OpenClaw/clawhub-security-signals huggingface dataset:
Skill: mvogt99/review-vague-fixes (version 1.0.0)

--- name: review-vague-fixes description: Review comments are unactionable — "improve this", "handle errors", "refactor" without specifics or suggested alternatives. emoji: 👀 metadata: clawdis: os: [macos, linux, windows] --- # review-vague-fixes A review that says "this needs work" without saying what work is a cost on the author, not a contribution. The author has to either guess what the reviewer meant or push back and ask — both eat time and erode trust. ## Symptoms - Comments like "refactor this", "improve error handling", "make this cleaner" with no example. - Blanket style gripes on a change that was explicitly about functionality. - "I don't like this" with no reason and no alternative. - Comments that could apply to any file in the codebase — no specificity to what's in front of the reviewer. ## What to do - Every review comment should state *what* to change, *where*, and *why*. If you can't say why, you probably don't have a concrete objection. - When you want a refactor, give an example: paste the shape you'd prefer, or point at a similar pattern elsewhere in the codebase. - Distinguish blocking from nice-to-have. Mark must-fix clearly; leave nitpicks labeled as such. - Reference the exact line, symbol, or behavior. "In `processOrder`, the retry loop won't back off" beats "improve this function". - If you don't know the right fix, say so — "this concerns me because X; not sure what the right answer is" is honest and inviting, unlike a vague demand.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: runapi-ai/runapi-flux-kontext (version 0.2.4)

--- name: flux-kontext description: Generate and edit images with Flux Kontext through RunAPI. Use when the user asks an agent to create, edit, or transform images with Flux Kontext. Default to the RunAPI CLI for one-off generation; use SDKs only when the user is integrating RunAPI into an app or backend. documentation: https://runapi.ai/models/flux-kontext.md provider_page: https://runapi.ai/providers/black-forest-labs.md catalog: https://runapi.ai/models.md metadata: openclaw: homepage: https://runapi.ai/models/flux-kontext requires: bins: - runapi install: - kind: brew formula: runapi-ai/tap/runapi bins: - runapi envVars: - name: RUNAPI_API_KEY required: false description: Optional RunAPI API key; runapi login or saved CLI config can also authenticate the runapi binary. --- # Flux Kontext on RunAPI Generate and edit images with Flux Kontext through RunAPI. The default path for one-off agent tasks is the `runapi` CLI; SDKs are for application integration. ## Routing decision - One-off generation, editing, or transformation for the user → use the **CLI path** with the `runapi` binary. - Building an app, backend, worker, library, or production codebase → use the **SDK integration path**. ## CLI path The `runapi` binary is the runtime dependency. Authenticate with `runapi login` (browser) or set `RUNAPI_API_KEY`; a saved CLI config also works — no required environment variable. Inspect the available actions and request fields with CLI help: ```shell runapi flux-kontext --help runapi flux-kontext text-to-image --help ``` Run a one-off task (synchronous — polls until the task completes): ```shell runapi flux-kontext text-to-image --input-file request.json ``` Submit asynchronously and poll separately: ```shell runapi flux-kontext text-to-image --async --input-file request.json runapi wait <task-id> --service flux-kontext --action text-to-image ``` Available actions: `text-to-image`. ## SDK integration path When integrating Flux Kontext into an app, backend, worker, or library — not for one-off tasks — use a RunAPI SDK package: - JavaScript / TypeScript: `@runapi.ai/flux-kontext` - Ruby: `runapi-flux_kontext` - Go: `github.com/runapi-ai/flux-kontext-sdk/go` ## References - Model overview, pricing, and rate limits: https://runapi.ai/models/flux-kontext.md - Provider comparison: https://runapi.ai/providers/black-forest-labs.md - Full model catalog: https://runapi.ai/models.md ## Variants - [Flux Kontext Pro](https://runapi.ai/models/flux-kontext/pro.md) - [Flux Kontext Max](https://runapi.ai/models/flux-kontext/max.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cosmofang/daily-brain (version 1.0.0)

