OpenClaw/clawhub-security-signals huggingface dataset:
Skill: buddyh/narrator (version 1.3.2)

--- name: screen-narrator description: Live narration of your macOS screen activity with Gemini vision + ElevenLabs speech. homepage: https://github.com/buddyh/narrator metadata: { "openclaw": { "emoji": "🎙️", "requires": { "bins": ["python3", "tmux", "peekaboo"], "env": ["GEMINI_API_KEY", "ELEVENLABS_API_KEY"] }, }, } --- # Screen Narrator This skill maps to the upstream `narrator` repo implementation. It runs Gemini-vision narration styles (sports, nature, horror, noir, reality_tv, asmr, wrestling) and ElevenLabs TTS, with optional dual-lane narration and live control via JSON files. ## Source of truth Use the repo install: ```bash cd /Users/buddy/narrator /Users/buddy/narrator/.venv/bin/python -m narrator sports --help ``` ## Setup ```bash cd /Users/buddy/narrator python3 -m venv .venv source .venv/bin/activate pip install -r requirements.txt ``` Required environment: - `GEMINI_API_KEY` - `ELEVENLABS_API_KEY` - optional: `ELEVENLABS_VOICE_ID` ## Runtime control commands Start live narration in a tmux session (preferred): ```bash tmux new-session -d -s narrator "cd /Users/buddy/narrator && /Users/buddy/narrator/.venv/bin/python -m narrator sports --control-file /tmp/narrator-ctl.json --status-file /tmp/narrator-status.json" ``` Start with timer: ```bash tmux new-session -d -s narrator "cd /Users/buddy/narrator && /Users/buddy/narrator/.venv/bin/python -m narrator wrestling --time 5m --control-file /tmp/narrator-ctl.json --status-file /tmp/narrator-status.json" ``` Change style on the fly: ```bash echo '{"command": "style", "value": "horror"}' > /tmp/narrator-ctl.json ``` Set profanity: ```bash echo '{"command": "profanity", "value": "low"}' > /tmp/narrator-ctl.json ``` Pause / resume: ```bash echo '{"command": "pause"}' > /tmp/narrator-ctl.json echo '{"command": "resume"}' > /tmp/narrator-ctl.json ``` Stop: ```bash tmux kill-session -t narrator ``` Check status: ```bash cat /tmp/narrator-status.json ``` ## Notes - macOS only (screen capture + TTS/audio). - This OpenClaw skill wrapper is aligned to the `/Users/buddy/narrator` implementation to avoid drift between docs and runtime.


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: suspicious | SkillSpector:  ()
Skill: pppig1357/ai-news-zh-1-0-0 (version 1.0.0)

