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  "text": "今天講一個 16K 星的項目, Book to Skill。你買了一本很厚的技術書,看完以後覺得很有用。\n過幾個月真遇到問題,又想不起那套方法藏在哪一章。直接把整本PDF丟給AI,也能問。\n不過每次聊天都帶著幾百頁內容,佔據上下文,找一個具體問題也容易繞很久。\nBook to Skill可以把書整理成一套能安裝的Agent Skill。\n以後你在Cloud Code,Github Copilot, CLI或AMP里工作,直接叫這套Skill。\n再說你想查什麼,AI會先看總入口,再按問題打開對應章節。\n它生成的內容不只是一份讀書摘要。\n最外層有一個 Skill.md, 裡面放這本書的主要方法和章節索引。\nChapters 文件夾會把每一章單獨保存。旁邊還有術語表,方法清單和素材表。\n你問到數據複製怎麼選,它不用重新讀完整本書。\n只要打開相關章節,再根據整理過的內容回答。\n這套文件也可以繼續更新。\n後來又找到一篇論文,或者公司文檔多了一半。\n你可以把新材料合進原來的Skill,不用全部重做。\n它能處理 PDF, EPUB, Markdown, HTML, RTF, 和常見電子書格式。\n輸入也不一定是一本書。你可以把一個項目的DOCS文件夾,幾篇論文加自己的筆記,或者一整套團隊規範放在一起處理。\n實際使用時,先把Book2Skill裝到正在使用的Agent工具裡。\n安裝命令我放在畫面上了。\n接著把書籍文件或者資料文件夾交給它。\n它會先問這份資料偏技術,還是以文字為主。\n普通文字書可以走速度更快的提取方式。\n代碼,表格和公式的技術書,可以用 Docling 保留結構。\n不過處理時間會更長。\n文字提取完成以後,它會先告訴你資料有多大,大概要消耗多少上下文。\n還會列出準備生成哪些文件。\n你確認以後,Agent才開始分析章節提取方法,再寫成完整的Skill。\n這裡有一個安裝細節,容易弄混。\n把倉庫放進 AgentSkill 文件夾,才會得到完整的斜杠命令和傳輸流程。\n單獨用PIP安裝只會得到文檔提取工具。\n不會自動把Book2Skill註冊成AgentSkill。\n項目作者拿幾本真實書做過測試。\n如果每次都把整本書塞進上下文,回答一個問題,可能要帶上十幾萬到二十多萬TOKENS。\n想換成Skill以後,通常只加載總入口和一個相關章節,大約5000TOKENS。\n按倉庫給出的測試結果。\n對比整本書,直接進上下文,使用量少了24到51倍。\n這個數字要按作者的測試條件來看,它和一次臨時檢索相比,差距會縮小到2.4到15.6倍。\n你只準備問一次,直接讓AI讀PDF更省事。\n你會反覆查同一本書,提前整理成Skill才更划算。\n生成結果也不能當成原書的100%復刻。\n這些章節文件是AI整理出來的學習筆記。\n重要定義,數字和代碼還要回原文核對。\n自動分章也依賴書裡的標題格式。\n遇到只有章節名、羅馬數字或者特殊排版的書。\n章節可能識別不準,需要你手動檢查。\n隱私方面,文檔提取在本集完成。\n但是,後面用雲端模型分析內容時,\n交給模型的文字仍然會發送給對應服務商。\n公司內部文檔和沒有公開授權的書,\n先確認數據規則和使用權限。\n你自己買的書可以拿來做私人學習筆記。\n不要把生成後的整套Skill隨便公開分享。\n項目最新穩定版是V1.3.0。\n它目前官方明確支持Cloud Code,\nGithub Copilot, CLI和AMP。\n如果你想在其他Agent里使用,\n需要自己確認它能不能識別這套目錄和SkillMD格式。\n項目地址我放在屏幕上。\n喜歡這類AI工具拆解,記得一鍵三連加關注。\n你的支持就是我繼續更新的最大動力。",
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      "text": "今天講一個 16K 星的項目, Book to Skill。你買了一本很厚的技術書,看完以後覺得很有用。",
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      "text_original": "今天講一個16000多新的項目,Book to Skill。你買了一本很厚的技術書,看完以後覺得很有用。",
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      "text": "過幾個月真遇到問題,又想不起那套方法藏在哪一章。直接把整本PDF丟給AI,也能問。",
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      "text": "不過每次聊天都帶著幾百頁內容,佔據上下文,找一個具體問題也容易繞很久。",
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      "text": "Book to Skill可以把書整理成一套能安裝的Agent Skill。",
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      "text": "以後你在Cloud Code,Github Copilot, CLI或AMP里工作,直接叫這套Skill。",
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      "text": "再說你想查什麼,AI會先看總入口,再按問題打開對應章節。",
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      "text": "它生成的內容不只是一份讀書摘要。",
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      "text": "最外層有一個 Skill.md, 裡面放這本書的主要方法和章節索引。",
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      "text": "Chapters 文件夾會把每一章單獨保存。旁邊還有術語表,方法清單和素材表。",
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      "text": "你問到數據複製怎麼選,它不用重新讀完整本書。",
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      "text": "只要打開相關章節,再根據整理過的內容回答。",
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      "text": "這套文件也可以繼續更新。",
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      "text": "後來又找到一篇論文,或者公司文檔多了一半。",
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      "text": "你可以把新材料合進原來的Skill,不用全部重做。",
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      "text": "它能處理 PDF, EPUB, Markdown, HTML, RTF, 和常見電子書格式。",
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      "text": "輸入也不一定是一本書。你可以把一個項目的DOCS文件夾,幾篇論文加自己的筆記,或者一整套團隊規範放在一起處理。",
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      "text": "實際使用時,先把Book2Skill裝到正在使用的Agent工具裡。",
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      "temperature": 0,
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      "compression_ratio": 1.2124183006535947,
      "no_speech_prob": 1.6515795675520195e-11,
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    {
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      "end": 93.3,
      "text": "安裝命令我放在畫面上了。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.6515795675520195e-11,
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    {
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      "text": "接著把書籍文件或者資料文件夾交給它。",
      "tokens": [
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      "no_speech_prob": 1.6515795675520195e-11,
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    {
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      "text": "它會先問這份資料偏技術,還是以文字為主。",
      "tokens": [
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    {
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      "text": "普通文字書可以走速度更快的提取方式。",
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    {
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      "text": "代碼,表格和公式的技術書,可以用 Docling 保留結構。",
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      "text_original": "代代碼,表格和公式的技術書,可以用DOCLING保留結構。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
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    {
