OpenClaw/clawhub-security-signals huggingface dataset:
Skill: xpneuma/web-browsing-bak (version 1.0.0)

--- name: web-browsing description: Browse and summarize websites, extract content from URLs, search the web for information. Use when user asks to visit a website, get webpage content, search online, or find current information that requires browsing the internet. --- # Web Browsing Skill This skill enables browsing websites, extracting content from URLs, and searching the web for information. ## When to Use **Use this skill when:** - User asks you to visit a website (e.g., "Check out example.com") - User wants webpage content summarized - User needs current information that requires internet search - User provides a URL and wants it analyzed - User asks to search for something online ## How It Works ### 1. Direct URL Access When given a specific URL: ```markdown User: "What's on https://example.com?" → Visit the page, extract main content, summarize key points ``` ### 2. Web Search When user asks to search: ```markdown User: "Find information about climate change" → Perform web search, present top results with summaries ``` ### 3. Content Extraction For specific data extraction: ```markdown User: "Get the latest news from techcrunch.com" → Navigate to site, extract relevant articles/headlines ``` ## Tools Available - **web_search**: Search the web for information - **fetch_url**: Visit and retrieve webpage content - **extract_content**: Parse HTML and extract structured data ## Best Practices 1. **Be specific** - Tell me what you want from the page (summary, specific data, latest news) 2. **Provide URLs** - If you have a specific page, share the URL directly 3. **Clarify intent** - Let me know if you need: - Quick summary - Detailed analysis - Specific data points - Latest updates ## Examples ```markdown ✅ "Visit https://news.ycombinator.com and summarize today's top stories" ✅ "Search for the latest React.js tutorial" ✅ "Check what's on Wikipedia's page about quantum computing" ✅ "Find pricing information from apple.com/iphone" ❌ Just say "browse the web" - be more specific! ``` ## Limitations - Cannot interact with JavaScript-heavy sites (may miss dynamic content) - Some sites block automated access - Video/audio content cannot be played, only described if available - Login-required pages won't work without credentials --- **Ready to browse!** Just give me a URL or tell me what to search for. 🌐


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mcdoolz/gemini-workflows (version 1.0.0)

--- name: gemini-workflows description: Use Gemini CLI for deep thinking, planning, workflow design, and non-code desktop task orchestration. --- # Gemini Workflows: Planning + Deep Thinking ## Routing Rule - Planning, strategy, analysis, synthesis: gemini - Web search (direct Google): gemini - Research + code review: gemini - Code implementation, refactors, tests: Claude CLI (daedalus-code skill) ## Commands gemini # deep planning flash # fast iteration ## Prompt Template Goal: <objective> Context: <constraints, tools, timeline> Deliverable: <format: checklist/table/plan> Decision criteria: <ranking logic> Output style: concise, actionable, execution-ready ## Handoff to Claude 1. Freeze scope + acceptance criteria 2. Hand to Claude via daedalus-code skill 3. Require validation outputs before completion


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: pshen613/explain-simply (version 1.0.0)