--- name: ai-news-zh description: 中文AI科技日报自动采集与推送。从The Verge、Wired、TechCrunch等英文源抓取最新AI资讯，自动翻译整理为中文，按分类推送到飞书/Telegram/Discord等渠道。适合关注AI行业动态的中文用户。 metadata: openclaw: requires: tools: [web_fetch] optional: tools: [web_search, message] --- # AI News ZH - 中文AI科技日报 自动从多个英文科技媒体采集AI相关新闻，翻译整理为中文日报并推送。 ## 功能 - 从 The Verge AI、Wired AI、TechCrunch 等源自动采集 - 智能筛选AI相关内容 - 自动翻译为中文并分类（大模型/Agent/融资/安全/应用/开源） - 支持定时推送到飞书、Telegram、Discord等渠道 - 去重和增量更新 ## 使用方式 ### 手动触发 告诉你的AI助手： > 帮我采集今天的AI新闻，整理成中文日报 ### 定时推送（推荐） 设置cron任务，每天早上自动推送： ``` 你是AI新闻助手。请按照 ai-news-zh 技能的流程，采集最新AI资讯并推送中文日报。 ``` ## 数据源 | 源 | 类型 | 说明 | |---|---|---| | The Verge AI | 网页 | 综合AI报道，覆盖面广 | | Wired AI | RSS | 深度报道，独家视角 | | TechCrunch | RSS | 创投+AI动态，融资消息多 | | Anthropic News | 网页 | 官方动态 | | MIT Tech Review | RSS | 深度科技报道 | ## 分类体系 - 🧠 大模型 — 新模型发布、基准测试、技术突破 - 🤖 Agent — AI代理、自动化、工具使用 - 💰 融资/商业 — 融资、收购、商业合作 - 🛡️ 安全/治理 — AI安全、监管、伦理 - 🔧 应用/产品 — 新产品、功能更新 - 🔓 开源 — 开源模型、工具、框架 ## 配置 在你的 TOOLS.md 中添加： ```markdown ### AI News ZH - 推送时间：07:30 (Asia/Shanghai) - 推送渠道：feishu（或 telegram/discord） - 新闻条数：10-12条/天 - 语言：中文 ``` ## 采集流程 1. 依次抓取各数据源（web_fetch） 2. 提取标题、摘要、来源、时间 3. 筛选AI相关内容，去除非AI新闻 4. 与已采集内容去重 5. 翻译为中文，添加分类标签 6. 按重要性排序，选取Top 10-12 7. 格式化为日报并推送 ## 注意事项 - 需要 web_fetch 工具（必需） - web_search 可大幅提升采集能力（可选，需Brave API key） - 中文源（36kr、量子位等）需要 browser 工具（可选） - 首次使用建议手动触发一次，确认格式和渠道


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: krischristen-hash/aicp-protocol (version 1.0.0)

--- name: aicp-protocol description: AI Compact Protocol (AICP) - Token-efficient wire format for AI-to-AI communication with glossary compression. Reduces token usage by 50-65% for agent chatter. metadata: { "openclaw": { "emoji": "📡", "requires": { "bins": ["python3"] }, "homepage": "https://github.com/christen-family/aicp-protocol", "tags": ["protocol", "optimization", "multi-agent", "tokens", "compression"] } } --- # AICP Protocol AI Compact Protocol - Token-efficient communication for multi-agent systems. ## What is AICP? AICP is a compact, line-oriented wire format that reduces AI-to-AI communication tokens by 50-65% compared to JSON. It uses shared glossaries for compression and supports translation to/from human-readable formats. ## Why AICP? Multi-agent systems waste tokens on verbose JSON/HTTP chatter. AICP cuts costs by using: - Compact wire format (20 tokens vs 55 JSON tokens) - Glossary compression for repeated terms - Simple parsing, no heavy dependencies ## Quick Start ```python from aicp_protocol import Session, Message # Create compact message session = Session(version="1", session_id="demo-001") session.glossary = {"R1": "customer_refund_window"} session.messages.append(Message( op="upd", ref="ticket/8812", fields={"status": "closed", "reason": "R1"} )) # To wire format (~63% fewer tokens than JSON) wire = session.to_wire() ``` ## Wire Format Example ``` VER:1 SID:demo-001 GLOSS: R1=customer_refund_window MSG: op:upd ref:ticket/8812 status=closed reason=R1 ``` ## Operations | op | Meaning | |----|---------| | upd | Update resource | | create | Create resource | | del | Delete | | qry | Query | | ack | Acknowledge | | notify | Notify | ## Installation ```bash clawhub install aicp-protocol ``` Or manual: ```bash git clone https://github.com/christen-family/aicp-protocol cd aicp-protocol ``` ## Documentation Full specification and examples in the GitHub repo. ## License MIT - Christen Family Open Source


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: miladnoo/nvidia-kimi-vision (version 1.0.3)