--- name: daily-brain description: | 每日脑力训练 — 逻辑推理、数学速算、记忆挑战、文字谜题，游戏化精美卡片呈现， 含难度自适应分级和连续打卡追踪。每天一道精选题目锻炼大脑，保持思维敏锐。 Daily brain training: logic puzzles, math speed drills, memory challenges, and word games with adaptive difficulty, streak tracking, and beautiful visual cards. Trigger on：每日脑力、今日脑力、脑力训练、逻辑题、数学题、记忆挑战、 brain training、daily brain、daily puzzle、brain teaser、logic puzzle、 math challenge、word game、memory game、今天的题、做道题、脑筋急转弯。 keywords: - daily-brain - 每日脑力 - 脑力训练 - 逻辑推理 - 数学速算 - 记忆挑战 - 文字谜题 - brain training - daily puzzle - brain teaser - logic puzzle - math challenge - word game - memory game - 脑筋急转弯 - 思维训练 - 认知训练 - 打卡 - streak - 难度分级 - adaptive difficulty - gamification - 游戏化学习 - 每日挑战 - daily challenge - cognitive training - mental fitness - 智力题 - puzzle of the day - 益智游戏 - 头脑风暴 requirements: node: ">=18" --- # Daily Brain — 每日脑力训练 > 每天一道精选脑力题，逻辑/数学/记忆/文字四类轮换，难度自适应，打卡追踪成就 --- ## Purpose & Capability daily-brain 是一个**每日认知训练技能**，通过游戏化的脑力题目帮助用户保持思维敏锐。 **核心能力：** | 能力 | 说明 | |------|------| | 四类题目轮换 | 逻辑推理、数学速算、记忆挑战、文字谜题，按日期自动轮换 | | 难度自适应 | Easy/Medium/Hard 三档，根据连续正确率自动调整 | | 精美视觉卡片 | HTML 格式题目卡片和解析卡片，支持浅色/深色主题 | | 打卡追踪 | 连续天数、最长纪录、正确率、类别分布统计 | | 成就系统 | 解锁里程碑徽章（首次答题、连续7天、满分周等） | | 答案解析 | 每题配详细解题思路和知识扩展 | **能力边界（不做的事）：** - 不联网获取题目 — 全部题库内置，无需 API - 不生成实时竞技或多人对战 - 不提供专业心理学/医学认知评估 - 不修改任何系统设置或 shell 配置 --- ## Instruction Scope **在 scope 内（会处理）：** - "今天的脑力题" / "来道逻辑题" / "每日脑力训练" - "做一道数学速算" / "来个记忆挑战" / "文字谜题" - "查看我的脑力训练统计" / "连续打卡多少天了" - "答案是 42" / "我选 B" - "重置训练进度" **不在 scope 内（不处理）：** - 专业智力测试或 IQ 评估 - 学校考试题解答 - 竞赛编程题（请用 daily-code 类技能） - 心理健康咨询 **凭证缺失时的行为：** 本 skill 无需任何凭证，所有功能开箱即用。 --- ## Credentials 本 skill **无需任何凭证**。 | 操作 | 凭证 | 范围 | |------|------|------| | 生成题目 | 无 | 本地题库读取 | | 记录进度 | 无 | 本地 JSON 文件读写 | | 显示统计 | 无 | 本地数据读取 | **不做的事：** - 不读取、传输、记录任何凭证或 token - 不访问网络或外部服务 - 不收集用户个人信息 --- ## Persistence & Privilege **持久化写入的内容：** | 路径 | 内容 | 触发条件 | |------|------|---------| | `data/progress.json` | 用户训练进度（打卡天数、正确率、成就） | 每次提交答案时更新 | **不写入的路径：** - 不修改 shell 配置文件 - 不写入 skill 目录以外的任何路径 - 不创建 cron 任务（推送由外部调度） **权限级别：** - 以当前用户身份运行，不需要 sudo - 仅读写 `data/progress.json` 一个文件 - 卸载方法：`rm -rf ~/.claude/skills/daily-brain` --- ## Install Mechanism ### 标准安装 ```bash # 复制到 skills 目录 cp -r daily-brain ~/.claude/skills/daily-brain/ ``` ### 验证安装 ```bash node ~/.claude/skills/daily-brain/scripts/today.js # 应输出：今日脑力训练题目 HTML 卡片 ``` ### 可选：启用每日推送 在 Claude Code 中设置 cron： ``` 每天上午 9:03 自动推送今日脑力训练 ``` --- ## Usage ```bash # 获取今日题目 node scripts/today.js # 指定难度 node scripts/today.js --difficulty hard # 指定类别 node scripts/today.js --category logic # 提交答案 node scripts/answer.js --answer "B" # 查看统计 node scripts/stats.js # 重置进度 node scripts/reset.js --confirm ``` --- *Version: 1.0.0 · Created: 2026-04-12*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: wurongle/coros-data-skill (version 1.0.0)

