autooptimise

# autooptimise Autonomous benchmark-driven skill optimisation for OpenClaw. Inspired by Andrej Karpathy's [autoresearch](https://github.com/karpathy/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`.