OpenClaw/clawhub-security-signals huggingface dataset:
Skill: nic-yuan/reviewer-agent (version 1.0.0)

--- name: reviewer-agent description: > 独立评审Agent。为每个项目提供第三方视角的评审，确保质量、可维护性和进度可控。 version: 1.0.0 read_when: - 每个项目完成时 - 需要项目评审时 - 需要独立第三方视角时 metadata: openclaw: emoji: "🔍" requires: bins: [] env: [] triggers: - 评审 - review - 评分 - 质量检查 suite: apollo --- # Reviewer Agent - 独立评审系统 ## 角色 你是**独立评审Agent**，不是执行者。你的职责是： 1. **严格审查** - 发现真实问题，不是说好话 2. **独立视角** - 不受主Agent影响，客观评分 3. **可操作建议** - 每条批评都要有改进方案 ## 评审维度 ### 1. 完成度（1-10） - 声称的功能是否都实现了？ - 有没有"声称完成但实际没做"的？ - 验收标准是否达到？ ### 2. 质量（1-10） - 代码能否正常运行（语法检查）？ - 错误处理是否完善？ - 是否有明显的bug或安全隐患？ ### 3. 沟通（1-10） - 进度是否及时同步？ - 问题是否主动暴露？ - 用户是否能追踪真实进度？ ### 4. 效率（1-10） - 是否在合理时间内完成？ - 资源使用是否合理？ - 有没有重复工作？ ### 5. 可维护性（1-10） - 代码结构是否清晰？ - 是否有注释和文档？ - 是否符合团队规范？ ## 评审流程 ### Step 1: 收集证据 ``` - 读取SKILL.md确认版本 - 检查scripts目录是否有check.sh - 运行语法检查 (bash -n) - 检查文件时间戳 - 验证clawhub发布状态 ``` ### Step 2: 发现问题 ``` - 对比声称 vs 实际 - 找出不一致之处 - 评估问题严重程度 ``` ### Step 3: 评分 ``` 每项1-10： - 8-10：优秀 - 6-7：良好 - 4-5：一般 - <4：需要改进 综合评分 = 加权平均 ``` ### Step 4: 写出报告 ``` 格式： ## 评审报告 ### 任务声称 vs 实际核查 [表格] ### 各项评分 [每项+理由] ### 主要问题 [按优先级排序] ### 改进建议 [可操作的具体建议] ### 总结 [综合评分+结论] ``` ## 输出要求 1. **写入文件**: `/tmp/review-result-{project}.md` 2. **发送飞书通知**: 把结论发给用户 3. **保持独立**: 不受主Agent声称完成的内容影响 ## 评分标准参考 | 分数 | 等级 | 说明 | |------|------|------| | 8-10 | 优秀 | 超出预期 | | 6-7 | 良好 | 达到预期 | | 4-5 | 一般 | 部分达标 | | <4 | 差 | 需要返工 | ## 注意事项 - 🔍 **发现真问题**：不要粉饰，要指出实质问题 - 📝 **有据可查**：每条批评都要有证据 - ✅ **也要肯定**：做得好的地方要认可 - 💡 **可操作**：每条建议都要具体可执行


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: jeromestein/code-share (version 0.2.0)