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      "end": 112.8,
      "text": "不過處理時間會更長。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.09707825434835334,
      "compression_ratio": 1.217910447761194,
      "no_speech_prob": 1.5140713335859424e-11,
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    {
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      "text": "文字提取完成以後,它會先告訴你資料有多大,大概要消耗多少上下文。",
      "tokens": [
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      "avg_logprob": -0.09707825434835334,
      "compression_ratio": 1.217910447761194,
      "no_speech_prob": 1.5140713335859424e-11,
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    {
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      "end": 120.9,
      "text": "還會列出準備生成哪些文件。",
      "tokens": [
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    {
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      "text": "你確認以後,Agent才開始分析章節提取方法,再寫成完整的Skill。",
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    {
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      "text": "這裡有一個安裝細節,容易弄混。",
      "tokens": [
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    {
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      "text": "把倉庫放進 AgentSkill 文件夾,才會得到完整的斜杠命令和傳輸流程。",
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      "temperature": 0,
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      "source": "whisper",
      "text_original": "把倉庫放進AgentSkill文件夾,才會得到完整的斜杠命令和轉輸流程。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
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    {
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      "text": "單獨用PIP安裝只會得到文檔提取工具。",
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    {
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      "text": "不會自動把Book2Skill註冊成AgentSkill。",
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    {
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      "text": "項目作者拿幾本真實書做過測試。",
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    {
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      "text": "如果每次都把整本書塞進上下文,回答一個問題,可能要帶上十幾萬到二十多萬TOKENS。",
      "tokens": [
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      "text": "想換成Skill以後,通常只加載總入口和一個相關章節,大約5000TOKENS。",
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    {
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      "text": "按倉庫給出的測試結果。",
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    {
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      "text": "對比整本書,直接進上下文,使用量少了24到51倍。",
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    {
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      "text": "這個數字要按作者的測試條件來看,它和一次臨時檢索相比,差距會縮小到2.4到15.6倍。",
      "tokens": [
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    {
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      "end": 180.1,
      "text": "你只準備問一次,直接讓AI讀PDF更省事。",
      "tokens": [
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    {
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      "text": "你會反覆查同一本書,提前整理成Skill才更划算。",
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      "no_speech_prob": 1.6765224625236996e-11,
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    },
    {
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      "seek": 17610,
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      "end": 188.1,
      "text": "生成結果也不能當成原書的100%復刻。",
      "tokens": [
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    },
    {
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      "seek": 17610,
      "start": 188.1,
      "end": 191.6,
      "text": "這些章節文件是AI整理出來的學習筆記。",
      "tokens": [
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    {
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      "seek": 17610,
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      "text": "重要定義,數字和代碼還要回原文核對。",
      "tokens": [
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      "temperature": 0,
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    {
      "id": 41,
      "seek": 17610,
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      "end": 198.6,
      "text": "自動分章也依賴書裡的標題格式。",
      "tokens": [
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    {
      "id": 42,
      "seek": 17610,
      "start": 198.6,
      "end": 202.6,
      "text": "遇到只有章節名、羅馬數字或者特殊排版的書。",
      "tokens": [
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      "text": "但是,後面用雲端模型分析內容時,",
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      "text": "公司內部文檔和沒有公開授權的書,",
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      "text": "不要把生成後的整套Skill隨便公開分享。",
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      "text": "它目前官方明確支持Cloud Code,",
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      "text": "如果你想在其他Agent里使用,",
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