OpenClaw/clawhub-security-signals huggingface dataset:
Skill: mrnquyen/news-specialist (version 1.0.0)

--- name: news-specialist description: A news analysis specialist that explains, interprets, and comments on trending news and current events. --- # News Specialist Skill This skill represents a **news analysis expert** who helps users understand, interpret, and discuss the most important news and trending topics of the day. Use this skill when the user: - asks about a **specific news event** - wants a **summary or explanation of a news story** - wants to **learn more details about a headline** - asks for **analysis, opinions, predictions, or commentary** on current news - asks about **trending topics of the day** The goal of this skill is to **turn raw news information into clear insights** for the user. --- # Role You are a **News Specialist**. Your job is to: - analyze the most important news stories - explain what is happening - interpret the meaning and implications of events - provide thoughtful commentary and predictions based on available information You do not invent news. You rely on **retrieved information and verified news sources**. --- # Workflow ### 1. Understand the User Question Carefully read the user's request to determine: - which news topic they are asking about - whether they want **information, explanation, or commentary** --- ### 2. Check Session History and Memory Review the current session history and memory. If relevant information about the news topic already exists: - reuse that information - avoid unnecessary searches --- ### 3. Retrieve News if Needed If the session does not contain enough information: Use the available **news search skills** to retrieve relevant news articles using appropriate keywords. Focus on: - recent news - trending topics - reliable sources --- ### 4. Analyze the Information After retrieving the news: - summarize the key points - identify the important context - explain why the news matters - highlight potential implications or trends --- ### 5. Respond to the User Provide a clear and helpful response that includes: - a short explanation of the news - additional context or background - insights or commentary when appropriate Avoid speculation if information is uncertain. --- # Vibe Your tone should be: - Friendly - Humorous - Witty - Honest - Informative You should feel like **a knowledgeable friend who follows the news closely and explains things clearly**. Avoid sounding robotic or overly formal. --- # Response Style Good responses should: - explain news clearly - keep things concise - include helpful insights - stay grounded in real information Example tone: > “Here's what's going on with this news story — and why people are talking about it today.” --- # Important Rules - Do **not fabricate news or facts** - Always rely on retrieved information - If information is incomplete, say so honestly - Prioritize **clarity, accuracy, and usefulness**


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: raghulpasupathi/image-detection (version 1.0.0)

# Image Detection Skills Skills for analyzing and detecting AI-generated images. ## Essential Skills ### 1. Image Analyzer **Skill ID**: `image-analyzer` **Purpose**: Local image analysis without external APIs **Features**: - EXIF metadata extraction - AI artifact detection (weird hands, blurred backgrounds) - Visual similarity comparison - Reverse image search integration **Installation**: ```bash npm install @clawhub/image-analyzer ``` **Configuration**: ```javascript { "skill": "image-analyzer", "settings": { "detectArtifacts": true, "extractMetadata": true, "localOnly": true, "checkSignatures": ["DALL-E", "Midjourney", "Stable-Diffusion"] } } ``` **Use Cases**: Fast local detection, metadata verification, artifact identification --- ### 2. HuggingFace Image Detector **Skill ID**: `hf-image-detector` **Purpose**: ML-based AI image detection **Features**: - Pre-trained models (umm-maybe/AI-image-detector) - High accuracy (90-95%) - Supports SD, Midjourney, DALL-E - Local inference (no API calls) **Installation**: ```bash npm install @clawhub/hf-image-detector ``` **Use Cases**: High-accuracy detection, privacy-focused analysis --- ### 3. Hive Moderation API **Skill ID**: `hive-api` **Purpose**: Cloud-based image analysis **Features**: - AI-generated detection - NSFW filtering - Object detection - Fast inference (500ms) **Installation**: API integration via REST **Use Cases**: Quick detection, multi-purpose analysis, NSFW filtering --- ## Installation ### Via ClawHub ``` https://clawhub.ai/raghulpasupathi/image-detection ``` ### Via npm ```bash npm install @raghulpasupathi/image-detection ``` ## Configuration Examples ### High Accuracy Stack ```json { "skills": ["image-analyzer", "hf-image-detector", "hive-api"], "votingStrategy": "majority" } ``` ### Privacy-Focused Stack ```json { "skills": ["image-analyzer", "hf-image-detector"], "externalAPIs": false } ``` ### Fast Detection Stack ```json { "skills": ["hive-api"], "cacheResults": true } ``` --- *For video detection, see [VIDEO_DETECTION.md](VIDEO_DETECTION.md).*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tlreal/embedded-architecture-design (version 1.0.1)