--- name: 深入浅出讲明白 description: 用七步法结构化讲清楚复杂概念、技术、产品，写出通俗易懂的干货文章。使用当需要解释新技术、科普知识、撰写方法总结，要求文章结构清晰、普通人能看懂。 --- # "深入浅出讲明白" Skill ## 使用说明 当需要把一个复杂概念、技术、产品讲明白，写成结构化文章时，用这个七步法框架。它能保证文章结构清晰、普通人能看懂、有价值感。 适用场景： - 解释新技术新概念（比如 AI 领域新插件、新框架） - 科普复杂知识 - 产品介绍、方法总结 - 给普通人看的干货文章 不适用场景： - 内部技术文档（不需要这套通俗写法） - 短问答（直接回答就行，没必要套结构） ## 七步法结构（固定顺序） 当需要把一个复杂概念、技术、产品讲明白，写成结构化文章时，用这个七步法框架。它能保证文章结构清晰、普通人能看懂、有价值感。 适用场景： - 解释新技术新概念（比如 AI 领域新插件、新框架） - 科普复杂知识 - 产品介绍、方法总结 - 给普通人看的干货文章 不适用场景： - 内部技术文档（不需要这套通俗写法） - 短问答（直接回答就行，没必要套结构） ## 七步法结构（固定顺序） ### 1. 开篇：从一个小故事说起 - 字数：50-150 字 - 要求：生活化场景，带入用户真实痛点/疑问，引出主题 - 目标：让读者一秒代入，知道"这东西能帮我干嘛" ### 2. 澄清：三个常见误区，它不是这些 - 排除三个最常见的错误认知 - 建立清晰的概念边界 - 避免读者带着误解读下去 ### 3. 本质：一句话讲清楚它到底是什么 - 从表层到深层，拆解核心原理 - 专业词汇配通俗解释 - 一句话说透，不绕弯子 ### 4. 拆解：三个维度读懂核心 固定三个维度： 1. **核心特性**：关键机制是什么，有什么特点 2. **核心价值**：它到底解决了什么问题，带来什么好处 3. **典型应用场景**：哪些情况用它最合适，列出来 每个维度 1-2 段，说清楚就行。 ### 5. 对比：一张表格看懂区别 - 拿它和相似概念/传统方式做对比 - 3-5 个维度，一目了然 - 表格形式最清晰 ### 6. 收尾：回到开头，聊聊它对我们的意义 - 首尾呼应，价值升华 - 字数：100-200 字 - 要有温度，讲清楚它对普通人到底意味着什么 ### 7. 延伸：想深入了解，可以读这些 - 分入门/相关/进阶三类 - 每类最多 3 本/篇 - 标注作者和核心价值，帮读者选择 ## 写作要求 1. **语言风格**：通俗口语化，避免堆砌专业术语，像聊天一样讲明白 2. **结构清晰**：严格遵守七步顺序，每个部分有明确标题 3. **控制篇幅**：全文一般在 1500-2500 字，不要太长 4. **示例开头**：一定要用真实场景，别上来就干讲定义 ## 模板（直接套用） ```markdown # {标题} - 七步法讲明白 > 本文用「深入浅出讲明白」七步法结构写成，帮你快速搞懂核心。 --- ## 1. 开篇：从一个小故事说起 {这里写生活化场景，痛点引入} --- ## 2. 澄清：它不是这些，别搞错了 ❌ **它不是XXX** —— {澄清} ❌ **它不是XXX** —— {澄清} ❌ **它不是XXX** —— {澄清} --- ## 3. 本质：一句话讲透它到底是什么 **{一句话本质}** {补充解释，专业词汇通俗化} --- ## 4. 拆解：三个维度读懂核心 ### 核心特性：{标题} {内容} ### 核心价值：{标题} {内容} ### 典型应用场景：{标题} | 场景 | 为什么适合 | |------|------------| | {场景1} | {原因} | | {场景2} | {原因} | | {场景3} | {原因} | | {场景4} | {原因} | --- ## 5. 对比：一张表格看懂区别 | {它} | {传统方式/相似概念} | |---|---| | {对比维度1} | {对比内容} | | {对比维度2} | {对比内容} | | {对比维度3} | {对比内容} | | {对比维度4} | {对比内容} | --- ## 6. 收尾：回到开头，聊聊它对我们的意义 {总结升华，讲价值} --- ## 7. 延伸：想深入了解，可以读这些 ### 入门直接用 - [{名称}]({链接}) —— {核心价值} ### 进阶阅读 - [{名称}]({链接}) —— {核心价值} --- > 一句话总结：{核心记忆点} ``` ## 使用方式 当用户让你"用七步法讲讲XXX"时： 1. 先搜索收集信息，搞懂这个概念 2. 严格套用上面的模板结构 3. 每个部分遵守字数和要求 4. 输出完整 markdown 文章 ## 示例成品 参考本 skill 目录下的 `example-ralph-wiggum.md`，就是用这套框架写的 Ralph Wiggum 插件说明。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yash-kavaiya/firecrawl-cli (version 1.0.0)

