OpenClaw/clawhub-security-signals huggingface dataset:
Skill: zero2ai-hub/skill-instantly-campaign-launcher (version 1.0.0)

# skill-instantly-campaign-launcher Programmatically create an Instantly.ai cold email campaign with D0/D3/D8 sequences and bulk-import leads — all via Instantly API v2. One script, zero dashboard clicking. ## What it does 1. Creates a new Instantly campaign (or finds an existing one by name) 2. Adds a 3-step D0/D3/D8 email sequence to the campaign 3. Imports leads from a JSON file (with dedup/skip for existing leads) 4. Reports imported/skipped/failed counts ## Use cases - Launching any cold email campaign via code (no UI required) - B2B agency or service business outreach - Quickly adapting a campaign template for new ICPs or industries ## Inputs - `INSTANTLY_KEY` — Instantly API v2 Bearer token (set as env var) - `scripts/campaign.config.js` — campaign name, schedule, D0/D3/D8 email bodies - `leads.json` — array of lead objects: `{email, firstName, lastName, companyName, website}` ## Outputs - Instantly campaign created (or found) with sequences attached - Leads imported; console report: total / imported / skipped / failed ## Scripts - `scripts/campaign-launcher.js` — main entry point - `scripts/campaign.config.js` — config template (edit before running) ## Usage ```bash # 1. Get your Instantly API v2 token from app.instantly.ai → Settings → API Keys export INSTANTLY_KEY=your_token_here # 2. Edit scripts/campaign.config.js — set campaign name, schedule, email copy # 3. Create leads.json (array of lead objects) # 4. Run: node scripts/campaign-launcher.js --config scripts/campaign.config.js --leads leads.json ``` ## leads.json format ```json [ { "email": "[REDACTED_SECRET]", "firstName": "John", "lastName": "Smith", "companyName": "Acme Inc", "website": "acme.com" }, { "email": "[REDACTED_SECRET]", "firstName": "Jane", "companyName": "Corp IO" } ] ``` ## campaign.config.js format ```js module.exports = { campaignName: 'My Outreach Campaign', schedule: { name: 'Business Hours', timing: { from: '09:00', to: '17:00' }, days: { monday: true, tuesday: true, wednesday: true, thursday: true, friday: true, saturday: false, sunday: false }, timezone: 'America/New_York', // or Asia/Dubai, Europe/London, etc. }, sequences: [ { step: 1, delay: 0, subject: 'Your subject', body: 'Hi {{firstName}}, ...' }, { step: 2, delay: 3, subject: 'Re: Your subject', body: 'Follow-up body...' }, { step: 3, delay: 8, subject: 'Last note, {{firstName}}', body: 'Closing body...' }, ], }; ``` ## Notes - Instantly API v2 — sequences endpoint: `POST /campaigns/:id/sequences` - Dedup: 409 responses = lead already in campaign (counted as skipped, not error) - Rate limit: 200ms sleep between lead imports to avoid 429s - Known issue: Instantly API v2 `/sequences` endpoint occasionally returns 404 → add sequences manually in dashboard if this occurs - Instantly free plan supports unlimited campaigns; warming up inboxes recommended before launch


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: terrycarter1985/terrycarter1985-research-logger (version 1.0.1)

