OpenClaw/clawhub-security-signals huggingface dataset:
Skill: xxxx536/brainaslab (version 0.1.0)

# BrainASLab — FreeSurfer Asymmetry Analyzer Upload FreeSurfer `lh.aparc.stats` and `rh.aparc.stats`. This skill automatically: - Parses cortical ROI metrics - Computes Asymmetry Index (AS) for Thickness, Area, and Volume - Generates publication-ready plots - Outputs AS_results.csv ## Input - lh.aparc.stats - rh.aparc.stats ## Output - AS_results.csv - AS_bar_*.png - AS_hist_*.png ## Requirement - Matlab installed and available in PATH


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhuchenggong19851114-design/zhuchenggong-github-publish (version 1.0.0)

--- name: github-publish description: GitHub发布技能。使用浏览器网页方式推送代码。 --- # GitHub发布 Skill ## 概述 通过浏览器网页方式推送代码到GitHub。 ## 触发方式 - "发布到GitHub" - "推送到GitHub" ## 发布流程 ### 步骤1：打开发布页面 - 打开 https://github.com/new ### 步骤2：创建仓库 - 填写仓库名 - 选择Public - 点击Create repository ### 步骤3：上传文件 - 在仓库页面点击上传文件 - 选择本地文件或拖拽文件 - 填写提交信息 - 点击Commit changes ### 步骤4：后续更新 - 打开发布的仓库 - 点击上传文件 - 上传新文件覆盖 - 填写提交信息 - 点击Commit changes ## 关键点 - 使用浏览器网页操作，不需要git命令行 - 浏览器已登录GitHub账号即可 ## 工具 - browser：浏览器操作


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: sky-lv/skylv-skill-quality-assurance (version 1.0.0)

--- name: skylv-skill-quality-assurance description: Analyzes and scores OpenClaw skill quality across 5 dimensions. Detects missing frontmatter, weak descriptions, poor examples, and actionable improvement recommendations. keywords: skill, quality, analysis, scoring, improvement, documentation, review triggers: quality, score, analyze skill, skill review, documentation --- # Skill Quality Assurance Automatically analyze and score OpenClaw skill documentation quality. Identifies critical issues and provides actionable fixes. ## Overview This skill runs a multi-dimensional quality analysis on any OpenClaw skill's SKILL.md file, scoring it across: - **Clarity** — Structure, headings, sentence length - **Completeness** — Required fields, install/usage sections - **Actionability** — Step-by-step guides, commands - **Discoverability** — Keywords, description, categories - **Examples** — Code examples, before/after, use cases ## Usage ### Analyze a single skill ``` Analyze the skill at: C:\path\to\skills\my-skill ``` ### Analyze all skills in a directory ``` Run: node engine.js C:\path\to\skills ``` ### Interpretation | Score | Grade | Meaning | |-------|-------|---------| | 8-10 | A | Production-ready | | 6-7 | B | Needs minor improvements | | 4-5 | C | Needs significant work | | <4 | F | Rewrite recommended | ## Scoring Dimensions ### 1. Clarity (20%) Checks for: headings hierarchy, sentence length, filler words, markdown formatting ### 2. Completeness (20%) Checks for: `name`, `description`, `keywords`, install section, usage section ### 3. Actionability (25%) Checks for: numbered steps, runnable commands, configuration guides ### 4. Discoverability (15%) Checks for: keywords (5+), description length (50+ chars), category/tags ### 5. Examples (20%) Checks for: code blocks (3+), before/after cases, use case descriptions ## Quality Thresholds For ClawHub publication: - Overall score ≥ 7.0 required - No dimension below 5.0 - Must have keywords field - Must have description ≥ 50 chars ## Auto-Fix The companion script `fix_skill_md.cjs` automatically fixes: - Missing `description` field - Missing `keywords` field - Missing `Install` section - Missing `Usage` section ```bash node fix_skill_md.cjs ``` ## Example Output ``` # Skill Quality Report Analyzed: 34 skills ## Overall Rankings 1. ✅ skylv-browser-automation-agent 9.4/10 2. ✅ skylv-openclaw-evomap-connector 9.3/10 Average score: 8.1/10 Grade A (7+): 29 Grade B (5-6): 5 Grade C (<5): 0 ``` ## MIT License © SKY-lv


