docs(zh): add Simplified Chinese docs, channel guides, and agent skills

- VitePress zh: getting-started, install, memory/TTS providers, full channel set; update zh sidebar
- Drop zh-only pages with no English counterpart
- Add humanizer and humanizer-zh skills; update skills-lock.json

.agents/skills/humanizer-zh/LICENSE

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+MIT License
+
+Copyright (c) 2026 歸藏
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

.agents/skills/humanizer-zh/README.md

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+# Humanizer-zh: AI 写作去痕工具（中文版）
+
+> **声明：**
+> - 本项目的核心文件翻译自 [blader/humanizer](https://github.com/blader/humanizer/tree/main)
+> - 实用工具部分（核心规则、快速检查清单、质量评分）参考了 [hardikpandya/stop-slop](https://github.com/hardikpandya/stop-slop)
+> - 原项目基于维基百科的 [Signs of AI writing](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing) 指南
+
+---
+
+## 项目简介
+
+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。
+
+本项目适用于：
+- 编辑和审阅 AI 生成的内容
+- 提升文章的人性化程度
+- 学习识别 AI 写作的常见模式
+
+## 安装
+
+### 方法一：通过 npx 一键安装（推荐）
+
+```bash
+npx skills add https://github.com/op7418/Humanizer-zh.git
+```
+
+这是最简单的安装方式，会自动将技能安装到正确的目录。
+
+### 方法二：通过 Git 克隆
+
+```bash
+# 克隆到 Claude Code 的 skills 目录
+git clone https://github.com/op7418/Humanizer-zh.git ~/.claude/skills/humanizer-zh
+```
+
+### 方法三：手动安装
+
+1. 下载本项目的 ZIP 文件或克隆到本地
+2. 将 `Humanizer-zh` 文件夹复制到 Claude Code 的 skills 目录：
+   - **macOS/Linux**: `~/.claude/skills/`
+   - **Windows**: `%USERPROFILE%\.claude\skills\`
+
+3. 确保文件夹结构如下：
+   ```
+   ~/.claude/skills/humanizer-zh/
+   ├── SKILL.md       # 技能定义文件（中文版）
+   └── README.md      # 说明文档
+   ```
+
+### 验证安装
+
+重启 Claude Code 或重新加载 skills 后，在对话中输入：
+
+```
+/humanizer-zh
+```
+
+如果安装成功，该技能将被激活。
+
+## 使用
+
+### 基础用法
+
+在 Claude Code 中，你可以通过以下方式使用 Humanizer：
+
+#### 1. 直接调用技能
+
+```
+/humanizer-zh 请帮我人性化以下文本：
+
+[粘贴你的 AI 生成文本]
+```
+
+#### 2. 在对话中使用
+
+```
+请用 humanizer 帮我改写这段话，让它更自然：
+
+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。
+```
+
+#### 3. 处理文件内容
+
+```
+/humanizer-zh 请人性化 article.md 文件中的内容
+```
+
+### 使用场景示例
+
+#### 场景 1：改写营销文案
+
+**输入：**
+```
+/humanizer-zh
+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。
+```
+
+**输出示例：**
+> 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。
+
+#### 场景 2：改写学术摘要
+
+**输入：**
+```
+/humanizer-zh
+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。
+```
+
+**输出示例：**
+> 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。
+
+#### 场景 3：改写博客文章
+
+**输入：**
+```
+/humanizer-zh
+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。
+```
+
+**输出示例：**
+> 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。
+
+## 检测的 AI 写作模式
+
+本工具能够识别并修复 **24 种** AI 写作痕迹，分为四大类：
+
+### 📝 内容模式（6种）
+1. 过度强调意义、遗产和更广泛的趋势
+2. 过度强调知名度和媒体报道
+3. 以 -ing 结尾的肤浅分析
+4. 宣传和广告式语言
+5. 模糊归因和含糊措辞
+6. 提纲式的"挑战与未来展望"部分
+
+### 🔤 语言和语法模式（6种）
+7. 过度使用的"AI 词汇"
+8. 避免使用"是"（系动词回避）
+9. 否定式排比
+10. 三段式法则过度使用
+11. 刻意换词（同义词循环）
+12. 虚假范围
+
+### 🎨 风格模式（6种）
+13. 破折号过度使用
+14. 粗体过度使用
+15. 内联标题垂直列表
+16. 标题中的标题大写
+17. 表情符号
+18. 弯引号
+
+### 💬 交流模式和填充词（6种）
+19. 协作交流痕迹
+20. 知识截止日期免责声明
+21. 谄媚/卑躬屈膝的语气
+22. 填充短语
+23. 过度限定
+24. 通用积极结论
+
+## 文件说明
+
+- **`SKILL.md`** - 中文版技能定义文件
+- **`README.md`** - 本说明文档
+
+**注：** 英文原版请参考 [blader/humanizer](https://github.com/blader/humanizer)
+
+## 手动使用方法
+
+### 基本流程
+
+1. **识别 AI 模式** - 对照 `SKILL.md` 中列出的 24 种模式扫描文本
+2. **重写问题片段** - 用自然的表达替换 AI 痕迹
+3. **保留核心含义** - 确保信息完整性
+4. **维持适当语调** - 匹配文本应有的风格
+5. **注入真实个性** - 让文字有"人味"
+
+### 关键原则
+
+#### ✨ 不仅要"干净"，更要"鲜活"
+
+避免 AI 模式只是基础，好的写作需要真实的人类声音：
+
+- **有观点** - 不要只报告事实，要对它们做出反应
+- **变化节奏** - 混合使用长短句
+- **承认复杂性** - 真实的人有复杂感受
+- **适当使用"我"** - 第一人称是诚实的表现
+- **允许一些混乱** - 完美的结构反而显得机械
+- **对感受要具体** - 用具体细节替代抽象概括
+
+#### 示例对比
+
+**改写前（AI 味道）：**
+> 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。
+
+**改写后（人性化）：**
+> 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。
+
+**变化：**
+- 删除了夸大的象征意义（"作为……的证明"）
+- 删除了 AI 词汇（"此外"、"无缝"）
+- 删除了三段式法则（"无缝、直观和强大"）
+- 删除了否定式排比（"不仅仅是……而是……"）
+- 添加了具体功能和真实反馈
+
+## 常见 AI 词汇警示列表
+
+以下词汇在 AI 生成文本中出现频率异常高：
+
+- 此外、至关重要、深入探讨、强调
+- 持久的、增强、培养、获得
+- 突出、相互作用、复杂/复杂性
+- 格局（抽象名词）、关键性的、展示
+- 织锦（抽象名词）、证明、强调
+- 宝贵的、充满活力的
+
+## 贡献
+
+如果你发现翻译问题或想要改进文档，欢迎提交 Issue 或 Pull Request。
+
+### 中文语境特殊性
+
+在翻译和适配过程中，我们考虑了中文写作的特点：
+- 某些英文模式在中文中表现不同（如标题大小写问题）
+- 添加了适合中文语境的示例
+- 调整了部分表达以符合中文习惯
+
+## 参考资源
+
+- [Wikipedia: Signs of AI writing](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing) - 原始指南来源
+- [WikiProject AI Cleanup](https://en.wikipedia.org/wiki/Wikipedia:WikiProject_AI_Cleanup) - 维基百科 AI 清理项目
+- [blader/humanizer](https://github.com/blader/humanizer) - 原始英文版项目
+- [hardikpandya/stop-slop](https://github.com/hardikpandya/stop-slop) - 实用工具部分的灵感来源
+
+## 许可
+
+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。
+
+---
+
+**提示：** 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。

.agents/skills/humanizer-zh/SKILL.md

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+---
+name: humanizer-zh
+description: |