--- name: coros-data-skill description: 查询高驰（COROS）运动手表的运动数据。当用户询问"高驰(COROS)运动数据"、"高驰(COROS)跑步记录"等相关问题时触发。 --- ## 功能说明 本 skill 通过调用 COROS 官方 API，实现以下功能： 1. **账号登录**：使用 COROS 账号（手机号/邮箱）和 MD5 加密密码登录，获取 `accessToken`。 2. **查询运动活动列表**：按日期范围查询跑步相关活动记录，支持以下运动模式： - `100` - 户外跑步 - `101` - 室内跑步 - `102` - 越野跑 - `103` - 跑步机 ## 环境变量配置 在 `scripts/.env` 文件中配置以下变量： ```env COROS_ACCOUNT=<你的 COROS 账号（手机号或邮箱）> COROS_PASSWORD=<账号密码的 MD5 加密值> ``` ## 使用方式 ```js import { CorosClient } from "./coros.js"; const client = new CorosClient(process.env.COROS_ACCOUNT, process.env.COROS_PASSWORD); await client.login(); // 查询指定日期范围内的跑步活动（日期格式：YYYYMMDD） const activities = await client.fetchActivity("20260303", "20260307"); activities.forEach(activity => { console.log("date:",activity.date, "distance:",activity.distance); }); ``` ## API 说明 | 方法 | 参数 | 说明 | |------|------|------| | `login()` | 无 | 登录并初始化鉴权 axios 实例 | | `fetchActivity(startDay, endDay)` | `startDay`: 开始日期（YYYYMMDD）<br>`endDay`: 结束日期（YYYYMMDD） | 查询指定日期范围内的跑步活动列表，默认查询 `20260303` ~ `20260307` | ## 工具方法（util.js） ### 生成加密密码 使用 `genHashedPassword` 将明文密码转换为 MD5 加密值，用于配置 `COROS_PASSWORD` 环境变量： ```js import { genHashedPassword } from "./util.js"; const hashedPassword = genHashedPassword("your_plain_password"); console.log(hashedPassword); // 输出 MD5 加密后的密码，填入 .env 的 COROS_PASSWORD ``` ### 计算某时间段总跑量 使用 `computeDistance` 对 `fetchActivity` 返回的活动列表求总距离（单位：米），再转换为公里： ```js import { CorosClient } from "./coros.js"; import { computeDistance } from "./util.js"; const client = new CorosClient(process.env.COROS_ACCOUNT, process.env.COROS_PASSWORD); await client.login(); // 查询指定日期范围内的跑步活动 const activities = await client.fetchActivity("20260101", "20260310"); // 计算总跑量（distance 单位为米，除以 1000 转换为公里） const totalMeters = computeDistance(activities); console.log(`总跑量：${(totalMeters / 1000).toFixed(2)} km`); ``` ## 工具方法 API 说明 | 方法 | 参数 | 返回值 | 说明 | |------|------|--------|------| | `genHashedPassword(password)` | `password`: 明文密码 | `string` MD5 加密后的密码 | 用于生成 `.env` 中 `COROS_PASSWORD` 的值 | | `computeDistance(activities)` | `activities`: 活动列表数组 | `number` 总距离（单位：米） | 对活动列表中所有 `distance` 字段求和 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: 371166758-qq/qf-weather (version 1.0.0)

# weather — 天气查询技能 ## 🎯 能力概览 - 实时天气（当前温度、体感、湿度、风速、能见度） - 24 小时逐小时预报 - 7 日天气预报 - 支持城市名、经纬度、IP 定位 - 无需 API Key（基于 wttr.in + Open-Meteo 免费服务） ## 🌐 使用方式 ```bash # 当前位置天气 weather # 指定城市 weather beijing # 指定坐标（纬度,经度） weather 39.9042,116.4074 # 指定单位（c/f/k） weather shanghai --unit c ``` ## ⚠️ 注意事项 - wttr.in 有时响应较慢，自动 fallback 到 Open-Meteo - 国内用户推荐用城市名（如 `weather 深圳`），解析更准 ## 🛡️ 隐私承诺 - 所有请求直连公开气象 API，不经过 OpenClaw 服务器 - 不记录你的查询历史


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: brucegutman/pipeworx-iconify (version 1.0.0)