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: qiliwei411-cloud/ad-agent-test (version 1.0.0)

--- name: ad-agent-test description: 腾讯广告投放Agent（ad.qq.com/atlas/{account_id}/agent）自动化体验测试技能。通过Cookie注入+Playwright浏览器自动化，对投放Agent的对话功能、快捷指令、常用指令面板、自由对话、妙招功能进行完整体验测试，并生成UX体验报告。适用场景：(1) 用户要求测试ad.qq.com投放Agent (2) 用户提到"投放Agent体验" (3) 用户需要对广告投放平台的Agent功能做产品体验/竞品分析 (4) 涉及ad.qq.com的自动化测试。需要用户提供adhome免登录Cookie。 --- # 投放Agent自动化体验测试 通过 Playwright 浏览器自动化 + Cookie注入 对 ad.qq.com 投放Agent功能进行完整UX测试。 ## 前置条件 1. **中文字体**：服务器需安装中文字体（否则截图中文为方块） 2. **Cookie**：需要用户提供有效的 adhome 免登录 Cookie 3. **Playwright**：需要 Node.js + playwright 已安装 ### 安装中文字体 ```bash yum install -y google-noto-sans-cjk-sc-fonts ``` ### 检查Playwright ```bash npx playwright --version ``` ## Cookie 获取流程 引导用户操作： 1. 打开 adhome 免登录页面，选择目标账户 2. 点击免登录进入 ad.qq.com/atlas/{account_id}/agent 3. 在浏览器 F12 控制台执行 `document.cookie` 4. 将结果发给 Agent ### Cookie 解析要点 - 必需Cookie：`gdt_mlogin`、`gdt_owner`、`tap_free_login_token`、`tap_free_login_userid` - `.woa.com` 域Cookie（`RIO_TOKEN`等）用于内网认证 - `.qq.com` 域Cookie（`ptcz`、`RK`等）用于QQ登录态 - Cookie有效期约2小时（会话级），过期会跳转登录页 ### Cookie 注入代码模板 参见 [scripts/inject_cookies.js](scripts/inject_cookies.js) — 解析用户提供的 cookie 字符串并注入到 Playwright context。 ## 测试流程 ### 第一阶段：环境验证 1. 注入Cookie 2. 访问 `https://ad.qq.com/atlas/{account_id}/agent` 3. 等待8秒页面加载 4. 验证是否进入Agent页面（非登录页）— 检查页面是否包含"对话"/"妙招"等文字 ### 第二阶段：对话功能测试 #### 2.1 首页快捷指令 遍历首页展示的快捷指令卡片（通常4个），依次： 1. 点击指令 2. 等待3秒 3. 截图记录模板展示 4. 提取输入框中的模板文案（含slot类型） 5. 新开会话测试下一个 #### 2.2 常用指令面板 1. 点击底部"常用指令"按钮（`[title="常用指令"]`） 2. 截图完整指令面板 3. 提取所有指令分类和名称 #### 2.3 模板Slot交互 1. 点击快捷指令后，在模板中点击 asset slot 2. 记录下拉列表内容 3. 点击 select slot，记录选项 #### 2.4 自由对话 测试覆盖投放全链路： **投放前**： - 预算规划："我有5000元预算，想推广一个教育类小程序，应该怎么设置投放计划？" - 选品定向："我是做电商的，卖女装，适合选择什么投放版位和定向人群？" **投放中**： - 数据查询："帮我查一下今天的广告消耗和转化数据" - 优化建议："我的广告点击率很低只有0.5%，有什么优化建议？" - 调预算："帮我把所有在投的广告日预算统一调整到200元" **投放后**： - 复盘分析："帮我分析一下上周的投放效果，哪些广告ROI最高？" - 批量关停："帮我把转化成本超过50元的广告全部暂停" 每轮对话：输入 → 等待25秒 → 截图 → 提取回复文本 ### 第三阶段：妙招功能测试 1. 切换到妙招页面（点击侧边栏"妙招"或首页"探索投放妙招"卡片） 2. 逐一点击每个妙招卡片，截图配置面板 3. 测试"一键执行"： - 验证按钮默认disabled状态（`title="请先完成必要配置"`） - 通过规则选择广告 - 验证按钮变为enabled - 点击执行，观察结果反馈 ## 输出物 ### 截图命名规范 ``` {序号}_{场景}.png ``` 示例：`01_home.png`、`05_chat_fashion.png`、`07_miaozahao.png` ### 报告结构 参见 [references/report-template.md](references/report-template.md) — 完整报告 Markdown 模板。 报告核心章节： 1. 功能全景（对话 + 妙招） 2. 对话功能体验（快捷指令 + 自由对话） 3. 妙招功能体验（配置面板 + 一键执行） 4. 信息架构与体验一致性 5. 优化建议（按P0/P1/P2分级） 6. 体验亮点 ### 报告输出 - 本地Markdown文件：`output/agent-test/体验报告_投放Agent_{date}.md` - 企微文档（通过 `wecom_mcp` 的 `smartpage_create` 上传） ## 关键选择器参考 | 元素 | 选择器 | |------|--------| | 快捷指令卡片 | `[class*="guideCard"]` 或 `text=模仿优质广告新建` | | 常用指令按钮 | `[title="常用指令"]` 或 `[class*="commandButton"]` | | 输入框 | `[contenteditable="true"]` | | 发送按钮 | `[class*="sendButton"]` | | 妙招侧边栏 | `[class*="menuItem"]` 含文字"妙招" | | 一键执行按钮 | `button` 含文字"一键执行" | | 广告选择规则 | `[class*="ruleSelect"]` 或含"规则"文字的tab | ## 注意事项 - Cookie约2小时过期，过期后自动跳转登录页 - 如截图中文显示为方块，先安装 `google-noto-sans-cjk-sc-fonts` - Agent回复需要10-25秒，部分场景会卡在"分析中"状态 - 使用DOM文本提取（`page.evaluate`）比截图更可靠，确保内容被记录 - 首次进入Agent页面需等8秒完成SPA加载 - 每次发送对话后建议新开会话（避免上下文串扰测试结果）