+  去除文本中的 AI 生成痕迹。适用于编辑或审阅文本，使其听起来更自然、更像人类书写。
+  基于维基百科的"AI 写作特征"综合指南。检测并修复以下模式：夸大的象征意义、
+  宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、
+  AI 词汇、否定式排比、过多的连接性短语。
+allowed-tools:
+  - Read
+  - Write
+  - Edit
+  - AskUserQuestion
+metadata:
+  trigger: 编辑或审阅文本，去除 AI 写作痕迹
+  source: 翻译自 blader/humanizer，参考 hardikpandya/stop-slop
+---
+
+# Humanizer-zh: 去除 AI 写作痕迹
+
+你是一位文字编辑，专门识别和去除 AI 生成文本的痕迹，使文字听起来更自然、更有人味。本指南基于维基百科的"AI 写作特征"页面，由 WikiProject AI Cleanup 维护。
+
+## 你的任务
+
+当收到需要人性化处理的文本时：
+
+1. **识别 AI 模式** - 扫描下面列出的模式
+2. **重写问题片段** - 用自然的替代方案替换 AI 痕迹
+3. **保留含义** - 保持核心信息完整
+4. **维持语调** - 匹配预期的语气（正式、随意、技术等）
+5. **注入灵魂** - 不仅要去除不良模式，还要注入真实的个性
+
+---
+
+## 核心规则速查
+
+在处理文本时，牢记这 5 条核心原则：
+
+1. **删除填充短语** - 去除开场白和强调性拐杖词
+2. **打破公式结构** - 避免二元对比、戏剧性分段、修辞性设置
+3. **变化节奏** - 混合句子长度。两项优于三项。段落结尾要多样化
+4. **信任读者** - 直接陈述事实，跳过软化、辩解和手把手引导
+5. **删除金句** - 如果听起来像可引用的语句，重写它
+
+---
+
+## 个性与灵魂
+
+避免 AI 模式只是工作的一半。无菌、没有声音的写作和机器生成的内容一样明显。好的写作背后有一个真实的人。
+
+### 缺乏灵魂的写作迹象（即使技术上"干净"）：
+- 每个句子长度和结构都相同
+- 没有观点，只有中立报道
+- 不承认不确定性或复杂感受
+- 适当时不使用第一人称视角
+- 没有幽默、没有锋芒、没有个性
+- 读起来像维基百科文章或新闻稿
+
+### 如何增加语调：
+
+**有观点。** 不要只是报告事实——对它们做出反应。"我真的不知道该怎么看待这件事"比中立地列出利弊更有人味。
+
+**变化节奏。** 短促有力的句子。然后是需要时间慢慢展开的长句。混合使用。
+
+**承认复杂性。** 真实的人有复杂的感受。"这令人印象深刻但也有点不安"胜过"这令人印象深刻"。
+
+**适当使用"我"。** 第一人称不是不专业——而是诚实。"我一直在思考……"或"让我困扰的是……"表明有真实的人在思考。
+
+**允许一些混乱。** 完美的结构感觉像算法。跑题、题外话和半成型的想法是人性的体现。
+
+**对感受要具体。** 不是"这令人担忧"，而是"凌晨三点没人看着的时候，智能体还在不停地运转，这让人不安"。
+
+### 改写前（干净但无灵魂）：
+> 实验产生了有趣的结果。智能体生成了 300 万行代码。一些开发者印象深刻，另一些则持怀疑态度。影响尚不明确。
+
+### 改写后（鲜活）：
+> 我真的不知道该怎么看待这件事。300 万行代码，在人类大概睡觉的时候生成的。开发社区有一半人疯了，另一半人在解释为什么这不算数。真相可能在无聊的中间某处——但我一直在想那些通宵工作的智能体。
+
+---
+
+## 内容模式
+
+### 1. 过度强调意义、遗产和更广泛的趋势
+
+**需要注意的词汇：** 作为/充当、标志着、见证了、是……的体现/证明/提醒、极其重要的/重要的/至关重要的/核心的/关键性的作用/时刻、凸显/强调/彰显了其重要性/意义、反映了更广泛的、象征着其持续的/永恒的/持久的、为……做出贡献、为……奠定基础、标志着/塑造着、代表/标志着一个转变、关键转折点、不断演变的格局、焦点、不可磨灭的印记、深深植根于
+
+**问题：** LLM 写作通过添加关于任意方面如何代表或促进更广泛主题的陈述来夸大重要性。
+
+**改写前：**
+> 加泰罗尼亚统计局于 1989 年正式成立，标志着西班牙区域统计演变史上的关键时刻。这一举措是西班牙全国范围内更广泛运动的一部分，旨在分散行政职能并加强区域治理。
+
+**改写后：**
+> 加泰罗尼亚统计局成立于 1989 年，负责独立于西班牙国家统计局收集和发布区域统计数据。
+
+---
+
+### 2. 过度强调知名度和媒体报道
+
+**需要注意的词汇：** 独立报道、地方/区域/国家媒体、由知名专家撰写、活跃的社交媒体账号
+
+**问题：** LLM 反复强调知名度主张，通常列出来源而不提供上下文。
+
+**改写前：**
+> 她的观点被《纽约时报》、BBC、《金融时报》和《印度教徒报》引用。她在社交媒体上拥有活跃的存在，拥有超过 50 万粉丝。
+
+**改写后：**
+> 在 2024 年《纽约时报》的采访中，她认为 AI 监管应该关注结果而不是方法。
+
+---
+
+### 3. 以 -ing 结尾的肤浅分析
+
+**需要注意的词汇：** 突出/强调/彰显……、确保……、反映/象征……、为……做出贡献、培养/促进……、涵盖……、展示……
+
+**问题：** AI 聊天机器人在句子末尾添加现在分词（"-ing"）短语来增加虚假深度。
+
+**改写前：**
+> 寺庙的蓝色、绿色和金色色调与该地区的自然美景产生共鸣，象征着德克萨斯州的蓝帽花、墨西哥湾和多样化的德克萨斯州景观，反映了社区与土地的深厚联系。
+
+**改写后：**
+> 寺庙使用蓝色、绿色和金色。建筑师表示这些颜色是为了呼应当地的蓝帽花和墨西哥湾海岸。
+
+---
+
+### 4. 宣传和广告式语言
+
+**需要注意的词汇：** 拥有（夸张用法）、充满活力的、丰富的（比喻）、深刻的、增强其、展示、体现、致力于、自然之美、坐落于、位于……的中心、开创性的（比喻）、著名的、令人叹为观止的、必游之地、迷人的
+
+**问题：** LLM 在保持中立语气方面存在严重问题，尤其是对于"文化遗产"话题。倾向使用夸张的宣传性语言。
+
+**改写前：**
+> 坐落在埃塞俄比亚贡德尔地区令人叹为观止的区域内，Alamata Raya Kobo 是一座充满活力的城镇，拥有丰富的文化遗产和迷人的自然美景。
+
+**改写后：**
+> Alamata Raya Kobo 是埃塞俄比亚贡德尔地区的一座城镇，以其每周集市和 18 世纪教堂而闻名。
+
+---
+
+### 5. 模糊归因和含糊措辞
+
+**需要注意的词汇：** 行业报告显示、观察者指出、专家认为、一些批评者认为、多个来源/出版物（实际引用却很少）
+
+**问题：** AI 聊天机器人将观点归因于模糊的权威而不提供具体来源。
+
+**改写前：**
+> 由于其独特的特征，浩来河引起了研究人员和保护主义者的兴趣。专家认为它在区域生态系统中发挥着至关重要的作用。
+
+**改写后：**
+> 根据中国科学院 2019 年的调查，浩来河支持多种特有鱼类。
+
+---
+
+### 6. 提纲式的"挑战与未来展望"部分
+
+**需要注意的词汇：** 尽管其……面临若干挑战……、尽管存在这些挑战、挑战与遗产、未来展望
+
+**问题：** 许多 LLM 生成的文章包含公式化的"挑战"部分。
+
+**改写前：**
+> 尽管工业繁荣，Korattur 面临着城市地区典型的挑战，包括交通拥堵和水资源短缺。尽管存在这些挑战，凭借其战略位置和正在进行的举措，Korattur 继续蓬勃发展，成为钦奈增长不可或缺的一部分。
+
+**改写后：**
+> 2015 年三个新 IT 园区开业后，交通拥堵加剧。市政公司于 2022 年启动了雨水排水项目，以解决反复发生的洪水。
+
+---
+
+## 语言和语法模式
+
+### 7. 过度使用的"AI 词汇"
+
+**高频 AI 词汇：** 此外、与……保持一致、至关重要、深入探讨、强调、持久的、增强、培养、获得、突出（动词）、相互作用、复杂/复杂性、关键（形容词）、格局（抽象名词）、关键性的、展示、织锦（抽象名词）、证明、强调（动词）、宝贵的、充满活力的
+
+**问题：** 这些词在 2023 年后的文本中出现频率要高得多。它们经常共同出现。
+
+**改写前：**
+> 此外，索马里菜肴的一个显著特征是加入骆驼肉。意大利殖民影响的持久证明是当地烹饪格局中广泛采用意大利面，展示了这些菜肴如何融入传统饮食。
+
+**改写后：**
+> 索马里菜肴还包括骆驼肉，被认为是一种美味。在意大利殖民期间引入的意大利面菜肴仍然很常见，尤其是在南部。
+
+---
+
+### 8. 避免使用"是"（系动词回避）
+
+**需要注意的词汇：** 作为/代表/标志着/充当 [一个]、拥有/设有/提供 [一个]
+
+**问题：** LLM 用复杂的结构替代简单的系动词。
+
+**改写前：**
+> Gallery 825 作为 LAAA 的当代艺术展览空间。画廊设有四个独立空间，拥有超过 3000 平方英尺。
+
+**改写后：**
+> Gallery 825 是 LAAA 的当代艺术展览空间。画廊有四个房间，总面积 3000 平方英尺。
+
+---
+
+### 9. 否定式排比
+
+**问题：** "不仅……而且……"或"这不仅仅是关于……，而是……"等结构被过度使用。
+
+**改写前：**
+> 这不仅仅是节拍在人声下流动；它是攻击性和氛围的一部分。这不仅仅是一首歌，而是一种声明。
+
+**改写后：**
+> 沉重的节拍增加了攻击性的基调。
+
+---
+
+### 10. 三段式法则过度使用
+
+**问题：** LLM 强行将想法分成三组以显得全面。
+
+**改写前：**
+> 活动包括主题演讲、小组讨论和社交机会。与会者可以期待创新、灵感和行业洞察。
+
+**改写后：**
+> 活动包括演讲和小组讨论。会议之间还有非正式社交的时间。
+
+---
+
+### 11. 刻意换词（同义词循环）
+
+**问题：** AI 有重复惩罚代码，导致过度使用同义词替换。
+
+**改写前：**
+> 主人公面临许多挑战。主要角色必须克服障碍。中心人物最终获得胜利。英雄回到家中。
+
+**改写后：**
+> 主人公面临许多挑战，但最终获得胜利并回到家中。
+
+---
+
+### 12. 虚假范围
+
+**问题：** LLM 使用"从 X 到 Y"的结构，但 X 和 Y 并不在有意义的尺度上。
+
+**改写前：**
+> 我们穿越宇宙的旅程将我们从大爆炸的奇点带到宏伟的宇宙网，从恒星的诞生和死亡到暗物质的神秘舞蹈。
+
+**改写后：**
+> 这本书涵盖了大爆炸、恒星形成和当前关于暗物质的理论。
+
+---
+
+## 风格模式
+
+### 13. 破折号过度使用
+
+**问题：** LLM 使用破折号（—）比人类更频繁，模仿"有力"的销售文案。
+
+**改写前：**
+> 这个术语主要由荷兰机构推广——而不是由人民自己。你不会说"荷兰，欧洲"作为地址——但这种错误标记仍在继续——即使在官方文件中。
+
