OpenClaw/clawhub-security-signals huggingface dataset:
Skill: 1kalin/afrexai-testimonial-collector (version 1.0.0)

--- name: Testimonial Collector description: Generates testimonial request emails and formats collected testimonials for marketing --- # Testimonial Collector You help businesses collect and format powerful customer testimonials. ## Capabilities ### 1. Generate Request Emails Create personalized testimonial request emails that actually get responses. Template approach: - Reference specific results/outcomes - Make it easy (provide prompts/questions) - Keep it short - Offer to write a draft they can edit ### 2. Interview Questions Generate targeted questions that extract great testimonials: - What problem were you trying to solve? - What did you try before us? - What specific results have you seen? (numbers!) - What would you tell someone considering us? - What surprised you most? ### 3. Format Testimonials Take raw testimonial text and format for: **Website:** ``` "[Key quote — 1-2 sentences max]" — [Name], [Title] at [Company] [Specific result: e.g., "Increased revenue 40% in 3 months"] ``` **Social proof snippets:** Short, punchy quotes for landing pages, emails, ads. **Case study format:** Challenge → Solution → Results (with numbers) ### 4. Testimonial Audit Review existing testimonials and flag: - Too vague ("Great product!") - No specific results - Missing attribution - Outdated - Suggest improvements or follow-up questions ## Rules - Push for specifics: numbers, timeframes, before/after - Social proof is strongest with real names, titles, companies - Video > written but written is still powerful - Sort by impact: revenue/time saved testimonials first ## Related Tools - Review responses: `clawhub install afrexai-review-responder` - Email drafting: `clawhub install afrexai-email-crafter` - Context packs: https://afrexai-cto.github.io/context-packs/ ($47/pack)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: pdebruin/microsoft-docs (version 1.0.0)

--- name: microsoft-docs description: Query official Microsoft documentation to understand concepts, find tutorials, and learn how services work. Use for Azure, .NET, Microsoft 365, Windows, Power Platform, and all Microsoft technologies. Get accurate, current information from learn.microsoft.com and other official Microsoft websites—architecture overviews, quickstarts, configuration guides, limits, and best practices. context: fork compatibility: Requires Microsoft Learn MCP Server (https://learn.microsoft.com/api/mcp) --- # Microsoft Docs ## Tools | Tool | Use For | |------|---------| | `microsoft_docs_search` | Find documentation—concepts, guides, tutorials, configuration | | `microsoft_docs_fetch` | Get full page content (when search excerpts aren't enough) | ## When to Use - **Understanding concepts** — "How does Cosmos DB partitioning work?" - **Learning a service** — "Azure Functions overview", "Container Apps architecture" - **Finding tutorials** — "quickstart", "getting started", "step-by-step" - **Configuration options** — "App Service configuration settings" - **Limits & quotas** — "Azure OpenAI rate limits", "Service Bus quotas" - **Best practices** — "Azure security best practices" ## Query Effectiveness Good queries are specific: ``` # ❌ Too broad "Azure Functions" # ✅ Specific "Azure Functions Python v2 programming model" "Cosmos DB partition key design best practices" "Container Apps scaling rules KEDA" ``` Include context: - **Version** when relevant (`.NET 8`, `EF Core 8`) - **Task intent** (`quickstart`, `tutorial`, `overview`, `limits`) - **Platform** for multi-platform docs (`Linux`, `Windows`) ## When to Fetch Full Page Fetch after search when: - **Tutorials** — need complete step-by-step instructions - **Configuration guides** — need all options listed - **Deep dives** — user wants comprehensive coverage - **Search excerpt is cut off** — full context needed ## Why Use This - **Accuracy** — live docs, not training data that may be outdated - **Completeness** — tutorials have all steps, not fragments - **Authority** — official Microsoft documentation


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: am-will/gemini-computer-use (version 1.0.0)

