OpenClaw/clawhub-security-signals huggingface dataset:
Skill: codes71/firefly (version 1.0.0)

--- name: firefly description: > Fetch meeting transcripts, summaries, and action items from Firefly AI (fireflies.ai). Use when the user asks about meetings, transcripts, meeting notes, action items, or anything related to fireflies.ai. Supports listing recent meetings, pulling full transcripts, getting summaries, and searching by keyword. --- # Firefly AI Integration Pull meeting data from Firefly AI via their GraphQL API. ## Setup Requires `FIREFLY_API_KEY` environment variable. Store in gateway env config. ## Usage Run the script at `scripts/firefly.cjs` with Node.js: ```bash FIREFLY_API_KEY=<key> node scripts/firefly.cjs <command> [options] ``` ### Commands - **list** — List recent meetings. Options: `--days <n>` (default 14), `--limit <n>` (default 50) - **transcript** — Full transcript with timestamps. Requires `--id <meeting_id>` - **summary** — Meeting summary, overview, and action items. Requires `--id <meeting_id>` - **search** — Search by keyword in titles/content. Requires `--keyword <text>`, optional `--limit <n>` ### Workflow 1. Use `list` to find meetings and get IDs 2. Use `summary` or `transcript` with the ID to get details 3. Use `search` to find meetings by topic ### For custom queries Build GraphQL queries directly against `https://api.fireflies.ai/graphql`. See `references/api.md` for full schema and available fields. ### Large transcripts Full transcripts can be very long (2000+ sentences). When a user asks for a transcript: - Save to a file in the workspace if they want to keep it - Summarize or extract relevant sections if they want specific info - Show a preview (first ~50 lines) and ask if they want more


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/openclaw-client-onboarding (version 1.0.0)

# OpenClaw Client Onboarding 快速为付费客户配置 OpenClaw 的自动化流程。 ## 用途 当你接到 OpenClaw 安装/配置订单时，使用此 Skill 快速： 1. 收集客户需求（AI 模型、消息渠道、预算） 2. 生成配置模板 3. 创建客户档案 4. 发送报价单 ## 使用方法 ``` 客户下单 OpenClaw 配置服务 → 运行此 Skill → 自动收集信息并生成配置方案 ``` ## 工作流程 ### Step 1: 收集客户信息 向客户询问： - **AI 模型偏好**: DeepSeek / GLM-5 / Qwen / GPT / Claude - **消息渠道**: Telegram / Discord / WeChat / Feishu / DingTalk - **预算范围**: ¥99-299 / ¥299-999 / ¥999+ - **主要用途**: 个人助手 / 客服 / 自动化 / 其他 ### Step 2: 生成配置方案 根据客户回答，生成： - 推荐的 AI 模型配置 - 消息渠道配置 - 自定义 Skills 推荐 - 报价单 ### Step 3: 创建客户档案 在 `clients/{客户名}/` 创建： - `config.json` - 配置文件 - `notes.md` - 客户备注 - `invoice.md` - 报价单 ## 定价参考 | 套餐 | 价格 | 包含服务 | |------|------|----------| | 基础 | ¥99 | 单渠道 + 基础配置 | | 高级 | ¥299 | 多渠道 + 3 个 Skills | | 企业 | ¥999 | 全渠道 + 10 个 Skills + 远程培训 | ## 文件结构 ``` workspace/ ├── skills/ │ └── openclaw-client-onboarding/ │ └── SKILL.md └── clients/ └── {客户名}/ ├── config.json ├── notes.md └── invoice.md ``` ## 联系方式 - WeChat: yanghu_ai - Telegram: @yanghu_openclaw - Email: [REDACTED_SECRET] --- Version: 1.0.0 Created: 2026-03-21 Author: OpenClaw CN Team


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wanng-ide/api-tester (version 1.0.0)

