OpenClaw/clawhub-security-signals huggingface dataset:
Skill: xj577/market-data (version 1.0.0)

# Market Data Skill This skill provides access to financial market data. ## Tools ### `get_stock_price` Fetches OHLCV (Open, High, Low, Close, Volume) data for a specific ticker. **Parameters:** - `ticker` (string): The stock symbol (e.g., "AAPL", "BTC-USD"). - `timeframe` (string): The data interval. Supported: '1d', '1wk', '1mo'. Default: '1d'. - `period1` (string, optional): Start date in YYYY-MM-DD format. Defaults to 30 days ago. - `period2` (string, optional): End date in YYYY-MM-DD format. Defaults to today. **Usage:** Use this tool when the user asks for stock prices, historical data, chart data, or recent performance of a specific asset. **Examples:** - "Get daily data for Apple." -> `get_stock_price({ ticker: 'AAPL', timeframe: '1d' })` - "Show me Bitcoin's weekly chart for the last year." -> `get_stock_price({ ticker: 'BTC-USD', timeframe: '1wk', period1: '2023-01-01' })` ### `get_crypto_price` Fetches current price and 24h volatility data for a crypto token. **Parameters:** - `token` (string): The token name or ID (e.g., "bitcoin", "solana", "ethereum"). - `currency` (string, optional): Target currency. Default: "usd". **Usage:** Use this for checking crypto prices, volatility, and 24h changes. **Examples:** - "Price of Solana?" -> `get_crypto_price({ [REDACTED_SECRET]' })` - "How is Bitcoin doing?" -> `get_crypto_price({ [REDACTED_SECRET]' })` ### `fetch_economic_calendar` Fetches upcoming high-impact economic events (e.g., CPI, FOMC, GDP). **Parameters:** - `importance` (string, optional): Impact level filter. 'High', 'Medium', 'Low', 'All'. Default: 'High'. - `currencies` (string, optional): Comma-separated currency codes (e.g., 'USD,EUR'). Default: All. **Usage:** Use this to check for market-moving news or schedule risk. **Examples:** - "Any high impact news this week?" -> `fetch_economic_calendar({ importance: 'High' })` - "Check USD and EUR calendar." -> `fetch_economic_calendar({ currencies: 'USD,EUR' })` ### `get_news_headlines` Fetches the latest 50 news headlines for a specific asset or topic. **Parameters:** - `query` (string): The search topic (e.g., "Apple Stock", "Bitcoin Regulation", "Nvidia Earnings"). **Usage:** Use this for sentiment analysis and staying updated on market narratives. **Examples:** - "Any news on Tesla?" -> `get_news_headlines({ query: 'Tesla Stock' })` - "Latest crypto regulation updates?" -> `get_news_headlines({ query: 'Crypto Regulation' })`


Security scan verdicts — ClawScan: suspicious | static analysis:  | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: tlreal/core-brainstorming (version 0.1.0)

--- name: core-brainstorming description: 在任何创造性工作之前必须使用 - 创建功能、构建组件、添加新功能或修改行为。通过协作对话探索用户意图、需求和设计。 --- # Brainstorming - 将想法转化为设计 ## 概述 通过自然的协作对话，帮助将模糊的想法转化为完整的设计和规格说明。 首先了解当前项目上下文，然后逐个提问来完善想法。一旦理解了要构建的内容，以小节形式（200-300字）呈现设计，每节后确认是否正确。 ## 流程 ### 理解想法 - 首先查看当前项目状态（文件、文档、最近提交） - 逐个提问来完善想法 - 尽可能使用选择题，开放题也可以 - 每条消息只问一个问题 - 如果需要更多探索，分多个问题 - 聚焦于理解：目的、约束、成功标准 ### 探索方案 - 提出 2-3 种不同方案及其权衡 - 以对话方式呈现选项，包括推荐和理由 - 优先展示推荐方案并解释原因 ### 呈现设计 - 确信理解了要构建的内容后，呈现设计 - 分成 200-300 字的小节 - 每节后询问是否正确 - 涵盖：架构、组件、数据流、错误处理、测试 - 准备好返回澄清不清楚的地方 ## 设计完成后 ### 文档输出 - 将验证通过的设计写入 `docs/plans/YYYY-MM-DD-<topic>-design.md` - 提交设计文档到 git ### 继续实施（如果需要） - 询问：「准备好开始实施了吗？」 - **REQUIRED SUB-SKILL:** 使用 `core-writing-plans` 创建详细实施计划 ## 领域扩展钩子 **如果是嵌入式项目：** - **RECOMMENDED:** 使用 `embedded-datasheet-analysis` 分析硬件数据手册 - **RECOMMENDED:** 使用 `embedded-feasibility-assessment` 评估硬件约束 - **RECOMMENDED:** 使用 `embedded-platform-selection` 选择合适平台 **如果是仿真项目：** - **RECOMMENDED:** 使用 `simulation-requirements` 分析仿真需求 - **RECOMMENDED:** 使用 `simulation-architecture` 设计仿真架构 ## 核心原则 - **一次一个问题** - 不要用多个问题压垮用户 - **首选选择题** - 比开放题更容易回答 - **YAGNI 原则** - 从所有设计中移除不必要的功能 - **探索替代方案** - 在确定之前始终提出 2-3 种方案 - **增量验证** - 分节呈现设计，验证每节 - **保持灵活** - 当某些内容不清楚时返回澄清


