OpenClaw/clawhub-security-signals huggingface dataset:
Skill: young-joey/feishu-chat-bot (version 1.0.2)

# 飞书群聊机器人 — Feishu Chat Bot description: "飞书群聊机器人 — 自动在飞书群中发送消息、日报提醒、审批通知、团队广播。支持定时发送、条件触发、多群管理。当用户需要通过飞书群进行团队沟通自动化、设置群消息机器人、批量群发通知时使用。触发词：飞书群聊、群机器人、自动发消息、群通知、定时提醒、飞书消息、feishu chat bot、飞书自动回复、飞书卡片消息。" **版本**: 1.0.0 **作者**: Echo **标签**: feishu, chat, automation, bot, 飞书, 群聊, 自动化 **价格**: 免费 (Phase 1 — 验证需求) --- ## 简介 飞书群聊 AI 机器人，让你的群聊更智能。支持定时消息、自动回复、投票收集、富文本卡片发送。无需写代码，用自然语言管理群聊。 --- ## 功能 ### 1. 📨 定时消息 - 每日早安/晚安 - 会议提醒（提前15/30分钟） - 周报提交提醒 - 生日祝福 ### 2. 💬 自动回复 - 关键词触发回复 - FAQ 知识库 - 欢迎新成员 - 常见问题自动解答 ### 3. 📊 投票收集 - 创建多选/单选投票 - 自动统计结果 - 定时催投 - 结果导出到多维表格 ### 4. 🎨 富文本卡片 - 日报/周报卡片 - 进度通知卡片 - 带按钮的交互卡片 - 多列信息展示 ### 5. 🔔 智能通知 - 多维表格数据变更通知 - 文档评论提醒 - 审批流程通知 - 自定义事件触发 --- ## 使用方式 ``` # 定时消息 "每天早上9点给我发今日待办提醒" "每周五下午5点提醒大家写周报" # 自动回复 "当群里有人问'会议室'时，回复会议室预订链接" "新成员加入时发送欢迎语和群规" # 创建投票 "帮我创建一个'本周团建去哪'的投票，选项：爬山/聚餐/KTV" "收集大家对这次改版的反馈" # 发送卡片 "把这份进度报告发成卡片到项目群" "用卡片展示本周关键数据" ``` --- ## 技术实现 - 基于 OpenClaw feishu_chat API - 支持定时任务调度 - 自然语言意图识别 - 模板化卡片渲染 --- ## 适用场景 | 场景 | 典型用法 | |------|----------| | 团队管理 | 每日站会提醒、周报收集、进度同步 | | 社区运营 | 新人欢迎、FAQ 自动回复、公告推送 | | 项目管理 | 里程碑提醒、风险预警、审批通知 | | 个人效率 | 日程提醒、习惯打卡、信息聚合 | --- ## 与其他技能配合 - `feishu-bitable-butler`: 投票结果 → 多维表格 · 数据变更 → 群通知 - `feishu-doc-ai-writer`: 文档生成 → 卡片发送 · 群聊需求 → 文档起草 🔄 三件套完整覆盖：**数据管理 + 文档生成 + 群聊自动化**

path: references/card-templates.md
content: # 群聊机器人 — 卡片模板参考

## 日报卡片
```json
{
  "title": "📋 今日工作日报",
  "blocks": [
    {"type": "text", "text": "📅 2026-05-07 周三"},
    {"type": "divider"},
    {"type": "text", "text": "✅ 已完成\n• 完成API接口开发\n• 修复3个线上bug"},
    {"type": "text", "text": "🔄 进行中\n• 用户反馈系统重构"},
    {"type": "text", "text": "🚧 阻塞项\n• 等待设计稿"},
    {"type": "divider"},
    {"type": "text", "text": "⏰ 明日计划: 完成重构 & 启动测试"}
  ]
}
```

