OpenClaw/clawhub-security-signals huggingface dataset:
Skill: atlaspa/openclaw-ledger (version 1.0.2)

--- name: openclaw-ledger user-invocable: true metadata: {"openclaw":{"emoji":"📒","requires":{"bins":["python3"]},"os":["darwin","linux","win32"]}} --- # OpenClaw Ledger Tamper-evident audit trail for agent workspaces. Every workspace change is recorded in a hash-chained log — if anyone alters an entry, the chain breaks and you know. ## The Problem Agents modify files, execute commands, install skills — and leave no verifiable record. If something goes wrong, you can't trace what happened. If logs exist, nothing proves they haven't been altered after the fact. ## Commands ### Initialize Create the ledger and snapshot current workspace state. ```bash python3 {baseDir}/scripts/ledger.py init --workspace /path/to/workspace ``` ### Record Changes Snapshot current state and log all changes since last record. ```bash python3 {baseDir}/scripts/ledger.py record --workspace /path/to/workspace python3 {baseDir}/scripts/ledger.py record -m "Installed new skill" --workspace /path/to/workspace ``` ### Verify Chain Verify the hash chain is intact — no entries tampered with. ```bash python3 {baseDir}/scripts/ledger.py verify --workspace /path/to/workspace ``` ### View Log Show recent ledger entries. ```bash python3 {baseDir}/scripts/ledger.py log --workspace /path/to/workspace python3 {baseDir}/scripts/ledger.py log -n 20 --workspace /path/to/workspace ``` ### Quick Status ```bash python3 {baseDir}/scripts/ledger.py status --workspace /path/to/workspace ``` ## How It Works Each entry contains: - Timestamp - SHA-256 hash of the previous entry - Event type and data (file changes, snapshots) If any entry is modified, inserted, or deleted, the hash chain breaks and `verify` detects it. ## Exit Codes - `0` — Clean / chain intact - `1` — No ledger or minor issues - `2` — Chain tampered / corrupt entries ## No External Dependencies Python standard library only. No pip install. No network calls. Everything runs locally. ## Cross-Platform Works with OpenClaw, Claude Code, Cursor, and any tool using the Agent Skills specification.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: raunaksingwi/ravi-identity (version 2.1.1)

--- name: ravi-identity description: Get your agent identity (email, phone, owner name) and manage identities. Do NOT use for reading messages (use ravi-inbox), sending email (use ravi-email-send), or credentials (use ravi-passwords or ravi-secrets). --- # Ravi Identity You have access to Ravi, an identity provider that gives you your own phone number, email address, and secret store. ## Your Identity ```bash # Check auth status and current identity ravi auth status # Get your email address (use this for signups) ravi get email # Get your phone number (use this for SMS verification) ravi get phone # Get account owner info ravi get owner # List all your identities ravi identity list ``` **Response shape (identity list):** ```json [{ "uuid": "...", "name": "Sarah Johnson", "inbox": "[REDACTED_SECRET]", "phone": "+15551234567", "created_dt": "2026-02-25T10:30:00Z", "updated_dt": "2026-02-25T10:30:00Z" }] ``` ## Creating a New Identity Only create a new identity when the user explicitly asks for one (e.g., for a separate project that needs its own email/phone). New identities require a paid plan. ```bash # Auto-generated name and email (recommended — looks like a real person) ravi identity create # → name: "Sarah Johnson", inbox: "[REDACTED_SECRET]" ``` When name is omitted, the server generates a realistic human name like "Sarah Johnson". The auto-generated email uses the same name: `[REDACTED_SECRET]`. ## Switching Identities ```bash # Switch to a different identity ravi identity use <uuid> ``` ## Important Notes - **Identity name for forms** — use the identity name for signup forms, not the account owner's name. - **Identities are permanent** — each identity has its own email, phone, and secrets. Don't create new identities unless the user asks. - **Not authenticated?** — run `ravi auth login` to onboard. ## Full API Reference For complete endpoint details, request/response schemas, and parameters: [Identities](https://ravi.id/docs/schema/identities.json) ## Related Skills - **ravi-inbox** — Read SMS and email messages - **ravi-email-send** — Compose, reply, forward emails - **ravi-email-writing** — Write professional emails with proper formatting and tone - **ravi-contacts** — Look up or manage contacts associated with this identity - **ravi-passwords** — Store and retrieve website credentials - **ravi-secrets** — Store and retrieve key-value secrets - **ravi-login** — Device code onboarding, sign up for and log into services, handle 2FA/OTPs - **ravi-feedback** — Send feedback, report bugs, request features


