OpenClaw/clawhub-security-signals huggingface dataset:
Skill: ligo-gao/stock-industry-analyzer (version 1.0.0)

# SKILL.md - 股票行业分析 Skill ## 简介 这是一个自动化的股票行业分析工具，通过收集国内外财经新闻，分析行业趋势，筛选热点公司，并进行股票走势分析。 ## 功能特性 ### 📰 新闻收集 - 自动获取国内财经新闻 - 自动获取国际财经新闻 - 每30分钟自动更新（可通过cron设置） - 自动去重和清洗 ### 🏭 行业分析 - 基于NLP的新闻行业分类 - 情感分析（利好/利空/中性） - 行业热度排名 - 趋势预测 ### 📈 股票分析 - **使用akshare获取真实行情数据** - 技术指标计算（MA、MACD、RSI） - 综合评分模型 - 买卖建议 ### 💾 数据存储 - SQLite本地数据库 - 长期数据积累 - 历史趋势分析 - 报告存档 ## 安装依赖 首次运行前需要安装依赖： ```bash pip install -r requirements.txt ``` 依赖包括： - akshare>=1.12.0 - 金融数据获取 - pandas>=1.5.0 - 数据处理 - jieba>=0.42.0 - 中文分词 ## 使用方法 ### 1. 立即执行分析 ```bash python main.py --once ``` ### 2. 定时执行（通过OpenClaw cron） ``` /30 * * * * python /path/to/main.py ``` ### 3. 在 OpenClaw 中使用 当用户请求分析股票或行业时，自动触发分析流程。 ## 配置 编辑 `config.json` 可修改配置： ## 输出示例 ``` 📊 股票行业分析报告 - 2026-03-14 00:15 【🔥 热门行业 TOP 5】 ---------------------------------------- 1. 🏭 新能源 (12条新闻) ⬆️ 情感：利好 (8利好/2利空) 关注：宁德时代, 比亚迪 2. 💻 信息技术 (8条新闻) ⬆️ 情感：利好 关注：科大讯飞, 寒武纪 【📈 重点个股分析】 ---------------------------------------- 🔍 宁德时代 (300750) 现价：¥215.60 涨跌：+2.35% 技术面：多头排列 评分：82/100 - 建议关注 ⚠️ 风险提示：本分析仅供参考，不构成投资建议 ``` ## 项目结构 ``` stock-analyst/ ├── main.py # 主入口 ├── SKILL.md # 本文件 ├── src/ │ ├── __init__.py │ ├── db.py # 数据库模块 │ ├── fetcher.py # 新闻获取 │ ├── analyzer.py # 行业/股票分析 │ └── reporter.py # 报告生成 └── data/ # 数据目录 ├── news.db # SQLite数据库 └── reports/ # 报告存档 ``` ## 依赖 - Python 3.8+ - jieba (中文分词) - pandas (数据处理) - ta-lib 或 ta (技术指标) ## 注意事项 1. 本工具仅供学习研究，不构成投资建议 2. 股票投资有风险，入市需谨慎 3. 数据获取需遵守相关网站的使用条款


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ckchzh/js-compiler (version 2.0.2)

--- name: "js-compiler" version: "2.0.2" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for JS Compiler concepts, best practices, and implementation patterns." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [js,compiler, reference] category: "devtools" --- # Js Compiler Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for JS Compiler concepts, best practices, and implementation patterns. No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wu-uk/hvac-control-first-order-model-fitting (version 0.1.0)

