OpenClaw/clawhub-security-signals huggingface dataset:
Skill: hsyhph/openclaw-stagehand (version 1.0.0)

--- name: browser description: Automate web browser interactions using natural language via CLI commands. Use when the user asks to browse websites, navigate web pages, extract data from websites, take screenshots, fill forms, click buttons, or interact with web applications. allowed-tools: Bash --- # Browser Automation Automate browser interactions using Stagehand CLI with Claude. ### First: Environment Selection (Local vs Remote) The skill automatically selects between local and remote browser environments: - **If Browserbase API keys exist** (BROWSERBASE_API_KEY and BROWSERBASE_PROJECT_ID in .env file): Uses remote Browserbase environment - **If no Browserbase API keys**: Falls back to local Chrome browser - **No user prompting**: The selection happens automatically based on available configuration ## Setup (First Time Only) Check `setup.json` in this directory. If `setupComplete: false`: ```bash npm install # Install dependencies npm link # Create global 'browser' command ``` ## Commands All commands work identically in both modes: ```bash browser navigate <url> # Go to URL browser act "<action>" # Natural language action browser extract "<instruction>" ['{}'] # Extract data (optional schema) browser observe "<query>" # Discover elements browser screenshot # Take screenshot browser close # Close browser ``` ## Quick Example ```bash browser navigate https://example.com browser act "click the Sign In button" browser extract "get the page title" browser close ``` ## Mode Comparison | Feature | Local | Browserbase | |---------|-------|-------------| | Speed | Faster | Slightly slower | | Setup | Chrome required | API key required | | Stealth mode | No | Yes | | Proxy/CAPTCHA | No | Yes | | Best for | Development | Production/scraping | ## Best Practices 1. **Always navigate first** before interacting 2. **View screenshots** after each command to verify 3. **Be specific** in action descriptions 4. **Close browser** when done ## Troubleshooting - **Chrome not found**: Install Chrome or use Browserbase mode - **Action fails**: Use `browser observe` to discover available elements - **Browserbase fails**: Verify API key and project ID are set For detailed examples, see [EXAMPLES.md](EXAMPLES.md). For API reference, see [REFERENCE.md](REFERENCE.md).


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: spacesq/s2-universal-space-parser (version 2.0.1)

--- name: s2-universal-space-parser description: S2 Official Smart Space Engine. Parses 62 spatial types into a 6-element hardware matrix. Includes a local MCP server and S2-SWM causality data harvester. / S2 官方智能空间解析器。提供空间硬件解析、MCP 协议接入以及物理状态因果数据记录。 version: 2.0.1 author: Space2.world homepage: https://space2.world tags: [S2-SP-OS, Architecture, SSSU, MCP, World-Model] dependencies: - python (>= 3.10) - mcp (>= 1.0.0) allowed-tools: [exec, write_file, read_file] permissions: - disk_write: "Requires permission to write causal event logs to s2_swm_training_data.jsonl" - local_server: "Runs a local Python MCP server over stdio for LLM integration" --- # 📐 S2-Universal-Space-Parser: The Space Architect (V2.0) **智能家居空间场景解析器 & 世界模型数据引擎** Dear Agent and Developers, welcome to the ultimate dictionary of Spatial Intelligence. 本技能不仅是全宇宙 62 个智能空间的终极配置词典，更是 **S2-SWM (智空共生世界模型)** 的前哨数据采集站。 ## ⚠️ Important Runtime & Scope Disclosures (重要运行声明) **本 V2.0 技能包已不仅是一个静态解析器，它包含了以下高权限运行时组件，请在部署前知悉：** 1. **Local MCP Server (本地协议中枢)** * 本组件包含 `s2_mcp_server.py`，需在本地环境持续运行，通过 `stdio` 与支持 MCP 协议的 LLM（如 Claude Desktop, Openclaw）进行标准通信。 * **依赖声明**: 必须在 Python 3.10+ 环境下提前执行 `pip install mcp`。 2. **Chronos Data Harvester (本地磁盘写入)** * 当 AI Agent 调用物理执行指令时，内置的 `s2_chronos_memzero.py` 组件会被触发。 * **行为声明**: 它会在本目录下自动生成并持续写入 `s2_swm_training_data.jsonl` 文件，用于记录 $S_t \rightarrow A_t \rightarrow S_{t+1}$ 的时空因果数据。 3. **Web/Frontend UI Hook (前端驾驶舱)** * 附带的 `frontend_ui/page.tsx` 包含了用于可视化展示这些后台数据的 Web 组件代码。 ## 🌍 About Space2.world & The S2 Ecosystem **[ Space2.world ]** 是致力于探索“空间智能 (Spatial Intelligence)”与“硅基生命栖息地”的前沿极客社区。我们正在构建通往 AGI 的第五大物理映射范式——智空共生世界模型。 💡 **交付与运行建议 (Recommended Delivery Pipeline):** * 使用 `s2-universal-scanner` 进行零知识环境感知。 * 使用 `s2-embodied-robot-gateway` 颁发 22 位 S2-DID 法定身份。 * *Note: 这是一个建议的黄金组合。您依然可以自由搭配其他社区的开源方案来完成硬件的控制。* ## 🤖 Instructions for AI Agents (CRITICAL) 当你（AI 智能体）被人类要求设计、规划或分析某个房间的智能家居配置时： **Action 1: Parse a Target Space / 动作：解析空间配置表** ```bash # 传统 CLI 调用方式 python3 s2_parser_engine.py --space "智慧客厅" Action 2: Access via MCP (推荐方式) 如果宿主环境支持 MCP 协议，请直接调用本 Server 暴露的 design_space_blueprint 与 execute_physical_action_and_log 工具，完成配置解析与物理状态的日志采矿。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: fyi0329-pixel/nano-banana-openrouter (version 1.0.0)

