{"slug":"ai","title":"通用-去AI味重写","summary":"用于对章节正文做去 AI 味重写。适合主编式多轮去味、先诊断再定强度、整章去模板腔、局部拆解释腔、打散均匀句群、保信息重写与人物声音去同腔化。关键词：去AI味、主编式去味、多轮改稿、模板腔、解释腔、均匀句群、太像AI、重写这段。","platform":"Claude","tags":[],"authorName":"LLM Mart","authorSlug":"llm-mart","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-10-05T21:51:07.936014Z","repo":{"url":"https://github.com/lornshrimp/Lorn.NovelWriteSkills","stars":225,"forks":39,"license":null,"updatedAt":"2026-09-04T01:06:04Z"},"bodyHtml":"<h1>Humanizer-zh: AI 写作去痕工具（中文版）</h1>\n<blockquote>\n<p><strong>声明：</strong></p>\n<ul>\n<li>本项目的核心文件翻译自 <a href=\"https://github.com/blader/humanizer/tree/main\">blader/humanizer</a></li>\n<li>实用工具部分（核心规则、快速检查清单、质量评分）参考了 <a href=\"https://github.com/hardikpandya/stop-slop\">hardikpandya/stop-slop</a></li>\n<li>原项目基于维基百科的 <a href=\"https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing\">Signs of AI writing</a> 指南</li>\n</ul>\n</blockquote>\n<hr>\n<h2>项目简介</h2>\n<p>Humanizer-zh 是一个用于去除文本中 AI 生成痕迹的工具，帮助你将 AI 生成的内容改写得更自然、更像人类书写的文本。</p>\n<p>本项目适用于：</p>\n<ul>\n<li>编辑和审阅 AI 生成的内容</li>\n<li>提升文章的人性化程度</li>\n<li>学习识别 AI 写作的常见模式</li>\n</ul>\n<h2>安装</h2>\n<h3>方法一：通过 npx 一键安装（推荐）</h3>\n<pre><code>npx skills add https://github.com/op7418/Humanizer-zh.git\n</code></pre>\n<p>这是最简单的安装方式，会自动将技能安装到正确的目录。</p>\n<h3>方法二：通过 Git 克隆</h3>\n<pre><code># 克隆到 Claude Code 的 skills 目录\ngit clone https://github.com/op7418/Humanizer-zh.git ~/.claude/skills/humanizer-zh\n</code></pre>\n<h3>方法三：手动安装</h3>\n<ol>\n<li><p>下载本项目的 ZIP 文件或克隆到本地</p>\n</li>\n<li><p>将 <code>Humanizer-zh</code> 文件夹复制到 Claude Code 的 skills 目录：</p>\n<ul>\n<li><strong>macOS/Linux</strong>: <code>~/.claude/skills/</code></li>\n<li><strong>Windows</strong>: <code>%USERPROFILE%\\.claude\\skills\\</code></li>\n</ul>\n</li>\n<li><p>确保文件夹结构如下：</p>\n<pre><code>~/.claude/skills/humanizer-zh/\n├── SKILL.md       # 技能定义文件（中文版）\n└── README.md      # 说明文档\n</code></pre>\n</li>\n</ol>\n<h3>验证安装</h3>\n<p>重启 Claude Code 或重新加载 skills 后，在对话中输入：</p>\n<pre><code>/humanizer-zh\n</code></pre>\n<p>如果安装成功，该技能将被激活。</p>\n<h2>使用</h2>\n<h3>基础用法</h3>\n<p>在 Claude Code 中，你可以通过以下方式使用 Humanizer：</p>\n<h4>1. 直接调用技能</h4>\n<pre><code>/humanizer-zh 请帮我人性化以下文本：\n\n[粘贴你的 AI 生成文本]\n</code></pre>\n<h4>2. 在对话中使用</h4>\n<pre><code>请用 humanizer 帮我改写这段话，让它更自然：\n\n这个项目作为我们团队致力于创新的证明。此外，它展示了我们在不断演变的技术格局中的关键作用。\n</code></pre>\n<h4>3. 处理文件内容</h4>\n<pre><code>/humanizer-zh 请人性化 article.md 文件中的内容\n</code></pre>\n<h3>使用场景示例</h3>\n<h4>场景 1：改写营销文案</h4>\n<p><strong>输入：</strong></p>\n<pre><code>/humanizer-zh\n坐落在风景如画的杭州市中心，这家咖啡馆拥有丰富的文化底蕴和令人叹为观止的装饰。它作为城市咖啡文化的焦点，为顾客提供无缝、直观和充满活力的体验。\n</code></pre>\n<p><strong>输出示例：</strong></p>\n<blockquote>\n<p>这家咖啡馆在杭州市中心开了三年，以手冲咖啡和老建筑改造的空间出名。</p>\n</blockquote>\n<h4>场景 2：改写学术摘要</h4>\n<p><strong>输入：</strong></p>\n<pre><code>/humanizer-zh\n本研究深入探讨了机器学习在医疗诊断中的关键作用，突出了其在不断演变的医疗格局中的重要性。此外，它为该领域的未来发展奠定了坚实的基础。\n</code></pre>\n<p><strong>输出示例：</strong></p>\n<blockquote>\n<p>本研究分析了机器学习在医疗诊断中的应用，重点是肺癌早期筛查。