autoresearch
SolidKarpathy의 autoresearch 패턴을 스킬 프롬프트 최적화에 적용. 체크리스트(3~6개 예/아니오 기준) 기반 hill-climbing 루프로 SKILL.md를 자동 개선합니다. 한 번에 하나만 바꾸고, 채점하고, 유지하거나 되돌립니다. 95%+ × 3회 연속 달성 시 종료. /autoresearch로 실행.
Install
Quality Score: 84/100
Skill Content
Details
- Author
- Dannykkh
- Repository
- Dannykkh/skill-olympus
- Created
- 8 months ago
- Last Updated
- 2 days ago
- Language
- Python
- License
- MIT
Similar Skills
Semantically similar based on skill content — not just same category
autoresearch
Autonomous skill-prompt optimization — Karpathy-style mutate/score/keep loop on SKILL.md. Triggers "autoresearch", "optimize skill", "tune", "evolve" a skill, "prompt optimization".
skill-improver
Autoresearch loop for Claude Code skills — greedy keep/discard hill climbing on a 10-dimension quality rubric, with blind subagent validation for self-scoring bias. Given only a skill name, it reads that skill's history and picks which modes to run. Modes: `score` rates a skill out of 100 without editing it; `freshen` probes external references (release notes, docs, deprecation signals) and applies verified updates; `trigger` measures and tunes the frontmatter description until it fires when it should and stays silent when it shouldn't (60/40 train/test, 7 runs/query); `ages` prints every skill's verification age vs last content change; `floor` measures what a bare model already knows about a skill's subject.
autoresearch
Autonomously optimize any Claude Code skill by running it repeatedly, scoring outputs against binary evals, mutating the prompt, and keeping improvements. Based on Karpathy's autoresearch methodology. Use when: optimize this skill, improve this skill, run autoresearch on, make this skill better, self-improve skill, benchmark skill, eval my skill, run evals on. Outputs: an improved SKILL.md, a results log, and a changelog of every mutation tried.