# Autoresearch

> Karpathy-style keep/revert experiment loop for Atris experiment packs. Use when improving prompts, tools, workers, or bounded repo targets.

- Skill: `atrislabs/autoresearch` (Agent Skill)
- Install (CLI): `npx skillmds@latest add atrislabs/autoresearch`
- Raw SKILL.md: https://api.skillmd.com/api/skills/atrislabs/autoresearch/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: atrislabs (https://skillmd.com/u/atrislabs)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/atrislabs/autoresearch

---


# Autoresearch Skill

Autoresearch means one bounded target, one external metric, one keep/revert loop, one append-only log.

## When to use

- prompt optimization
- worker routing
- tool behavior
- evaluation harnesses
- any repo-local target that can be measured honestly

## Process

1. Read `atris/experiments/<slug>/program.md`
2. Confirm the target is bounded
3. Run the baseline with `measure.py`
4. Apply one candidate change
5. Rerun the metric
6. Keep only if the score improves
7. Write the outcome to `results.tsv`
8. Revert losses

## Rules

- external metric only
- no unlogged keeps
- no broad refactors inside an experiment
- one experiment pack = one target
- if variance exists, define the keep margin first

## Commands

```bash
atris experiments init <slug>
atris experiments validate
atris experiments benchmark
```

## Good output

- short `program.md`
- honest `measure.py`
- deterministic `loop.py`
- append-only `results.tsv`

## Bad output

- "felt better"
- changed three things at once
- kept a patch without a measured win
- no reset/revert path