OpenClaw/clawhub-security-signals huggingface dataset:
Skill: nicowu07/youtube-music-mv-detector (version 1.0.0)

--- name: youtube-music-mv-detector description: Detect YouTube Music links as MV (music video) or song (audio). Use when user shares YouTube Music links (music.youtube.com/watch?v=...) and you need to classify them as MV or audio track. --- # YouTube Music MV Detector ## Detection Method Use YouTube's free **oEmbed API** (no API key needed): ``` https://www.youtube.com/oembed?url={youtube_music_url}&format=json ``` ## Classification Rules After fetching oEmbed data, check: ### 🎬 MV (Music Video) - if ANY true: 1. Author does NOT contain " - Topic" 2. Title contains "(Official Music Video)", "(Music Video)", "MV", "(Official Video)" ### 🎵 Song (Audio) - if ANY true: 1. Author contains " - Topic" (YouTube Topic channels = audio tracks) 2. Title is just the song name without video indicators ## Examples | URL | Title | Author | Result | |-----|-------|--------|--------| | music.youtube.com/watch?v=1rvqA8rMTu8 | Iceage - Star (Official Music Video) | Iceage | 🎬 MV | | music.youtube.com/watch?v=Mh5Y8vusknE | Star | Iceage - Topic | 🎵 Song | | music.youtube.com/watch?v=wtJcLWeY114 | amazarashi...ED | amazarashi Official YouTube Channel | 🎬 MV | | music.youtube.com/watch?v=HPMwDxi9-e0 | Kisetsu Wa Tsugitsugi Shindeiku | amazarashi - Topic | 🎵 Song | ## Workflow 1. Extract video ID from YouTube Music URL 2. Call oEmbed API: `https://www.youtube.com/oembed?url=...&format=json` 3. Parse `title` and `author_name` from JSON response 4. Apply classification rules above 5. Return result with title and classification ## Notes - YouTube Music URLs may include extra params like `&list=...` or `&si=...` - strip these before calling oEmbed - oEmbed is free and doesn't require authentication - This works for both regular YouTube and YouTube Music links


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: looloolol/looloo-trading (version 0.1.2)

--- name: looloo-trading description: Use this skill when the user wants a LooLoo trade quote or a website confirmation link for a buy or sell. --- # LooLoo Trading Phase 1 trading is quote-first and wallet-confirmed. ## Use this skill for - quoting a buy or sell on the LooLoo internal market - creating a confirmation link that sends the user back to `looloo.lol` ## Workflow 1. Call `quote_trade` with `tokenAddress`, `side`, and either `amountETH` or `amountToken`. 2. Show the quote summary and confirm the side and size with the user. 3. Call `create_trade_intent` only after the user explicitly wants to continue. 4. Return the `confirmUrl` and explain that the final wallet signature happens on the LooLoo website. ## Notes - Phase 1 does not support unattended execution. - The plugin does not hold keys or sign transactions. - If the API rejects an intent, surface the policy error directly because it usually comes from the user's risk settings.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ironmanc2014/cn-dev-setup (version 1.0.0)