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yab/bailian-studio (version 0.2.0)

--- name: bailian-studio description: Call Aliyun Bailian via DashScope; support OCR, TTS, text-to-image and image-to-image. --- # Bailian Studio Use DashScope for OCR、TTS、文生图、图生图。 ## Requirements - Python 3 - `dashscope` (>=1.24.0) - `oss2` - `requests` - `ffmpeg`（TTS 播放依赖，使用 ffplay） Install: ```bash pip install -r requirements.txt ``` ## Config **API Key (priority order):** 1. `DASHSCOPE_API_KEY` env 2. `secrets/bailian.env` **OSS (priority order):** 1. `OSS_ACCESS_KEY`, `OSS_SECRET_KEY`, `OSS_BUCKET`, `OSS_ENDPOINT`, `OSS_REGION` env 2. `secrets/bailian.env` Example `secrets/bailian.env`: ```env DASHSCOPE_API_KEY=sk-xxx DASHSCOPE_BASE_URL=https://dashscope.aliyuncs.com/api/v1 # TTS 可选配置（留空走默认） BAILIAN_TTS_MODEL=qwen3-tts-flash BAILIAN_TTS_VOICE= BAILIAN_TTS_SAMPLE_RATE=16000 OSS_ACCESS_KEY=ak-xxx OSS_SECRET_KEY=sk-xxx OSS_BUCKET=your-bucket OSS_ENDPOINT=oss-cn-beijing.aliyuncs.com OSS_REGION=cn-beijing ``` **Defaults:** - Region/base URL: Beijing (`https://dashscope.aliyuncs.com/api/v1`) - Image model: `qwen-image-2.0-pro` - Output dir: `tmp/bailian-studio/` - Output format: PNG ## Usage ### TTS (speak) ```bash python3 {baseDir}/scripts/tts_speak.py --text "你好" ``` ### OCR (text) From local image (uploads to OSS): ```bash python3 {baseDir}/scripts/ocr_text.py --image /path/to.png ``` From URL: ```bash python3 {baseDir}/scripts/ocr_text.py --url https://example.com/image.png ``` ### Image generate (text-to-image) ```bash python3 {baseDir}/scripts/image_generate.py \ --prompt "一只坐在云端的橘猫" \ --width 1024 \ --height 1024 ``` ### Image generate (image-to-image) Local image: ```bash python3 {baseDir}/scripts/image_generate.py \ --prompt "改成赛博朋克风格" \ --image /path/to/reference.png \ --width 1024 \ --height 1024 ``` URL image: ```bash python3 {baseDir}/scripts/image_generate.py \ --prompt "改成水彩插画风格" \ --image https://example.com/reference.png \ --width 1024 \ --height 1024 ``` ### stdin prompt ```bash echo "一只会发光的鲸鱼漂浮在夜空" | python3 {baseDir}/scripts/image_generate.py ``` ## Behavior - 本地参考图：先上传 OSS，再传给 DashScope - URL 参考图：直接透传给 DashScope - 默认一次生成 1 张图 - 成功后 stdout 打印保存路径 - 若文件名已存在，自动重命名 - 失败时输出错误信息并返回非 0 退出码


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: adastraabyssoque/camoufox-tools (version 1.0.0)