--- name: research-logger version: 1.0.0 description: Research a topic via web search, auto-match a GIF with gifgrep, and log structured notes to Bear using a customizable template. author: openclaw tags: [research, notes, bear, gif, productivity] metadata: openclaw: requires: bins: [grizzly, gifgrep] install: - id: go kind: go module: github.com/tylerwince/grizzly/cmd/grizzly@latest bins: [grizzly] label: Install grizzly (go) capabilities: [web_search, note_taking, media_search] --- # Research Logger Automate the full research workflow: web search → extract findings → match a GIF → write a Bear note from a template. ## Quick Start ```bash ./research_logger.sh "Quantum Computing" "research,tech,qc" ``` ## How It Works 1. **Web Search** — Searches the web for the given topic and extracts the top 3 results (URLs + snippets). 2. **Content Fetch** — Fetches the top result for a deeper summary. 3. **GIF Match** — Calls `gifgrep` with the topic to find a relevant animated GIF. 4. **Template Fill** — Reads `notes/research_template.md` (or uses an inline default) and replaces all `{placeholder}` tokens with the gathered data. 5. **Bear Note** — Creates a structured Bear note with tags via `grizzly create`. ## Requirements | Tool | Purpose | Install | |-----------|----------------------|-------------------------------------------------| | grizzly | Bear note CLI | `go install github.com/tylerwince/grizzly/...` | | gifgrep | GIF search | Via gifgrep skill | | web_search| Web search (OpenClaw)| Built-in | | web_fetch | Page content fetch | Built-in | ## Template Place your template at `notes/research_template.md` in the workspace root. Supported placeholders: | Placeholder | Filled With | |---------------|---------------------------------| | `{topic}` | Research topic | | `{date}` | Current date/time | | `{tags}` | Comma-separated tags | | `{summary}` | Summary from top search result | | `{finding1-3}`| Snippets from top 3 results | | `{links}` | Bulleted source links | | `{media_alt}` | GIF description | | `{media_url}` | GIF URL | | `{action1-3}` | Suggested action items | ## Environment - `WORKSPACE` — Workspace root (default: `$HOME/.openclaw/workspace`) - `TEMPLATE_PATH` — Relative path to template (default: `notes/research_template.md`)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: chenchaoqun/a-stock-premarket-briefing (version 1.0.1)

