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 Jeff-Kazzee/growth-engine --skill growth-retrogit clone --depth 1 https://github.com/Jeff-Kazzee/growth-engineWrote 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/jeff-kazzee/growth-engine/growth-retro)<a href="https://agentmods.dev/skills/jeff-kazzee/growth-engine/growth-retro"><img src="https://agentmods.dev/badge/skills/jeff-kazzee/growth-engine/growth-retro/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/jeff-kazzee/growth-engine/growth-retro"><img src="https://agentmods.dev/badge/skills/jeff-kazzee/growth-engine/growth-retro.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00068 | $0.00615 |
| Opus 5 | $0.00034 | $0.00308 |
| Sonnet 5 | $0.00014 | $0.00123 |
| Haiku 4.5 | $0.00007 | $0.00061 |
Grade A, and why
growth-retro 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 10d 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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Growth Retro
Run a retrospective on the Growth Engine system itself. The plugin's skill files are fixed, but growth-engine/playbook.md is not — it is the system's writable brain, read and obeyed by every skill. This retro is how the system learns.
Procedure
1. Gather evidence
Read growth-engine/playbook.md, decision-log.md, all files in reviews/ since the last retro (especially "Notes for next retro" sections), and skim recent radar/wiki/positioning outputs.
2. Analyze outcomes
- Which radar verdicts turned out right or wrong? (Build-now items that shipped and got traction vs died; watchlist triggers that fired vs never did.)
- Which review commitments got done vs repeatedly killed — and what did the done ones have in common (size, type, framing)?
- Which proof assets actually moved the user's goals (replies, subscribers, interviews, users)?
- Which playbook directives earned their keep; which are stale or ignored?
- Where did the system create friction — too many questions, too-long reports, wrong cadence, scores that didn't match reality?
3. Interview the user
Ask 3–5 pointed questions, for example: What advice from this system did you actually use? What did you ignore, and why? What felt like busywork? Where was the system too soft or too harsh? What's missing? What should it stop doing?
4. Update the playbook
Rewrite playbook.md:
- Add/edit numbered Directives — concrete, testable rules (e.g., "Cap radar reports at 5 opportunities," "Weekend-size commitments only," "Always propose a Substack angle for shipped proof").
- Move confirmed patterns into What works / What doesn't work with evidence and dates.
- Update the Open experiments table: close finished ones with results, add 1–2 new experiments with hypotheses and check-by dates.
- Prune directives that no longer apply. Keep the file under ~150 lines — a playbook nobody reads improves nothing.
5. Report
Summarize to the user: what the system learned, what changed in the playbook, what experiments are running, and the date the next retro should happen. Log the retro in decision-log.md.
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.
- 10d ago First seen · 46 lines · 68 tokens per session scan A f02eb7283444
growth-retro is a skill published in the GitHub repository Jeff-Kazzee/growth-engine (4 stars, last pushed 10d ago), licensed MIT. It adds 68 tokens to every session and 615 once invoked, about $0.0003 per session on Opus 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 2026-08-31.
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