feat(market-research): 8-phase flow — dual-store charts, numeric enrich, Play links
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fatih.bulut
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@ -36,51 +36,86 @@ used lately. Choose 2–3 categories, 1–2 regions, and 1 niche lens you did NO
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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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## Phase 1: Gather charts (free)
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For each chosen region pull BOTH stores. Apple RSS (iOS signal — bundle ids, not
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Android packages):
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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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play.google.com charts (Android signal — REAL packages). Pull the overall top
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plus one category page per chosen category (Play category code, e.g.
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`PRODUCTIVITY`, `GAME_PUZZLE`, `FINANCE`):
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```bash
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python3 ${CLAUDE_PLUGIN_ROOT}/skills/market-research/scripts/fetch-play-charts.py \
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top --region <region> --limit 25 > "$WORK/play-top-<region>.json"
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python3 ${CLAUDE_PLUGIN_ROOT}/skills/market-research/scripts/fetch-play-charts.py \
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category --category <CAT> --region <region> --limit 25 > "$WORK/play-<CAT>.json"
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```
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If any fetch fails (Play may rotate its obfuscated title class), note it and
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continue with the feeds you got — Apple RSS + web signal still stand.
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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: Trend + numeric signal
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Read `${CLAUDE_PLUGIN_ROOT}/skills/market-research/references/numeric-sources.md`.
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For the chosen categories/niches, WebSearch the named numeric sources and pull
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real figures (market size, downloads, revenue, YoY) WITH their source URLs. For
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the top themes, attempt Trends momentum:
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```bash
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python3 ${CLAUDE_PLUGIN_ROOT}/skills/market-research/scripts/trends.py "<theme>"
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```
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If a `trends.py` result is `{"ok": false, "fallback": "websearch"}`, WebSearch the
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same momentum signal instead. Vary queries by the run's angles.
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## Phase 2: Synthesize candidates
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Cluster the chart entries + web findings into **at least 12** distinct app/game
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ideas (synthesize more than 10 so dedup in Phase 4 still leaves ≥10). For each:
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name, category, what-it-does, why-now (trend signal), incumbent(s), monetization
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model. Note that App Store `bundle_id`s from the charts are iOS — treat them as
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signal, not Android packages. Write the working list as a JSON array (objects
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with at least `name`, optional `package`, `category`) to `$WORK/candidates.json`.
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## Phase 3: Synthesize ≥12 candidates
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Cluster chart entries (Apple RSS + play.google.com) + web findings into ≥12
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distinct ideas (synthesize > 10 so dedup still leaves ≥10). For each: name,
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category, what-it-does, why-now (with a cited number where available),
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incumbent(s), monetization model. play.google.com entries already give an
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Android `package`; carry it.
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Write the working list as a JSON array (objects with at least `name`, optional
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`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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## Phase 4: Cheap score + history dedup
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Score each candidate on the chart/trend signal you ALREADY have (don't enrich
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yet — that's Phase 5). Then drop anything suggested before:
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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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If fewer than 10 survive, loop back to Phase 1/2 with a DIFFERENT angle and
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synthesize more, then re-filter. Never present a padded or repeated list.
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## Phase 5: Present + handoff
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## Phase 5: Enrich survivors only (efficiency)
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For the surviving top ~10 ONLY (never the full raw set), enrich each:
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1. Resolve 1–2 Play links + stats:
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```bash
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python3 ${CLAUDE_PLUGIN_ROOT}/skills/market-research/scripts/play.py \
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resolve "<candidate name>"
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```
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2. **Verify** each resolved `play_url` with WebFetch — confirm HTTP 200 and the
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`id=<package>` is present on the page. Drop dead/mismatched links. A candidate
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with no verifiable link is flagged "Play link unresolved" (kept in report,
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skipped on handoff).
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3. Saturation:
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```bash
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python3 ${CLAUDE_PLUGIN_ROOT}/skills/market-research/scripts/play.py \
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count "<category or core feature>"
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```
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4. Momentum (`trends.py` as in Phase 2) if not already pulled.
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## Phase 6: Re-score with numbers
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Read `${CLAUDE_PLUGIN_ROOT}/skills/market-research/references/scoring-guide.md`.
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Re-score every surviving candidate using the enriched numbers; attach the
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`evidence` object (cited installs / trend % / saturation / ARPU). Rank by `total`
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descending. Subscores with no supporting number are capped per the guide.
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## Phase 7: 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 `$WORK/research-<YYYY-MM-DD>.md` (use
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the actual run date). Show the user the ranked table (≥10 rows) and your top-3
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recommended picks.
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Fill it from the enriched, re-scored survivors and write
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`$WORK/research-<YYYY-MM-DD>.md`. Show the user the ranked table (≥10 rows, with
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Play links + saturation) and your top-3 picks.
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Record this run's suggestions so they won't repeat:
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```bash
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@ -88,20 +123,20 @@ 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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Then ask which candidate(s) to pursue. Each pick already has a verified Play URL —
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invoke the `clone-app` skill on it. If the user picks nothing, stop — the report
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stands on its own.
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## Error Handling Summary
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| Scenario | Action |
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| `fetch-charts.py` fails for a region | note it, continue with other feeds/web search |
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| `fetch-charts.py` / `fetch-play-charts.py` fails | note it, continue with other feeds/web search |
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| play.google.com chart fetch fails / title class rotated | continue on Apple RSS + web signal; package link stays stable |
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| `play.py resolve` finds no package | flag candidate "Play link unresolved", skip handoff |
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| Play link fails WebFetch verify | drop that link; if none verify, flag unresolved |
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| `trends.py` returns `{"ok": false}` | WebSearch the same momentum signal |
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| numeric source paywalled/JS-only | use only free-visible figures; never invent a number |
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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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