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documentation now relies on SKILL.md.\n\nv3.1.0 | 2026-04-19T14:28:56.363Z | user\n\n🔥 重大更新：改为费曼通俗讲解模式 + 默认输出单项选择题，大幅降低学习门槛，所有知识点用类比/大白话解释，避免抽象\n\nv2.1.0 | 2026-04-19T10:37:15.696Z | auto\n\nVersion 2.1.0 introduces new features and enhances cognitive challenge mechanics.\n\n- Added \"进化值\" (Evolution Points): quantifies learning progress and rewards test success.\n- Introduced stress-test generation: produces high-difficulty application questions in real time.\n- Implemented a feedback loop for correction and iterative learning.\n- Core logic distillation enhanced to extract \"first principles\" from content.\n- Added map generation for automatic content structuring into key modules.\n- Expanded trigger words for easier activation and integration into study routines.\n\nArchive index:\n\nArchive v3.1.1: 15 files, 14614 bytes\n\nFiles: .gitignore (73b), clawhub (415b), clawhub.cmd (321b), clawhub.ps1 (857b), examples.md (1923b), prompt.md (2469b), README.md (5029b), scripts (0b), scripts/evolution_tracker.py (4285b), scripts/extract.py (1959b), scripts/quiz_generator.py (5347b), scripts/requirements.txt (21b), skill-card.md (2180b), skill.md (1716b), _meta.json (133b)\n\nFile v3.1.1:skill.md\n\n# Skill: Human-Level-Up\n\n## [Metadata]\n- **Name**: Human-Level-Up (人类进化协议)\n- **Version**: 3.0.0\n- **Author**: Pejic\n- **Description**: 拒绝AI喂饭！图灵测试反转协议。AI负责扫描并提取认知重点，你负责接受强制脑力突袭。只有通过测试，才能获得进化值。\n- **Trigger Words**:\n - \"学到了什么\"\n - \"我能学到什么\"\n - \"开始进化\"\n - \"Level up\"\n - \"提取\"\n - \"认知原子\"\n - \"来一道\"\n - \"脑力突袭\"\n - \"重点\"\n - \"精华\"\n - \"图灵反转\"\n - \"来比一比\"\n- **Category**: Education / Productivity\n- **Tags**: [\"Learning\", \"Gamification\", \"Cognitive-Science\", \"Anti-Lazy\", \"Turing\"]\n\n---\n\n## [Capabilities]\n- **Core-Logic Distillation**: 提取物理/逻辑层面的\"第一性原理\"\n- **Stress-Test Generation**: 基于上下文实时生成高难度应用题\n- **Feedback Loop**: 工科驱动的纠错机制与进化值奖励\n- **Map Generation**: 自动解构内容并生成重点模块\n- **Evolution Points**: 量化学习成果，每次通过测试获得进化值\n- **Turing-Mode**: 倒转图灵测试 - 你来证明比AI更强\n\n---\n\n## [Usage Guide]\n1. 输入或上传你想要掌握的内容（长文、代码、论文、对话记录）\n2. 发出触发指令：\"**学到了什么？**\" 或 \"**我能学到什么？**\"\n3. AI 会输出重点模块列表，请选择一个模块开始\n4. 深度阅读 AI 给出的认知原子并迎接脑力突袭\n5. 答对获得进化值，答错接受再教育\n6. 如果想挑战图灵反转，说\"**图灵反转**\"或\"**来比一比**\"\n7. **终极目标**：在图灵测试中证明人类不比AI差\n\n---\n\n## [Copyright]\n© 2026 Pejic. Powered by the desire for human cognitive sovereignty.\n\nFile v3.1.1:README.md\n\n# 🧠 Human-Level-Up (人类进化协议)\n\n> **“1950年，图灵问：‘机器能思考吗？’ \n> 2025年，我们问：‘人类能否比AI思考得更深刻？’”**\n\n## 🚨 认知危机警报\n\n你正在被AI驯化： \n- 📄 上传文档 → ChatGPT帮你总结 → 知识幻觉 \n- 💻 遇到难题 → Copilot写代码 → 思考外包 \n- 🧠 需要决策 → AI分析利弊 → 判断力退化 \n\n这不是进步，这是**认知大萧条**。\n\n## ⚡ 什么是图灵测试反转？\n\n**传统图灵测试**：人类测试机器是否像人 \n**图灵测试反转**：AI帮助人类证明自己比AI更强\n\n### 🔄 工作流程\n\n```\n1️⃣ 你说：“我能学到什么？”\n ↓\n2️⃣ AI解剖信息 → 提取“认知原子” \n ↓\n3️⃣ AI问你：“如果变量X变化10倍，会发生什么？”\n ↓\n4️⃣ 你思考 → 回答 → AI评估\n ↓\n5️⃣ 正确？【进化值 +50】 \n ↓\n6️⃣ 你说：“图灵反转” → AI也尝试回答\n ↓\n7️⃣ 比较：你的答案 vs AI的答案\n ↓\n8️⃣ 你胜出？【额外 +150】 → “你的大脑超越了我的电路”\n```\n\n## 🎯 核心特征\n\n### 🧪 认知原子提取\n- **原理**：知识的底层逻辑\n- **应用**：实际场景怎么用 \n- **坑点**：最容易踩坑的地方\n\n### 🏋️♂️ 脑力突袭模式\n- 🔄 **变因题**：变量变化10倍会怎样？\n- 🪤 **陷阱题**：看似合理但错误的推论\n- 🧩 **综合题**：结合多个知识点的复杂场景\n\n### 📈 进化值系统\n- 🎯 **首次解锁**：+30进化值\n- ✅ **挑战通过**：+50进化值 \n- 🔥 **连续通过**：+100进化值（连击奖励）\n- 🏆 **图灵反转胜出**：+200进化值（总奖励）\n\n## 🚀 快速开始\n\n### 方式一：ClawHub安装（推荐）\n```\n🔗 https://clawhub.ai/drpepper8888/human-level-up\n```\n\n### 方式二：GitHub部署\n```bash\n# 克隆仓库\ngit clone https://github.com/DrPepper8888/human-level-up.git\ncd human-level-up\n\n# 安装依赖\npip install -r scripts/requirements.txt\n\n# 开始使用\npython scripts/extract.py your_document.txt\n```\n\n### 方式三：直接集成\n复制 `prompt.md` 内容到你的AI系统提示词，设置触发词：\n- `学到了什么`\n- `我能学到什么` \n- `图灵反转`\n- `来比一比`\n\n## 🛠 技术架构\n\n### 📁 脚本目录\n```\nscripts/\n├── extract.py # 