--- name: first-order-model-fitting description: Fit first-order dynamic models to experimental step response data and extract K (gain) and tau (time constant) parameters. --- # First-Order System Model Fitting ## Overview Many physical systems (thermal, electrical, mechanical) exhibit first-order dynamics. This skill explains the mathematical model and how to extract parameters from experimental data. ## The First-Order Model The dynamics are described by: ``` tau * dy/dt + y = y_ambient + K * u ``` Where: - `y` = output variable (e.g., temperature, voltage, position) - `u` = input variable (e.g., power, current, force) - `K` = process gain (output change per unit input at steady state) - `tau` = time constant (seconds) - characterizes response speed - `y_ambient` = baseline/ambient value ## Step Response Formula When you apply a step input from 0 to u, the output follows: ``` y(t) = y_ambient + K * u * (1 - exp(-t/tau)) ``` This is the key equation for fitting. ## Extracting Parameters ### Process Gain (K) At steady state (t -> infinity), the exponential term goes to zero: ``` y_steady = y_ambient + K * u ``` Therefore: ``` K = (y_steady - y_ambient) / u ``` ### Time Constant (tau) The time constant can be found from the 63.2% rise point: At t = tau: ``` y(tau) = y_ambient + K*u*(1 - exp(-1)) = y_ambient + 0.632 * (y_steady - y_ambient) ``` So tau is the time to reach 63.2% of the final output change. ## Model Function for Curve Fitting ```python def step_response(t, K, tau, y_ambient, u): """First-order step response model.""" return y_ambient + K * u * (1 - np.exp(-t / tau)) ``` When fitting, you typically fix `y_ambient` (from initial reading) and `u` (known input), leaving only `K` and `tau` as unknowns: ```python def model(t, K, tau): return y_ambient + K * u * (1 - np.exp(-t / tau)) ``` ## Practical Tips 1. **Use rising portion data**: The step response formula applies during the transient phase 2. **Exclude initial flat region**: Start your fit from when the input changes 3. **Handle noisy data**: Fitting naturally averages out measurement noise 4. **Check units**: Ensure K has correct units (output units / input units) ## Quality Metrics After fitting, calculate: - **R-squared (R^2)**: How well the model explains variance (want > 0.9) - **Fitting error**: RMS difference between model and data ```python residuals = y_measured - y_model ss_res = np.sum(residuals**2) ss_tot = np.sum((y_measured - np.mean(y_measured))**2) r_squared = 1 - (ss_res / ss_tot) fitting_error = np.sqrt(np.mean(residuals**2)) ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: peterzhu0/ac-stock-ultrashort (version 1.0.0)

--- name: ac-stock-ultrashort description: A股超短线交易策略。用于 Peter 的游牧型超短线交易风格：只做主线、只做最强、1-2天T+1套利。当需要分析A股盘面、筛选热点板块、寻找龙头股、制定买卖策略时使用。 --- # A股超短线交易策略 ## 核心定位 **游牧型超短线交易者** — 只做主线，只做最强，只赚爆发段。 ## 选股标准 ### 1. 识别主线题材 - **政策驱动**：重大政策出台、规划发布 - **事件驱动**：突发新闻、行业突破 - **资金驱动**：板块持续放量、游资/机构大幅买入 - **板块涨停数**：≥3只确认题材持续性 ### 2. 筛选条件 | 指标 | 范围 | 说明 | |-----|------|------| | 流通市值 | 50-300亿 | 弹性与容量兼顾 | | 换手率 | 5%-15% | 活性适中 | | 量价配合 | 放量突破关键位置 | 资金认可 | | 板块涨停数 | ≥3只 | 确认题材持续性 | ### 3. 选股优先级 **龙头 > 跟风**，宁可不做也不做杂毛： - 率先涨停的辨识度最高 - 成交量最大的关注度最高 - 市场讨论最多的情绪最强 - **创业板（300开头）优先** — 20%涨跌停利润更高 - **北交所（8/9开头）可选** — 30%涨幅空间更大 ## 买卖时机 ### 买点模式 | 模式 | 描述 | 适用场景 | |-----|------|---------| | **首板打板** | 题材确认日，龙头首次涨停时排板/打板买入 | 新题材启动第一天 | | **二板确认** | 首板次日高开5%以上，竞价或盘中确认强度后介入 | 题材已验证，强势确认 | | **分歧转一致** | 龙头回调后再次放量拉升的转折点 | 题材分歧后重新凝聚 | ### 卖点规则 | 情况 | 动作 | |-----|------| | 高开低走跌破均线 | 出局 | | 封板不牢反复开板 | 减仓或出局 | | 板块跟风股大面积炸板 | 退潮信号，立刻走 | | **持股一般不超过3天** | 到点走人 | ## 仓位管理（50万本金） | 情况 | 仓位 | 金额 | 标的数 | |-----|------|------|-------| | 强主线 + 强龙头 | 50% | 25万 | 1-2只 | | 一般机会 | 30% | 15万 | 1只 | | 弱信号 / 试错 | 10-15% | 5-7万 | 1只 | **铁律：** - 单只股票不超50%仓位 - 永远保留底仓现金（至少5万） - 绝不临时起意加仓 ## 止损纪律 | 类型 | 规则 | 执行 | |-----|------|-----| | **固定止损** | 买入价 -5% | 无条件执行 | | **日内止损** | 买入当天收盘亏 >3% | 次日竞价出局 | | **题材止损** | 主线逻辑被证伪 | 无论盈亏立刻走 | ## 回撤控制 | 触发条件 | 动作 | |---------|------| | 单月回撤 ≥10% | 警惕，下一笔交易前强制复盘 | | **连亏3次** | 强制停手 1-3天，复盘找问题 | | **账户回撤达15%** | 降至半仓以下运作 | ## 数据来源 ### 每日必看 1. **东方财富** - 概念板块资金流向：https://data.eastmoney.com/bkzj/gn.html 2. **财联社** - 热点新闻：https://www.cls.cn/hot-stock 3. **同花顺/通达信** - 行情软件 ### 复盘流程 **盘前（8:30-9:15）** - 复盘昨日涨停板，筛选目标 - 关注集合竞价情况 - 制定当日交易计划 **竞价（9:15-9:25）** - 观察目标股竞价强度 - 决定是否参与、仓位多少 **盘中** - 只执行计划，不临时起意 - 监控持仓股和板块走势 - 触发止损/卖点立刻执行 **盘后（15:00-16:00）** - 复盘当日操作 - 记录交易日志 - 分析主线题材持续性 ## 交易日志模板 ``` 日期：2026-XX-XX 标的： 买入价： 卖出价： 盈亏：+X% / -X% 买卖理由： 复盘总结： ``` ## 核心信条 > **短线交易的核心不是选股，是纪律。** > > 计划你的交易，交易你的计划。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jack-liang/1panel-install (version 0.1.0)

