microsoft/SkillOpt：README 來源編輯指南

microsoft/SkillOpt 的 README 將專案描述為「SkillOpt is a text-space optimizer that trains reusable natural-language skills for frozen LLM agents through trajectory-driven edits, validation-gated updates, and deployable bestskill.md artifacts.」。本文只整理倉庫可直接核對的內容，不把 star、Fork 或宣傳語當成品質證明。README 在「SkillOpt: Executive Strategy for Self-Evolving Agent Skills」下寫到：Train agent skills like you train neural networks , with epochs, (mini-)batchsize, learning rates, and validation gates , but without touching model weights.。這說明的是專案邊界，不是已完成的生產驗證。

從 README 的「News 🔥🔥🔥」與相關條目，可以先判斷它是否處理你的實際問題：[2026-07-02] 🚀 SkillOpt v0.2.0 for full release details and contributor acknowledgements.。若需求不同，不應只因專案熱度就採用。本文保留原始專案名、命令與元件名，方便回到一手來源核對。 README 另外列出一項可核對的資訊：[2026-07-24] 📰 SkillOpt in the news. Read the official Microsoft Research feature.。這類原文條目可用來設計試跑步驟，但不能取代實際環境測試。

README 將運作方式分散在「Overview」等段落。可確認的線索包括：Modern agent skills are usually hand-crafted, generated one-shot by a strong LLM, or evolved through loosely controlled self-revision , none of which behaves like a deep-learning optimizer for the skill itself, and none of which reliably。本文不把未寫出的架構、效能或安全邊界補成結論；真正的執行鏈仍要配合目錄、設定檔與版本標籤檢查。

第一次安裝應從 README 指出的入口開始。目前可核對的命令是： pip install -e ".[webui]" python -m skillopt_webui.app 如果倉庫沒有命令，本文不會自行編造步驟，而是建議先閱讀「News 🔥🔥🔥」，確認系統依賴、預設埠與首次初始化。

日常使用取決於專案文件。README 的「Overview」段落提到：SkillOpt treats the skill document as the trainable state of a frozen agent, and trains it with the discipline that makes weight-space optimization reproducible.。設定檔、環境變數、權限與資料目錄只在來源明確時才會記錄；沒有寫出的預設值，應在測試環境驗證並保留回滾副本。 同一部分也提到：[2026-06-15] 😴 SkillOpt-Sleep (preview) , a nightly offline self-evolution companion for local coding agents (Claude Code / Codex / Copilot): review past sessions, replay recurring tasks, and consolidate validated skills behind a held-out。