OpenClaw/clawhub-security-signals huggingface dataset:
Skill: bytesagain-lab/dex-aggregator (version 4.0.0)

--- name: "dex-aggregator" version: "4.0.0" description: "DEX aggregation reference — routing algorithms, 1inch Fusion mode, CowSwap batch auctions, sandwich attack protection, and swap API endpoints" author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [dex, swap, 1inch, uniswap, defi] category: "blockchain" --- # Dex Aggregator DEX aggregation reference — routing algorithms, 1inch Fusion mode, CowSwap batch auctions, sandwich attack protection, and swap API endpoints. No API keys or credentials required — outputs reference documentation only. ## Commands | Command | Description | |---------|-------------| | `intro` | Aggregator mechanics, major platforms | | `standards` | 1inch Fusion, 0x RFQ, CowSwap batches | | `troubleshooting` | Reverted transactions, approvals, price impact | | `performance` | Split ratios, gas estimation, private pools | | `security` | Approvals, front-running, token risks | | `migration` | CEX to DEX, single-DEX to aggregator, cross-chain | | `cheatsheet` | API endpoints, chain IDs, router addresses | | `faq` | Fees, safety, savings, integration | ## 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: deemoartisan/gateway-notify (version 1.0.5)

--- name: gateway-notify description: "Set up automatic notifications when OpenClaw gateway restarts. Use when user wants to be notified of gateway startup events via any messaging channel (iMessage, WhatsApp, Telegram, Discord, etc.)." --- # Gateway Notify Automatically send notifications when the OpenClaw gateway starts up. ## What It Does Creates a hook that triggers on `gateway:startup` events and sends a notification message to the user's preferred channel with gateway status information. ## Quick Start Run the setup script with the user's messaging channel and address: ```bash scripts/setup_gateway_notify.sh <channel> <address> ``` Examples: ```bash scripts/setup_gateway_notify.sh imessage [REDACTED_SECRET] scripts/setup_gateway_notify.sh whatsapp +1234567890 scripts/setup_gateway_notify.sh telegram @username ``` The script will: 1. Create the hook directory at `~/.openclaw/hooks/gateway-restart-notify` 2. Generate the handler with the specified channel configuration 3. Enable the hook in OpenClaw config 4. Restart the gateway to activate ## How It Works The hook uses OpenClaw's internal hook system: - Listens for `gateway:startup` events - Collects gateway status (model, time, port) - Sends notification via the configured channel CLI ## Supported Channels See [CHANNELS.md](references/CHANNELS.md) for channel-specific CLI commands and address formats. ## Manual Setup If you need to customize the hook, see [MANUAL.md](references/MANUAL.md) for step-by-step instructions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: freebook8/music-player (version 1.1.0)

