aeon-autoresearch

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).