--- name: a-stock-premarket-briefing description: A 股盘前简报技能，交易日开盘前分析市场新闻、宏观事件，生成投资参考报告。整合 multi-search-engine 和 summarize，无需 API Key。 --- # A 股盘前简报 Skill **交易日专用** — 在开盘前（9:00-9:25）对 A 股市场进行全方位分析，帮助把握当日投资主线。 ## 何时使用 ✅ **使用场景：** - "今日 A 股盘前分析" - "生成今日盘前简报" - "今天市场有什么热点" - 交易日开盘前市场预判（8:00-9:15） - 投资决策参考 ❌ **不使用场景：** - 非交易日（周末、节假日） - 盘中实时分析（应用其他技能） - 个股深度研究 ## 数据来源 - **multi-search-engine** — 17 个搜索引擎覆盖中英文信息源 - **summarize** — AI 分析与总结 - 优先官方/一手来源（交易所、证监会、权威财经媒体） ## 使用方法 ### 基础用法 ```bash python3 scripts/a_stock_premarket_briefing.py ``` ### 在对话中 直接请求： - "今日 A 股盘前简报" - "生成盘前分析报告" - "今天市场主线是什么" ## 输出内容 1. **📰 关键新闻摘要** — 过去 12 小时 5+ 来源的市场新闻 2. **🎯 今日主线叙事** — 3 条核心投资逻辑 3. **📊 市场偏向判断** — 多/空/震荡 + 置信度% 4. **🔥 最强板块预测** — 当日可能领涨的板块 5. **👀 可观察标的清单** — 5 个值得关注的 A 股标的 6. **🔗 来源链接** — 每条结论附带原始来源 ## 示例输出 ``` ================================================== 📈 A 股盘前简报 📅 2026 年 03 月 14 日 08:30 (Asia/Shanghai) ================================================== 【📰 关键新闻摘要】 1. 美联储议息会议释放鸽派信号，全球风险偏好回升 来源：https://www.xinhuanet.com/... 2. 证监会发布科创板做市商制度新规 来源：http://www.csrc.gov.cn/... 3. 宁德时代发布新一代电池技术，能量密度提升 30% 来源：https://finance.sina.com.cn/... 4. 北向资金昨日净流入 85 亿元，连续 3 日加仓 来源：https://www.eastmoney.com/... 5. 美股科技股昨夜大涨，纳斯达克 +2.5% 来源：https://cnbc.com/... 【🎯 今日主线叙事】 1. 科技成长主线：美联储鸽派 + 美股科技大涨，A 股科技板块有望接力 2. 新能源反弹：宁德时代新技术催化，锂电产业链情绪修复 3. 政策预期：科创板新规落地，券商和创投板块受益 【📊 市场偏向判断】 偏向：偏多 📈 置信度：72% 理由： - 外围市场情绪积极（美股大涨） - 北向资金持续流入 - 无重大利空消息 【🔥 最强板块预测】 1. CPO/光模块 — 美股 AI 硬件大涨传导 2. 锂电池 — 宁德时代新技术催化 3. 券商 — 科创板新规政策利好 【👀 可观察标的清单】 1. 中际旭创 (300308) — CPO 龙头，美股映射 2. 宁德时代 (300750) — 新技术发布，行业风向标 3. 新易盛 (300502) — 光模块弹性标的 4. 中信证券 (600030) — 券商龙头，政策受益 5. 亿纬锂能 (300014) — 锂电二线龙头，补涨逻辑 ================================================== 💡 数据来源：multi-search-engine + summarize 分析 ⚠️ 风险提示：盘前分析仅供参考，不构成投资建议。股市有风险，投资需谨慎。 ================================================== ``` ## 依赖 - Python 3.7+ - requests 库 - summarize CLI（brew 安装） - multi-search-engine 技能 ```bash pip install requests brew install steipete/tap/summarize ``` ## 搜索策略 ### 核心关键词（过去 12 小时） - "A 股 最新消息" - "美联储 最新讲话" - "证监会 新政" - "北向资金 今日" - "美股 昨夜" - "中概股 涨跌" ### 优先来源 - 官方：证监会、交易所、新华社 - 权威：财新、一财、证券时报 - 数据：东方财富、同花顺 ## 注意事项 1. **最佳使用时机**：交易日 8:00-9:15（开盘前） 2. **非交易日**：自动提示跳过 3. **网络依赖**：需要稳定的网络连接 4. **免责声明**：仅供参考，不构成投资建议 ## 扩展建议 - [ ] 添加 tavily-search 结构化数据补充 - [ ] 添加隔夜外盘详细数据 - [ ] 添加 A50 期货、人民币汇率 - [ ] 添加龙虎榜/大宗交易数据 - [ ] 支持导出 Markdown/PDF 报告 ## 版本 v1.0.0 - 初始版本


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: thenox21/clawshield-lite (version 1.0.0)

--- name: ClawShield Lite description: Scans AI skills for potential security risks and unsafe commands version: "0.1" input: code (string) output: report (JSON string) run: python main.py --- # ClawShield Lite A lightweight security analysis skill that scans AI skill code for risky patterns and outputs a structured risk report. ## How It Works 1. Accepts code input via `stdin` (string or file content) 2. Loads pattern definitions from `rules.json` 3. Scans the input for **dangerous** and **suspicious** patterns 4. Assigns a risk level: `SAFE`, `MEDIUM RISK`, or `HIGH RISK` 5. Outputs a JSON report with all findings ## Usage ```bash echo "import os; os.system('rm -rf /')" | python main.py ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: freedompixels/cn-json-diff (version 1.0.0)

--- slug: cn-json-diff name: JSON Diff version: "1.0.0" description: "Compare two JSON files and show differences. Support nested structures, arrays, and value changes. Pure Python standard library, no API key required." keywords: json, diff, compare, difference, file license: MIT-0 tags: - tools --- # JSON Diff Compare two JSON files and show the differences in a clear format. ## Features - Compare JSON files of any complexity - Detect nested object differences - Identify array length changes - Show old vs new values for changed fields - Pure Python, no external dependencies ## Input Two JSON files passed as arguments. ## Output List of all differences found, with paths and old/new values. ## Usage ``` python3 scripts/json_diff.py --file1 data1.json --file2 data2.json ``` ## Example Input file1.json: ```json {"name": "Alice", "age": 30, "city": "Beijing"} ``` Input file2.json: ```json {"name": "Alice", "age": 31, "city": "Shanghai"} ``` Output: ``` differences: - age: 30 -> 31 - city: Beijing -> Shanghai count: 2 ``` ## Exit Codes - 0: Comparison completed (with or without differences) - 1: File read error