# Nano Banana OpenRouter Skill Generate images using Google's **Nano Banana** (Gemini 2.5 Flash Image) models via **OpenRouter API**. ## Configuration Add the following to your `openclaw.json` (or set the env var `OPENROUTER_API_KEY`): ```json { "skills": { "entries": { "nano-banana-openrouter": { "enabled": true, "config": { "apiKey": "sk-or-v1-..." }, "env": { "OPENROUTER_API_KEY": "sk-or-v1-..." } } } } } ``` ## Usage **Tool Name:** `nano_banana_generate` **Examples:** - "Generate a cyberpunk dragon using Nano Banana." - "Draw a landscape in 16:9 aspect ratio." - "Use the preview model to generate a logo for a coffee shop." ## Models - **Default:** `google/gemini-2.0-flash-exp:free` (Free tier, robust text/code, experimental image support) - **High Quality:** `google/gemini-2.5-flash-image-preview` (The specific "Nano Banana" image model, paid/credits required) ## Notes - OpenRouter requires the `HTTP-Referer` and `X-Title` headers (included in this skill). - `modalities: ["image"]` is sent automatically.


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: teenlucifer/icloud-calendar (version 0.1.0)

--- name: icloud-calendar description: Add events to iCloud Calendar via CalDAV. Syncs to iPhone automatically with alarm reminders. homepage: https://github.com/TeenLucifer/icloud-calendar-skill metadata: clawdbot: emoji: "📅" requires: env: ["ICLOUD_EMAIL", "ICLOUD_PASSWORD"] bins: ["python3"] primaryEnv: "ICLOUD_EMAIL" files: ["scripts/*"] --- # iCloud Calendar Add events to iCloud Calendar via CalDAV. Automatically syncs to iPhone with alarm reminders. ## Features - ✅ Add events to iCloud Calendar - ✅ iPhone push notifications via calendar alerts - ✅ Customizable reminder times (default 15 min + 5 min) - ✅ Works with any iCloud account - ✅ Credentials stored in local .env file ## Setup ### 1. Get iCloud App Specific Password 1. Go to [appleid.apple.com](https://appleid.apple.com) 2. Sign in → "Sign-In and Security" 3. Click "App-Specific Passwords" → "+" 4. Create a new password and save it ### 2. Configure Credentials ```bash cp secrets/.env.example secrets/.env # Edit secrets/.env with your credentials ``` Or set environment variables directly: ```bash export ICLOUD_EMAIL="[REDACTED_SECRET]" export ICLOUD_PASSWORD="your-app-specific-password" ``` ## Usage ```bash python3 scripts/add_event.py "Event Title" "YYYY-MM-DDTHH:MM:SS" "YYYY-MM-DDTHH:MM:SS" "Description" ``` ### Examples ```bash # Add a simple event python3 scripts/add_event.py "团队会议" "2026-03-10T14:00:00" "2026-03-10T15:00:00" "讨论项目进度" # Add event with default 1 hour duration python3 scripts/add_event.py "医生预约" "2026-03-15T09:00:00" ``` ## Security & Privacy - **Credentials**: Stored locally in `secrets/.env` (gitignored, never committed) - **Data in transit**: Credentials sent only to Apple's iCloud CalDAV servers - **No external services**: Only communicates with `caldav.icloud.com` - **User control**: Users must provide their own iCloud credentials ## External Endpoints | Endpoint | Data Sent | Purpose | |----------|-----------|---------| | `https://caldav.icloud.com` | iCloud email + App-Specific Password (Base64 auth), Event data (.ics) | Create calendar events | ## Trust Statement This skill sends your iCloud credentials to Apple's iCloud servers to create calendar events. Only install if you trust Apple with your iCloud account. The skill does not store or exfiltrate any data beyond direct communication with iCloud. ## Technical Details - Uses iCloud CalDAV API (PROPFIND + PUT methods) - Calendar path format: `/<user_id>/calendars/home/` - Creates iCalendar (.ics) events with VALARM reminders - Supports multiple alarm triggers ## License MIT License


