Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add acogood/diffmode_free --skill growth-factors-mininggit clone --depth 1 https://github.com/acogood/diffmode_freeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/acogood/diffmode_free/growth-factors-mining)<a href="https://agentmods.dev/skills/acogood/diffmode_free/growth-factors-mining"><img src="https://agentmods.dev/badge/skills/acogood/diffmode_free/growth-factors-mining/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/acogood/diffmode_free/growth-factors-mining"><img src="https://agentmods.dev/badge/skills/acogood/diffmode_free/growth-factors-mining.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00129 | $0.03258 |
| Opus 5.5 | $0.00052 | $0.01303 |
| Sonnet 5.5 | $0.00026 | $0.00652 |
| Haiku 4.5 | $0.00013 | $0.00326 |
Grade A, and why
growth-factors-mining scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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-study search toward relevant growth stories (a bootstrapped B2B SaaS should mine indie SaaS / community-led / content / PLG case studies, not enterprise ad-spend stories). - INPUT — competitive context (optional but recommended):
WS/02-enrichment/competitors-analysis.mdandWS/02-enrichment/acquisition-tactics.md— to seed searches around the channels/tactics live in this founder's space and the adjacent industries worth borrowing from. - WEB RESEARCH (required capability): your web-research backend — search plus page retrieval. This is the ONLY source of vectors.
- OUTPUT: write
WS/03-think-tanks/demand-generation/growth-factors.json.
Caching & bounded research (cost control — surface this tradeoff)
This stage is deliberately "fresh per run," which is slower / pricier / less deterministic than a static asset. Mitigate:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 212 lines · 129 tokens per session scan A c5081b932b32
growth-factors-mining is a skill published in the GitHub repository acogood/diffmode_free (165 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 129 tokens to every session and 3,258 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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