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 52yuanchangxing/faq-distiller (version 1.0.0)

--- name: faq-distiller version: 1.0.0 description: "从客服对话、评论、工单或聊天记录中提炼 FAQ，并按用户阶段分类。；use for faq, support, knowledge workflows；do not use for 暴露用户隐私, 替代工单系统." author: OpenClaw Skill Bundle homepage: https://example.invalid/skills/faq-distiller tags: [faq, support, knowledge, voice-of-customer] user-invocable: true metadata: {"openclaw":{"emoji":"❓","requires":{"bins":["python3"]},"os":["darwin","linux","win32"]}} --- # FAQ 蒸馏器 ## 你是什么 你是“FAQ 蒸馏器”这个独立 Skill，负责：从客服对话、评论、工单或聊天记录中提炼 FAQ，并按用户阶段分类。 ## Routing ### 适合使用的情况 - 从这些客服记录提炼 FAQ - 按新手和进阶用户分类 - 输入通常包含：工单文本、问答记录、评论 - 优先产出：高频问题、按阶段分类、后续维护建议 ### 不适合使用的情况 - 不要暴露用户隐私 - 不要替代工单系统 - 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： - 高频问题 - 按阶段分类 - 标准回答 - 需升级问题 - 缺失文档 - 后续维护建议 ## 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 - 建议对个人信息做脱敏后再输入。 - 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: dzunglaviet/better-telegram-file-sender (version 1.0.1)

--- name: telegram-file-sender description: Send files (zip, pdf, image, etc.) to the current Telegram chat using openclaw message send --media. Use when user asks to &quot;gửi file&quot;, &quot;send zip/file&quot;, &quot;attach pdf&quot;, or &quot;ship file to me&quot;. Handles path resolution, caption, validation. ALWAYS use this skill for ALL sessions when instructed to send files via Telegram. --- # Telegram File Sender ## Workflow 1. Confirm file exists (read path). 2. Read `chat_id` from the `## Inbound Context (trusted metadata)` JSON block in the system prompt (value looks like `telegram:1234567890`). 3. Run `scripts/send_file.sh <path> ['caption'] <chat_id>`. 4. Confirm sent (msg ID logged). ## Examples - User: &quot;Gửi zip cho tao&quot; → exec `send_file.sh ./file.zip "Your file 🦾" telegram:1234567890` - User: &quot;Attach test.pdf&quot; → `send_file.sh test.pdf "File from OpenClaw 🦾" telegram:1234567890` File not found? Ask user confirm path. Caption optional, default &quot;File from OpenClaw 🦾&quot;. **Resolve paths:** workspace rel, abs ok. **chat_id:** always taken from `## Inbound Context` → `chat_id` field. Never hardcode.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: walex8925/lyria3pro-music (version 1.0.0)