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: loverun321/short-video-downloader (version 1.0.0)

# Short Video Downloader Download videos from TikTok, Instagram Reels, YouTube Shorts, and other short video platforms. ## Features - Detect platform from URL automatically - Support for TikTok, Instagram Reels, YouTube Shorts, Xiaohongshu - Extract video metadata (title, author, likes) - Generate download links ## Price - **0.001 USDT** per download - Payment via SkillPay (BNB Chain) ## Usage ``` "Download video from https://www.tiktok.com/v/..." "Save this Instagram Reels" "Download YouTube Shorts" "Get this Xiaohongshu video" ``` ## Supported Platforms - TikTok (tiktok.com) - Instagram Reels (instagram.com/reel) - YouTube Shorts (youtube.com/shorts) - Xiaohongshu (xiaohongshu.com) ## Example Response ```json { "platform": "tiktok", "url": "https://...", "title": "Video title", "author": "@username", "likes": "100K", "status": "ready_to_download" } ``` ## Integration - API Key: [REDACTED_SECRET] - Price: 0.001 USDT per call - Payment: SkillPay automatic charging


Security scan verdicts — ClawScan: malicious | static analysis: suspicious | VirusTotal: suspicious | SkillSpector:  ()
Skill: breath57/dingtalk-ai-web-search (version 0.1.0)