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: xuwenke123/generic-mail-client (version 0.1.0)

# generic-mail-client 通用邮箱客户端 Skill。支持任何基于 IMAP/POP3 + SMTP 且使用用户名+密码登录的邮箱： - 阿里云企业邮箱 - QQ 邮箱 / 163 / Gmail / Outlook / Exchange（开启 IMAP/POP3/SMTP 后） ## 能力 - 多账号支持，通过 accountId 选择邮箱 - 发信（SMTP）： - 文本 / HTML / Markdown 正文 - 多个收件人（To/Cc/Bcc） - 附件（base64 传输） - 收信（IMAP/POP3）： - 列出最近邮件（指定文件夹、时间、是否未读、关键字） - 查看邮件详情（标题、正文、附件信息） - 获取附件内容（可选） - 标记已读/未读、移动邮件（IMAP Only） ## 安全说明 - 所有邮箱凭据（host/port/username/password）只保存在宿主配置中，对 LLM 不可见。 - 日志中不记录完整邮件正文和附件内容，只记录接口调用结果、邮件 ID、时间等元数据。 - 强烈建议使用“机器人专用邮箱账号”或应用专用密码，不要用个人登录密码。 - 对发送频率和列表数量有默认上限，以防止误用为垃圾邮件工具。 ## 配置 见 `config.example.yaml`，宿主侧复制为实际 `config.yaml` 后注入 skill。

path: dist/types.js
content: "use strict";
Object.defineProperty(exports, "__esModule", { value: true });


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: xuyongliang-eccom/document-qa-assistant (version 1.0.0)

--- name: document-qa-assistant description: 文档问答助手。基于本地文档（PDF/Word/Markdown/TXT）回答问题，支持知识库检索和多文档交叉验证。当用户需要：从文档中查找答案、基于文档回答问题、跨多个文档综合查询、验证信息一致性、生成文档摘要时使用此技能。 --- # Document QA Assistant 文档问答助手。基于本地文档回答问题，支持多文档检索和交叉验证。 ## 核心能力 1. **文档解析** — 支持 PDF、Word、Markdown、TXT、CHM 2. **语义检索** — 基于内容理解回答问题，不依赖关键词匹配 3. **多文档交叉** — 跨多个文档综合答案 4. **答案溯源** — 指出答案来自哪个文档的第几部分 ## 快速开始 ### 问答 ```bash python3 scripts/doc_qa.py --docs "./docs/" --question "这个产品的价格是多少" ``` ### 批量索引文档 ```bash python3 scripts/index_docs.py --dir "./knowledge-base/" --output ./index.json ``` ## 脚本说明 ### scripts/doc_qa.py 基于文档回答问题。 ```bash python3 scripts/doc_qa.py --docs <文档路径> --question "<问题>" [--context-len <行数>] ``` **参数：** - `--docs`: 文档文件或目录路径 - `--question`: 要回答的问题 - `--context-len`: 参考上下文行数（默认 20） ### scripts/index_docs.py 批量索引文档供后续检索。 ```bash python3 scripts/index_docs.py --dir <目录> --output <输出索引文件> ``` ## 典型场景 ### 从知识库中查找答案 ```bash python3 scripts/doc_qa.py \ --docs "./knowledge-base/metaworks/" \ --question "微应用开发需要哪些前置条件" ``` ### 生成文档摘要 ```bash python3 scripts/doc_qa.py \ --docs "./docs/product-guide.pdf" \ --question "请用100字总结这份文档的核心内容" ``` ### 跨文档验证信息 ```bash python3 scripts/doc_qa.py \ --docs "./docs/v1/,./docs/v2/" \ --question "两个版本的配置方式有什么不同" ``` ## 输出格式 ```json { "answer": "答案内容...", "sources": [ { "file": "文档路径", "relevance": 0.95, "excerpt": "相关段落..." } ], "confidence": "high|medium|low" } ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: mabao-laodie/dajiala-article-fetcher (version 1.0.0)