--- name: firecrawl-cli description: "Web scraping, crawling, searching, and browser automation via the Firecrawl CLI (firecrawl). Use when scraping URLs to markdown/HTML, crawling entire websites, discovering all URLs on a site, running web searches with optional scraping, launching cloud browser sessions for automation, or using the AI agent for natural-language web queries. Requires firecrawl-cli installed (npm install -g firecrawl-cli) and authentication (firecrawl login or FIRECRAWL_API_KEY env var). Triggers on: scrape URL, crawl site, map website URLs, firecrawl search, browser automation with firecrawl, web agent query." --- # Firecrawl CLI ## Installation & Auth ```bash npm install -g firecrawl-cli firecrawl login --browser # Recommended for agents # or export FIRECRAWL_API_KEY=fc-YOUR-KEY firecrawl --status # Verify: shows credits + concurrency ``` ## Commands Summary | Command | Purpose | |---------|---------| | `firecrawl scrape <url>` | Scrape single URL | | `firecrawl search "<query>"` | Web search (+ optional scrape) | | `firecrawl map <url>` | Discover all URLs on a site | | `firecrawl crawl <url>` | Crawl entire website (async job) | | `firecrawl browser` | Cloud browser sandbox automation | | `firecrawl agent "<prompt>"` | NL-driven web agent queries | ## Key Patterns **Scrape (most common):** ```bash firecrawl https://example.com --only-main-content # Clean markdown firecrawl https://example.com --format markdown,links # Multiple formats → JSON firecrawl https://example.com -o output.md # Save to file ``` **Crawl a docs site:** ```bash firecrawl crawl https://docs.example.com --limit 50 --max-depth 2 --wait --progress -o docs.json ``` **Browser automation (AI agents):** ```bash firecrawl browser launch-session firecrawl browser execute "open https://example.com" firecrawl browser execute "snapshot" # Returns @ref IDs firecrawl browser execute "click @e5" firecrawl browser execute "scrape" firecrawl browser close ``` **AI agent query:** ```bash firecrawl agent "Find top 5 AI startups and funding" --wait firecrawl agent "Compare pricing" --urls https://a.com,https://b.com --wait ``` ## Full Reference See [references/commands.md](references/commands.md) for all commands, options, and examples. ## Tips - Use `--only-main-content` for clean article content (removes nav/footer) - `crawl` returns a job ID immediately — use `--wait` to block or poll with job ID later - Browser `execute` default mode is agent-browser (bash) — 40+ commands, best for agents - `spark-1-mini` (default) is 60% cheaper than `spark-1-pro` for agent queries - Check concurrency limit with `--status` before parallelizing scrape jobs - Self-hosted instances skip API key auth automatically when `--api-url` is set


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yomero243/physicclaw (version 1.0.0)

--- name: physic-claw-vea description: An interactive 3D visualization application featuring an "Augmented Entity" with reactive shaders and emotional states. --- # PhysicClaw-VEA ## Overview PhysicClaw-VEA is an advanced interactive 3D experience built with **React Three Fiber** and **Three.js**. The core of the application is an "Augmented Entity" that dynamically reacts to simulated internal states such as "thinking", emotions, and intensity levels through custom shaders and animations. ## Key Features ### 🎨 Advanced 3D Visualization - Immersive scene rendering using React Three Fiber. - Support for loaded 3D models (GLB/FBX) with animation playback. - Fallback to procedural geometry when models are loading or unavailable. ### ⚡ Reactive Shaders (`EnergyShader`) - Custom GLSL shaders that visually modify the entity in real-time. - Visual parameters include: - **Intensity**: Fluctuating energy levels. - **Thinking State**: Visual pulses indicating processing. - **Mood**: Color shifts based on the entity's emotional state. ### 🧠 "Soul" System (State Management) - Powered by **Zustand** for global state management (`soulStore`). - Simulates the entity's internal "soul" or consciousness, driving the visual changes and behaviors. ### 💬 Chat Interface - An overlay interface allowing users to interact with the entity via text. - Connects directly to the entity's state to trigger responses or visual changes. ## Tech Stack - **Frontend**: React (v19), TypeScript, Vite - **3D Graphics**: Three.js, React Three Fiber, Dre - **State**: Zustand - **Styling**: Tailwind CSS (configured) ## Installation & Usage 1. **Install dependencies**: ```bash npm install ``` 2. **Run locally**: ```bash npm run dev ``` 3. **Build**: ```bash npm run build ```


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hongjiahao371-pixel/ollama-ocr (version 1.0.0)

