lab:autoresearch
Self-improving loop for plugin skills. Reads program.md, proposes one mutation per iteration, evaluates against deterministic scorer, keeps improvements via git, reverts failures. Targets weakest skill+dimension. Use with /loop for overnight runs.
npx skills add oliver-kriska/claude-elixir-phoenix --skill autoresearch --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 — Plugin Skill Self-Improvement Iteratively improve plugin skills via the autoresearch pattern: propose one mutation -> eval -> keep/revert -> repeat. ## Usage ``` /lab:autoresearch # Targeted: attack weakest skill+dimension /lab:autoresearch --skill review # Focus on one skill /lab:autoresearch --strategy sweep # Process all skills alphabetically /lab:autoresearch --dry-run # Show what would change, don't commit ``` For overnight runs: ``` /loop 5m /lab:autoresearch --strategy sweep --max-iterations 200 ``` ## Iron Laws 1. **ONE mutation per iteration** — if description needs "and", split into two 2. **NEVER mutate read-only files** — check program.md before every write 3. **EVAL is deterministic** — always use the wrapper script, never LLM-judge 4. **REVERT on regression OR checks failure** — no exceptions 5. **LOG every iteration** — use `keep` or `revert` command (never skip) 6. **CHECK ideas.md before proposing** — don't rediscover known optimizations ## Wrapper Script Commands All eval/git/journal operations go through ONE script. Do NOT run these manually. ```bash # Find the weakest skill+dimension python3 lab/autoresearch/scripts/run-iteration.py target --str
- Usage
- Iron Laws
- Wrapper Script Commands
- Core Loop (ONE iteration)
- Step 1: Read State
- Step 2: Select Target
- Step 3: Read + Propose
- Step 4: Apply + Evaluate
- Step 5: Keep or Revert
- Step 6: Ideas Backlog
- Step 7: Continue or Stop
- References
Find the weakest skill+dimension python3 lab/autoresearch/scripts/run-iteration.py target --strategy targeted Score a skill (before mutation, to get baseline) python3 lab/autoresearch/scripts/run-iteration.py score <skill-name> After mutation: score + checks + compare → verdict (KEEP or REVERT) python3 lab/autoresearch/scripts/run-iteration.py eval <skill-name> Act on verdict: python3 lab/autoresearch/scripts/run-iteration.py keep <skill> <dim> <old> <new> \ python3 lab/autoresearch/scripts/run-iteration.py revert <skill> <dim> <old> <new> \ Check overall progress
What does the lab:autoresearch skill do?
Self-improving loop for plugin skills. Reads program.md, proposes one mutation per iteration, evaluates against deterministic scorer, keeps improvements via git, reverts failures. Targets weakest skill+dimension. Use with /loop for overnight runs.
How do I install it?
Run `npx skills add oliver-kriska/claude-elixir-phoenix --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 oliver-kriska/claude-elixir-phoenix, a repository with 515 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.