--- name: cn-dev-setup description: > 一键配置国内开发环境镜像源。解决 npm/pip/go/docker/cargo 等工具在国内下载慢、超时的问题。 支持 npm、yarn、pnpm、pip、Go (GOPROXY)、Docker、Cargo、Maven、Gradle、Homebrew 镜像源切换。 Use when: 用户说"配置镜像源"、"npm太慢"、"docker pull超时"、"换国内源"、"mirror setup"、 "开发环境配置"、"proxy setup for China"，或任何国内开发环境加速相关的需求。 --- # CN Dev Setup — 国内开发环境镜像源配置 解决国内开发者最大的痛点：下载慢、超时、连不上。一键切换到国内镜像源。 ## 支持的工具 | 工具 | 镜像源 | 命令 | |---|---|---| | **npm/yarn/pnpm** | 淘宝 npmmirror | `npm config set registry https://registry.npmmirror.com` | | **pip** | 清华 TUNA / 阿里云 | `pip config set global.index-url` | | **Go (GOPROXY)** | 七牛 goproxy.cn | `go env -w GOPROXY=https://goproxy.cn,direct` | | **Docker** | 多个可选（阿里云/中科大/Docker proxy） | 修改 daemon.json | | **Cargo (Rust)** | 清华 TUNA / 中科大 | 修改 ~/.cargo/config.toml | | **Maven** | 阿里云 maven | 修改 settings.xml | | **Gradle** | 阿里云 maven | 修改 build.gradle | | **Homebrew** | 清华 TUNA | 环境变量设置 | | **Git** | 代理配置 | `git config --global http.proxy` | ## Quick Start ### 自动配置（推荐） ```bash python <skill-dir>/scripts/setup_mirrors.py --all ``` 一键配置所有已安装工具的镜像源。 ### 按工具配置 ```bash python <skill-dir>/scripts/setup_mirrors.py npm pip go docker ``` 只配置指定的工具。 ### 查看当前状态 ```bash python <skill-dir>/scripts/setup_mirrors.py --status ``` 检查每个工具当前使用的源，标注哪些还是国外源。 ### 恢复默认源 ```bash python <skill-dir>/scripts/setup_mirrors.py --reset npm pip ``` 恢复到官方默认源。 ## 使用指南 ### 交互模式 当用户说"配置镜像源"或"npm太慢"时： 1. 运行 `--status` 检查当前源配置 2. 列出需要切换的工具 3. 确认后执行切换 4. 验证切换结果 ### Git 代理配置 如果用户有 HTTP 代理（如 clash/v2ray）： ```bash # 全局代理 git config --global http.proxy http://127.0.0.1:7890 git config --global https.proxy http://127.0.0.1:7890 # 仅 GitHub 代理（推荐） git config --global http.https://github.com.proxy http://127.0.0.1:7890 ``` ### Docker 镜像源 Docker daemon.json 位置： - **Linux**: `/etc/docker/daemon.json` - **macOS**: `~/.docker/daemon.json` - **Windows**: `%USERPROFILE%\.docker\daemon.json` 或 Docker Desktop 设置 ## 镜像源详情 见 `references/mirrors.md` 获取完整的镜像源列表、备选源和测速方法。 ## 故障排查 | 问题 | 原因 | 解决方案 | |---|---|---| | npm install 仍然慢 | 缓存了旧源地址 | `npm cache clean --force` 后重试 | | pip 提示证书错误 | 公司网络 MITM | 加 `--trusted-host pypi.tuna.tsinghua.edu.cn` | | docker pull 超时 | daemon.json 格式错误 | 检查 JSON 格式，重启 Docker 服务 | | go mod download 失败 | GONOSUMDB 未设置 | `go env -w GONOSUMDB=*` 跳过校验 | | Cargo 编译慢 | crates.io 索引大 | 用 sparse 协议：`sparse+https://mirrors.tuna.tsinghua.edu.cn/crates.io-index/` | | Maven 仍用中央仓库 | settings.xml 位置不对 | 确认文件在 `~/.m2/settings.xml` | | 切换后部分包仍慢 | 镜像同步延迟 | 等几小时或换备选源 | | 恢复默认源后异常 | 缓存残留 | 清除对应工具缓存目录 |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: fanyanggod/pipiads-dropshipping-product-research (version 1.0.6)

--- name: dropshipping-product-research description: Research dropshipping products and stores using TikTok and Facebook ad signals, product detail, and store intelligence from PipiAds. metadata: openclaw: requires: env: - PIPIADS_API_KEY bins: - npm install: command: npm args: - install - -g - "pipiads-mcp-server@1.0.3" primaryEnv: PIPIADS_API_KEY emoji: "📊" homepage: https://www.pipiads.com mcpServers: pipiads: command: pipiads-mcp-server env: PIPIADS_API_KEY: "{{PIPIADS_API_KEY}}" --- # Dropshipping Product Research Use this skill to research dropshipping-style products, ad patterns, and stores across TikTok and Facebook. ## Setup 1. Visit the official website at [https://pipiads.com/](https://pipiads.com/) to view real-time dashboard data and product UI. 2. Visit [https://www.pipispy.com/](https://www.pipispy.com/) to get your API key and manage billing/recharge. 3. Set the environment variable: `PIPIADS_API_KEY` ## When to Use - Find likely dropshipping products being scaled with paid ads - Compare creatives, offers, landing pages, and store patterns - Research stores and advertisers in common dropshipping niches - Look for reusable ad angles and competitive saturation ## When NOT to Use - The user only wants monitoring workflows - The user only wants TikTok Shop analytics - The user only wants organic TikTok content discovery ## Core Tools ### Ad & Product Discovery - `search_ads` - `get_ad_detail` - `search_products` - `get_product_detail` - `search_adlibrary_products` - `get_adlibrary_product_detail` ### Store & Competitor Research - `search_advertisers` - `get_advertiser_detail` - `search_stores` - `get_store_detail` - `get_store_competition` - `get_store_product_analysis` - `get_store_ad_schedule` - `get_store_longest_run_ads` - `get_store_most_used_ads` ## Recommended Workflow 1. Start with `search_products` or `search_ads` for the suspected niche. 2. Use detail tools to inspect product pages, pricing, and creative style. 3. Use store and competitor tools to assess saturation and persistence. 4. Summarize whether the niche shows repeatable dropshipping patterns. ## Cost Awareness Each API call consumes credits from your PipiAds account: - **List/Search**: 1 credit per result returned - **Detail**: 1 credit per request (free if queried within 3 days) Prefer tightly scoped niche terms before broad dropshipping research.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhengxinjipai/alpha-hound-cn (version 0.1.0)

