Why you'd want it
SkillOpt treats the skill document as the trainable state of a frozen agent and trains it without touching model weights. A separate optimizer model turns scored rollouts into bounded add, delete or replace edits, and in the default path an edit is accepted only when it strictly improves a held-out validation score. The deployed artifact is a best_skill.md file that runs against the unchanged target model, and the README says it adds zero inference-time model calls at deployment. A skillopt-sleep CLI, labelled preview, applies the same gate to past coding-agent sessions and stages proposed updates for you to adopt.
Best for
- Training a skill file against scored tasks
- Tuning skills for Codex or Claude Code runs
- Reviewing past coding-agent sessions into skills (preview)
Use it when
You have tasks you can score and a held-out split, and you want a skill document for an agent whose model you keep frozen improved by measured edits instead of hand-tuning.
Skip it if
You have no scored tasks or validation split to gate edits against, or no access to a configured model backend for the rollout and optimizer calls. The README calls SkillOpt-Sleep a preview and some multi-objective, replay and dream-rollout controls experimental, and the docs say its interfaces and defaults may change. With a real backend, Sleep sends truncated excerpts of harvested session transcripts to the provider you pick, and the docs say outbound prompts are not guaranteed secret-free, so review transcripts first.
Editor's note
PyPI serves 0.2.0. The docs say some newer features, such as Cursor and Pi support and the openai_compatible research backend, need a source install from main, and the benchmark configs and agent integration files live in the repository rather than the wheel.