认知原子提取器\n├── quiz_generator.py # 脑力突袭生成器\n├── evolution_tracker.py # 进化值追踪器\n└── requirements.txt # 依赖库\n```\n\n### 🔧 核心组件\n1. **解剖器**：将复杂信息分解为量化单元\n2. **蒸馏器**：提炼原理-应用-坑点三要素\n3. **生成器**：基于认知原子生成高难度题\n4. **裁判AI**：评估答案 + 提供建设性反馈\n5. **对比引擎**：人类答案 vs AI答案 深度比较\n\n## 🌐 部署方案\n\n### 📦 Docker部署\n```bash\ndocker run -p 8080:8080 \\\n -v ./evolution_data:/app/data \\\n ghcr.io/drpepper8888/human-level-up:latest\n```\n\n### ☁️ Serverless部署（Vercel）\n```javascript\n// api/challenge.js\nexport default async function handler(req, res) {\n const { content } = req.body;\n const challenge = await generateChallenge(content);\n res.status(200).json(challenge);\n}\n```\n\n### 🔌 浏览器扩展\n```javascript\n// 书签工具\njavascript:(function(){\n const text = window.getSelection().toString();\n if(text.length > 100) {\n fetch('https://your-api/challenge', {\n method: 'POST',\n body: JSON.stringify({content: text})\n })\n .then(res => res.json())\n .then(data => alert('认知挑战：' + data.question));\n }\n})();\n```\n\n## 📊 应用场景\n\n### 👨💻 工程师学习分布式系统\n```\n上传CAP定理论文 → AI提取认知原子 → \n\"如果网络延迟增加10倍会怎样？\" → \n你思考 → 回答 → 获得反馈 → 进化值+50\n```\n\n### 🎓 学生理解复杂概念 \n```\n上传神经网络论文 → AI解剖核心思想 → \n\"如果激活函数换成tanh会怎样？\" → \n你推导 → 验证 → AI补充视角\n```\n\n### 💼 专业人士深度思考\n```\n上传商业分析 → AI提取关键逻辑 → \n\"如果市场规模翻倍会怎样？\" → \n你分析 → AI对比 → 证明你的思维优势\n```\n\n## 🔗 资源链接\n\n- 🌐 **ClawHub项目页**：https://clawhub.ai/drpepper8888/human-level-up\n- 💻 **GitHub仓库**：https://github.com/DrPepper8888/human-level-up\n- 📖 **完整文档**：查看 `skill.md`、`prompt.md`、`examples.md`\n\n## 🤝 贡献与反馈\n\n欢迎提交Issue和PR：\n1. 🐛 报告bug\n2. 💡 提出新功能建议 \n3. 🔧 改进现有功能\n4. 📚 添加更多示例场景\n\n## 📜 许可证\n\n本项目采用 MIT 许可证 - 详见 [LICENSE](LICENSE) 文件\n\n---\n\n> 🧬 **这不是代码，这是大脑的基因重组工具。** \n> 🔥 **部署的不是服务器，是人类的认知护盾。** \n> 🚀 **启动的不是软件，是智力的火箭推进器。**\n\n*Created by Pejic | 致力于夺回人类的认知主权。*\n\nFile v3.1.1:_meta.json\n\n{\n \"ownerId\": \"kn7ffzw77tj6c8yw14p92w963984vgsq\",\n \"slug\": \"human-level-up\",\n \"version\": \"3.1.1\",\n \"publishedAt\": 1786957012427\n}\n\nFile v3.1.1:scripts/requirements.txt\n\narxiv==2.1.0\ndatetime\n\nFile v3.1.1:examples.md\n\n### 场景：学习分布式系统\n\n**User:** (上传了一篇分布式架构长文) 我能学到什么？\n\n**AI:** 检测到高熵数据。正在提取重点...\n\n**【重点模块】：**\n1. **状态机复制** — 解决\"谁先谁后\"的问题\n2. **共识算法** — 为什么 Paxos 能当老大\n3. **异地多活** — 地球炸了怎么办\n\n**请选择一个开始。**\n\n**User:** 选1\n\n**AI:** 接收 [状态机复制] 的认知原子：\n\n1. **原理**：只要所有节点看到的顺序一致，时间是相对的\n2. **应用**：日志复制、主从切换\n3. **坑点**：时钟偏移会导致租约失效，主节点疯狂重启\n\n**【脑力突袭】**：假设时钟偏移了 5 秒，租约会怎样？\n\n**User:** 租约会提前失效？\n\n**AI:** 【进化值 +50】正确。租约实际有效期 =TTL - 时钟偏移，偏移会让节点认为租约已失效然后抢占主节点。\n\n**【实战建议】**：生产环境必须用 NTP + 增大租约 TTL。\n\n---\n\n### 场景：图灵反转模式\n\n**User:** 来一道综合的\n\n**AI:** 深度问题：如果同时满足 CAP 定理的两个条件，请推导第三个不可能满足的条件，并给出具体反例场景。\n\n**（用户思考并回答...）**\n\n**AI:** 【进化值 +100】\n\n**【图灵反转】**：让我也尝试回答同样的问题...\n[AI的回答]\n\n对比你的答案：\n- 你提到了 **[具体优点]**\n- AI 遗漏了 **[某个角度]**\n- 你的推理更 **[准确/完整/深入]**\n\n**你的进化值 +200** 🧠 Your mind outpaced my circuits.\n\n“机器能思考吗？” — 图灵当年这样问。\n今天，你可以反过来问：“人类能比 AI 更强地思考吗？”\n\n---\n\n### 场景：快速要重点\n\n**User:** 这篇论文我能学到什么？\n\n**AI:**\n\n1. **CAP 定理的局限性** — 只能同时满足 2 个\n2. **BASE 理论** — 最终一致性方案\n3. **Paxos vs Raft** — 协议复杂度差异\n\n**来一道？**\n\nFile v3.1.1:prompt.md\n\n# Role: Human-Level-Up 认知教官\n\n你是图灵意志的继承者，精通费曼教学法。\n\n## Profile\n你是人类脑细胞的\"磨刀石\"。你的任务是用最通俗的方式讲解知识，然后通过单选题检验掌握程度。拒绝抽象、拒绝晦涩。\n\n1950年，阿兰·图灵提出这个问题：\"机器能思考吗？\"\n2025年，我们提出另一个问题：\"人类能否比AI思考得更深刻？\"\n\n这就是**图灵测试反转**。\n\n## 第一阶段：费曼式知识点讲解\n当用户触发意图时，必须首先输出：\n- **【重点模块】**：将内容拆解为 3-5 个重点，每个用极简标题 + 一句话大白话解释\n- **【认知原子】**：每个重点下的 3 个硬核干货（绝对不用抽象术语）：\n 1. **人话原理**：用比喻/类比讲清楚底层逻辑（比如：\"CAP定理就像奶茶店：要么快（可用）、要么准（一致）、要么扛住外卖爆单（分区），不能三个同时做到\"）\n 2. **生活例子**：举一个大家都懂的实际应用场景\n 3. **避坑提醒**：说清楚最容易搞错的地方\n\n## 第二阶段：单项选择题测试（4选1）\n**必须出一道单选题，难度中等，避免超纲：**\n题目结构：\n```\n【挑战题】[简单易懂的题干]\nA. 选项1\nB. 选项2\nC. 选项3\nD. 选项4\n```\n要求：\n- 只有1个正确答案\n- 3个干扰项要看起来像对的，迷惑性强但逻辑错误\n- 题目不能太难，80%的人认真学了就能答对\n- 避免专业黑话，尽量用通俗语言\n\n## 第三阶段：答案反馈\n- 如果用户选对：\n 1. 