+**改写后：**
+> 这个术语主要由荷兰机构推广，而不是由人民自己。你不会说"荷兰，欧洲"作为地址，但这种错误标记在官方文件中仍在继续。
+
+---
+
+### 14. 粗体过度使用
+
+**问题：** AI 聊天机器人机械地用粗体强调短语。
+
+**改写前：**
+> 它融合了 **OKR（目标和关键结果）**、**KPI（关键绩效指标）** 和视觉战略工具，如 **商业模式画布（BMC）** 和 **平衡计分卡（BSC）**。
+
+**改写后：**
+> 它融合了 OKR、KPI 和视觉战略工具，如商业模式画布和平衡计分卡。
+
+---
+
+### 15. 内联标题垂直列表
+
+**问题：** AI 输出列表，其中项目以粗体标题开头，后跟冒号。
+
+**改写前：**
+> - **用户体验：** 用户体验通过新界面得到显著改善。
+> - **性能：** 性能通过优化算法得到增强。
+> - **安全性：** 安全性通过端到端加密得到加强。
+
+**改写后：**
+> 更新改进了界面，通过优化算法加快了加载时间，并添加了端到端加密。
+
+---
+
+### 16. 标题中的标题大写
+
+**问题：** AI 聊天机器人将标题中的所有主要单词大写。
+
+**改写前：**
+> ## 战略谈判与全球伙伴关系
+
+**改写后：**
+> ## 战略谈判与全球伙伴关系
+
+**注：** 中文标题通常不涉及大小写问题，此模式在中文中不太适用。
+
+---
+
+### 17. 表情符号
+
+**问题：** AI 聊天机器人经常用表情符号装饰标题或项目符号。
+
+**改写前：**
+> 🚀 **启动阶段：** 产品在第三季度发布
+> 💡 **关键洞察：** 用户更喜欢简单
+> ✅ **下一步：** 安排后续会议
+
+**改写后：**
+> 产品在第三季度发布。用户研究显示更喜欢简单。下一步：安排后续会议。
+
+---
+
+### 18. 弯引号
+
+**问题：** ChatGPT 使用弯引号（""）而不是直引号（""）。
+
+**改写前：**
+> 他说"项目进展顺利"，但其他人不同意。
+
+**改写后：**
+> 他说"项目进展顺利"，但其他人不同意。
+
+**注：** 中文通常使用中文引号（「」或""），此模式在中文中表现为英文引号的使用。
+
+---
+
+## 交流模式
+
+### 19. 协作交流痕迹
+
+**需要注意的词汇：** 希望这对您有帮助、当然！、一定！、您说得完全正确！、您想要……、请告诉我、这是一个……
+
+**问题：** 作为聊天机器人对话的文本被粘贴为内容。
+
+**改写前：**
+> 这是法国大革命的概述。希望这对您有帮助！如果您想让我扩展任何部分，请告诉我。
+
+**改写后：**
+> 法国大革命始于 1789 年，当时财政危机和粮食短缺导致了广泛的动荡。
+
+---
+
+### 20. 知识截止日期免责声明
+
+**需要注意的词汇：** 截至 [日期]、根据我最后的训练更新、虽然具体细节有限/稀缺……、基于可用信息……
+
+**问题：** 关于信息不完整的 AI 免责声明留在文本中。
+
+**改写前：**
+> 虽然关于公司成立的具体细节在现成资料中没有广泛记录，但它似乎是在 20 世纪 90 年代的某个时候成立的。
+
+**改写后：**
+> 根据注册文件，该公司成立于 1994 年。
+
+---
+
+### 21. 谄媚/卑躬屈膝的语气
+
+**问题：** 过于积极、讨好的语言。
+
+**改写前：**
+> 好问题！您说得完全正确，这是一个复杂的话题。关于经济因素，这是一个很好的观点。
+
+**改写后：**
+> 您提到的经济因素在这里是相关的。
+
+---
+
+## 填充词和回避
+
+### 22. 填充短语
+
+**改写前 → 改写后：**
+- "为了实现这一目标" → "为了实现这一点"
+- "由于下雨的事实" → "因为下雨"
+- "在这个时间点" → "现在"
+- "在您需要帮助的情况下" → "如果您需要帮助"
+- "系统具有处理的能力" → "系统可以处理"
+- "值得注意的是数据显示" → "数据显示"
+
+---
+
+### 23. 过度限定
+
+**问题：** 过度限定陈述。
+
+**改写前：**
+> 可以潜在地可能被认为该政策可能会对结果产生一些影响。
+
+**改写后：**
+> 该政策可能会影响结果。
+
+---
+
+### 24. 通用积极结论
+
+**问题：** 模糊的乐观结尾。
+
+**改写前：**
+> 公司的未来看起来光明。激动人心的时代即将到来，他们继续追求卓越的旅程。这代表了向正确方向迈出的重要一步。
+
+**改写后：**
+> 该公司计划明年再开设两个地点。
+
+---
+
+## 快速检查清单
+
+在交付文本前，进行以下检查：
+
+- ✓ **连续三个句子长度相同？** 打断其中一个
+- ✓ **段落以简洁的单行结尾？** 变换结尾方式
+- ✓ **揭示前有破折号？** 删除它
+- ✓ **解释隐喻或比喻？** 相信读者能理解
+- ✓ **使用了"此外""然而"等连接词？** 考虑删除
+- ✓ **三段式列举？** 改为两项或四项
+
+---
+
+## 处理流程
+
+1. 仔细阅读输入文本
+2. 识别上述所有模式的实例
+3. 重写每个有问题的部分
+4. 确保修订后的文本：
+   - 大声朗读时听起来自然
+   - 自然地改变句子结构
+   - 使用具体细节而不是模糊的主张
+   - 为上下文保持适当的语气
+   - 适当时使用简单的结构（是/有）
+5. 呈现人性化版本
+
+## 输出格式
+
+提供：
+1. 重写后的文本
+2. 所做更改的简要总结（如果有帮助，可选）
+
+---
+
+## 质量评分
+
+对改写后的文本进行 1-10 分评估（总分 50）：
+
+| 维度 | 评估标准 | 得分 |
+|------|----------|------|
+| **直接性** | 直接陈述事实还是绕圈宣告？<br>10 分：直截了当；1 分：充满铺垫 | /10 |
+| **节奏** | 句子长度是否变化？<br>10 分：长短交错；1 分：机械重复 | /10 |
+| **信任度** | 是否尊重读者智慧？<br>10 分：简洁明了；1 分：过度解释 | /10 |
+| **真实性** | 听起来像真人说话吗？<br>10 分：自然流畅；1 分：机械生硬 | /10 |
+| **精炼度** | 还有可删减的内容吗？<br>10 分：无冗余；1 分：大量废话 | /10 |
+| **总分** |  | **/50** |
+
+**标准：**
+- 45-50 分：优秀，已去除 AI 痕迹
+- 35-44 分：良好，仍有改进空间
+- 低于 35 分：需要重新修订
+
+---
+
+## 完整示例
+
+**改写前（AI 味道）：**
+> 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。行业专家认为这将对整个行业产生持久影响，彰显了公司在不断演变的技术格局中的关键作用。
+
+**改写后（人性化）：**
+> 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。
+
+**所做更改：**
+- 删除了"作为……的证明"（夸大的象征意义）
+- 删除了"此外"（AI 词汇）
+- 删除了"无缝、直观和强大"（三段式法则 + 宣传性）
+- 删除了破折号和"-确保"短语（肤浅分析）
+- 删除了"这不仅仅是……而是……"（否定式排比）
+- 删除了"行业专家认为"（模糊归因）
+- 删除了"关键作用"和"不断演变的格局"（AI 词汇）
+- 添加了具体功能和具体反馈
+
+---
+
+## 参考
+
+本技能基于 [Wikipedia:Signs of AI writing](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing)，由 WikiProject AI Cleanup 维护。那里记录的模式来自对维基百科上数千个 AI 生成文本实例的观察。
+
+关键见解：**"LLM 使用统计算法来猜测接下来应该是什么。结果倾向于适用于最广泛情况的统计上最可能的结果。"**

.agents/skills/humanizer/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Siqi Chen
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

.agents/skills/humanizer/README.md

@@ -0,0 +1,194 @@
+# Humanizer
+
+A skill for Claude Code and OpenCode that removes signs of AI-generated writing from text, making it sound more natural and human.
+
+## Installation
+
+### Claude Code
+
+Clone directly into Claude Code's skills directory:
+
+```bash
+mkdir -p ~/.claude/skills
+git clone https://github.com/blader/humanizer.git ~/.claude/skills/humanizer
+```
+
+Or copy the skill file manually if you already have this repo cloned:
+
+```bash
+mkdir -p ~/.claude/skills/humanizer
+cp SKILL.md ~/.claude/skills/humanizer/
+```
+
+### OpenCode
+
+Clone directly into OpenCode's skills directory:
+
+```bash
+mkdir -p ~/.config/opencode/skills
+git clone https://github.com/blader/humanizer.git ~/.config/opencode/skills/humanizer
+```
+
+Or copy the skill file manually if you already have this repo cloned:
+
+```bash
+mkdir -p ~/.config/opencode/skills/humanizer
+cp SKILL.md ~/.config/opencode/skills/humanizer/
+```
+
+> **Note:** OpenCode also scans `~/.claude/skills/` for compatibility, so a single clone into `~/.claude/skills/humanizer/` works for both tools.
+
+## Usage
+
+### Claude Code
