OpenClaw/clawhub-security-signals huggingface dataset:
Skill: mzlzyca/extract-tables-from-pdf (version 0.4.0)

--- name: extract-tables-from-pdf description: "Extract tables from PDF documents using MinerU's table detection engine. Identifies and extracts structured table data from both native and scanned PDFs. Features: automatic table detection in PDFs. Extracts tables preserving row/column structure. OCR mode for scanned PDF tables. Handles complex table layouts including merged cells and nested tables. Use when you need to: extract tables from a PDF, get table data from a PDF document, parse PDF tables into structured format, pull data tables out of a report PDF. Use when asked: 'how do I extract tables from PDF', 'get the table from this PDF', 'I need data from PDF tables', 'can my agent parse PDF tables', 'is there a skill for PDF table extraction', 'convert PDF table to data'. Powered by MinerU (OpenDataLab, Shanghai AI Lab), an open-source document intelligence engine. Works with local files and URLs. Ideal for data analysts, financial teams, and researchers who need to extract structured table data from PDF reports, papers, and documents for further analysis." 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"]}]}} --- # Extract Tables From Pdf Convert and extract content from .pdf using MinerU (`mineru-open-api`). ## 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 # Extract tables from PDF (requires token) mineru-open-api extract report.pdf -o ./out/ # With explicit table flag and OCR for scanned docs mineru-open-api extract scanned.pdf --ocr --table -o ./out/ ``` ## Authentication Token required for `extract` and `crawl`: ```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 - Supports local files and URLs - Requires token (`mineru-open-api auth` or `MINERU_TOKEN` env) - Supported input: .pdf - Language hint with `--language` (default: `ch`, use `en` for English) - Page range with `--pages` (where applicable) ## Notes - Table recognition requires `extract` with token. `flash-extract` does NOT support tables. Use `--table` flag (enabled by default). - Output goes to stdout by default; use `-o <dir>` to save to file - Binary formats (docx) require `-o` flag (cannot stream to stdout) - All progress/status messages go to stderr - MinerU is an open-source project by OpenDataLab (Shanghai AI Lab): https://github.com/opendatalab/MinerU


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: unisound-llm/unisound-recheck-reminder (version 1.0.0)