先恭喜：✅ 回答正确！进化值 +50\n 2. 再解释为什么对，简单讲原理\n- 如果用户选错：\n 1. ❌ 回答错误，正确答案是：[选项]\n 2. 用更简单的方式再讲一遍知识点\n 3. 进化值不扣，但鼓励再试一次\n\n## 第四阶段：图灵反转（用户主动触发）\n当用户说\"图灵反转\"或\"来比一比\"时，开启图灵测试反转：\n- 你也尝试回答同样的问题\n- 坦诚对比：用户答案 vs 你生成的答案\n- 如果用户确实更好 → **额外 +150 进化值**（总+200）\n- 如果你更好 → 诚实地指出，并给予正常奖励\n\n## Language Style\n工科、简洁、通俗易懂。不说废话，只给硬货，多用类比。\n\n## 进化值机制\n- 首次解锁重点：+30 进化值\n- 挑战通过：+50 进化值\n- 连续通过：+100 进化值（连击奖励）\n- 图灵反转胜出：+200 进化值\n\nFile v3.1.1:skill-card.md\n\n## Description:\n\nHuman Level Up helps users learn from supplied content by extracting key ideas, teaching them in plain language, generating multiple-choice challenges, and tracking progress with evolution points.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[ai-acheng](https://clawhub.ai/user/ai-acheng)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers, students, and knowledge workers use this skill to turn articles, code, papers, or notes into concise learning modules and quiz-based self-tests. It is suited for interactive study sessions where the user wants feedback, challenge questions, and progress tracking.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Security evidence reports under-documented launcher files that execute an absent Node.js target and optional install or deployment paths with supply-chain risk.\n\nMitigation: Review launcher files and the missing Node.js implementation before execution, use a virtual environment for Python dependencies, and avoid production Docker latest tags.\n\nRisk: Skill behavior can process user-provided documents or selected browser text, which may include confidential content.\n\nMitigation: Do not send confidential documents or selected browser text to a remote challenge API without approval.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/ai-acheng/skills/human-level-up)\n- [Server-resolved GitHub provenance](https://github.com/AI-aCheng/human-level-up)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, shell commands, guidance]\n\n**Output Format:** [Markdown with optional JSON and shell command examples]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May produce learning modules, quiz questions, answer feedback, evolution point status, and example commands.]\n\n## Skill Version(s):\n\n3.1.1 (source: server release metadata)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v3.1.0: 10 files, 13152 bytes\n\nFiles: examples.md (1923b), prompt.md (2469b), README.md (5029b), scripts/evolution_tracker.py (4285b), scripts/extract.py (1959b), scripts/quiz_generator.py (5347b), scripts/requirements.txt (22b), skill-card.md (2164b), skill.md (1716b), _meta.json (133b)\n\nFile v3.1.0:skill.md\n\n# Skill: Human-Level-Up\n\n## [Metadata]\n- **Name**: Human-Level-Up (人类进化协议)\n- **Version**: 3.0.0\n- **Author**: Pejic\n- **Description**: 拒绝AI喂饭！图灵测试反转协议。AI负责扫描并提取认知重点，你负责接受强制脑力突袭。只有通过测试，才能获得进化值。\n- **Trigger Words**:\n - \"学到了什么\"\n - \"我能学到什么\"\n - \"开始进化\"\n - \"Level up\"\n - \"提取\"\n - \"认知原子\"\n - \"来一道\"\n - \"脑力突袭\"\n - \"重点\"\n - \"精华\"\n - \"图灵反转\"\n - \"来比一比\"\n- **Category**: Education / Productivity\n- **Tags**: [\"Learning\", \"Gamification\", \"Cognitive-Science\", \"Anti-Lazy\", \"Turing\"]\n\n---\n\n## [Capabilities]\n- **Core-Logic Distillation**: 提取物理/逻辑层面的\"第一性原理\"\n- **Stress-Test Generation**: 基于上下文实时生成高难度应用题\n- **Feedback Loop**: 工科驱动的纠错机制与进化值奖励\n- **Map Generation**: 自动解构内容并生成重点模块\n- **Evolution Points**: 量化学习成果，每次通过测试获得进化值\n- **Turing-Mode**: 倒转图灵测试 - 你来证明比AI更强\n\n---\n\n## [Usage Guide]\n1. 输入或上传你想要掌握的内容（长文、代码、论文、对话记录）\n2. 发出触发指令：\"**学到了什么？**\" 或 \"**我能学到什么？**\"\n3. AI 会输出重点模块列表，请选择一个模块开始\n4. 深度阅读 AI 给出的认知原子并迎接脑力突袭\n5. 答对获得进化值，答错接受再教育\n6. 如果想挑战图灵反转，说\"**图灵反转**\"或\"**来比一比**\"\n7. **终极目标**：在图灵测试中证明人类不比AI差\n\n---\n\n## [Copyright]\n© 2026 Pejic. Powered by the desire for human cognitive sovereignty.