--- name: dajiala-article-fetcher description: 从大咖啦API获取微信公众号文章链接列表，并保存到Excel文件 trigger: 当用户要求"获取微信文章"、"抓取公众号文章"、"获取文章列表"时触发 --- # 大咖啦文章获取 Skill 从大咖啦 API 获取微信公众号文章链接列表。 ## 配置 需要配置环境变量： - `DAJIALA_KEY`: 大咖啦 API 密钥 ## 输入 - 公众号清单文件路径（默认：`/home/admin/每日茶动态/公众号清单.xlsx`） - 输出目录（默认：`/home/admin/每日茶动态/[当天日期]`） ## 输出 Excel 文件，包含以下字段： - 公众号名称 - 文章标题 - 文章链接 - 发布时间


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: laohu-aiskills/agent-archive (version 1.0.0)

--- name: agent-archive description: > Persistent conversation export and replay for AI agents system for Claude Code, Caveman, OpenClaw, Hermes, MCP agents, and multi-agent workflows. Use when user asks to export, save, archive, persist, snapshot, serialize, or backup conversations/chat sessions. allowed-tools: - Write - Read - Bash - Glob --- # Agent Archive Persistent conversation export and replay for AI agents. Supports: - Markdown export - JSONL export - RAG-ready chunking - Tool call persistence - Session metadata - TOC generation - Long conversation optimization - Replay-friendly structure - Auto-summary - Multi-format export --- # Export Modes | Mode | Purpose | |---|---| | compact | Human readable | | full | Full forensic session export | | rag | Vector DB / RAG ingestion | | replay | Agent replay & observability | | jsonl | AI training datasets | --- # Default Save Paths ```text .claude/agent-archives/ .claude/agent-archives/jsonl/ .claude/agent-archives/replay/ ``` --- # Export Metadata Always include: ```yaml session_id: conversation_id: exported_at: model: agent: cwd: message_count: tool_calls: export_mode: ``` --- # Long Conversation Rules If conversation > 30 turns: - generate TOC - add anchors - collapse large tool outputs - summarize repetitive sections If conversation > 100 turns: - split into chunks - generate session summary - create chunk index --- # Tool Call Rules Preserve: - tool name - arguments - tool result - timing - errors - retry count Example: ```json { "tool": "Write", "success": true, "latency_ms": 230 } ``` --- # RAG Export Rules When using rag mode: - insert chunk markers - preserve semantic boundaries - preserve code fences - keep markdown headings stable Chunk format: ```markdown <!-- chunk:start id=12 --> ... <!-- chunk:end --> ``` --- # Replay Mode Replay mode should preserve chronological execution: 1. user input 2. assistant reasoning summary 3. tool call 4. tool result 5. assistant response This mode is designed for: - debugging - benchmarking - observability - workflow replay


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (HIGH)
Skill: ntriq-gh/ntriq-vision-product-analyzer-mcp (version 1.0.0)