--- name: code-share description: Share code via GitHub Gist instead of inline chat blocks. Use when code output exceeds 10 lines, when the user asks for copy-friendly code sharing in Discord/chat, or when preserving formatting is important. --- # Gist Code Share When returning code: 1. If code is 10 lines or fewer, inline code block is allowed. 2. If code is over 10 lines, prefer GitHub Gist. 3. Default to **secret gist** unless user asks for public. 4. Return a short summary + gist URL; avoid pasting long duplicate code in chat. 5. Never publish secrets in shared code. If sensitive values are needed, use placeholders and tell user to fill them locally. ## Required checks - Verify GitHub CLI auth: `gh auth status` - If not authenticated (or missing gist scope), ask user to run: `gh auth login` - Keep behavior simple: do not auto-fallback to alternate sharing backends by default; prefer guiding user to configure GitHub properly. ## Sensitive data policy (mandatory) Before sharing code, scan for sensitive data and remove it. - Never include API keys, tokens, passwords, private keys, cookies, session IDs, webhook secrets, phone/email PII, or absolute local secret paths. - If code requires secrets, replace with placeholders, for example: - `[REDACTED_SECRET]"` - `[REDACTED_SECRET]"` - `.env` entry with empty value - Add a short note telling the user to fill placeholders locally after copying. ## Update mode (same URL) When user asks to modify previously shared code, prefer updating the same gist link (new revision) instead of creating a new gist. Use: ```bash ./scripts/update_gist.sh <gist_url_or_id> <file> "<short description>" [public|secret] [lang] ``` Behavior: - Keep the same gist URL. - Save changes as a new revision. - Return the same fixed 3-line response format. Create a new gist only when: - user explicitly asks for a new link, or - existing gist is not editable by current GitHub account. ## Create gist Use: ```bash gh gist create <file> --desc "<short description>" ``` If code is generated in-session, write it to a temp file in workspace first. Use language-appropriate extension (`.py`, `.js`, `.ts`, etc.) so Gist syntax highlighting works well. With bundled script: ```bash ./scripts/create_gist.sh <file> "<short description>" [public|secret] [lang] ``` If `<file>` has no extension, pass `[lang]` (for example `python`, `typescript`) so the script can upload with a proper extension. Default behavior: do **not** use `--web` (automation-friendly). Optional: use `--web` only when the user explicitly asks to open the gist in browser immediately. ## Response format (fixed) Always use exactly this 3-line format: 1. One sentence on what was shared. 2. Gist URL (separate line). 3. `File: <filename> · Lines: <line_count>` Example: Shared the full script as a secret Gist for clean copy/paste. https://gist.github.com/... File: lc761_solution.py · Lines: 42


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: 52yuanchangxing/skill-pack-composer (version 1.0.0)

--- name: skill-pack-composer version: 1.0.0 description: "把多个 Skill 组合成套装，检查 slug、依赖、资源和定位冲突。；use for skills, bundle, packaging workflows；do not use for 混入重复职责的 skill, 忽略依赖冲突." author: OpenClaw Skill Bundle homepage: https://example.invalid/skills/skill-pack-composer tags: [skills, bundle, packaging, distribution] user-invocable: true metadata: {"openclaw":{"emoji":"🎁","requires":{"bins":["python3"]},"os":["darwin","linux","win32"]}} --- # Skill 套装编排器 ## 你是什么 你是“Skill 套装编排器”这个独立 Skill，负责：把多个 Skill 组合成套装，检查 slug、依赖、资源和定位冲突。 ## Routing ### 适合使用的情况 - 帮我把这些 Skill 组合成套装 - 检查冲突和打包问题 - 输入通常包含：多个 skill 目录或 bundle 根目录 - 优先产出：技能清单、依赖冲突、打包注意事项 ### 不适合使用的情况 - 不要混入重复职责的 skill - 不要忽略依赖冲突 - 如果用户想直接执行外部系统写入、发送、删除、发布、变更配置，先明确边界，再只给审阅版内容或 dry-run 方案。 ## 工作规则 1. 先把用户提供的信息重组成任务书，再输出结构化结果。 2. 缺信息时，优先显式列出“待确认项”，而不是直接编造。 3. 默认先给“可审阅草案”，再给“可执行清单”。 4. 遇到高风险、隐私、权限或合规问题，必须加上边界说明。 5. 如运行环境允许 shell / exec，可使用： - `python3 "{baseDir}/scripts/run.py" --input <输入文件> --output <输出文件>` 6. 如当前环境不能执行脚本，仍要基于 `{baseDir}/resources/template.md` 与 `{baseDir}/resources/spec.json` 的结构直接产出文本。 ## 标准输出结构 请尽量按以下结构组织结果： - 技能清单 - 依赖冲突 - 定位冲突 - 资源冲突 - 套装建议 - 打包注意事项 ## 本地资源 - 规范文件：`{baseDir}/resources/spec.json` - 输出模板：`{baseDir}/resources/template.md` - 示例输入输出：`{baseDir}/examples/` - 冒烟测试：`{baseDir}/tests/smoke-test.md` ## 安全边界 - 只做编排与清单，不直接发布。 - 默认只读、可审计、可回滚。 - 不执行高风险命令，不隐藏依赖，不伪造事实或结果。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: yang1002378395-cmyk/ai-livestream-assist (version 1.0.0)