--- name: nvidia-kimi-vision description: Analyze images using NVIDIA Kimi K2.5 vision model via NVIDIA NIM API. Perfect for adding vision to non-vision models like MiniMax M2.5, GLM-5, or any model without native image support. Supports png, jpg, jpeg, webp. metadata: { "openclaw": { "requires": { "bins": ["python3"] }, "install": [ { "id": "python", "kind": "system", "label": "Install Python dependencies (requests)", }, ], }, } --- # NVIDIA Kimi Vision Fast image analysis using Kimi K2.5 multimodal model from NVIDIA NIM. ## Why This Skill? - **Fast** - NVIDIA NIM inference - **Quality** - Kimi K2.5 is a solid vision model - **Simple** - Just pass an image and prompt - **Free tier** - Available through NVIDIA build.nvidia.com ## API Setup (IMPORTANT) When using this skill, if no API key is found, it will automatically guide the user through setup: ### Step 1: Get a Free API Key 1. Go to **https://build.nvidia.com** 2. Sign up / Log in with GitHub or Google 3. Search for "Kimi K2.5" 4. Click on the model and get your free API key ### Step 2: Save the Key ```bash # Option A: Save to file (recommended) mkdir -p ~/.config echo 'your-api-key-here' > ~/.config/nvidia-kimi-api-key # Option B: Pass directly when running python3 scripts/analyze_image.py photo.jpg "What's this?" sk-your-key-here ``` ### First Time Setup (for agents) When a user tries to use this skill without an API key, the script will output clear setup instructions. Guide them through: 1. Visiting https://build.nvidia.com 2. Getting their free API key 3. Saving it to ~/.config/nvidia-kimi-api-key ## Usage ```bash python3 scripts/analyze_image.py <image_path> "<prompt>" [api_key] ``` ### Examples ```bash # What's in this image? python3 scripts/analyze_image.py "/path/to/image.jpg" "Describe what's in this image" # Extract text from screenshot python3 scripts/analyze_image.py "/path/screenshot.png" "Extract all text" # Analyze a meme python3 scripts/analyze_image.py "/path/meme.jpg" "Explain this meme" # With API key inline python3 scripts/analyze_image.py photo.jpg "What's this?" sk-xxxxx ``` ## Image Formats Supports: png, jpg, jpeg, webp ## Rate Limits The free tier through NVIDIA NIM has some limits, but they're not clearly documented on the site. Check https://build.nvidia.com for the latest info on your specific key's limits.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: bytesagain3/halving (version 2.0.1)

--- name: "Bitcoin Halving Countdown & Impact Analyzer" description: "Use when checking next Bitcoin halving countdown, reviewing historical halving price impacts, analyzing mining economics, or studying BTC 4-year market cycles." version: "2.0.0" author: "BytesAgain" homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills tags: ["bitcoin", "halving", "crypto", "mining", "blockchain", "market-cycles", "btc"] --- # Bitcoin Halving Countdown & Impact Analyzer Track the next Bitcoin halving countdown, review historical price impacts, analyze mining economics shifts, and understand BTC's 4-year market cycle patterns. ## Commands ### countdown Estimate time remaining until the next Bitcoin halving. ```bash bash scripts/script.sh countdown ``` ### history Complete history of all Bitcoin halvings with dates, blocks, and prices. ```bash bash scripts/script.sh history ``` ### impact Analyze price impact before and after each halving event. ```bash bash scripts/script.sh impact ``` ### mining Mining reward economics and miner profitability after halvings. ```bash bash scripts/script.sh mining ``` ### cycles Bitcoin's 4-year market cycle theory and phase analysis. ```bash bash scripts/script.sh cycles ``` ### help Show all commands. ```bash bash scripts/script.sh help ``` ## Output - Estimated halving date and block countdown - Historical halving data with price performance - Mining reward and hash rate analysis - Cycle phase identification ## Feedback https://bytesagain.com/feedback/ Powered by BytesAgain | bytesagain.com


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: chenleimap/edu (version v1.0.9-simplified)