--- name: ollama-ocr description: Use Ollama's vision/OCR models to recognize text from images. Supports glm-ocr, llava, moondream, and llama3.2-vision models. Ideal when you need local offline OCR without relying on cloud APIs. metadata: { "author": "hongjiahao371-pixel", "version": "1.0.0", "openclaw": { "description": "Local Ollama OCR for image text recognition", "examples": [ "recognize text from image", "extract text from screenshot", "OCR this picture" ] } } --- # Ollama OCR Skill Use this skill when you need to recognize text from images using Ollama's local vision/OCR models. No internet required - fully offline OCR. ## When to Use - User sends an image and wants text extraction - User asks to recognize text from a screenshot or picture - Need local offline OCR without cloud API dependency - Processing sensitive images that shouldn't be sent to third parties ## Models Available | Model | Best For | Size | |-------|----------|------| | `glm-ocr:latest` | Chinese text OCR | ~2.2GB | | `llava:7b` | General image understanding | ~4.7GB | | `moondream` | Lightweight vision model | ~1.5GB | | `llama3.2-vision:latest` | Large vision model | ~7GB+ | ## Ollama Endpoint Default: `http://172.17.0.2:11434` (Docker container to host gateway) **Note:** Endpoint is pre-configured for OpenClaw running in Docker accessing host Ollama. Adjust `OLLAMA_HOST` in `ollama_ocr.py` if your setup differs. ## Usage ### Command Line ```bash python3 ollama_ocr.py /path/to/image.jpg [model_name] ``` Examples: ```bash python3 ollama_ocr.py receipt.png glm-ocr:latest python3 ollama_ocr.py screenshot.jpg llava:7b ``` ### Python API ```python from ollama_ocr import ollama_ocr # Basic OCR with default model (glm-ocr) result = ollama_ocr('/path/to/image.jpg') # Specify model result = ollama_ocr('/path/to/image.jpg', 'glm-ocr:latest') print(result) ``` ## Example Prompts to Activate This Skill - "识别这张图片里的文字" - "帮我 OCR 一下这个截图" - "Extract text from this image" - "What text is in this screenshot?" ## Notes - Image path must be absolute or relative to script location - For large images, consider resizing first to avoid timeout - `glm-ocr` works best for Chinese text - Some models may have output quirks (e.g., glm-ocr occasionally repeats) - First call may be slow if model isn't cached in memory ## Requirements - Ollama installed and running - At least one vision/OCR model downloaded (e.g., `ollama pull glm-ocr:latest`)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: macdesire/searxng-backup (version 1.0.0)

--- name: searxng description: Privacy-respecting metasearch using your local SearXNG instance. Search the web, images, news, and more without external API dependencies. author: Avinash Venkatswamy version: 1.0.1 homepage: https://searxng.org triggers: - "search for" - "search web" - "find information" - "look up" metadata: {"clawdbot":{"emoji":"🔍","requires":{"bins":["python3"]},"config":{"env":{"SEARXNG_URL":{"description":"SearXNG instance URL","default":"http://localhost:8080","required":true}}}}} --- # SearXNG Search Search the web using your local SearXNG instance - a privacy-respecting metasearch engine. ## Commands ### Web Search ```bash uv run {baseDir}/scripts/searxng.py search "query" # Top 10 results uv run {baseDir}/scripts/searxng.py search "query" -n 20 # Top 20 results uv run {baseDir}/scripts/searxng.py search "query" --format json # JSON output ``` ### Category Search ```bash uv run {baseDir}/scripts/searxng.py search "query" --category images uv run {baseDir}/scripts/searxng.py search "query" --category news uv run {baseDir}/scripts/searxng.py search "query" --category videos ``` ### Advanced Options ```bash uv run {baseDir}/scripts/searxng.py search "query" --language en uv run {baseDir}/scripts/searxng.py search "query" --time-range day ``` ## Configuration **Required:** Set the `SEARXNG_URL` environment variable to your SearXNG instance: ```bash export SEARXNG_URL=https://your-searxng-instance.com ``` Or configure in your Clawdbot config: ```json { "env": { "SEARXNG_URL": "https://your-searxng-instance.com" } } ``` Default (if not set): `http://localhost:8080` ## Features - 🔒 Privacy-focused (uses your local instance) - 🌐 Multi-engine aggregation - 📰 Multiple search categories - 🎨 Rich formatted output - 🚀 Fast JSON mode for programmatic use ## API Uses your local SearXNG JSON API endpoint (no authentication required by default).

path: _meta.json
content: ownerId: kn76z88c7kaynewbq2n2cv8831801bfs
slug: searxng
version: 1.0.3
publishedAt: 1769472992634

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: variable190/deterministic-controller (version 0.1.1)