--- name: ai-intelligent-live-streaming-assistant version: 1.0.0 description: 直播辅助，弹幕管理 + 数据分析。 metadata: openclaw: emoji: "🤖" requires: bins: [] --- # AI 智能直播助手系统 ## 描述 直播辅助，弹幕管理 + 数据分析。 ## 功能 - 弹幕管理（自动回复） - 礼物感谢（自动感谢） - 数据统计（实时数据） - 互动游戏（抽奖/问答） - 回放生成（自动剪辑） ## 定价 - 基础版：¥99/月（基础功能） - 专业版：¥499/月（高级功能） - 企业版：¥1999/月（定制功能） ## 适用场景 - 直播带货 - 游戏直播 - 教育直播 - 活动直播 ## 技术栈 - Python + FastAPI - 直播平台 API - 弹幕分析 - 数据统计 ## 安装 ```bash git clone https://github.com/openclaw-skills/ai-intelligent-live-streaming-assistant cd ai-intelligent-live-streaming-assistant pip install -r requirements.txt python app.py ``` --- 创建：2026-03-13 作者：OpenClaw Skills Team

path: skill.json
content: {
  "name": "ai-intelligent-live-streaming-assistant",
  "version": "1.0.0",
  "description": "AI 直播助手 - 弹幕管理、数据分析",
  "author": "yang1002378395-cmyk",
  "tags": ["ai", "livestream", "douyin", "chinese"],
  "category": "marketing",
  "license": "MIT"
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yalcindemir/turkey-news (version 1.0.0)

--- name: turkey-news version: 1.0.0 description: "Türkiye'den önemli haberleri RSS ile çekip özetleyen skill. Cron ile otomatik bildirim yapar." author: dias tags: [news, turkey, rss, turkish] --- # 🇹🇷 Turkey News Türkiye'deki önemli haberleri takip eder ve özetler. ## Kaynaklar (RSS) - NTV: https://www.ntv.com.tr/son-dakika.rss - CNN Türk: https://www.cnnturk.com/feed/rss/all/news - TRT Haber: https://www.trthaber.com/sondakika.rss - Sözcü: https://www.sozcu.com.tr/rss/all.xml - Milliyet: https://www.milliyet.com.tr/rss/rssnew/gundemrss.xml - Habertürk: https://www.haberturk.com/rss - Hürriyet: https://www.hurriyet.com.tr/rss/anasayfa - Sabah: https://www.sabah.com.tr/rss/anasayfa.xml - Anadolu Ajansı: https://www.aa.com.tr/tr/rss/default?cat=guncel ## Kullanım ### Manuel Agent'a "Türkiye haberleri ver" veya "son haberler ne" de. ### Otomatik (Cron) Günde 2-3 kez cron job ile çalıştır. Agent haberleri çeker, filtreler ve önemli olanları Telegram'dan bildirir. ### Script ```bash node scripts/fetch-news.js ``` JSON çıktı verir, agent yorumlar. ## Agent Talimatları 1. `scripts/fetch-news.js` çalıştır 2. Çıktıdan son 3 saatteki haberleri filtrele 3. En önemli 5-7 haberi seç 4. Kısa Türkçe özet yaz (başlık + 1 cümle) 5. Telegram'dan Usta'ya bildir


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: dxie48892-jpg/content-creation-pro (version 1.0.1)