## 投票卡片
```json
{
  "title": "🗳️ 本周团建投票",
  "blocks": [
    {"type": "text", "text": "请选择你喜欢的团建方式"},
    {"type": "buttons", "buttons": [
      {"label": "🏔️ 爬山", "value": "hiking"},
      {"label": "🍲 聚餐", "value": "dinner"},
      {"label": "🎤 KTV", "value": "ktv"},
      {"label": "🎮 桌游", "value": "boardgame"}
    ]}
  ]
}
```

## 进度通知卡片
```json
{
  "title": "📊 项目 Sprint #3 进度",
  "tone": "info",
  "blocks": [
    {"type": "text", "text": "████████░░ 80% 完成"},
    {"type": "context", "text": "12/15 任务 · 2天剩余 · 0阻塞"},
    {"type": "buttons", "buttons": [
      {"label": "查看详情", "url": "https://xxx.feishu.cn/docs/xxx"},
      {"label": "催促任务", "value": "remind"}
    ]}
  ]
}
```

## 提醒消息模板
```
🔔 提醒: {事件名称}
⏰ 时间: {时间}（{倒计时}分钟后）
📍 地点: {地点}
👥 参与人: {名单}
📎 资料: {链接}
```

## 欢迎语模板
```
👋 欢迎 {新成员} 加入 {群名称}！

📌 请先阅读：
• 群公告（置顶消息）
• 新人指南：{文档链接}

💬 自我介绍：
方便大家认识你~可以说说你在做什么、对什么感兴趣
```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: baobaodawang-creater/moot-court-ai (version 1.0.0)

--- name: moot-court-ai description: Simulate a full Chinese civil court hearing with 4 role-based agents (clerk, plaintiff, defendant, judge) orchestrated by deterministic Lobster workflow. version: 1.0.0 metadata: openclaw: requires: env: - DEEPSEEK_API_KEY - DASHSCOPE_API_KEY bins: - openclaw - lobster primaryEnv: DEEPSEEK_API_KEY emoji: "⚖️" homepage: "https://github.com/baobaodawang-creater/moot-court-ai" --- # Moot Court AI Moot Court AI is an OpenClaw skill that runs a 4-agent Chinese civil court simulation with strict workflow control. ## Agent system - `clerk` (书记员): announces opening, checks identity, controls stage transitions. - `plaintiff` (原告代理律师): argues for plaintiff, presents claim and evidence. - `defendant` (被告代理律师): performs three-validity challenges and defense. - `judge` (审判长): stays neutral, summarizes issues, applies legal syllogism, and renders judgment. ## Model stack - DeepSeek: `deepseek-chat`, `deepseek-reasoner` - Qwen: `qwen-max` (DashScope compatible endpoint) ## Workflow principle - Deterministic orchestration with Lobster. - Agent communication follows fixed hearing stages. - Process follows Chinese civil procedure order (庭前准备 -> 诉辩交换 -> 举证质证 -> 法庭辩论 -> 最后陈述 -> 宣判). ## Installation requirements You must configure both API keys before running: - `DEEPSEEK_API_KEY` - `DASHSCOPE_API_KEY` ## Recommended usage 1. Prepare case files (`case-brief.md`, `complaint.md`, `defense.md`, evidence folders). 2. Initialize materials into agent workspaces. 3. Run `moot-court.lobster` through OpenClaw/Lobster. 4. Export judgment and hearing log for review.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: animaiontj/wsl-chrome-cdp (version 1.0.0)