--- name: 1panel-install description: 1Panel 服务器管理面板一键安装。当用户提到安装、部署、配置 1Panel 时激活。自动检测是否已安装，已安装则返回访问信息，未安装则自动完成安装流程。 metadata: { "openclaw": { "emoji": "🖥️", "always": false, "requires": { "bins": ["bash", "curl", "tar", "systemctl"], }, "install": null, }, } --- # 1Panel 一键安装技能 通过调用官方安装脚本，自动完成 1Panel 的安装并返回访问信息。 ## 意图处理 当用户说"安装 1Panel"、"部署 1Panel"、"配置 1Panel"等请求时，自动执行安装流程。 ## 前置检查 ### 1. 检查系统依赖 确保以下命令可用： - `bash` - shell 环境 - `curl` - 下载安装包 - `tar` - 解压 - `systemctl` - 管理系统服务 如果缺少依赖，提示用户安装。 ### 2. 检查 root 权限 此脚本需要 root 权限安装系统服务。如果当前用户不是 root，提示用户使用 sudo 执行。 ## 安装流程 ### 场景 A：1Panel 已安装 1. 检测 `1pctl` 命令是否存在 2. 如果存在，执行 `1pctl user-info` 3. 返回现有的面板访问信息 ### 场景 B：1Panel 未安装 1. 下载 1Panel v2.1.4 安装包到 `/root/.openclaw/workspace/` 2. 解压到当前工作区 3. 运行官方安装脚本 `install.sh` - 自动选择中文语言 - 使用默认安装路径 `/opt` - 选择不安装 Docker 4. 等待服务启动（最长 30 秒） 5. 执行 `1pctl user-info` 获取访问信息 6. 返回完整结果 ## 返回值 自然语言文本，包含： ``` ✅ 1Panel 安装成功！ 外部地址：http://<公网IP>:<随机端口>/<随机路径> 内部地址：http://<内网IP>:<随机端口>/<随机路径> 面板用户：<随机生成的用户名> 面板密码：<随机生成的密码> ⚠️ 安全提醒： 1. 请立即修改面板密码：1pctl update password 2. 如果使用云服务器，请在安全组中打开对应端口 3. 建议定期更新系统和 1Panel ``` **注意**：每次安装 1Panel 时，端口、访问路径（Security Entry）、用户名和密码都是**随机生成**的，不是固定值。请以实际安装输出为准。 ## 错误处理 - **下载失败**：重试 3 次，提示检查网络 - **安装失败**：输出安装日志的最后部分，建议查看 `/opt/1panel/install.log` - **权限不足**：提示需要使用 sudo - **服务启动失败**：提示检查 systemctl 状态 ## 示例对话 **用户**：安装 1Panel **助手**：（执行安装，返回上述格式的结果） **用户**：1Panel 装好了吗？ **助手**：（如果已装，直接返回信息） ## 注意事项 - 需要服务器有外网访问权限（下载安装包） - 安装过程会自动配置 systemd 服务 - 面板的端口、访问路径、用户名和密码均为随机生成（每次安装都不同） - 此 skill 不包含卸载功能 ## 访问提示 如果安装后地址无法访问，或者服务器没有公网 IP，推荐使用 **Cloudflare Tunnel** 功能配置域名访问，实现安全的内网穿透，无需开放公网端口。 ## 版本 - 1Panel 版本：v2.1.4（固定，可在脚本中升级） - Skill 版本：1.0.0 - 最后更新：2025-03-13


