autoresearch
Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy''s autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without
Profile →npx skills add github/awesome-copilot --skill autoresearch --agent copilot
Same command for any agent — swap --agent for claude-code, codex, cursor.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Agent Behavior Rules
- Phase 1: Setup (Interactive)
- 1.1 Define the Goal
- 1.2 Define the Metric
- 1.3 Define the Scope
- 1.4 Define Constraints
- 1.5 Define the Experiment Budget (Optional)
- 1.6 Simplicity Criterion
- 1.7 Confirm Setup
- Phase 2: Branch & Baseline
- Phase 3: Experiment Loop
- For each experiment:
- Experiment Strategy
- Handling Constraints
What does the autoresearch skill do?
Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy''s autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without
How do I install it?
Run `npx skills add github/awesome-copilot --skill autoresearch --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From github/awesome-copilot, a repository with 37,432 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.