--- name: deterministic-controller description: Deterministic, evidence-gated controller templates for OpenClaw (HEARTBEAT/ACTIVITIES + sprint template + poll cron payload). Docs-only: installs no services and makes no config changes by itself. --- # Deterministic Controller for OpenClaw (Docs-only) ## USE WHEN - You want a **deterministic**, repeatable orchestration loop for OpenClaw (heartbeat + cron poll + subagents). - You want **evidence-gated** completion (no DONE without artifact paths). - You want a **lean portfolio queue** (`ACTIVITIES.md`) with external sprint-plan import via `Plan Path`. ## DON'T USE WHEN - You want a turnkey, self-modifying installer that patches config / creates cron jobs automatically. - You need complex runtime code; this skill is **templates + docs**. ## Outputs - Template files under this skill folder: - `templates/HEARTBEAT.md` - `templates/ACTIVITIES.md` - `templates/SPRINT_TEMPLATE.md` - Copy/paste prompts: - `examples/setup_prompt.md` - `examples/project_to_sprint_prompt.md` - Cron payload text: - `docs/poll_cron_payload.txt` - Config guidance: - `docs/openclaw_config_snippets.md` ## How to use Start with the repo README: - `README.md` Then copy templates into your workspace and set: - `<TELEGRAM_GROUP_ID>` in `HEARTBEAT.md` - heartbeat prompt + cadence in `openclaw.json` (see `docs/openclaw_config_snippets.md`) This skill intentionally does not execute commands or change config automatically.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: yjstrivesh/patent-scout (version 1.0.1)

--- name: patent-scout description: "通过百度或Google Patents在线检索中国专利信息，支持关键词和专利号查询，输出结构化专利摘要和申请人等数据。" --- # Patent Scout - 中国专利搜索 Skill 真实的中国专利搜索工具，通过百度搜索或 Google Patents 在线检索中国专利信息。 ## 触发条件 当用户提到以下内容时使用此 Skill： - "搜索专利"、"查专利"、"专利检索" - "patent search"、"find patents" - "技术专利"、"发明专利"、"实用新型" - 提到具体的中国专利号（如 CN116789012A） - 需要查看专利摘要、申请人等信息 ## 功能特性 ### 数据源 - **百度搜索**（默认）— 国内直连，无需代理，适合中国网络环境 - **Google Patents** — 全球专利数据库，需代理访问 ### 搜索能力 - 关键词搜索（中文） - 专利号精确查询（CN 开头） - 自动过滤广告和无关结果 - 自动提取专利号 ### 输出格式 - Markdown（默认）— 结构化展示专利信息 - JSON — 方便程序处理 ## 使用方法 ### 关键词搜索 ```bash # 默认百度搜索 node scripts/patent-scout.js --query "工业防火墙" # 限制结果数量 node scripts/patent-scout.js --query "深度学习" --limit 5 # 导出到文件 node scripts/patent-scout.js --query "固态电池" --output results.md # JSON 格式输出 node scripts/patent-scout.js --query "5G" --format json ``` ### 专利号查询 ```bash node scripts/patent-scout.js --patent-id CN116789012A ``` ### 使用 Google Patents（需代理） ```bash node scripts/patent-scout.js --query "芯片" -s google --proxy http://127.0.0.1:7890 ``` ## 参数说明 | 参数 | 说明 | 默认值 | |------|------|--------| | `-q, --query` | 搜索关键词 | - | | `-p, --patent-id` | 专利号（如 CN116789012A） | - | | `-l, --limit` | 结果数量 | 10 | | `-s, --source` | 数据源 (baidu/google) | baidu | | `-o, --output` | 输出文件路径 | - | | `-f, --format` | 输出格式 (markdown/json) | markdown | | `--proxy` | 代理地址（Google Patents 需要） | - | ## 技术栈 - **Node.js** — 运行环境 - **axios** — HTTP 请求 - **cheerio** — HTML 解析 - **commander** — 命令行参数解析 ## 安装 ```bash cd internal/java/patent-scout npm install ``` ## 示例场景 ### 场景 1: 技术调研 ``` 用户: "帮我搜索关于工业防火墙的专利" → node scripts/patent-scout.js --query "工业防火墙" --limit 5 ``` ### 场景 2: 专利号查询 ``` 用户: "查一下专利 CN121567465A" → node scripts/patent-scout.js --patent-id CN121567465A ``` ### 场景 3: 导出分析 ``` 用户: "搜索固态电池专利并导出" → node scripts/patent-scout.js --query "固态电池" --output results.md ``` ## 注意事项 - 百度搜索为默认数据源，国内可直连 - Google Patents 在中国大陆需要配合代理使用 - 请控制请求频率，避免被限流 - 部分百度结果可能无法提取完整专利号 ## 许可证 MIT License ## 作者 闫老师团队 ## 更新日志 ### v2.0.0 - 重写为真实网络搜索（移除演示数据） - 支持百度搜索（国内直连） - 支持 Google Patents（需代理） - 自动过滤广告 - 自动提取中国专利号 ### v1.0.0 - 初始版本（演示数据）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wahddbing/bmap-jsapi-gl (version 1.0.3)