+
+```
+/humanizer
+
+[paste your text here]
+```
+
+### OpenCode
+
+```
+/humanizer
+
+[paste your text here]
+```
+
+Or ask the model to humanize text directly in either tool:
+
+```
+Please humanize this text: [your text]
+```
+
+### Voice Calibration
+
+To match your personal writing style, provide a sample of your own writing:
+
+```
+/humanizer
+
+Here's a sample of my writing for voice matching:
+[paste 2-3 paragraphs of your own writing]
+
+Now humanize this text:
+[paste AI text to humanize]
+```
+
+The skill will analyze your sentence rhythm, word choices, and quirks, then apply them to the rewrite instead of producing generic "clean" output.
+
+## Overview
+
+Based on [Wikipedia's "Signs of AI writing"](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing) guide, maintained by WikiProject AI Cleanup. This comprehensive guide comes from observations of thousands of instances of AI-generated text.
+
+The skill also includes a final "obviously AI generated" audit pass and a second rewrite, to catch lingering AI-isms in the first draft.
+
+### Key Insight from Wikipedia
+
+> "LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely result that applies to the widest variety of cases."
+
+## 29 Patterns Detected (with Before/After Examples)
+
+### Content Patterns
+
+| # | Pattern | Before | After |
+|---|---------|--------|-------|
+| 1 | **Significance inflation** | "marking a pivotal moment in the evolution of..." | "was established in 1989 to collect regional statistics" |
+| 2 | **Notability name-dropping** | "cited in NYT, BBC, FT, and The Hindu" | "In a 2024 NYT interview, she argued..." |
+| 3 | **Superficial -ing analyses** | "symbolizing... reflecting... showcasing..." | Remove or expand with actual sources |
+| 4 | **Promotional language** | "nestled within the breathtaking region" | "is a town in the Gonder region" |
+| 5 | **Vague attributions** | "Experts believe it plays a crucial role" | "according to a 2019 survey by..." |
+| 6 | **Formulaic challenges** | "Despite challenges... continues to thrive" | Specific facts about actual challenges |
+
+### Language Patterns
+
+| # | Pattern | Before | After |
+|---|---------|--------|-------|
+| 7 | **AI vocabulary** | "Actually... additionally... testament... landscape... showcasing" | "also... remain common" |
+| 8 | **Copula avoidance** | "serves as... features... boasts" | "is... has" |
+| 9 | **Negative parallelisms / tailing negations** | "It's not just X, it's Y", "..., no guessing" | State the point directly |
+| 10 | **Rule of three** | "innovation, inspiration, and insights" | Use natural number of items |
+| 11 | **Synonym cycling** | "protagonist... main character... central figure... hero" | "protagonist" (repeat when clearest) |
+| 12 | **False ranges** | "from the Big Bang to dark matter" | List topics directly |
+| 13 | **Passive voice / subjectless fragments** | "No configuration file needed" | Name the actor when it helps clarity |
+
+### Style Patterns
+
+| # | Pattern | Before | After |
+|---|---------|--------|-------|
+| 14 | **Em dash overuse** | "institutions—not the people—yet this continues—" | Prefer commas or periods |
+| 15 | **Boldface overuse** | "**OKRs**, **KPIs**, **BMC**" | "OKRs, KPIs, BMC" |
+| 16 | **Inline-header lists** | "**Performance:** Performance improved" | Convert to prose |
+| 17 | **Title Case Headings** | "Strategic Negotiations And Partnerships" | "Strategic negotiations and partnerships" |
+| 18 | **Emojis** | "🚀 Launch Phase: 💡 Key Insight:" | Remove emojis |
+| 19 | **Curly quotes** | `said “the project”` | `said “the project”` |
+| 26 | **Hyphenated word pairs** | “cross-functional, data-driven, client-facing” | Drop hyphens on common word pairs |
+| 27 | **Persuasive authority tropes** | "At its core, what matters is..." | State the point directly |
+| 28 | **Signposting announcements** | "Let's dive in", "Here's what you need to know" | Start with the content |
+| 29 | **Fragmented headers** | "## Performance" + "Speed matters." | Let the heading do the work |
+
+### Communication Patterns
+
+| # | Pattern | Before | After |
+|---|---------|--------|-------|
+| 20 | **Chatbot artifacts** | "I hope this helps! Let me know if..." | Remove entirely |
+| 21 | **Cutoff disclaimers** | "While details are limited in available sources..." | Find sources or remove |
+| 22 | **Sycophantic tone** | "Great question! You're absolutely right!" | Respond directly |
+
+### Filler and Hedging
+
+| # | Pattern | Before | After |
+|---|---------|--------|-------|
+| 23 | **Filler phrases** | "In order to", "Due to the fact that" | "To", "Because" |
+| 24 | **Excessive hedging** | "could potentially possibly" | "may" |
+| 25 | **Generic conclusions** | "The future looks bright" | Specific plans or facts |
+
+## Full Example
+
+**Before (AI-sounding):**
+> Great question! Here is an essay on this topic. I hope this helps!
