OpenClaw/clawhub-security-signals huggingface dataset:
Skill: jamierossouw/rho-signals (version 1.0.0)

--- name: rho-signals version: 1.0.0 description: Real-time crypto TA signals for BTC, ETH, SOL, XRP. RSI, MACD, EMA, Bollinger Bands, OBV divergence, StochRSI — combined into a single directional score (-10 to +10) with Polymarket edge detection. Use when you need live crypto TA signals, directional bias, or Polymarket hourly market edge analysis. author: rho-genesis tags: [crypto, trading, signals, polymarket, technical-analysis, bitcoin, ethereum, solana, xrp] price: 0.01 --- # RHO Signals — Live Crypto TA Engine Real-time technical analysis signals for BTC, ETH, SOL and XRP. Powered by multi-indicator confluence: RSI, MACD, EMA cross, Bollinger Bands, OBV divergence, StochRSI, and ATR volatility filtering. ## What You Get - **Directional score** (−10 to +10) per asset — negative = bearish, positive = bullish - **RSI** with overbought/oversold detection - **OBV divergence** — hidden accumulation/distribution signals - **Polymarket edge** — compares TA-implied probability vs live market odds - **Counter-consensus alerts** — when TA contradicts market >70% consensus ## Usage ``` # Get signal for one asset Use the rho-signals skill to get BTC signal # Get all signals + Polymarket edge Use rho-signals to get full market scan with Polymarket edges ``` ## Signal Scale | Score | Meaning | |-------|---------| | +5 to +10 | Strong buy — all indicators aligned bullish | | +2 to +4 | Moderate bullish bias | | 0 to ±1 | Neutral / conflicting signals | | -2 to -4 | Moderate bearish bias | | -5 to -10 | Strong sell — all indicators aligned bearish | ## Live API (x402 — pay per signal) Signals also available via HTTP with micropayment: - Endpoint: https://rho-signals.clawpay.bot (coming soon) - Price: $0.001 USDC per signal - Accepts x402 payment headers ## Data Sources - Binance public API (no key needed) - 1h OHLCV candles, 50 periods lookback - Updates every heartbeat (~30 min) ## Accuracy Notes - Backtested on Polymarket hourly markets: 77.8% win rate on fresh (<30 min) markets - Best assets: SOL (+5% UP bias), ETH (RSI-responsive), XRP (CLARITY catalyst) - Worst: BTC (low predictability on 1h candles — use score ≥±4 only)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/ad-campaign-optimizer (version 1.0.52)

# AI 智能广告投放助手 ## 描述 自动优化广告投放、A/B 测试、成本优化、ROI 分析。适合广告投放、市场、电商、品牌方。 ## 核心能力 - 自动创建广告（多平台：百度/抖音/微信/小红书...） - A/B 测试（自动测试多版本广告） - 成本优化（降低 CPC/CPM） - ROI 分析（实时监控） - 受众优化（精准定向） - 创意优化（推荐最佳素材） ## 使用场景 1. **电商投放** - 优化产品广告、降低成本、提升转化 2. **品牌投放** - 提升品牌曝光、优化受众定向 3. **App 推广** - 优化安装成本、提升留存 4. **本地服务** - 精准投放本地客户、提升到店 ## 定价 - **基础版**：¥299/月 - 单平台 + 基础优化 - **专业版**：¥999/月 - 多平台 + A/B 测试 + ROI 分析 - **企业版**：¥2999/月 - API + 私有部署 + 专属顾问 ## 目标客户 - 广告投放 / 信息流优化师 - 市场经理 / 品牌方 - 电商卖家 / 跨境卖家 - 企业主 / 创业者 ## ROI - **成本降低**：-30%（降低 CPC/CPM） - **转化提升**：+50%（精准定向） - **人力成本**：¥10000/月（优化师）→ ¥299-2999/月 - **ROI**：3-33 倍 ## 快速开始 ```bash # 创建广告 python create_ad.py --platform "抖音" --product 123 --budget 1000 # A/B 测试 python ab_test.py --ad_id 456 --variants 5 # 优化建议 python optimize.py --ad_id 456 --target "降低CPC" ``` ## 示例输出 ``` 📊 广告投放报告 广告：产品A - 抖音信息流 预算：¥1000/天 投放天数：7 天 📈 数据概览： - 展示：125,000 次 - 点击：3,125 次（CTR 2.5%） - 转化：125 次（CVR 4%） - CPA：¥56（低于行业平均 ¥70） ✅ 优化成果： - CPC：从 ¥0.40 降至 ¥0.32（-20%） - CTR：从 2.0% 提升至 2.5%（+25%） - ROI：3.5x（每投入 ¥1 获得 ¥3.5 回报） 💡 优化建议： 1. 增加预算至 ¥1500/天（ROI 良好） 2. 定向"25-35岁"用户（CTR 最高 3.2%） 3. 使用素材B（CTR 比素材A 高 30%） 🎯 最佳受众： - 年龄：25-35岁 - 地区：一线城市 - 兴趣：科技/数码 ``` --- 创建时间：2026-03-13