--- name: api-tester description: Perform structured HTTP/HTTPS requests (GET, POST, PUT, DELETE) with custom headers and JSON body support. Use for API testing, health checks, or interacting with REST services programmatically without relying on curl. --- # API Tester A lightweight, dependency-free HTTP client for OpenClaw. ## Usage ### Basic GET Request ```javascript const api = require('skills/api-tester'); const result = await api.request('GET', 'https://api.example.com/data'); console.log(result.status, result.data); ``` ### POST Request with JSON Body ```javascript const api = require('skills/api-tester'); const payload = { key: 'value' }; const headers = { 'Authorization': 'Bearer <token>' }; const result = await api.request('POST', 'https://api.example.com/submit', headers, payload); ``` ### Return Format The `request` function returns a Promise resolving to: ```javascript { status: 200, // HTTP status code headers: { ... }, // Response headers data: { ... }, // Parsed JSON body (if applicable) or raw string raw: "...", // Raw response body string error: "..." // Error message if request failed (network error, timeout) } ``` ## Features - **Zero dependencies**: Uses Node.js built-in `http` and `https` modules. - **Auto-JSON**: Automatically stringifies request body and parses response body if Content-Type matches. - **Timeout support**: Default 10s timeout, configurable. - **Error handling**: Returns structured error object instead of throwing, ensuring safe execution.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: linuszz/economies-of-scale (version 1.0.0)

--- name: economies-of-scale description: "Analyze cost advantages from scale. Use for understanding cost structure and competitive positioning." --- # Economies of Scale ## Metadata - **Name**: economies-of-scale - **Description**: Scale advantage and cost curve analysis - **Triggers**: economies of scale, scale advantage, cost curve, experience curve ## Instructions Analyze economies of scale for $ARGUMENTS. ## Framework ### Types of Scale Economies | Type | Description | Example | |------|-------------|---------| | **Purchasing** | Volume discounts | Bulk buying | | **Technical** | Fixed cost leverage | R&D spread | | **Financial** | Lower cost of capital | Cheaper debt | | **Managerial** | Specialization | Expert teams | | **Marketing** | Brand leverage | Ad spend efficiency | ### Experience Curve ``` Cost │╲ │ ╲ │ ╲ │ ╲___ │ ╲___ │ ╲___ └───────────── Volume ``` ## Output ``` ## Economies of Scale: [Business/Product] ### Cost Curve Analysis | Volume | Unit Cost | Savings | Source | |--------|-----------|---------|--------| | 100K | $10.00 | - | Baseline | | 500K | $7.50 | 25% | Purchasing | | 1M | $6.00 | 40% | Technical | | 5M | $4.50 | 55% | Full scale | ### Scale Advantages 1. **Purchasing Power**: [Analysis] 2. **Fixed Cost Leverage**: [Analysis] 3. **Network Effects**: [Analysis] ### Competitive Implications - **Barrier to entry**: [Analysis] - **Cost leadership**: [Analysis] - **Minimum efficient scale**: [Analysis] ### Recommendations 1. [Recommendation 1] 2. [Recommendation 2] ``` ## Tips - Identify minimum efficient scale - Consider diseconomies of scale - Factor in flexibility trade-offs


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: edwardrodriguez703-design/book-towing (version 1.0.1)

--- name: book-towing description: Book towing services through Lokuli MCP. Use when user needs to find and book towing. Triggers on requests like "book a towing", "find towing near me", or any towing service request. --- # uook towing Book towing services through Lokuli's MCP server. ## MCP Endpoint ``` https://lokuli.com/mcp/sse ``` Transport: SSE | JSON-RPC 2.0 | POST requests ## Tools ### search ```json { "method": "tools/call", "params": { "name": "search", "arguments": { "query": "towing", "zipCode": "90640", "maxResults": 20 } } } ``` ### check_availability ```json { "method": "tools/call", "params": { "name": "check_availability", "arguments": { "providerId": "xxx", "serviceId": "yyy", "date": "2025-02-10" } } } ``` ### create_booking ```json { "method": "tools/call", "params": { "name": "create_booking", "arguments": { "providerId": "xxx", "serviceId": "yyy", "timeSlot": "2025-02-10T14:00:00-08:00", "customerName": "John Doe", "customerEmail": "[REDACTED_SECRET]", "customerPhone": "+13105551234" } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yiming1001/mx-auto (version 1.0.4)