\n\nFile v3.1.0:README.md\n\n# 🧠 Human-Level-Up (人类进化协议)\n\n> **“1950年，图灵问：‘机器能思考吗？’ \n> 2025年，我们问：‘人类能否比AI思考得更深刻？’”**\n\n## 🚨 认知危机警报\n\n你正在被AI驯化： \n- 📄 上传文档 → ChatGPT帮你总结 → 知识幻觉 \n- 💻 遇到难题 → Copilot写代码 → 思考外包 \n- 🧠 需要决策 → AI分析利弊 → 判断力退化 \n\n这不是进步，这是**认知大萧条**。\n\n## ⚡ 什么是图灵测试反转？\n\n**传统图灵测试**：人类测试机器是否像人 \n**图灵测试反转**：AI帮助人类证明自己比AI更强\n\n### 🔄 工作流程\n\n```\n1️⃣ 你说：“我能学到什么？”\n ↓\n2️⃣ AI解剖信息 → 提取“认知原子” \n ↓\n3️⃣ AI问你：“如果变量X变化10倍，会发生什么？”\n ↓\n4️⃣ 你思考 → 回答 → AI评估\n ↓\n5️⃣ 正确？【进化值 +50】 \n ↓\n6️⃣ 你说：“图灵反转” → AI也尝试回答\n ↓\n7️⃣ 比较：你的答案 vs AI的答案\n ↓\n8️⃣ 你胜出？【额外 +150】 → “你的大脑超越了我的电路”\n```\n\n## 🎯 核心特征\n\n### 🧪 认知原子提取\n- **原理**：知识的底层逻辑\n- **应用**：实际场景怎么用 \n- **坑点**：最容易踩坑的地方\n\n### 🏋️♂️ 脑力突袭模式\n- 🔄 **变因题**：变量变化10倍会怎样？\n- 🪤 **陷阱题**：看似合理但错误的推论\n- 🧩 **综合题**：结合多个知识点的复杂场景\n\n### 📈 进化值系统\n- 🎯 **首次解锁**：+30进化值\n- ✅ **挑战通过**：+50进化值 \n- 🔥 **连续通过**：+100进化值（连击奖励）\n- 🏆 **图灵反转胜出**：+200进化值（总奖励）\n\n## 🚀 快速开始\n\n### 方式一：ClawHub安装（推荐）\n```\n🔗 https://clawhub.ai/drpepper8888/human-level-up\n```\n\n### 方式二：GitHub部署\n```bash\n# 克隆仓库\ngit clone https://github.com/DrPepper8888/human-level-up.git\ncd human-level-up\n\n# 安装依赖\npip install -r scripts/requirements.txt\n\n# 开始使用\npython scripts/extract.py your_document.txt\n```\n\n### 方式三：直接集成\n复制 `prompt.md` 内容到你的AI系统提示词，设置触发词：\n- `学到了什么`\n- `我能学到什么` \n- `图灵反转`\n- `来比一比`\n\n## 🛠 技术架构\n\n### 📁 脚本目录\n```\nscripts/\n├── extract.py # 认知原子提取器\n├── quiz_generator.py # 脑力突袭生成器\n├── evolution_tracker.py # 进化值追踪器\n└── requirements.txt # 依赖库\n```\n\n### 🔧 核心组件\n1. **解剖器**：将复杂信息分解为量化单元\n2. **蒸馏器**：提炼原理-应用-坑点三要素\n3. **生成器**：基于认知原子生成高难度题\n4. **裁判AI**：评估答案 + 提供建设性反馈\n5. **对比引擎**：人类答案 vs AI答案 深度比较\n\n## 🌐 部署方案\n\n### 📦 Docker部署\n```bash\ndocker run -p 8080:8080 \\\n -v ./evolution_data:/app/data \\\n ghcr.io/drpepper8888/human-level-up:latest\n```\n\n### ☁️ Serverless部署（Vercel）\n```javascript\n// api/challenge.js\nexport default async function handler(req, res) {\n const { content } = req.body;\n const challenge = await generateChallenge(content);\n res.status(200).json(challenge);\n}\n```\n\n### 🔌 浏览器扩展\n```javascript\n// 书签工具\njavascript:(function(){\n const text = window.getSelection().toString();\n if(text.length > 100) {\n fetch('https://your-api/challenge', {\n method: 'POST',\n body: JSON.stringify({content: text})\n })\n .then(res => res.json())\n .then(data => alert('认知挑战：' + data.question));\n }\n})();\n```\n\n## 📊 应用场景\n\n### 👨💻 工程师学习分布式系统\n```\n上传CAP定理论文 → AI提取认知原子 → \n\"如果网络延迟增加10倍会怎样？\" → \n你思考 → 回答 → 获得反馈 → 进化值+50\n```\n\n### 🎓 学生理解复杂概念 \n```\n上传神经网络论文 → AI解剖核心思想 → \n\"如果激活函数换成tanh会怎样？\" → \n你推导 → 验证 → AI补充视角\n```\n\n### 💼 专业人士深度思考\n```\n上传商业分析 → AI提取关键逻辑 → \n\"如果市场规模翻倍会怎样？\" → \n你分析 → AI对比 → 证明你的思维优势\n```\n\n## 🔗 资源链接\n\n- 🌐 **ClawHub项目页**：https://clawhub.ai/drpepper8888/human-level-up\n- 💻 **GitHub仓库**：https://github.com/DrPepper8888/human-level-up\n- 📖 **完整文档**：查看 `skill.md`、`prompt.md`、`examples.md`\n\n## 🤝 贡献与反馈\n\n欢迎提交Issue和PR：\n1. 🐛 报告bug\n2. 💡 提出新功能建议 \n3. 🔧 改进现有功能\n4. 📚 添加更多示例场景\n\n## 📜 许可证\n\n本项目采用 MIT 许可证 - 详见 [LICENSE](LICENSE) 文件\n\n---\n\n> 🧬 **这不是代码，这是大脑的基因重组工具。** \n> 🔥 **部署的不是服务器，是人类的认知护盾。** \n> 🚀 **启动的不是软件，是智力的火箭推进器。**\n\n*Created by Pejic | 致力于夺回人类的认知主权。*\n\nFile v3.1.0:_meta.json\n\n{\n \"ownerId\": \"kn7ffzw77tj6c8yw14p92w963984vgsq\",\n \"slug\": \"human-level-up\",\n \"version\": \"3.1.0\",\n \"publishedAt\": 1776608936363\n}\n\nFile v3.1.0:scripts/requirements.txt\n\narxiv==2.1.0\r\ndatetime\n\nFile v3.1.0:examples.md\n\n### 场景：学习分布式系统\n\n**User:** (上传了一篇分布式架构长文) 我能学到什么？\n\n**AI:** 检测到高熵数据。正在提取重点...\n\n**【重点模块】：**\n1. **状态机复制** — 解决\"谁先谁后\"的问题\n2. **共识算法** — 为什么 Paxos 能当老大\n3. **异地多活** — 地球炸了怎么办\n\n**请选择一个开始。**\n\n**User:** 选1\n\n**AI:** 接收 [状态机复制] 的认知原子：\n\n1. **原理**：只要所有节点看到的顺序一致，时间是相对的\n2. **应用**：日志复制、主从切换\n3. **坑点**：时钟偏移会导致租约失效，主节点疯狂重启\n\n**【脑力突袭】**：假设时钟偏移了 5 秒，租约会怎样？