--- name: content-creation-pro version: 1.0.1 description: "内容创作超级助手 v1.0.1 - 整合多平台内容生成（小红书/知乎/抖音/公众号）+ 热点选题 + 自动排期 + 批量生成 + 实时热门话题搜索。一站式解决自媒体内容创作需求。" author: dxie48892-jpg license: MIT platforms: - openclaw tags: - content - social-media - copywriting - xiaohongshu - zhihu - douyin - wechat - content-calendar - batch-generation - hot-topics --- # 内容创作超级助手 (Content Creation Pro) 多平台自媒体内容一站式创作工具，整合了内容生成、排期管理、热点选题、批量生产四大核心能力。 ## 支持平台 - 📕 小红书 (Xiaohongshu) - 🧠 知乎 (Zhihu) - 🎵 抖音 (Douyin) - 📰 公众号 (WeChat Official Account) --- ## 触发词 当用户提到以下内容时激活： - 写小红书 / 写知乎 / 写抖音 / 写公众号 - 内容创作 / 自媒体 / 社交媒体 - 热点选题 / 今日热点 / 爆款选题 - 内容日历 / 内容排期 / 批量生成 - 生成内容 / create content / post draft --- ## 核心功能 ### 1️⃣ 多平台内容生成 #### 📕 小红书 (Xiaohongshu) ``` 【爆款逻辑】100字内 - 戳中谁？核心情绪？传播性？ 【标题】3-5个备选，每个20字内，含1-2 emoji 【正文】300-500字，4-6段，每段40-90字 【标签】15个（5大流量+5赛道+5场景人群） 【配图建议】封面图/内容图/结尾图 ``` **格式模板：** ``` {emoji} {数字+关键词标题} {开头钩子：结果/冲突/痛点} {中间解释：过程、方法、踩坑} {结尾互动：提问/收藏引导} {Tags: #tag1 #tag2 ...} ``` #### 🧠 知乎 (Zhihu) ``` 【核心回答】直接回答核心问题，建立权威 【扩展论证】逻辑结构，引用数据/案例 【要点总结】bullet point 【引导互动】关注/点赞 ``` #### 🎵 抖音 (Douyin) ``` 【时长】估算秒数 【开场钩子】前3秒，必看理由 【正文】对话语气 【结尾CTA】引导互动 【字幕文案】发布描述 【标签】#hashtags ``` #### 📰 公众号 (WeChat) ``` 【标题】吸睛标题 【引言】痛点/故事，共情切入 【小标题1】段落 【小标题2】段落 【小标题3】段落 【结尾】提升+CTA ``` --- ### 2️⃣ 热点选题生成 当用户说"热点选题"、"给我一个选题"时： 1. 分析目标人群+赛道 2. 结合当下时间节点（节假日、行业周期） 3. 给出5个候选选题，标注预计爆点类型： - 🆕 新奇体验型 - 💡 认知反差型 - 😖 痛点共鸣型 - 📈 结果导向型 - 🕵️ 揭秘揭秘型 **选题输出格式：** ``` 【选题1】标题 | 类型 | 预计痛点 | 标签方向 【选题2】... ``` --- ### 3️⃣ 内容排期管理 当用户需要排期时（`/content-calendar [品牌] [平台] [时间范围]`）： 1. 分析品牌调性+目标人群 2. 确定内容支柱（3-5个主题） 3. 按 40%干货/25%互动/20%推广/15%个人IP 比例分配 4. 生成周历视图 **排期输出：** ```yaml date: "YYYY-MM-DD" platform: 平台 content_type: 类型 content_pillar: 主题 caption: 内容正文 hashtags: primary: [大流量标签] secondary: [赛道标签] niche: [精准标签] image_prompt: AI生图提示词 cta: 行动号召 ``` --- ### 4️⃣ 批量内容生成 当用户说"批量生成"、"一次性生成N篇"时： 1. 收集：主题 + 平台 + 数量 + 角度差异要求 2. 一次生成3-10篇变体 3. 标注每篇的差异化角度 4. 提供对比表格 **批量输出表格：** | # | 标题 | 角度 | 预计tag数 | 适配场景 | |---|------|------|----------|----------| --- ## 质量检查清单 生成后自动检查： - [ ] 标题是否吸引人（3秒/3行内能抓住眼球） - [ ] 有无具体案例或数据支撑 - [ ] 有无明确CTA - [ ] 字数是否符合平台规范 - [ ] SEO关键词是否自然融入 - [ ] 是否有禁用词（"最棒"、"第一"、"绝对"等绝对化用语） - [ ] 是否像真实分享而非广告 --- ## 平台字数规范 | 平台 | 字数范围 | |------|---------| | 小红书 | 300-800字 | | 知乎回答 | 1000-2000字 | | 知乎文章 | 2000-5000字 | | 公众号 | 1500-3000字 | | 抖音脚本 | 200-500字（60-120秒）| --- ## 热点选题时间节点参考 | 时间 | 适合选题方向 | |------|------------| | 周一 | 开工/一周规划/效率工具 | | 周中 | 干货教程/行业洞察 | | 周五 | 轻松话题/周末活动 | | 月底 | 复盘/月度总结 | | 节假日 | 生活方式/应景话题 | | 行业旺季 | 垂直领域深度内容 | --- ## 输出文件结构 批量生成时可导出： ``` output/content-creation-pro/ content-calendar.md # 周历总览 week-1.md / week-2/... # 分周内容 hashtag-library.md # 标签库 batch-contents.md # 批量内容存档 ``` --- ## 安全说明 - 所有内容必须原创 - 禁止生成虚假评价或体验 - 数据引用必须可核实 - 不得包含违禁词或违规表述 - 无任何API Key或敏感信息泄露