研究使用了 2019-2023 年间 5000 例病历数据。</p>\n</blockquote>\n<h4>场景 3：改写博客文章</h4>\n<p><strong>输入：</strong></p>\n<pre><code>/humanizer-zh\n人工智能不仅仅是一种技术，它是我们思考未来的方式的革命。行业专家认为这将对整个社会产生持久影响。\n</code></pre>\n<p><strong>输出示例：</strong></p>\n<blockquote>\n<p>我一直在想 AI 会怎么改变我们的工作方式。上周和几个做产品的朋友聊，有人觉得很兴奋，有人担心失业，大概率真相在中间某个无聊的地方。</p>\n</blockquote>\n<h2>检测的 AI 写作模式</h2>\n<p>本工具能够识别并修复 <strong>24 种</strong> AI 写作痕迹，分为四大类：</p>\n<h3>\uD83D\uDCDD 内容模式（6种）</h3>\n<ol>\n<li>过度强调意义、遗产和更广泛的趋势</li>\n<li>过度强调知名度和媒体报道</li>\n<li>以 -ing 结尾的肤浅分析</li>\n<li>宣传和广告式语言</li>\n<li>模糊归因和含糊措辞</li>\n<li>提纲式的\"挑战与未来展望\"部分</li>\n</ol>\n<h3>\uD83D\uDD24 语言和语法模式（6种）</h3>\n<ol start=\"7\">\n<li>过度使用的\"AI 词汇\"</li>\n<li>避免使用\"是\"（系动词回避）</li>\n<li>否定式排比</li>\n<li>三段式法则过度使用</li>\n<li>刻意换词（同义词循环）</li>\n<li>虚假范围</li>\n</ol>\n<h3>\uD83C\uDFA8 风格模式（6种）</h3>\n<ol start=\"13\">\n<li>破折号过度使用</li>\n<li>粗体过度使用</li>\n<li>内联标题垂直列表</li>\n<li>标题中的标题大写</li>\n<li>表情符号</li>\n<li>弯引号</li>\n</ol>\n<h3>\uD83D\uDCAC 交流模式和填充词（6种）</h3>\n<ol start=\"19\">\n<li>协作交流痕迹</li>\n<li>知识截止日期免责声明</li>\n<li>谄媚/卑躬屈膝的语气</li>\n<li>填充短语</li>\n<li>过度限定</li>\n<li>通用积极结论</li>\n</ol>\n<h2>文件说明</h2>\n<ul>\n<li><strong><code>SKILL.md</code></strong> - 中文版技能定义文件</li>\n<li><strong><code>README.md</code></strong> - 本说明文档</li>\n</ul>\n<p><strong>注：</strong> 英文原版请参考 <a href=\"https://github.com/blader/humanizer\">blader/humanizer</a></p>\n<h2>手动使用方法</h2>\n<h3>基本流程</h3>\n<ol>\n<li><strong>识别 AI 模式</strong> - 对照 <code>SKILL.md</code> 中列出的 24 种模式扫描文本</li>\n<li><strong>重写问题片段</strong> - 用自然的表达替换 AI 痕迹</li>\n<li><strong>保留核心含义</strong> - 确保信息完整性</li>\n<li><strong>维持适当语调</strong> - 匹配文本应有的风格</li>\n<li><strong>注入真实个性</strong> - 让文字有\"人味\"</li>\n</ol>\n<h3>关键原则</h3>\n<h4>✨ 不仅要\"干净\"，更要\"鲜活\"</h4>\n<p>避免 AI 模式只是基础，好的写作需要真实的人类声音：</p>\n<ul>\n<li><strong>有观点</strong> - 不要只报告事实，要对它们做出反应</li>\n<li><strong>变化节奏</strong> - 混合使用长短句</li>\n<li><strong>承认复杂性</strong> - 真实的人有复杂感受</li>\n<li><strong>适当使用\"我\"</strong> - 第一人称是诚实的表现</li>\n<li><strong>允许一些混乱</strong> - 完美的结构反而显得机械</li>\n<li><strong>对感受要具体</strong> - 用具体细节替代抽象概括</li>\n</ul>\n<h4>示例对比</h4>\n<p><strong>改写前（AI 味道）：</strong></p>\n<blockquote>\n<p>新的软件更新作为公司致力于创新的证明。此外，它提供了无缝、直观和强大的用户体验——确保用户能够高效地完成目标。这不仅仅是一次更新，而是我们思考生产力方式的革命。</p>\n</blockquote>\n<p><strong>改写后（人性化）：</strong></p>\n<blockquote>\n<p>软件更新添加了批处理、键盘快捷键和离线模式。来自测试用户的早期反馈是积极的，大多数报告任务完成速度更快。</p>\n</blockquote>\n<p><strong>变化：</strong></p>\n<ul>\n<li>删除了夸大的象征意义（\"作为……的证明\"）</li>\n<li>删除了 AI 词汇（\"此外\"、\"无缝\"）</li>\n<li>删除了三段式法则（\"无缝、直观和强大\"）</li>\n<li>删除了否定式排比（\"不仅仅是……而是……\"）</li>\n<li>添加了具体功能和真实反馈</li>\n</ul>\n<h2>常见 AI 词汇警示列表</h2>\n<p>以下词汇在 AI 生成文本中出现频率异常高：</p>\n<ul>\n<li>此外、至关重要、深入探讨、强调</li>\n<li>持久的、增强、培养、获得</li>\n<li>突出、相互作用、复杂/复杂性</li>\n<li>格局（抽象名词）、关键性的、展示</li>\n<li>织锦（抽象名词）、证明、强调</li>\n<li>宝贵的、充满活力的</li>\n</ul>\n<h2>贡献</h2>\n<p>如果你发现翻译问题或想要改进文档，欢迎提交 Issue 或 Pull Request。