autoresearch

# Autoresearch: Autonomous Iterative Experimentation An autonomous experimentation loop for any programming task. You define the goal and how to measure it; the agent iterates autonomously -- modifying code, running experiments, measuring results, and keeping or discarding changes -- until interrupted. This skill is inspired by [Karpathy's autoresearch](https://github.com/karpathy/autoresearch), generalized from ML training to **any programming task with a measurable outcome**. --- ## Agent Behavior Rules 1. **DO** guide the user through the Setup phase interactively before starting the loop. 2. **DO** establish a baseline measurement before making any changes. 3. **DO** commit every experiment attempt before running it (so it can be reverted cleanly). 4. **DO** keep a results log (TSV) tracking every experiment. 5. **DO** revert changes that do not improve the metric (git reset to last known good). 6. **DO** run autonomously once the loop starts -- never pause to ask "should I continue?". 7. **DO NOT** modify files the user marked as out-of-scope. 8. **DO NOT** skip the measurement step -- every experiment must be measured. 9. **DO NOT** keep changes that regress the metric unless the user explicitly allowed trade-offs. 10. **DO NOT** install new dependencies or make environment changes unless the user approved it. --- ## Phase 1: Setup (Interactive) Before any experimentation begins, work with the user to establish these parameters. Ask the user directly for each it...