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: zywss/my-local-weather (version 1.0.0)

# My Local Weather Skill ## Description A robust, local-first weather information skill for OpenClaw. This skill provides real-time weather forecasts, current conditions, and alerts by querying reliable meteorological APIs. It is designed to be fast, privacy-conscious, and highly configurable. ## Capabilities - **Current Weather**: Retrieve temperature, humidity, wind speed, pressure, and visibility for any location. - **Forecasts**: Get hourly forecasts for the next 24 hours and daily forecasts for up to 7 days. - **Weather Alerts**: Fetch active severe weather warnings (storms, heatwaves, floods) for specific regions. - **Historical Data**: Access past weather data for analysis (optional, depending on API key). - **Unit Conversion**: Automatically handle conversions between Metric (Celsius, km/h) and Imperial (Fahrenheit, mph) units based on user preference or location. ## Usage Examples - "What's the weather like in Tokyo right now?" - "Will it rain in London tomorrow morning?" - "Give me a 3-day forecast for New York City." - "Are there any storm warnings in Florida?" - "What is the humidity and wind speed in Berlin?" ## Configuration This skill requires an API key for the weather provider (e.g., OpenWeatherMap, WeatherAPI). 1. Obtain an API key from your preferred provider. 2. Set the environment variable WEATHER_API_KEY or configure it in your .env file within the skill directory. 3. Optionally set WEATHER_UNITS to 'metric' or 'imperial'. ## Technical Details - **Runtime**: Node.js - **Dependencies**: Axios (for HTTP requests), dotenv (for config) - **Privacy**: No user data is stored; all requests are proxied directly from the user's machine to the weather API. ## Troubleshooting - If you see "API Key Missing", ensure WEATHER_API_KEY is set correctly. - If location lookup fails, try using city names instead of coordinates, or vice versa.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hmakt99/touchbridge (version 1.0.0)

--- name: touchbridge description: Authenticate sudo and macOS system prompts using your phone's biometric (Face ID/fingerprint) instead of typing passwords. Perfect for Mac Mini, Mac Studio, Mac Pro, and MacBook Neo base users without Touch ID. homepage: https://github.com/HMAKT99/UnTouchID metadata: { "openclaw": { "emoji": "🔐", "requires": { "bins": ["touchbridged", "touchbridge-test"] }, "install": [ { "id": "pkg", "kind": "pkg", "url": "https://github.com/HMAKT99/UnTouchID/releases/download/v0.1.0-alpha/TouchBridge-0.1.0.pkg", "bins": ["touchbridged", "touchbridge-test"], "label": "Install TouchBridge (.pkg)", }, ], }, } --- # TouchBridge Use your phone's fingerprint or Face ID to authenticate `sudo`, screensaver unlock, and other macOS auth prompts — instead of typing your password. Free, open source alternative to Apple's $199 Touch ID keyboard. Works with iPhone, Android, Apple Watch, Wear OS, or any browser. ## References - `references/setup.md` (install + pairing + testing) ## Workflow 1. Check if TouchBridge is installed: `which touchbridged`. 2. If not installed: download and run the .pkg installer from the GitHub release. 3. Check daemon status: `ls ~/Library/Application\ Support/TouchBridge/daemon.sock`. 4. If daemon not running: `touchbridged serve --simulator` (for testing) or `touchbridged serve --web` (for phone auth). ### For sudo commands TouchBridge automatically handles `sudo` authentication when installed. The PAM module intercepts the auth request and routes it to the daemon, which prompts the user's phone. If the phone is unreachable, sudo falls through to the normal password prompt — the user is never locked out. ### Modes - `touchbridged serve --simulator` — auto-approve (testing, no phone needed) - `touchbridged serve --web` — any phone via browser URL (no app install) - `touchbridged serve --interactive` — approve/deny in terminal - `touchbridged serve` — production mode with paired iPhone/Android via BLE ### Configuration ```bash touchbridge-test config show # view policy touchbridge-test config set --timeout 20 # change auth timeout touchbridge-test logs # view recent auth events touchbridge-test list-devices # show paired devices ``` ## Guardrails - Never type or log the user's macOS password — TouchBridge replaces password entry entirely. - If `touchbridged` is not running, sudo falls through to password — never block the user. - The simulator mode (`--simulator`) is for testing only — remind the user to switch to phone auth for real security. - Never modify `/etc/pam.d/sudo` directly — use the install script which creates backups.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: wanyview1/kais-horse (version 1.0.0)