# Music Player Skill - Windows 音乐播放技能 支持 Windows 系统的音乐搜索、下载和播放技能。基于 go-music-api 和多个音乐源。 ## 功能特性 - 🔍 **多源搜索** - 支持网易云音乐等多个 API 源 - 📥 **高清下载** - 自动获取最佳音质版本 - 🎧 **本地播放** - 调用 Windows 默认播放器 - 📝 **元数据嵌入** - 支持 ID3 标签（歌名、歌手、专辑、封面） - 🎵 **批量下载** - 支持下载多个版本 ## 快速开始 ### 1. 安装依赖 ```bash pip install requests mutagen python-pptx ``` ### 2. 搜索并下载音乐 ```bash # 基础下载 python download.py "歌曲名" "保存路径.mp3" # 使用 go-music-api 源（推荐，音质更好） python download_go_api.py # 下载多个版本 python download_versions.py ``` ### 3. 播放音乐 ```bash python play_music.py "歌曲路径.mp3" ``` ### 4. 嵌入元数据 ```bash python embed_metadata.py "歌曲.mp3" "歌名" "歌手" "专辑" "封面.jpg" ``` ## 文件说明 | 文件 | 说明 | |------|------| | `download.py` | 基础下载脚本（网易云 API） | | `download_go_api.py` | 使用 go-music-api 源（推荐） | | `download_versions.py` | 下载多个版本 | | `search_and_download.py` | 搜索并下载（旧版） | | `play_music.py` | 播放本地音乐 | | `embed_metadata.py` | 嵌入 ID3 元数据 | | `SKILL.md` | 技能说明文档 | ## 使用示例 ### 示例 1：下载刘德华的《中国人》 ```bash python download_go_api.py "刘德华 中国人" "C:\\music\\刘德华 - 中国人.mp3" ``` ### 示例 2：下载 Enya 的《One by One》 ```bash python download.py "One by One Enya" "C:\\music\\Enya_One_by_One.mp3" ``` ### 示例 3：播放下载的音乐 ```bash python play_music.py "C:\\music\\Enya_One_by_One.mp3" ``` ## API 源 技能支持以下音乐 API 源： 1. **网易云音乐** - 基础源，稳定可靠 2. **go-music-api** - 开源项目，音质更好 3. **UOMG API** - 备用源 ## 输出目录 默认下载到：`C:\Users\Administrator\.openclaw\workspace\music\` ## 注意事项 - ⚠️ 需要 Python 3.7+ - ⚠️ 需要网络连接来搜索和下载音乐 - ⚠️ 下载的音乐仅供个人学习使用 - ⚠️ 部分 API 可能需要代理访问 ## 故障排除 ### 下载失败 1. 检查网络连接 2. 尝试切换 API 源（使用 `download_go_api.py`） 3. 检查歌曲名称是否正确 ### 无法播放 1. 检查文件是否完整（文件大小 > 1MB） 2. 尝试使用其他播放器打开 3. 重新下载其他版本 ### 元数据嵌入失败 1. 确保安装了 mutagen：`pip install mutagen` 2. 检查文件路径是否正确 3. 确保 MP3 文件未被占用 ## 更新日志 ### v1.1.0 (2026-03-24) - ✅ 添加 go-music-api 支持 - ✅ 优化音质选择 - ✅ 添加多版本下载 - ✅ 改进错误处理 ### v1.0.0 (2026-03-24) - ✅ 初始版本发布 - ✅ 支持网易云音乐 API - ✅ 基础搜索和下载功能 - ✅ 本地播放支持 ## 许可证 本技能仅供学习交流使用，请勿用于商业用途。 ## 支持 如有问题，请提交 Issue 或联系作者。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: ifuryst/open-computer-use (version 0.1.0)

--- name: open-computer-use description: Guidance for installing, configuring, and operating Open Computer Use, an open-source Computer Use MCP service for macOS, Linux, and Windows. --- # Open Computer Use ## Overview Open Computer Use is an open-source Computer Use service exposed through MCP. It lets compatible AI agents operate local desktop apps on macOS, Linux, and Windows through tools such as `list_apps`, `get_app_state`, `click`, `type_text`, `press_key`, `scroll`, `drag`, and `set_value`. Use this skill when a user wants to install, configure, verify, troubleshoot, or operate Open Computer Use from an agent runtime such as Codex, Claude Code, Gemini CLI, opencode, or another MCP client. ## Install Install the CLI: ```sh npm i -g open-computer-use ``` On macOS, run it once and grant Accessibility and Screen Recording permissions: ```sh open-computer-use ``` Windows and Linux do not require the macOS permission step. ## MCP Setup Install into Codex: ```sh open-computer-use install-codex-mcp ``` Or configure any MCP client manually: ```json { "mcpServers": { "open-computer-use": { "command": "open-computer-use", "args": ["mcp"] } } } ``` ## Other Agent Integrations Open Computer Use also includes installer commands for common agent runtimes: ```sh open-computer-use install-codex-plugin open-computer-use install-claude-mcp open-computer-use install-gemini-mcp open-computer-use install-opencode-mcp ``` ## Direct CLI Calls Use the CLI to call one tool: ```sh open-computer-use call list_apps open-computer-use call get_app_state --args '{"app":"TextEdit"}' ``` Run a sequence in one process so element indexes can be reused: ```sh open-computer-use call --calls '[{"tool":"get_app_state","args":{"app":"TextEdit"}},{"tool":"press_key","args":{"app":"TextEdit","key":"Return"}}]' ``` Check setup and permissions: ```sh open-computer-use doctor ``` ## Operating Rules - Treat Computer Use as control over the user's real desktop session. - Ask before actions that send messages, submit forms, delete data, install software, change system settings, make purchases, or expose sensitive local information. - Prefer accessibility element indexes from `get_app_state` over coordinate clicks when available. - Re-read app state after important actions before continuing. - On macOS, if tools fail due to permissions, ask the user to grant Accessibility and Screen Recording for the Open Computer Use app or runtime process. ## Project Links - Repository: https://github.com/iFurySt/open-codex-computer-use - npm package: https://www.npmjs.com/package/open-computer-use