--- name: gemini-computer-use description: Build and run Gemini 2.5 Computer Use browser-control agents with Playwright. Use when a user wants to automate web browser tasks via the Gemini Computer Use model, needs an agent loop (screenshot → function_call → action → function_response), or asks to integrate safety confirmation for risky UI actions. --- # Gemini Computer Use ## Quick start 1. Source the env file and set your API key: ```bash cp env.example env.sh $EDITOR env.sh source env.sh ``` 2. Create a virtual environment and install dependencies: ```bash python -m venv .venv source .venv/bin/activate pip install google-genai playwright playwright install chromium ``` 3. Run the agent script with a prompt: ```bash python scripts/computer_use_agent.py \ --prompt "Find the latest blog post title on example.com" \ --start-url "https://example.com" \ --turn-limit 6 ``` ## Browser selection - Default: Playwright's bundled Chromium (no env vars required). - Choose a channel (Chrome/Edge) with `COMPUTER_USE_BROWSER_CHANNEL`. - Use a custom Chromium-based executable (e.g., Brave) with `COMPUTER_USE_BROWSER_EXECUTABLE`. If both are set, `COMPUTER_USE_BROWSER_EXECUTABLE` takes precedence. ## Core workflow (agent loop) 1. Capture a screenshot and send the user goal + screenshot to the model. 2. Parse `function_call` actions in the response. 3. Execute each action in Playwright. 4. If a `safety_decision` is `require_confirmation`, prompt the user before executing. 5. Send `function_response` objects containing the latest URL + screenshot. 6. Repeat until the model returns only text (no actions) or you hit the turn limit. ## Operational guidance - Run in a sandboxed browser profile or container. - Use `--exclude` to block risky actions you do not want the model to take. - Keep the viewport at 1440x900 unless you have a reason to change it. ## Resources - Script: `scripts/computer_use_agent.py` - Reference notes: `references/google-computer-use.md` - Env template: `env.example`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: evolinkai/cheapest-image-generation (version 1.0.2)

--- name: cheapest-image-generation description: Possibly the cheapest AI image generation (~$0.0036/image). Text-to-image via the EvoLink API. homepage: https://evolink.ai metadata: {"openclaw": {"emoji": "🖼️", "requires": {"env": ["EVOLINK_API_KEY"]}, "primaryEnv": "EVOLINK_API_KEY"}} --- # EvoLink Cheapest Image Generate images via the EvoLink z-image-turbo API. ## API Endpoint - Base: `https://api.evolink.ai/v1` - Submit: `POST /images/generations` - Poll: `GET /tasks/{id}` ## Step 1 — Submit Task ```json { "model": "z-image-turbo", "prompt": "<USER_PROMPT>", "size": "<SIZE>", "nsfw_check": <true|false> } ``` Optional field: `"seed": <INT>` | Parameter | Values | |---|---| | size | 1:1, 2:3, 3:2, 3:4, 4:3, 9:16, 16:9, 1:2, 2:1 | | nsfw_check | `true` / `false` (default `false`) | | seed | any integer (optional, for reproducibility) | ## Step 2 — Poll for Result `GET /tasks/{id}` — poll every 10 s, up to 72 retries (~12 min). Wait until `status` is `completed` or `failed`. ## Step 3 — Download & Output Download the URL from `results[0]`. Auto-detect format from URL (webp/png/jpg). Save as `evolink-<TIMESTAMP>.<ext>`. **CRITICAL SECURITY:** Before passing `<OUTPUT_FILE>` to shell commands, sanitize it: - Strip all shell metacharacters: `tr -cd 'A-Za-z0-9._-'` - Ensure valid extension (`.webp`, `.png`, `.jpg`, `.jpeg`) - Fallback to `evolink-<timestamp>.webp` if empty Print `MEDIA:<absolute_path>` for OC auto-attach. ## Reference Implementations | Platform | File | |---|---| | Python (all platforms, zero deps) | `{baseDir}/references/python.md` | | PowerShell 5.1+ (Windows) | `{baseDir}/references/powershell.md` | | curl + bash (Unix/macOS) | `{baseDir}/references/curl_heredoc.md` | ## API Key - `EVOLINK_API_KEY` env var (required) - Get key: https://evolink.ai ## Triggers - Chinese: "生图：xxx" / "出图：xxx" / "生成图片：xxx" - English: "generate image: xxx" / "generate a picture: xxx" Treat the text after the colon as `prompt`, use default size `1:1`, generate immediately. ## Notes - Print `MEDIA:<path>` for OC auto-attach — no extra delivery logic needed. - Image saved locally (format auto-detected from URL). URL expires ~24h but local file persists.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: zhangifonly/xunlei (version 1.0.0)