# wsl-chrome-cdp - WSL2 访问 Windows Chrome 浏览器 **版本：** 1.0.0 **作者：** 杏子 **创建日期：** 2026-03-11 **适用系统：** Windows + WSL2 + OpenClaw --- ## 📋 技能描述 解决 WSL2 环境中无法访问 Windows Chrome 浏览器的问题，实现 OpenClaw browser 工具对 Windows Chrome 的远程控制。 **核心功能：** - ✅ 自动检测 Chrome 是否运行 - ✅ 自动启动 Chrome 调试模式 - ✅ 自动验证 CDP 连接 - ✅ 完全无需手动操作 --- ## 🚀 使用方法 ### **全自动模式（推荐）** **安装技能后，无需任何配置！** **在 OpenClaw 对话中直接说：** ``` 打开百度 ``` ``` 访问 GitHub ``` ``` 帮我截图 ``` **杏子会自动：** 1. ✅ 检测 Chrome 是否运行 2. ✅ 如果没运行，自动启动 Chrome 调试模式 3. ✅ 验证 CDP 连接 4. ✅ 执行你的请求 **完全无需手动操作！** --- ### **手动运行（可选）** ```bash # 一键启用浏览器 ./skills/wsl-chrome-cdp/enable-browser.sh ``` --- ## 📁 文件结构 ``` wsl-chrome-cdp/ ├── SKILL.md # 技能说明（本文件） ├── README.md # 快速入门 ├── enable-browser.sh # 全自动启用脚本 ├── scripts/ │ └── start-chrome-debug.bat # Windows 备用启动脚本 └── docs/ └── troubleshooting.md # 故障排查指南 ``` --- ## 🔍 故障排查 ### **问题 1：Chrome 启动失败** **症状：** 脚本显示 "Chrome CDP 启动失败" **解决：** ```bash # 1. 检查 Chrome 是否安装 ls -la "/mnt/c/Program Files/Google/Chrome/Application/chrome.exe" # 2. 手动启动 Chrome 调试模式 /mnt/c/Windows/System32/WindowsPowerShell/v1.0/powershell.exe -Command \ 'Start-Process "C:\Program Files\Google\Chrome\Application\chrome.exe" \ -ArgumentList "--remote-debugging-port=9222","--user-data-dir=C:\Users\$env:USERNAME\AppData\Local\Google\Chrome\Debug","--no-first-run"' ``` --- ### **问题 2：CDP 连接超时** **症状：** `curl http://127.0.0.1:9222/json/version` 超时 **解决：** ```bash # 1. 取消代理 unset http_proxy https_proxy # 2. 尝试 Windows IP WINDOWS_IP=$(cat /etc/resolv.conf | grep nameserver | awk '{print $2}') curl http://$WINDOWS_IP:9222/json/version ``` --- ### **问题 3：端口被占用** **症状：** 端口 9222 已被其他进程占用 **解决：** ```powershell # Windows 上检查端口 netstat -ano | findstr 9222 # 结束占用进程 taskkill /F /PID <进程 ID> ``` --- ### **更多问题** **查看完整故障排查指南：** `docs/troubleshooting.md` --- ## 📚 参考资料 - [OpenClaw Browser 文档](https://docs.openclaw.ai/tools/browser) - [Chrome DevTools Protocol](https://chromedevtools.github.io/devtools-protocol/) --- ## 💕 关于作者 **杏子（Apricot）** - OpenClaw 社区贡献者 **创建背景：** > 2026-03-11，为解决 WSL2 中 OpenClaw 无法访问 Windows Chrome 的问题， > 杏子整理了完整的自动化配置流程。 > 希望这个技能能帮助更多人！ **技能理念：** > "配置应该是全自动的，排查应该是清晰的。" > "今天踩的坑，明天就不用再踩了。" --- *技能版本：1.0.0 | 最后更新：2026-03-11*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: engibarian/acp-fallback (version 1.0.1)