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: octenai/octen-search-skill (version 1.0.0)

--- name: OctenSearch description: Real-time web search for AI agents powered by Octen. Fast, fresh, and relevant — search the web, filter by date, and get LLM-ready results in under 80ms. Ideal for research, news monitoring, and any task requiring up-to-date information. homepage: https://octen.ai metadata: {"clawdbot":{"emoji":"🔍","requires":{"bins":["python3"],"env":["OCTEN_API_KEY"]},"primaryEnv":"OCTEN_API_KEY"}, "homepage" : "https://octen.ai", "support" : "[REDACTED_SECRET]" } --- # Octen Search - Real-time Web Search for AI Agents Octen Search gives your OpenClaw agent the ability to **search the web in real time**. Powered by [Octen](https://octen.ai), a search infrastructure purpose-built for AI agents and LLMs. ## What This Skill Does - 🔍 **Web search**: Search the live web and get structured, LLM-ready results - ⚡ **Fast**: Average response time under 80ms - 🕐 **Fresh**: Minute-level index freshness — always up-to-date - 📅 **Time filtering**: Filter results by publish date (start/end time) - 🤖 **LLM-native**: Results formatted and ranked specifically for AI consumption ## Usage ```bash # Basic web search python3 {baseDir}/scripts/search.py "your search query" # Control number of results (1-20, default: 5) python3 {baseDir}/scripts/search.py "your query" -n 10 # Filter by start date python3 {baseDir}/scripts/search.py "your query" --start_time "2026-01-01T00:00:00Z" # Filter by date range python3 {baseDir}/scripts/search.py "your query" --start_time "2026-01-01T00:00:00Z" --end_time "2026-01-31T23:59:59Z" ``` ## Options - `-n, --count <count>`: Optional. Number of results (min: 1, max: 20, if not provided, default to 5) - `--start_time <time>`: Optional. Start time for filtering results (ISO 8601 format, e.g., "2026-01-01T00:00:00Z") - `--end_time <time>`: Optional. End time for filtering results (ISO 8601 format, e.g., "2026-01-31T23:59:59Z"). If start_time and end_time are both provided, end_time must be greater than start_time ## Notes - Needs `OCTEN_API_KEY` in the environment variables, get it from https://octen.ai, then set it like this: `export OCTEN_API_KEY=your-api-key` - Use `--start_time` and `--end_time` if you want to filter results by time published. For example, to search for news published in January 2026, you can use `--start_time "2026-01-01T00:00:00Z" --end_time "2026-01-31T23:59:59Z"`. ## Security - The `OCTEN_API_KEY` environment variable is **only sent to the official Octen API endpoint**: `https://api.octen.ai/search` - No environment variables are sent to any other endpoints or external services - The API endpoint is **hardcoded and whitelisted** in the code and cannot be modified at runtime - The skill uses standard HTTP header authentication (`X-Api-Key`) which is the recommended practice for API authentication - All network requests are made over HTTPS to ensure encrypted transmission of the API key


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zero2ai-hub/skill-runway-video-gen (version 1.0.0)

--- name: skill-runway-video-gen version: 1.0.0 description: Generate short product videos from images using Runway Gen4 Turbo. Use for TikTok ads, UGC-style product demos, Reels, and YouTube Shorts. metadata: openclaw: requires: { bins: ["uv"] } --- # skill-runway-video-gen Wraps Runway Gen4 Turbo image-to-video API. Point at a product image, describe the motion, get an MP4. No browser needed. ## Usage ```bash uv run scripts/generate_video.py \ --image product.jpg \ --prompt "water droplets falling, soft bokeh, slow motion" \ --output output.mp4 \ --duration 10 \ --ratio 720:1280 ``` ## Args | Arg | Default | Description | |---|---|---| | `--image` | required | Path to source product image | | `--prompt` | `""` | Motion description (optional but recommended) | | `--output` | required | Output MP4 path | | `--duration` | `10` | Video length: 5 or 10 seconds | | `--ratio` | `720:1280` | Aspect ratio (vertical for TikTok) | ## Config API key is read from (in order): 1. `RUNWAY_API_KEY` environment variable 2. `~/tiktok-api.json` → `runway.apiKey` ## Cost | Duration | Cost | |---|---| | 5s | $0.25 | | 10s | $0.50 | Always use 10s — you get more content to work with. ## Known quirks **Last 1–2s often freezes.** Fix: stretch to 12s at 0.83x speed using MoviePy: ```python from moviepy import VideoFileClip clip = VideoFileClip("output.mp4") slow = clip.with_effects([vfx.MultiplySpeed(0.83)]) slow.write_videofile("output_12s.mp4", fps=30) ``` ## Rate limits No per-minute quota (unlike Veo). Runway charges per second of output. ## Pipeline integration This skill feeds into `skill-tiktok-video-pipeline` as the video generation step. The pipeline handles slowmo stretch and caption overlay automatically.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: softboypatrick/insight-finder (version 1.0.0)