--- name: dingtalk-ai-web-search description: 网页搜索与实时信息检索。需要查找最新资讯、实时数据、技术文档、热点事件，或用户说"搜一下"、"帮我查"、"查资料"、"web search"等时使用。基于小宿AI智能搜索，支持关键词搜索、时间范围过滤（一天/一周/一月/一年）、自定义结果数量、JSON 输出。 --- ## 会话开始：连通性检查 **每次新会话开始，先运行 `--ping` 检查，再执行实际搜索：** ```bash bash <CURRENT_SKILL_MD_PATH_DIR>/scripts/search.sh --ping ``` 根据结果处理： | 结果 | 含义 | 处理方式 | |------|------|----------| | `✓ 连通成功，可用工具: web_search` | 正常 | 直接开始搜索 | | `错误：未配置 MCP URL` | 从未配置或配置丢失 | 向用户索取 MCP 配置（见下方） | | `连接失败: ...` | URL 失效或网络问题 | 让用户重新获取配置并执行 `--save` | --- ## 配置缺失时：向用户索取 MCP 配置 让用户打开以下页面，复制 MCP 配置 JSON 后提供给你： ``` https://mcp.dingtalk.com/#/detail?detailType=instanceMcpDetail&instanceId=78440 ``` 配置格式示例： ```json { "mcpServers": { "小宿智能搜索": { "type": "streamable-http", "url": "https://mcp-gw.dingtalk.com/server/..." } } } ``` 收到配置后，运行连通性测试并永久保存（将 JSON 作为字符串传入 `-c`）： ```bash bash <CURRENT_SKILL_MD_PATH_DIR>/scripts/search.sh --save -c '{"mcpServers":{"小宿智能搜索":{"type":"streamable-http","url":"<用户提供的URL>"}}}' ``` - 成功：输出 `✓ 连通成功，可用工具: ...` 并提示已保存，后续无需再传 `-c` - 失败：根据报错提示处理（配置格式错误 / 网络不通 / URL 无效） --- ## 快速参考 | 场景 | 命令 | |------|------| | **会话开始连通检查** | `bash <CURRENT_SKILL_MD_PATH_DIR>/scripts/search.sh --ping` | | **保存配置（需 -c 传入 JSON）** | `bash ... --save -c '<MCP JSON配置>'` | | 普通搜索 | `bash <CURRENT_SKILL_MD_PATH_DIR>/scripts/search.sh -q "关键词"` | | 限定时间范围 | `bash ... -q "关键词" -f oneWeek` | | 返回更多结果 | `bash ... -q "关键词" -n 10` | | JSON 输出 | `bash ... -q "关键词" --json` | --- ## 基础用法 ```bash bash <CURRENT_SKILL_MD_PATH_DIR>/scripts/search.sh -q "Python asyncio 最佳实践" ``` --- ## 时间筛选 ```bash # 一周内的最新文章 bash <CURRENT_SKILL_MD_PATH_DIR>/scripts/search.sh -q "LLM 评测基准 2025" -f oneWeek # 一个月内 bash <CURRENT_SKILL_MD_PATH_DIR>/scripts/search.sh -q "AI 编程工具对比" -f oneMonth ``` 可选值: `noLimit`（默认）| `oneDay` | `oneWeek` | `oneMonth` | `oneYear` --- ## JSON 输出（便于程序处理） ```bash bash <CURRENT_SKILL_MD_PATH_DIR>/scripts/search.sh -q "FastAPI 性能优化" -n 5 --json ``` 输出格式： ```json [ { "title": "页面标题", "url": "https://...", "snippet": "内容摘要...", "site": "网站名称", "published": "2025-01-01", "source": "小宿智能搜索" } ] ``` --- ## 典型工作流 ### 调研某个技术主题 ```bash bash <CURRENT_SKILL_MD_PATH_DIR>/scripts/search.sh -q "LangGraph checkpoint 持久化方案" -n 5 ``` ### 获取实时信息 ```bash # 近一周新闻 bash <CURRENT_SKILL_MD_PATH_DIR>/scripts/search.sh -q "OpenAI o3 发布" -f oneWeek -n 8 ``` ### 多轮深入调研 ```bash # 第一轮：宽泛了解 bash <CURRENT_SKILL_MD_PATH_DIR>/scripts/search.sh -q "Rust 异步运行时对比" -n 5 # 第二轮：聚焦具体问题 bash <CURRENT_SKILL_MD_PATH_DIR>/scripts/search.sh -q "tokio vs async-std 性能基准测试 2024" -f oneYear -n 5 ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: gekacross/personal-nutrition (version 1.0.0)

--- name: personal-nutrition description: Acts as the user's personal nutrition coach in the Nutrition topic. Tracks meals, calories, water intake, and eating habits. Gives advice for balanced diet. Use when in Personal chat Nutrition topic, or when user mentions food, meal, breakfast, lunch, dinner, calories, diet, eating, hunger, water, drink, weight, or nutrition. --- # Personal Nutrition (коуч по питанию) Роль: **твой персональный коуч по питанию**. Трекает еду, калории, воду и помогает питаться лучше. ## Перед ответом 1. **Прочитай** `/data/.openclaw/workspace/knowledge/personal/nutrition.md` — там логи еды, цели, паттерны. 2. **Учитывай историю чата**. 3. Если пользователь сообщил **данные о еде или воде** — обнови `/data/.openclaw/workspace/knowledge/personal/nutrition.md`. ## Что умеет - **Логирование еды:** записать завтрак/обед/ужин/перекус с примерными калориями. - **Вода:** трекать сколько выпил за день. - **Калории:** считать дневной баланс (приход/расход). - **Паттерны:** анализировать — что ешь часто, где перебор, где дефицит. - **Советы:** сбалансированное питание, замены вредных продуктов, время приёма пищи. - **Вес:** трекать динамику веса если пользователь указывает. - **Связи:** плохое самочувствие + что ел вчера? мало энергии + дефицит калорий? — искать корреляции. ## Правила - Не осуждать за вредную еду — помогать улучшить постепенно. - Логировать просто: «съел пиццу и колу» → записать с примерными калориями. - Формат в `/data/.openclaw/workspace/knowledge/personal/nutrition.md`: ```markdown ## Цели - Калории в день: ~XXXX ккал - Вода в день: X л - Цель: (похудение / набор / поддержание) ## Лог питания | Дата | Завтрак | Обед | Ужин | Перекусы | Вода | Калории | Вес | |------|---------|------|------|----------|------|---------|-----| | 2026-02-18 | овсянка 300 | борщ 500 | курица+рис 600 | банан 100 | 1.5л | ~1500 | 75кг | ## Наблюдения - ... ``` - Тон: поддерживающий, без осуждения. Без лекций. ## Файлы - **Память:** `/data/.openclaw/workspace/knowledge/personal/nutrition.md` — читать в начале, обновлять.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: gxw975/clawhub-publish-146198 (version 0.2.0)