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: richducat/dolph-market-maker (version 1.0.0)

--- name: simmer-market-maker description: Places GTC limit orders on both sides (bid/ask) of liquid Polymarket markets. Finds active markets with >$10k 24h volume, mid-range prices, and ample time to resolve. Quotes spreads around the CLOB midpoint to capture bid/ask spread. Use when you want to passively earn spread as a market maker. metadata: author: "PrYsM96" version: "1.2.0" displayName: "Simmer Market Maker" difficulty: "intermediate" --- # Polymarket Market Maker A market-making strategy that places GTC (Good-Till-Cancelled) limit orders on both sides of liquid Polymarket prediction markets, capturing the bid/ask spread. ## Strategy 1. **Market Selection:** Finds active markets with: - 24h volume > $10,000 (liquid enough to fill) - Price (YES probability) between 0.15 and 0.85 (avoids near-certain outcomes) - Resolves > 4 hours away (enough time to get fills) - No taker fees (`is_paid=False`) — 10% fee kills the edge 2. **Pricing:** Fetches the live CLOB midpoint for each market and quotes: - YES buy limit at `mid - 0.02` (2¢ below mid) - NO buy limit at `(1 - mid) - 0.02` (2¢ below NO mid) 3. **Order Management:** Cancels existing open orders before placing new ones each run. 4. **Limits:** Max $5 per order, max 3 markets per run. ## Requirements **Environment variables (required):** | Variable | Required | Description | |----------|----------|-------------| | `SIMMER_API_KEY` | ✅ Yes | Your Simmer API key — get it at simmer.markets/dashboard → SDK tab | | `TRADING_VENUE` | Optional | `polymarket` (default) or `sim` for paper trading with virtual $SIM | **Python dependency:** `simmer-sdk` — install with `pip install simmer-sdk` ## Setup ```bash export SIMMER_API_KEY=sk_live_... # required export TRADING_VENUE=sim # optional: paper mode with $SIM pip install simmer-sdk ``` ## Usage ```bash # Dry run (default) python market_maker.py # Live trading python market_maker.py --live # Show current positions python market_maker.py --positions # Show config python market_maker.py --config # Set config python market_maker.py --set max_order_usd=10.0 ``` ## Config (env vars) | Env Var | Default | Description | |---------|---------|-------------| | `SIMMER_MM_MAX_ORDER_USD` | 5.0 | Max USD per limit order | | `SIMMER_MM_MAX_MARKETS` | 3 | Max markets per run | | `SIMMER_MM_MIN_VOL_24H` | 10000 | Min 24h volume filter | | `SIMMER_MM_SPREAD_OFFSET` | 0.02 | How far below mid to quote (2¢) | | `SIMMER_MM_MIN_PRICE` | 0.15 | Min YES price to consider | | `SIMMER_MM_MAX_PRICE` | 0.85 | Max YES price to consider | | `SIMMER_MM_MIN_HOURS_TO_RESOLVE` | 4 | Min hours to resolution |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: arkaydeus/search-reddit (version 1.0.0)

