autoresearch

# 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](https://github.com/karpathy/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. 1. **Parse target skill**: Get `<skill-name>` from `$ARGUMENTS`. Validate `skills/<skill-name>/SKILL.md` exists. 2. **Load or create RESEARCH.md**: Check for `skills/<skill-name>/RESEARCH.md`. If it exists, read it — a skill born from `/harvest` arrives with a seeded RESEARCH.md whose `## Test Inputs` are the harvest trap prompts and wh...