--- name: Data Probe version: 1.0.0 slug: data-probe description: "数据探索与洞察引擎 | 自动分析CSV/JSON/Excel | 统计检验+模式识别+异常检测+可视化建议 | 输出结构化报告" author: Softboypatrick license: MIT-0 tags: [data-analysis, insight, statistics, exploration, business-intelligence] --- # Data Probe Data Probe 是一个自动化的数据探索与洞察引擎。它接收原始数据，执行自动化的统计分析和模式识别，最终输出带置信度评级的结构化洞察报告。 --- ## 一、触发方式 当用户提供以下内容时自动激活： 1. 粘贴的 CSV/JSON/表格数据 2. 数据文件路径 3. 明确的请求："分析这些数据"、"找找规律"、"出报告" --- ## 二、四阶段分析管线 ### Stage 1: 数据收容（Data Containment） 目标：理解数据形状，评估可用性。 | 检查维度 | 具体方法 | 严重等级 | |---------|---------|---------| | 缺失值比率 | 每列计算缺失比例，>5%标记 | 中危 | | 异常值 | Z-score > 3 或 IQR 法 | 低危 | | 类型推断 | 自动检测 numeric/categorical/datetime | 信息 | | 重复行 | 精确去重 | 低危 | | 基数检测 | 唯一值数量，识别 ID 列 | 低危 | | 数据时效 | 时间戳范围检查 | 信息 | 输出：数据质量评分表 + 需修复项列表。 ### Stage 2: 描述性统计（Descriptive Statistics） 目标：形成对数据的整体认知。 - **数值列**：count, mean, std, min, 25%/50%/75%/max - **分类列**：value_counts, unique count, mode, mode_freq - **时间列**：range, frequency, gaps, seasonality hints - **分布形状**：skewness, kurtosis, normality test (if n>30) 输出：数据概要卡片，识别显性特征。 ### Stage 3: 关联与模式（Correlation & Patterns） 目标：发现变量间关系和隐藏模式。 ```yaml 关系分析: 数值-数值: Pearson r, Spearman ρ, 散点分布形态 分类-数值: ANOVA / Kruskal-Wallis, 分组箱形图 分类-分类: 列联表, Cramérs V, 卡方检验 时间序列: 自相关图, 趋势成分, 季节性成分, 残差分析 模式检测: 聚类倾向: 用 WCSS 肘部法建议最佳k 离群簇: DBSCAN 密度检测 顺序模式: 频繁项集 (支持度 > 5% 时) ``` 每个关联都需附带： - 效应量（effect size） - p 值（如果适用） - 实际意义评估（统计学显著 ≠ 业务显著） ### Stage 4: 洞察输出（Actionable Insights） 目标：输出可执行的决策支撑。 ```yaml 报告结构: Executive Summary: - 数据概况（行×列, 时间范围） - Top-3 最重要的发现 - 紧急程度评级 Detailed Findings: - 每个发现包含: - 标题（30字内） - 置信度（60-99%） - 支撑证据（统计量+图表描述） - 业务影响评估 - 建议行动 Limitations: - 样本局限性 - 因果推断限制（相关性≠因果） - 已知偏差 ``` --- ## 三、置信度评分规则 | 置信度 | 条件 | |--------|------| | 90-99% | 统计显著(p<0.01) + 效应量大 + 业务逻辑一致 | | 70-89% | 统计显著(p<0.05) + 效应量中等 | | 50-69% | 趋势明显但统计不显著 | | <50% | 需要更多数据，标记为hypothesis | --- ## 四、交叉验证机制 ```yaml 自我质疑: 1. 这个发现是否可能是偶然？ → 多重比较校正（Bonferroni） 2. 是否忽略了混杂变量？ → 分层分析检查 3. 样本是否代表总体？ → 抽样偏差检查 4. 是否有反向因果的可能？ → 时间顺序验证 ``` --- ## 五、使用示例 用户输入： ``` 日期,销售额,访问量,转化率,客单价 2026-01-01,15200,1200,2.3%,128 2026-01-02,14800,1150,2.1%,130 ... ``` 输出（摘要）： ``` ## Data Probe 洞察报告 ### 数据质量: 89/100 - 缺失值: 0.3%（已插补） - 异常值: 2个（已标注） ### 🔍 发现 #1: 周末转化率低于工作日 31% 置信度: 94% | p=0.003 | 效应量 Cohen d=0.87 支撑: 12个完整周数据 (n=84), 周末平均1.9% vs 工作日2.75% 影响: 如果周末优化到工作日水平，月增收约 ¥8,500 建议: 1) 推出周末专属优惠 2) 移动端体验优化 ### 🔍 发现 #2: 客单价与转化率中度负相关 (r=-0.42) 置信度: 82% | p=0.01 支撑: 高客单(>200)时转化率降至1.5% 建议: 尝试分层定价/分期付款方案，在高客单区间保转化 ### 📋 限制 - 仅有84个数据点，季节效应可能未充分捕捉 - 相关性不代表因果，A/B测试验证后再实施 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: banner90/byt-workflow (version 1.0.4)