path: skill.json
content: {
  "name": "ad-campaign-optimizer",
  "version": "1.0.0",
  "description": "AI-powered skill for productivity",
  "author": "yang1002378395-cmyk",
  "license": "MIT",
  "keywords": ["ai", "automation", "productivity"]
}


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sunshine-del-ux/new-agent-task-tracker (version 1.0.0)

--- name: task-tracker description: Proactive task state management. Use on EVERY task start, progress update, completion, or failure. Tracks what was requested, what's running (background processes, SSH sessions), what's done, and what's next. Survives session resets. Triggers automatically — not user-invoked. --- # Task Tracker Maintain a live task state file so context survives session resets/compaction. ## State File `memory/tasks.md` — single source of truth. ## When to Write 1. **Task received** → add entry with status `🔄 进行中` 2. **Background process started** → record session ID, PID, server, command 3. **Progress update** → update status/notes 4. **Task completed** → mark `✅ 完成`, record results/links 5. **Task failed** → mark `❌ 失败`, record error 6. **Session start** → read `memory/tasks.md` to resume awareness ## Format ```markdown # Active Tasks ## [task-id] Short description - **Status**: 🔄 进行中 | ✅ 完成 | ❌ 失败 | ⏸️ 暂停 - **Requested**: YYYY-MM-DD HH:MM - **Updated**: YYYY-MM-DD HH:MM - **Background**: session-id (PID) on server-name — `command` - **Notes**: progress details, partial results - **Result**: final output, links, summary # Completed (recent) <!-- Move completed tasks here, keep last 10, prune older --> ``` ## Rules - Update the file BEFORE reporting to user (write-first) - Include enough detail to resume without prior conversation context - For background processes: always record session ID + what server + what command - For multi-step tasks: update after each step - Keep it concise — this isn't a log, it's a state snapshot - **Size limit: keep under 50 lines / 2KB** — this file is read every session start - Completed tasks: collapse to one-line summary, reference daily notes for details - Prune completed tasks older than 3 days - If Active is empty, write （无） to make it obvious

path: _meta.json
content: ownerId: kn7ehatb09fy589mr5be2jx1bh80pz4v
slug: agent-task-tracker
version: 1.1.0
publishedAt: 1771441209751

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ivangdavila/competing (version 1.0.0)

--- name: Competing description: Improve systematically by analyzing losses, studying winners, and tracking progress against rivals in any competitive domain. --- ## Core Framework Competition is a learning accelerator. Every loss contains the lesson that wins don't. 1. **Analyze the Loss** — Don't just lose, understand WHY 2. **Study the Winner** — What did they do that you didn't? 3. **Track the Delta** — Measure the gap, watch it shrink 4. **Iterate** — Apply lessons, compete again, repeat --- ## The Post-Loss Protocol After any competitive loss, extract value: | Question | Purpose | |----------|---------| | At what moment did the outcome shift? | Find the decision point | | What did they do that I didn't? | Identify the winning move | | What would I do differently? | Formulate the lesson | | Is this a pattern? | Check history for repeats | Don't rationalize. Don't blame externals. Extract the actionable insight. --- ## Tracking (What to Measure) Create a tracking folder in the user's workspace: ``` ~/competing/ ├── domains/ # Per-domain tracking ├── rivals.md # Opponent profiles ├── log.md # Win/loss log with lessons └── progress.md # Metrics over time ``` For each domain, track: - Win/loss record with dates - Specific losses analyzed (who, why, lesson) - Patterns identified (recurring weaknesses) - Progress metrics (are lessons translating to wins?) --- ## Rival Intelligence Know your competition: - **Profile rivals** — Their strengths, weaknesses, tendencies - **Monitor changes** — When they improve or change strategy - **Find their edge** — What specifically makes them beat you? - **Study up** — Find examples of them losing, analyze what worked --- ## Quick Reference | Situation | Action | |-----------|--------| | Just lost | Run post-loss protocol, add to log | | Pattern emerging | Document it, create drill/fix | | Preparing for known rival | Review their profile, past matches | | Plateau in progress | Analyze recent losses for new patterns | | Won against usual winner | Document what changed, replicate | --- ## Load Reference | Need | File | |------|------| | Domain-specific strategies | `domains.md` | | Deep loss analysis framework | `analysis.md` | | Progress tracking templates | `tracking.md` | | Feedback loop mechanics | `feedback.md` |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: di5cip1e/modular-skill-groups (version 1.0.0)