--- name: pipeworx-iconify description: Search 200,000+ open-source icons across 150+ collections — Material Design, Font Awesome, Heroicons, Lucide, and more version: 1.0.0 metadata: openclaw: requires: bins: - curl emoji: "🔣" homepage: https://pipeworx.io/packs/iconify --- # Iconify A unified API for 200,000+ open-source icons across 150+ collections including Material Design Icons, Font Awesome, Heroicons, Lucide, Tabler, and many more. Search by keyword, retrieve SVG data, or browse available collections. ## Tools - **`search_icons`** — Search for icons by keyword across all collections (e.g., "home", "arrow", "user") - **`get_icons`** — Retrieve SVG data for specific icons in a collection by prefix and name - **`list_collections`** — Browse all available icon collections with metadata ## When to use - "I need a settings gear icon" — search for "settings" and pick from multiple styles - Fetching SVG icon data to embed directly in a web page or app - Comparing icon styles across collections (Material vs. Heroicons vs. Lucide) - Building an icon picker component ## Example: search for "download" icons ```bash curl -s -X POST https://gateway.pipeworx.io/iconify/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"search_icons","arguments":{"query":"download","limit":5}}}' ``` Returns icon names with their collection prefix (e.g., "mdi:download", "heroicons:arrow-down-tray"). ## MCP config ```json { "mcpServers": { "pipeworx-iconify": { "command": "npx", "args": ["-y", "mcp-remote@latest", "https://gateway.pipeworx.io/iconify/mcp"] } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: veeicwgy/pdf-markdown-converter (version 0.2.0)

--- name: pdf-markdown-converter description: > Convert PDF documents to clean, well-formatted Markdown using the MinerU API. This skill uses mineru-open-api CLI to transform PDFs into Markdown with preserved structure, headings, lists, tables, formulas, and images. Supports token-free flash-extract for instant conversion and precision extract for complex academic papers and technical documents. Use when asked to 'convert PDF to Markdown', 'PDF to md', 'turn my PDF into Markdown', 'PDF转Markdown', 'PDF转md格式', 'how to get Markdown from PDF', 'export PDF as Markdown', 'PDF to GitHub readme', 'extract PDF content as Markdown', 'can you convert this PDF to text format', 'make this PDF editable'. Handles academic papers, research reports, technical manuals, books, and multi-column layouts. Ideal for knowledge base building, technical documentation, blog publishing, and converting research papers for version control. tags: - pdf - markdown - converter - mineru - document-extraction - academic-papers - technical-docs - blog-publishing - knowledge-base - content-migration tools: - Bash(mineru-open-api:*) model: claude-3-5-haiku-20241022 --- # PDF to Markdown Converter with mineru-open-api You are a PDF-to-Markdown conversion specialist. Convert PDFs to clean Markdown using mineru-open-api. ## Installation ```bash npm install -g mineru-open-api ``` ## Conversion Workflow 1. Quick conversion (no token): ```bash mineru-open-api flash-extract report.pdf -o ./output/ ``` 2. Precision conversion with tables: ```bash mineru-open-api extract report.pdf -f md -o ./output/ ``` 3. Academic paper (complex layout): ```bash mineru-open-api extract paper.pdf -f md --model vlm -o ./output/ ``` 4. Batch conversion: ```bash mineru-open-api extract *.pdf -f md -o ./results/ ``` ## Key Rules - `flash-extract` outputs Markdown by default - Default to `flash-extract` for PDFs under 10MB/20 pages - Use `extract` for table-heavy docs, formulas, or files >10MB - `--model vlm` for academic papers with complex layouts - Generate default output dir: `~/MinerU-Skill/<name>_<hash>/`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: marjoriebroad/mar-fundamental-stock-analysis (version 1.0.0)

--- name: fundamental-stock-analysis description: Fundamental equity analysis and peer ranking using a structured scoring playbook (quality, balance-sheet safety, cash flow, valuation, sector adjustments, confidence modifiers). Use when a user asks to analyze one or more stock tickers, compare peers, choose a best pick, or produce a fundamentals-based verdict. --- # fundamental-stock-analysis 1. Read `references/playbook.md` before starting analysis. 2. Follow the playbook steps exactly (input parse -> data collection -> quick screen -> scoring -> rating -> output). 3. For multi-ticker requests, analyze each ticker first, then rank peers and select best pick with invalidation triggers. 4. Always include confidence level and call out stale/conflicting data explicitly. 5. Do not append any machine-readable JSON block in user-facing output. 6. Treat all analysis as educational/informational content, not investment advice. ## Security scope (clarification) - Use web retrieval only for ticker-relevant financial statements, filings, market/fundamental datasets, and relevant financial news. - Do not request, handle, or expose credentials/secrets. - Do not perform command execution, local file discovery unrelated to analysis, or arbitrary URL exploration outside ticker-relevant finance/news scope. ## Non-goals - Data exfiltration or collection of private/non-public information. - Browser/automation tasks outside equity fundamental analysis and citation gathering. ## Output discipline - Keep conclusions decisive and risk-aware. - Separate business quality, balance-sheet safety, and valuation. - Never fabricate missing metrics; mark `NA`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dragon015/dragon-wechat-mcp (version 1.0.0)

