feat(market-research): add 6-phase SKILL.md orchestrator with clone-app handoff
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---
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description: Autonomously research the app and game market — rotate search angles, pull App Store chart feeds, synthesize emerging trends, score candidates by cloneability + market opportunity + monetization fit, exclude anything suggested before, and hand chosen candidates to the clone-app skill. Use when the user wants market research, fresh app/game ideas to clone, trending apps, or "what should I build next". 中文触发词：市场调研、找应用创意、热门应用、值得克隆的app
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trigger: market research|app ideas|what to build|what should i clone|trending apps|find apps to clone|top apps|market scan|市场调研|应用创意|热门应用
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---
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# Market Research — Discover Clone Candidates
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Scan the app/game market with free sources, score candidates, and hand the ones
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you pick to the `clone-app` skill. Every run rotates its search angles and
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excludes everything suggested before, so results stay fresh.
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This skill orchestrates 6 phases (0–5). Deterministic steps are factored into
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helper scripts under `${CLAUDE_PLUGIN_ROOT}/skills/market-research/scripts/`;
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AI-judgment steps follow rubrics under `.../references/`.
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## Legal note
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This produces market research and ideas only. Actual cloning is gated later by
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the `clone-app` skill's own legal note (analyze only apps you are authorized to).
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## State & working dir
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All state lives under `./work/market-research/` in the user's cwd (never inside
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the plugin):
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- `history.json` — every candidate ever suggested (the non-repeat memory).
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- `research-<YYYY-MM-DD>.md` — this run's report.
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Create it: `WORK="./work/market-research"` and `mkdir -p "$WORK"`.
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Pick a `RUN_ID` for this run that encodes the chosen angles (e.g.
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`2026-06-22-games-br`); it is stored with each suggestion so future runs can see
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which angles were used recently.
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## Phase 0: Seed rotation
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Read `${CLAUDE_PLUGIN_ROOT}/skills/market-research/references/research-angles.md`.
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If `$WORK/history.json` exists, skim recent `run_id`s to see which angles were
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used lately. Choose 2–3 categories, 1–2 regions, and 1 niche lens you did NOT use
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last run. If the command passed a focus argument, force one category to match it.
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State the chosen angles to the user in one line before continuing.
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## Phase 1: Gather (free web)
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Hard chart data — for each chosen region, pull the relevant feeds:
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```bash
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python3 ${CLAUDE_PLUGIN_ROOT}/skills/market-research/scripts/fetch-charts.py \
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topfreeapplications --region <region> --limit 25 > "$WORK/charts-<region>-free.json"
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python3 ${CLAUDE_PLUGIN_ROOT}/skills/market-research/scripts/fetch-charts.py \
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topgrossingapplications --region <region> --limit 25 > "$WORK/charts-<region>-grossing.json"
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```
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(Top-grossing = monetization signal; top-free = demand signal. Add
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`toppaidapplications` if willingness-to-pay matters for the angle.) If a fetch
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fails, note it and continue with the feeds you got.
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Trend signal — use WebSearch for the chosen categories/niches: new releases,
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ProductHunt launches, Reddit/news chatter in the last ~90 days, "fastest growing
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<category> apps 2026", dated-incumbent complaints. Vary the queries by the
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run's angles so two runs don't search the same terms. These results are the
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qualitative half the charts can't give.
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## Phase 2: Synthesize candidates
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Cluster the chart entries + web findings into **at least 12** distinct app/game
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ideas (synthesize more than 10 so dedup in Phase 4 still leaves ≥10). For each:
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name, category, what-it-does, why-now (trend signal), incumbent(s), monetization
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model. Note that App Store `bundle_id`s from the charts are iOS — treat them as
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signal, not Android packages. Write the working list as a JSON array (objects
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with at least `name`, optional `package`, `category`) to `$WORK/candidates.json`.
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## Phase 3: Score
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Read `${CLAUDE_PLUGIN_ROOT}/skills/market-research/references/scoring-guide.md`.
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Score every candidate's four components and weighted total. Add the subscores and
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`total` to each object in `$WORK/candidates.json`. Rank by `total` descending.
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## Phase 4: History dedup
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Drop anything already suggested:
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```bash
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python3 ${CLAUDE_PLUGIN_ROOT}/skills/market-research/scripts/history.py \
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filter --history "$WORK/history.json" < "$WORK/candidates.json" > "$WORK/fresh.json"
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```
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If fewer than 10 candidates survive, go back to Phase 1/2 with a different angle
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and synthesize more, then re-filter — never present a padded or repeated list.
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## Phase 5: Present + handoff
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Read `${CLAUDE_PLUGIN_ROOT}/skills/market-research/references/report-template.md`.
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Fill it from `$WORK/fresh.json` and write `$WORK/research-<YYYY-MM-DD>.md` (use
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the actual run date). Show the user the ranked table (≥10 rows) and your top-3
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recommended picks.
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Record this run's suggestions so they won't repeat:
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```bash
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python3 ${CLAUDE_PLUGIN_ROOT}/skills/market-research/scripts/history.py \
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add --history "$WORK/history.json" --date <YYYY-MM-DD> --run-id "<RUN_ID>" \
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< "$WORK/fresh.json"
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```
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Then ask which candidate(s) to pursue. For each pick:
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1. Resolve it to a Google Play package/URL. If you don't already have the package,
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WebSearch `"<name>" site:play.google.com` (or the developer + app name) and
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confirm the `play.google.com/store/apps/details?id=...` URL.
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2. Invoke the `clone-app` skill on that URL/package to run full feasibility.
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If the user picks nothing, stop — the report stands on its own.
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## Error Handling Summary
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| Scenario | Action |
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|---|---|
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| `fetch-charts.py` fails for a region | note it, continue with other feeds/web search |
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| Web search returns thin results | broaden queries within the chosen angle, try another region |
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| < 10 candidates survive dedup | loop back to Phase 1/2 with a new angle, re-filter |
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| history.json missing/first run | treat as empty; all candidates are fresh |
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| Candidate has no resolvable Play package | skip the handoff for it, keep it in the report as iOS-only/unresolved |
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| User picks nothing | stop after writing the report |
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@ -0,0 +1,29 @@
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#!/usr/bin/env bash
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set -uo pipefail
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ROOT="$(cd "$(dirname "$0")/../../.." && pwd)"
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SKILL="$ROOT/plugins/market-research/skills/market-research/SKILL.md"
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fail=0
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has() { grep -q "$1" "$SKILL" && echo "PASS contains: $1" || { echo "FAIL missing: $1"; fail=1; }; }
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[[ -f "$SKILL" ]] || { echo "FAIL: SKILL.md missing"; exit 1; }
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# frontmatter
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has "description:"
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has "trigger:"
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# all six phases
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has "Phase 0"
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has "Phase 1"
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has "Phase 2"
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has "Phase 3"
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has "Phase 4"
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has "Phase 5"
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# wires both scripts and the rubrics
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has "fetch-charts.py"
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has "history.py"
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has "scoring-guide.md"
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has "research-angles.md"
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has "report-template.md"
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# state contract + handoff
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has "work/market-research"
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has "history.json"
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has "clone-app"
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exit $fail
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