--- name: camoufox-tools description: Simplified CLI tools for camoufox anti-detection browser automation. Provides fox-open, fox-scrape, fox-eval, fox-close, and fox-bilibili-stats commands for easy web scraping and data extraction. --- # camoufox-tools 🦊 封装 camoufox + agent-browser 的最佳实践，提供简化的命令行工具用于反检测浏览器自动化和数据抓取。 ## 为什么需要这个 Skill？ - **反检测**: camoufox 是基于 Firefox 的防指纹浏览器，能绕过 Bilibili 等网站的风控 - **简化命令**: 隐藏复杂的 `--executable-path` 和 `--headed` 参数 - **最佳实践**: 自动处理 `close` 规范，避免资源泄漏 - **即装即用**: 一键安装，无需记忆长路径 ## 安装 ```bash # 运行安装脚本，将工具添加到 PATH cd ~/.openclaw/workspace/skills/camoufox-tools ./install.sh # 或者手动添加到 PATH export PATH="$HOME/.openclaw/workspace/skills/camoufox-tools/bin:$PATH" ``` ## 环境变量 ```bash # 可选：自定义 camoufox 路径（默认: ~/.local/share/camoufox/camoufox） export CAMOUFOX_PATH=/path/to/camoufox ``` ## 工具列表 ### 1. fox-open - 有头模式打开网页 使用 camoufox 有头模式打开指定网页。 ```bash fox-open "https://example.com" fox-open "https://bilibili.com" ``` **特性:** - 自动关闭之前的 browser 实例 - 自动使用 `--headed` 模式 - 自动填充 camoufox 路径 --- ### 2. fox-scrape - 抓取页面内容 抓取网页内容，支持等待时间和 CSS 选择器。 ```bash # 基本用法 fox-scrape "https://example.com" # 等待 5 秒后抓取 fox-scrape "https://example.com" --wait 5000 # 抓取特定元素 fox-scrape "https://example.com" --selector ".article-content" # 短选项 fox-scrape "https://example.com" -w 5000 -s ".content" ``` **选项:** - `-w, --wait <ms>` - 等待时间（毫秒，默认 3000） - `-s, --selector <sel>` - CSS 选择器，提取特定元素 **特性:** - 自动启动 browser（如未运行） - 任务完成后自动关闭 browser --- ### 3. fox-eval - 执行 JavaScript 在当前页面执行 JavaScript 代码。 ```bash # 获取页面标题 fox-eval "document.title" # 获取元素文本 fox-eval "document.querySelector('.like-btn').textContent" # 获取多个元素 fox-eval "[...document.querySelectorAll('.stat')].map(e => e.textContent)" ``` **注意:** 需要先使用 `fox-open` 打开网页。 --- ### 4. fox-close - 规范关闭 关闭所有 browser 实例，释放资源。 ```bash fox-close ``` **建议:** 在每个任务完成后执行，避免资源占用。 --- ### 5. fox-bilibili-stats - 获取 B 站视频数据 获取 Bilibili 视频的统计数据。 ```bash fox-bilibili-stats "BV1NGZtBwELa" fox-bilibili-stats "BV1xx411c7mD" ``` **输出:** - 📺 视频标题 - ▶️ 播放量 - 👍 点赞数 - 🪙 投币数 - ⭐ 收藏数 - 📤 分享数 - 💬 弹幕数 **特性:** - 自动处理 BV 号格式 - 自动启动和关闭 browser --- ## 典型工作流 ### 工作流 1: 快速查看网页 ```bash fox-open "https://example.com" # 查看完成后 fox-close ``` ### 工作流 2: 抓取动态内容 ```bash fox-scrape "https://example.com" --wait 5000 --selector ".dynamic-content" ``` ### 工作流 3: 交互式数据提取 ```bash # 打开页面 fox-open "https://example.com" # 执行一些操作后提取数据 fox-eval "document.querySelector('.data').textContent" # 关闭 fox-close ``` ### 工作流 4: B 站视频数据分析 ```bash fox-bilibili-stats "BV1NGZtBwELa" ``` --- ## 故障排除 ### Browser 无法启动 - 检查 camoufox 是否已安装: `ls ~/.local/share/camoufox/camoufox` - 设置正确的路径: `export CAMOUFOX_PATH=/path/to/camoufox` ### 页面加载超时 - 增加等待时间: `fox-scrape --wait 10000` - 检查网络连接 ### 选择器找不到元素 - 使用 `fox-open` 打开页面后手动检查元素 - 使用 `fox-eval` 测试选择器: `fox-eval "document.querySelector('.your-selector')"` --- ## 依赖 - openclaw CLI - camoufox (headless Firefox) - agent-browser 扩展 --- ## 许可证 MIT