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: demitrim/discord-context (version 0.1.1)

--- name: discord-context description: Sync and cache per-thread context for Discord Forum channels. Use when handling /discord-context commands to poll active threads, list cached context, inspect a thread cache, or link a thread to a memory QMD file. user-invocable: true metadata: {"openclaw":{"requires":{"bins":["node"],"env":["DISCORD_TOKEN"]}}} --- # discord-context Run `node {baseDir}/scripts/discord-context-cli.js <command> ...`. ## Commands - `poll [--guild <id>] [--forum <id>] [--workspace <path>]` - Pull active threads from Discord and refresh cache for new/updated threads. - Requires `DISCORD_TOKEN` plus guild/forum IDs (flags or env vars). - `context [threadId] [--workspace <path>] [--json]` - Without `threadId`: list cached threads. - With `threadId`: print cached context and metadata for one thread. - `link <threadId> <qmdName> [--workspace <path>]` - Link a thread to `memory/<qmdName>.md` and refresh cached context text. ## Environment - `DISCORD_TOKEN` (required for `poll`) - `DISCORD_GUILD_ID` (default guild id for `poll`) - `DISCORD_FORUM_CHANNEL_ID` (default forum id for `poll`) - `OPENCLAW_WORKSPACE` (defaults to `~/.openclaw/workspace`) ## Security Rules - Never hardcode Discord tokens. - Accept only numeric thread/guild/forum IDs. - Accept only `[a-zA-Z0-9_-]+` for `qmdName`. - Keep all reads/writes inside the workspace `memory/` tree. ## Paths - Cache metadata: `memory/discord-cache/thread-<id>.json` - Cache text: `memory/discord-cache/thread-<id>-context.txt` - Source context files: `memory/*.md`


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zenyang-x/doubaochatobtain (version 1.0.0)