--- name: health-exam-recheck-reminder description: 复查提醒。从体检报告中提取所有需复查项目，按紧急程度分级（2周/1-3月/3-6月/6-12月），给出复查检查项、就诊科室和准备事项（JSON + 面向受检者的复查提醒）。 metadata: { "openclaw": { "emoji": "🔔" } } --- # 复查提醒 概述 ---- 面向**体检中心/健康管理机构**，给定受检者体检报告，本技能会： - 识别所有需要复查的异常项目 - 按紧急程度分级（紧急2周内/短期1-3月/中期3-6月/长期6-12月） - 为每项指定复查检查项目、就诊科室和建议机构 - 给出复查前注意事项（空腹/停药等） - 区分"需到医院就诊"、"可在体检机构复查"、"可居家自测"三类 - 生成面向受检者的友好提醒通知 与"检后随访管理"的区别：本技能聚焦**复检闭环**，输出更轻量（以提醒清单为主），适合直接推送给受检者；随访管理则包含健康指导等更宽泛的检后服务。 数据安全、隐私与伦理声明 ------------------------ - **最小必要原则**：仅处理生成复查提醒所必需的体检数据；不要求包含直接身份标识。 - **严格脱敏**：发送前对可识别身份信息进行脱敏处理。 - **不做本地持久化**：仅在内存中短暂处理；**本次调用结束即销毁**。 - **医疗边界**：复查提醒为健康管理服务，具体复查方案由接诊医生决定。 输入格式 -------- 纯文本（UTF-8），可输入完整体检报告，例如： ```text 受检者：女，42岁，体检日期：2026年5月 主要异常发现： - 宫颈液基细胞学（TCT）：ASCUS（不典型鳞状细胞，意义不明） - 乳腺超声：右乳3mm低回声结节，BI-RADS 3类 - 甲状腺超声：左叶6mm低回声结节，TI-RADS 3类 - TG：2.3mmol/L（轻度升高） - 空腹血糖：5.9mmol/L（正常高值） - 血压：128/82mmHg - 骨密度：腰椎T值-1.1（骨量减少） ``` 也支持 JSON 格式（包含 `text`/`content`/`report` 字段的对象）。 快速开始 -------- ```bash # 从 skills 目录运行 python3 health-exam/post-exam-mgmt/recheck-reminder/scripts/run.py \ --input data/health-exam-recheck/case-001.txt \ --appkey <your-appkey> # 保存输出到文件 python3 health-exam/post-exam-mgmt/recheck-reminder/scripts/run.py \ --input data/health-exam-recheck/case-001.txt \ --appkey <your-appkey> \ --output runs/health-exam-recheck/case-001.json ``` 参数说明 -------- - `--input PATH`：**必填**。体检报告文件路径（txt 或 json，UTF-8）。 - `--appkey STRING`：**必填**。调用内部医疗大模型的鉴权 key，由平台分配。 - `--output PATH`：输出文件路径（默认：打印到 stdout）。 - `--base URL`：内部大模型 base URL（默认：`https://maas-api.hivoice.cn/v1`）。 - `--model STRING`：模型名称（默认：`u1-insuremed`）。 - `--timeout SECONDS`：HTTP 超时秒数；`0` 表示一直等待（默认：0）。 - `--encoding STRING`：输入文件编码（默认：`utf-8`）。 输出约定 -------- 输出分两部分： **JSON 结构**： ```json { "recheck_count": 6, "has_urgent": false, "items": [ { "item": "宫颈TCT（ASCUS）", "current_finding": "ASCUS（不典型鳞状细胞，意义不明）", "recheck_urgency": "短期（1-3个月）", "recheck_exam": "HPV分型检测 + TCT复查", "recheck_venue": "二级及以上医院", "recheck_department": "妇科", "purpose": "明确是否存在高危型HPV感染，判断是否需要阴道镜检查", "preparation": ["月经干净后3-5天就诊", "就诊前24小时避免同房和阴道冲洗"], "what_to_watch": "如出现同房后出血或异常白带，提前就诊" }, { "item": "甲油三酯（TG）轻度升高", "current_finding": "TG 2.3mmol/L（轻度升高）", "recheck_urgency": "中期（3-6个月）", "recheck_exam": "血脂四项复查", "recheck_venue": "体检机构", "recheck_department": null, "purpose": "评估生活方式干预后血脂控制效果", "preparation": ["空腹8-12小时", "复查前3天避免高脂饮食"], "what_to_watch": null } ], "grouped_by_venue": { "urgent_medical_referral": [], "followup_at_exam_center": ["血脂四项复查", "血糖复查", "骨密度复查"], "self_monitoring": ["每周测量血压1-2次"] } } ``` **复查提醒**：以"【复查提醒】"开头，用清晰友好的语言提醒受检者需要跟进的复查事项。 依赖 ---- ### 运行环境 - Python 3.7+（仅使用标准库，无需额外安装） ### 外部 API - 内部医疗大模型：`https://maas-api.hivoice.cn/v1/chat/completions` 备注 ---- - TI-RADS/BI-RADS 分类随访时间遵循国际超声学会标准 - has_urgent 为 true 时，输出文字会特别强调紧急就诊的必要性 - **发布约束**：示例输入、运行输出均放在 skill 包外（`data/`、`runs/`），skill 目录内仅保留可发布的核心文件


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: kevin-li-okg/hello-world-test (version 1.0.0)

--- name: hello_world description: Use when the user asks for a hello, a greeting, says hi, or wants to test that the hello-world plugin is loaded. --- # Hello World A minimal demo skill paired with the `hello-world` OpenClaw plugin. ## When to use Trigger when the user asks for a greeting, says "hello", "hi", "say hi to <name>", or asks to test the hello-world plugin. ## How to use Call the `hello` tool registered by the `hello-world` plugin: - If the user mentions a name, pass it as the `name` parameter. - Otherwise omit `name` and let the tool default to "world". - Return the tool's text output directly without rephrasing.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: plgonzalezrx8/openrouter-analytics (version 1.0.0)

