aeon-autoresearch
Self-improvement loop. Given a target SKILL.md, generates four parallel improved variations, scores each, applies the winner.
Inputs
| Param | Description |
|---|---|
| target | Skill name or path to SKILL.md. Required. |
| mode | evolve (default) writes the diff. dry-run scores and prints, writes nothing. |
The four variations
- A — Better inputs: replace deprecated APIs, add fallbacks, fix broken endpoints.
- B — Sharper output: tighter format, signal over noise, explicit verdicts, banned filler.
- C — More robust: empty-data handling, retries, dedup state, rate-limit awareness.
- D — Rethink: fundamentally different methodology for the same goal.
Each is a complete runnable SKILL.md. Frontmatter shape preserved.
Scoring
1-5 per axis, weighted total max 50:
| Axis | Weight | |---|---| | Improvement vs original | 3× | | Output value | 2× | | Clarity, data quality, robustness | 1.5× each | | Conventions | 1× |
Tie-break (within 2 points): prefer the variation making the biggest single improvement over many small ones.
Safety guarantee
If every variation scores ≤ original on Improvement, the skill aborts with AUTORESEARCH_NO_IMPROVEMENT. No file written. Working skills are never downgraded.
Preserves the original's core purpose, frontmatter shape, and declared env vars.
Versioning
Inside a git repo, changes land in a branch (autoresearch/${target}) — operator reviews the diff before merging. Outside a repo, the original is preserved at ${target}/SKILL.md.before-autoresearch for rollback.
Output
The diff, plus a report with the scoring table for all four variations and a one-paragraph rationale for the winner.
Pairs with aeon-skill-evals (surfaces what's underperforming) and aeon-skill-repair (deterministic bugs; autoresearch handles quality lifts).