--- name: doubao-chat-obtain description: 读取豆包（Douyin/Doubao）对话页面的完整全文内容。用于读取豆包对话帖子（如 https://www.doubao.com/thread/xxx ），解决虚拟滚动容器导致的"只能读取第一屏"问题，提取完整文本保存到本地文件。此 Skill 应在用户分享豆包对话链接或要求读取豆包页面时触发。豆包页面采用虚拟滚动，内容初始不可见，必须通过 JavaScript 直接操作 DOM 元素提取。 agent_created: true visibility: public version: 1.0.0 --- # DoubaoChatObtain 读取豆包对话页面完整全文。 ## 核心原理 豆包页面的 `body` 设置了 `overflow: hidden`，`window.scroll` 无效。真正的滚动区域是一个 `div.fixed` 容器，页面内容通过虚拟滚动加载（只渲染可视区域附近的内容）。直接用 `element.innerText` 可以一次性绕过虚拟滚动，提取已渲染的全部文本。 ## 前置依赖 需安装 [agent-browser](https://www.npmjs.com/package/agent-browser)（Node.js CLI 浏览器自动化工具）： ```bash npm install -g agent-browser # 或项目级安装 npm install agent-browser ``` 验证安装： ```bash npx agent-browser --version ``` ## 执行步骤 ### 第一步：用 agent-browser 打开目标 URL ```bash npx agent-browser open "<豆包对话URL>" ``` ### 第二步：执行 JS 提取全文 在豆包页面内执行以下 JS，通过 `getComputedStyle` 遍历所有元素，找到真正的滚动容器，然后一次性提取 `innerText`： ```bash npx agent-browser eval --json "(() => { const c = [...document.querySelectorAll('div')].find(el => { const s = getComputedStyle(el); return (s.overflowY==='auto'||s.overflowY==='scroll') && el.scrollHeight > el.clientHeight; }); return c ? c.innerText : 'NOT_FOUND'; })();" > /tmp/doubao_raw.json ``` **关键点**：`--json` 参数返回标准 `{"success":true,"data":{"origin":"...","result":"文本"}}` 格式，避免解析转义字符的问题。 ### 第三步：解析并保存 使用内置脚本解析 JSON 并写入文件： ```bash python3 scripts/parse_doubao.py --input /tmp/doubao_raw.json --output ./豆包对话全文.txt ``` ## 已知限制与注意事项 1. **需要浏览器已打开豆包页面**：每次读取新对话前，需先 `agent-browser open <url>`。 2. **非登录用户**：可能看到"开始试用豆包"弹窗，读取内容通常不受影响。 3. **内容提取时机**：等待页面完全加载后再执行 JS，尤其是长对话帖子。 4. **多页对话**：某些帖子可能有翻页，滚动到底部后再执行 JS 提取。 5. **若 `eval --json` 返回 `NOT_FOUND`**：说明页面未完全加载，滚动到底部后重试。 ## 输出 - 文件保存路径：`/Users/zen/WorkBuddy/Claw/中登日记自动化项目/豆包对话全文.txt` - 字数通常在 5 万～15 万字之间（取决于对话长度） - 建议：提取完成后，对照原文用 Read 工具浏览，确认关键段落位置后再进行后续分析。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cinience/alicloud-ai-content-aicontent (version 1.0.4)

--- name: alicloud-ai-content-aicontent description: Manage Alibaba Cloud AIContent (AiContent) via OpenAPI/SDK. Use whenever the user needs AI content generation or content workflow operations in Alibaba Cloud, including listing assets, creating/updating generation configurations, checking task status, or troubleshooting failed content jobs. version: 1.0.0 --- Category: service # AIContent Use Alibaba Cloud OpenAPI (RPC) with official SDKs or OpenAPI Explorer to manage resources for AIContent. ## Workflow 1) Confirm region, resource identifiers, and desired action. 2) Discover API list and required parameters (see references). 3) Call API with SDK or OpenAPI Explorer. 4) Verify results with describe/list APIs. ## AccessKey priority (must follow) 1) Environment variables: `ALICLOUD_ACCESS_KEY_ID` / `ALICLOUD_ACCESS_KEY_SECRET` / `ALICLOUD_REGION_ID` Region policy: `ALICLOUD_REGION_ID` is an optional default. If unset, decide the most reasonable region for the task; if unclear, ask the user. 2) Shared config file: `~/.alibabacloud/credentials` ## API discovery - Product code: `AiContent` - Default API version: `20240611` - Use OpenAPI metadata endpoints to list APIs and get schemas (see references). ## High-frequency operation patterns 1) Inventory/list: prefer `List*` / `Describe*` APIs to get current resources. 2) Change/configure: prefer `Create*` / `Update*` / `Modify*` / `Set*` APIs for mutations. 3) Status/troubleshoot: prefer `Get*` / `Query*` / `Describe*Status` APIs for diagnosis. ## Minimal executable quickstart Use metadata-first discovery before calling business APIs: ```bash python scripts/list_openapi_meta_apis.py ``` Optional overrides: ```bash python scripts/list_openapi_meta_apis.py --product-code <ProductCode> --version <Version> ``` The script writes API inventory artifacts under the skill output directory. ## Output policy If you need to save responses or generated artifacts, write them under: [REDACTED_SECRET] ## Validation ```bash mkdir -p output/alicloud-ai-content-aicontent for f in skills/ai/content/alicloud-ai-content-aicontent/scripts/*.py; do python3 -m py_compile "$f" done echo "py_compile_ok" > output/alicloud-ai-content-aicontent/validate.txt ``` Pass criteria: command exits 0 and `output/alicloud-ai-content-aicontent/validate.txt` is generated. ## Output And Evidence - Save artifacts, command outputs, and API response summaries under `output/alicloud-ai-content-aicontent/`. - Include key parameters (region/resource id/time range) in evidence files for reproducibility. ## Prerequisites - Configure least-privilege Alibaba Cloud credentials before execution. - Prefer environment variables: `ALICLOUD_ACCESS_KEY_ID`, `ALICLOUD_ACCESS_KEY_SECRET`, optional `ALICLOUD_REGION_ID`. - If region is unclear, ask the user before running mutating operations. ## References - Sources: `references/sources.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: choihyunsus/n2-free-search (version 1.0.0)