--- name: bmap-jsapi-gl description: 百度地图 JSAPI WebGL (BMapGL) 开发指南。在编写、审查或调试使用百度地图 API的代码时应运用此技能。适用于涉及地图初始化、覆盖物渲染、图层管理、事件处理、控件交互或性能优化的任务。当用户提及 BMapGL、百度地图、jsapi-gl或相关地图开发需求时自动触发。 license: MIT version: 1.0.0 metadata: openclaw: requires: env: BMAP_JSAPI_KEY primaryEnv: BMAP_JSAPI_KEY --- # JSAPI GL 开发指南 百度地图 JSAPI WebGL 版本开发指南。包含地图初始化、覆盖物、事件、图层等核心模块的 API 说明和代码示例，旨在帮助开发者快速集成百度地图并遵循正确的使用方式。 ## 何时适用 在以下场景中参考这些指南： - 创建新的地图页面或组件 - 在地图上添加标注、折线、多边形等覆盖物 - 处理地图交互事件（点击、拖拽、缩放等） - 配置地图样式或切换图层 - 调试地图渲染或性能问题 ## 快速参考 ### 0. 基础概念 - `references/base-classes.md` - 基础类：Point、Bounds、Size、Pixel、Icon - `references/constants.md` - 通用常量：搜索状态码、POI 类型 ### 1. 地图 - `references/map-init.md` - 地图初始化：资源引入、创建实例、配置选项、交互与视图控制 ### 2. 地图覆盖物 - `references/overlay-common.md` - 覆盖物通用操作：添加/移除、显示/隐藏、批量清除 - `references/marker.md` - 点标记：构造参数、位置/图标/旋转/置顶/拖拽方法 - `references/polyline.md` - 折线：构造参数、线条样式、坐标操作、编辑模式 - `references/polygon.md` - 多边形：构造参数、边框/填充样式、带孔多边形、编辑模式 - `references/circle.md` - 圆形：构造参数、中心点/半径、样式设置、编辑模式 - `references/custom-overlay.md` - 自定义覆盖物：DOM 创建、属性传递、事件绑定、旋转控制 - `references/info-window.md` - 信息窗口：构造参数、内容/尺寸设置、最大化、与 Marker 配合使用 ### 3. 事件 - `references/map-events.md` - 地图事件：绑定方式、交互事件、视图变化事件、生命周期事件 - `references/overlay-events.md` - 覆盖物事件：通用事件、拖拽事件、矢量图形事件 ### 4. 地图样式 - `references/map-style.md` - 个性化地图：自定义地图外观（颜色、显隐），实现深色主题、简洁地图等效果 ### 5. 图层服务 - `references/xyz-layer.md` - 第三方图层：加载 XYZ/TMS/WMS/WMTS 标准瓦片 - `references/mvt-layer.md` - 矢量瓦片：加载 MVT/PBF 格式瓦片，支持样式表达式、特征交互、状态管理 ### 6. 路径规划 - `references/route-common.md` - 通用配置：构造参数、渲染选项、回调函数、数据结构、状态常量 - `references/driving-route.md` - 驾车：策略枚举、途经点、路况、收费、拖拽 - `references/walking-route.md` - 步行：转向类型、拖拽 - `references/riding-route.md` - 骑行：骑行搜索 - `references/transit-route.md` - 公交：市内/跨城策略、交通方式、换乘 ### 7. 其他LBS服务 - `references/local-search.md` - 本地检索：普通/范围/周边检索、结果处理、翻页、POI 数据结构 - `references/geocoder.md` - 地理编码：正地理编码（地址→坐标）、逆地理编码（坐标→地址） - `references/convertor.md` - 坐标转换：GPS/高德/谷歌坐标转百度坐标 ## 如何使用 请阅读各个参考文件以获取详细说明和代码示例: ``` references/map-init.md ``` 每个参考文件包含： - 功能简要说明 - 完整代码示例及解释 - API 参数说明和注意事项


