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/autoresearchnpx skills add darkroomengineering/cc-settings --skill autoresearchgit 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/autoresearch)<a href="https://agentmods.dev/skills/darkroomengineering/cc-settings/autoresearch"><img src="https://agentmods.dev/badge/skills/darkroomengineering/cc-settings/autoresearch.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 | $0.00050 | $0.04337 |
| Opus 5 | $0.00025 | $0.02168 |
| Sonnet 5 | $0.00010 | $0.00867 |
| Haiku 4.5 | $0.00005 | $0.00434 |
Grade A, and why
autoresearch 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 today.
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 — 414 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AutoResearch
Standalone Codex: unsupported
Stop here in standalone Codex. This workflow depends on a measured Claude subprocess isolation and model-pinning loop. Do not substitute native agents or claim comparable scores until a Codex equivalent has its own controlled, repeatable measurements. The remaining workflow is Claude-only.
Autonomous skill optimization. You modify a skill's prompt, test it, keep improvements, revert failures. Repeat forever.
Adapted from Karpathy's autoresearch. Same method: single editable file, single metric, git-based keep/revert, autonomous loop. The only difference: SKILL.md replaces train.py, checklist pass rate replaces val_bpb.
NEVER STOP. Once the loop begins, do NOT pause to ask the human if you should continue. The human might be away and expects you to work indefinitely until manually interrupted. If you run out of ideas, think harder — re-read failing outputs, try combining near-misses, try more radical prompt rewrites. The loop runs until the human interrupts you, period.
Setup
Work with the user to configure, then go autonomous.
-
Parse target skill: Get
<skill-name>from$ARGUMENTS. Validateskills/<skill-name>/SKILL.mdexists. -
Load or create RESEARCH.md: Check for
skills/<skill-name>/RESEARCH.md. If it exists, read it — a skill born from/harvestarrives with a seeded RESEARCH.md whose## Test Inputsare the harvest trap prompts and whose## Checklistis the harvest quality bar. If not, generate one:- Read the target SKILL.md
- Derive 3 test inputs from its description and use cases
- Derive 5-7 checklist items from its workflow steps and output format
- Write the generated RESEARCH.md and show it to the user for confirmation
Either way, validate the shape before measuring:
bun run lint:research skills/<skill-name>/RESEARCH.md(required sections present, ≥2 test inputs, 3-7 checklist items, numeric settings). A seed that fails this parses wrong in the loop below.
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.
- today Changed · +1 lines 2e19fbc20755
- 4d ago First seen · 413 lines · 50 tokens per session scan A 06d32729b85b
autoresearch is a skill published in the GitHub repository darkroomengineering/cc-settings (43 stars, last pushed 2d ago), licensed MIT. It adds 50 tokens to every session and 4,337 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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