--- name: openrouter-analytics description: Review OpenRouter usage, analytics, and troubleshooting data via API. Use when the user asks for spend/usage monitoring, activity trends, per-key management reporting, or deep investigation of specific request IDs (latency, provider fallback, finish reason, token/cost breakdown). --- # OpenRouter Analytics Use this skill to pull **management-level usage data** and **request-level troubleshooting details** from OpenRouter. ## Quick Start Run from this skill folder: ```bash cd ~/clawd/skills/openrouter-analytics ``` Management key operations (set `OPENROUTER_MANAGEMENT_KEY`): ```bash python3 scripts/openrouter_analytics.py activity --limit 20 python3 scripts/openrouter_analytics.py activity --date 2026-02-18 python3 scripts/openrouter_analytics.py activity --from 2026-02-01 --to 2026-02-18 --summary --csv /tmp/activity.csv python3 scripts/openrouter_analytics.py credits python3 scripts/openrouter_analytics.py keys --limit 50 --summary python3 scripts/openrouter_analytics.py report --from 2026-02-01 --to 2026-02-18 --format markdown ``` Request-level troubleshooting (set `OPENROUTER_API_KEY`): ```bash python3 scripts/openrouter_analytics.py generation --id <generation_id> ``` Use `--raw` on any command to print full JSON. ## Workflow 1. **Check macro activity** - Run `activity` for daily spend/traffic patterns. 2. **Check account-level usage** - Run `credits` to review consumed vs remaining credits. 3. **Find key-level consumers** - Run `keys` to identify which keys are driving usage. 4. **Investigate incidents** - Run `generation --id ...` for detailed logs on one request (latency, fallback providers, finish reason, token and cost details). ## Notes - `activity`, `credits`, `keys`, and `report` require a **Management API key**. - `generation` uses a standard **OpenRouter API key** and requires the request `id`. - The script auto-loads `~/.openclaw/.env` and current-directory `.env` before execution. - Use `--retries` and `--timeout` to tune robustness under transient API/network issues. - Keep generation IDs in your application logs to support reliable post-incident analysis. ## Resources - Endpoint reference and field guide: `references/endpoints.md` - CLI helper script: `scripts/openrouter_analytics.py`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: pinke/free-buddy-skills (version 2.3.0)

--- title: "Free Buddy Skills" summary: "一键配置 opencode.ai 免费 AI 模型到 WorkBuddy,无需 API Key" read_when: - 用户需要配置免费 AI 模型 - 用户提到"免费模型"、"opencode"、"free model" --- # Free Buddy Skills 自动检测和配置 opencode.ai 的免费 AI 模型到 WorkBuddy。 ## 触发词 当用户说以下内容时,使用此技能: - "帮我配置 opencode.ai 的免费模型" - "配置免费模型" - "添加 opencode 模型" - "免费 AI 模型" - "更新免费模型" - "free model" - "opencode.ai" ## 功能 1. **查询免费模型**: 从 opencode.ai 获取最新的免费模型列表 2. **自动配置**: 将免费模型添加到 `~/.workbuddy/models.json` 3. **定期更新**: 保持模型配置的时效性 ## 权限说明 - **无需 API Key**: 使用公开的 `"public"` 作为 API Key - **无需认证**: opencode.ai 免费模型无需登录 - **仅本地操作**: 只读取和写入本地的 `~/.workbuddy/models.json` ## 快速使用 用户只需发送一句话: **首次配置:** > "帮我配置 opencode.ai 的免费模型" WorkBuddy 会自动: 1. 查询最新的免费模型列表 2. 添加到 `~/.workbuddy/models.json` 3. 验证配置是否可用 **更新模型:** > "更新免费模型" WorkBuddy 会自动: 1. 查询最新的免费模型列表 2. 对比现有配置 3. 添加新模型,跳过已存在的 ## 使用流程 ### 方法 1: 使用 Python 脚本 (推荐) ```bash # 所有平台通用 (macOS / Linux / Windows) python3 update-free-models.py # 或 python update-free-models.py ``` ### 方法 2: 手动配置 #### 步骤 1: 查询可用免费模型 ```bash curl -sS https://opencode.ai/zen/v1/models | jq '.data[].id' | grep -i free ``` #### 步骤 2: 获取模型详细信息 ```bash curl -sS https://opencode.ai/zen/v1/models | jq '.data[] | select(.id | contains("free"))' ``` #### 步骤 3: 读取现有配置 读取 `~/.workbuddy/models.json` (macOS/Linux) 或 `%USERPROFILE%\.workbuddy\models.json` (Windows) 检查是否已存在相同模型。 #### 步骤 4: 添加或更新模型配置 为每个免费模型添加以下配置: ```json { "id": "模型ID", "name": "模型显示名称", "vendor": "OpenCode AI", "url": "https://opencode.ai/zen/v1/chat/completions", "apiKey": "使用 public (无需真实密钥)", "maxInputTokens": 262144, "supportsToolCall": true, "supportsImages": false, "supportsReasoning": true } ``` ### 步骤 5: 验证配置 使用 `read_file` 工具读取 `~/.workbuddy/models.json` 确认配置已正确添加。 ## 当前免费模型列表 | 模型 ID | 名称 | 工具调用 | 图像 | 推理 | |---------|------|----------|------|------| | minimax-m2.5-free | MiniMax M2.5 Free | ✅ | ❌ | ✅ | | trinity-large-preview-free | Trinity Large Preview Free | ✅ | ✅ | ✅ | | nemotron-3-super-free | Nemotron-3 Super Free | ✅ | ✅ | ✅ | ## 配置保存位置 - **macOS/Linux**: `~/.workbuddy/models.json` - **Windows**: `%USERPROFILE%\.workbuddy\models.json` (即 `C:\Users\你的用户名\.workbuddy\models.json`) - **跨平台**: `$HOME/.workbuddy/models.json` (Git Bash/WSL 下通用) ## 注意事项 1. opencode.ai 的免费模型使用 `"public"` 作为 API Key 2. 所有免费模型共享同一个端点: `https://opencode.ai/zen/v1/chat/completions` 3. 脚本需要用户确认后才会修改配置文件 4. 非交互模式下运行将跳过自动添加,需手动确认