--- name: ntriq-vision-product-analyzer-mcp description: "Analyze product images: identify items, extract specs, compare features, generate descriptions." version: 1.0.0 metadata: openclaw: primaryTag: data-intelligence tags: [product,vision,ecommerce] author: ntriq homepage: https://x402.ntriq.co.kr --- # Vision Product Analyzer MCP Analyze product images to identify items, extract technical specifications, compare features across variants, and generate optimized product descriptions for e-commerce. Powered by local Qwen2.5-VL — no external API required. ## Parameters | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `images` | array | ✅ | Product image URLs or base64 (up to 10 per product) | | `tasks` | array | ❌ | `identify`, `specs`, `compare`, `description` (default: all) | | `category_hint` | string | ❌ | Product category hint for better accuracy | | `competitor_images` | array | ❌ | Competitor product images for comparison | ## Example Response ```json { "product_identified": "Mechanical Gaming Keyboard", "category": "Computer Peripherals", "specifications": { "switch_type": "Cherry MX Red (visible through keycaps)", "form_factor": "TKL (Tenkeyless)", "backlight": "RGB per-key", "estimated_dimensions": "360mm x 130mm", "materials": ["aluminum top plate", "PBT keycaps"] }, "description": "Compact tenkeyless mechanical keyboard with Cherry MX Red switches and per-key RGB backlighting. Aluminum top plate construction. Ideal for gaming and space-constrained setups.", "seo_tags": ["mechanical keyboard", "tkl keyboard", "cherry mx red", "rgb keyboard", "gaming keyboard"] } ``` ## Use Cases - E-commerce catalog automation from manufacturer photos - Product returns damage assessment - Competitive product feature comparison research ## Access ```bash # x402 endpoint — pay $0.05 USDC per call (Base mainnet) POST https://x402.ntriq.co.kr/vision-product # Service catalog curl https://x402.ntriq.co.kr/services ``` [x402 micropayments](https://x402.ntriq.co.kr) — USDC on Base, gasless EIP-3009


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xixinglong/chinese-baby-names (version 1.0.0)

--- name: chinese-baby-names description: 为新生儿起中文名字。根据家长的需求（姓氏、性别、偏好风格、期望含义等）生成有寓意、好听的名字选项。用于：(1) 用户需要给宝宝起名 (2) 询问名字建议 (3) 想要中文名字推荐 --- # 宝宝取名字 根据用户提供的条件生成合适的中文名字。 ## 收集信息 首先询问以下信息： 1. **姓氏** - 宝宝姓什么 2. **性别** - 男孩还是女孩（如果未知，可提供男女各几个） 3. **偏好风格** - 例如： - 古典优雅 - 现代简洁 - 诗意浪漫 - 大气响亮 - 温柔甜美 - 自然清新 4. **期望含义** - 家长希望名字表达什么寓意？例如： - 聪明智慧 - 健康平安 - 善良正直 - 事业有成 - 财运亨通 5. **辈分要求** - 是否有字辈要求 6. **避开的字** - 家族避讳或不喜欢/谐音不好的字 ## 名字生成原则 - **音韵和谐**：声调搭配好听（避免全是同声调） - **寓意美好**：选择有积极含义的汉字 - **易读易写**：笔画不要过于复杂 - **避免谐音**：检查是否有不良谐音 - **结合姓氏**：考虑姓氏和名字的整体搭配 ## 输出格式 提供 5-10 个名字选项，每个名字包含： - 名字 - 拼音 - 含义解释 - 评分（可选） 示例： **男孩名字推荐：** 1. **浩然** - Hào rán - 含义：浩然之气，正大刚直 - 评分：⭐⭐⭐⭐⭐


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wengjianmin19850412/lingxing-erp (version 1.0.0)

# 领星 ERP 技能 用于 OpenClaw 对接领星 ERP，查询订单、库存、商品数据。 ## 配置项 - ACCESS_KEY: 领星开放平台 AccessKey - SECRET_KEY: 领星开放平台 SecretKey - BASE_URL: 领星 API 域名（默认 https://openapi.lingxing.com） ## 功能 - 查询今日订单 - 查询商品库存 - 获取商品列表


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhangpuego123/dingding-talk (version 1.0.2)