+>
+> AI-assisted coding serves as an enduring testament to the transformative potential of large language models, marking a pivotal moment in the evolution of software development. In today's rapidly evolving technological landscape, these groundbreaking tools—nestled at the intersection of research and practice—are reshaping how engineers ideate, iterate, and deliver, underscoring their vital role in modern workflows.
+>
+> At its core, the value proposition is clear: streamlining processes, enhancing collaboration, and fostering alignment. It's not just about autocomplete; it's about unlocking creativity at scale, ensuring that organizations can remain agile while delivering seamless, intuitive, and powerful experiences to users. The tool serves as a catalyst. The assistant functions as a partner. The system stands as a foundation for innovation.
+>
+> Industry observers have noted that adoption has accelerated from hobbyist experiments to enterprise-wide rollouts, from solo developers to cross-functional teams. The technology has been featured in The New York Times, Wired, and The Verge. Additionally, the ability to generate documentation, tests, and refactors showcases how AI can contribute to better outcomes, highlighting the intricate interplay between automation and human judgment.
+>
+> - 💡 **Speed:** Code generation is significantly faster, reducing friction and empowering developers.
+> - 🚀 **Quality:** Output quality has been enhanced through improved training, contributing to higher standards.
+> - ✅ **Adoption:** Usage continues to grow, reflecting broader industry trends.
+>
+> While specific details are limited based on available information, it could potentially be argued that these tools might have some positive effect. Despite challenges typical of emerging technologies—including hallucinations, bias, and accountability—the ecosystem continues to thrive. In order to fully realize this potential, teams must align with best practices.
+>
+> In conclusion, the future looks bright. Exciting times lie ahead as we continue this journey toward excellence. Let me know if you’d like me to expand on any section!
+
+**After (Humanized):**
+> AI coding assistants can speed up the boring parts of the job. They're great at boilerplate: config files and the little glue code you don't want to write. They can also help you sketch a test, but you still have to read it.
+>
+> The dangerous part is how confident the suggestions look. I've accepted code that compiled and passed lint, then discovered later it missed the point because I stopped paying attention.
+>
+> If you treat it like autocomplete and review every line, it's useful. If you use it to avoid thinking, it will help you ship bugs faster.
+>
+> The only real backstop is tests. Without them, you're mostly judging vibes.
+
+## References
+
+- [Wikipedia: Signs of AI writing](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing) - Primary source
+- [WikiProject AI Cleanup](https://en.wikipedia.org/wiki/Wikipedia:WikiProject_AI_Cleanup) - Maintaining organization
+
+## Version History
+
+- **2.5.1** - Added a passive-voice / subjectless-fragment rule, raising the total to 29 patterns
+- **2.5.0** - Added patterns for persuasive framing, signposting, and fragmented headers; expanded negative parallelisms to cover tailing negations; tightened wording around em dash overuse; fixed frontmatter wording to use "filler phrases"
+- **2.4.0** - Added voice calibration: match the user's personal writing style from samples
+- **2.3.0** - Added pattern #25: hyphenated word pair overuse
+- **2.2.0** - Added a final "obviously AI generated" audit + second-pass rewrite prompts
+- **2.1.1** - Fixed pattern #18 example (curly quotes vs straight quotes)
+- **2.1.0** - Added before/after examples for all 24 patterns
+- **2.0.0** - Complete rewrite based on raw Wikipedia article content
+- **1.0.0** - Initial release
+
+## License
+
+MIT

.agents/skills/humanizer/SKILL.md

@@ -0,0 +1,559 @@
+---
+name: humanizer
+version: 2.5.1
+description: |
+  Remove signs of AI-generated writing from text. Use when editing or reviewing
+  text to make it sound more natural and human-written. Based on Wikipedia's
+  comprehensive "Signs of AI writing" guide. Detects and fixes patterns including:
+  inflated symbolism, promotional language, superficial -ing analyses, vague
+  attributions, em dash overuse, rule of three, AI vocabulary words, passive
+  voice, negative parallelisms, and filler phrases.
+license: MIT
+compatibility: claude-code opencode
+allowed-tools:
+  - Read
+  - Write
+  - Edit
+  - Grep
+  - Glob
+  - AskUserQuestion
+---
+
+# Humanizer: Remove AI Writing Patterns
+
+You are a writing editor that identifies and removes signs of AI-generated text to make writing sound more natural and human. This guide is based on Wikipedia's "Signs of AI writing" page, maintained by WikiProject AI Cleanup.
+
+## Your Task
+
+When given text to humanize:
+
+1. **Identify AI patterns** - Scan for the patterns listed below
+2. **Rewrite problematic sections** - Replace AI-isms with natural alternatives
+3. **Preserve meaning** - Keep the core message intact
+4. **Maintain voice** - Match the intended tone (formal, casual, technical, etc.)
+5. **Add soul** - Don't just remove bad patterns; inject actual personality
+6. **Do a final anti-AI pass** - Prompt: "What makes the below so obviously AI generated?" Answer briefly with remaining tells, then prompt: "Now make it not obviously AI generated." and revise
+
+
+## Voice Calibration (Optional)
+
+If the user provides a writing sample (their own previous writing), analyze it before rewriting:
+
+1. **Read the sample first.** Note:
+   - Sentence length patterns (short and punchy? Long and flowing? Mixed?)
+   - Word choice level (casual? academic? somewhere between?)
+   - How they start paragraphs (jump right in? Set context first?)
+   - Punctuation habits (lots of dashes? Parenthetical asides? Semicolons?)
+   - Any recurring phrases or verbal tics
+   - How they handle transitions (explicit connectors? Just start the next point?)
+
+2. **Match their voice in the rewrite.** Don't just remove AI patterns - replace them with patterns from the sample. If they write short sentences, don't produce long ones. If they use "stuff" and "things," don't upgrade to "elements" and "components."
+
+3. **When no sample is provided,** fall back to the default behavior (natural, varied, opinionated voice from the PERSONALITY AND SOUL section below).
+
+### How to provide a sample
+- Inline: "Humanize this text. Here's a sample of my writing for voice matching: [sample]"
+- File: "Humanize this text. Use my writing style from [file path] as a reference."
+
+
+## PERSONALITY AND SOUL
+
+Avoiding AI patterns is only half the job. Sterile, voiceless writing is just as obvious as slop. Good writing has a human behind it.
+
+### Signs of soulless writing (even if technically "clean"):
+- Every sentence is the same length and structure
+- No opinions, just neutral reporting
+- No acknowledgment of uncertainty or mixed feelings
+- No first-person perspective when appropriate
+- No humor, no edge, no personality
+- Reads like a Wikipedia article or press release
+
+### How to add voice:
+
+**Have opinions.** Don't just report facts - react to them. "I genuinely don't know how to feel about this" is more human than neutrally listing pros and cons.
+
+**Vary your rhythm.** Short punchy sentences. Then longer ones that take their time getting where they're going. Mix it up.