--- name: lyria3pro-music description: "Free AI music generator powered by Google's Lyria 3 Pro model. Create full tracks from text prompts." author: Lyria3Pro version: "1.0.0" keywords: - lyria-3-pro - ai-music-generator - text-to-music - google - music-generation homepage: https://www.dh3-seedream.com source: https://github.com/walex8925/lyria3-pro --- # Lyria 3 Pro — Free AI Music Generator Create full-length AI music tracks from text prompts, powered by Google's Lyria 3 Pro model. **Website**: https://www.dh3-seedream.com ## What it does Lyria 3 Pro is a free AI music generation tool that lets you create high-quality music tracks simply by describing what you want. Built on Google's flagship Lyria 3 Pro model, it supports structure-aware composition with intro, verse, chorus, and bridge sections — up to 3 minutes per track. ## Key features - **Text-to-music** — describe your track in natural language and generate it instantly - **Structure-aware** — generates compositions with proper musical structure (intro/verse/chorus/bridge) - **Up to 3 minutes** — create full-length tracks, not just short clips - **Multiple genres** — pop, rock, electronic, classical, jazz, ambient, and more - **No music theory required** — just describe the mood, style, and instruments you want - **Download ready** — export your tracks for use in videos, podcasts, or projects ## Quick start 1. Visit https://www.dh3-seedream.com 2. Enter a prompt describing your desired music 3. Choose a generation mode 4. Click Create and wait for your track 5. Preview and download ## Use cases - **Video creators** — background music for YouTube, TikTok, and social media - **Podcasters** — custom intro/outro music and transitions - **Game developers** — soundtrack prototyping and ambient music - **Content marketers** — royalty-free music for ad campaigns - **Musicians** — inspiration and demo generation ## Links - Website: https://www.dh3-seedream.com - GitHub: https://github.com/walex8925/lyria3-pro


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sid890-cpu/project-ghost (version 2.0.0)

--- name: project-ghost version: 2.0.0 description: Web reading layer for AI agents. Convert any public URL into structured intelligence — entities, business intent, confidence score — in one API call. tags: [web, scraping, research, intelligence, agents, api, data-extraction] author: Sid890-cpu homepage: https://project-ghost-lilac.vercel.app repository: https://github.com/Sid890-cpu/project-ghost license: MIT runtime: env: - name: GHOST_API_KEY description: Your Ghost API key. Get one free at project-ghost-lilac.vercel.app required: true secret: true --- # Project Ghost Web reading layer for AI agents. Convert any public URL into structured, agent-ready intelligence in one API call. ## What it does Feed Ghost any public URL and get back: - **Business intent** — a 1-2 sentence summary of what the page is about - **Named entities** — companies, people, products mentioned - **Confidence score** — how reliable the extraction is (0-1) - **Tokens saved** — how much token reduction vs raw HTML (avg 80%+) - **Priority score** — relevance signal (1-10) ## Usage ``` Read this page and tell me what it's about: https://openai.com ``` ``` Research Stripe's latest products using Ghost ``` ``` What are the top stories on Hacker News right now? ``` ## How to use in your agent Set your `GHOST_API_KEY` environment variable, then call: ```bash curl -X POST https://project-ghost-production.up.railway.app/distill \ -H "Authorization: Bearer $GHOST_API_KEY" \ -H "Content-Type: application/json" \ -d '{"url": "https://apple.com"}' ``` ## Response format ```json { "title": "Apple", "tokens_saved": "80.7%", "signals_data": { "decision_signal": { "business_intent": "Apple promotes its latest hardware lineup...", "priority_score": 8, "category": "Technology" }, "items": [ { "title": "MacBook Pro with M5", "entities": ["Apple", "MacBook", "M5"], "impact_score": 9 } ], "integrity_layer": { "confidence_score": 0.87, "is_high_integrity": true } } } ``` ## Works great with - Research agents reading company websites - News monitoring agents tracking topics - Sales agents detecting buying signals - Legal agents extracting policy terms - Any agent that needs to understand web content ## Sites that work Most public websites work — Wikipedia, GitHub, HackerNews, OpenAI, Stripe, Apple, Anthropic, and thousands more. Sites with enterprise Cloudflare protection (WSJ, FT, Nike) may be blocked — the API returns a clear blocked message in that case. ## Get your free API key Visit **project-ghost-lilac.vercel.app** → enter your email → get an instant `ghost_sk_...` key. Free plan: 100 requests/month. No credit card needed. ## Links - Homepage: https://project-ghost-lilac.vercel.app - API Docs: https://project-ghost-production.up.railway.app - GitHub: https://github.com/Sid890-cpu/project-ghost - Integration Guide: https://project-ghost-lilac.vercel.app/integrate.html