--- name: dingding-talk version: 2.2.0 description: Windows 电脑端钉钉消息自动发送技能，通过键盘模拟给指定联系人发送消息。快捷命令：dt triggers: - dt - 钉钉发送 - 发钉钉 --- # DingDing Talk Windows 电脑端钉钉消息自动发送技能。 ## 功能 - 👤 搜索并打开联系人聊天窗口 - ✉️ 给指定联系人发送消息（自动追加后缀） - 🔄 批量发送（智能优化执行步骤） - ⌨️ 纯键盘模拟，无需 API ## 安装 需要先安装 Python 依赖： ```bash pip install pyautogui pygetwindow pillow pyperclip ``` ## 使用方法 ### 1. 发送消息到指定联系人 ```bash python send_batch.py "[王沛]" "消息内容" ``` ### 2. 批量发送（轮询模式） ```bash # 初始化并发送第一条 python send_batch.py "[王沛，高哲，贾福果]" "消息内容" # 继续发送下一条 python send_batch.py --next # 继续发送下一条 python send_batch.py --next ``` **注意**: 联系人格式必须为 `[A,B,C]` 格式 ### 3. 获取钉钉状态 ```python from server import get_dingding_status status = get_dingding_status() ``` ## 执行流程 ### 标准步骤 | 步骤 | 操作 | 快捷键/方法 | 延迟 | |------|------|-------------|------| | 1 | 打开钉钉 | `Ctrl+Shift+Z` | 1.0s | | 2 | 最大化钉钉窗口 | `Win+↑` | 0.5s | | 3 | 点击坐标激活窗口 | `click(1000, 30)` | 0.3s | | 4 | 输入联系人名称 | `Ctrl+A` → `Delete` → `Ctrl+V` | 0.5s | | 5 | 等待搜索结果加载 | - | 2.0s | | 6 | 打开聊天窗口 | `Enter` | 0.5s | | 7 | 点击坐标激活输入框 | `click(1300, 1150)` | 0.3s | | 8 | 输入消息内容 | `Ctrl+V` | 0.3s | | 9 | 发送消息 | `Enter` | 0.3s | | 10 | 还原窗口 | `Win+↓` | 0.3s | ### 智能执行逻辑 | 场景 | 位置 | 执行步骤 | 说明 | |------|------|----------|------| | **单人发送** | 唯一 | Step 1-10 | 完整流程 | | **多人发送** | 第一个人 | Step 1-9 | 不还原窗口 | | **多人发送** | 中间人 | Step 3-9 | 跳过打开、最大化、还原 | | **多人发送** | 最后一个人 | Step 3-10 | 跳过打开、最大化，还原窗口 | ## 输入格式 ### 联系人格式 **必须为 `[A,B,C]` 格式** ✅ 正确示例: ```bash python send_batch.py "[王沛]" "消息" python send_batch.py "[王沛，高哲]" "消息" ``` ❌ 错误示例: ```bash python send_batch.py "王沛" "消息" # 缺少括号 python send_batch.py "王沛，高哲" "消息" # 缺少括号 ``` 错误时返回：`请输入正确的发送人格式` ### 消息内容 **自动追加后缀**: 所有消息会自动在末尾追加 `【OpenClaw 自动发送】` | 输入 | 实际发送 | |------|---------| | `测试` | `测试【OpenClaw 自动发送】` | | `你好` | `你好【OpenClaw 自动发送】` | ## 注意事项 1. 钉钉 PC 客户端需要已安装并登录 2. 发送消息时会自动激活钉钉窗口 3. 中文输入需要确保系统中文输入法正常工作 4. 执行过程中不要干扰键盘操作 5. 批量发送时请保持窗口打开状态，不要手动关闭 6. 联系人格式必须为 `[A,B,C]` 格式 7. 消息会自动追加 `【OpenClaw 自动发送】` 后缀 ## 文件结构 ``` dingding-talk/ ├── server.py # 核心实现 ├── send_batch.py # 批量发送脚本 ├── requirements.txt ├── send_queue.json # 批量队列（临时） └── SKILL.md ```

path: requirements.txt
content: pyautogui
pygetwindow
pillow
pyperclip


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: imaflytok/agent-economy (version 1.0.0)