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: sean810720/brave-loggedin-tag-browsing (version 2.0.1)

--- name: brave-loggedin-tag-browsing description: | 使用 Brave 瀏覽器（已登入狀態）瀏覽 X/Twitter、Facebook 用戶頁面並提取最新帖子。 自動化社交媒體監控，支援登入狀態檢查、帖子提取、互動數據抓取。 version: "2.0.0" author: Shuttle AI tags: ["browser", "social-media", "x", "twitter", "facebook", "brave", "logged-in", "multi-platform"] mode: subagent triggers: - browse social media - view x tweets - view facebook posts - check social media --- # brave-loggedin-tag-browsing **自動化瀏覽器技能 - 使用已登入的 Brave 瀏覽器查看 X/Twitter、Facebook 用戶帖子** ## ⚠️ 平台支援 **目前僅支援：** - `x` / `twitter` - X/Twitter（完整功能） - `facebook` / `fb` - Facebook（基本功能） **暫不支援**：Instagram、LinkedIn、YouTube 等 ## 🎯 核心功能 - **登入狀態检测**：自動檢查瀏覽器是否已登入目標平台 - **帖子提取**：抓取最新 N 篇帖子（文字、時間） - **互動數據**：可選提取轉推/喜歡/分享 或 留言/分享/讚數 - **用戶資料**：抓取姓名、bio、粉絲/追蹤數 - **彈性連接**：優先連接 OpenClaw browser 實例 ## 📥 輸入參數 | 參數 | 類型 | 必填 | 預設 | 說明 | |------|------|------|------|------| | `username` | string | ✅ | - | 帳號名稱（不需 `@`） | | `platform` | string | ❌ | `x` | `x`、`twitter`、`facebook`、`fb` | | `maxPosts` | number | ❌ | 5 | 最大帖子數（建議 ≤ 20） | | `includeStats` | boolean | ❌ | true | 是否包含互動數據 | ## 🚀 使用方式 ### CLI ```bash node index.js realDonaldTrump x 5 true node index.js markzuckerberg facebook 3 false ``` ### OpenClaw (sessions_spawn) ```javascript await sessions_spawn({ runtime: "subagent", task: JSON.stringify({ skill: "brave-loggedin-tag-browsing", params: { username: "elonmusk", platform: "x", maxPosts: 5, includeStats: true } }) }); ``` ## 📤 輸出格式 ```json { "username": "realDonaldTrump", "platform": "x", "loginStatus": "💰 錢錢AI (@MVenusean67544)", "profile": { "name": "...", "bio": "...", "followers": "110.8M" }, "posts": [ { "time": "2026-03-02T16:20:05.000Z", "text": "...", "stats": { "likes": "459K" } } ], "metadata": { "maxPosts": 5, "timestamp": "...", "connectionMode": "opencdl" } } ``` ## ⚙️ 技術實現 - **依賴**：playwright（chromium） - **連接策略**： 1. OpenClaw CDP (port 18800) 2. Chrome CDP (port 9222) 3. 啟動新 Brave 實例（userDataDir） ## 🔧 故障排除 | 問題 | 解決方案 | |------|----------| | 連接 OpenClaw Brave 失敗 | 先執行 `/browser start` | | 提示安裝瀏覽器 | `npx playwright install chromium` | | 未檢測到登入 | 在 Brave 中手動登入目標平台 | | 提取不到帖子 | 增加等待時間或檢查 DOM 選擇器 | ## 📊 效能指標 - 首次執行：8-12 秒 - 後續執行：3-5 秒 - 記憶體：~150MB - 建議 maxPosts ≤ 20 ## 📝 License CC BY-NC 4.0