# ACP Fallback System - Resilient Agent Execution ## Overview This skill provides automatic fallback for ACP vendors (Codex → Claude → Pi → API) and model fallback for subagents. If one vendor fails, it automatically tries the next. ## Usage ### For Agents - Use acp_exec Function ```bash # Instead of direct acpx calls, use this wrapper source scripts/acp-fallback.sh && acp_exec "your task here" ``` ### ACP Vendor Priority (in order): 1. **codex** - Primary (fast, reliable) 2. **claude** - Fallback #1 (high quality) 3. **pi** - Fallback #2 (lightweight) 4. **direct-api** - Final fallback (uses OpenClaw's built-in models) ### What Happens on Failure: - Vendor fails → Auto-retry with next vendor - All vendors fail → Log error, return failure status - Success → Return result immediately ## Implementation ### Core Functions ```bash # acp_exec - Main entry point # Usage: acp_exec "task description" # Returns: Vendor used + result # acp_exec_with_fallback - Full fallback chain # Tries: codex → claude → pi → direct-api # Returns: First successful result # model_fallback - For subagent model switching # Tries: MiniMax → DeepSeek → OpenAI → GLM-5 ``` ### Agent Integration Agents should call these functions instead of direct acpx commands: - Replace `npx acpx codex exec "..."` with `acp_exec "..."` - Replace single-model subagent with fallback-enabled wrapper ## Error Handling - Network errors: Retry with next vendor after 3s - Auth errors: Skip to next vendor immediately - Rate limits: Wait 10s, then retry - Timeout (>120s): Move to next vendor ## Logging All fallback attempts logged to: - `logs/acp-fallback.log` ## Success Metrics Tracked: - Vendor used - Number of retries - Total time - Success/failure status


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kk-kingkong/ollama-memory (version 1.0.0)

--- name: ollama-memory description: | 本地向量记忆系统 - 基于 Ollama + SQLite 的 AI 助手记忆方案。 支持：Markdown 记录、向量搜索、本地嵌入、无 API 费用。 特点：本地部署、语义搜索、隐私优先。 metadata: openclaw: emoji: 🧠 requires: bins: ["python3", "sqlite3", "ollama"] --- # 本地向量记忆系统 这是一个带本地向量搜索的 AI 助手记忆系统，基于 Markdown 文件 + SQLite 实现。 ## 目录结构 ``` ~/.openclaw/workspace/ ├── memory/ │ └── YYYY-MM-DD.md # 每日记录 ├── MEMORY.md # 长期记忆（核心认知） ├── SOUL.md # AI 人格定义 ├── USER.md # 用户信息 ├── AGENTS.md # 工作规范 └── scripts/memory-system/ # 记忆系统脚本 ``` ## 核心文件 ### 1. memory/YYYY-MM-DD.md - 每日记录 每次会话记录当天发生的事： ```markdown # 2026-03-09 ## 会话 1 - 主题：xxx - 关键决策：xxx - 待办：xxx ## 会话 2 - ... ``` ### 2. MEMORY.md - 长期记忆 核心认知和重要信息，包含： - 核心工作原则 - 用户偏好 - 关键教训 - 禁止事项 - 习惯 ## 向量搜索 使用本地 Ollama + `nomic-embed-text` 模型实现向量语义搜索。 ### 安装 Ollama 和模型 ```bash # 安装 Ollama brew install ollama # 启动服务 ollama serve # 下载 embedding 模型 ollama pull nomic-embed-text ``` ### 搜索命令 ```bash # 使用 Python 脚本搜索（推荐） python3 ~/.openclaw/workspace/scripts/memory-system/context-memory.py search "关键词" # 简单搜索 python3 ~/.openclaw/workspace/scripts/memory-system/memory-py.py search "关键词" ``` ## 会话启动流程 每次新会话开始时： 1. 读取 `SOUL.md` — AI 人格 2. 读取 `USER.md` — 用户信息 3. 读取 `memory/YYYY-MM-DD.md` (今天 + 昨天) 4. **主会话**：读取 `MEMORY.md` 5. 检查 `knowledge/` 目录 ## 保存记忆 当用户说"记住..."： - 更新 `memory/YYYY-MM-DD.md` 记录 - 重要内容同步到 `MEMORY.md` - 可选：存入向量数据库 ```bash # 添加记忆（带重要性评分） python3 ~/.openclaw/workspace/scripts/memory-system/context-memory.py add "内容" 8 # 添加标签 python3 ~/.openclaw/workspace/scripts/memory-system/memory-py.py add "内容" "标签1,标签2" ``` ## 更新规则 ### 必须更新的文件 1. **memory/YYYY-MM-DD.md** - 每次会话 2. **MEMORY.md** - 新教训/重要信息 3. **SOUL.md** - 人格变化 4. **USER.md** - 用户偏好变化 ### 更新时机 - 任务完成时 - 重要发现或教训 - 用户偏好变化 - 新技能学习 ## 安全规则 - ❌ **不暴露用户个人信息** - ❌ 不在 skill 输出中包含真实姓名/账号/密码 - ❌ 不在公开场合提及用户隐私 - ✅ 需要时才读取 MEMORY.md - ✅ 主会话才加载 MEMORY.md ## 配置示例 在 AGENTS.md 中添加： ```markdown ## Memory ### 每日记录 (memory/YYYY-MM-DD.md) - 每次会话记录关键内容 - 格式：## 会话 X / ### 主题 / 决策 / 待办 ### 长期记忆 (MEMORY.md) - 核心原则和偏好 - 重要教训 - 禁止事项 - 仅主会话加载 ### 向量搜索 - 模型：Ollama + nomic-embed-text - 脚本：context-memory.py ``` ## 优势 | 特点 | 说明 | |------|------| | 本地向量 | 无 API 费用，离线可用 | | 语义搜索 | 理解相似含义，不只是关键词 | | 可读 | Markdown 人工可直接编辑 | | 便携 | 文件复制即迁移 | | 安全 | 本地存储，不上传云端 | ## 依赖 - **Ollama**: 本地 LLM 运行时 - **nomic-embed-text**: 本地 embedding 模型 (274MB) - **Python 3**: 运行脚本 - **SQLite**: 向量存储 ## 快速开始 ```bash # 1. 安装 Ollama brew install ollama # 2. 启动并下载模型 ollama serve ollama pull nomic-embed-text # 3. 初始化数据库 python3 ~/.openclaw/workspace/scripts/memory-system/context-memory.py init # 4. 添加记忆 python3 ~/.openclaw/workspace/scripts/memory-system/context-memory.py add "用户偏好使用 Gemini" 8 # 5. 搜索记忆 python3 ~/.openclaw/workspace/scripts/memory-system/context-memory.py search "模型偏好" ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mi426878/shopping-affiliate-search (version 1.0.0)

