Autooptimise

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name: autooptimise description: "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." homepage: https://github.com/WealthVisionAI-Source/autooptimise metadata: { "openclaw": { "emoji": "🔬" } }

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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

| 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.