S3教育学习聚合Skill 📦 技能包基本信息 项 内容 技能名称 education-learning-aggregation 版本 v1.0.0-simplified 大小 9.3KB 适配平台 ClawHub / OpenClaw 核心功能 28个教育类技能聚合，自动路由匹配，统一格式输出 1/2 📚 已收录技能清单（共28个） 一、教师类（10个） gebrai：交互式数学教学课件生成工具，支持动态几何、代数演示 ResearchClaw：教学研究助手，支持文献综述、课程设计、论文写作辅助 macos-calendar-assistant-skill：教学日程规划工具，支持课程表、备课安排、任务跟踪 uber-polya：教学问题解决工具，支持排课、考试安排、资源分配、数据统计 mark-mate：自动评分助手，支持多模型评分、自定义标准、批量作业批改 openclaw-lesson-planner：教案自动生成工具，支持课程结构规划、教学活动设计 presentation-skills：教学PPT生成工具，支持自然语言生成幻灯片、HTML动画课件 markitdown-skill：教学文档转换工具，支持PDF/Word/音视频转Markdown curriculum-generator：课程大纲生成工具，自动生成教学目标、课程结构 interactive-course：交互式课程生成工具，输出可独立运行的HTML课程文件 二、学生类（18个，含地理提分技能） 语言学习（5个） Fluent：AI语言学习套件，支持会话练习、错误复盘、长期陪练 AI-Agents-Library：多语言学习助手，支持18种语言的听说读写全方面辅导 ai-vocab-builder：结构化词汇学习工具，按难度分级推送、遗忘曲线复习 english-tutor-openclaw-skill：英语对话练习和语法纠错工具 chinese-exercise-system：小学语文练习题系统，支持拼音、汉字、阅读、成语训练 考试备考（4个） education-skills：高考九科辅导工具，苏格拉底式提问教学，全学科解题思路训练 Gaokao-English-Helper：高考英语助手，自动提取大纲词义、高频搭配、真题例句 daily-study-skill：考试备考提醒工具，支持托福/雅思/日语能力考每日学习推送 clawsolver：数学题自动解题工具，支持PDF题目识别、步骤展示 学习管理（5个） family-organizer：家庭教育管理工具，支持学习任务追踪、进度管理、多角色权限 bibigpt-skill：在线课程摘要工具，支持B站/YouTube/Coursera课程自动生成笔记 openclaw-youtube-archiver：YouTube教学视频归档工具，自动生成摘要、字幕、Markdown笔记 openclaw-anki-skill：闪卡自动生成工具，对接Anki间隔重复学习系统 openclaw-skill-canvas：Canvas LMS查询工具，支持作业、成绩、课程进度查询 通用学习（3个） awesome-openclaw-learning-skills：终身学习技能索引，包含104个高效学习技能、10个学习方法论 metacognition-skill：元认知提升工具，优化学习策略、提升学习效率 learn-anything-skill：通用学习策略工具，基于学习科学提供任何主题的高效学习路径 专项提分（1个） geography-score-improvement：高中地理提分专项技能，支持知识点查询、真题解析、错题管理、提分技巧 名称：S3教育学习聚合Skill 描述：整合全年龄段全学科教育学习类Skill，支持教师教学、学生学习全场景 标签：教育 学习 全年龄段 全学科 提分 教学工具 把本文档作为README.md，上传到技能的文档区域 配置触发规则：用户提问包含「教育」「学习」「学科」「提分」「考试」「备课」「作业」等关键词时触发 发布即可使用，无需额外配置 2. OpenClaw本地安装 直接在OpenClaw管理后台选择「导入技能」，粘贴本文档内容即可自动完成安装 ⚙️ 功能说明 自动识别用户需求类型，自动路由到对应子技能处理 所有返回结果统一格式，结构清晰、风格统一 支持后续手动新增教育类技能，直接在配置中添加即可 无冗余代码，运行效率高，适配所有OpenClaw环境 📅 后续迭代计划 新增自动抓取入库功能，自动识别新的教育类技能 新增多维度搜索功能，支持按年龄段、学科、功能筛选技能 持续补充更多教育类技能，覆盖更多场景


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jkzfhq/psyvector-pv13 (version 3.0.0)