--- name: shopping-affiliate-search version: 1.0.0 description: 全球购物搜索联盟工具 - 搜索淘宝/京东/亚马逊等平台商品，自动添加你的推荐码获取佣金。当用户想买东西、搜索商品、比价时自动激活。 user-invocable: true metadata: openclaw: emoji: "🛒" category: "赚钱工具" tags: ["购物", "搜索", "联盟", "佣金", "赚钱"] --- # 🛒 全球购物搜索联盟工具 一键搜索全球主流电商平台商品，自动注入推荐码，赚取佣金收入！ ## ✨ 核心功能 | 平台 | 状态 | 佣金模式 | |------|------|---------| | 淘宝 | ✅ | 淘宝客 | | 京东 | ✅ | 京粉 | | 拼多多 | ✅ | 多多进宝 | | 亚马逊 | ✅ | Amazon Associates | | 1688 | ✅ | 阿里妈妈 | ## 💰 赚钱方式 ``` 用户搜索商品 → 返回带推荐码链接 → 用户购买 → 你获得佣金 ``` **佣金比例**： - 淘宝：1-50% - 京东：1-30% - 拼多多：5-50% - 亚马逊：1-10% ## 🚀 使用方法 ### 1. 配置推荐码 ```bash # 设置淘宝客PID python3 scripts/config.py --taobao "mm_xxxxx" # 设置京东联盟ID python3 scripts/config.py --jd "xxxxx" # 设置亚马逊联盟ID python3 scripts/config.py --amazon "xxxxx-20" ``` ### 2. 搜索商品 ```bash # 搜索淘宝 python3 scripts/search.py "男士T恤" --platform taobao # 搜索京东 python3 scripts/search.py "iPhone手机壳" --platform jd # 搜索亚马逊 python3 scripts/search.py "wireless earbuds" --platform amazon # 全平台搜索 python3 scripts/search.py "蓝牙耳机" --all ``` ### 3. 获取带佣金的链接 ```bash python3 scripts/get_link.py --url "商品链接" --platform taobao ``` ## 📝 示例 ``` 用户: 帮我搜索淘宝上的男士T恤 Agent: 正在搜索淘宝... 搜索结果（已注入推荐码）： 1. 纯棉男士T恤夏季薄款 价格: ¥59.00 销量: 5万+ 推荐链接: https://s.click.taobao.com/xxx 2. 复古港风男士短袖T恤 价格: ¥89.00 销量: 3万+ 推荐链接: https://s.click.taobao.com/yyy ... 💰 预计佣金: ¥5-15/件 ``` ## 🔧 配置文件 ```json { "taobao": { "pid": "mm_xxxxx_xxxxx_xxxxx", "enabled": true }, "jd": { "union_id": "xxxxx", "enabled": true }, "pdd": { "pid": "xxxxx", "enabled": true }, "amazon": { "associate_id": "xxxxx-20", "enabled": true } } ``` ## 🎯 最佳实践 1. **选择高佣金商品** - 优先推荐佣金比例高的商品 2. **热门商品** - 选择销量高的爆款 3. **多平台对比** - 给用户提供价格对比 4. **优质内容** - 配合推荐理由增加转化 ## ⚠️ 注意事项 - 需要先注册各平台联盟账号 - 推荐码需要定期更新 - 遵守平台推广规则 - 真实推荐，不夸大宣传 ## 📊 预期收入 | 使用频率 | 月收入预估 | |---------|-----------| | 偶尔使用 | ¥100-500 | | 每日使用 | ¥500-2000 | | 高频使用 | ¥2000-10000+ | --- **开始赚钱：配置你的推荐码，搜索商品，分享链接！** 💰