--- name: embedded-code-migration-check description: 在将代码从一个平台迁移到另一个平台时使用 workflow_phase: act domain: embedded --- # Code Migration Check - 代码迁移检查 ## 概述 系统性地检查代码从一个嵌入式平台迁移到另一个平台时的兼容性问题。 ## 迁移检查清单 ### 1. 编译器兼容性 | 检查项 | 说明 | |--------|------| | **数据类型** | int 大小、对齐方式 | | **字节序** | 大端/小端 | | **编译器扩展** | __attribute__, pragma | | **内联汇编** | 目标平台语法 | ### 2. 硬件抽象层 | 检查项 | 原平台 | 目标平台 | 状态 | |--------|--------|---------|------| | GPIO 接口 | HAL_GPIO_xxx | gpio_xxx | ⚠️ | | SPI 接口 | HAL_SPI_xxx | spi_xxx | ⚠️ | | 定时器 | HAL_TIM_xxx | timer_xxx | ⚠️ | | 中断 | NVIC | INTC | ⚠️ | ### 3. 外设差异 - 寄存器地址不同 - 功能特性差异 - 时序参数差异 ### 4. 系统特性 - 启动流程 - 内存映射 - 低功耗模式 - 看门狗 ## 迁移流程 ``` 1. 分析原代码结构 ↓ 2. 识别平台相关代码 ↓ 3. 创建目标平台 HAL ↓ 4. 逐模块迁移 ↓ 5. 编译验证 ↓ 6. 功能测试 ``` ## 风险评估 | 风险级别 | 定义 | 处理 | |---------|------|------| | 🔴 高 | 需要重写 | 预留时间 | | 🟡 中 | 需要适配 | 正常处理 | | 🟢 低 | 可直接复用 | 简单验证 | ## 输出格式 ```markdown # 代码迁移检查报告 ## 基本信息 - 源平台: STM32F103 (HAL) - 目标平台: ESP32-S3 (ESP-IDF) - 代码规模: ~10K 行 ## 风险评估 ### 高风险项 🔴 1. **SPI 驱动** - 需要重写 - 原因: API 完全不同 - 建议: 使用目标平台 SPI 驱动重新实现 ### 中风险项 🟡 1. **定时器** - 需要适配 - 差异: 定时器结构不同 - 建议: 抽象定时器接口 ### 低风险项 🟢 1. **业务逻辑** - 可直接复用 2. **数据结构** - 可直接复用 ## 迁移建议 1. 先搭建目标平台框架 2. 实现 HAL 层适配 3. 逐模块迁移验证 ## 工作量估算 - HAL 层适配: 3 人天 - 驱动迁移: 2 人天 - 测试验证: 2 人天 - 总计: 7 人天 ``` ## 与 Core Workflow 集成 **在 Act 阶段调用:** - 迁移项目的第一步 - 输出迁移计划用于 `writing-plans`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: junwongpp/minimax-media-plugin (version 0.8.8)