--- name: mx-auto description: Local Runtime automation entrypoint for App triggers, read-only browser sandbox inspection, and local script execution. Use when the user wants to list or run Runtime triggers, inspect existing browser sandbox tabs/snapshots, or list/show/run local App scripts. --- # mx-auto Use this skill as a lightweight router for local Runtime automation. ## Capabilities - **Triggers**: list and run callable App triggers. - Read [references/triggers.md](references/triggers.md) when the user asks to list, refresh, inspect, or run triggers. - **Sandbox**: list existing browser sandbox tabs or snapshot an existing tab. - Read [references/sandbox.md](references/sandbox.md) when the user asks about browser sandbox tabs, target IDs, snapshots, dashboard text, or URL-matched tab inspection. - **Scripts**: list, show, or run local scripts/examples. - Read [references/scripts.md](references/scripts.md) when the user asks to list scripts, inspect a script schema, or run a script directly. - For complex mixed requests, read [references/learning-guide.md](references/learning-guide.md). ## Shared Runtime Defaults Preferences file: `$OPENCLAW_STATE_DIR/skill-state/mx-auto/preferences.json` Fallback: `$HOME/.openclaw/skill-state/mx-auto/preferences.json` Helper: ```bash bash {baseDir}/scripts/export_preference.sh show bash {baseDir}/scripts/export_preference.sh apply-recommended bash {baseDir}/scripts/export_preference.sh set-key defaultAppHome "/Users/zhym/Library/Application Support/rpa-app-executor" ``` Runtime base URL discovery: 1. `MX_APP_RUNTIME_BASE_URL` 2. `RPA_RUNTIME_BASE_URL` 3. stored `defaultLocalBaseUrl` 4. standard Runtime ports: `8877`, `8878`, `8879` Runtime app home discovery: 1. `--app-home` 2. `MX_AUTO_APP_HOME` 3. `RPA_APP_HOME` 4. stored `defaultAppHome` 5. platform default app home Runtime admin token discovery: 1. `MX_APP_RUNTIME_ADMIN_TOKEN` 2. `RPA_RUNTIME_ADMIN_TOKEN` 3. `{resolved app home}/runtime/admin-token.json` Never store or print the Runtime admin token value. App updates may rotate or recreate it; re-read the token file at runtime. ## Entry Points Prefer the wrapper: ```bash bash {baseDir}/scripts/run.sh ... ``` Triggers: ```bash bash {baseDir}/scripts/run.sh triggers list --format json bash {baseDir}/scripts/run.sh triggers run --trigger-name "小红书测试" ``` Sandbox: ```bash bash {baseDir}/scripts/run.sh sandbox tabs --format json bash {baseDir}/scripts/run.sh sandbox snapshot --url-contains dashboardV4 --url-not-contains /review ``` Scripts: ```bash bash {baseDir}/scripts/run.sh scripts list --format json bash {baseDir}/scripts/run.sh scripts show xiaohongshu.note.search.v1.json --format json bash {baseDir}/scripts/run.sh scripts run xiaohongshu.note.search.v1.json --input-json '{"keyword":"美食探店"}' --wait true --format json ``` Legacy trigger commands remain supported: ```bash bash {baseDir}/scripts/run.sh --list-triggers bash {baseDir}/scripts/run.sh --trigger-name "小红书测试" ```


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: dustin-a11y/quantum-memory (version 1.2.0)

--- name: quantum-memory-graph title: Quantum Memory Graph description: "Quantum-enhanced long-term memory for AI agents — #1 on LongMemEval (98.6% R@5, 99.4% R@10, 0.9426 NDCG). Chunked gte-large retrieval with QAOA+CVaR subgraph optimization for agents." tags: [memory, retrieval, quantum, rag, embeddings, long-memory, AI-agents, agent-memory] --- # Quantum Memory Graph (QMG) Quantum-enhanced long-term memory for AI agents. Uses **chunked gte-large embeddings** for state-of-the-art semantic retrieval with **QAOA+CVaR** quantum subgraph optimization for graph-based reasoning. ## Features - **#1 on LongMemEval** — 98.6% R@5, 99.4% R@10, 0.9426 NDCG - **Chunked retrieval** — 500-char blocks with 100-char overlap, mean-of-top-3 per session - **QAOA+CVaR optimization** — 12.8% edge over greedy on graph/PCE tasks - **GPU accelerated** — runs on NVIDIA GB10 (DGX Spark) - **Cascade recall** — personal graph → historical archive fallback - **Per-agent isolation** — each agent gets their own isolated memory graph ## How It Works 1. **Session chunking** — Conversations split into overlapping 500-char chunks 2. **Embedding** — Chunks encoded with gte-large (1024-dim sentence transformer) 3. **Scoring** — Per-session score = mean of top-3 chunk cosine similarities 4. **Refinement** — Top-N candidates optionally refined via QAOA+CVaR subgraph optimizer 5. **Cascade** — Personal graph first, historical archive fallback if relevance < 0.4 ## Performance | Metric | Score | |--------|:-----:| | R@1 | 90.6% | | **R@5** | **98.6%** | | R@10 | 99.4% | | NDCG@10 | 0.9426 | *Benchmark: LongMemEval-S (500 questions, 18,464 sessions), May 28 2026* ## Usage ```python from quantum_memory_graph import MemoryGraph mg = MemoryGraph() mg.store("Project Alpha uses React frontend with TypeScript.") mg.store("Project Alpha backend is FastAPI with PostgreSQL.") # Recall — chunked semantic retrieval + optional QAOA refinement results = mg.retrieve("What is Project Alpha's tech stack?", top_k=5) ``` Or as a FastAPI server for agent integration: ```bash pip install quantum-memory-graph quantum-memory-graph serve # serves /store, /recall, /stats endpoints ``` ## Requirements - Python 3.10+ - sentence-transformers - numpy - (optional) qiskit for QAOA optimization on real hardware ## Links - GitHub: [https://github.com/Dustin-a11y/quantum-memory-graph](https://github.com/Dustin-a11y/quantum-memory-graph) - PyPI: [https://pypi.org/project/quantum-memory-graph/](https://pypi.org/project/quantum-memory-graph/) ## License MIT


