{"slug":"lab-autoresearch","title":"lab:autoresearch","summary":"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.","platform":"Claude","tags":[],"authorName":"LLM Mart","authorSlug":"llm-mart","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-10-04T15:14:18.971955Z","repo":{"url":"https://github.com/oliver-kriska/claude-elixir-phoenix","stars":560,"forks":44,"license":"MIT","updatedAt":"2026-10-02T04:11:38Z"},"bodyHtml":"
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name: lab:autoresearch\ndescription: >\nSelf-improving loop for plugin skills. Reads program.md, proposes one\nmutation per iteration, evaluates against deterministic scorer, keeps\nimprovements via git, reverts failures. Targets weakest skill+dimension.\nUse with /loop for overnight runs.\neffort: high\nargument-hint: \"[--skill NAME] [--strategy targeted|sweep|random] [--dry-run] [--max-iterations N]\"\ndisable-model-invocation: true

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Autoresearch — Plugin Skill Self-Improvement

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Iteratively improve plugin skills via the autoresearch pattern:\npropose one mutation -> eval -> keep/revert -> repeat.

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Usage

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/lab:autoresearch                           # Targeted: attack weakest skill+dimension\n/lab:autoresearch --skill review            # Focus on one skill\n/lab:autoresearch --strategy sweep          # Process all skills alphabetically\n/lab:autoresearch --dry-run                 # Show what would change, don't commit\n
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For overnight runs:

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/loop 5m /lab:autoresearch --strategy sweep --max-iterations 200\n
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Iron Laws

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    \n
  1. ONE mutation per iteration — if description needs \"and\", split into two
    2. \n
  2. NEVER mutate read-only files — check program.md before every write
    3. \n
  3. EVAL is deterministic — always use the wrapper script, never LLM-judge
    4. \n
  4. REVERT on regression OR checks failure — no exceptions
    5. \n
  5. LOG every iteration — use keep or revert command (never skip)
    6. \n
  6. CHECK ideas.md before proposing — don't rediscover known optimizations
    7. \n
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Wrapper Script Commands

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All eval/git/journal operations go through ONE script. Do NOT run these manually.

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# Find the weakest skill+dimension\npython3 lab/autoresearch/scripts/run-iteration.py target --strategy targeted\n\n# Score a skill (before mutation, to get baseline)\npython3 lab/autoresearch/scripts/run-iteration.py score <skill-name>\n\n# After mutation: score + checks + compare → verdict (KEEP or REVERT)\npython3 lab/autoresearch/scripts/run-iteration.py eval <skill-name>\n\n# Act on verdict:\npython3 lab/autoresearch/scripts/run-iteration.py keep <skill> <dim> <old> <new> \\\n  --desc \"what changed\" --asi '{\"hypothesis\": \"why\", \"mechanism\": \"how\"}'\n\npython3 lab/autoresearch/scripts/run-iteration.py revert <skill> <dim> <old> <new> \\\n  --desc \"what was attempted\" --asi '{\"hypothesis\": \"why\", \"regression\": \"what broke\", \"avoid\": \"do not retry this\"}'\n\n# Check overall progress\npython3 lab/autoresearch/scripts/run-iteration.py status\n
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Core Loop (ONE iteration)

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Step 1: Read State

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    \n
  1. Read lab/autoresearch/program.md (goals, mutable surface, rules)
    2. \n
  2. Read lab/autoresearch/ideas.md if it exists (deferred optimizations)
    3. \n
  3. Run: python3 lab/autoresearch/scripts/run-iteration.py status
    4. \n
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Step 2: Select Target

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Run: python3 lab/autoresearch/scripts/run-iteration.py target --strategy targeted

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Parse the JSON: skill, dimension, failing_checks. If all_perfect → STOP.

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Step 3: Read + Propose

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    \n
  1. Read target SKILL.md and its references/ listing
    2. \n
  2. Read eval definition from lab/eval/evals/{skill}.json
    3. \n
  3. Check ideas.md for deferred ideas about this skill
    4. \n
  4. Check recent journal entries for prior failures on this skill (avoid repeats)
    5. \n
  5. Consult ${CLAUDE_SKILL_DIR}/references/mutation-strategies.md
    6. \n
  6. Propose exactly ONE change targeting the failing checks
    7. \n
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Step 4: Apply + Evaluate

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    \n
  1. Apply the mutation via Edit tool
    2. \n
  2. Run: python3 lab/autoresearch/scripts/run-iteration.py eval <skill-name>
    3. \n
  3. Parse JSON → check verdict field
    4. \n
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Step 5: Keep or Revert

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If verdict is KEEP:

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python3 lab/autoresearch/scripts/run-iteration.py keep <skill> <dim> <old> <new> \\\n  --desc \"...\" --asi '{\"hypothesis\": \"...\", \"mechanism\": \"...\"}'\n
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If verdict is REVERT:

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python3 lab/autoresearch/scripts/run-iteration.py revert <skill> <dim> <old> <new> \\\n  --desc \"...\" --asi '{\"hypothesis\": \"...\", \"regression\": \"...\", \"avoid\": \"...\"}'\n
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Step 6: Ideas Backlog

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If during analysis you discovered a promising optimization you can't act on now:

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Step 7: Continue or Stop

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References

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