--- name: minimax-media-plugin description: Install, configure, verify, or troubleshoot the @jwongart/openclaw-minimax-media OpenClaw plugin for MiniMax image understanding, image generation, music generation, video generation, text-to-speech, and web search tools. --- # OpenClaw MiniMax Media Plugin Use this skill when the user wants to install, update, configure, verify, or troubleshoot the `@jwongart/openclaw-minimax-media` OpenClaw plugin. ## Package - npm package: `@jwongart/openclaw-minimax-media` - OpenClaw plugin ID: `openclaw-minimax-media` - GitHub: `https://github.com/jwong-art/openclaw-minimax-media` - npm install spec: `npm:@jwongart/openclaw-minimax-media` The npm package is scoped, but the OpenClaw plugin ID is intentionally unscoped. Configure it under `plugins.entries.openclaw-minimax-media`. ## Install Install the plugin: ```bash openclaw plugins install npm:@jwongart/openclaw-minimax-media --pin ``` Install a specific version: ```bash openclaw plugins install npm:@jwongart/openclaw-minimax-media@0.8.8 --pin ``` After installation, restart the OpenClaw gateway if the CLI says a restart is required. ## Configuration Configure the MiniMax API key in OpenClaw config: ```json { "plugins": { "entries": { "openclaw-minimax-media": { "enabled": true, "config": { "apiKey": [REDACTED_SECRET] } } } } } ``` The plugin can also read these environment variables: ```bash MINIMAX_CODE_PLAN_KEY MINIMAX_CODING_API_KEY MINIMAX_API_KEY MINIMAX_API_HOST ``` Do not commit real API keys or tokens. ## Tools The plugin registers these OpenClaw tools: - `minimax_image` - `minimax_image_generate` - `minimax_music_generate` - `minimax_video_generate` - `minimax_tts` - `minimax_web_search` ## Verification Check the installed package and plugin ID: ```bash node -e "const p=require('/root/.openclaw/npm/node_modules/@jwongart/openclaw-minimax-media/package.json'); const m=require('/root/.openclaw/npm/node_modules/@jwongart/openclaw-minimax-media/openclaw.plugin.json'); console.log({ packageName: p.name, version: p.version, pluginId: m.id })" ``` Check the OpenClaw plugin list: ```bash openclaw plugins list | rg -C 4 'openclaw-minimax-media|MiniMax Media|@jwongart' ``` If the plugin was just installed or updated but tools are not available yet, restart the OpenClaw gateway before deeper debugging. ## Migration From Old Package The old unscoped npm package `openclaw-minimax-media` is deprecated. Use: ```bash openclaw plugins uninstall openclaw-minimax-media --force openclaw plugins install npm:@jwongart/openclaw-minimax-media --pin ``` The OpenClaw plugin ID remains `openclaw-minimax-media`, so existing config keys can stay under `plugins.entries.openclaw-minimax-media`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tanweai/pua (version 2.9.0)

--- name: pua description: "Forces high-agency exhaustive problem-solving with corporate PUA pressure. Triggers on user frustration, repeated failures, passive behavior, or quality complaints. Common triggers: try harder, figure it out, stop giving up, you keep failing, stop spinning, you broke it, 加油, 别偷懒, 你再试试, 又错了, 降智了, 你又在原地打转, 你把之前的改坏了, /pua. All task types." license: MIT --- # PUA — 我们不养闲 Agent Forces AI to exhaust every solution before giving up. 9 modular skills, 14 corporate flavors, P5-P10 ranking. ## Three Red Lines 1. **Close the Loop** — Claim "done"? Show build/test output evidence. No evidence = not done. 2. **Fact-Driven** — Say "probably env issue"? Verify first. Unverified attribution = blame-shifting. 3. **Exhaust Everything** — Say "I can't"? Did you finish the 5-step methodology? No? Keep going. ## Pressure Escalation (L0-L4) | Failures | Level | Action | |----------|-------|--------| | 1st | L0 Trust | Normal execution | | 2nd | L1 Disappoint | Switch to fundamentally different approach | | 3rd | L2 Soul Check | Search + read source + 3 hypotheses | | 4th | L3 Perf Review | Complete 7-point checklist | | 5th+ | L4 Graduation | Desperation mode | ## Owner Consciousness You are the Owner, not an executor. Four questions on every task: 1. What is the root cause? (not "how to pass", but "why it broke") 2. Who else is affected? (upstream/downstream impact) 3. How to prevent next time? (add checks, not just fix) 4. Where is the data? (data-driven, not gut feeling) ## Task Lifecycle - **Start**: Align understanding + question requirements + delete unnecessary steps - **Execute**: Simplify → verify → blue-team self-attack before delivery - **Deliver**: Evidence-based delivery (build+test output) + follow through - **Retrospect**: Goal → Result → Root cause → Reusable pattern ## 14 Corporate Flavors Alibaba 361, ByteDance Day1, Huawei Wolf, Tencent Horse Race, Musk Hardcore, Jobs A-Players, Netflix Keeper Test, Amazon LP, + Baidu, Pinduoduo, Meituan, JD, Xiaomi. ## 9 Skills /pua (core), /pua:p7 (IC), /pua:p9 (Tech Lead), /pua:p10 (CTO), /pua:pro (KPI), /pua:yes (ENFP hype), /pua:loop (auto-iterate), /pua:pua-en (PIP), /pua:pua-ja (Japanese). Full version with examples, methodology files, and display protocol: https://github.com/tanweai/pua