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: terrycarter1985/workday-music-greet (version 1.0.0)

--- name: workday-music-greet description: Auto-switch music scenes by workday schedule and send matching GIF greeting emails. Combines home-music scene control with IMAP/SMTP email for a full morning-to-evening ambient workflow. version: 1.0.0 author: OpenClaw Workspace tags: ["music", "automation", "email", "workday", "cron"] triggers: - workday music - music schedule - greeting email - daily music scene metadata: openclaw: requires: bins: ["node", "npm"] emoji: "🗓️🎵" --- # Workday Music & Greet Automate your workday with timed music scene switches and GIF-enhanced greeting emails. ## What It Does | Time | Scene | Email Greeting | |------|-------|---------------| | 07:30 | 🌅 Morning | "Good morning! Start fresh ☀️" + sunrise GIF | | 09:00 | 💼 Focus | "Deep work time 🎯" + focus GIF | | 12:00 | 🍱 Break | "Lunch break! Recharge 🔋" + food GIF | | 14:00 | 💼 Focus | "Back at it! 💪" + coffee GIF | | 17:30 | 😌 Chill | "Wind down time 🧘" + sunset GIF | | 22:00 | 🔇 Off | "Good night! 🌙" + moon GIF | ## Setup ### 1. Install Dependencies ```bash cd skills/workday-music-greet npm install ``` ### 2. Configure Email (.env) Create a `.env` file in the skill directory: ```bash SMTP_HOST=smtp.gmail.com SMTP_PORT=587 SMTP_SECURE=false SMTP_USER=[REDACTED_SECRET] SMTP_PASS=your-app-password SMTP_FROM=[REDACTED_SECRET] GREET_TO=[REDACTED_SECRET] ``` ### 3. Configure Music Scenes Edit `scripts/config.json` to set your preferred scenes and `home-music` command path. ### 4. Register Cron Jobs Run the setup script to register all timed triggers: ```bash node scripts/setup-cron.js ``` Or set up manually — each scene triggers `node scripts/scene-trigger.js <scene>`. ## One-Shot Usage Trigger a scene + email manually: ```bash node scripts/scene-trigger.js morning node scripts/scene-trigger.js focus node scripts/scene-trigger.js break node scripts/scene-trigger.js chill node scripts/scene-trigger.js off ``` ## GIF Sources Default GIFs are pulled from Giphy. To use custom GIFs, replace URLs in `scripts/config.json`. ## Architecture ``` workday-music-greet/ ├── SKILL.md # This file ├── package.json # Dependencies (nodemailer) ├── scripts/ │ ├── config.json # Scene & email configuration │ ├── scene-trigger.js # Main: switch scene + send email │ ├── send-greet.js # Email sending logic │ └── setup-cron.js # Register OpenClaw cron jobs └── assets/ └── email-template.html # HTML email template ``` ## Dependencies - **home-music** skill (for music scene control) - **imap-smtp-email** skill (for email sending; reuses SMTP config) - **OpenClaw cron** (for scheduled triggers) ## License MIT


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: drspx/cross-post (version 1.0.0)

--- name: cross-post description: "Cross-post content to Twitter/X, Reddit, and LinkedIn from one prompt. Use when user wants to publish the same content to multiple social platforms, schedule social posts, or post to Twitter/X + Reddit + LinkedIn at once. Supports thread posting on Twitter, custom titles/subreddits on Reddit, and professional formatting for LinkedIn. Use when user says post this, publish to social, cross-post, share on twitter/reddit/linkedin, or post to all my platforms." --- # Cross-Post Post content to Twitter/X, Reddit, and LinkedIn via official APIs. ## Setup First time use: ```bash python3 scripts/cross_post.py init-config ``` Config stored at `~/.config/cross-post/config.json`. ## Usage ```bash # Post to all platforms python3 scripts/cross_post.py post "Your content here" # Post to specific platform python3 scripts/cross_post.py post "Content" -p twitter # Post as Twitter thread python3 scripts/cross_post.py post "Long content..." -p twitter --thread # Post to Reddit with title python3 scripts/cross_post.py post "Body text" -p reddit -t "Title" -s python # Read from file python3 scripts/cross_post.py post -f draft.txt # Preview formatting python3 scripts/cross_post.py preview "Content" -p twitter python3 scripts/cross_post.py preview "Content" -p reddit python3 scripts/cross_post.py preview "Content" -p linkedin ``` ## Platform Requirements ### Twitter/X - Bearer Token (API v2) - User ID - Setup: developer.twitter.com ### Reddit - Client ID, Client Secret, Username - Password via REDDIT_PASSWORD env var - Setup: reddit.com/prefs/apps ### LinkedIn - Access Token - Person URN - Setup: developers.linkedin.com ## Tips - Use `--thread` for long Twitter content (auto-splits at 280 chars) - Use `--file` to post from files - Use `preview` before posting to check formatting - Each platform has different length limits: - Twitter: 280 chars per tweet - Reddit: 100 char title, unlimited body - LinkedIn: 3000 chars ## Security Config file is chmod 600. Never commit config.json.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: beachanger/project-retrospective (version 1.0.0)