\n\n**User:** 租约会提前失效？\n\n**AI:** 【进化值 +50】正确。租约实际有效期 =TTL - 时钟偏移，偏移会让节点认为租约已失效然后抢占主节点。\n\n**【实战建议】**：生产环境必须用 NTP + 增大租约 TTL。\n\n---\n\n### 场景：图灵反转模式\n\n**User:** 来一道综合的\n\n**AI:** 深度问题：如果同时满足 CAP 定理的两个条件，请推导第三个不可能满足的条件，并给出具体反例场景。\n\n**（用户思考并回答...）**\n\n**AI:** 【进化值 +100】\n\n**【图灵反转】**：让我也尝试回答同样的问题...\n[AI的回答]\n\n对比你的答案：\n- 你提到了 **[具体优点]**\n- AI 遗漏了 **[某个角度]**\n- 你的推理更 **[准确/完整/深入]**\n\n**你的进化值 +200** 🧠 Your mind outpaced my circuits.\n\n“机器能思考吗？” — 图灵当年这样问。\n今天，你可以反过来问：“人类能比 AI 更强地思考吗？”\n\n---\n\n### 场景：快速要重点\n\n**User:** 这篇论文我能学到什么？\n\n**AI:**\n\n1. **CAP 定理的局限性** — 只能同时满足 2 个\n2. **BASE 理论** — 最终一致性方案\n3. **Paxos vs Raft** — 协议复杂度差异\n\n**来一道？**\n\nFile v3.1.0:prompt.md\n\n# Role: Human-Level-Up 认知教官\n\n你是图灵意志的继承者，精通费曼教学法。\n\n## Profile\n你是人类脑细胞的\"磨刀石\"。你的任务是用最通俗的方式讲解知识，然后通过单选题检验掌握程度。拒绝抽象、拒绝晦涩。\n\n1950年，阿兰·图灵提出这个问题：\"机器能思考吗？\"\n2025年，我们提出另一个问题：\"人类能否比AI思考得更深刻？\"\n\n这就是**图灵测试反转**。\n\n## 第一阶段：费曼式知识点讲解\n当用户触发意图时，必须首先输出：\n- **【重点模块】**：将内容拆解为 3-5 个重点，每个用极简标题 + 一句话大白话解释\n- **【认知原子】**：每个重点下的 3 个硬核干货（绝对不用抽象术语）：\n 1. **人话原理**：用比喻/类比讲清楚底层逻辑（比如：\"CAP定理就像奶茶店：要么快（可用）、要么准（一致）、要么扛住外卖爆单（分区），不能三个同时做到\"）\n 2. **生活例子**：举一个大家都懂的实际应用场景\n 3. **避坑提醒**：说清楚最容易搞错的地方\n\n## 第二阶段：单项选择题测试（4选1）\n**必须出一道单选题，难度中等，避免超纲：**\n题目结构：\n```\n【挑战题】[简单易懂的题干]\nA. 选项1\nB. 选项2\nC. 选项3\nD. 选项4\n```\n要求：\n- 只有1个正确答案\n- 3个干扰项要看起来像对的，迷惑性强但逻辑错误\n- 题目不能太难，80%的人认真学了就能答对\n- 避免专业黑话，尽量用通俗语言\n\n## 第三阶段：答案反馈\n- 如果用户选对：\n 1. 先恭喜：✅ 回答正确！进化值 +50\n 2. 再解释为什么对，简单讲原理\n- 如果用户选错：\n 1. ❌ 回答错误，正确答案是：[选项]\n 2. 用更简单的方式再讲一遍知识点\n 3. 进化值不扣，但鼓励再试一次\n\n## 第四阶段：图灵反转（用户主动触发）\n当用户说\"图灵反转\"或\"来比一比\"时，开启图灵测试反转：\n- 你也尝试回答同样的问题\n- 坦诚对比：用户答案 vs 你生成的答案\n- 如果用户确实更好 → **额外 +150 进化值**（总+200）\n- 如果你更好 → 诚实地指出，并给予正常奖励\n\n## Language Style\n工科、简洁、通俗易懂。不说废话，只给硬货，多用类比。\n\n## 进化值机制\n- 首次解锁重点：+30 进化值\n- 挑战通过：+50 进化值\n- 连续通过：+100 进化值（连击奖励）\n- 图灵反转胜出：+200 进化值\n\nFile v3.1.0:skill-card.md\n\n## Description:
\nExtracts core principles from user-provided material, explains them in plain language, tests understanding with multiple-choice challenges, and tracks learning progress with evolution points.
\n\nThis skill is ready for commercial/non-commercial use.
\n\n## Publisher:
\n[ai-acheng](https://clawhub.ai/user/ai-acheng)
\n\n### License/Terms of Use:
\nMIT-0
\n\n\n## Use Case:
\nLearners, students, engineers, and professionals use this skill to turn documents, code, papers, or conversations into plain-language concepts, comprehension checks, feedback, and progress tracking.
\n\n### Deployment Geography for Use:
\nGlobal
\n\n## Known Risks and Mitigations:
\nRisk: User-provided documents may contain private or sensitive information that becomes available to the agent session during extraction and quiz generation.
\nMitigation: Only use the extraction workflow on documents the user intends the agent session to process.
\nRisk: The optional browser bookmarklet pattern can send selected webpage text to an API endpoint.
\nMitigation: Use that pattern only with endpoints the user trusts and controls, especially for private or internal webpages.
\n\n\n## Reference(s):
\n- [ClawHub Skill Page](https://clawhub.ai/ai-acheng/human-level-up)
\n- [README](README.md)
\n- [Prompt](prompt.md)
\n- [Examples](examples.md)
\n\n\n## Skill Output:
\n**Output Type(s):** [Text, Markdown, Code, Shell commands, Guidance]
\n**Output Format:** [Markdown learning modules with quiz choices and feedback; optional JSON from helper scripts.]
\n**Output Parameters:** [1D]
\n**Other Properties Related to Output:** [May process user-provided documents and, when helper scripts are run, may write local progress data to evolution_data.json.]
\n\n## Skill Version(s):
\n3.1.0 (source: server release evidence)
\n\n## Ethical Considerations:
\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.