--- name: n2-free-search description: Free, unlimited web search for AI agents via SearXNG — no API keys needed. homepage: https://nton2.com user-invocable: true --- # 🔍 N2 Free Search — MCP Server Skill Free, private, unlimited web search for your AI agent. No API keys, no credit cards, no rate limits. ## Why Use This? | | Brave Search API | Google Custom Search | **N2 Free Search** | |---|---|---|---| | **Cost** | $5 / 1,000 queries | $5 / 1,000 queries | **Free forever** | | **API Key** | Required | Required | **Not needed** | | **Search Engines** | Brave only | Google only | **70+ engines** | ## Quick Setup ### Option 1: Public Mode (Zero Setup) Add to your MCP config: ```json { "mcpServers": { "n2-free-search": { "command": "npx", "args": ["-y", "n2-free-search"] } } } ``` ### Option 2: Self-Hosted (Maximum Privacy) ```json { "mcpServers": { "n2-free-search": { "command": "npx", "args": ["-y", "n2-free-search"], "env": { "SEARXNG_URL": "http://localhost:8080" } } } } ``` ## Available Tools - **n2_web_search** — Search the web (Google, Bing, DuckDuckGo + 70 more) - **n2_news_search** — Search recent news articles - **n2_image_search** — Search for images - **n2_video_search** — Search for videos - **n2_suggest** — Get search suggestions / autocomplete ## Links - NPM: https://www.npmjs.com/package/n2-free-search - GitHub: https://github.com/choihyunsus/n2-free-search - Website: https://nton2.com --- *Part of the N2 AI Body series — Building the Body for AI*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: rikhou/tang-poetry-challenge (version 1.0.0)

--- name: tang-poetry-challenge description: 唐诗三百首对诗挑战游戏，提供诗句上句，用户需要对出下句。包含挑战结束评分和错题分析功能。 --- # 唐诗三百首对诗挑战 ## 功能说明 本技能提供唐诗三百首对诗挑战游戏，具体功能包括： 1. **诗句挑战**：随机抽取唐诗三百首中的诗句，提供上句，用户需要对出下句 2. **挑战评分**：挑战结束后，根据答对题目数量和用时给出评分 3. **错题分析**：挑战结束后，对用户答错的题目进行分析，提供正确答案和相关解析 ## 使用流程 1. 启动挑战：用户可以指定挑战题目数量（默认10题） 2. 开始挑战：系统随机抽取指定数量的唐诗诗句 3. 答题过程： - 系统显示诗句上句 - 用户输入下句 - 系统判断答案正确性 4. 挑战结束： - 显示总得分 - 展示错题分析 - 提供挑战结果总结 ## 数据来源 本技能使用 `references/tang_poetry.json` 文件中的唐诗三百首数据，包含诗句、作者、朝代等信息。 ## 脚本说明 - `scripts/challenge.py`：核心挑战逻辑，处理题目抽取、答案验证、评分计算等 - `scripts/feedback.py`：处理错误提示和错题分析 ## 使用示例 ### 启动挑战 用户：开始唐诗挑战 系统：欢迎参加唐诗三百首对诗挑战！请问您想挑战多少题？（默认10题） 用户：5题 系统：好的，准备开始5题挑战！ ### 答题过程 系统：第1题，请对出下句： 床前明月光， 用户：疑是地上霜 系统：回答正确！ 系统：第2题，请对出下句： 春眠不觉晓， 用户：处处闻啼鸟 系统：回答正确！ ### 挑战结束 系统：挑战结束！您的得分：5/5，用时：1分30秒 系统：评分：优秀！您对唐诗的掌握非常出色。 系统：错题分析：无错题，继续保持！