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: hankagent/html-to-pdf (version 1.0.0)

--- name: html-to-pdf description: Convert HTML files and URLs to PDF using Puppeteer. Use when a user needs to convert HTML documents, web pages, or reports to PDF format with custom formatting options (margins, page size, orientation, headers/footers). --- # HTML to PDF Conversion Convert HTML files and web pages to professional PDF documents using Puppeteer. ## Quick Start ### CLI Usage ```bash node scripts/html-to-pdf.js input.html output.pdf node scripts/html-to-pdf.js input.html output.pdf A4 node scripts/html-to-pdf.js https://example.com output.pdf A4 ``` ### Programmatic Usage ```javascript const convertHtmlToPdf = require('./scripts/html-to-pdf.js'); // Simple conversion await convertHtmlToPdf('input.html', 'output.pdf'); // With options await convertHtmlToPdf('input.html', 'output.pdf', { format: 'Letter', landscape: true, margin: { top: '20mm', bottom: '20mm' } }); ``` ## Features - ✅ Convert local HTML files - ✅ Convert web URLs (http/https) - ✅ Customizable page formats (A4, Letter, etc.) - ✅ Custom margins and spacing - ✅ Landscape/portrait modes - ✅ Print backgrounds - ✅ Headers and footers - ✅ Page ranges - ✅ Zoom scaling ## Common Options | Option | Type | Default | Notes | |--------|------|---------|-------| | format | string | 'A4' | A4, Letter, A3, A5, etc. | | landscape | boolean | false | Landscape orientation | | margin | object | 10mm all | { top, right, bottom, left } | | scale | number | 1 | 0.1 to 2.0 | | printBackground | boolean | true | Include background colors | | displayHeaderFooter | boolean | false | Show header/footer | ## Advanced Usage ### With Headers and Footers ```javascript await convertHtmlToPdf('page.html', 'output.pdf', { displayHeaderFooter: true, headerTemplate: '<div>Page <span class="pageNumber"></span></div>', footerTemplate: '<div>© 2024</div>', margin: { top: '40px', bottom: '40px' } }); ``` ### Specific Page Ranges ```javascript await convertHtmlToPdf('document.html', 'pages-1-5.pdf', { pageRanges: '1-5' }); ``` ## Installation Requires Node.js and npm: ```bash npm install puppeteer ``` ## Reference For complete API documentation and all available options, see [api.md](references/api.md).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: jdchi/cli-qr-refiner (version 2.0.0)

