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  "text": "欢迎来到WorkBetty进阶蓝皮书的22章。这一章我们聊一个特别实用的是,怎么把一本书,一系列视频蒸留成Agent能主动调用的Skill。核心就三步,读书,蒸留,用起来。\n很多人都有这个困惑,书读了不少,真要用的时候却想不起来。恋爱也一样,他训练时读过很多经典,但回答常常是一堆正确的废话,每个字都对,就是没有可落地的步骤。\n知识经流要解决的,就是这种学了用不上的问题。\n知识经流,简单来说,就是把书或视频里的方法论,提纯成一个个代处发条件,代执行步骤的Skill。\n就像化学经流暗费点分离混合物,它按框架、原则、暗粒、反粒,数于五个维度,把知识分离成纯净组分,只把真正有用的提纯成可执行单元。\nTanger Scale用六个阶段把一整本书蒸留成一套Skill,先整书理解,在五个Agent并行提取,然后三重验证筛选,接着构造Skill,建立链接,最后压力测试。\n以文案创作完全手册为例,整本书最终变成一套可以调用,可以测试的Skill集合。\n阶段0很关键,不从摘金句开始,而是先读清整本书的骨架,这一步决定了后面提取的质量上限。\n阶段1让五个Agent从五个维度并行扫描全文,各自独立工作,互不干扰,避免单线阅读时的视角遗漏。\n阶段1.5是三重验证筛选,每个候选单元必须过三关,没过就淘汰,宁缺无赖。\n一本书通常50到100个候选,最后只留10到25个。\n阶段2构造Skill,最核心的是设计触发条件,没有触发条件,Agent根本不知道什么时候该调用它。\n阶段4座链接,把Skill之间的关系连成知识网络,这样遇到复杂问题,Agent能选一组Skill而不是单个。\n阶段5是压力测试,用诱饵测试故意给不该触发的场景,看Skill能不能忍住不激活,再用真实问题验证它给的是不是可落地的步骤。\n征流完的产物是一套结构清晰的Skill集合,有Rimi说明,有每个Skill的独立文件,有记录关系的Index,还有待测试用力的Test目录。\n而且测试用力兼容Darwin Scale,可以自动进化,持续优化,分数只升不降。\n有人会问,这和Rack有什么区别?\nRack解决的是知识管理,让你能查到书里有什么,知识经流解决的是知识运用,让Agent在对的时刻主动拿出对的框架。\n当你不知道该问什么的时候,Rack帮不了你。\n他也吸收了Carpacy的LLM wiki思路,但目标不同,两者并不忽斥。\nTanger Scale第二版增加了视频蒸留,先用Video Downloader把视频下载,提取音频,转写成文字。\n再进入六阶段Sub。\n支持YouTube和B站,长视频,推荐用ASR API转写。\n多个同主题视频还能合并蒸留,自动取重,而视频获取和文本蒸留职责分离,各自演进。\n什么材料适合蒸留?\n方法论密度高的书最适合,五星,访谈和课程视频也不错,长视频播客可用,金句散文类就一般了,小说叙事文学基本不适合。\n还有一个潜质条件,蒸留前最好自己先读过一遍,蒸留是阅读后的结构化工具,不是替代阅读。\n知识经流是Token消耗密集型的,主要花在多Agent并行提取和验证上,建议用轻量模型做提取,强模型做验证和构造。\n好在产物可以直接分享复用,把GitHub仓库地址给Agent,它就能自动安装使用,社区里同一本书不必每个人重复蒸留。\n最后提醒几个误区,训练过的书也要蒸留,因为小众和新书,AI大概率没读过,而且蒸留的价值在触发条件,蒸留完还是要读书,它是补充不是替代。\nAI给建议不等于能直接执行,决策还是人的责任,覆盖也不是越广越好,边界要控住。\n以温达的AI入门课为例,26个视频就能蒸留成一套Skill集合。\n知识经流是Skill的一种生产方式和Seg封装并行,产物可以在同一个Agent框架下混合使用。",
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      "text": "欢迎来到WorkBetty进阶蓝皮书的22章。这一章我们聊一个特别实用的是,怎么把一本书,一系列视频蒸留成Agent能主动调用的Skill。核心就三步,读书,蒸留,用起来。",
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      "text": "很多人都有这个困惑,书读了不少,真要用的时候却想不起来。恋爱也一样,他训练时读过很多经典,但回答常常是一堆正确的废话,每个字都对,就是没有可落地的步骤。",
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      "text": "知识经流,简单来说,就是把书或视频里的方法论,提纯成一个个代处发条件,代执行步骤的Skill。",
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      "text": "就像化学经流暗费点分离混合物,它按框架、原则、暗粒、反粒,数于五个维度,把知识分离成纯净组分,只把真正有用的提纯成可执行单元。",
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      "text": "Tanger Scale用六个阶段把一整本书蒸留成一套Skill,先整书理解,在五个Agent并行提取,然后三重验证筛选,接着构造Skill,建立链接,最后压力测试。",
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      "text": "以文案创作完全手册为例,整本书最终变成一套可以调用,可以测试的Skill集合。",
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      "text": "阶段0很关键,不从摘金句开始,而是先读清整本书的骨架,这一步决定了后面提取的质量上限。",
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      "text": "阶段1.5是三重验证筛选,每个候选单元必须过三关,没过就淘汰,宁缺无赖。",
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      "text": "征流完的产物是一套结构清晰的Skill集合,有Rimi说明,有每个Skill的独立文件,有记录关系的Index,还有待测试用力的Test目录。",
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      "text": "而且测试用力兼容Darwin Scale,可以自动进化,持续优化,分数只升不降。",
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      "text": "有人会问,这和Rack有什么区别?",
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      "text": "Rack解决的是知识管理,让你能查到书里有什么,知识经流解决的是知识运用,让Agent在对的时刻主动拿出对的框架。",
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      "text": "当你不知道该问什么的时候,Rack帮不了你。",
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      "text": "他也吸收了Carpacy的LLM wiki思路,但目标不同,两者并不忽斥。",
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      "text": "Tanger Scale第二版增加了视频蒸留,先用Video Downloader把视频下载,提取音频,转写成文字。",
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      "text": "再进入六阶段Sub。",
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      "text": "支持YouTube和B站,长视频,推荐用ASR API转写。",
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      "text": "多个同主题视频还能合并蒸留,自动取重,而视频获取和文本蒸留职责分离,各自演进。",
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      "text": "什么材料适合蒸留?",
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      "text": "方法论密度高的书最适合,五星,访谈和课程视频也不错,长视频播客可用,金句散文类就一般了,小说叙事文学基本不适合。",
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      "text": "还有一个潜质条件,蒸留前最好自己先读过一遍,蒸留是阅读后的结构化工具,不是替代阅读。",
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      "text": "知识经流是Token消耗密集型的,主要花在多Agent并行提取和验证上,建议用轻量模型做提取,强模型做验证和构造。",
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      "text": "好在产物可以直接分享复用,把GitHub仓库地址给Agent,它就能自动安装使用,社区里同一本书不必每个人重复蒸留。",
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