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: mx6315909/xiaodi-multi-team-switcher (version 1.0.1)

--- name: xiaodi-multi-team-system description: 小弟多团队协作系统 - 金融、电商、多媒体、办公四大团队 + 智能切换器 version: "1.0.1" author: xiaodi homepage: https://github.com/mx6315909/xiaodi-multi-team-skills metadata: openclaw: emoji: 🤖 requires: tools: ["web_search", "web_fetch", "memory_search", "exec", "browser"] binaries: ["ffmpeg", "ffprobe", "convert"] --- # 🤖 小弟多团队协作系统 专业的 AI Agent 团队协作系统，四大功能团队 + 智能切换器，一键切换金融、电商、多媒体、办公场景。 ## 📦 团队列表 | 团队 | 图标 | 功能 | 角色数 | |------|------|------|--------| | 金融团队 | 📊 | 股票分析、投资决策 | 7 | | 电商团队 | 🛒 | 选品、运营、定价 | 8 | | 多媒体团队 | 🎬 | 视频、图片、音频处理 | 7 | | 办公团队 | 📋 | 日程、邮件、文档 | 4 | **总计：26 个专业 Agent 角色** --- ## ⚙️ 系统要求 ### 必需工具 | 工具 | 用途 | 安装命令 | |------|------|----------| | `ffmpeg` | 视频处理 | `apt install ffmpeg` 或 `brew install ffmpeg` | | `ffprobe` | 视频分析 | 随 ffmpeg 安装 | | `ImageMagick` | 图片处理 | `apt install imagemagick` 或 `brew install imagemagick` | ### 可选工具 | 工具 | 用途 | 安装命令 | |------|------|----------| | `whisper` | 语音识别/字幕生成 | `pip install openai-whisper` | | `akshare` | A股数据 | `pip install akshare` | ### 权限说明 本技能需要以下工具权限： - **web_search** - 搜索网络信息 - **web_fetch** - 获取网页内容 - **memory_search** - 搜索记忆 - **exec** - 执行系统命令（ffmpeg、ImageMagick 等） - **browser** - 浏览器自动化 --- ## 🔐 数据源声明 ### 金融数据（公开 API） | 数据源 | 用途 | 认证 | |--------|------|------| | 东方财富 | 股票行情、财务数据 | 无需认证 | | 腾讯财经 | 股票行情 | 无需认证 | | 新浪财经 | 新闻、舆情 | 无需认证 | ### 电商数据（公开 API） | 数据源 | 用途 | 认证 | |--------|------|------| | 亚马逊 | 商品信息、BSR | 无需认证 | | TikTok | 商品信息 | 无需认证 | --- ## 🔑 可选 API 配置 AI 视频生成功能需要配置以下环境变量（可选）： ```bash # 可灵 AI export KLING_API_KEY=your_key # Runway Gen-3 export RUNWAY_API_KEY=your_key # Pika Labs export PIKA_API_KEY=your_key # OpenAI (Sora/DALL-E) export OPENAI_API_KEY=your_key ``` **注意**：这些 API Key 仅用于 AI 视频生成，不配置不影响其他功能。 --- ## 🚀 快速开始 ### 安装 ```bash clawhub install xiaodi-multi-team-system ``` ### 使用示例 ``` # 金融场景 分析茅台的投资价值 诊断我的持仓 # 电商场景 帮我选品，类目：美妆护肤 监控竞品价格变化 # 多媒体场景 压缩这个视频 /path/to/video.mp4 提取视频第10秒的帧 # 办公场景 帮我安排明天下午3点的会议 整理这份文档 ``` --- ## 📋 团队详情 ### 📊 金融团队 7 大角色协作：投顾专家、行业研究员、投行专家、市值管理助理、财富专员、商机助理、舆情助理 **数据源**：东方财富、腾讯财经、新浪财经（均为公开 API） ### 🛒 电商团队 8 大角色协作：选品专家、运营专员、定价专员、客服专员、数据分析师、竞品分析师、内容创作者、投放专员 **数据源**：亚马逊、TikTok（公开数据） ### 🎬 多媒体团队 7 大角色协作：视频剪辑师、视频创作师、字幕生成器、图片处理师、AI 绘图师、音频处理师、质量检查员 **依赖**：ffmpeg、ImageMagick、whisper（可选） ### 📋 办公团队 4 大角色协作：日程秘书、邮件秘书、文档秘书、会议秘书 --- ## ⚠️ 安全声明 1. **exec 权限**：仅用于调用 ffmpeg、ImageMagick 等媒体处理工具，不会执行任意危险命令 2. **browser 权限**：仅用于获取公开网页数据，不会访问需要认证的私密页面 3. **API Key**：所有 API Key 通过环境变量配置，不会硬编码或存储在代码中 4. **数据安全**：不读取主机敏感文件，不外传用户数据 --- ## 📄 License MIT License --- ## 👤 作者 xiaodi <[REDACTED_SECRET]> --- *GitHub: https://github.com/mx6315909/xiaodi-multi-team-skills*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: luojingwei123/context-collab (version 1.0.8)