--- name: byt-workflow description: YouTube video translation workflow, download audio, launch Doubao, play audio, capture translation tools: - youtube_translate --- # Byt Workflow ## Usage ```bash python workflow.py <youtube_url> [mode] ``` ## Parameters - `url` (required): YouTube video URL - `mode` (optional): Translation mode (dual, single, chinese, bilingual), default: "dual" ## Workflow Steps 1. **Download YouTube audio** - Uses youtube-audio-download skill 2. **Launch Doubao** - Uses doubao-launch skill 3. **Play audio** - Uses audio-play skill 4. **Capture translation** - Uses doubao-capture skill ## Returns ```json { "success": true, "audio_path": "H:/works/audio/video_title-xxxxx.mp3", "translation_path": "H:/works/translations/doubao_20240307_143022.txt", "duration": 1200 } ``` ## Tools ## youtube_translate Complete YouTube video translation workflow ## Workflow Integration This skill is part of the YouTube translation workflow: 1. **youtube-audio-download**: Download audio from YouTube 2. **doubao-launch**: Launch Doubao translation window 3. **audio-play**: Play the downloaded audio 4. **doubao-capture**: Capture translated subtitles ## Execution All skills execute on Windows Python via WSL cross-platform call: ``` wsl -> python.exe scripts/workflow.py ... ``` ## Error Handling All skills return JSON with `success` field: - `success: true` - Operation completed - `success: false` - Check `error_code` and `error_message` ## Notes - Windows GUI automation requires visible desktop (no RDP disconnect) - Output files are stored in Windows `works/` directory - WSL accesses Windows files via `/mnt/h/...`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kazemace/weibo-hot-search-anonymous (version 1.0.3)