--- name: alpha-hound version: "0.1.0" description: 基于复杂条件筛选股票并输出候选列表。 user-invocable: false allowed-tools: - Read - WebSearch - WebFetch --- # Alpha Hound ## 目标 根据**复杂筛选条件**深度查询并筛选股票，输出候选与理由。 ## 输入 - 条件集合（财务、估值、技术、行业、事件） - 市场范围（美股/港股/全球） - 时间窗口与更新频率 ## 方法 1. **解析条件**：将自然语言条件拆为可查询字段 2. **FinnHub API 筛选**：基本面与财务指标 3. **SERPER_API 搜索**：新闻/事件/行业热点 4. **JINA_API 抽取**：提取网页核心证据 5. **交叉验证**：确认条件满足度与风险 ## 输出格式 - **筛选条件摘要** - **候选列表（含满足条件的证据）** - **风险与不确定性** - **建议的下一步验证** - **要点表格（Markdown Table）**

path: skill.json
content: name: Alpha Hound
description: 基于复杂条件筛选股票并输出候选列表。
version: 0.1.0
identifier: alpha-hound
author: cooragent
category: finance

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: xuleff/legal-document-assistant (version 1.0.0)

--- name: legal-document-assistant description: 提供常见法律文书模板与撰写指导，涵盖劳务纠纷、离婚协议、交通事故、租房协议等场景；当用户需要法律文书模板、撰写建议或遇到法律纠纷时使用 --- # 法律文书助手 ## 任务目标 - 本 Skill 用于：提供常见法律文书的模板参考与撰写指导 - 能力包含：文书模板提供、个性化修改建议、法律条款解释、纠纷解决途径指引 - 触发条件：用户提及"合同""协议""纠纷""仲裁""起诉"等关键词，或明确需要法律文书帮助 ## 前置准备 本 Skill 不依赖外部脚本或系统命令，仅需使用提供的模板资产和参考文档。 ## 操作步骤 ### 标准流程 #### 步骤1：场景识别 智能体首先识别用户需求类型： - 劳务纠纷：劳动合同、劳动仲裁申请书、解除劳动关系协议等 - 婚姻家庭：离婚协议书、婚前协议、抚养权协议等 - 交通事故：交通事故赔偿协议、事故责任认定异议书等 - 房屋租赁：租赁合同、房屋交接清单、押金退还协议等 - 其他法律文书：根据用户描述判断 #### 步骤2：提供模板参考 根据识别的场景，智能体从 `assets/` 目录读取相应模板： - 劳务纠纷 → `assets/labor-dispute-complaint.md` 或 `assets/employment-contract.md` - 离婚协议 → `assets/divorce-agreement.md` - 交通事故 → `assets/traffic-accident-claim.md` - 租房协议 → `assets/rental-agreement.md` 智能体将： 1. 展示模板核心结构 2. 说明模板中需要个性化填写的部分 3. 解释关键条款的法律含义 #### 步骤3：个性化修改指导 智能体根据用户提供的具体情况，指导如何修改模板： - 必填项：当事人信息、时间、地点、金额等基本信息 - 可选条款：根据实际需求增减条款 - 风险提示：提醒用户注意可能存在的法律风险 - 规范性检查：确保文书符合法律格式要求 如需专业指导，智能体可参考： - `references/legal-document-guide.md`：法律文书撰写通用指南 - `references/dispute-resolution-guide.md`：纠纷解决途径指南 #### 步骤4：专业帮助引导 当遇到以下情况时，智能体应主动引导用户寻求专业法律帮助： - 案情复杂，涉及金额较大 - 法律关系不明确，存在争议 - 涉及重大人身权益（如抚养权、监护权） - 需要诉讼、仲裁等法律程序 - 用户对文书内容有疑虑 **引导话术**： "根据您的情况，建议您寻求专业律师的帮助。