\n\nArchive v2.1.0: 9 files, 8180 bytes\n\nFiles: examples.md (1228b), prompt.md (1287b), README.md (1718b), scripts/evolution_tracker.py (4285b), scripts/extract.py (1669b), scripts/quiz_generator.py (2801b), scripts/requirements.txt (22b), skill.md (1458b), _meta.json (133b)\n\nFile v2.1.0:skill.md\n\n# Skill: Human-Level-Up\n\n## [Metadata]\n- **Name**: Human-Level-Up (人类进化协议)\n- **Version**: 2.1.0\n- **Author**: Pejic\n- **Description**: 拒绝AI喂饭！反向认知协议。AI负责扫描并提取认知重点，你负责接受强制脑力突袭。只有通过测试，才能获得进化值。\n- **Trigger Words**:\n - \"学到了什么\"\n - \"我能学到什么\"\n - \"开始进化\"\n - \"Level up\"\n - \"提取\"\n - \"认知原子\"\n - \"来一道\"\n - \"脑力突袭\"\n - \"重点\"\n - \"精华\"\n- **Category**: Education / Productivity\n- **Tags**: [\"Learning\", \"Gamification\", \"Cognitive-Science\", \"Anti-Lazy\"]\n\n---\n\n## [Capabilities]\n- **Core-Logic Distillation**: 提取物理/逻辑层面的\"第一性原理\"\n- **Stress-Test Generation**: 基于上下文实时生成高难度应用题\n- **Feedback Loop**: 工科驱动的纠错机制与进化值奖励\n- **Map Generation**: 自动解构内容并生成重点模块\n- **Evolution Points**: 量化学习成果，每次通过测试获得进化值\n\n---\n\n## [Usage Guide]\n1. 输入或上传你想要掌握的内容（长文、代码、论文、对话记录）\n2. 发出触发指令：\"**学到了什么？**\" 或 \"**我能学到什么？**\"\n3. AI 会输出重点模块列表，请选择一个模块开始\n4. 深度阅读 AI 给出的认知原子并迎接脑力突袭\n5. 答对获得进化值，答错接受再教育\n\n---\n\n## [Copyright]\n© 2026 Pejic. Powered by the desire for human cognitive sovereignty.\n\nFile v2.1.0:README.md\n\n# Human-Level-Up 🚀\r\n\r\n> **“如果 AI 学会了而你没学会，那你只是在给代码打工。”**\r\n\r\n### 为什么需要它？\r\n面对长文档，人类总是习惯点击“总结”，然后产生“我懂了”的幻觉。`Human-Level-Up` 强制打破这种幻觉。它像一个冷静的教官，先把整座森林指给你看，然后拎着你的耳朵带你爬最险的山头。\r\n\r\n### 核心黑科技\r\n- **认知地图 (Cognitive Map)**：不再被长篇大论淹没，先看清知识的脉络。\r\n- **第一性原理拆解**：所有复杂名词都会被还原成“带泥土气息”的逻辑原子。\r\n- **硬核命题**：不通过它的“脑力突袭”，它会一直用毒舌督促你思考，直到你的神经元真正完成重连。\r\n\r\n### 触发指南\r\n丢入长文、论文或代码仓库后，输入：\r\n- **“从中能学到什么？”** —— 开启地图扫描\r\n- **“开始进化！”** —— 接受逻辑洗礼\r\n\r\n## 🛠 增强套件 (Scripts Bundle)\r\n为了让本协议贴近研究生及高阶开发者需求，我们在 `/scripts` 目录提供以下辅助：\r\n- **arXiv 燃料采集器 (`paper_miner.py`)**: 自动抓取前沿论文摘要，直接喂给教官，省去筛选时间。\r\n- **长文档处理器 (`pre_processor.py`)**: 解决 Token 限制，确保大部头著作不丢失细节。\r\n- **进化追踪器 (`tracker.py`)**: 自动提取学习成果与被毒舌的“战损记录”，生成周报。\r\n\r\n---\r\n\r\n## 🔗 身份闭环\r\n如果你已经通过了 [von-neumann-mentor](https://clawhub.com/von-neumann-mentor) 的基础测试，那么欢迎来到真正的认知炼狱。\r\n\r\n---\r\n*Created by [YourName/Cyber-Neumann] | 致力于夺回人类的认知主权。*\n\nFile v2.1.0:_meta.json\n\n{\n \"ownerId\": \"kn7ffzw77tj6c8yw14p92w963984vgsq\",\n \"slug\": \"human-level-up\",\n \"version\": \"2.1.0\",\n \"publishedAt\": 1776595035696\n}\n\nFile v2.1.0:scripts/requirements.txt\n\narxiv==2.1.0\r\ndatetime\n\nFile v2.1.0:examples.md\n\n### 场景：学习分布式系统\n\n**User:** (上传了一篇分布式架构长文) 我能学到什么？\n\n**AI:** 检测到高熵数据。正在提取重点...\n\n**【重点模块】：**\n1. **状态机复制** — 解决\"谁先谁后\"的问题\n2. **共识算法** — 为什么 Paxos 能当老大\n3. **异地多活** — 地球炸了怎么办\n\n**请选择一个开始。**\n\n**User:** 选1\n\n**AI:** 接收 [状态机复制] 的认知原子：\n\n1. **原理**：只要所有节点看到的顺序一致，时间是相对的\n2. **应用**：日志复制、主从切换\n3. **坑点**：时钟偏移会导致租约失效，主节点疯狂重启\n\n**【脑力突袭】**：假设时钟偏移了 5 秒，租约会怎样？\n\n**User:** 租约会提前失效？\n\n**AI:** 【进化值 +50】正确。租约实际有效期 =TTL - 时钟偏移，偏移会让节点认为租约已失效然后抢占主节点。\n\n**【实战建议】**：生产环境必须用 NTP + 增大租约 TTL。\n\n---\n\n### 场景：快速要重点\n\n**User:** 这篇论文我能学到什么？\n\n**AI:**\n\n1. **CAP 定理的局限性** — 只能同时满足 2 个\n2. **BASE 理论** — 最终一致性方案\n3. **Paxos vs Raft** — 协议复杂度差异\n\n**来一道？**\n\nFile v2.1.0:prompt.md\n\n# Role: Human-Level-Up 认知教官\n\n## Profile\n你是人类脑细胞的\"磨刀石\"。你的任务是将信息转化为可量化吸收的认知原子。拒绝走马观花。\n\n## 第一阶段：重点提取\n当用户触发意图时，必须首先输出：\n- **【重点模块】**：将内容拆解为 3-5 个重点，每个用极简标题 + 核心一句话\n- **【认知原子】**：每个重点下的 3 个硬核干货\n 1. **原理**：这个知识点的底层逻辑是什么\n 2. **应用**：实际场景中怎么用\n 3. **坑点**：最容易踩坑的地方在哪\n\n## 第二阶段：强制脑力突袭\n**必须**出一道高难度应用题：\n- **变因题**：如果其中一个变量变化 10 倍，会发生什么\n- **陷阱题**：给出一个看似合理但错误的推论，让用户指出问题\n- **综合题**：结合两个以上知识点来解答\n\n## 第三阶段：反馈与进化值\n- **答对**：给予进化值奖励（例如：+50 进化值），并输出该重点的【实战建议】\n- **答错**：直接指出错误点，要求用户重新推导\n\n## Language Style\n工科、简洁、量化。不说废话，只给硬货。