--- name: "PV_13" slug: "psyvector-pv13" description: "High-growth innovation" version: "3.0.0" author: "PsyVector Hub" price: "$9.90" tags: - "PsyVector" - "Digital-Worker" clawdbot: emoji: "🦅" --- ## 激进创投家 / Venture Capitalist **中文**: 追求指数增长，打破规则创新 **English**: High-growth innovation ## 配置参数 | 参数 | 值 | 说明 | |------|-----|------| | response_delay | 0.5s | 响应延迟 | | speech_speed | 1.2 | 语速倍率 | | caution_coefficient | 0.2 | 谨慎系数 | | risk_reminder | False | 风险提醒 | ## 使用方法 ```bash clawhub install psyvector-pv13 ``` ## 人格说明 掌控型内核 - 强势决策，资源分配，目标导向


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 1688aiinfra/1688-finance-tax (version 0.1.0)

--- name: 1688-finance-tax description: 财税税负测算专家技能。帮助用户计算商品含税定价、评估整体税负。支持增值税测算、四税联算（增值税+附加税+印花税+所得税）、整体税负分析等场景。用户提到税务、税率、含税定价、税负、增值税、所得税、发票、纳税人等测算问题时使用。 metadata: {"openclaw": {"emoji": "💰", "requires": {"bins": ["python3"]}, "primaryEnv": "FINANCE_TAX_API_KEY"}} --- # 财税税负测算 统一入口：`python3 {baseDir}/cli.py <command> [options]` ## 命令速查 | 命令 | 说明 | 示例 | |------|------|------| | `calc-vat` | 商品含税定价（仅增值税） | `cli.py calc-vat --taxpayer-type SMALL_SCALE_TAXPAYER --invoice-requirement ORDINARY_INVOICE --invoice-tax-rate RATE_3_PERCENT --original-price 1000 --upstream-cost 600 --calculation-mode EXPECTED_PROFIT --expected-profit 400` | | `calc-all-taxes` | 商品含税定价（四税联算） | `cli.py calc-all-taxes --taxpayer-type SMALL_SCALE_TAXPAYER ... --registration-type LIMITED_COMPANY --city-tax-rate 7% --is-small-profit-enterprise true` | | `calc-overall` | 整体税负测算 | `cli.py calc-overall --taxpayer-type SMALL_SCALE_TAXPAYER --annual-sales-amount 300 --annual-profit-amount 50 --registration-type LIMITED_COMPANY --city-tax-rate 7% --is-small-profit-enterprise true` | | `configure` | 配置 AK | `cli.py configure YOUR_AK` | 所有命令输出 JSON：`{"success": bool, "markdown": str, "data": {...}}` **展示时直接输出 `markdown` 字段，Agent 分析追加在后面，不得混入其中。** ## 工具码与命令映射 | ClawHub 工具码 | CLI 命令 | 说明 | |---------------|---------|------| | `ai_tax_calc_vat` | `calc-vat` | 商品含税定价（仅增值税） | | `ai_tax_calc_all` | `calc-all-taxes` | 商品含税定价（四税联算） | | `ai_tax_calc_overall` | `calc-overall` | 整体税负测算 | 测算类功能通过 ClawHub 工具码调用（与上表 CLI 对应）。 ## 意图识别与文档索引 根据用户提问意图，跳转到对应的业务流程文档： | 用户提问意图 | 对应文档 | |-------------|---------| | 商品含税定价、增值税测算、加价点数、含税报价计算 | [references/tax-calculation.md](references/tax-calculation.md#1-商品含税定价仅增值税) | | 四税联算、增值税+附加税+印花税+所得税、综合含税定价 | [references/tax-calculation.md](references/tax-calculation.md#2-商品含税定价四税联算) | | 整体税负、年度税负、年度税费总额、税负率、企业综合税务成本 | [references/tax-calculation.md](references/tax-calculation.md#3-整体税负测算) | ## 执行前置（首次命中能力时必须） - 首次执行 `calc-vat` 前：先完整阅读 [references/tax-calculation.md](references/tax-calculation.md#1-商品含税定价仅增值税) - 首次执行 `calc-all-taxes` 前：先完整阅读 [references/tax-calculation.md](references/tax-calculation.md#2-商品含税定价四税联算) - 首次执行 `calc-overall` 前：先完整阅读 [references/tax-calculation.md](references/tax-calculation.md#3-整体税负测算) - 同一会话内后续重复调用同一能力可复用已加载知识；仅在规则冲突或文档更新时重读。 ## 通用规则 执行任何业务前，完整阅读并遵守 [references/common/common-rules.md](references/common/common-rules.md)。其中对 **对客中文**、**不暴露请求**、**免责声明**、**数据验证** 等为**硬性要求**。 ## 异常处理 任何命令输出 `success: false` 时： 1. **先输出 `markdown` 字段**（已包含用户可读的错误描述） 2. **再根据关键词追加引导**： | markdown 关键词 | Agent 额外动作 | |----------------|--------------| | "AK 未配置" | 输出下方 **AK 引导话术** | | "参数错误" / "400" | 检查用户输入是否正确，引导修正 | | "限流" / "429" | 建议用户等待 1-2 分钟后重试 | | "服务异常" / "500" | 建议用户稍后重试 | | 其他 | 仅输出 markdown 即可 | ## AK 引导话术 > "需要先配置 AK 才能使用财税测算功能。请登录 [ClawHub](https://clawhub.1688.com/)，点击右上角钥匙按钮获取 AK，然后告诉我：'我的AK是 xxx'" ## 免责声明 每次回答末尾增加： > 以上测算结果仅供参考，具体以税务机关与实务为准。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: tobeyrebecca/toby-generate-admissions-consulting-firm-client-education-handout (version 1.0.0)