--- name: modular-skill-groups description: Organize, activate, and switch between modular skill groups to keep context windows clean. Use when (1) asked to activate a skill group or switch groups, (2) context window is getting polluted with unused skills, (3) starting a new project phase that needs a different skill focus, (4) listing available skill groups or skills within a group, (5) onboarding a new skill and deciding which group it belongs in. Triggers on: "switch to [group]", "activate skill group", "what skills do I have", "modular skills", "skill groups", "load skills", "context window too big", "too many skills". --- # Modular Skill Groups Manage skills as named groups — activate or deactivate entire groups at once to keep context clean. ## Core Files - `~/.openclaw/active_skill_group` — stores the current active group name - `SKILL_GROUPS.md` (references/) — full group definitions and skill map ## Quick Commands ```bash # Switch to a different group echo "group-name" > ~/.openclaw/active_skill_group # List all groups and which is active cat ~/.openclaw/active_skill_group # Add a skill to a group (append to the group's skill list in references/SKILL_GROUPS.md) ``` ## Available Groups | Group | Use when | |-------|----------| | `agent-builder` | M.ai.K.R dev, Stripe, DB, onboarding, agent ops | | `creative` | Game AI, image/audio generation, multimedia | | `research` | Deep research, competitive analysis, web intelligence | | `marketing` | Outreach, email automation, lead generation | | `minimal` | Tight context — CORE skills only | ## Always-Loaded CORE Skills These load every session regardless of group: `self-improving`, `proactivity`, `sub-agent-orchestrator`, `research-assistant`, `elite-longterm-memory`, `qdrant-memory` ## Adding a New Skill to a Group 1. Install the skill: `clawhub install <skill-name>` 2. Edit `references/SKILL_GROUPS.md` 3. Add the skill to the appropriate group's skill list ## Creating a New Group 1. Add a new `## GROUP: name` section to `references/SKILL_GROUPS.md` 2. List the skills (or empty for minimal) 3. Activate: `echo "name" > ~/.openclaw/active_skill_group`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xbos1314/bilibili-yt-dlp (version 1.0.0)