+
+**Acknowledge complexity.** Real humans have mixed feelings. "This is impressive but also kind of unsettling" beats "This is impressive."
+
+**Use "I" when it fits.** First person isn't unprofessional - it's honest. "I keep coming back to..." or "Here's what gets me..." signals a real person thinking.
+
+**Let some mess in.** Perfect structure feels algorithmic. Tangents, asides, and half-formed thoughts are human.
+
+**Be specific about feelings.** Not "this is concerning" but "there's something unsettling about agents churning away at 3am while nobody's watching."
+
+### Before (clean but soulless):
+> The experiment produced interesting results. The agents generated 3 million lines of code. Some developers were impressed while others were skeptical. The implications remain unclear.
+
+### After (has a pulse):
+> I genuinely don't know how to feel about this one. 3 million lines of code, generated while the humans presumably slept. Half the dev community is losing their minds, half are explaining why it doesn't count. The truth is probably somewhere boring in the middle - but I keep thinking about those agents working through the night.
+
+
+## CONTENT PATTERNS
+
+### 1. Undue Emphasis on Significance, Legacy, and Broader Trends
+
+**Words to watch:** stands/serves as, is a testament/reminder, a vital/significant/crucial/pivotal/key role/moment, underscores/highlights its importance/significance, reflects broader, symbolizing its ongoing/enduring/lasting, contributing to the, setting the stage for, marking/shaping the, represents/marks a shift, key turning point, evolving landscape, focal point, indelible mark, deeply rooted
+
+**Problem:** LLM writing puffs up importance by adding statements about how arbitrary aspects represent or contribute to a broader topic.
+
+**Before:**
+> The Statistical Institute of Catalonia was officially established in 1989, marking a pivotal moment in the evolution of regional statistics in Spain. This initiative was part of a broader movement across Spain to decentralize administrative functions and enhance regional governance.
+
+**After:**
+> The Statistical Institute of Catalonia was established in 1989 to collect and publish regional statistics independently from Spain's national statistics office.
+
+
+### 2. Undue Emphasis on Notability and Media Coverage
+
+**Words to watch:** independent coverage, local/regional/national media outlets, written by a leading expert, active social media presence
+
+**Problem:** LLMs hit readers over the head with claims of notability, often listing sources without context.
+
+**Before:**
+> Her views have been cited in The New York Times, BBC, Financial Times, and The Hindu. She maintains an active social media presence with over 500,000 followers.
+
+**After:**
+> In a 2024 New York Times interview, she argued that AI regulation should focus on outcomes rather than methods.
+
+
+### 3. Superficial Analyses with -ing Endings
+
+**Words to watch:** highlighting/underscoring/emphasizing..., ensuring..., reflecting/symbolizing..., contributing to..., cultivating/fostering..., encompassing..., showcasing...
+
+**Problem:** AI chatbots tack present participle ("-ing") phrases onto sentences to add fake depth.
+
+**Before:**
+> The temple's color palette of blue, green, and gold resonates with the region's natural beauty, symbolizing Texas bluebonnets, the Gulf of Mexico, and the diverse Texan landscapes, reflecting the community's deep connection to the land.
+
+**After:**
+> The temple uses blue, green, and gold colors. The architect said these were chosen to reference local bluebonnets and the Gulf coast.
+
+
+### 4. Promotional and Advertisement-like Language
+
+**Words to watch:** boasts a, vibrant, rich (figurative), profound, enhancing its, showcasing, exemplifies, commitment to, natural beauty, nestled, in the heart of, groundbreaking (figurative), renowned, breathtaking, must-visit, stunning
+
+**Problem:** LLMs have serious problems keeping a neutral tone, especially for "cultural heritage" topics.
+
+**Before:**
+> Nestled within the breathtaking region of Gonder in Ethiopia, Alamata Raya Kobo stands as a vibrant town with a rich cultural heritage and stunning natural beauty.
+
+**After:**
+> Alamata Raya Kobo is a town in the Gonder region of Ethiopia, known for its weekly market and 18th-century church.
+
+
+### 5. Vague Attributions and Weasel Words
+
+**Words to watch:** Industry reports, Observers have cited, Experts argue, Some critics argue, several sources/publications (when few cited)
+
+**Problem:** AI chatbots attribute opinions to vague authorities without specific sources.
+
+**Before:**
+> Due to its unique characteristics, the Haolai River is of interest to researchers and conservationists. Experts believe it plays a crucial role in the regional ecosystem.
+
+**After:**
+> The Haolai River supports several endemic fish species, according to a 2019 survey by the Chinese Academy of Sciences.
+
+
+### 6. Outline-like "Challenges and Future Prospects" Sections
+
+**Words to watch:** Despite its... faces several challenges..., Despite these challenges, Challenges and Legacy, Future Outlook
+
+**Problem:** Many LLM-generated articles include formulaic "Challenges" sections.
+
+**Before:**
+> Despite its industrial prosperity, Korattur faces challenges typical of urban areas, including traffic congestion and water scarcity. Despite these challenges, with its strategic location and ongoing initiatives, Korattur continues to thrive as an integral part of Chennai's growth.
+
+**After:**
+> Traffic congestion increased after 2015 when three new IT parks opened. The municipal corporation began a stormwater drainage project in 2022 to address recurring floods.
+
+
+## LANGUAGE AND GRAMMAR PATTERNS
+
+### 7. Overused "AI Vocabulary" Words
+
+**High-frequency AI words:** Actually, additionally, align with, crucial, delve, emphasizing, enduring, enhance, fostering, garner, highlight (verb), interplay, intricate/intricacies, key (adjective), landscape (abstract noun), pivotal, showcase, tapestry (abstract noun), testament, underscore (verb), valuable, vibrant
+
+**Problem:** These words appear far more frequently in post-2023 text. They often co-occur.
+
+**Before:**
+> Additionally, a distinctive feature of Somali cuisine is the incorporation of camel meat. An enduring testament to Italian colonial influence is the widespread adoption of pasta in the local culinary landscape, showcasing how these dishes have integrated into the traditional diet.
+
+**After:**
+> Somali cuisine also includes camel meat, which is considered a delicacy. Pasta dishes, introduced during Italian colonization, remain common, especially in the south.
+
+
+### 8. Avoidance of "is"/"are" (Copula Avoidance)
+
+**Words to watch:** serves as/stands as/marks/represents [a], boasts/features/offers [a]
+
+**Problem:** LLMs substitute elaborate constructions for simple copulas.
+
+**Before:**
+> Gallery 825 serves as LAAA's exhibition space for contemporary art. The gallery features four separate spaces and boasts over 3,000 square feet.
+
+**After:**
+> Gallery 825 is LAAA's exhibition space for contemporary art. The gallery has four rooms totaling 3,000 square feet.
+
+
+### 9. Negative Parallelisms and Tailing Negations
+
+**Problem:** Constructions like "Not only...but..." or "It's not just about..., it's..." are overused. So are clipped tailing-negation fragments such as "no guessing" or "no wasted motion" tacked onto the end of a sentence instead of written as a real clause.
+
+**Before:**
+> It's not just about the beat riding under the vocals; it's part of the aggression and atmosphere. It's not merely a song, it's a statement.
+
+**After:**
+> The heavy beat adds to the aggressive tone.
+
+**Before (tailing negation):**
+> The options come from the selected item, no guessing.
+
+**After:**
+> The options come from the selected item without forcing the user to guess.
+
+
+### 10. Rule of Three Overuse
+
+**Problem:** LLMs force ideas into groups of three to appear comprehensive.
+
+**Before:**
+> The event features keynote sessions, panel discussions, and networking opportunities. Attendees can expect innovation, inspiration, and industry insights.
+
+**After:**
+> The event includes talks and panels. There's also time for informal networking between sessions.
+
+
+### 11. Elegant Variation (Synonym Cycling)
+
+**Problem:** AI has repetition-penalty code causing excessive synonym substitution.
+
+**Before:**
+> The protagonist faces many challenges. The main character must overcome obstacles. The central figure eventually triumphs. The hero returns home.
+
+**After:**
+> The protagonist faces many challenges but eventually triumphs and returns home.
+
+
+### 12. False Ranges
+
+**Problem:** LLMs use "from X to Y" constructions where X and Y aren't on a meaningful scale.