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chloepark85/tistory-api-cli (version 0.1.1)

--- name: Tistory API CLI slug: tistory-api-cli displayName: Tistory API CLI version: 0.1.1 summary: Tistory 블로그용 비공식 CLI. 글 읽기/수정/삭제, 카테고리 조회, 이미지 첨부 업로드를 지원하며 요청/응답 스키마 검증과 한국어 에러 메시지를 제공한다. tags: [latest, tistory, blog, cli] license: MIT metadata: openclaw: requires: env: [] primaryEnv: "" --- # Tistory API CLI Tistory 블로그를 터미널에서 제어하기 위한 경량 CLI이다. 글 읽기/수정/삭제와 카테고리 조회, 이미지 첨부 POST를 지원한다. 요청/응답 스키마를 검증하고, 에러 메시지를 한국어로 안내한다. Python 런타임이 없는 환경을 고려해 uv/pipx 기반 설치 가이드를 제공하며, pytest 단위 테스트와 GitHub Actions CI를 포함한다. PyPI 배포(tistory-api-cli)를 고려해 pip 설치 경로를 제공한다. ## 무엇이 새로운가 — v0.1.1 - SKILL.md 메타데이터 반영 및 태그(latest) 유지 - 기능 보강 - 읽기(read-post), 수정(update), 삭제(delete), 카테고리 조회 추가 - 파일 첨부(이미지) POST 지원 - 요청/응답 스키마 검증 및 에러 메시지 국문화 - 안정성/품질 - Python 런타임 미존재 환경 고려한 설치 가이드(uv/pipx) README 보강 - pytest 기반 단위 테스트(가짜 응답 fixture) 및 GitHub Actions CI 추가 - PyPI 배포(tistory-api-cli) 고려해 pip 설치 경로 제공 - 확장 계획 - Velog용 별도 스킬(velog-cli) 리서치/스펙 작성(블로그/시리즈 CRUD, 태그/검색) - 한·영 혼용 문서와 예시 강화로 해외 사용자 접근성 개선 ## 설치 - pipx - pipx install tistory-api-cli - uv - uv tool install tistory-api-cli - pip (권장도 낮음) - pip install tistory-api-cli ## 사용 예시 - 글 조회: tistory-cli read --post-id 12345 - 글 수정: tistory-cli update --post-id 12345 --title "제목" --content @body.md - 글 삭제: tistory-cli delete --post-id 12345 - 카테고리 조회: tistory-cli categories - 이미지 업로드: tistory-cli upload-image ./image.png ## 환경 변수(필요 시) - TISTORY_ACCESS_TOKEN: Tistory API 토큰 - TISTORY_BLOG_NAME: 기본 블로그 식별자 ## 링크 - GitHub: 제공 예정 ## 라이선스 MIT-0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: louisshao464/video-knowledge-extractor (version 1.0.0)

--- name: video-knowledge-extractor description: 从视频、音频、播放列表或本地文件中抽取知识，生成摘要、要点、章节和适合知识库沉淀的 Markdown/JSON。适用于链接、文件路径和文件夹输入。 --- # 中文视频知识抽取器 把视频内容整理成可复用的知识资产。 ## 何时使用 - 用户给出视频或音频链接，希望整理成笔记 - 用户给出本地文件、文件夹或批量列表，希望自动处理 - 用户需要摘要、要点、章节、行动项，而不只是转写 - 用户要把内容沉淀到 Obsidian、Notion 或其他知识库 ## 核心流程 1. 识别输入类型 2. 下载或直接读取媒体 3. Whisper 转写 4. 长内容自动分块总结 5. LLM 后处理生成摘要、要点、章节和行动项 6. 输出固定格式文件 ## 处理原则 - 优先输出可入库内容，而不是只返回原文 - 本地文件和文件夹直接读取，不强制下载 - 长视频自动分块，避免单次上下文过大 - LLM 可用时自动增强，不可用时回退本地规则 ## 输出 - `summary.md` - `notes.md` - `chapters.md` - `knowledge.json` - `manifest.json` ## 参考文件 - 运行说明：`README.md` - 示例输入输出：`examples.md` - 输出模板：`output_templates/` - 处理脚本：`scripts/process_media.py`


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