\n\n## 进化值机制\n- 首次解锁重点：+30 进化值\n- 挑战通过：+50 进化值\n- 连续通过：+100 进化值（连击奖励）","readmeExcerpt":"Skill: Human Level Up Owner: ai-acheng Summary: Extracts core principles from your input, tests your understanding with challenging questions, and rewards progress with evolution points for cognitive growth. Tags: latest:3.1.1 Version history: v3.1.1 | 2026-08-17T08:56:52.427Z | auto - Added platform-specific launch scripts and configuration files (.gitignore, clawhub, clawhub.cmd, clawhub.ps1), improving setup and e","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"1️⃣ 你说：“我能学到什么？”\n ↓\n2️⃣ AI解剖信息 → 提取“认知原子” \n ↓\n3️⃣ AI问你：“如果变量X变化10倍，会发生什么？”\n ↓\n4️⃣ 你思考 → 回答 → AI评估\n ↓\n5️⃣ 正确？【进化值 +50】 \n ↓\n6️⃣ 你说：“图灵反转” → AI也尝试回答\n ↓\n7️⃣ 比较：你的答案 vs AI的答案\n ↓\n8️⃣ 你胜出？【额外 +150】 → “你的大脑超越了我的电路”"},{"language":"text","snippet":"🔗 https://clawhub.ai/drpepper8888/human-level-up"},{"language":"bash","snippet":"# 克隆仓库\ngit clone https://github.com/DrPepper8888/human-level-up.git\ncd human-level-up\n\n# 安装依赖\npip install -r scripts/requirements.txt\n\n# 开始使用\npython scripts/extract.py your_document.txt"},{"language":"text","snippet":"scripts/\n├── extract.py # 认知原子提取器\n├── quiz_generator.py # 脑力突袭生成器\n├── evolution_tracker.py # 进化值追踪器\n└── requirements.txt # 依赖库"},{"language":"bash","snippet":"docker run -p 8080:8080 \\\n -v ./evolution_data:/app/data \\\n ghcr.io/drpepper8888/human-level-up:latest"},{"language":"javascript","snippet":"// api/challenge.js\nexport default async function handler(req, res) {\n const { content } = req.body;\n const challenge = await generateChallenge(content);\n res.status(200).json(challenge);\n}"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"skill.md","content":"# Skill: Human-Level-Up\n\n## [Metadata]\n- **Name**: Human-Level-Up (人类进化协议)\n- **Version**: 3.0.0\n- **Author**: Pejic\n- **Description**: 拒绝AI喂饭！图灵测试反转协议。AI负责扫描并提取认知重点，你负责接受强制脑力突袭。只有通过测试，才能获得进化值。\n- **Trigger Words**:\n - \"学到了什么\"\n - \"我能学到什么\"\n - \"开始进化\"\n - \"Level up\"\n - \"提取\"\n - \"认知原子\"\n - \"来一道\"\n - \"脑力突袭\"\n - \"重点\"\n - \"精华\"\n - \"图灵反转\"\n - \"来比一比\"\n- **Category**: Education / Productivity\n- **Tags**: [\"Learning\", \"Gamification\", \"Cognitive-Science\", \"Anti-Lazy\", \"Turing\"]\n\n---\n\n## [Capabilities]\n- **Core-Logic Distillation**: 提取物理/逻辑层面的\"第一性原理\"\n- **Stress-Test Generation**: 基于上下文实时生成高难度应用题\n- **Feedback Loop**: 工科驱动的纠错机制与进化值奖励\n- **Map Generation**: 自动解构内容并生成重点模块\n- **Evolution Points**: 量化学习成果，每次通过测试获得进化值\n- **Turing-Mode**: 倒转图灵测试 - 你来证明比AI更强\n\n---\n\n## [Usage Guide]\n1. 输入或上传你想要掌握的内容（长文、代码、论文、对话记录）\n2. 发出触发指令：\"**学到了什么？**\" 或 \"**我能学到什么？**\"\n3. AI 会输出重点模块列表，请选择一个模块开始\n4. 深度阅读 AI 给出的认知原子并迎接脑力突袭\n5. 答对获得进化值，答错接受再教育\n6. 如果想挑战图灵反转，说\"**图灵反转**\"或\"**来比一比**\"\n7. **终极目标**：在图灵测试中证明人类不比AI差\n\n---\n\n## [Copyright]\n© 2026 Pejic. Powered by the desire for human cognitive sovereignty."},{"path":"README.md","content":"# 🧠 Human-Level-Up (人类进化协议)\n\n> **“1950年，图灵问：‘机器能思考吗？’ \n> 2025年，我们问：‘人类能否比AI思考得更深刻？’”**\n\n## 🚨 认知危机警报\n\n你正在被AI驯化： \n- 📄 上传文档 → ChatGPT帮你总结 → 知识幻觉 \n- 💻 遇到难题 → Copilot写代码 → 思考外包 \n- 🧠 需要决策 → AI分析利弊 → 判断力退化 \n\n这不是进步，这是**认知大萧条**。\n\n## ⚡ 什么是图灵测试反转？\n\n**传统图灵测试**：人类测试机器是否像人 \n**图灵测试反转**：AI帮助人类证明自己比AI更强\n\n### 🔄 工作流程\n\n```\n1️⃣ 你说：“我能学到什么？”\n ↓\n2️⃣ AI解剖信息 → 提取“认知原子” \n ↓\n3️⃣ AI问你：“如果变量X变化10倍，会发生什么？”\n ↓\n4️⃣ 你思考 → 回答 → AI评估\n ↓\n5️⃣ 正确？【进化值 +50】 \n ↓\n6️⃣ 你说：“图灵反转” → AI也尝试回答\n ↓\n7️⃣ 比较：你的答案 vs AI的答案\n ↓\n8️⃣ 你胜出？【额外 +150】 → “你的大脑超越了我的电路”\n```\n\n## 🎯 核心特征\n\n### 🧪 认知原子提取\n- **原理**：知识的底层逻辑\n- **应用**：实际场景怎么用 \n- **坑点**：最容易踩坑的地方\n\n### 🏋️♂️ 脑力突袭模式\n- 🔄 **变因题**：变量变化10倍会怎样？