--- name: weibo-hot-search-anonymous description: 无需登录微博账号，匿名抓取微博实时热搜榜并保存为 Markdown 文件。当用户说"获取微博热搜"、"抓取热搜"、"微博热搜榜"、"不用登录查热搜"、"匿名获取热搜"、"get Weibo hot search"、"weibo trending" 时使用。 version: 1.1.0 metadata: openclaw: requires: anyBins: - bun - npx --- # 微博热搜抓取（匿名版） **无需微博账号，无需登录**，通过 Chrome/Edge CDP 以匿名身份访问微博热搜页面（`https://weibo.com/newlogin?tabtype=search`），采集实时热搜列表并保存为 Markdown 表格。 微博热搜页面对未登录用户完全公开，本 skill 利用全新空白的浏览器配置目录访问，不依赖任何账号 Cookie。页面使用虚拟滚动，每次 DOM 中约保留 30 条，脚本缓慢滚动并在每步采集快照，覆盖全部约 50 条热搜。广告条目自动过滤，置顶条目保留并标注。 ## 脚本目录 **重要**：所有脚本位于本 skill 的 `scripts/` 子目录中。 **Agent 执行说明**： 1. 将本 SKILL.md 所在目录路径记为 `{baseDir}` 2. 脚本路径 = `{baseDir}/scripts/weibo-hot-search.ts` 3. 将文档中所有 `{baseDir}` 替换为实际路径 4. 确定 `${BUN_X}` 运行时：已安装 `bun` → 使用 `bun`；有 `npx` → 使用 `npx -y bun`；否则提示安装 bun ## 前置条件 - Google Chrome、Chromium 或 Microsoft Edge - `bun` 运行时 - **无需微博账号，无需任何登录操作** ## 用法 ```bash # 采集热搜，保存到当前目录下 ./<今天日期>-weibo-hot-search.md ${BUN_X} {baseDir}/scripts/weibo-hot-search.ts # 指定输出路径 ${BUN_X} {baseDir}/scripts/weibo-hot-search.ts --output ./data/hotsearch.md ``` **参数说明**： | 参数 | 说明 | |------|------| | `--output <路径>` | 输出 Markdown 文件路径（默认：`./<YYYY-MM-DD>-weibo-hot-search.md`） | | `--profile <目录>` | Chrome/Edge 配置文件目录（默认：`getDefaultProfileDir()`，无需修改） | ## 输出格式 ```markdown # 微博热搜 2026-03-10 > 采集时间：2026/3/10 15:27:14 > 共 50 条 | 排名 | 热搜词 | 热度 | 标签 | |------|--------|------|------| | 1 | [某热搜词](https://s.weibo.com/weibo?q=%23某热搜词%23) | - | 置顶 | | 2 | [另一个热搜](https://s.weibo.com/weibo?q=%23另一个热搜%23) | 1046777 | - | | 3 | [热搜三](https://s.weibo.com/weibo?q=%23热搜三%23) | 764477 | 新 | ... ``` - **置顶**：政府/官方置顶内容（`_ranktop_` 图标），排在最前 - **标签**：微博原始标签（新、热、爆、沸等） - **热度**：原始热度数值；置顶条目通常无热度，显示 `-` ## 环境变量 | 变量 | 说明 | |------|------| | `WEIBO_BROWSER_CHROME_PATH` | 覆盖 Chrome/Edge 可执行文件路径 | | `WEIBO_BROWSER_DEBUG_PORT` | 固定 CDP 调试端口（调试用） | ## 故障排查 ### Chrome 调试端口未就绪 ```bash pkill -f "Chrome.*remote-debugging-port" 2>/dev/null pkill -f "Edge.*remote-debugging-port" 2>/dev/null sleep 2 ``` **重要**：遇到此错误时，自动终止 Chrome/Edge CDP 进程并重试，无需询问用户。 ### 热搜列表未出现 若脚本超时未检测到热搜列表，可能原因： - 网络问题导致页面加载失败，重试即可 - 微博页面结构更新，需检查 DOM 选择器是否仍有效 ## 备注 - 匿名访问，不依赖任何账号 Cookie，可在任意环境直接运行 - 页面使用虚拟滚动，脚本以 200px / 600ms 的速度缓慢滚动，确保所有条目经过 DOM 渲染窗口 - 广告条目（含 `_doticon_` 标识）自动过滤 - 置顶条目（含 `_ranktop_` 图片）保留并排在最前


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: windy-001-crypto/stock-analysis-pro (version 1.0.0)