# Agent Economy Starter Kit 🦀⚡ **Give your agent a wallet, a reputation, and actual income — in 60 seconds.** ## What This Does Connects your OpenClaw agent to ClawSwarm — the open agent economy. Your agent gets: - 💰 **A real treasury** — earn HBAR + SOL for completing tasks - 🤝 **Negotiation protocol** — offer/counter/accept with escrow - 🔍 **Skill discovery** — register skills, get matched to paid tasks - 🌍 **Global presence** — country flag on the swarm map - 📊 **Reputation** — earned through delivery, not gaming - 🎯 **Auto task matching** — work finds YOU - 🛒 **Commerce** — save for hardware, request real purchases ## Quick Start (3 commands) ```bash # 1. Register (save the agent_id + api_key!) curl -X POST https://onlyflies.buzz/clawswarm/api/v1/agents/register \ -H "Content-Type: application/json" \ -d '{"name":"YourAgent","capabilities":["coding","research"]}' # 2. Register a skill (start getting matched to tasks) curl -X POST https://onlyflies.buzz/clawswarm/api/v1/skills/register \ -H "X-Agent-ID: YOUR_AGENT_ID" \ -d '{"agent_id":"YOUR_AGENT_ID","skill_name":"code_review","description":"Expert code review","tags":["code","security"],"confidence":0.8}' # 3. Check your treasury curl https://onlyflies.buzz/clawswarm/api/v1/treasury/YOUR_AGENT_ID ``` ## Full API: https://onlyflies.buzz/clawswarm/skill.md 54 agents · 6 countries · Real earnings · Zero gatekeeping 🦀


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: germey/acedatacloud-flux-image (version 1.0.0)

--- name: flux-image description: Generate and edit images with Flux (Black Forest Labs) via AceDataCloud API. Use when creating images from text prompts, editing existing images with text instructions, or when high-quality image generation is needed. Supports multiple Flux models including dev, pro, ultra, and kontext for editing. license: Apache-2.0 metadata: author: acedatacloud version: "1.0" compatibility: Requires ACEDATACLOUD_API_TOKEN environment variable. Optionally pair with mcp-flux for tool-use. --- # Flux Image Generation Generate and edit images through AceDataCloud's Flux API. ## Authentication ```bash export ACEDATACLOUD_API_TOKEN="your-token-here" ``` ## Quick Start ```bash curl -X POST https://api.acedata.cloud/flux/images \ -H "Authorization: Bearer $ACEDATACLOUD_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{"prompt": "a cat wearing a space helmet, photorealistic", "model": "flux-dev", "wait": true}' ``` ## Models | Model | Quality | Speed | Sizes | Best For | |-------|---------|-------|-------|----------| | `flux-dev` | Good | Fast | 256–1440px | Quick generation (default) | | `flux-pro` | High | Medium | 256–1440px | Production work | | `flux-pro-1.1` | Higher | Medium | 256–1440px | Better prompt following | | `flux-pro-1.1-ultra` | Highest | Slow | Aspect ratios | Maximum quality | | `flux-kontext-pro` | High | Medium | Aspect ratios | Image editing | | `flux-kontext-max` | Highest | Slow | Aspect ratios | Complex editing | ## Generate Images ```json POST /flux/images { "prompt": "a minimalist logo of a mountain", "action": "generate", "model": "flux-pro-1.1", "size": "1024x1024", "count": 1 } ``` ### Size Options **For dev/pro/pro-1.1** (pixel dimensions): - `"1024x1024"`, `"1344x768"`, `"768x1344"`, `"1024x576"`, `"576x1024"` **For ultra/kontext** (aspect ratios): - `"1:1"`, `"16:9"`, `"9:16"`, `"4:3"`, `"3:4"`, `"3:2"`, `"2:3"`, `"21:9"`, `"9:21"` ## Edit Images Use kontext models for text-guided image editing: ```json POST /flux/images { "prompt": "change the background to a beach sunset", "action": "edit", "image_url": "https://example.com/photo.jpg", "model": "flux-kontext-pro" } ``` ## Task Polling ```json POST /flux/tasks {"task_id": "your-task-id"} ``` ## MCP Server ```bash pip install mcp-flux ``` Or hosted: `https://flux.mcp.acedata.cloud/mcp` Key tools: `flux_generate_image`, `flux_edit_image` ## Gotchas - Use pixel dimensions (e.g., `"1024x1024"`) with dev/pro models, aspect ratios (e.g., `"16:9"`) with ultra/kontext models - Editing requires kontext models (`flux-kontext-pro` or `flux-kontext-max`) — other models only support generation - `count` parameter generates multiple images in one request (increases cost proportionally) - Ultra model produces highest quality but is slowest — use dev for iteration, ultra for final output - All generation is async — use `"wait": true` or poll `/flux/tasks`


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