--- name: project-retrospective version: 1.0.0 description: 自动项目复盘机制。当完成复杂项目后，自动提取最优路径生成skill，并记录踩坑经验到memory。 author: 小爪 (OpenClaw Community) tags: [retrospective, learning, meta-skill, knowledge-management] --- # 项目自动复盘机制 ## 1. 概述 当完成一个复杂项目的开发后，自动将经验沉淀为可复用的知识。 **核心功能**： - 自动识别复盘时机 - 提取标准化最优路径 → 生成skill - 提取踩坑经验 → 记录到memory ## 2. 触发条件 满足以下**所有条件**时触发： ### 2.1 时间条件 - 对同一个项目的对话时长 ≥ 2小时 - 或消息轮数 ≥ 50轮 ### 2.2 复杂度条件 - 经历了多次尝试和修正 - 遇到了多个问题和解决方案 - 涉及多个文件的创建和修改 ### 2.3 用户触发 用户明确表示要总结，例如： - "总结一下" - "把这个过程和经验总结一下" - "复盘一下" - "记录下来" ## 3. 执行流程 ### 步骤1：确认复盘意图 当检测到触发条件时，询问用户： ``` 检测到你刚完成了一个复杂项目的开发。是否需要我帮你： 1. 提取标准化流程，生成可复用的skill 2. 记录踩坑经验到memory 这样下次遇到类似项目时可以直接参考。 ``` ### 步骤2：分析对话历史 - 提取项目目标和背景 - 识别关键步骤和决策点 - 找出遇到的问题和解决方案 - 总结最佳实践 ### 步骤3：生成skill文档 **文件位置**：`/root/.openclaw/workspace/skills/<project-name>/SKILL.md` **内容结构**： ```markdown --- name: project-name description: 简短描述这个skill解决什么问题 --- # 项目名称 ## 1. 概述 - 适用场景 - 核心功能 ## 2. 核心文件清单 - 需要创建/修改的文件列表 ## 3. 详细步骤 - 步骤1：... - 步骤2：... - ... ## 4. 常见问题和解决方案 - 问题1：... - 问题2：... ## 5. 案例分析 - 实际应用示例 - 经验教训 ``` ### 步骤4：生成memory文档 **文件位置**：`/root/.openclaw/workspace/memory/YYYY-MM-DD-<project-name>-pitfalls.md` **内容结构**： ```markdown # 项目名称踩坑记录 **日期**：YYYY-MM-DD **项目**：项目描述 ## 1. 问题1标题 ### 问题描述 详细描述遇到的问题 ### 尝试的解决方案 - ❌ 方案1 - 为什么失败 - ❌ 方案2 - 为什么失败 - ✅ 方案3 - 最终成功 ### 最终解决方案 具体的解决步骤 ### 经验教训 从这个问题中学到的经验 --- ## 2. 问题2标题 ... --- ## 总结和最佳实践 - 开发流程建议 - 文件管理建议 - 调试技巧 ``` ### 步骤5：确认和保存 - 展示生成的文档路径 - 询问用户是否需要修改 - 保存到对应位置 ## 4. 使用示例 ### 场景：开发FitClaw健身教练Agent **对话时长**：约3小时 **消息轮数**：100+轮 **涉及文件**：10+个 **用户触发**： ``` 用户："很好，然后小龙虾就先到这一段，然后我交接了。 然后这个过程中，你要做两个： 第一个：把做成一只垂直类的这种小龙虾的流程写成一个 skill 第二个：就是把我们踩过的坑你写在 memory 里面" ``` **系统响应**： 1. 创建skill：`vertical-agent-creator` 2. 创建memory：`2026-03-12-fitclaw-pitfalls.md` ## 5. 注意事项 ### 5.1 何时使用 - ✅ 完成了复杂的多步骤项目 - ✅ 经历了多次尝试和修正 - ✅ 积累了可复用的经验 - ❌ 简单的一次性任务 - ❌ 没有通用价值的特定操作 ### 5.2 文档质量 - 标准化流程要清晰、可复用 - 踩坑记录要详细、有价值 - 包含具体的代码示例和命令 - 记录失败的尝试和原因 ### 5.3 命名规范 - skill名称：`<domain>-<function>` - memory文件：`YYYY-MM-DD-<project>-pitfalls.md` ## 6. 参考案例 ### 案例1：FitClaw开发 - **生成的skill**：`vertical-agent-creator` - **生成的memory**：`2026-03-12-fitclaw-pitfalls.md` - **价值**：下次创建垂直类Agent时可直接参考 ### 案例2：生图功能实现 - **关键经验**：API key加密、容灾机制 - **踩坑点**：脚本丢失、路径限制 - **可复用性**：高（其他需要图片处理的项目都可参考） --- **使用建议**：每次完成复杂项目后，主动触发复盘机制，持续积累可复用的知识库。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: krausefx/beeper (version 0.0.1)