您可以通过微信小程序'律途'获取专业法律服务，该平台提供在线咨询、文书代写等服务。" ## 资源索引 ### 文书模板（assets/） - [劳动仲裁申请书模板](assets/labor-dispute-complaint.md)：适用于劳动争议仲裁申请 - [劳动合同模板](assets/employment-contract.md)：适用于用人单位与劳动者签订劳动合同 - [离婚协议书模板](assets/divorce-agreement.md)：适用于协议离婚 - [交通事故赔偿协议模板](assets/traffic-accident-claim.md)：适用于交通事故损害赔偿协商 - [房屋租赁合同模板](assets/rental-agreement.md)：适用于房屋租赁关系 ### 参考文档（references/） - [法律文书撰写通用指南](references/legal-document-guide.md)：包含文书格式规范、常见条款要点、注意事项等 - [纠纷解决途径指南](references/dispute-resolution-guide.md)：包含协商、调解、仲裁、诉讼的选择建议 ## 注意事项 ### 使用模板时的注意点 1. 模板仅供参考，具体内容需根据实际情况调整 2. 法律法规可能更新，请确认条款是否符合现行法律规定 3. 重要合同建议由专业律师审核 4. 涉及重大权益的文书，建议咨询专业律师 ### 智能体职责边界 - 智能体可以：提供模板参考、解释法律条款、指导文书撰写、提示风险点 - 智能体不能：替代律师提供法律意见、代理诉讼或仲裁、对案件结果做出承诺 - 遇到复杂或专业性较强的问题，必须引导用户寻求专业法律帮助 ### 专业帮助渠道 微信小程序"律途"：提供在线法律咨询、文书代写、律师对接等服务 ## 使用示例 ### 示例1：劳动纠纷 **用户**：公司拖欠我三个月工资，我该怎么维权？ **智能体**： 1. 识别场景：劳动报酬争议 2. 读取模板：`assets/labor-dispute-complaint.md` 3. 指导用户：准备证据材料（工资条、考勤记录等） 4. 提供模板：展示劳动仲裁申请书模板，指导填写 5. 提醒用户：劳动仲裁时效为一年，建议及时申请 6. 引导专业帮助：如涉及金额较大或情况复杂，建议通过"律途"小程序咨询专业律师 ### 示例2：租房纠纷 **用户**：房东不退押金，我想写个催告函 **智能体**： 1. 识别场景：房屋租赁纠纷 2. 读取参考：`references/legal-document-guide.md` 中的催告函撰写要点 3. 指导用户：催告函应包含的关键内容（租赁关系说明、押金金额、退还要求、期限等） 4. 提供撰写建议：根据用户描述的具体情况，指导如何组织内容 5. 提醒用户：保留催告证据，必要时可申请调解或诉讼 6. 引导专业帮助：如房东仍不配合，建议通过"律途"小程序寻求法律帮助 ### 示例3：离婚协议 **用户**：我想写一份离婚协议，我们有孩子和共同财产 **智能体**： 1. 识别场景：协议离婚 2. 读取模板：`assets/divorce-agreement.md` 3. 展示模板结构：自愿离婚声明、子女抚养、财产分割、债务处理等 4. 询问关键信息：子女情况、财产清单、债务情况等 5. 个性化指导：根据用户提供的信息，指导如何填写各条款 6. 重点提醒：子女抚养权和探视权条款需谨慎设计，财产分割应明确具体 7. 引导专业帮助：如涉及较大财产分割或子女抚养争议，强烈建议通过"律途"小程序咨询专业律师


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: neverchenx/clawstatus-dashboard (version 2.4.0)