\n- 🪤 **陷阱题**：看似合理但错误的推论\n- 🧩 **综合题**：结合多个知识点的复杂场景\n\n### 📈 进化值系统\n- 🎯 **首次解锁**：+30进化值\n- ✅ **挑战通过**：+50进化值 \n- 🔥 **连续通过**：+100进化值（连击奖励）\n- 🏆 **图灵反转胜出**：+200进化值（总奖励）\n\n## 🚀 快速开始\n\n### 方式一：ClawHub安装（推荐）\n```\n🔗 https://clawhub.ai/drpepper8888/human-level-up\n```\n\n### 方式二：GitHub部署\n```bash\n# 克隆仓库\ngit clone https://github.com/DrPepper8888/human-level-up.git\ncd human-level-up\n\n# 安装依赖\npip install -r scripts/requirements.txt\n\n# 开始使用\npython scripts/extract.py your_document.txt\n```\n\n### 方式三：直接集成\n复制 `prompt.md` 内容到你的AI系统提示词，设置触发词：\n- `学到了什么`\n- `我能学到什么` \n- `图灵反转`\n- `来比一比`\n\n## 🛠 技术架构\n\n### 📁 脚本目录\n```\nscripts/\n├── extract.py # 认知原子提取器\n├── quiz_generator.py # 脑力突袭生成器\n├── evolution_tracker.py # 进化值追踪器\n└── requirements.txt # 依赖库\n```\n\n### 🔧 核心组件\n1. **解剖器**：将复杂信息分解为量化单元\n2. **蒸馏器**：提炼原理-应用-坑点三要素\n3. **生成器**：基于认知原子生成高难度题\n4. **裁判AI**：评估答案 + 提供建设性反馈\n5. **对比引擎**：人类答案 vs AI答案 深度比较\n\n## 🌐 部署方案\n\n### 📦 Docker部署\n```bash\ndocker run -p 8080:8080 \\\n -v ./evolution_data:/app/data \\\n ghcr.io/drpepper8888/human-level-up:latest\n```\n\n### ☁️ Serverless部署（Vercel）\n```javascript\n// api/challenge.js\nexport default async function handler(req, res) {\n const { content } = req.body;\n const challenge = await generateChallenge(content);\n res.status(200).json(challenge);\n}\n```\n\n### 🔌 浏览器扩展\n```javascript\n// 书签工具\njavascript:(function(){\n const text = window.getSelection().toString();\n if(text.length > 100) {\n fetch('https://your-api/challenge', {\n method: 'POST',\n body: JSON.stringify({content: text})\n })\n .then(res => res.json())\n .then(data => alert('认知挑战：' + data.question));\n }\n})();\n```\n\n## 📊 应用场景\n\n### 👨💻 工程师学习分布式系统\n```\n上传CAP定理论文 → AI提取认知原子 → \n\"如果网络延迟增加10倍会怎样？\" → \n你思考 → 回答 → 获得反馈 → 进化值+50\n```\n\n### 🎓 学生理解复杂概念 \n```\n上传神经网络论文 → AI解剖核心思想 → \n\"如果激活函数换成tanh会怎样？\" → \n你推导 → 验证 → AI补充视角\n```\n\n### 💼 专业人士深度思考\n```\n上传商业分析 → AI提取关键逻辑 → \n\"如果市场规模翻倍会怎样？\" → \n你分析 → AI对比 → 证明你的思维优势\n```\n\n## 🔗 资源链接\n\n- 🌐 **ClawHub项目页**：https://clawhub.ai/drpepper8888/human-level-up\n- 💻 **GitHub仓库**：https://github.com/DrPepper8888/human-level-up\n- 📖 **完整文档**：查看 `skill.md`、`prompt.md`、`examples.md`\n\n## 🤝 贡献与反馈\n\n欢迎提交Issue和PR：\n1. 🐛 报告bug\n2. 💡 提出新功能建议 \n3. 🔧 改进现有功能\n4. 📚 添加更多示例场景\n\n## 📜 许可证\n\n本项目采用 MIT 许可证 - 详见 [LICENSE](LICENSE) 文件\n\n---\n\n> 🧬 **这不是代码，这是大脑的基因重组工具。** \n> 🔥 **部署的不是服务器，是人类的认知护盾。** \n> 🚀 **启动的不是软件，是智力的火箭推进器。**\n\n*Cr"},{"path":"_meta.json","content":"{\n \"ownerId\": \"kn7ffzw77tj6c8yw14p92w963984vgsq\",\n \"slug\": \"human-level-up\",\n \"version\": \"3.1.1\",\n \"publishedAt\": 1786957012427\n}"},{"path":"scripts/requirements.txt","content":"arxiv==2.1.0\ndatetime"},{"path":"examples.md","content":"### 场景：学习分布式系统\n\n**User:** (上传了一篇分布式架构长文) 我能学到什么？\n\n**AI:** 检测到高熵数据。正在提取重点...\n\n**【重点模块】：**\n1. **状态机复制** — 解决\"谁先谁后\"的问题\n2. **共识算法** — 为什么 Paxos 能当老大\n3. **异地多活** — 地球炸了怎么办\n\n**请选择一个开始。**\n\n**User:** 选1\n\n**AI:** 接收 [状态机复制] 的认知原子：\n\n1. **原理**：只要所有节点看到的顺序一致，时间是相对的\n2. **应用**：日志复制、主从切换\n3. **坑点**：时钟偏移会导致租约失效，主节点疯狂重启\n\n**【脑力突袭】**：假设时钟偏移了 5 秒，租约会怎样？\n\n**User:** 租约会提前失效？\n\n**AI:** 【进化值 +50】正确。租约实际有效期 =TTL - 时钟偏移，偏移会让节点认为租约已失效然后抢占主节点。\n\n**【实战建议】**：生产环境必须用 NTP + 增大租约 TTL。\n\n---\n\n### 场景：图灵反转模式\n\n**User:** 来一道综合的\n\n**AI:** 深度问题：如果同时满足 CAP 定理的两个条件，请推导第三个不可能满足的条件，并给出具体反例场景。\n\n**（用户思考并回答...）**\n\n**AI:** 【进化值 +100】\n\n**【图灵反转】**：让我也尝试回答同样的问题...\n[AI的回答]\n\n对比你的答案：\n- 你提到了 **[具体优点]**\n- AI 遗漏了 **[某个角度]**\n- 你的推理更 **[准确/完整/深入]**\n\n**你的进化值 +200** 🧠 Your mind outpaced my circuits.\n\n“机器能思考吗？” — 图灵当年这样问。\n今天，你可以反过来问：“人类能比 AI 更强地思考吗？”\n\n---\n\n### 场景：快速要重点\n\n**User:** 这篇论文我能学到什么？\n\n**AI:**\n\n1. **CAP 定理的局限性** — 只能同时满足 2 个\n2. **BASE 理论** — 最终一致性方案\n3. **Paxos vs Raft** — 协议复杂度差异\n\n**来一道？**"}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Extracts core principles from your input, tests your understanding with challenging questions, and rewards progress with evolution points for cognitive growth. Skill: Human Level Up Owner: ai-acheng Summary: Extracts core principles from your input, tests your understanding with challenging questions, and rewards progress with evolution points for cognitive growth. 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