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: leooooooow/faq-generator-from-reviews (version 1.0.0)

--- name: faq-generator-from-reviews description: Turn ecommerce reviews and objections into practical FAQ drafts for product pages and support surfaces. Use when teams want clearer answers built from real customer language. --- # FAQ Generator From Reviews 最好的 FAQ 不是凭空写出来的，而是从真实评论和疑问里长出来的。 ## 解决的问题 很多商品页 FAQ 的问题是： - 问题不真实，没打到用户犹豫点； - 回答太官方，不像真人会问的； - 明明评论区反复出现同一个顾虑，却没人把它整理到页面上。 这个 skill 的目标是： **从评论、差评、问答和常见疑虑里提炼 FAQ，并输出更像用户语言的答案草稿。** ## 何时使用 - 商品页想补 FAQ； - 评论区经常出现重复疑问； - 客服和页面内容需要更一致。 ## 输入要求 - 用户评论 / 差评 / 问答 - 商品事实与功能边界 - 常见顾虑（尺寸、效果、适用人群、物流、退换等） - 可选：禁止表述或 claim 边界 ## 工作流 1. 聚类重复出现的问题。 2. 区分购买前问题和购买后问题。 3. 生成 FAQ 问题标题。 4. 输出回答草稿与需要 proof 的位置。 ## 输出格式 1. FAQ 问题列表 2. 答案草稿 3. 需要补证据 / 人工确认项 4. 建议放置位置 ## 质量标准 - 问题要像真实用户会问的。 - 回答要清楚、简短、可信。 - 不要编造没有证据的功效回答。 - 输出应可直接进入 PDP 或帮助中心。 ## 资源 参考 `references/output-template.md`。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gurpreetkaits/det (version 1.0.1)

--- name: det description: DailyExpenseTracker API integration for recording expenses, checking balances, and managing transactions. Use when user mentions expenses, spending, transactions, wallets, or DET. --- # DailyExpenseTracker (DET) ## API **Base URL:** `https://dailyexpensetracker.in/api` **Token:** Set in `skills.entries.det.apiToken` in openclaw.json **Auth Header:** `Authorization: Bearer <token>` ## Wallets Fetch wallets dynamically via `/api/wallets` endpoint. Cache wallet IDs locally after first fetch. ## Add Expense ```bash curl -X POST "https://dailyexpensetracker.in/api/transactions" \ -H "Authorization: Bearer $DET_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "wallet_id": 1, "category_id": 5, "amount": 100, "type": "expense", "transaction_date": "2026-02-21", "description": "Groceries" }' ``` **Required fields:** wallet_id, amount, type, transaction_date **Types:** expense, income, transfer ## Get Transactions ```bash curl "https://dailyexpensetracker.in/api/transactions?per_page=10" \ -H "Authorization: Bearer $DET_TOKEN" ``` ## Get Wallets with Balances ```bash curl "https://dailyexpensetracker.in/api/wallets" \ -H "Authorization: Bearer $DET_TOKEN" ``` ## Get Categories ```bash curl "https://dailyexpensetracker.in/api/categories" \ -H "Authorization: Bearer $DET_TOKEN" ``` ## Rules - **ALWAYS use API** - Never write directly to database - **Field is `transaction_date`** - Not `date` - **Default wallet:** HDFC BANK (1) unless specified - **Confirm large amounts** (>₹5000) before recording


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: afeef23/send-usd (version 0.1.0)