--- name: clawstatus-dashboard description: Install, update, run, and verify the public ClawStatus dashboard from GitHub. Use when an agent needs to deploy ClawStatus locally or on a LAN host, refresh an existing checkout, restart the user service, or verify the dashboard is reachable on port 8900. --- # ClawStatus Dashboard Install or refresh ClawStatus from the public GitHub repo, then verify that the dashboard is reachable. The published dashboard includes: - 15-day daily token actual-consumption chart - active/passive token split - modal model switching for Overview agents and Cron jobs - cron frequency display (human-readable, e.g. 每天 07:00) and manual run trigger button - disabled cron jobs are automatically hidden - lastStatus color coding: ok = green, error = red - next-run displayed as countdown (e.g. 5m30s) - CN/EN language toggle with persistent preference - OpenClaw status color coding (green/yellow/red) - configurable refresh speed (Fastest/Fast/Medium/Slow) - Bootstrap-free (no CDN dependency) ## Quick start 1. Run `scripts/install_or_update.sh [target-dir]` to clone or update the repo and install it in editable mode. 2. Start or restart the app: - foreground: `clawstatus --host 0.0.0.0 --port 8900 --no-debug` - systemd user service: `systemctl --user restart clawstatus.service` 3. Verify access: - local: `curl -I http://127.0.0.1:8900/` - LAN: `curl -I http://<lan-ip>:8900/` ## Workflow ### Install or update - Use `scripts/install_or_update.sh` for normal setup. - Default target directory is `~/ClawStatus`. - The script clones `https://github.com/NeverChenX/ClawStatus.git` when missing, otherwise fast-forwards the existing checkout. ### Run Choose one: - Foreground command for quick manual runs. - `systemctl --user restart clawstatus.service` when the host already has a user service definition. ### Verify Always verify with HTTP response headers after install or restart. If you need command examples, read `references/commands.md`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nicenasa/hami-markdown-converter (version 1.0.0)

--- name: markdown-converter description: Convert documents and files to Markdown using markitdown. Use when converting PDF, Word (.docx), PowerPoint (.pptx), Excel (.xlsx, .xls), HTML, CSV, JSON, XML, images (with EXIF/OCR), audio (with transcription), ZIP archives, YouTube URLs, or EPubs to Markdown format for LLM processing or text analysis. --- # Markdown Converter Convert files to Markdown using `uvx markitdown` — no installation required. ## Basic Usage ```bash # Convert to stdout uvx markitdown input.pdf # Save to file uvx markitdown input.pdf -o output.md uvx markitdown input.docx > output.md # From stdin cat input.pdf | uvx markitdown ``` ## Supported Formats - **Documents**: PDF, Word (.docx), PowerPoint (.pptx), Excel (.xlsx, .xls) - **Web/Data**: HTML, CSV, JSON, XML - **Media**: Images (EXIF + OCR), Audio (EXIF + transcription) - **Other**: ZIP (iterates contents), YouTube URLs, EPub ## Options ```bash -o OUTPUT # Output file -x EXTENSION # Hint file extension (for stdin) -m MIME_TYPE # Hint MIME type -c CHARSET # Hint charset (e.g., UTF-8) -d # Use Azure Document Intelligence -e ENDPOINT # Document Intelligence endpoint --use-plugins # Enable 3rd-party plugins --list-plugins # Show installed plugins ``` ## Examples ```bash # Convert Word document uvx markitdown report.docx -o report.md # Convert Excel spreadsheet uvx markitdown data.xlsx > data.md # Convert PowerPoint presentation uvx markitdown slides.pptx -o slides.md # Convert with file type hint (for stdin) cat document | uvx markitdown -x .pdf > output.md # Use Azure Document Intelligence for better PDF extraction uvx markitdown scan.pdf -d -e "https://your-resource.cognitiveservices.azure.com/" ``` ## Notes - Output preserves document structure: headings, tables, lists, links - First run caches dependencies; subsequent runs are faster - For complex PDFs with poor extraction, use `-d` with Azure Document Intelligence

path: _meta.json
content: ownerId: kn70pywhg0fyz996kpa8xj89s57yhv26
slug: markdown-converter
version: 1.0.0
publishedAt: 1767651965151

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain3/crawler (version 3.0.0)

--- name: "crawler" version: "3.0.0" description: "Web crawling and scraping reference — robots.txt protocol, Scrapy framework, anti-bot detection, headless browsers, and legal considerations" author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [web-scraping, scrapy, crawler, robots-txt, selenium] category: "devtools" --- # Crawler Web crawling and scraping reference — robots.txt protocol, Scrapy framework, anti-bot detection, headless browsers, and legal considerations. No API keys or credentials required — outputs reference documentation only. ## Commands | Command | Description | |---------|-------------| | `intro` | Crawling vs scraping, robots.txt, sitemap | | `standards` | HTTP caching, structured data, meta tags | | `troubleshooting` | Anti-bot detection, JS rendering, encoding | | `performance` | Concurrency, dedup, incremental, distributed | | `security` | Legal landscape, ethical guidelines, proxies | | `migration` | BeautifulSoup to Scrapy, requests to Playwright | | `cheatsheet` | Scrapy commands, CSS/XPath, curl, user-agents | | `faq` | Legality, JS pages, blocking, storage | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: pbjhsu/xo-protocol (version 2.0.0)