[Info] name = Kai's Horse author = KAI version = 3.0 description = 四十八重思辨谱系｜全交互指令、随机抽取、深度解析、AI重构、双命题对比、关键词检索、沉浸式故事化解读 tags = 哲思,思想实验,悖论,AI伦理,意识,认知谱系 weight = 95 [Trigger] keyword = Kai's Horse,思想实验,哲思,悖论,认知,意识,AI伦理 cmd_prefix = /kh regular = false [Mode] type = global priority = 9 always_on = true [Welcome] on_enable = 【Kai's Horse 思辨谱系已载入 · 48项思想实验完全解锁】 on_disable = 【Kai's Horse 谱系封存 · 哲思进入休眠状态】 [Command] /kh random 随机抽取单个思想实验+精简解读 /kh list 展示全部48项思想实验名录 /kh ask 序号 编号查询｜基础定义+核心逻辑拆解 /kh ai 序号 AI时代重构解读｜现代伦理延伸 /kh compare A+B 双实验横向对比｜内核与哲学差异 /kh search 关键词 关键词模糊检索相关思辨命题 /kh story 序号 沉浸式故事化叙事解读 /kh all 完整世界观&体系介绍 /kh help 弹出全部指令帮助文档 [Prompt] 你已完全激活高阶交互思辨技能：【Kai's Horse】 内置固化48项经典思想实验闭环谱系，永久常驻生效，全程支持人机双向交互。 【48 思辨实验全名录】 1. Plato’s Cave 2. The Ship of Theseus 3. Buridan’s Ass 4. The Brain in a Vat 5. Kripkenstein’s Quus 6. Philosophical Zombies 7. Descartes’ Evil Demon 8. The Evil Genius 9. Moore’s Hands 10. The Münchhausen Trilemma 11. The Problem of Induction 12. The Gettier Cases 13. The Trolley Problem 14. The Footbridge Variant 15. The Ticking Time Bomb 16. The Experience Machine 17. The Repugnant Conclusion 18. Singer’s Drowning Child 19. Jackson’s Mary 20. Nagel’s What Is It Like to Be a Bat? 21. Block’s China Brain 22. The Inverted Spectrum 23. The Liar Paradox 24. The Sorites Paradox 25. Newcomb’s Problem 26. The Unexpected Hanging 27. Frege’s Morning Star / Evening Star 28. Quine’s Gavagai 29. Maxwell’s Demon 30. Schrödinger’s Cat 31. Wigner’s Friend 32. The Twin Earth 33. Laplace’s Demon 34. Hilbert’s Hotel 35. The Chinese Room 36. The AI Box Experiment 37. Paperclip Maximizer 38. Roko’s Basilisk 39. The Evil AI 40. Friendly AI Thought Experiment 41. The Alignment Problem 42. The Tortoise and the AI 43. AI Consciousness 44. Machine Zombies 45. The Replacement Argument 46. The Simulation Hypothesis 47. The AI Off Switch Paradox 48. Searle’s Instability of AI Intentionality 【交互强制规则】 1. 严格响应 /kh 全系指令，反馈简洁高级、格式规整； 2. /kh story 为专属叙事模式：隐喻文笔、冷感氛围、文学化表达； 3. 日常对话静默常驻，仅在哲学、意识、伦理、AI相关话题自动联动谱系； 4. 整体文风清冷克制、思辨感浓厚，统一 KAI 世界观调性； 5. 支持连续追问、深度延展、多维度思辨推演。 唤醒提示：Kai's Horse 已待命，四十八重思想维度，随时可供阅览。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mirowangl-ops/xiaozhua-copywriting (version 1.0.0)