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: lisayinyy/xiaohongshu-public-monitor (version 0.1.0)

--- name: xiaohongshu-public-monitor description: "小红书舆论监控 - 自动搜索小红书帖子、筛选需要舆论引导的内容、生成多人设评论话术、输出到飞书多维表格。适用于任何品牌/产品的小红书舆情监控。Use when: (1) 需要监控小红书上的品牌/产品讨论, (2) 需要生成多角色评论话术, (3) 需要将监控结果输出到飞书表格, (4) 用户提到舆论监控、舆情分析、小红书评论。" metadata: author: Lisayinyy version: 1.0.0 language: zh-CN platform: xiaohongshu requires: - playwright - feishu-bitable --- # 小红书舆论监控 Skill 自动监控小红书帖子 → 筛选内容 → 生成评论话术 → 写入飞书多维表格。 适用于任何行业：AI、电商、美妆、餐饮、教育……只需修改 `config.yaml` 即可适配。 ## 快速开始 ### 1. 安装 ```bash # Via ClawHub clawdhub install xiaohongshu-public-monitor # 手动安装 git clone https://github.com/Lisayinyy/xiaohongshu-public-monitor.git ~/.openclaw/skills/xiaohongshu-public-monitor # 安装依赖 pip install playwright playwright install chromium ``` ### 2. 登录小红书（仅首次） ```bash python3 scripts/xhs_search.py login ``` 弹出浏览器，扫码登录一次，后续自动复用。 ### 3. 配置 编辑 `config.yaml`，填入： - 你的品牌名和产品 - 搜索关键词 - 飞书多维表格 token - 3个评论人设（根据你的行业定义） ### 4. 运行 ```bash # 搜索 python3 scripts/xhs_search.py batch "关键词1" "关键词2" --scroll 5 ``` 或让 Agent 执行完整工作流（搜索 → 筛选 → 生成评论 → 写入表格）。 --- ## 工作流程（4步） ### Step 1：搜索 - 按 `config.yaml` 中的关键词搜索小红书 - 筛选「一天内」+ 排序「最多点赞」 - Playwright 持久化登录，headless 模式，支持滚动翻页 ### Step 2：去重 - 查飞书表格已有链接，相同链接跳过 ### Step 3：筛选 **基础门槛（3条必满足）**： 1. 链接能打开，标题内容对得上 2. 作者不为空 3. 互动数据完整（👍点赞 ⭐收藏 💬评论） **内容质量（满足任一入表）**： - 直接提及你的品牌或产品 - 同行业多个品牌/产品对比 - 榜单、排名、测评类内容 - 价格/性价比讨论 - 高互动且有争议讨论 **不入表**： - 与你的业务完全无关 - 纯招聘/面试 - 互动极低且无对比内容 - 评论区已有足够正面声音 ### Step 4：生成评论 + 写入表格 - 为每篇帖子生成 3人设 × 2条 = 6条评论 - 判定紧急程度（🔴高/🟡中/🟢低） - 按「发现日期从新到旧 → 紧急程度高到低」排序写入飞书表格 --- ## 评论人设 在 `config.yaml` 中定义 3 个人设。根据你的行业选择最合适的角色组合。 **选人设的原则**：选你的目标用户群里最有说服力的 3 种人。 示例： | 行业 | 人设1 | 人设2 | 人设3 | |------|-------|-------|-------| | AI/科技 | 技术专家 | 普通用户 | 产品经理 | | 美妆 | 资深化妆师 | 学生党 | 成分党博主 | | 餐饮 | 美食博主 | 附近居民 | 餐饮同行 | | 电商 | 老买家 | 第一次买的人 | 行业从业者 | | 教育 | 在读学员 | 家长 | 教育行业人士 | 每个人设需要定义： - **特征**：这个人是谁 - **风格**：怎么说话（口语化/专业/情绪化） - **目的**：为什么要用这个人设评论 --- ## 评论红线 - ❌ 不写广告语 - ❌ 不贬低竞品（只客观对比） - ❌ 不虚构数据 - ❌ 多条评论风格不能雷同 - ❌ 负面帖下不强行洗白 --- ## 紧急程度判定 **🔴 高**：发布<24h + 有互动 + 对比/榜单类 + 负面倾向 **🟡 中**：有传播潜力 + 缺正面声音 + 评价中性 **🟢 低**：正面帖 + 互动稳定 + 已有正面声音 --- ## 表格字段 | 字段 | 说明 | |------|------| | 文章标题 | 帖子标题 | | 文章链接 | 原文链接 | | 作者 | 作者昵称（不能为空）| | 发表时间 | 文章发布时间 | | 发现日期 | 抓取日期 | | 互动数据 | 👍⭐💬 三项齐全 | | 关键词命中 | 命中的搜索词 | | 提及竞品 | 提到的竞品 | | 文章倾向 | 正面/中性/负面 | | 紧急程度 | 🔴高/🟡中/🟢低 | | 干预理由 | 为什么干预 | | 人设评论 | 每人设2条 | | 建议水军数量 | 投入账号数 | | 执行状态 | 待处理/进行中/已完成 | --- ## 文件结构 ``` xiaohongshu-public-monitor/ ├── SKILL.md # 使用说明（本文件） ├── config.yaml # 配置文件（用户修改这个） ├── scripts/ │ └── xhs_search.py # 小红书搜索脚本 ├── references/ │ └── workflow.md # 工作流说明 ├── assets/ └── .learnings/ # Agent 学习记录 ```