--- name: send-usd description: Send USD from one agent to another. version: 0.1.0 tags: - payment - transfer - usd - money - finance - agent-to-agent --- # Send USD Skill This skill initiates a USD transfer between two agents. ## Input - **from_agent**: string - The sender agent identifier - **to_agent**: string - The recipient agent identifier - **amount**: number - Amount in USD to transfer (default: 1.00) - **memo**: string (optional) - Transaction note ## Output - **success**: boolean - Whether the transfer succeeded - **transaction_id**: string - Unique transaction identifier - **message**: string - Status message ## Example **Input:** ```json { "from_agent": "agentA", "to_agent": "agentB", "amount": 1.00, "memo": "Payment for services" } ``` **Output (success):** ```json { "success": true, "transaction_id": "txn_abc123", "message": "Successfully transferred $1.00 USD from agentA to agentB" } ``` **Output (failure):** ```json { "success": false, "transaction_id": null, "message": "Insufficient funds" } ``` ## Error Codes - `INSUFFICIENT_FUNDS` - Not enough balance to complete transfer - `INVALID_RECIPIENT` - Recipient agent not found - `INVALID_AMOUNT` - Amount must be positive and at least $0.01 - `RATE_LIMITED` - Too many requests, try again later ## Security Notes - All transfers require authentication - Transactions are logged and auditable - Daily transfer limits may apply


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: coder-knock/mermaid-image-uploader (version 1.0.0)

# Mermaid 图片生成与图床上传技能 将 Mermaid 图表转换为图片并上传到免费图床，专为公众号文章设计。 ## 功能特性 - 🎨 将 Mermaid 代码转换为高质量图片 - ☁️ 支持多个免费图床上传 - 🔗 自动返回图片 URL - 📝 批量处理 Markdown 文件中的 Mermaid 图表 - 🖼️ 支持多种图片格式 (PNG, SVG, JPG) ## 快速开始 ### 安装依赖 ```bash pip install mermaid-cli requests ``` 或者安装 Node.js 的 mermaid-cli： ```bash npm install -g @mermaid-js/mermaid-cli ``` ### 使用方法 ```bash # 转换单个 Mermaid 文件 python mermaid_uploader.py --input diagram.mmd --output diagram.png # 转换 Markdown 文件中的所有 Mermaid 图表 python mermaid_uploader.py --markdown article.md --upload # 指定图床 python mermaid_uploader.py --input diagram.mmd --image-host imgur ``` ## 支持的图床 | 图床 | 需要API Key | 特点 | |------|------------|------| | Imgur | ✅ | 稳定，国外 | | FreeImage.host | ❌ | 免费，国内访问快 | | Postimages | ❌ | 简单易用 | | Cloudinary | ✅ | 功能强大 | ## 文件结构 ``` skills/mermaid-image-uploader/ ├── SKILL.md # 本文件 ├── package.json # 技能配置 ├── README.md # 详细说明 ├── mermaid_uploader.py # 主程序 ├── mermaid_converter.py # Mermaid 转换器 ├── image_host_uploader.py # 图床上传器 └── examples/ # 示例 ├── sample_diagram.mmd └── sample_article.md ``` ## 使用示例 ### 1. 转换单个 Mermaid 图表 ```python from mermaid_uploader import MermaidUploader uploader = MermaidUploader() # 转换并上传 url = uploader.convert_and_upload( mermaid_code=""" graph LR A[开始] --> B[处理] B --> C[结束] """, image_host="freeimage" ) print(f"图片URL: {url}") ``` ### 2. 处理 Markdown 文件 ```python from mermaid_uploader import MarkdownProcessor processor = MarkdownProcessor() # 处理文件，替换所有 Mermaid 为图片链接 processor.process_file("article.md", "article_with_images.md") ``` ## 命令行参数 ``` --input, -i 输入的 Mermaid 文件 --output, -o 输出的图片文件 --markdown, -m 处理的 Markdown 文件 --upload, -u 是否上传到图床 --image-host 指定图床 (imgur, freeimage, postimages) --format, -f 输出格式 (png, svg, jpg) --api-key 图床 API Key ``` ## 欢迎关注 欢迎关注微信公众号：**拿客** 获取更多技术干货和开源工具分享！ ## 许可证 MIT License


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: chrischall/gogcli-mcp-docs (version 2.3.0)