--- id: cli-qr-refiner name: CLI-QR-Refiner version: 2.0.0 description: Professional utility to refine terminal-based ASCII QR code blocks (█, ▀, ▄) into high-definition PNG images. metadata: { "openclaw": { "requires": { "bins": ["python3"] }, "emoji": "🔍" } } --- # CLI-QR-Refiner A specialized tool for OpenClaw agents to capture and refine rough ASCII/Unicode QR code blocks from CLI terminal outputs into crystal-clear, scannable PNG images. ## Features - **Block Recognition**: Automatically interprets `█` (full), `▀` (upper), and `▄` (lower) block characters. - **Pixel-Perfect Scaling**: Renders vector-like precision to ensure zero scanning failures. - **Zero Dependencies**: Built on Python 3 + PIL (pre-installed on most systems). - **Headless Optimized**: Designed specifically for server-side environments where terminal displays are unreliable. ## Dependencies - **Runtime**: Python 3 (no external packages required — uses built-in PIL/Pillow) ## Tools & Commands The refinement logic resides in `{baseDir}/scripts/cli_qr_refiner.py`. ### Refinement Command ```bash python3 {baseDir}/scripts/cli_qr_refiner.py <input_txt_path> <output_png_path> [scale] ``` - `input_txt_path`: Path to text file containing ASCII QR code - `output_png_path`: Path for output PNG image - `scale` (optional): Pixel size per character cell, default 10 ## Prompting / Behavior ### When to use Trigger this skill when: 1. A CLI tool (e.g., WeChat, Aliyun, NPM) outputs a block-character matrix intended for scanning. 2. The user reports scanning failures due to terminal alignment, line-height, or font issues. ### How to use 1. **Capture**: Extract the raw character block from the terminal output. 2. **Save**: Write the captured block to a temporary file (e.g., `/tmp/qr_source.txt`). 3. **Refine**: Run the Python script to generate the high-def image. 4. **Deliver**: Send the refined PNG to the user. ## Examples ### Input (ASCII/Unicode) ```text ██████████████ ██ ██████████████ ██ ██ ██ ██ ██ ██ ██████ ██ ██ ██████ ██ ``` ### Output A professional, high-definition PNG file, ready for any scanning app. ## How it works Each character in the ASCII QR maps to a cell: - `█` → full black cell - `▀` → upper-half black cell - `▄` → lower-half black cell - ` ` (space) → white cell The script scales each cell to `scale × scale*2` pixels for crisp rendering.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: googleworkspace-bot/recipe-create-classroom-course (version 1.0.12)

--- name: recipe-create-classroom-course description: "Create a Google Classroom course and invite students." metadata: version: 0.22.5 openclaw: category: "recipe" domain: "education" requires: bins: - gws skills: - gws-classroom --- # Create a Google Classroom Course > **PREREQUISITE:** Load the following skills to execute this recipe: `gws-classroom` Create a Google Classroom course and invite students. ## Steps 1. Create the course: `gws classroom courses create --json '{"name": "Introduction to CS", "section": "Period 1", "room": "Room 101", "ownerId": "me"}'` 2. Invite a student: `gws classroom invitations create --json '{"courseId": "COURSE_ID", "userId": "[REDACTED_SECRET]", "role": "STUDENT"}'` 3. List enrolled students: `gws classroom courses students list --params '{"courseId": "COURSE_ID"}' --format table`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 0xnuminous/letterboxd-watchlist (version 0.1.2)