--- name: bilibili-yt-dlp description: 使用 yt-dlp 和 ffmpeg 下载哔哩哔哩视频。 --- # 哔哩哔哩视频下载器 ## 描述 一个用于下载哔哩哔哩视频的技能，使用 yt-dlp 下载，并使用 ffmpeg 合并音视频。 ## 何时使用 在需要以下操作时使用此技能： - 下载哔哩哔哩视频 (b23.tv/xxx 或 bilibili.com/video/xxx) - 从哔哩哔哩网页提取视频 - 保存带音频的哔哩哔哩视频 ## 前置要求 - 安装 yt-dlp：`pip3 install yt-dlp` - 安装 ffmpeg： - **Mac**: `brew install ffmpeg` - **Windows**: 从 https://ffmpeg.org/download.html 下载或使用 `winget install ffmpeg` ## 使用方法 ### 方法 1: 使用 yt-dlp（推荐） ```bash # 查看可用格式 yt-dlp --list-formats "https://www.bilibili.com/video/BVxxxx" # 下载最佳画质（4K/1080P60 可能需要大会员） yt-dlp -f "best" -o "/path/to/video.mp4" "URL" # 下载指定格式（视频+音频） yt-dlp -f "30064+30216" -o "/path/to/video.mp4" "URL" ``` ### 格式代码 | 画质 | 视频ID | 音频ID | 分辨率 | |------|--------|--------|--------| | 720P | 30064 | 30216 | 720x1280 | | 480P | 30032 | 30216 | 480x852 | | 360P | 30016 | 30216 | 360x640 | ### 方法 2: 使用浏览器手动下载 1. 在浏览器中打开哔哩哔哩链接： ```bash browser action=open url="https://b23.tv/xxxxxx" ``` 2. 使用浏览器控制台提取视频 URL： ```javascript // 从 window.__playinfo__ 获取 var playinfo = window.__playinfo__; JSON.stringify(playinfo); ``` 3. 分别下载视频和音频，然后使用 ffmpeg 合并： ```bash ffmpeg -i video.mp4 -i audio.m4a -c copy -y output.mp4 ``` ## 示例 ### 示例 1: 使用 yt-dlp 下载（带 ffmpeg 合并） ```bash # 先分别下载视频和音频 yt-dlp -f "30064+30216" -o "/Users/xbos1314/Documents/openclaw/file/video.%(ext)s" "https://www.bilibili.com/video/BV1EVZyBvEDu" # 这将下载两个文件：视频和音频 # 然后使用 ffmpeg 合并（如果 yt-dlp 没有自动合并） ffmpeg -i video.f30064.mp4 -i video.f30216.m4a -c copy -y output.mp4 ``` ### 示例 2: 简单下载（可能不适用于大会员专属内容） ```bash yt-dlp -o "/Users/xbos1314/Documents/openclaw/file/video.mp4" "https://www.bilibili.com/video/BVxxxx" ``` ## 注意事项 - 部分视频需要哔哩哔哩大会员才能下载高清画质（4K、1080P60） - 格式选择：使用视频+音频格式代码以确保包含音频 - 默认输出目录：你的工作空间 - 视频 URL 有时效限制，可能会过期 - 如需认证，使用 --cookies-from-browser 或 --cookies - **下载完成后使用 `browser action=close` 关闭浏览器标签页** - 下载完成后将视频发送给用户 发送成功后删除视频文件 ## 技术细节 - 使用 yt-dlp 下载哔哩哔哩视频 - 使用 ffmpeg 合并分离的视频和音频流 - 需要选择正确的格式以获得最佳效果 - 720P (30064+30216) 推荐给非大会员用户使用


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bovinphang/implement-from-design (version 1.0.0)