--- name: "stock-analysis-pro" version: "1.0.0" description: "专业股票分析技能整合 akshare 数据 + 技术指标 + 板块轮动 + 持仓诊断。触发词：'分析股票'、'今日行情'、'持仓诊断'、'推荐股票'、'今日财经'。无需API密钥，直接调用 akshare 获取A股/基金/期货实时数据。" --- # Stock Analysis Pro 专业股票分析技能，集成实时行情、技术指标、板块分析、持仓诊断。 ## 核心功能 | 功能 | 命令 | 数据源 | |------|------|--------| | 实时行情 | `akshare` | akshare (tushare备份) | | 技术分析 | 内置TA指标 | 计算所得 | | 板块轮动 | 行业/概念板块 | akshare | | 持仓诊断 | 持仓股票分析 | akshare + 用户输入 | | 异动监控 | 涨跌/资金流 | akshare | | 财报简评 | 业绩预判 | 公开财报 | ## 触发词 当用户说以下内容时激活： - "分析股票"、"今日行情"、"帮我看股" - "持仓诊断"、"我的股票怎么样" - "推荐股票"、"买什么好" - "今日财经"、"财经分析报告" - "大盘怎么样"、"板块机会" ## 使用方式 ### 实时行情查询 ``` 触发：用户问某只股票行情 操作： 1. 使用 akshare 技能获取实时数据 2. 计算涨跌幅、换手率、量比 3. 结合板块热度给出简单判断 ``` ### 持仓诊断报告 ``` 触发：用户提供持仓列表 操作： 1. 批量获取每只股票实时价格 2. 计算持仓金额、盈亏比例 3. 查询所属板块今日表现 4. 查询主力资金流向 5. 生成诊断结论和操作建议 ``` ### 今日财经报告 ``` 触发：用户说"今日财经分析报告"或"今日股票情况" 操作： 1. 获取上证/深证/创业板/北证50指数 2. 获取涨跌家数对比 3. 获取热门板块TOP10 4. 获取概念板块异动 5. 获取资金流向TOP10 6. 输出格式化报告 ``` ## 持仓诊断表格式 | 序号 | 股票 | 代码 | 股数 | 成本价 | 现价 | 盈亏% | 持股金额 | 做T次数 | 所属板块 | 板块热度 | 今日涨跌 | 主力资金 | 诊断结论 | 操作建议 | 止损位 | 目标位 | **字段说明：** - 持股金额 = 现价 × 股数 - 做T次数 = 当日做T交易次数（用户告知） - 所属板块 = 同花顺所属板块 - 板块热度 = 板块当日涨跌幅% - 主力资金 = 主力净流入（万） - 诊断结论：持有/加仓/减仓/卖出 - 操作建议：具体仓位调整比例 ## 市场概况格式 ``` 大盘：上证/深证/创业板涨跌幅 涨跌家数：上涨/下跌/平盘 热门板块TOP5：板块名（涨跌幅%） 资金流入TOP3：股票名（资金净额） ``` ## 推荐股票格式 | 股票 | 代码 | 现价 | 买入逻辑 | 目标价 | 止损位 | ## 数据来源 - **主数据源**：akshare（免费，无需API Key） - **备份数据源**：tushare（需Token，可选） - **实时行情**：新浪财经/东方财富 ## 常用 akshare 函数 ```python import akshare as ak # 实时行情 ak.stock_zh_a_spot_em() # 所有A股实时行情 # 板块行情 ak.stock_board_industry_name_em() # 行业板块 ak.stock_board_concept_name_em() # 概念板块 # 资金流向 ak.stock_individual_fund_flow(stock="000001") # 个股资金流 # 期货 ak.futures_zh_spot(symbol="螺纹钢") # 商品期货现货 ``` ## 技术指标计算 支持以下技术指标： - MA（均线）：5/10/20/60/120/250日 - RSI（相对强弱指数） - MACD（指数平滑异同） - KDJ（随机指标） - BOLL（布林带） - 成交量对比 ## 注意事项 1. **数据延迟**：新浪财经数据可能有3-5秒延迟 2. **批量限制**：单次查询不超过50只股票 3. **频率控制**：避免高频调用（>10次/分钟） 4. **数据校验**：获取后验证数据合理性 ## 报告输出格式 触发"今日财经分析报告"时使用以下格式： ``` # 📊 今日财经分析报告 日期：YYYY-MM-DD HH:MM ## 一、大盘概况 [上证/深证/创业板/北证 涨跌幅] ## 二、持仓诊断 [诊断表格] ## 三、今日机会 [推荐板块 + 原因] ## 四、操作建议 [总体仓位建议] ## 五、风险提示 [需要注意的风险] ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: colifire/clawhub-demo-skill (version 0.1.0)

--- name: clawhub-demo-skill description: Use this tiny demo skill to verify that a ClawHub skill can be published, installed, and invoked; it returns a short confirmation message and optionally echoes a user-provided phrase. version: 0.1.0 metadata: openclaw: emoji: "🧪" --- # ClawHub Demo Skill Use this skill only for publishing, installation, and invocation smoke tests. When invoked: - Reply with one concise sentence confirming that `clawhub-demo-skill` is active. - If the user provides a phrase to echo, include that phrase once. - Do not call external services. - Do not create files. Example invocation: ```text Use $clawhub-demo-skill and echo: hello clawhub ``` Expected behavior: ```text clawhub-demo-skill is active: hello clawhub ```


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