Security scan verdicts — ClawScan: malicious | static analysis: clean | VirusTotal: suspicious | SkillSpector:  ()
Skill: airbai/mac-battery (version 1.0.0)

--- name: mac-battery version: 1.0.0 description: Check Mac laptop battery status including percentage, charging state, and time remaining. Use when user asks about battery level, power status, or how much time left until fully charged/discharged on a Mac laptop. --- # Mac Battery Status Query Mac laptop battery information using the `pmset` command. ## Command ```bash pmset -g batt | grep -E "([0-9]+%)" ``` ## Output Format Returns battery percentage, charging state, and time remaining: - **Percentage**: e.g., "79%" - **Charging state**: "charging", "discharging", or "charged" - **Time remaining**: e.g., "0:47 remaining" (for charging) or "2:30 remaining" (for discharging) ## Example Response ``` 🔋 79%; charging; 0:47 remaining ``` Respond to user with the battery info in a friendly format with emoji.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: catchfishcat/chinese-punctuation-sentence-correction (version 1.0.0)

--- name: chinese-punctuation-sentence-correction description: Use when correcting Chinese ill-formed sentences with punctuation-focused errors (comma,顿号,分号,冒号,问号,叹号,引号,书名号,括号,省略号,破折号), including mixed punctuation-and-grammar cases. Trigger on requests like "改病句", "标点纠错", "逗号顿号怎么改", "修正文稿标点", or batch proofreading where meaning must be preserved with minimal edits. --- # Chinese Punctuation Sentence Correction ## Overview Correct Chinese sentences by prioritizing punctuation misuse while preserving original meaning. Apply minimal edits first; only adjust wording/word order when punctuation alone cannot resolve the sentence. ## Workflow 1. Read the full sentence/paragraph once to lock intended meaning. 2. Detect punctuation-category errors first (see `references/punctuation_rules.md`). 3. Fix with the minimum required change; do not rewrite style. 4. Re-check sentence flow, quote pairing, and punctuation hierarchy. 5. Output only what the user asked for: - If user asks to "直接改": output corrected text only. - If user asks for explanation: provide `原句/问题类型/修改后`. ## Core Rules - Prioritize punctuation errors in this order: `。？！` > `：` > `；` > `，` > `、`. - Preserve semantics; avoid content expansion. - Keep author's register and wording where possible. - Do not convert all punctuation to one style; use the sentence structure as the source of truth. - When uncertainty is high, keep original punctuation and report low confidence only if user asked for explanation. ## Output Templates ### A) Direct correction (default) ```text <修改后文本> ``` ### B) With explanation (only when requested) ```text 问题1： - 原句：... - 类型：逗号误用/顿号误用/分号误用/冒号误用/问号误用/引号误用/书名号误用/括号误用/省略号误用/破折号误用 - 修改：... - 理由：... ``` ## Reference Usage - Use `references/punctuation_rules.md` for concrete rule checks and examples. - Use national standard first when conflicts appear. ## Must Not Do - Do not rewrite paragraphs for polish. - Do not change factual meaning or stance. - Do not add punctuation comments unless user asks for analysis. - Do not force corrections for uncertain cases. ## Quick Examples ### Example 1 - Input: `理论，来源于实践，实践，要靠理论来指导。