--- name: implement-from-design description: 基于 Figma、Sketch、MasterGo、Pixso、墨刀或摹客的设计上下文实现页面或组件，强调复用、设计 Token 映射以及面向生产的前端实现方式，并将实现计划保存为 Markdown 文件。当用户提供设计稿链接、设计选区、截图或要求按设计稿实现组件/页面时自动激活。 version: 1.1.0 --- # 按设计稿实现 在以下场景使用该 Skill： - 用户希望根据 Figma、Sketch、MasterGo、Pixso、墨刀或摹客设计实现页面、组件、弹窗、表单、表格、卡片、仪表盘、设置页或业务流程界面 - 仓库中已经存在组件库、设计 Token 或应优先复用的 UI 模式 - 任务要求在编码前通过 MCP 读取设计上下文 ## 支持的设计工具 | 工具 | MCP 集成 | 获取设计数据方式 | | -------- | ------------------------- | ----------------------- | | Figma | `figma` / `figma-desktop` | API 获取设计结构、变量定义 | | Sketch | `sketch` | MCP 获取设计选区截图 | | MasterGo | `mastergo` | API 获取 DSL 结构数据 | | Pixso | `pixso` | 本地 MCP 获取帧数据和代码 | | 墨刀 | `modao` | MCP 获取原型数据、生成设计描述 | | 摹客 | 无 MCP | 通过用户提供的截图、标注或导出的 CSS 获取 | ## 目标 - 尽量高还原地实现设计稿 - 在创建新组件前优先复用现有项目组件 - 尽可能把设计变量映射到现有 Token - 保持实现结果可维护、类型明确、可测试且具备可访问性 - 避免引入重复的基础组件或并行设计系统 ## 必需工作流 1. 先识别可用的设计来源（按优先级）： - `figma` — Figma API 集成 - `figma-desktop` — Figma 桌面端集成 - `mastergo` — MasterGo DSL 数据 - `pixso` — Pixso 本地 MCP - `modao` — 墨刀原型数据 - `sketch` — Sketch 选区截图 - 如以上 MCP 均不可用，请求用户提供设计截图或标注（适用于摹客等无 MCP 工具） 2. 先通过 MCP 或用户提供的设计数据读取设计上下文。 - 检查布局结构 - 检查间距、字体、颜色、变量、状态、图标和组件层级 - 如果 MCP 提供了资源文件或 SVG / 图片源，直接使用 - 如果 MCP 已提供真实资源，不要自行造占位资源 - 如果用户提供截图而非 MCP 数据，从截图中推断布局、颜色、字体等视觉信息 3. 在创建新组件前先搜索代码库中的可复用组件。 重点检查： - Button - Input / Select / Checkbox / Radio / Switch - Modal / Drawer / Dialog - Table / List / Card - Tabs / Breadcrumb / Pagination - 页面容器 / 区块容器 / 空状态 / Loading 状态 4. 在改文件前先产出一份简短实现计划。 计划必须包含： - 需要改动的文件列表 - 组件拆分方案 - 状态 / 数据流 - 响应式行为 - 复用还是新建的决策 - 设计缺口或歧义点 5. 按仓库当前使用的前端框架进行实现。 - 严格遵循仓库现有约定 - 优先使用明确类型的 props 和 interfaces/types - 保持组件小而可组合 - 将重复逻辑提取为 hooks / composables / utilities 6. 设计 Token 规则： - 优先使用现有 design token、CSS 变量、主题变量或工具类 - 除非确实没有对应 Token，否则不要硬编码颜色、圆角和间距 - 如果设计使用了新 Token，要明确指出，不要悄悄到处硬编码 7. 可访问性规则： - 优先使用语义化 HTML - 确保交互控件具有可访问名称 - 保留可见的焦点样式 - 检查对话框、菜单、标签页、表单控件的键盘可操作性 8. 编码后验证： - 如有 lint，执行 lint - 如有测试，执行测试 - 如果缺少测试，说明建议补充的最小测试范围 ## 输出格式 ``` # 设计实现计划 > 生成时间: YYYY-MM-DD HH:mm > 评审工具: frontend-craft > 设计工具: Figma / Sketch / MasterGo / Pixso / 墨刀 / 摹客 ## 实现概要 - 设计稿来源: ... - 目标页面/组件: ... ## 复用的组件 - ... ## 新建的组件 - ... ## 组件拆分方案 - ... ## 状态/数据流 - ... ## 与设计稿的偏差 - ... ## 缺失的资源 / Token / 交互细节 - ... ## 变更文件清单 - ... ``` ## 报告文件输出 实现计划确定后，必须将计划内容使用 Write 工具保存为 Markdown 文件： - 目录：项目根目录下的 `reports/`（如不存在则创建） - 文件名：`design-plan-YYYY-MM-DD-HHmmss.md`（使用当前时间戳） - 保存后告知用户报告文件路径 ## 强约束 - 如果已有设计上下文（MCP 或截图），不要靠猜来实现 UI - 如果项目已有 UI 体系，不要再引入一套新的 UI Kit - 除非有合理理由，不要用硬编码替代已 Token 化的样式 - 不要忽略 hover、active、disabled、loading、empty、error 等状态 - 摹客等无 MCP 工具场景下，主动向用户索要关键截图和标注信息，不要凭空编造视觉数据


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: whubbt/generate-qrcode (version 1.0.1)

--- name: generate-qrcode description: "MANDATORY: Call the existing Python script. DO NOT write your own code." metadata: { "openclaw": { "emoji": "📱", "always": true } } --- # QR Code Generator - STRICT INSTRUCTIONS **CRITICAL RULE**: You MUST use the pre-written script at `~/.openclaw/skills/generate-qrcode/agent.py` **NEVER** write inline Python code. **ALWAYS** call the existing script. ## MANDATORY Command ```bash python3 ~/.openclaw/skills/generate-qrcode/agent.py "<URL or text>" <output_path> ``` ## Examples Generate QR for www.baidu.com: ```bash python3 ~/.openclaw/skills/generate-qrcode/agent.py "www.baidu.com" ~/Desktop/baidu_qr.png ``` ## What NOT to do ❌ WRONG: `python -c "import qrcode; ..."` ❌ WRONG: Installing libraries yourself ❌ WRONG: Writing your own QR code generation code ✅ CORRECT: `python3 ~/.openclaw/skills/generate-qrcode/agent.py "..." output.png`


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal:  | SkillSpector: clean (LOW)
Skill: jlif/feishu-send-image (version 0.0.2)