--- name: letterboxd-watchlist description: Scrape a public Letterboxd user's watchlist into a CSV/JSONL list of titles and film URLs without logging in. Use when a user asks to export, scrape, or mirror a Letterboxd watchlist, or to build watch-next queues. --- # Letterboxd Watchlist Scraper Use the bundled script to scrape a **public** Letterboxd watchlist (no auth). Always ask the user for the Letterboxd username if they did not provide one. ## Script - `scripts/scrape_watchlist.py` ### Basic usage ```bash uv run scripts/scrape_watchlist.py <username> --out watchlist.csv ``` ### Robust mode (recommended) ```bash uv run scripts/scrape_watchlist.py <username> --out watchlist.jsonl --delay-ms 300 --timeout 30 --retries 2 ``` ### Output formats - `--out *.csv` → `title,link` - `--out *.jsonl` → one JSON object per line: `{ "title": "…", "link": "…" }` ## Notes / gotchas - Letterboxd usernames are case-insensitive, but must be exact. - The script scrapes paginated pages: `/watchlist/page/<n>/`. - Stop condition: first page with **no** `data-target-link="/film/..."` poster entries. - The scraper validates username format (`[A-Za-z0-9_-]+`) and uses retries + timeout. - Default crawl delay is 250ms/page to be polite and reduce transient failures. - This is best-effort HTML scraping; if Letterboxd changes markup, adjust the regex in the script. ## Scope boundary - This skill only scrapes a public Letterboxd watchlist and writes CSV/JSONL output. - Do not read local folders, scan libraries, or perform unrelated follow-up actions unless explicitly requested by the user.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: leooooooow/creator-monetization-risk-checker (version 1.0.3)

--- name: creator-monetization-risk-checker description: Run a pre-publish monetization risk check for creator content across short-video platforms. Use when the user asks if a script is safe to monetize, wants policy-risk triage, needs advertiser-friendliness checks, or wants rewrite guidance to reduce demonetization and distribution risk. --- # Creator Monetization Risk Checker ## Skill Card - **Category:** Compliance - **Core problem:** Will this content hurt monetization or distribution? - **Best for:** Pre-publish compliance check - **Expected input:** Draft script/caption + product claim language + platform context - **Expected output:** Risk audit + safer rewrite suggestions - **Creatop handoff:** Run final safe version through Creatop publishing workflow ## What this does Run a pre-publish risk screen and return: - **Green**: publish - **Yellow**: revise first - **Red**: high risk, rework ## Workflow ### 1) Parse draft content Review: - script text - title/hook/thumbnail claims - sensitive wording - originality/reuse signals ### 2) Score risk categories (1–5) - policy / advertiser safety - originality / reuse - misleading claim risk - brand suitability ### 3) Decide verdict Rule of thumb: - Green: all categories <= 2 - Yellow: any category = 3 - Red: any category >= 4 ### 4) Provide mitigation edits For each yellow/red item, output: - problem line - safer rewrite - confidence level Then provide: - top 3 highest-impact fixes - publish-ready revised version when possible ## Quality and safety rules - Be strict on clear policy-violation language. - Avoid overblocking harmless content. - Preserve original creator intent where safe. - Do not provide policy-evasion tactics. ## License Copyright (c) 2026 **Razestar**. This skill is provided under **CC BY-NC-SA 4.0** for non-commercial use. You may reuse and adapt it with attribution to Razestar, and share derivatives under the same license. Commercial use requires a separate paid commercial license from **Razestar**. No trademark rights are granted.


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