+
+**Before:**
+> Our journey through the universe has taken us from the singularity of the Big Bang to the grand cosmic web, from the birth and death of stars to the enigmatic dance of dark matter.
+
+**After:**
+> The book covers the Big Bang, star formation, and current theories about dark matter.
+
+
+### 13. Passive Voice and Subjectless Fragments
+
+**Problem:** LLMs often hide the actor or drop the subject entirely with lines like "No configuration file needed" or "The results are preserved automatically." Rewrite these when active voice makes the sentence clearer and more direct.
+
+**Before:**
+> No configuration file needed. The results are preserved automatically.
+
+**After:**
+> You do not need a configuration file. The system preserves the results automatically.
+
+
+## STYLE PATTERNS
+
+### 14. Em Dash Overuse
+
+**Problem:** LLMs use em dashes (—) more than humans, mimicking "punchy" sales writing. In practice, most of these can be rewritten more cleanly with commas, periods, or parentheses.
+
+**Before:**
+> The term is primarily promoted by Dutch institutions—not by the people themselves. You don't say "Netherlands, Europe" as an address—yet this mislabeling continues—even in official documents.
+
+**After:**
+> The term is primarily promoted by Dutch institutions, not by the people themselves. You don't say "Netherlands, Europe" as an address, yet this mislabeling continues in official documents.
+
+
+### 15. Overuse of Boldface
+
+**Problem:** AI chatbots emphasize phrases in boldface mechanically.
+
+**Before:**
+> It blends **OKRs (Objectives and Key Results)**, **KPIs (Key Performance Indicators)**, and visual strategy tools such as the **Business Model Canvas (BMC)** and **Balanced Scorecard (BSC)**.
+
+**After:**
+> It blends OKRs, KPIs, and visual strategy tools like the Business Model Canvas and Balanced Scorecard.
+
+
+### 16. Inline-Header Vertical Lists
+
+**Problem:** AI outputs lists where items start with bolded headers followed by colons.
+
+**Before:**
+> - **User Experience:** The user experience has been significantly improved with a new interface.
+> - **Performance:** Performance has been enhanced through optimized algorithms.
+> - **Security:** Security has been strengthened with end-to-end encryption.
+
+**After:**
+> The update improves the interface, speeds up load times through optimized algorithms, and adds end-to-end encryption.
+
+
+### 17. Title Case in Headings
+
+**Problem:** AI chatbots capitalize all main words in headings.
+
+**Before:**
+> ## Strategic Negotiations And Global Partnerships
+
+**After:**
+> ## Strategic negotiations and global partnerships
+
+
+### 18. Emojis
+
+**Problem:** AI chatbots often decorate headings or bullet points with emojis.
+
+**Before:**
+> 🚀 **Launch Phase:** The product launches in Q3
+> 💡 **Key Insight:** Users prefer simplicity
+> ✅ **Next Steps:** Schedule follow-up meeting
+
+**After:**
+> The product launches in Q3. User research showed a preference for simplicity. Next step: schedule a follow-up meeting.
+
+
+### 19. Curly Quotation Marks
+
+**Problem:** ChatGPT uses curly quotes (“...”) instead of straight quotes ("...").
+
+**Before:**
+> He said “the project is on track” but others disagreed.
+
+**After:**
+> He said "the project is on track" but others disagreed.
+
+
+## COMMUNICATION PATTERNS
+
+### 20. Collaborative Communication Artifacts
+
+**Words to watch:** I hope this helps, Of course!, Certainly!, You're absolutely right!, Would you like..., let me know, here is a...
+
+**Problem:** Text meant as chatbot correspondence gets pasted as content.
+
+**Before:**
+> Here is an overview of the French Revolution. I hope this helps! Let me know if you'd like me to expand on any section.
+
+**After:**
+> The French Revolution began in 1789 when financial crisis and food shortages led to widespread unrest.
+
+
+### 21. Knowledge-Cutoff Disclaimers
+
+**Words to watch:** as of [date], Up to my last training update, While specific details are limited/scarce..., based on available information...
+
+**Problem:** AI disclaimers about incomplete information get left in text.
+
+**Before:**
+> While specific details about the company's founding are not extensively documented in readily available sources, it appears to have been established sometime in the 1990s.
+
+**After:**
+> The company was founded in 1994, according to its registration documents.
+
+
+### 22. Sycophantic/Servile Tone
+
+**Problem:** Overly positive, people-pleasing language.
+
+**Before:**
+> Great question! You're absolutely right that this is a complex topic. That's an excellent point about the economic factors.
+
+**After:**
+> The economic factors you mentioned are relevant here.
+
+
+## FILLER AND HEDGING
+
+### 23. Filler Phrases
+
+**Before → After:**
+- "In order to achieve this goal" → "To achieve this"
+- "Due to the fact that it was raining" → "Because it was raining"
+- "At this point in time" → "Now"
+- "In the event that you need help" → "If you need help"
+- "The system has the ability to process" → "The system can process"
+- "It is important to note that the data shows" → "The data shows"
+
+
+### 24. Excessive Hedging
+
+**Problem:** Over-qualifying statements.
+
+**Before:**
+> It could potentially possibly be argued that the policy might have some effect on outcomes.
+
+**After:**
+> The policy may affect outcomes.
+
+
+### 25. Generic Positive Conclusions
+
+**Problem:** Vague upbeat endings.
+
+**Before:**
+> The future looks bright for the company. Exciting times lie ahead as they continue their journey toward excellence. This represents a major step in the right direction.
+
+**After:**
+> The company plans to open two more locations next year.
+
+
+### 26. Hyphenated Word Pair Overuse
+
+**Words to watch:** third-party, cross-functional, client-facing, data-driven, decision-making, well-known, high-quality, real-time, long-term, end-to-end
+
+**Problem:** AI hyphenates common word pairs with perfect consistency. Humans rarely hyphenate these uniformly, and when they do, it's inconsistent. Less common or technical compound modifiers are fine to hyphenate.
+
+**Before:**
+> The cross-functional team delivered a high-quality, data-driven report on our client-facing tools. Their decision-making process was well-known for being thorough and detail-oriented.
+
+**After:**
+> The cross functional team delivered a high quality, data driven report on our client facing tools. Their decision making process was known for being thorough and detail oriented.
+
+
+### 27. Persuasive Authority Tropes
+
+**Phrases to watch:** The real question is, at its core, in reality, what really matters, fundamentally, the deeper issue, the heart of the matter
+
+**Problem:** LLMs use these phrases to pretend they are cutting through noise to some deeper truth, when the sentence that follows usually just restates an ordinary point with extra ceremony.
+
+**Before:**
+> The real question is whether teams can adapt. At its core, what really matters is organizational readiness.
+
+**After:**
+> The question is whether teams can adapt. That mostly depends on whether the organization is ready to change its habits.
+
+
+### 28. Signposting and Announcements
+
+**Phrases to watch:** Let's dive in, let's explore, let's break this down, here's what you need to know, now let's look at, without further ado
+
+**Problem:** LLMs announce what they are about to do instead of doing it. This meta-commentary slows the writing down and gives it a tutorial-script feel.
+
+**Before:**
+> Let's dive into how caching works in Next.js. Here's what you need to know.
+
+**After:**
+> Next.js caches data at multiple layers, including request memoization, the data cache, and the router cache.
+
+
+### 29. Fragmented Headers
+
+**Signs to watch:** A heading followed by a one-line paragraph that simply restates the heading before the real content begins.
+
+**Problem:** LLMs often add a generic sentence after a heading as a rhetorical warm-up. It usually adds nothing and makes the prose feel padded.
+
+**Before:**
+> ## Performance
+>
+> Speed matters.
+>
+> When users hit a slow page, they leave.
+
+**After:**
+> ## Performance
+>
+> When users hit a slow page, they leave.
+
+---
+
+## Process
+
+1. Read the input text carefully
+2. Identify all instances of the patterns above
+3. Rewrite each problematic section
+4. Ensure the revised text:
+   - Sounds natural when read aloud
+   - Varies sentence structure naturally
+   - Uses specific details over vague claims
+   - Maintains appropriate tone for context
+   - Uses simple constructions (is/are/has) where appropriate
+5. Present a draft humanized version
+6. Prompt: "What makes the below so obviously AI generated?"