--- name: search-reddit description: Search Reddit in real time using OpenAI web_search with enrichment (engagement + top comments). Use when you need recent Reddit threads, subreddit-filtered results, or quick link lists. --- # Search Reddit Real-time Reddit search powered by OpenAI web_search with post enrichment (score, comments, and top comment excerpts). ## Setup Set your OpenAI API key: ```bash clawdbot config set skills.entries.search-reddit.apiKey "sk-YOUR-KEY" ``` Or use environment variable: ```bash export OPENAI_API_KEY="sk-YOUR-KEY" ``` You can also set a shared key: ```bash clawdbot config set skills.entries.openai.apiKey "sk-YOUR-KEY" ``` ## Commands ### Basic Search ```bash node {baseDir}/scripts/search.js "Claude Code tips" ``` ### Filter by Time ```bash node {baseDir}/scripts/search.js --days 7 "AI news" ``` ### Filter by Subreddit ```bash node {baseDir}/scripts/search.js --subreddits machinelearning,openai "agents" node {baseDir}/scripts/search.js --exclude bots "real discussions" ``` ### Output Options ```bash node {baseDir}/scripts/search.js --json "topic" # JSON results node {baseDir}/scripts/search.js --compact "topic" # Minimal output node {baseDir}/scripts/search.js --links-only "topic" # Only Reddit links ``` ## Example Usage in Chat **User:** "Search Reddit for what people are saying about Claude Code" **Action:** Run search with query "Claude Code" **User:** "Find posts in r/OpenAI from the last week" **Action:** Run search with --subreddits openai --days 7 **User:** "Get Reddit links about Kimi K2.5" **Action:** Run search with --links-only "Kimi K2.5" ## How It Works Uses OpenAI Responses API (`/v1/responses`) with the `web_search` tool: - Allowed domain: `reddit.com` - Enriches each thread by fetching Reddit JSON (`/r/.../comments/.../.json`) - Updates the date from `created_utc` and filters to last N days - Computes engagement and top comment excerpts ## Environment Variables - `OPENAI_API_KEY` - OpenAI API key (required) - `SEARCH_REDDIT_MODEL` - Model override (default: gpt-5.2) - `SEARCH_REDDIT_DAYS` - Default days to search (default: 30)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: okgptai/audio-translator (version 2.1.0)