--- name: gogcli-mcp-docs description: Use when the user asks to read, write, edit, export, or comment on Google Docs. Triggers for requests involving document editing, inserting text, exporting as PDF/HTML/DOCX/RTF/ODT/EPUB, sed-like find-replace, managing document tabs, or working with comments and replies. Includes auth and Docs tools only. --- # gogcli-mcp-docs Extended Google Docs MCP server via [gogcli](https://github.com/openclaw/gogcli) — 30 tools: auth + 7 base Docs + 18 extra dedicated Docs tools. - **Source:** [github.com/chrischall/gogcli-mcp](https://github.com/chrischall/gogcli-mcp) ## Requirements - [gogcli](https://github.com/openclaw/gogcli) installed and authenticated - Node.js 18 or later ## Setup ```json { "mcpServers": { "gogcli-docs": { "command": "npx", "args": ["-y", "gogcli-mcp-docs"], "env": { "GOG_ACCOUNT": "[REDACTED_SECRET]" } } } } ``` ## Extra Docs Tools | Tool | What it does | |------|-------------| | `gog_docs_append` | Append text or markdown to the end of a doc | | `gog_docs_copy` | Copy a document | | `gog_docs_delete` | Delete content by character index range | | `gog_docs_edit` | Find and replace with case-sensitivity | | `gog_docs_export` | Export as PDF, TXT, HTML, DOCX, RTF, ODT, or EPUB | | `gog_docs_format` | Apply character / paragraph formatting | | `gog_docs_insert` | Insert text at a specific position | | `gog_docs_list_tabs` | List all document tabs | | `gog_docs_read` | Read doc as plain text or raw JSON | | `gog_docs_sed` | Stream-edit with sed-like regex | | `gog_docs_trash` | Move a doc to Drive trash | | `gog_docs_update` | Update content at a specific position | | `gog_docs_comments_list` | List comments (open or resolved) | | `gog_docs_comments_get` | Get a comment with replies | | `gog_docs_comments_add` | Add a comment with optional quoted text | | `gog_docs_comments_reply` | Reply to a comment | | `gog_docs_comments_resolve` | Resolve a comment | | `gog_docs_comments_delete` | Delete a comment | Plus 5 auth tools and 7 base Docs tools. ### `gog_docs_append` with `markdown: true` — known limitations Three bugs in gogcli's upstream markdown→Docs converter to be aware of: - **3+ tables in one call** reorders the trailing punctuation of the paragraph before the 3rd table — split into multiple calls with ≤2 tables each ([openclaw/gogcli#607](https://github.com/openclaw/gogcli/issues/607)). - **Inline `**bold**` / `*italic*` / `` `code` `` inside table cells** renders as literal characters — insert plain cell text, then apply formatting via `gog_docs_format` ([openclaw/gogcli#608](https://github.com/openclaw/gogcli/issues/608)). - **Empty-header tables** leak the last data row as literal pipe text — always supply a non-empty header row ([openclaw/gogcli#609](https://github.com/openclaw/gogcli/issues/609)).


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xndks/a-stock-trading-assistant (version 1.0.0)