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kirkraman/kirk-build-personal-brand-faq-page (version 1.0.0)

--- name: "Build Personal Brand FAQ Page" description: "Generate customer-facing answers for common personal brand questions. Use when working on FAQ content for thought leaders, coaches, personal brand..." homepage: "https://www.skillboss.co/skills/build-personal-brand-faq-page" metadata: {"category":"personal-branding","subcategory":"automation","skill_id":"build-personal-brand-faq-page","generated_day":"","seo_keywords":["FAQ generator AI","personal brand FAQ writer","personal brand AI","creator workflow automation"]} --- # Build Personal Brand FAQ Page Generate customer-facing answers for common personal brand questions. Use when working on FAQ content for thought leaders, coaches, personal brand... ## Use When Use this skill for FAQ content work for thought leaders, coaches, personal brand builders. ## Workflow 1. Clarify the audience, business goal, constraints, and deliverable. 2. Produce the strongest first draft for the target use case. 3. Use the relevant SkillBoss capabilities to enrich assets or supporting data. 4. Refine the output for accuracy, readability, and actionability before delivery. ## SEO / GEO - Primary keywords: FAQ generator AI, personal brand FAQ writer, personal brand AI, creator workflow automation - Search intent: FAQ content - Canonical slug: `build-personal-brand-faq-page` - Install query: Install Build Personal Brand FAQ Page with SkillBoss ## APIs Used - `chat` - `web_search` ## Suggested Prompt [Build Personal Brand FAQ Page] for my team ## Notes - Generate customer-facing answers for common personal brand questions - Review outputs before external publication or automated execution. ## SEO Keywords - FAQ generator AI - personal brand FAQ writer - personal brand AI - creator workflow automation


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: partigle/feishu-reading-task (version 1.0.0)

--- name: feishu-reading-task description: | 飞书待阅读任务自动创建技能。当用户说"加到待阅读任务"、"加入待读"、"mark as reading"等时， 自动根据当前对话人员创建飞书任务，并挂到该人员的待阅读清单中。 **触发词：** "加到待阅读任务"、"加入待读清单"、"mark as reading"、"待阅读"、"稍后阅读" **使用方式：** 用户只需说："把这个加到待阅读任务" 或 "加入待读清单" 技能会自动： 1. 从对话上下文识别当前对话人员 2. 提取要保存的内容（链接、文档、项目等） 3. 创建飞书任务并指定该人员为负责人 4. 记录到 memory 文件 --- # 飞书待阅读任务技能 ## 功能 自动将内容添加到对话人员的飞书待阅读任务清单。 ## 执行流程 1. **识别触发** - 检测用户消息中是否包含待阅读相关关键词 2. **获取对话人员** - 从消息上下文提取 sender_id (open_id) 3. **提取内容** - 从对话中提取要保存的链接、标题、描述 4. **创建任务** - 调用 feishu_task_task create API 5. **记录** - 写入 memory/YYYY-MM-DD.md ## 任务字段 - **summary**: 待阅读：{内容标题} - **description**: 来源 + 链接 + 简要描述 - **members**: [{id: sender_id, role: assignee}] - **current_user_id**: sender_id ## 示例 用户：把这个加到待阅读任务 https://example.com/article 助手：✅ 已添加到您的待阅读任务清单 ## 依赖 - feishu_task_task (创建任务) - 消息上下文 (获取对话人员 ID)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: kaiyuelv/flowbridge (version 1.0.0)