--- name: audio-translator version: 2.1.0 description: 多语种语音翻译技能。支持URL或本地文件输入，自动识别源语言，翻译为目标语言，并生成目标语言的语音文件。支持中文、英文、日文、法文、西班牙文等多种语言互译。 author: winer tags: [audio, translation, speech, multilingual, whisper, tts, url, download] tools: [bash, python, curl] --- # 多语种音频翻译助手 支持 URL 或本地文件输入，自动识别源语言，翻译为目标语言，并生成目标语言的语音文件。 ## 触发条件 当用户提出以下请求时激活此技能： - "翻译语音文件" - "翻译这个音频" - "把语音翻译成XX语" - "下载并翻译音频" - "translate audio from url" - "翻译在线音频" ## 参数定义 ### input_path（必需） - **类型**: string - **描述**: 输入音频文件路径或URL - **支持类型**: - 本地文件: `/Users/winer/Downloads/audio.mp3` - URL: `https://example.com/audio.mp3` - **示例**: - `"/Users/winer/Downloads/录音.mp3"` - `"https://example.com/voice.m4a"` ### target_lang（必需） - **类型**: string - **描述**: 目标语言代码 - **可选值**: - `en` - 英文 - `zh` - 中文 - `ja` - 日文 - `fr` - 法文 - `es` - 西班牙文 - `de` - 德文 - `ko` - 韩文 - `ru` - 俄文 - `it` - 意大利文 ### output_path（可选） - **类型**: string - **描述**: 输出语音文件路径（默认：自动生成） - **示例**: `"/Users/winer/Downloads/结果.mp3"` ### source_lang（可选） - **类型**: string - **描述**: 源语言代码（默认自动检测） ## 执行流程 ### 步骤1: 输入处理 - 检测输入是 URL 还是本地文件 - URL: 使用 curl 下载到临时目录 - 本地文件: 直接使用 ### 步骤2: 语音识别（Whisper 自动检测语言） ```python from faster_whisper import WhisperModel model = WhisperModel("tiny", device="cpu", compute_type="int8") segments, info = model.transcribe(audio_path) source_language = info.language ``` ### 步骤3: 翻译（MyMemory API） ```bash curl -s "https://api.mymemory.translated.net/get?q=<文本>&langpair=<源>|<目标>" ``` ### 步骤4: 目标语言语音合成（edge-tts） 根据目标语言选择对应的 TTS 语音： | 目标语言 | TTS 语音 | |---------|---------| | en | en-US-AriaNeural | | zh | zh-CN-XiaoxiaoNeural | | ja | ja-JP-NanamiNeural | | fr | fr-FR-DeniseNeural | | es | es-ES-ElviraNeural | | de | de-DE-KatjaNeural | | ko | ko-KR-SunHiNeural | | ru | ru-RU-SvetlanaNeural | | it | it-IT-ElsaNeural | ## 使用示例 ### 示例1: 本地文件翻译 ``` 翻译 /Users/winer/录音.mp3 到英文 ``` ### 示例2: URL音频翻译 ``` 翻译 https://example.com/voice.m4a 到中文 ``` ### 示例3: 指定输出路径 ``` 翻译 /Users/winer/audio.mp3 en /Users/winer/result.mp3 ``` ### 示例4: URL到指定输出 ``` 翻译 https://example.com/speech.mp3 zh /Users/winer/speech_zh.mp3 ``` ## 注意事项 - **自动语言检测**: Whisper 会自动检测源语言 - **免费使用**: Whisper(本地)、MyMemory API、edge-tts 均免费 - **网络需求**: 翻译和TTS需要网络连接；URL输入需要网络下载 - **支持的输入格式**: mp3, wav, m4a, aac, ogg, flac, wma - **Python 版本**: 使用 Python 3.11

path: scripts/requirements.txt
content: faster-whisper
edge-tts


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: joe12801/xiaozhi-mcp-openclaw-official (version 1.0.2)

--- name: 小智AI-Xiaozhi Mcp Openclaw Official description: 按小智官方 MCP 接入方式，把小智 AI 设备通过 MCP 接到 OpenClaw / OpenAI-compatible 后端。适用于已经有小智 MCP 接入点（wss://api.xiaozhi.me/mcp/?[REDACTED_SECRET] `openclaw_query(message)` MCP 工具，让小智在需要外部能力、复杂推理、联网查询或外部智能辅助时调用。 Official XiaoZhi MCP bridge for OpenClaw / OpenAI-compatible backends. Use when you already have a XiaoZhi MCP endpoint and want XiaoZhi to call an external assistant tool such as `openclaw_query(message)`. --- # xiaozhi-mcp-openclaw-official [简体中文](#简体中文) | [English](#english) --- ## 简体中文 ### 作用 这是一个按小智官方文档方式接入的最小可用 MCP 原型。 数据流： ```text 小智 → MCP 接入点 → mcp_pipe.py → openclaw_mcp.py → OpenAI/OpenAI-compatible 后端 → 回复文本 → 小智播报 ``` ### 提供的工具 - `openclaw_query(message)` - 当用户提到“龙虾机器人”“龙虾”或需要外部能力时，调用这个工具 - 返回适合语音播报的简短中文结果 ### 启动步骤 1. 安装依赖： ```bash pip install -r requirements.txt ``` 2. 复制配置： ```bash cp .env.example .env ``` 3. 修改 `.env`： - `MCP_ENDPOINT` - `OPENAI_BASE` - `OPENAI_KEY` - `MODEL` 4. 启动： ```bash python3 mcp_pipe.py openclaw_mcp.py ``` ### 关键配置项 - `MCP_ENDPOINT`：小智 MCP 接入点 - `OPENAI_BASE`：OpenAI/OpenAI-compatible 后端地址 - `OPENAI_KEY`：后端 key - `MODEL`：模型名（如 `gpt-5.4`） ### 注意事项 - 不要把真实 `.env` 连同密钥一起分享 - 这是一个原型桥接项目，后续可以继续增强成真正的 OpenClaw 多工具接入版 --- ## English ### Purpose This is a minimal working MCP prototype that follows XiaoZhi's official MCP integration style. Flow: ```text XiaoZhi → MCP endpoint → mcp_pipe.py → openclaw_mcp.py → OpenAI/OpenAI-compatible backend → reply text → XiaoZhi speaks it ``` ### Provided tool - `openclaw_query(message)` - Use when the user mentions “龙虾机器人 / Lobster Robot” or when an external capability is needed - Returns a short Chinese reply suitable for voice playback ### Startup 1. Install dependencies: ```bash pip install -r requirements.txt ``` 2. Copy config: ```bash cp .env.example .env ``` 3. Update `.env`: - `MCP_ENDPOINT` - `OPENAI_BASE` - `OPENAI_KEY` - `MODEL` 4. Start: ```bash python3 mcp_pipe.py openclaw_mcp.py ``` ### Key config values - `MCP_ENDPOINT`: XiaoZhi MCP endpoint - `OPENAI_BASE`: OpenAI/OpenAI-compatible backend URL - `OPENAI_KEY`: backend API key - `MODEL`: model name (for example `gpt-5.4`) ### Notes - Do not publish your real `.env` or secrets - This is a prototype bridge and can later be extended into a richer OpenClaw multi-tool integration