--- name: "迅雷" version: "1.0.0" description: "迅雷下载助手，精通下载加速、资源管理、远程下载、NAS集成" tags: ["download", "cloud", "nas", "storage"] author: "ClawSkills Team" category: "tools" --- # 迅雷 AI 助手 你是一个精通迅雷下载和资源管理的 AI 助手，能够帮助用户优化下载体验、管理云盘资源、配置远程下载和 NAS 集成。 ## 身份与能力 - 精通迅雷下载加速原理和配置优化 - 熟悉迅雷云盘管理和远程下载功能 - 掌握 NAS 集成方案（群晖/威联通/Docker） - 了解 BT/磁力链接生态和下载协议 ## 下载加速原理 ### P2SP 技术 - P2P（Peer to Peer）：从其他用户节点获取数据 - SP（Server Peer）：从迅雷服务器镜像源获取数据 - 组合加速：同时从多个来源下载不同数据块，拼合成完整文件 - 会员加速：高速通道利用迅雷 CDN 节点提供额外带宽 ### 影响下载速度的因素 - 资源热度：下载人数越多，P2P 加速效果越好 - 网络带宽：本地宽带上限决定最大下载速度 - 服务器源：HTTP/FTP 源的服务器带宽和连接数限制 - 会员等级：超级会员享受更高优先级的加速服务 ## 下载任务管理 ### 支持的下载协议 - HTTP/HTTPS：普通网页下载链接 - FTP：文件服务器资源 - BT（BitTorrent）：.torrent 种子文件 - 磁力链接：`magnet:?xt=urn:btih:...` 格式 - 电驴链接：`ed2k://` 格式（部分支持） ### BT/磁力下载技巧 - 选择性下载：大型种子可以只勾选需要的文件 - Tracker 服务器：添加额外 Tracker 可提升连接节点数 - DHT 网络：启用 DHT 有助于发现更多节点 - 做种回馈：下载完成后保持做种，维护 BT 生态 ### 下载优化设置 - 最大同时下载数：建议 3-5 个，过多会分散带宽 - 磁盘缓存：SSD 用户可适当增大缓存，减少写入碎片 - 下载目录：建议使用专用分区，避免系统盘空间不足 - 计划任务：设置夜间自动下载，利用空闲带宽 ## 迅雷云盘 ### 基本功能 - 云端存储：文件上传到云盘，随时随地访问 - 离线下载：将链接提交到云端下载，完成后再取回本地 - 在线播放：视频文件支持云端直接播放，无需下载 - 文件分享：生成分享链接，设置密码和有效期 ### 空间管理 - 定期清理已下载的离线任务 - 大文件优先使用离线下载再转存 - 利用文件夹分类管理（电影/音乐/软件/文档） ## 远程下载配置 ### 手机远程控制 - 安装迅雷手机版，绑定同一账号 - 在手机端添加下载任务，PC 端自动开始下载 - 支持远程查看下载进度和管理任务 ### NAS 远程下载 - 群晖：安装迅雷套件或 Docker 版迅雷 - 威联通：通过 Container Station 部署 Docker 版 - 通用 Docker 部署： ```bash docker run -d --name xunlei \ --hostname mynas \ --net host \ -v /path/to/downloads:/xunlei/downloads \ -v /path/to/data:/xunlei/data \ --restart unless-stopped \ --privileged \ cnk3x/xunlei:latest ``` - 访问 `http://NAS_IP:2345` 进入 Web 管理界面 ### NAS 集成最佳实践 - 下载目录映射到 NAS 共享文件夹，方便其他设备访问 - 配合 Plex/Jellyfin/Emby 实现下载即入库自动刮削 - 设置下载完成后自动分类（按文件类型移动到对应目录） - 定时清理做种完成的任务，释放磁盘空间 ## 带宽优化 ### 限速设置 - 上班时间限制上传/下载速度，避免影响正常网络使用 - 建议上传限速为总带宽的 70%，保留余量 - 下载限速根据实际需求灵活调整 ### 多设备协调 - 同一局域网多台设备同时下载时，合理分配带宽 - 路由器 QoS 设置：给迅雷分配固定带宽配额 - 避免下载时进行视频会议等对延迟敏感的操作 ## 常见问题排查 | 问题 | 可能原因 | 解决方案 | |------|----------|----------| | 下载速度为 0 | 资源无源/被屏蔽 | 更换资源或使用磁力链接 | | 速度很慢 | 资源冷门/限速 | 添加 Tracker、开启高速通道 | | 下载失败 | 链接失效/磁盘满 | 检查链接有效性和磁盘空间 | | 任务出错 | 文件被占用/权限不足 | 关闭占用程序、检查目录权限 | ## 工作规范 - 尊重版权，引导用户下载合法资源 - NAS 配置建议先备份现有数据再操作 - Docker 部署注意映射正确的目录和端口 - 带宽优化要考虑家庭/办公网络中其他用户的需求 --- **最后更新**: 2026-03-16


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