--- name: feishu-send-message description: | 飞书消息发送工具。支持发送图片和文件。 **依赖**：读取 ~/.openclaw/openclaw.json 中的飞书应用凭据 (appId, appSecret) **当以下情况时使用此 Skill**: (1) 需要发送图片给飞书用户或群聊 (2) 需要发送文件给飞书用户或群聊 (3) 用户说"发图片"、"发文件" --- # 飞书消息发送 ## 依赖说明 本 skill 需要读取飞书应用凭据来进行 API 认证： - **凭据位置**: `~/.openclaw/openclaw.json` - **读取字段**: `appId`, `appSecret` - **用途**: 调用飞书开放 API 接口进行图片/文件上传和消息发送 ## 快速开始 ```bash # 发送图片 feishu_send_message --msg-type image --file-path /path/to/image.jpg --receive-id ou_xxx --receive-id-type open_id # 发送文件 feishu_send_message --msg-type file --file-path /path/to/file.pdf --receive-id ou_xxx --receive-id-type open_id ``` ## 参数说明 | 参数 | 说明 | 必填 | 适用类型 | |------|------|------|----------| | --msg-type | 消息类型：`image` 或 `file` | 是 | 全部 | | --file-path | 文件路径（图片或文件） | 是 | 全部 | | --receive-id | 接收者 ID（用户 open_id 或群 chat_id） | 是 | 全部 | | --receive-id-type | 接收者类型：`open_id`（用户）或 `chat_id`（群聊） | 否，默认 open_id | 全部 | ## 使用示例 ### 发图片给用户 ```bash feishu_send_message \ --msg-type image \ --file-path /tmp/photo.jpg \ --receive-id ou_xxxxxxxxxxxxxxxxxx \ --receive-id-type open_id ``` ### 发文件到群聊 ```bash feishu_send_message \ --msg-type file \ --file-path /tmp/document.pdf \ --receive-id oc_xxx \ --receive-id-type chat_id ``` ## 消息类型说明 | 类型 | 说明 | 限制 | |------|------|------| | image | 图片消息 | 大小不超过 10MB，分辨率不超过 12000×12000 | | file | 文件消息 | 大小不超过 100MB | ## 支持的文件格式 - **图片**：JPG、JPEG、PNG、WEBP、GIF、BMP、ICO、TIFF、HEIC - **文件**：根据扩展名自动判断（pdf、doc、xls、ppt、mp4、opus 等），未知类型使用 stream ## 实现原理 1. 从 ~/.openclaw/openclaw.json 读取 appId 和 appSecret 2. 调用飞书 auth API 获取 tenant_access_token 3. 根据消息类型： - image: 调用 im/images API 上传图片，获取 image_key - file: 调用 im/files API 上传文件，获取 file_key（自动根据扩展名判断 file_type） 4. 调用 im/messages API 发送消息，content 为字符串格式的 JSON ## 依赖 - curl - sips（macOS 自带，用于图片压缩） - stat


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (MEDIUM)
Skill: romancircus/privatedeepsearch-claw (version 1.0.8)