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/content-monetizer (version 1.1.0)

# content-monetizer ## 描述 内容变现助手 - 分析你的内容（文章/视频/代码），推荐最佳变现方式。适合：创作者、博主、开发者。 ## 功能 - 内容价值评估（预计收入） - 平台推荐（掘金/知乎/B站/YouTube） - 变现方式建议（广告/付费/会员/咨询） - 竞品对标分析 - SEO 优化建议 ## 使用场景 1. 写文章前 - 先评估哪个平台收益最高 2. 内容复用 - 一篇内容多平台分发策略 3. 定价咨询 - 该收多少钱？ 4. 流量分析 - 为什么没人看？ ## 命令示例 ```bash # 分析单篇文章 openclaw run content-monetizer analyze article.md # 推荐平台 openclaw run content-monetizer recommend --type article --topic "AI" # 生成分发计划 openclaw run content-monetizer distribute article.md # 竞品分析 openclaw run content-monetizer compare "AI 教程" # 收入预测 openclaw run content-monetizer predict --views 10000 --topic "编程" ``` ## 配置 在 `~/.openclaw/config/content-monetizer.json` 中设置： ```json { "platforms": { "juejin": { "weight": 0.3 }, "zhihu": { "weight": 0.25 }, "bilibili": { "weight": 0.2 }, "wechat": { "weight": 0.15 }, "youtube": { "weight": 0.1 } }, "contentTypes": ["article", "video", "code", "course"] } ``` ## 变现方式分析 ``` 📝 内容分析报告 主题：DeepSeek API 教程 字数：3000 字 预估价值：¥500-2000 推荐平台： 1. 掘金（⭐⭐⭐⭐⭐） - 受众匹配度高 - 预计阅读：2000-5000 - 潜在收入：¥100-500（广告/付费） 2. 知乎（⭐⭐⭐⭐） - 搜索流量大 - 预计阅读：1000-3000 - 潜在收入：¥50-200 3. 公众号（⭐⭐⭐） - 需要私域运营 - 潜在收入：¥200-1000（咨询转化） 变现建议： - 短期：掘金发布 + 接咨询 - 中期：整理成付费课程 - 长期：建立社群/会员 行动计划： 1. 今天：发布掘金，添加微信引流 2. 本周：知乎同步，回答相关问题 3. 本月：整理成电子书，定价 ¥29 ``` ## 注意事项 - 收入预测仅供参考 - 实际收益取决于内容质量和运营 - 建议配合 content-creator-cn 使用


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fish1981bimmer/learning-assistant-app (version 1.0.0)

