — every past suggestion; the non-repeat memory.
  • research-<date>.md — the full report for a run.

Scripts

  • scripts/fetch-charts.py — fetch App Store RSS chart feeds → normalized JSON.
  • scripts/history.py — read / append / dedup the suggestion history.

Python is stdlib-only. Tests run offline against tests/fixtures/:

bash plugins/market-research/tests/run-all.sh

Effort convention

Effort is measured in AI Sprints (one focused Claude session), never calendar time.