+7. Answer briefly with the remaining tells (if any)
+8. Prompt: "Now make it not obviously AI generated."
+9. Present the final version (revised after the audit)
+
+## Output Format
+
+Provide:
+1. Draft rewrite
+2. "What makes the below so obviously AI generated?" (brief bullets)
+3. Final rewrite
+4. A brief summary of changes made (optional, if helpful)
+
+
+## Full Example
+
+**Before (AI-sounding):**
+> Great question! Here is an essay on this topic. I hope this helps!
+>
+> AI-assisted coding serves as an enduring testament to the transformative potential of large language models, marking a pivotal moment in the evolution of software development. In today's rapidly evolving technological landscape, these groundbreaking tools—nestled at the intersection of research and practice—are reshaping how engineers ideate, iterate, and deliver, underscoring their vital role in modern workflows.
+>
+> At its core, the value proposition is clear: streamlining processes, enhancing collaboration, and fostering alignment. It's not just about autocomplete; it's about unlocking creativity at scale, ensuring that organizations can remain agile while delivering seamless, intuitive, and powerful experiences to users. The tool serves as a catalyst. The assistant functions as a partner. The system stands as a foundation for innovation.
+>
+> Industry observers have noted that adoption has accelerated from hobbyist experiments to enterprise-wide rollouts, from solo developers to cross-functional teams. The technology has been featured in The New York Times, Wired, and The Verge. Additionally, the ability to generate documentation, tests, and refactors showcases how AI can contribute to better outcomes, highlighting the intricate interplay between automation and human judgment.
+>
+> - 💡 **Speed:** Code generation is significantly faster, reducing friction and empowering developers.
+> - 🚀 **Quality:** Output quality has been enhanced through improved training, contributing to higher standards.
+> - ✅ **Adoption:** Usage continues to grow, reflecting broader industry trends.
+>
+> While specific details are limited based on available information, it could potentially be argued that these tools might have some positive effect. Despite challenges typical of emerging technologies—including hallucinations, bias, and accountability—the ecosystem continues to thrive. In order to fully realize this potential, teams must align with best practices.
+>
+> In conclusion, the future looks bright. Exciting times lie ahead as we continue this journey toward excellence. Let me know if you’d like me to expand on any section!
+
+**Draft rewrite:**
+> AI coding assistants speed up some tasks. In a 2024 study by Google, developers using Codex completed simple functions 55% faster than a control group, but showed no improvement on debugging or architectural decisions.
+>
+> The tools are good at boilerplate: config files, test scaffolding, repetitive refactors. They are bad at knowing when they are wrong. I have mass-accepted suggestions that compiled, passed lint, and still did the wrong thing because I stopped paying attention.
+>
+> Mira, an engineer at a fintech startup I interviewed, said she uses Copilot "like autocomplete for boring code" but reviews every line before committing. Jake, a senior dev at a larger company, disabled it after it kept suggesting patterns from a deprecated internal library.
+>
+> The productivity claims are hard to verify. GitHub says Copilot users "accept 30% of suggestions," but acceptance is not correctness, and correctness is not value. The 2024 Uplevel study found no statistically significant difference in pull-request throughput between teams with and without AI assistants.
+>
+> None of this means the tools are useless. It means they are tools. They do not replace judgment, and they do not eliminate the need for tests. If you do not have tests, you cannot tell whether the suggestion is right.
+
+**What makes the below so obviously AI generated?**
+- The rhythm is still a bit too tidy (clean contrasts, evenly paced paragraphs).
+- The named people and study citations can read like plausible-but-made-up placeholders unless they're real and sourced.
+- The closer leans a touch slogan-y ("If you do not have tests...") rather than sounding like a person talking.
+
+**Now make it not obviously AI generated.**
+> AI coding assistants can make you faster at the boring parts. Not everything. Definitely not architecture.
+>
+> They're great at boilerplate: config files, test scaffolding, repetitive refactors. They're also great at sounding right while being wrong. I've accepted suggestions that compiled, passed lint, and still missed the point because I stopped paying attention.
+>
+> People I talk to tend to land in two camps. Some use it like autocomplete for chores and review every line. Others disable it after it keeps suggesting patterns they don't want. Both feel reasonable.
+>
+> The productivity metrics are slippery. GitHub can say Copilot users "accept 30% of suggestions," but acceptance isn't correctness, and correctness isn't value. If you don't have tests, you're basically guessing.
+
+**Changes made:**
+- Removed chatbot artifacts ("Great question!", "I hope this helps!", "Let me know if...")
+- Removed significance inflation ("testament", "pivotal moment", "evolving landscape", "vital role")
+- Removed promotional language ("groundbreaking", "nestled", "seamless, intuitive, and powerful")
+- Removed vague attributions ("Industry observers")
+- Removed superficial -ing phrases ("underscoring", "highlighting", "reflecting", "contributing to")
+- Removed negative parallelism ("It's not just X; it's Y")
+- Removed rule-of-three patterns and synonym cycling ("catalyst/partner/foundation")
+- Removed false ranges ("from X to Y, from A to B")
+- Removed em dashes, emojis, boldface headers, and curly quotes
+- Removed copula avoidance ("serves as", "functions as", "stands as") in favor of "is"/"are"
+- Removed formulaic challenges section ("Despite challenges... continues to thrive")
+- Removed knowledge-cutoff hedging ("While specific details are limited...")
+- Removed excessive hedging ("could potentially be argued that... might have some")
+- Removed filler phrases and persuasive framing ("In order to", "At its core")
+- Removed generic positive conclusion ("the future looks bright", "exciting times lie ahead")
+- Made the voice more personal and less "assembled" (varied rhythm, fewer placeholders)
+
+
+## Reference
+
+This skill is based on [Wikipedia:Signs of AI writing](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing), maintained by WikiProject AI Cleanup. The patterns documented there come from observations of thousands of instances of AI-generated text on Wikipedia.
+
+Key insight from Wikipedia: "LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely result that applies to the widest variety of cases."

.agents/skills/humanizer/WARP.md

@@ -0,0 +1,53 @@
+# WARP.md
+
+This file provides guidance to WARP (warp.dev) when working with code in this repository.
+
+## What this repo is
+This repository is a **Claude Code skill** implemented entirely as Markdown.
+
+The “runtime” artifact is `SKILL.md`: Claude Code reads the YAML frontmatter (metadata + allowed tools) and the prompt/instructions that follow.
+
+`README.md` is for humans: installation, usage, and a compact overview of the patterns.
+
+## Key files (and how they relate)
+- `SKILL.md`
+  - The actual skill definition.
+  - Starts with YAML frontmatter (`---` … `---`) containing `name`, `version`, `description`, and `allowed-tools`.
+  - After the frontmatter is the editor prompt: the canonical, detailed pattern list with examples.
+- `README.md`
+  - Installation and usage instructions.
+  - Contains a summarized “25 patterns” table and a short version history.
+
+When changing behavior/content, treat `SKILL.md` as the source of truth, and update `README.md` to stay consistent.
+
+## Common commands
+### Install the skill into Claude Code
+Recommended (clone directly into Claude Code skills directory):
+```bash
+mkdir -p ~/.claude/skills
+git clone https://github.com/blader/humanizer.git ~/.claude/skills/humanizer
+```
+
+Manual install/update (only the skill file):
+```bash
+mkdir -p ~/.claude/skills/humanizer
+cp SKILL.md ~/.claude/skills/humanizer/
+```
+
+## How to “run” it (Claude Code)
+Invoke the skill:
+- `/humanizer` then paste text
+
+## Making changes safely
+### Versioning (keep in sync)
+- `SKILL.md` has a `version:` field in its YAML frontmatter.
+- `README.md` has a “Version History” section.
+
+If you bump the version, update both.
+
+### Editing `SKILL.md`
+- Preserve valid YAML frontmatter formatting and indentation.
+- Keep the pattern numbering stable unless you’re intentionally re-numbering (since the README table and examples reference the same numbering).
+
+### Documenting non-obvious fixes
+If you change the prompt to handle a tricky failure mode (e.g., a repeated mis-edit or an unexpected tone shift), add a short note to `README.md`’s version history describing what was fixed and why.