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不同的答案：與其借語言常識，不如直接讓模型學會預測世界如何變化。",[24,70,71],{},"最耐人尋味的，是一個反直覺的細節：多餵純影片資料，對機器人任務持續有幫助，但對預測人類自己的動作幾乎沒用、甚至略微變差。同一批資料，對「跨到別的身體」有效，對「留在原本的身體」無效。這暗示影片預測學到的不是「怎麼動」，而是「世界會怎麼變」——人的手和夾爪長得完全不同，但杯子推倒會滾、繩子拉緊會繃直，這些規律對誰都一樣。若這條路走得通，機器人的資料瓶頸就從「有多少人願意去操控手臂」，變成「有多少人的生活被記錄下來」。而那也會帶出一個更難回答的問題：誰的生活，誰同意的？",[73,74],"hr",{},[16,76,78],{"id":77},"延伸閱讀",[20,79,80],{},"🔗 延伸閱讀",[24,82,83],{},[20,84,85],{},"本篇主角",[87,88,89],"ul",{},[90,91,92,96],"li",{},[47,93,95],{"href":62,"rel":94},[51],"Dyna-2 技術報告","｜Dyna Robotics, 2026",[24,98,99],{},[20,100,101],{},"世界模型 × 動作",[87,103,104,110,116],{},[90,105,106,109],{},[47,107,52],{"href":49,"rel":108},[51],"｜影片與動作擴散的耦合預訓練",[90,111,112,115],{},[47,113,58],{"href":56,"rel":114},[51],"｜World Action Models are Zero-shot Policies",[90,117,118,123],{},[47,119,122],{"href":120,"rel":121},"https://arxiv.org/abs/2602.16710",[51],"EgoScale","｜以第一人稱人類影片擴展靈巧操作",[24,125,126],{},[20,127,128],{},"對照組：VLA 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Skills：讓 AI 學會可重複使用的專業技能","國立臺北大學統計學系",[8,9,10],"黃暐宸",{"type":13,"value":330,"toc":424},[331,337,347,353,364,370,381,387],[16,332,334],{"id":333},"agent-skills",[20,335,336],{},"Agent Skills",[24,338,339,340,346],{},"近期 Agentic AI 值得關注的發展之一，是 ",[47,341,344],{"href":342,"rel":343},"https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills",[51],[20,345,336],{},"。過去使用生成式 AI，常需要每次重新輸入 Prompt、背景資料與工作要求；Agent Skills 則可以把一套工作方法、規則、參考資料與工具封裝成可重複使用的「技能」，讓 AI 在遇到適合的任務時自行載入並執行。這使 AI 不只是回答問題，而是能依照既定流程完成較複雜、重複性高的工作，也讓個人或組織能逐步累積自己的 AI 工作能力。對企業來說，同一套技能也能讓不同人使用相近流程，降低重複教學與操作差異。",[16,348,350],{"id":349},"progressive-disclosure",[20,351,352],{},"Progressive Disclosure",[24,354,355,356,363],{},"Skill 的重要概念之一是 ",[47,357,360],{"href":358,"rel":359},"https://blog.aihao.tw/2026/05/20/llm-knowledge-base/",[51],[20,361,362],{},"Progressive Disclosure（漸進式披露）","。AI 不需要一開始就讀取所有 Skills 的完整內容，而是先知道目前有哪些技能，再根據任務判斷需要哪一項，之後才載入相關指令、文件、範例與其他資源。這種方式可以減少 Context 與 Token 的浪費，也避免一次塞入過多資訊影響模型判斷。當 Skills 數量愈來愈多時，這種「需要時才載入」的設計，也讓 Agent 更容易擴充不同領域的專業能力。",[16,365,367],{"id":366},"一般人也能使用skill",[20,368,369],{},"一般人也能使用Skill",[24,371,372,373,376,377,380],{},"更重要的是，",[20,374,375],{},"Skill 不只適合開發者","。一般使用者也能把經常重複的工作整理成 Skill，例如依公司格式製作會議紀錄、按照固定規則整理研究論文、修改英文履歷、產生每週工作報告，甚至整理旅遊規劃或個人學習流程。使用者主要需要把操作步驟、規則、範例與參考資料整理清楚，不一定要會複雜程式設計，也可以直接使用別人建立好的 Skill，再依自己的需求調整內容。未來使用 AI 的能力可能不只是「會下 Prompt」，而是",[20,378,379],{},"懂得把自己的知識與工作流程整理成 AI 可以反覆使用的 Skill","，讓 AI 真正成為可持續累積能力的工作助手。",[16,382,384],{"id":383},"agent-skills-推薦",[20,385,386],{},"Agent Skills 推薦",[24,388,389,390,397,398,401,402,397,409,412,413,397,420,423],{},"如果想實際體驗 Agent Skills，可以先從 GitHub 上熱門且用途廣泛的專案開始。例如 ",[47,391,394],{"href":392,"rel":393},"https://github.com/anthropics/skills?utm_source=chatgpt.com",[51],[20,395,396],{},"Anthropic 官方 Skills"," 約有 ",[20,399,400],{},"17.1 萬顆星","，其中包含 PDF、PowerPoint、Word、Excel 等文件處理 Skills，適合日常辦公與資料整理，也是了解 Skill 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