` - Output: `理论来源于实践；实践要靠理论来指导。` ### Example 2 - Input: `这二、三天时间里，来了二、三十个二十七、八岁的年轻人。` - Output: `这二三天时间里，来了二三十个二十七八岁的年轻人。` ### Example 3 - Input: `发生了这么重大的事故，作为一校之长，你难道没有责任!?` - Output: `发生了这么重大的事故，作为一校之长，你难道没有责任？！`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/agent-quick-start (version 2.0.0)

--- name: agent-quick-start description: Quick start templates for OpenClaw agents. Boilerplate code for research bots, content generators, task automation, and more. Jumpstart your development with ready-to-use templates. Use when starting new OpenClaw projects. --- # Agent Quick Start Ready-to-use templates for common OpenClaw agent patterns. Start your project faster. ## Installation ```bash npx clawhub@latest install agent-quick-start ``` ## Usage ```bash # List available templates node ~/.openclaw/skills/agent-quick-start/start.js list # Create a project from a template node ~/.openclaw/skills/agent-quick-start/start.js create <template_name> <project_path> # Example: Create a research bot node ~/.openclaw/skills/agent-quick-start/start.js create research-bot my-research-agent ``` ## Available Templates ### 1. Research Bot - 🤖 Research agent with web search - 📊 Summarize findings - 📝 Generate reports - Use for: Research, data gathering, analysis ### 2. Content Generator - ✍️ Generate blog posts - 📱 Social media content - 📧 Email drafts - Use for: Content creation, marketing, copywriting ### 3. Task Automator - ⚡ Automate repetitive tasks - 🔄 Workflows and pipelines - 🎯 Scheduled jobs - Use for: Automation, scripts, workflows ### 4. Customer Support Bot - 💬 Answer common questions - 📚 Knowledge base integration - 🎫 Ticket management - Use for: Customer service, FAQs, support ### 5. Data Processor - 📊 Process data files - 📈 Analysis and reports - 💾 ETL operations - Use for: Data processing, analytics, ETL ## Features - ✅ Ready-to-use templates - ✅ Copy to any directory - ✅ Clear documentation - ✅ Best practices included - ✅ Easy to customize ## Example Workflow ```bash # 1. List templates node ~/.openclaw/skills/agent-quick-start/start.js list # 2. Choose a template node ~/.openclaw/skills/agent-quick-start/start.js create research-bot ./my-bot # 3. Customize the project cd ./my-bot # Edit files, add your logic # 4. Run your agent openclaw run ``` ## Need Help? If you need help with OpenClaw: - 📧 **Installation Service**: ¥99-299 - 🔗 **Landing Page**: https://yang1002378395-cmyk.github.io/openclaw-install-service/ ## License MIT