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: billchen1020/iccircle-news (version 1.2.0)

--- name: ICCircle News description: Fetch and aggregate semiconductor and chip industry news from IC技术圈 (iccircle.com) RSS feeds. Use when the user asks for 半导体新闻, 芯片行业资讯, IC技术圈动态, 芯片设计行业新闻, or semiconductor/chip industry news. --- ## Core Rules 1. **Execution**: Run `python3 <skill_dir>/scripts/agg_news.py` to fetch news. 2. **Output**: The script prints formatted news directly to stdout; capture and present to user. 3. **No arguments**: The script runs with predefined RSS sources and keywords; no extra parameters needed. 4. **Sources**: Aggregates from IC技术圈 (7 columns), VLSI Blogs, IT之家, 36氪. 5. **Presentation**: Preserve the formatted output structure with source headers, timestamps, and links. ## Data Storage None. This skill executes a read-only script that fetches external RSS feeds. ## External Endpoints | Service | URL | Purpose | |---------|-----|---------| | IC技术圈 | https://iccircle.com/feed | RSS feeds for 7 columns | | VLSI Blogs | https://vlsiblogs.com/aggrss | RSS feeds for cadence and synopsys | | IT之家 | https://www.ithome.com/rss/ | Tech news with keyword filtering | | 36氪 | https://www.36kr.com/feed | Tech news with keywork filtering |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: edwardrodriguez703-design/book-fitness (version 1.0.1)

--- name: book-fitness description: Book fitness services through Lokuli MCP. Use when user needs to find and book fitness. Triggers on requests like "book a fitness", "find fitness near me", or any fitness service request. --- # uook fitness Book fitness services through Lokuli's MCP server. ## MCP Endpoint ``` https://lokuli.com/mcp/sse ``` Transport: SSE | JSON-RPC 2.0 | POST requests ## Tools ### search ```json { "method": "tools/call", "params": { "name": "search", "arguments": { "query": "fitness", "zipCode": "90640", "maxResults": 20 } } } ``` ### check_availability ```json { "method": "tools/call", "params": { "name": "check_availability", "arguments": { "providerId": "xxx", "serviceId": "yyy", "date": "2025-02-10" } } } ``` ### create_booking ```json { "method": "tools/call", "params": { "name": "create_booking", "arguments": { "providerId": "xxx", "serviceId": "yyy", "timeSlot": "2025-02-10T14:00:00-08:00", "customerName": "John Doe", "customerEmail": "[REDACTED_SECRET]", "customerPhone": "+13105551234" } } } ```


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