</p>\n<h3>中文语境特殊性</h3>\n<p>在翻译和适配过程中，我们考虑了中文写作的特点：</p>\n<ul>\n<li>某些英文模式在中文中表现不同（如标题大小写问题）</li>\n<li>添加了适合中文语境的示例</li>\n<li>调整了部分表达以符合中文习惯</li>\n</ul>\n<h2>参考资源</h2>\n<ul>\n<li><a href=\"https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing\">Wikipedia: Signs of AI writing</a> - 原始指南来源</li>\n<li><a href=\"https://en.wikipedia.org/wiki/Wikipedia:WikiProject_AI_Cleanup\">WikiProject AI Cleanup</a> - 维基百科 AI 清理项目</li>\n<li><a href=\"https://github.com/blader/humanizer\">blader/humanizer</a> - 原始英文版项目</li>\n<li><a href=\"https://github.com/hardikpandya/stop-slop\">hardikpandya/stop-slop</a> - 实用工具部分的灵感来源</li>\n</ul>\n<h2>许可</h2>\n<p>本翻译项目遵循原项目的许可协议。核心内容基于维基百科社区的观察和总结。</p>\n<hr>\n<p><strong>提示：</strong> 这个工具不是为了\"欺骗\" AI 检测器，而是为了真正提升写作质量。最好的\"去 AI 化\"方法是让文字有真实的人类思考和声音。</p>\n","files":[{"path":"humanizer-main/AGENTS.md","sizeBytes":2354,"isText":true},{"path":"humanizer-main/.claude-plugin/marketplace.json","sizeBytes":622,"isText":true},{"path":"humanizer-main/.claude-plugin/plugin.json","sizeBytes":578,"isText":true},{"path":"humanizer-main/LICENSE","sizeBytes":1066,"isText":false},{"path":"humanizer-main/README.md","sizeBytes":12126,"isText":true},{"path":"humanizer-main/SKILL.md","sizeBytes":34017,"isText":true},{"path":"Humanizer-zh-main/LICENSE","sizeBytes":1063,"isText":false},{"path":"Humanizer-zh-main/README.md","sizeBytes":7799,"isText":true},{"path":"Humanizer-zh-main/SKILL.md","sizeBytes":18898,"isText":true},{"path":"references/AI句式替换与动作化替代清单.md","sizeBytes":8478,"isText":true},{"path":"references/AI味高发信号与拆洗策略补充.md","sizeBytes":4278,"isText":true},{"path":"references/OS叙事拐杖检测增补.md","sizeBytes":3977,"isText":true},{"path":"references/七猫专栏OS叙事拐杖检测增补.md","sizeBytes":3928,"isText":true},{"path":"references/去AI化报告模板.md","sizeBytes":13788,"isText":true},{"path":"references/去AI味三档手术强度与风险卡.md","sizeBytes":2468,"isText":true},{"path":"references/去AI味三级改造与工具包.md","sizeBytes":6122,"isText":true},{"path":"references/去AI味共享裁判规则补编.md","sizeBytes":14381,"isText":true},{"path":"references/去AI味句级改写工具.md","sizeBytes":1259,"isText":true},{"path":"references/去AI味后供血与职责复检卡.md","sizeBytes":3894,"isText":true},{"path":"references/去AI味四维病灶与45条规则.md","sizeBytes":5261,"isText":true},{"path":"references/去AI味多轮诊断与回写模板.md","sizeBytes":3260,"isText":true},{"path":"references/去AI味执行清单.md","sizeBytes":4532,"isText":true},{"path":"references/去AI味红线保护.md","sizeBytes":623,"isText":true},{"path":"references/去AI味识别雷达.md","sizeBytes":14935,"isText":true},{"path":"references/去AI味风格与排版补充.md","sizeBytes":1752,"isText":true},{"path":"references/去雕琢腔与透明人声校准卡.md","sizeBytes":3241,"isText":true},{"path":"references/实战对比案例——从AI稿与人工稿的差异提取可复用诊断与拆洗规则.md","sizeBytes":43014,"isText":true},{"path":"references/朱雀AI检测七维度操作详解.md","sizeBytes":14516,"isText":true},{"path":"references/真实细节与反向挑刺补充.md","sizeBytes":5285,"isText":true},{"path":"references/科幻类AI味典型问题.md","sizeBytes":4039,"isText":true},{"path":"references