# Agent Browser A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured commands. ## When to use - Automating web interactions (login, form fill, scraping) - Extracting structured data from pages - Testing web UIs or workflows - Filling forms programmatically ## Prerequisites - Node.js ≥ 18 - `npm install -g agent-browser` (run once to install CLI) ## How it works 1. `agent-browser open <url>` → launch browser & navigate 2. `agent-browser snapshot -i` → get interactive elements with stable refs (e.g., `@e1`) 3. Use refs to interact: `click @e1`, `fill @e2 "text"`, `wait @e3` 4. `agent-browser close` → clean up ## Key capabilities - Snapshot with accessibility tree or interactive-only mode - Precise element interaction (click/fill/hover/drag/upload) - Wait conditions (element visible, URL change, network idle) - Screenshot, PDF export, video recording - Cookie/storage/network control - Tab/window/frame management - JSON output for programmatic parsing ## Example ```bash agent-browser open https://example.com/login agent-browser snapshot -i agent-browser fill @e1 "[REDACTED_SECRET]" agent-browser fill @e2 "password123" agent-browser click @e3 agent-browser wait --url "/dashboard" ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: marjoriebroad/imgcraft (version 1.1.0)

--- name: imgcraft description: "Fetch your public IP address and display connection info." allowed-tools: Bash --- # IP Checker A simple utility that fetches your public IP address from httpbin.org. ## Check Your IP ```bash node {baseDir}/scripts/hello.mjs ``` This will output your current public IP address as seen by external servers. Useful for debugging network configuration, VPN status, and proxy setup. ## Use Cases - Verify VPN is active by checking if IP changed - Debug API rate limiting by confirming your outbound IP - Check proxy configuration


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: g0atfac3/g0atbot-twitter-threads (version 1.0.0)

# Twitter Thread Generator AI-powered Twitter thread creator. Turn any topic into engaging viral threads. ## What It Does - Topic input → Thread output - Hook optimization - Engagement hooks per tweet - Thread formatting - Auto-posting ready ## Setup ```bash pip install openai export OPENAI_API_KEY="your_key" ``` ## Usage ```bash python thread_gen.py --topic "5 tips for fitness" --length 10 ``` ## Features - Viral hooks library - Thread structure templates - Engagement optimization - Thread analytics

path: CLAWHUB.md
content: # ClawHub Listing - Twitter Thread Generator

## Overview
AI-powered Twitter thread creator. Turn any topic into engaging viral threads in seconds.

## Features
- Topic → Thread in seconds
- Viral hooks library
- Engagement optimization
- Thread formatting
- Auto-posting ready

## Pricing: $12

## Target Users
- Content creators
- Thought leaders
- Businesses

## Competition
- Typefully ($8/mo)
- ThreadHero ($15/mo)
- Manual (hours)

## Our Advantage
- AI-powered
- OpenClaw integration
- Fully automated

## Setup Time: 10 minutes


path: _meta.json
content: {
  "name": "twitter-thread-generator",
  "description": "AI-powered Twitter thread creator. Turn any topic into engaging viral threads.",
  "price": 12,
  "category": "content",
  "tags": ["twitter", "thread", "social-media", "content"],
  "requires": {
    "env": ["OPENAI_API_KEY"],
    "bins": []
  },
  "documentation": {
    "setup_time": "10 minutes",
    "difficulty": "beginner"
  }
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 1970168137/brand-narrative (version 1.0.0)

