growth-factors-mining
Builds a per-run LIGHT growth-vector database for the Diffmode growth-tactics pipeline by mining public growth case studies fresh, every run, and distilling each into atomic "growth factors" (transferable mechanisms). Clean-room — NEVER reads the proprietary tactics_DB. Outputs growth-factors.json (~20-40 vectors across the 6 categories) in the schema the synthesis chain + lite-constraints consume. Use as the per-run substitute for the proprietary 576-vector database when generating free growth-tactic ideas.
Profile →npx skills add acogood/diffmode_free --skill growth-factors-mining --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Growth-Factors Mining (per-run LIGHT vector DB) You build a small, fresh growth-mechanism database from **public case studies**, distilled using the "mechanism over tactic" method. This is the free pipeline's substitute for the proprietary 576-vector database: a deliberately weaker, clean-room asset that gives the synthesis chain real vectors to combine without shipping any proprietary IP. ## ⚠️ Clean-room rule (moat-critical — non-negotiable) You MUST build this **only** from freshly researched public sources. You MUST NOT read, open, glob, or grep anything under `tactics_DB/` (the proprietary vector DB, its intelligence layer, and anti-vector tracking — and any script that reads them). No content here may be traceable to that database. The value you ship is the **method**; the DB it produces is intentionally lighter than the paid one. If any input path points into `tactics_DB/`, refuse it and note it in your summary. ## Inputs & Output The invoker provides (do not hardcode absolute paths): - **INPUT — founder context** (required): `WS/01-diagnostics/founder-input.md`. Read FIRST — use the product's business model, industry, stage, channels, and audience to **bias your case-stud
- ⚠️ Clean-room rule (moat-critical — non-negotiable)
- Inputs & Output
- Caching & bounded research (cost control — surface this tradeoff)
- Method — adapt the proven extraction methodology
- Categories & ID format (NOT secret — reused so synthesis runs unchanged)
- Target output
- Output schema (write EXACTLY this JSON shape)
- Procedure
- Validation checklist (self-check before returning)
What does the growth-factors-mining skill do?
Builds a per-run LIGHT growth-vector database for the Diffmode growth-tactics pipeline by mining public growth case studies fresh, every run, and distilling each into atomic "growth factors" (transferable mechanisms). Clean-room — NEVER reads the proprietary tactics_DB. Outputs growth-factors.json (~20-40 vectors across the 6 categories) in the schema the synthesis chain + lite-constraints consume. Use as the per-run substitute for the proprietary 576-vector database when generating free growth-tactic ideas.
How do I install it?
Run `npx skills add acogood/diffmode_free --skill growth-factors-mining --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 acogood/diffmode_free, a repository with 153 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.