Autooptimise

Autonomously optimise any OpenClaw skill using a benchmark-driven experiment loop. Scores skill outputs 0-10 across 4 dimensions, identifies the lowest-scoring pattern, proposes a targeted SKILL.md change, re-tests, and keeps or discards based on measured improvement. Use when asked to: optimise my [skill] skill, run autooptimise on [skill], benchmark my [skill] skill, improve my skill overnight.

Autonomous benchmark-driven skill optimisation for OpenClaw. Inspired by Andrej Karpathy's autoresearch — the same modify → test → score → keep/discard loop, applied to agent skill quality instead of GPU training.

Trigger Phrases

• "optimise my weather skill"
• "run autooptimise on [skill-name]"
• "benchmark my [skill-name] skill"
• "improve my skill overnight"

Key Files

File	Purpose
benchmark/tasks.json	Test task suite (prompts + expected qualities)
benchmark/scorer.md	LLM judge scoring rubric
runner/run_experiment.md	Autonomous loop instructions (load this next)
runner/experiment_log.md	Auto-created run log (gitignored)

How to Run

1. Read runner/run_experiment.md — it contains the full loop instructions
2. Confirm the target skill with the user if not specified
3. Execute the loop (max 3 iterations)
4. Present proposed changes for human approval — never auto-apply

Scoring

Use the best available LLM judge model (prefer a strong reasoning model). Score each task 0–10 on:

• Accuracy — correct answer / correct tool called
• Conciseness — no padding, no unnecessary text
• Tool usage — right tool, right parameters
• Formatting — output matches expected format

Full rubric: benchmark/scorer.md

Safety Rules

• Never auto-apply changes. Always present a diff and wait for explicit human approval.
• Never modify benchmark/tasks.json or benchmark/scorer.md during a run.
• Never exceed 3 iterations per run in v0.1.
• Log every action to runner/experiment_log.md.