autooptimise#

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#

FilePurposebenchmark/tasks.jsonTest task suite (prompts + expected qualities)benchmark/scorer.mdLLM judge scoring rubricrunner/run_experiment.mdAutonomous loop instructions (load this next)runner/experiment_log.mdAuto-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.