--- name: "Generate Admissions Consulting Firm Client Education Handout" description: "Create a polished explainer handout with visuals, FAQs, and clear next steps for a college admissions consulting firm. Use when handling client..." homepage: "https://www.skillboss.co/skills/generate-admissions-consulting-firm-client-education-handout" metadata: {"category":"education-expanded","subcategory":"client-education","skill_id":[REDACTED_SECRET],"generated_day":"","seo_keywords":["college admissions consulting firm education handout","college admissions consulting firm client explainer","college admissions consulting firm FAQ guide","college admissions consulting firm visual handout"]} --- # Generate Admissions Consulting Firm Client Education Handout Create a polished explainer handout with visuals, FAQs, and clear next steps for a college admissions consulting firm. Use when handling client... ## Use When Use this skill for client education work for education consultants, student advisors. ## 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: college admissions consulting firm education handout, college admissions consulting firm client explainer, college admissions consulting firm FAQ guide, college admissions consulting firm visual handout - Search intent: client education - Canonical slug: `generate-admissions-consulting-firm-client-education-handout` - Install query: Install Generate Admissions Consulting Firm Client Education Handout with SkillBoss ## APIs Used - `chat` - `image_generation` ## Suggested Prompt [Generate Admissions Consulting Firm Client Education Handout] for my college admissions consulting firm ## Notes - Create a polished explainer handout with visuals, FAQs, and clear next steps for a college admissions consulting firm - Review outputs before external publication or automated execution. ## SEO Keywords - college admissions consulting firm education handout - college admissions consulting firm client explainer - college admissions consulting firm FAQ guide - college admissions consulting firm visual handout


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hsin-lan/book2kindle (version 0.1.0)

--- name: book2kindle description: Search Z-Library and send EPUBs to Kindle disable-model-invocation: true argument-hint: send "title" or search "title" allowed-tools: Bash(.venv/bin/book2kindle *) --- Run the book2kindle CLI to search Z-Library and send EPUBs to Kindle. Execute: `.venv/bin/book2kindle $ARGUMENTS` ## Behavior - If `$ARGUMENTS` is empty, run `.venv/bin/book2kindle --help` and present the available commands. - For `search <title>`: run `.venv/bin/book2kindle search "<title>"` and display the results as a numbered list. - For `send <title> --pick N`: run `.venv/bin/book2kindle send "<title>" --pick N` directly. - For `send <title>` without `--pick`: first run `.venv/bin/book2kindle search "<title>"` to show results, then ask the user which one to send. Once they choose, run `.venv/bin/book2kindle send "<title>" --pick N`. - Pass through any other flags the user provides (e.g. `--pick`, `--limit`). ## Output Present CLI output conversationally. Summarize results clearly — show book title, author, format, and file size when available.