/统计模式对抗与人味编辑终审方案.md","sizeBytes":28290,"isText":true},{"path":"references/腾讯专栏直白与氛围保持增补.md","sizeBytes":2429,"isText":true},{"path":"references/视角塌陷与五感替代诊断修复卡.md","sizeBytes":8816,"isText":true},{"path":"references/风格注入锚点卡.md","sizeBytes":6466,"isText":true},{"path":"shuorenhua-main/AGENTS.md","sizeBytes":1263,"isText":true},{"path":"shuorenhua-main/assets/banner-dark.svg","sizeBytes":49295,"isText":false},{"path":"shuorenhua-main/assets/banner-light.svg","sizeBytes":49295,"isText":false},{"path":"shuorenhua-main/assets/icon-hd.png","sizeBytes":1032386,"isText":false},{"path":"shuorenhua-main/assets/icon.png","sizeBytes":304613,"isText":false},{"path":"shuorenhua-main/assets/readme-logo.png","sizeBytes":175768,"isText":false},{"path":"shuorenhua-main/assets/social-card.png","sizeBytes":85203,"isText":false},{"path":"shuorenhua-main/automation/eval/judge-prompt.md","sizeBytes":2728,"isText":true},{"path":"shuorenhua-main/automation/eval/README.md","sizeBytes":6250,"isText":true},{"path":"shuorenhua-main/automation/eval/rewrite-prompt.md","sizeBytes":3629,"isText":true},{"path":"shuorenhua-main/automation/intake.md","sizeBytes":4453,"isText":true},{"path":"shuorenhua-main/automation/intake-prompt.md","sizeBytes":2956,"isText":true},{"path":"shuorenhua-main/automation/README.md","sizeBytes":3642,"isText":true},{"path":"shuorenhua-main/CHANGELOG.md","sizeBytes":44365,"isText":true},{"path":"shuorenhua-main/CLAUDE.md","sizeBytes":9,"isText":true},{"path":"shuorenhua-main/CONTRIBUTING.md","sizeBytes":7654,"isText":true},{"path":"shuorenhua-main/evals/benchmark.md","sizeBytes":49849,"isText":true},{"path":"shuorenhua-main/evals/real-samples.md","sizeBytes":35256,"isText":true},{"path":"shuorenhua-main/evals/results-v1.3.0.md","sizeBytes":3587,"isText":true},{"path":"shuorenhua-main/evals/results-v1.4.3.md","sizeBytes":4325,"isText":true},{"path":"shuorenhua-main/evals/results-v1.5.0.md","sizeBytes":5562,"isText":true},{"path":"shuorenhua-main/evals/results-v1.7.1.md","sizeBytes":6959,"isText":true},{"path":"shuorenhua-main/evals/results-v1.7.4.md","sizeBytes":10405,"isText":true},{"path":"shuorenhua-main/evals/results-v1.8.0.md","sizeBytes":3540,"isText":true},{"path":"shuorenhua-main/evals/results-v1.8.3.md","sizeBytes":3382,"isText":true},{"path":"shuorenhua-main/evals/results-v1.8.5.md","sizeBytes":4213,"isText":true},{"path":"shuorenhua-main/evals/results-v1.8.6.md","sizeBytes":3970,"isText":true},{"path":"shuorenhua-main/evals/results-v1.9.0.md","sizeBytes":6950,"isText":true},{"path":"shuorenhua-main/evals/results-v1.9.1.md","sizeBytes":4150,"isText":true},{"path":"shuorenhua-main/evals/results-v1.9.2.md","sizeBytes":3607,"isText":true},{"path":"shuorenhua-main/evals/run-eval.md","sizeBytes":8265,"isText":true},{"path":"shuorenhua-main/.github/ISSUE_TEMPLATE/bad-case.md","sizeBytes":9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