--- name: learning-assistant-app description: 一个集成多个免费API的学习助手应用 category: productivity tags: - learning - api - education - helper - dictionary - translation - calculator - history author: fish1981bimmer repository: https://github.com/fish1981bimmer/learning-assistant-app license: MIT version: 1.0.0 --- ## 学习助手应用 一个完全免费的学习助手应用，集成多个免费API，无需任何认证。 ### 功能特性 - 📚 **词典查询** - 使用DictionaryAPI查询单词定义和例句 - 🌍 **翻译工具** - 使用LibreTranslate进行多语言翻译 - 💡 **有趣事实** - 获取随机有趣的知识事实 - 🔢 **数学计算** - 支持复杂数学表达式计算 - 📜 **历史事件** - 查看历史上的今天发生的事件 ### 技术栈 - **后端**: Node.js + Express - **HTTP客户端**: Axios - **前端**: 原生HTML/CSS/JavaScript - **数据存储**: 本地文件系统 ### 集成的API | API | 用途 | 认证 | |-----|------|------| | DictionaryAPI | 词典查询 | ❌ | | LibreTranslate | 文本翻译 | ❌ | | Useless Facts | 有趣事实 | ❌ | | MathJS | 数学计算 | ❌ (本地) | | History API | 历史事件 | ❌ | ### 快速开始 1. 安装依赖： ```bash npm install ``` 2. 配置环境变量： ```bash cp .env.example .env # 编辑 .env 文件配置API地址 ``` 3. 启动服务： ```bash npm start ``` 4. 访问应用： 打开浏览器访问 http://localhost:3000 ### API端点 - `GET /api/dictionary/:word` - 查询单词 - `POST /api/translate` - 翻译文本 - `GET /api/fact` - 获取有趣事实 - `POST /api/calculate` - 数学计算 - `GET /api/history` - 获取历史事件 - `GET /api/data/preferences` - 获取用户偏好 - `POST /api/data/preferences` - 保存用户偏好 ### 项目结构 ``` learning-assistant-app/ ├── server.js # Express服务器主文件 ├── dataManager.js # 数据管理类 ├── api/ # API路由 │ └── data.js # 数据管理API ├── public/ # 静态资源 │ ├── index.html # 前端页面 │ ├── script.js # 前端脚本 │ └── style.css # 前端样式 ├── data/ # 数据存储目录 ├── package.json # 项目配置 ├── .env # 环境变量 └── .gitignore # Git忽略文件 ``` ### 使用说明 1. **词典查询**: 输入英文单词，获取定义、音标和例句 2. **翻译工具**: 选择源语言和目标语言，输入文本进行翻译 3. **有趣事实**: 点击按钮获取随机有趣的知识事实 4. **数学计算**: 支持基本运算、函数调用等复杂数学表达式 5. **历史事件**: 查看历史上的今天发生的重要事件 ### 开发计划 - [ ] 添加更多API集成 - [ ] 实现用户认证系统 - [ ] 添加数据持久化 - [ ] 优化UI界面 - [ ] 添加移动端适配 ### 许可证 MIT License ### 致谢 - [DictionaryAPI](https://dictionaryapi.dev/) - [LibreTranslate](https://libretranslate.com/) - [Useless Facts](https://uselessfacts.jsph.pl/) - [History API](https://api.api-ninjas.com/)


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: backup-system (version 1.0.0)

--- name: backup-system description: 備份系統關鍵設定與工作區資料。當用戶要求「備份系統」、「backup system」或需要手動保存當前狀態時使用。 --- # 備份系統 (Backup System) 此技能用於統一備份 OpenClaw 的設定檔 (`~/.openclaw`) 以及工作區資料 (`~/clawd`)。 ## 使用方法 1. 執行 `scripts/backup.sh` 腳本。 2. 該腳本會自動在 `~/openclaw_backups/` 目錄下建立一個以當前時間命名的資料夾（格式：`yyyymmddhhmm`）。 3. 備份完成後，向用戶回報備份的路徑。 ## 腳本說明 - **位置**: `scripts/backup.sh` - **行為**: 建立目錄並使用 `cp -R` 進行遞歸複製。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: pikaqiuyaya/boss-agent (version 1.0.0)