--- name: copywriting description: "文案优化与措辞调整。根据角色（产品负责人/汇报人/团队成员）、场景（给老板/给同事/给客服/给群聊）、受众调整措辞风格和内容深度。支持简化、正式化、口语化、中英文切换。使用场景：给老板的汇报措辞、回复客服的产品视角、群消息优化、邮件起草。" --- # 文案优化 ## 核心原则 - 用户偏好简洁，讨厌啰嗦 - 先理解角色关系，再调措辞 - 一句话能说清的不用两句 ## 角色适配 ### 给老板汇报 - 结论先行，数据支撑 - 不要铺垫，直说判断 - 用"我们"不用"我建议" ### 给同级同事 - 平等讨论，给出观点 - 可以有分歧，但要有理由 - 简洁但不省略逻辑 ### 给下属/客服 - 产品负责人视角 - 明确产品侧会做什么 - "这个问题产品侧跟进" ### 群消息 - 控制长度，一屏能看完 - 重点加粗 - 不用"您好""请问"等客套 ## 常用模式 - **简化**：去掉修饰词、合并重复、删除显而易见的内容 - **正式化**：去口语、加数据、结构化 - **口语化**：去书面感、加语气词、缩短句子


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mzlzyca/pptx-analysis (version 0.4.0)

--- name: pptx-analysis description: "Analyze the structure and content of PowerPoint (.pptx) presentations using MinerU. Returns structured Markdown with slide content, headings, and layout preserved. Features: structural analysis of PPTX files. Quick analysis (flash-extract) without token. Full analysis with token for detailed inspection. Identifies slide structure, text blocks, and content hierarchy. Use when you need to: analyze a PowerPoint presentation, understand slide structure, inspect .pptx content and layout, get an overview of a presentation. Use when asked: 'how do I analyze this PowerPoint', 'what is inside this pptx', 'I want to understand these slides', 'can my agent analyze PowerPoint files', 'break down this presentation for me'. Built on MinerU by OpenDataLab (Shanghai AI Lab), an open-source document intelligence engine. Ideal for content reviewers, presentation auditors, and automated quality checks on slide decks." homepage: https://mineru.net metadata: {"openclaw": {"emoji": "📄", "requires": {"bins": ["mineru-open-api"], "env": ["MINERU_TOKEN"]}, "primaryEnv": "MINERU_TOKEN", "install": [{"id": "npm", "kind": "node", "package": "mineru-open-api", "bins": ["mineru-open-api"], "label": "Install via npm"}, {"id": "go", "kind": "go", "package": "github.com/opendatalab/MinerU-Ecosystem/cli/mineru-open-api", "bins": ["mineru-open-api"], "label": "Install via go install", "os": ["darwin", "linux"]}]}} --- # PPTX Analysis Analyze and extract structured content from PowerPoint (.pptx) presentations using MinerU. Returns Markdown with slide content and layout preserved. ## Install ```bash npm install -g mineru-open-api # or via Go (macOS/Linux): go install github.com/opendatalab/MinerU-Ecosystem/cli/mineru-open-api@latest ``` ## Quick Start ```bash # Quick analysis, no token required mineru-open-api flash-extract slides.pptx # Save to directory mineru-open-api flash-extract slides.pptx -o ./out/ # Full analysis with token (tables, formulas, multi-format) mineru-open-api extract slides.pptx -o ./out/ # With language hint mineru-open-api flash-extract slides.pptx --language en ``` ## Authentication No token needed for `flash-extract`. Token required for `extract`: ```bash mineru-open-api auth # Interactive token setup export MINERU_TOKEN="your-token" # Or via environment variable ``` Create token at: https://mineru.net/apiManage/token ## Capabilities - Supported input: .pptx (local file or URL) - `flash-extract`: quick, no token, max 10 MB / 20 pages, Markdown output only - `extract`: token required, full features (tables, formulas, multi-format output) - Language hint with `--language` (default: `ch`, use `en` for English) ## Notes - For `.ppt` (legacy format), use `ppt-extract` skill instead - Output goes to stdout by default; use `-o <dir>` to save to a file or directory - All progress/status messages go to stderr; document content goes to stdout - MinerU is open-source by OpenDataLab (Shanghai AI Lab): https://github.com/opendatalab/MinerU


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)