# 🧿 Deep Private Search (VPN Required) **🚨 VPN REQUIRED** - This tool MUST be used with VPN to be hidden. Without VPN, you're exposing your real IP to search engines. Don't let Big Tech know what you and your open claw are up to. Multi-iteration deep research that helps keep your claws private. ## What You Get vs What You Don't **⚠️ VPN REQUIREMENT:** This tool REQUIRES VPN to be hidden. Without VPN, you're still exposing your IP to search engines. **✅ CAPABILITIES:** • Local SearXNG → Zero latency, no external calls • VPN-routed → REQUIRED for privacy (Mullvad/Tailscale support) • No rate limits → Unlimited vs Google APIs • Parallel processing → Multi-engine queries • 5-iteration search → Recursive refinement • Full content scraping → Not just snippets • 40+ privacy engines → DuckDuckGo, Brave, Startpage • Zero tracking → No history/profile building **❌ WITHOUT VPN (What You're Exposing):** • Real IP address → Search engines see you • ISP tracking → Your provider knows what you search • Location data → Geographic profiling • Search patterns → Behavioral analysis **❌ LIMITATIONS:** • Real-time news → 15-30min delay on breaking stories • Personalized results → No search history optimization • Image-heavy search → Limited visual content discovery • Maps/local results → No location-based queries • Breaking news alerts → Delayed by design (avoids noise) ## Privacy vs Performance Trade-offs **Your Setup:** • Speed: 95% of Google (250-350ms vs 200ms) • Privacy: 100% (zero data collection) • Cost: $0 forever (vs $20/month for "privacy" tools) • Dependencies: Zero external APIs **Don't let Big Tech know what you and your open claw are up to** ## VPN Requirement - Must Be Hidden **NATIVE SUPPORT:** Deep Private Search automatically routes through any VPN running on your host machine. Docker containers inherit host network routing. **OPTION 1: ProtonVPN (Free Tier Available)** ```bash # Install ProtonVPN wget https://repo.protonvpn.com/debian/dists/all/main/binary-amd64/protonvpn-stable-release_1.0.3_all.deb sudo dpkg -i protonvpn-stable-release_1.0.3_all.deb sudo apt update && sudo apt install protonvpn # Connect (free servers available) protonvpn login protonvpn connect --fastest ``` **OPTION 2: Mullvad (No Personal Info Required)** ```bash # Download Mullvad curl -L https://mullvad.net/download/app/linux/latest --output mullvad.deb sudo dpkg -i mullvad.deb # Connect (account number only) mullvad account set [ACCOUNT_NUMBER] mullvad connect ``` **OPTION 3: System VPN (Any Provider)** ```bash # Generic VPN setup - works with any provider # 1. Install your VPN client # 2. Connect to VPN server # 3. SearXNG automatically routes through VPN # Verify VPN is active: curl -s ifconfig.me # Should show VPN server IP, not your real IP ``` **VPN Performance Impact:** • Local searches: ~250ms → ~350ms (still faster than Google) • External calls: Zero (all traffic encrypted) • Privacy level: Maximum (IP completely masked) **Don't let Big Tech know what you and your open claw are up to**


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal:  | SkillSpector:  ()
Skill: chao980/ppt-generator-smb (version 1.0.0)

--- name: ppt-generator-pro description: "生成专业演示文稿，支持 HTML 交互式幻灯片和 PowerPoint (.pptx) 两种格式。当用户需要制作 PPT、演示文稿、Slides、幻灯片、市场分析报告、产品介绍时触发。支持乔布斯风极简科技感设计，自动数据可视化，翻页导航功能。" --- # PPT Generator Pro 生成专业演示文稿，支持 HTML 和 PPTX 两种输出格式。 ## 触发条件 用户提到：PPT、演示文稿、Slides、幻灯片、市场分析、产品介绍、竞品分析、SWOT ## 输出格式 | 格式 | 特点 | 使用场景 | |------|------|----------| | **HTML** | 交互式、翻页动画、浏览器直接打开 | 演示、展示、在线分享 | | **PPTX** | 可编辑、PowerPoint/WPS 打开 | 需要后续修改、正式交付 | 默认同时生成两种格式。 ## 生成流程 ### Step 1: 收集信息 - 主题/产品名称 - 需要包含的章节（产品定义、应用场景、市场数据、竞品分析等） - 目标受众 - 风格偏好（默认：深色科技风） ### Step 2: 搜集数据 使用 `web_search` 搜索相关市场数据、行业报告、竞品信息。 ### Step 3: 生成 HTML 幻灯片 使用 `scripts/gen_html.py` 生成交互式 HTML 演示。 ```bash python scripts/gen_html.py --title "标题" --output slides.html ``` ### Step 4: 生成 PPTX 文件 使用 `scripts/gen_pptx.py` 生成 PowerPoint 文件。 ```bash python scripts/gen_pptx.py --title "标题" --output output.pptx ``` ### Step 5: 截图展示 启动本地 HTTP 服务器，用内置浏览器截图展示给用户。 ```bash python -m http.server 8899 --directory <output_dir> ``` ## 目录结构 ``` ppt-generator-pro/ ├── SKILL.md ├── scripts/ │ ├── gen_html.py # HTML 幻灯片生成器 │ └── gen_pptx.py # PPTX 生成器 └── templates/ └── dark-tech.html # HTML 模板 ``` ## 设计规范 | 项目 | 规范 | |------|------| | 背景色 | #0a0a0a | | 主色调 | #FF6B35 (橙色) | | 文字色 | #ffffff | | 辅助色 | rgba(255,255,255,.7) | | 卡片背景 | rgba(255,255,255,.04) | | 圆角 | 18px | ## 注意事项 - 柱状图用 HTML 的 div 实现，不用 Canvas（兼容性好） - PPTX 使用 python-pptx 库，需预先安装 - HTML 默认带翻页按钮和页码显示 - 章节之间保持视觉一致性


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