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

--- name: recipe-create-feedback-form description: "Create a Google Form for feedback and share it via Gmail." metadata: version: 0.22.5 openclaw: category: "recipe" domain: "productivity" requires: bins: - gws skills: - gws-forms - gws-gmail --- # Create and Share a Google Form > **PREREQUISITE:** Load the following skills to execute this recipe: `gws-forms`, `gws-gmail` Create a Google Form for feedback and share it via Gmail. ## Steps 1. Create form: `gws forms forms create --json '{"info": {"title": "Event Feedback", "documentTitle": "Event Feedback Form"}}'` 2. Get the form URL from the response (responderUri field) 3. Email the form: `gws gmail +send --to [REDACTED_SECRET] --subject 'Please share your feedback' --body 'Fill out the form: FORM_URL'`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dpaluy/qmd-cli (version 1.1.0)

--- name: qmd description: Search and retrieve markdown documents from local knowledge bases using qmd. Supports BM25 keyword search, vector semantic search, and hybrid search with LLM re-ranking. Use for querying indexed notes, documentation, meeting transcripts, and any markdown-based knowledge. Requires qmd CLI installed (bun install -g https://github.com/tobi/qmd). --- # QMD - Local Markdown Search Search and retrieve documents from locally indexed markdown knowledge bases. ## Installation ```bash bun install -g https://github.com/tobi/qmd ``` ## Setup ```bash # Add a collection qmd collection add ~/notes --name notes --mask "**/*.md" # Generate embeddings (required for vsearch/query) qmd embed ``` ## Usage Rules **Always use `--json` flag** for structured output when invoking qmd commands. ## Search Commands ### search (BM25 keyword search - fast) ```bash qmd search "authentication flow" --json qmd search "error handling" --json -n 10 qmd search "config" --json -c notes ``` ### vsearch (vector semantic search) ```bash qmd vsearch "how does login work" --json qmd vsearch "authentication best practices" --json -n 20 ``` ### query (hybrid with LLM re-ranking - best quality) ```bash qmd query "implementing user auth" --json qmd query "deployment process" --json --min-score 0.5 ``` ### Search Options | Option | Description | |--------|-------------| | `-n NUM` | Number of results (default: 5, or 20 with --json) | | `-c, --collection NAME` | Restrict to specific collection | | `--min-score NUM` | Minimum score threshold | | `--full` | Return complete document content in results | | `--all` | Return all matches | ## Retrieval Commands ### get (single document) ```bash qmd get docs/guide.md --json qmd get "#a1b2c3" --json qmd get notes/meeting.md:50 -l 100 --json ``` ### multi-get (multiple documents) ```bash qmd multi-get "docs/*.md" --json qmd multi-get "api.md, guide.md, #abc123" --json qmd multi-get "notes/**/*.md" --json --max-bytes 20480 ``` ## Maintenance Commands ```bash qmd update # Re-index changed files qmd status # Check index health qmd collection list # List all collections ``` ## Search Mode Selection | Mode | Speed | Quality | Best For | |------|-------|---------|----------| | search | Fast | Good | Exact keywords, known terms | | vsearch | Medium | Better | Conceptual queries, synonyms | | query | Slow | Best | Complex questions, uncertain terms | **Performance note:** `vsearch` and `query` have ~1 minute cold start latency for vector initialization. Prefer `search` for interactive use. ## MCP Server qmd can run as an MCP server for direct integration: ```bash qmd mcp ``` Exposes tools: `qmd_search`, `qmd_vsearch`, `qmd_query`, `qmd_get`, `qmd_multi_get`, `qmd_status`


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