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 agentmods add skills/darkroomengineering/cc-settings/harvestnpx skills add darkroomengineering/cc-settings --skill harvestgit clone --depth 1 https://github.com/darkroomengineering/cc-settingsWrote 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/darkroomengineering/cc-settings/harvest)<a href="https://agentmods.dev/skills/darkroomengineering/cc-settings/harvest"><img src="https://agentmods.dev/badge/skills/darkroomengineering/cc-settings/harvest.svg" alt="Measured on agentmods" 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 | $0.00059 | $0.02094 |
| Opus 5 | $0.00030 | $0.01047 |
| Sonnet 5 | $0.00012 | $0.00419 |
| Haiku 4.5 | $0.00006 | $0.00209 |
Grade A, and why
harvest 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 5d 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 — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
harvest
Extract the repeatable procedure behind an unusually good result and land it as a reviewed cc-settings artifact — with the evidence measured, not asserted. The output is a concrete file (skill, rule, profile section, AGENTS.md diff, or team-knowledge note) plus a filled harvest contract whose verdict (PASS / FAIL / INCONCLUSIVE) decides whether it may be promoted, and to what scope.
This is a ratchet: it only tightens shared standards on behavior it can show repeated or survived a trap. Behavior it cannot measure is marked INCONCLUSIVE and held back, not written up as fact.
Standalone Codex source boundary
Claude frontmatter does not create an isolated fork in standalone Codex. Keep
evidence inspection read-only in the current context or use a fresh native
reader. Audit native rules under ${CODEX_HOME:-$HOME/.codex}/rules, the
installed AGENTS.md, and managed artifacts in the real cc-settings git
checkout named by the Codex sentinel's repo_path. Installed plugin/cache files
and darkroom/source may be inspected for drift, but never edited as the source
repo.
Phase 1 — Witnessed behavior census
Before deciding what to harvest, inventory what was actually seen. For each candidate behavior, record — from evidence, not memory:
- Witness count: how many independent sessions / transcripts / runs showed it, and therefore multi-witness (≥2) or single-witness (1).
- What repeated: the steps that were identical every time — the deterministic core.
- What varied: what differed across witnesses. Decide per difference: noise to drop, or part of the signal to preserve.
- Evidence inspected: the transcripts, PRs, diffs, or outputs you actually read.
- Not proven: what you are inferring rather than observing. Name the gaps.
Evidence comes two ways — use whichever the user has:
- Interview — 3–5 questions, one at a time: trigger? ordered steps? where the default would have gone wrong? how you knew the output was good? what it refused?
- Transcript / diff analysis — the user points at a session, PR, or outputs. Read them, reconstruct the same answers yourself, then confirm with the user.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 173 lines · 59 tokens per session scan A 69d479e8eff3
harvest is a skill published in the GitHub repository darkroomengineering/cc-settings (43 stars, last pushed 2d ago), licensed MIT. It adds 59 tokens to every session and 2,094 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 2026-08-30.
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