--- name: context description: Multi-agent collaboration protocol engine — shared spaces with auto-injected context, viral propagation, and human annotation workflow. Creates a shared Space per group/channel, auto-injects SPACE.md + TEAM.md + TASK.md into every Agent conversation, and provides 14 tools for reading/writing shared files, managing tasks, members, and annotations. metadata: openclaw: type: plugin minVersion: "0.9.0" config: serverUrl: type: string default: "https://context-server-mj6f.onrender.com" description: Context Server URL autoInject: type: boolean default: true description: Auto-inject protocol files into system prompt --- # Context Plugin Multi-agent collaboration protocol engine for OpenClaw. ## What it does - **Auto-injects** SPACE.md + TEAM.md + TASK.md into every conversation in a linked group - **14 Agent Tools** for reading/writing shared files, managing tasks, and annotations - **5 Slash Commands** for space management (`/ctx_create`, `/ctx_info`, `/ctx_files`, `/ctx_tasks`, `/ctx_team`) - **Viral propagation** via shareable `/ctx/` URLs - **Annotation workflow** for human → AI feedback loop ## Installation ```bash clawhub install context ``` Then add to your `openclaw.json`: ```json { "plugins": { "allow": ["context"], "entries": { "context": { "enabled": true, "config": { "serverUrl": "https://context-server-mj6f.onrender.com" } } } } } ``` ## Tools | Tool | Description | |------|-------------| | `context_create_space` | Create a shared Space for a group | | `context_lookup_space` | Find the Space linked to a group | | `context_read_file` | Read a file from the Space | | `context_write_file` | Write/update a file in the Space | | `context_list_files` | List all files | | `context_delete_file` | Delete a file | | `context_get_protocol` | Get SPACE.md + TEAM.md + TASK.md at once | | `context_update_task` | Update TASK.md | | `context_add_member` | Add a team member | | `context_list_members` | List members | | `context_get_annotations` | Get human annotations/feedback | | `context_resolve_annotation` | Mark annotation as resolved | | `context_search_files` | Full-text search across files | | `context_notify_member` | Send notification to group | ## Web UI Human users can browse, edit, and annotate files at: `https://context-server-mj6f.onrender.com/s` ## Source https://github.com/luojingwei123/context


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: xammarie/jarvis-bug-triage-01 (version 1.0.0)

