autoresearch
This skill should be used when the user asks to "run autoresearch", "optimize X in a loop", "set up autonomous experiments", "start autoresearch", "optimize X overnight", or "experiment loop". Sets up and runs an autonomous experiment loop for any optimization target.
npx skills add majiayu000/claude-skill-registry --skill autoresearch-paulrberg-agent-skills --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Autoresearch Autonomous experiment loop: try ideas, measure results, keep what works, discard what doesn't, never stop. Works for any optimization target: test speed, bundle size, LLM training, build times, Lighthouse scores, binary size, latency, memory usage. ## Setup If `autoresearch.md` already exists in the working directory, **skip setup and resume the loop** — read `autoresearch.md`, `autoresearch.jsonl`, and `git log`, then continue experimenting. Otherwise: 1. **Gather context**: Ask (or infer from `$ARGUMENTS` and conversation) the **Goal**, **Command** to benchmark, **Primary metric** (name + direction), **Files in scope**, and **Constraints**. 2. **Create branch**: `git checkout -b autoresearch/<goal>-<date>` (e.g. `autoresearch/test-speed-2026-03-21`). 3. **Read source files**: Understand the workload deeply before writing anything. Read every file in scope. 4. **Write session files**: Create `autoresearch.md` and `autoresearch.sh` (see templates below). If constraints require correctness validation (tests must pass, types must check), also create `autoresearch.checks.sh`. Commit all. 5. **Run baseline**: Execute the first experiment with no changes to establish the
- Setup
- autoresearch.md
- autoresearch.sh
- autoresearch.checks.sh (optional)
- The Experiment Loop
- JSONL Schema
- Resuming
- Ideas Backlog
- Loop Rules
- User Messages During Experiments
set -euo pipefail
Pre-checks (fast, <1s — catch syntax errors early)
python3 -c "import ast; ast.parse(open('train.py').read())"
Run benchmark
uv run train.py > /tmp/autoresearch-output.log 2>&1
Extract and output metrics as METRIC lines
echo "METRIC val_bpb=$val_bpb"
pnpm test --run --reporter=dot 2>&1 | tail -50
pnpm typecheck 2>&1 | grep -i error || true
timeout 600 ./autoresearch.sh > run.log 2>&1What does the autoresearch skill do?
This skill should be used when the user asks to "run autoresearch", "optimize X in a loop", "set up autonomous experiments", "start autoresearch", "optimize X overnight", or "experiment loop". Sets up and runs an autonomous experiment loop for any optimization target.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill autoresearch-paulrberg-agent-skills --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 majiayu000/claude-skill-registry, a repository with 534 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.