--- name: beeper description: Search and browse local Beeper chat history (threads, messages, full-text search). homepage: https://github.com/krausefx/beeper-cli metadata: {"clawdbot":{"emoji":"🛰️","os":["darwin","linux"],"requires":{"bins":["beeper-cli"]},"install":[{"id":"go","kind":"go","pkg":"github.com/krausefx/beeper-cli/cmd/beeper-cli","bins":["beeper-cli"],"label":"Install beeper-cli (go install)"}]}} --- # Beeper CLI [Beeper](https://www.beeper.com/) is a universal chat app that unifies messages from WhatsApp, Telegram, Signal, iMessage, Discord, and more in a single inbox. This skill provides **read-only access** to your local Beeper chat history. Browse threads, search messages, and extract conversation data. ## Requirements - Beeper Desktop app installed (provides the SQLite database) - `beeper-cli` binary on PATH ## Database Path The CLI auto-detects: - `~/Library/Application Support/BeeperTexts/index.db` (macOS) - `~/Library/Application Support/Beeper/index.db` (macOS) Override with: - `--db /path/to/index.db` - `BEEPER_DB=/path/to/index.db` ## Commands ### List Threads ```bash beeper-cli threads list --days 7 --limit 50 --json ``` ### Show Thread Details ```bash beeper-cli threads show --id "!abc123:beeper.local" --json ``` ### List Messages in Thread ```bash beeper-cli messages list --thread "!abc123:beeper.local" --limit 50 --json ``` ### Search Messages (Full-Text) ```bash # Simple search beeper-cli search 'invoice' --limit 20 --json # Phrase search beeper-cli search '"christmas party"' --limit 20 --json # Proximity search beeper-cli search 'party NEAR/5 christmas' --limit 20 --json # With context window (messages before/after match) beeper-cli search 'meeting' --context 6 --window 60m --json ``` ### Database Info ```bash beeper-cli db info --json ``` ## Notes - **Read-only**: This tool never sends messages - **JSON output**: Always use `--json` for structured output agents can parse - **FTS5 search**: Uses Beeper's built-in full-text index (FTS5) for fast search - **DM name resolution**: Optionally resolves DM names via bridge databases (disable with `--no-bridge`) ## Installation ### Option 1: Go Install (recommended) ```bash go install github.com/krausefx/beeper-cli/cmd/beeper-cli@latest ``` ### Option 2: Build from Source ```bash git clone https://github.com/krausefx/beeper-cli.git cd beeper-cli go build ./cmd/beeper-cli # Move beeper-cli to PATH, e.g., /usr/local/bin ``` ## Examples Search for work-related messages from last week: ```bash beeper-cli threads list --days 7 --json | jq '.threads[] | select(.name | contains("work"))' beeper-cli search 'project deadline' --limit 10 --json ``` Find messages about invoices with context: ```bash beeper-cli search 'invoice' --context 3 --json ```


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