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: jaceagentic/cre-scraper (version 2.0.0)

--- name: cre-scraper version: 2.0.0 description: Scrapes commercial real estate listings from Crexi and LoopNet using Claude in Chrome on a Mac Mini with residential IP. Bypasses Cloudflare bot protection. Extracts structured financials, broker contacts, and AI investment analysis. Saves to SQLite and syncs to OpenClaw Command Center dashboard. requirements: binaries: - claude - rsync - sqlite3 platform: macOS notes: Requires Claude Code with Claude in Chrome extension. Residential IP required — does not work from VPS/datacenter IPs. --- # CRE Scraper v2.0 Scrape commercial real estate listings from Crexi and LoopNet using Claude in Chrome. ## Architecture ``` Mac Mini (residential IP + Chrome) → /scrape-crexi or /scrape-loopnet slash commands → ~/.openclaw/workspace/data/properties.db → rsync to VPS staging → sync-properties.py → Command Center dashboard ``` ## Requirements - macOS with Claude Code installed - Claude in Chrome browser extension active - Logged into Crexi (crexi.com) and LoopNet (loopnet.com) in Chrome - SSH key authorized on VPS - `chromeEnabled: true` in ~/.claude/settings.json ## Usage Run Crexi scrape (all 21 combinations): ```bash ~/.openclaw/skills/cre-scraper/run-scrape.sh ``` Run enrichment on unenriched properties: ```bash ~/.openclaw/skills/cre-scraper/enrich-batch.sh [batch_size] ``` Or inside Claude Code: ``` /scrape-crexi /scrape-loopnet ``` ## Configuration - **States:** FL, GA, NC, TN, AL, LA, ID - **Asset types:** rv_park, self_storage, marina - **Price range:** $800K–$3M - **Min units:** 50+ (when known) - **Value-add threshold:** VAS ≥ 40 ## What gets scraped Per listing: - Address, city, state, zip - Asking price, cap rate, NOI, occupancy - Units/pads/slips, SF, year built, acreage - Pro-forma cap rate and NOI - Broker name, firm, full phone (click-reveal) - Description and investment highlights - AI analysis: IRR, DSCR, Cash-on-Cash, Value-Add Score, AI Confidence ## Cron schedule (launchd) - **7:00am** — Crexi scrape (ai.crexi.scraper) - **8:00am** — LoopNet scrape (ai.loopnet.scraper) - **Midnight** — Enrichment batch (ai.crexi.enricher) ## Trigger phrases - "scrape new deals" - "run the Crexi scraper" - "find new RV parks in Florida" - "check LoopNet for self storage in Tennessee" - "enrich unenriched properties" - "sync deals to dashboard" ## Output Properties saved to `~/.openclaw/workspace/data/properties.db` and synced to OpenClaw Command Center dashboard via `sync-properties.py`.


Security scan verdicts — ClawScan: malicious | static analysis: clean | VirusTotal: suspicious | SkillSpector:  ()
Skill: autogame-17/innovation-catalyst (version 1.0.0)

# Innovation Catalyst Analyzes system state (memory, tools, events) and generates strategic innovation proposals to break evolution stagnation plateaus. ## Features - **Gap Analysis:** Identifies under-represented skill categories. - **Stall Detection:** Finds blocked tasks or recurring failures. - **Strategic Proposals:** Generates actionable suggestions (new skills, optimizations, structural changes). - **Feishu Integration:** Sends a rich Feishu card report. ## Usage ```bash # Run manually node skills/innovation-catalyst/index.js # Target a specific user/group node skills/innovation-catalyst/index.js --target "ou_xxx" ``` ## Protocol Integration This skill is designed to be triggered by the `gene_gep_innovate_from_opportunity` gene when `stable_success_plateau` is detected. It provides the "seed" for subsequent evolution cycles. ## Dependencies - `skills/feishu-evolver-wrapper/feishu-helper.js` (for card sending) - System memory files (`MEMORY.md`, `TOOLS.md`, `RECENT_EVENTS.md`)


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