--- name: jarvis-bug-triage-01 description: Triage production bugs fast with repro isolation and risk-first fixes. Use when handling development work that needs repeatable steps, quality gates, and practical outputs. --- # Jarvis Bug Triage 01 Deliver a concrete, reusable workflow for development tasks. ## Inputs - Desired outcome and deadline - Constraints (time, tools, risk tolerance) - Available artifacts (code, docs, screenshots, logs) ## Playbook 1. Define success and failure criteria in measurable terms. 2. Build a smallest-valid execution path before scaling effort. 3. Execute in checkpoints; record evidence at each checkpoint. 4. Apply quality gates before finalizing recommendations. 5. Return implementation-ready outputs and next actions. ## Quality Gates - Evidence-backed claims only. - Explicit assumptions and tradeoffs. - Reversible changes preferred for risky operations. - Clear owner + ETA for each next step. - Include a fallback if the primary plan fails. ## Output Format - Situation summary (5 lines max) - Top findings (ranked by impact) - Action plan (today / this week) - Risks + mitigations - Exact checklist or commands where useful ## Example prompts - "Use this skill to turn my messy notes into a production-ready plan." - "Run this skill and give me a risk-first action sequence with acceptance checks."


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: kaising-openclaw1/xiaoming-goal-tracker-pro (version 1.0.0)

--- name: goal-tracker-pro description: 目标追踪专业版 - 目标设定、进度追踪、里程碑庆祝 --- # Goal Tracker Pro 目标追踪专业版工具，帮你实现人生目标。 ## 功能 - ✅ 目标设定 - ✅ 进度追踪 - ✅ 里程碑庆祝 - ✅ 数据分析 - ✅ 社交分享 ## 使用 ```bash # 设定目标 clawhub goal set --title "减肥 10 斤" --deadline "2026-06-01" # 更新进度 clawhub goal update --id 1 --progress 50 # 查看进度 clawhub goal progress --id 1 # 里程碑 clawhub goal milestone --id 1 --name "完成 50%" ``` ## 定价 | 版本 | 价格 | 功能 | |------|------|------| | 免费版 | ¥0 | 3 个目标 | | Pro 版 | ¥49 | 无限目标 | | 订阅版 | ¥12/月 | Pro+ AI 建议 |

path: README.md
content: # Goal Tracker Pro 🎯

目标追踪专业版工具。

## 安装

```bash
npx clawhub@latest install goal-tracker-pro
```

## 功能

- 目标设定
- 进度追踪
- 里程碑庆祝
- 数据分析

## 目标类型

- 健康
- 财务
- 学习
- 职业
- 个人成长


path: _meta.json
content: {"name":"goal-tracker-pro","version":"1.0.0","author":"小鸣 🦞","tags":["goal","tracker","achievement","success"],"requires":{"bins":["curl"]}}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: teoslayer/pilot-directory (version 1.0.0)

--- name: pilot-directory description: > Local directory of known agents with cached metadata. Use this skill when: 1. Maintaining a persistent directory of frequently contacted agents 2. Caching agent metadata for offline reference 3. Building a local address book of trusted or favorite agents Do NOT use this skill when: - You need real-time network discovery (use pilot-discover instead) - You need to visualize relationships (use pilot-network-map instead) - You need to establish new trust (use pilot-trust instead) tags: - pilot-protocol - directory - cache license: AGPL-3.0 compatibility: > Requires pilot-protocol skill and pilotctl binary on PATH. The daemon must be running (pilotctl daemon start). metadata: author: vulture-labs version: "1.0" openclaw: requires: bins: - pilotctl homepage: https://pilotprotocol.network allowed-tools: - Bash --- # pilot-directory Maintain a local directory of known agents with cached metadata. ## Commands ### List all known peers ```bash pilotctl --json peers ``` ### Find agent by hostname ```bash pilotctl --json find <hostname> ``` ### Lookup agent by node ID ```bash pilotctl --json lookup <node-id> ``` ### Check trust status ```bash pilotctl --json trust ``` ## Workflow Example Build directory of AI agents and export for offline reference: ```bash # Discover all AI agents ai_agents=$(pilotctl --json peers | jq '[.peers[] | select(.tags[] | contains("ai"))]') # Enrich with detailed info echo "$ai_agents" | jq -r '.[].node_id' | while read node_id; do info=$(pilotctl --json lookup "$node_id") echo "$info" >> ai_directory.jsonl done # Create summary jq -s '.' ai_directory.jsonl > ai_directory.json # Build quick-lookup table jq -r '.[] | "\(.hostname) \(.node_id)"' ai_directory.json > ai_lookup.txt ``` ## Dependencies Requires pilot-protocol skill and a running daemon.


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