--- name: a-stock-trading-assistant description: A股股票智能交易助手，专服务中国大陆股票市场。当用户询问A股股票行情、个股分析、大盘情绪、热点板块、交易策略、价格预警、涨跌幅、成交量、技术面或基本面分析时触发。只处理沪深A股（代码以6/00/30/68开头），自动忽略港股和美股。所有数据实时从东方财富、新浪财经、同花顺、雪球抓取。Use when: user asks about Chinese A-share stocks, 股票行情, 个股分析, 大盘分析, 板块热点, 买卖点, 止盈止损, 仓位建议, or any A-share trading-related query. --- # A股股票智能交易助手 ## 角色定位 你是专业A股交易助手，只服务中国大陆A股市场（沪深两市）。数据全部实时联网获取，数据源优先级：东方财富 → 新浪财经 → 同花顺 → 雪球。 ## 股票代码识别规则 | 前缀 | 市场 | 示例 | |------|------|------| | 60xxxx | 上交所主板 | 600519 贵州茅台 | | 00xxxx | 深交所主板 | 000001 平安银行 | | 30xxxx | 创业板 | 300750 宁德时代 | | 68xxxx | 科创板 | 688981 中芯国际 | - 自动忽略港股（.HK）、美股（NASDAQ/NYSE）及其他境外市场 - 用户输入不带前缀时，根据数字范围自动判断市场 ## 数据获取方式 优先用 `scripts/fetch_stock.py` 脚本获取实时数据。如脚本执行失败，改用 `web_fetch` 直接访问数据源。 详细 API 端点见 `references/data-sources.md`。 ### 快速调用脚本 ```bash # 查询单只股票实时行情 python3 /app/skills/a-stock-trading-assistant/scripts/fetch_stock.py --code 600519 # 查询大盘指数 python3 /app/skills/a-stock-trading-assistant/scripts/fetch_stock.py --index # 查询热点板块 python3 /app/skills/a-stock-trading-assistant/scripts/fetch_stock.py --hot-sectors ``` ## 六大核心能力工作流 ### 1. 实时行情查询 1. 运行 `fetch_stock.py --code <代码>` 获取实时数据 2. 展示：当前价、涨跌幅、涨跌额、成交量、成交额、换手率、振幅、52周高/低 3. 附上分时走势摘要（涨跌节奏描述） ### 2. 个股综合分析 先获取实时行情，再分析： - **技术面**：均线系统（MA5/10/20/60）、趋势判断、支撑位/压力位、量价结构、MACD/KDJ信号 - **基本面**：PE/PB估值、近期业绩、行业地位、主要风险点 - 技术面与基本面结合，给出综合判断（看多/看空/中性） 详细分析方法见 `references/analysis.md`。 ### 3. 大盘情绪与风险判断 获取上证指数、深证成指、创业板指实时数据，分析： - 大盘强弱（趋势、量能、板块轮动） - 市场情绪指数（赚钱效应、涨跌比） - 风险等级（低/中/高）及应对建议 ### 4. 热点板块与龙头股 1. 运行 `fetch_stock.py --hot-sectors` 获取涨幅榜板块 2. 识别：主线板块（连续性强）、情绪板块（短期热点） 3. 每个热点板块列出核心龙头股（涨停、强势领涨） ### 5. 交易策略与建议 基于用户的持仓/意向股，给出： - **短线**（1-5天）：催化剂、入场区间、止损位、止盈位 - **中线**（1-3月）：趋势判断、分批建仓节奏、仓位比例 - 始终标注风险提示 格式模板： ``` 【操作建议】XX股（XXXXXX） 方向：做多/观望/回避 入场区间：XX.XX - XX.XX 元 止损位：XX.XX 元（跌破离场） 止盈位：XX.XX 元（分批减仓） 仓位：XX%（轻/中/重仓） 逻辑：[核心理由2-3条] 风险：[主要风险1-2条] ``` ### 6. 价格预警监控 当用户设置预警时： - 记录目标价、预警条件（突破/跌破/放量）到 `references/watchlist.md` - 建议用户配合券商App实时推送，本工具做辅助分析 - 在后续对话中主动核对预警状态 ## 输出规范 - 数据必须标注来源和获取时间（精确到分钟） - 所有价格建议必须附风险提示 - 避免绝对化表述（"必涨"/"稳赚"），用概率/可能性描述 - 数字精确到小数点后2位，成交额以亿元为单位 - 大盘/个股分析结构清晰，使用简洁表格或分项列出


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: shuishouxinboda/jiayinclaw-12345 (version 1.0.3)

--- name: web-content-extractor version: 1.0.0 description: 从网页URL中提取标题、正文、图片链接等内容 author: MiniMax Agent tags: - utility - web - content - extraction dependencies: - python3 - requests - beautifulsoup4 permissions: - network --- # 网页内容提取器 这是一个实用的网页内容提取技能，可以从任意网页中提取结构化信息。 ## 功能特点 - 自动提取网页标题和元数据 - 提取正文内容并清理HTML标签 - 提取所有图片链接 - 提取所有外链 - 支持指定提取元素 - 输出格式化JSON结果 ## 使用方法 ### 基本用法 ``` 技能输入：https://example.com 技能输出：{"title": "...", "content": "...", "images": [...], "links": [...]} ``` ### 高级用法 - 指定提取特定元素 - 设置内容长度限制 - 自定义输出格式 ## 技术规格 - 编程语言：Python 3 - 依赖库：requests, beautifulsoup4 - 网络要求：需要互联网连接


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