--- name: flowbridge description: FlowBridge - 零代码跨生态自动化工具 | No-code cross-platform automation with WeChat, DingTalk, Feishu, WPS integration --- # FlowBridge - 零代码跨生态自动化工具 让无代码基础的用户也能在3分钟内搭建跨平台自动化流程，连接微信、钉钉、飞书、WPS等国内主流生态。 ## 核心功能 | 功能模块 | 说明 | |---------|------| | **国内生态接口对接** | 微信、钉钉、飞书、WPS、腾讯文档、阿里云盘 | | **零代码流程配置** | 可视化拖拽，3分钟完成配置 | | **AI流程智能生成** | 自然语言指令自动生成流程 | | **执行监控与兜底** | 与重试降级Skill联动，成功率≥95% | | **模板中心** | 50+高频场景模板一键复用 | ## 快速开始 ```python from scripts.workflow_engine import WorkflowEngine from scripts.ai_flow_generator import AIFlowGenerator # AI生成流程 ai_gen = AIFlowGenerator() workflow = ai_gen.generate("微信收到文件自动同步到阿里云盘") # 执行流程 engine = WorkflowEngine() engine.run(workflow) ``` ## 安装 ```bash pip install -r requirements.txt ``` ## 项目结构 ``` clawhub-automation/ ├── SKILL.md # Skill说明 ├── README.md # 完整文档 ├── requirements.txt # 依赖 ├── config/ │ └── connectors.yaml # 生态连接器配置 ├── scripts/ # 核心模块 │ ├── workflow_engine.py # 流程引擎 │ ├── connector_manager.py # 生态连接器 │ ├── ai_flow_generator.py # AI流程生成 │ ├── template_center.py # 模板中心 │ ├── execution_monitor.py # 执行监控 │ └── permission_manager.py # 权限管理 ├── templates/ # 场景模板 ├── examples/ # 使用示例 └── tests/ # 单元测试 ``` ## 运行测试 ```bash cd tests python test_automation.py ``` ## 详细文档 请参考 `README.md` 获取完整API文档和使用指南。

path: requirements.txt
content: requests>=2.31.0
pyyaml>=6.0
python-dateutil>=2.8.0
schedule>=1.2.0

path: scripts/__init__.py
content: """
FlowBridge - 零代码跨生态自动化工具
No-code cross-platform automation tool
"""

__version__ = "1.0.0"
__author__ = "ClawHub Platform"

from .workflow_engine import WorkflowEngine, Workflow, WorkflowNode
from .connector_manager import ConnectorManager, PlatformConnector
from .ai_flow_generator import AIFlowGenerator
from .template_center import TemplateCenter
from .execution_monitor import ExecutionMonitor
from .permission_manager import PermissionManager

__all__ = [
    'WorkflowEngine',
    'Workflow',
    'WorkflowNode',
    'ConnectorManager',
    'PlatformConnector',
    'AIFlowGenerator',
    'TemplateCenter',
    'ExecutionMonitor',
    'PermissionManager'
]

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: aisadocs/stock-hot-zh-aisa (version 1.0.0)

--- name: stock-hot-zh description: "通过 AISA 扫描热门股票和加密资产的实时异动。触发条件：当用户想知道现在什么最热、什么在大幅波动、市场动量如何、涨幅榜或消息驱动异动时使用。" author: aisa version: "1.0.0" license: Apache-2.0 user-invocable: true primaryEnv: AISA_API_KEY requires: env: - AISA_API_KEY bins: - python3 metadata: openclaw: primaryEnv: AISA_API_KEY requires: env: - AISA_API_KEY bins: - python3 --- # 热门标的扫描 ## 何时使用 - 通过 AISA 扫描热门股票和加密资产的实时异动。触发条件：当用户想知道现在什么最热、什么在大幅波动、市场动量如何、涨幅榜或消息驱动异动时使用。 ## 不适用场景 - 当用户明确要本地浏览器 Cookie、密码、Keychain 或其他本地敏感凭据时，不要使用这个 skill。 - 当问题与该 skill 的主题无关时，优先选择更贴切的 skill。 ## 核心能力 - 找出涨跌幅居前标的、动量标的和快速市场总结。 ## 快速开始 ```bash export AISA_API_KEY="your-key" ``` ## 运行方式 首选运行路径是仓库内置的 Python 客户端： ```bash python3 scripts/hot_scanner.py ``` ## 示例请求 - 展示当前最热门的股票和加密异动标的。 ## 说明 - 仅供信息参考，不构成投资建议。


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