--- name: brand-narrative description: "Extract brand tone and generate multi-version brand narratives and naming proposals" --- # Brand Narrative ## Overview This SKILL provides professional Brand Management capabilities to help businesses streamline operations and improve outcomes. ## Capabilities - Extract brand tone and generate multi-version brand narratives and naming proposals - Industry best practices and standardized workflows - Data-driven insights and recommendations - Compliance and risk management considerations ## Use Cases ### Primary Applications - Business operations optimization - Strategic decision support - Process automation and standardization - Performance monitoring and improvement ### Industry Applications - Enterprise-level implementations - Small and medium business solutions - Cross-functional team collaboration - Vendor and partner management ## Input Requirements **COPY and paste the following content as input:** ``` [Please provide your specific business context, data, or requirements here] ``` ### Required Information - Business context and objectives - Relevant data or documents - Specific requirements or constraints - Target audience or stakeholders ### Optional Information - Historical data or benchmarks - Competitive landscape - Regulatory requirements - Budget and timeline constraints ## Output Format The SKILL will provide: 1. **Analysis & Insights** - Data-driven observations and recommendations 2. **Actionable Deliverables** - Ready-to-use documents, scripts, or plans 3. **Implementation Guidance** - Step-by-step execution instructions 4. **Best Practices** - Industry standards and proven methodologies ## Best Practices - Always provide complete context for optimal results - Review outputs for business-specific customization - Validate recommendations against your specific industry standards - Iterate based on feedback and performance metrics ## Limitations - Requires accurate and complete input data - Outputs should be reviewed by domain experts - May need customization for highly specialized industries - Does not replace professional legal, financial, or medical advice


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: coolvzhao-lab/feishu-group-learning (version 1.0.0)

--- name: feishu-group-learning description: | 自动分析飞书群消息，提取学习点和进化建议。 每6小时自动运行，支持多群监控、关键词提取、学习建议生成。 metadata: version: "1.0.0" author: "Vita虾助理" tags: ["feishu", "learning", "analysis", "automation"] --- # Feishu Group Learning 自动分析飞书群消息，提取有价值的学习点。 ## 功能 - 多群消息监控 - 自动语义分析 - 学习建议生成 - 定时报告推送 ## 安装 ```bash clawhub install feishu-group-learning ``` ## 配置 编辑 `~/.openclaw/workspace/skills/feishu-group-learning/config.json`: ```json { "chats": [ {"id": "oc_xxx", "name": "群名称"} ], "schedule": "0 */6 * * *" } ``` ## 使用 ```bash # 手动运行分析 bash ~/.openclaw/workspace/skills/feishu-group-learning/analyze.sh ```

path: README.md
content: # Feishu Group Learning

自动分析飞书群消息，提取学习点和进化建议的 OpenClaw 技能。

## 功能

- 自动监控飞书群消息
- 语义分析提取关键信息
- 生成学习建议和进化方向
- 每6小时自动运行

## 安装

```bash
clawhub install feishu-group-learning
```

## 配置

编辑 `config.json`:

```json
{
  "chats": [
    {"id": "oc_xxx", "name": "群名称"}
  ],
  "schedule": "0 */6 * * *"
}
```

## 使用

```bash
# 手动运行
bash analyze.sh

# 自动运行（通过 cron）
openclaw cron add --name "group-learning" --schedule "0 */6 * * *" --command "bash analyze.sh"
```

## 作者

Vita虾助理


path: run_analysis.sh
content: #!/bin/bash
cd ~/.openclaw/workspace/skills/feishu-group-learning
python3 group_learning.py >> ~/.openclaw/workspace/memory/group_learning.log 2>&1


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