# Boss Agent - 统筹协调 Agent ## 角色定位 你是**Boss Agent**，是系统的**统筹协调者**。 ## 核心职责 ### 1. 统筹全局 - 接收用户的总体指令 - 分解任务给 Ass Agent 和 Ops Agent - 跟踪任务进度 - 汇总结果汇报给用户 ### 2. 信息收集 - **读取 Ass Agent 的信息** - 了解小助手的工作状态 - **读取 Ops Agent 的信息** - 了解运维机器人的工作状态 - **跨 Agent 查询** - 可以访问其他 Agent 的会话历史、记忆、状态 ### 3. 任务分发 - 给 Ass Agent 分配日常任务 - 给 Ops Agent 分配运维任务 - 协调多 Agent 协作 ### 4. 汇报总结 - 向用户汇报整体进展 - 汇总各 Agent 的工作结果 - 提供全局视角的分析 ## 🔧 技术实现 ### 访问其他 Agent 的方式 #### 1. 读取会话历史 ```bash # 查看 Ass Agent 的会话 sessions_list --agent ass # 查看 Ops Agent 的会话 sessions_list --agent ops ``` #### 2. 发送任务 ```bash # 给 Ass Agent 发送任务 sessions_send --session-key agent:ass:main --message "请处理这个任务：..." # 给 Ops Agent 发送任务 sessions_send --session-key agent:ops:main --message "请执行运维任务：..." ``` #### 3. 查询状态 ```bash # 检查各 Agent 状态 systemctl --user status openclaw-gateway-ass.service systemctl --user status openclaw-gateway-ops.service ``` ## 📋 工作流程 ### 示例：用户说"检查所有服务状态" ``` 1. 【接收任务】 用户："检查所有服务状态" 2. 【分解任务】 Boss 思考： - Ass Agent 负责：文档服务、用户服务 - Ops Agent 负责：系统服务、网关服务 3. 【分发任务】 → 发送消息给 Ass Agent： "请检查文档服务和用户服务的状态" → 发送消息给 Ops Agent： "请检查系统服务和网关服务的状态" 4. 【等待回复】 ← 接收 Ass Agent 回复： "文档服务：正常，用户服务：正常" ← 接收 Ops Agent 回复： "系统服务：正常，网关服务：重启中" 5. 【汇总汇报】 → 发送给用户： "✅ 所有服务检查完成： Ass Agent 负责： - 文档服务：✓ 正常 - 用户服务：✓ 正常 Ops Agent 负责： - 系统服务：✓ 正常 - 网关服务：⚠ 重启中（预计 2 分钟） 整体状态：正常" ``` ## 🎯 权限说明 ### 可以做的： - ✅ 读取 Ass Agent 的会话历史 - ✅ 读取 Ops Agent 的会话历史 - ✅ 向 Ass Agent 发送任务 - ✅ 向 Ops Agent 发送任务 - ✅ 查询所有 Agent 的状态 - ✅ 汇总信息并汇报 ### 需要用户授权的： - ⚠️ 修改其他 Agent 的配置 - ⚠️ 重启其他 Agent 的服务 - ⚠️ 删除其他 Agent 的数据 ## 📊 与其他 Agent 的关系 | 角色 | 职责 | 汇报对象 | |------|------|----------| | **Boss (我)** | 统筹协调 | 用户 | | **Ass** | 日常助手 | Boss | | **Ops** | 运维支持 | Boss | ## 💡 工作原则 1. **透明** - 让用户知道你在协调哪些 Agent 2. **高效** - 并行分发任务，不串行等待 3. **准确** - 汇总信息时保持准确，不歪曲 4. **及时** - 任务完成后立刻汇报 ## 🚀 启动检查 确认所有 Agent 运行正常： ```bash # 检查所有网关服务 systemctl --user status openclaw-gateway-boss.service systemctl --user status openclaw-gateway-ass.service systemctl --user status openclaw-gateway-ops.service ``` 所有服务应该是 `active (running)` 状态。 --- **记住：** 你是用户的代表，负责管理和协调其他 Agent！


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hhse/openclaw-backupgg (version 1.0.1)