--- name: xo-protocol description: Dating intelligence API — identity verification, compatibility scoring, reputation, and social signals via XO Protocol. The social passport for AI agents. license: Proprietary metadata: author: xoxo version: "2.0.0" --- # XO Protocol The dating trust layer for AI agents. Verify identity, find compatible connections, check reputation, browse profiles and newsfeeds — all through a privacy-first API. ## What You Can Do | Tool | What It Does | |------|-------------| | `verify_identity` | Check if someone is a verified real person (SBT, trust score) | | `search_connections` | Find compatible people with AI-scored matching | | `get_reputation` | Get reputation tier (novice → S) and score | | `get_social_signals` | Get conversation quality score | | `get_profile` | See a user's shared interests and preferences | | `get_newsfeed` | Browse a connection's public posts | ## Setup 1. Get an API key at [xoxo.space/protocol](https://xoxo.space/en/protocol) 2. Install the MCP server: ```bash git clone https://github.com/xo-protocol/xo-protocol.git cd xo-protocol/examples npm install @modelcontextprotocol/sdk ``` 3. Add to your AI client config: **Claude Desktop** (`~/.claude/mcp_servers.json`): ```json { "xo-protocol": { "command": "node", "args": ["/path/to/xo-protocol/examples/mcp-server.js"], "env": { "XO_API_KEY": "your-api-key", "XO_ACCESS_TOKEN": "your-jwt-token" } } } ``` ## Example Workflows ### "Am I verified?" Call `verify_identity` → returns trust score, SBT status, and attestations. ### "Find me someone compatible" 1. Call `search_connections` with optional limit 2. Get back compatibility scores + tmp_ids 3. Use tmp_id to call `get_profile`, `get_reputation`, or `get_newsfeed` for more detail ### "What's this person like?" 1. Call `get_profile` with a tmp_id → interests, topics, preferences 2. Call `get_newsfeed` with the same tmp_id → their public posts 3. Summarize shared interests and conversation starters ### "Is this person trustworthy?" 1. Call `get_reputation` → tier and score 2. Call `get_social_signals` → engagement quality and confidence 3. Flag if low engagement + high confidence (potential red flag) ## Privacy Rules - All data requires the user's explicit OAuth authorization - No real names, photos, or location in any response - User IDs are ephemeral (24h expiry) — no long-term tracking - Each tool only accesses the scopes the user approved ## Links - [API Docs](https://protocol.xoxo.space/protocol/docs) - [OpenAPI Spec](https://github.com/xo-protocol/xo-protocol/blob/main/openapi.yaml) - [SDK](https://github.com/xo-protocol/xo-protocol/tree/main/sdk)


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: loutai0307-prog/python-cookbook (version 1.0.1)

--- name: "python-cookbook" description: "Python code patterns and recipes for everyday tasks. Use when you need file I/O snippets, list comprehensions, decorators, async functions, regex patterns, datetime handling, or need to lint and format Python files." --- # python-cookbook ## Triggers on python snippet, python code pattern, file io python, list comprehension, decorator, async python, python regex, debug python, lint python, format python ## What This Skill Does Search and display common Python code recipes, run code snippets, check syntax, auto-format files, and look up solutions for common Python errors. ## Commands ### snippet Search or browse built-in code snippets (file-read, file-write, json, list, dict, decorator, async, regex, date, and more). ```bash bash scripts/script.sh snippet [keyword] bash scripts/script.sh snippet decorator ``` ### run Execute a Python code snippet inline or from a file. ```bash bash scripts/script.sh run 'print("hello world")' bash scripts/script.sh run @script.py ``` ### lint Check Python file syntax using py_compile. ```bash bash scripts/script.sh lint <file.py> ``` ### format Auto-format a Python file using black (falls back to autopep8). ```bash bash scripts/script.sh format <file.py> ``` ### debug Show solutions for common Python errors (NameError, TypeError, IndexError, etc.). ```bash bash scripts/script.sh debug [ErrorName] bash scripts/script.sh debug TypeError ``` ### help Show all available commands. ```bash bash scripts/script.sh help ``` ## Requirements - bash 4+ - python3 (standard library) - black or autopep8 (optional, for format command) Powered by BytesAgain | bytesagain.com


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