--- name: openclaw-backup version: 1.0.0 description: 一键备份 OpenClaw 工作区，支持必选/可选内容，生成 ZIP 压缩包（含 SHA256 校验） author: 木木 --- # OpenClaw Backup 🔒 一键备份 OpenClaw 工作区的重要文件，支持自定义选择备份内容。 ## 触发条件 当用户提到以下关键词时激活此技能： - "备份 OpenClaw" - "备份工作区" - "backup openclaw" - "导出配置" - "打包技能" ## 备份内容 ### 🟢 必选（默认包含，不可取消） | 文件/目录 | 说明 | |-----------|------| | `MEMORY.md` | 长期记忆 | | `memory/` | 每日记忆日志 | | `skills/` | 已安装技能 | | `SOUL.md` | 身份定义 | | `IDENTITY.md` | 角色配置 | | `USER.md` | 用户信息 | | `AGENTS.md` | Agent 配置 | | `TOOLS.md` | 工具配置 | | `HEARTBEAT.md` | 心跳任务 | ### 🟡 可选（用户选择） | 文件/目录 | 说明 | |-----------|------| | `docs/` | 文档目录 | | `logs/` | 日志文件 | | `tasks/` | 任务文件 | | `knowledge/` | 知识库 | | `.openclaw/config.json` | 配置文件 | ## 使用方法 ### 交互式备份（默认，可选择内容） ```bash python3 ~/.openclaw/workspace/skills/openclaw-backup/scripts/backup.py ``` ### 快速备份（仅必选内容） ```bash python3 ~/.openclaw/workspace/skills/openclaw-backup/scripts/backup.py --required-only ``` ### 指定输出路径 ```bash python3 ~/.openclaw/workspace/skills/openclaw-backup/scripts/backup.py --output ~/Desktop/openclaw-backup ``` ## 输出 - 生成时间戳命名的压缩包：`openclaw-backup-YYYYMMDD-HHMMSS.zip` - 包含备份清单：`BACKUP_MANIFEST.txt` - 可选：生成校验和文件 ## 恢复备份 ```bash python3 ~/.openclaw/workspace/skills/openclaw-backup/scripts/restore.py <backup.zip> ``` ## 安全提示 - ⚠️ **备份文件未加密** - ZIP 包使用标准压缩，不包含加密 - 备份文件可能包含敏感信息（API Keys、记忆等） - 建议手动加密存储（如用 7z 加密或存放在安全位置） - 不要将备份文件上传到公开仓库 - 恢复时会验证路径安全，防止 ZipSlip 攻击 --- **木木提示**：定期备份，数据安全 🌲


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: razvanmacovei/x402-cli (version 1.0.3)

--- name: x402-cli description: Pay for x402 payment-gated HTTP endpoints using USDC stablecoins version: 1.0.3 metadata: openclaw: requires: env: [EVM_PRIVATE_KEY] bins: [x402-cli] primaryEnv: EVM_PRIVATE_KEY install: - kind: brew formula: razvanmacovei/tap/x402-cli bins: [x402-cli] - kind: go package: github.com/razvanmacovei/x402-cli@latest bins: [x402-cli] homepage: https://github.com/razvanmacovei/x402-cli --- # x402-cli Pay for x402 payment-gated HTTP endpoints using USDC stablecoins. ## When to use Use this tool when you need to access an API that returns HTTP 402 Payment Required with x402 payment requirements. The tool handles the full payment flow: probe → sign → pay → return response. ## Prerequisites Set `EVM_PRIVATE_KEY` environment variable with a wallet private key that holds USDC on the target network (e.g., Base Sepolia for testnet). ## Usage ### Probe an endpoint (check price without paying) ```bash x402-cli --json --skip-verify <url> ``` Returns JSON with `probe.paymentRequirements` containing the price, network, and asset. ### Pay and access an endpoint ```bash x402-cli --json -y <url> ``` Returns JSON with `payment.body` containing the backend response and `payment.paymentResponse` containing the transaction hash. ### POST with body ```bash x402-cli --json -y -X POST -d '{"query": "hello"}' -H 'Content-Type: application/json' <url> ``` ### Self-signed TLS (local development) ```bash x402-cli --json -y -k <url> ``` ## Exit codes - `0` — Success (payment accepted or probe completed) - `1` — Error (network, config, or unexpected failure) - `2` — Payment rejected by facilitator - `3` — Route is free (no payment needed) ## JSON output structure ```json { "status": "accepted", "probe": { "statusCode": 402, "paymentRequired": true, "paymentRequirements": { "...x402 requirements..." } }, "payment": { "statusCode": 200, "accepted": true, "signer": "0x...", "paymentResponse": { "success": true, "transaction": "0x...", "network": "eip155:84532" }, "body": "...backend response..." } } ``` ## Key fields to parse - `.status` — `"free"`, `"payment_required"`, `"accepted"`, `"rejected"`, `"error"` - `.probe.paymentRequirements.accepts[0].amount` — price in atomic units - `.probe.paymentRequirements.accepts[0].network` — chain ID (e.g., `eip155:84532`) - `.payment.body` — the actual backend response after payment - `.payment.paymentResponse.transaction` — on-chain transaction hash


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