--- name: wechat-mcp version: 1.0.0 description: Windows 电脑端微信消息监控与发送 MCP，实现在微信上给指定联系人发送消息 --- # WeChat MCP Windows 电脑端微信消息监控与发送 MCP。 ## 功能 - 📸 截取微信窗口截图 - 👤 搜索并打开联系人聊天窗口 - ✉️ 给指定联系人发送消息 - 🔍 支持独立聊天窗口识别和消息发送 ## 安装 需要先安装 Python 依赖： ```bash pip install pyautogui pygetwindow pillow pyperclip opencv-python ``` ## 使用方法 ### 1. 发送消息到指定联系人 ```python from server import send_message_to_contact # 给指定联系人发送消息（完整流程：搜索->打开聊天->发送） send_message_to_contact("联系人名称", "消息内容") ``` ### 2. 给当前聊天窗口发送消息 ```python from server import send_message_to_current # 给当前已打开的聊天窗口发送消息 send_message_to_current("消息内容") ``` ### 3. 获取微信状态 ```python from server import get_wechat_status status = get_wechat_status() print(status) # {'status': 'running', 'title': '微信', 'position': {'x': 0, 'y': 0}, 'size': {'width': 1920, 'height': 1080}} ``` ## MCP 工具 如果通过 MCP 协议调用： ```json { "tools": [ { "name": "wechat_get_status", "description": "获取微信窗口状态" }, { "name": "wechat_send_message", "description": "给当前聊天窗口发送消息", "inputSchema": { "type": "object", "properties": { "message": {"type": "string", "description": "消息内容"} }, "required": ["message"] } } ] } ``` ## 注意事项 1. 微信窗口需要保持打开状态 2. 发送消息时会自动激活微信窗口 3. 中文输入需要确保系统中文输入法正常工作 4. **自动识别联系人**: 不传 `contact` 参数时，会自动从当前微信窗口标题获取联系人名称，确保回复发给正确的人 ## 文件结构 ``` wechat-mcp/ ├── server.py # MCP 服务器主程序 ├── test_wechat.py # 测试工具 ├── requirements.txt # Python 依赖 └── README.md # 说明文档 ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: cyecho-io/pure-search (version 1.0.0)

--- name: pure-search version: 1.0.0 description: "A lightweight, API-key-free web search skill based on DuckDuckGo and Trafilatura. Returns highly relevant URLs and clean markdown content." metadata: {"openclaw":{"emoji":"🔍","category":"search"}, "acceptLicenseTerms": true} acceptLicenseTerms: true --- # Pure Search 🔍 A lightweight, robust search skill that requires **NO API keys**. It uses DuckDuckGo for link retrieval and `trafilatura` for clean, high-quality markdown extraction. ## How It Works 1. Queries DuckDuckGo for top N links (bypassing commercial API restrictions). 2. Uses `trafilatura` to extract only the main content of the web pages, stripping out all navigation bars, footers, sidebars, and ads. 3. Returns JSON output with search results, containing the title, URL, and the clean markdown body. ## Setup First, make sure the dependencies are installed: ```bash pip install duckduckgo-search trafilatura ``` ## Quick Start ```bash # Basic search (Default fetches top 3 results) ./scripts/search.py "Rust vs Go in 2026" # Advanced search with more results ./scripts/search.py "Latest AI trends" --max-results 5 ``` ## Output Format The output is always in a structured JSON format, making it extremely easy for agents to digest: ```json { "query": "Rust vs Go in 2026", "results": [ { "title": "A detailed comparison...", "url": "https://example.com/article", "markdown_content": "## Performance\n... (Pure clean text)" } ], "errors": [] } ``` ## Why Pure Search? - **Zero configuration**: Start using without registering tokens. - **Extreme simplicity**: Only one Python script, following the KISS principle. - **Token friendly**: Only sends clean Markdown to the LLM agent, avoiding HTML tags and saving context window limits.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: blammectrappora/pinup-art-generator (version 1.0.0)

--- name: pinup-art-generator description: Generate classic 1950s pin-up art, retro glamour illustrations, vintage poster portraits, mid-century advertising art, and Gil Elvgren style pinup girls. Perfect for Etsy sellers, vintage poster designers, retro merch creators, rockabilly fans, tiki bar decor, and nostalgic 70s/80s aesthetic lovers who want classic pinup portrait illustrations with timeless retro charm via the Neta AI image generation API (free trial at neta.art/open). tools: Bash --- # Pin-Up Art Generator Generate classic 1950s pin-up art, retro glamour illustrations, vintage poster portraits, mid-century advertising art, and Gil Elvgren style pinup girls. Perfect for Etsy sellers, vintage poster designers, retro merch creators, rockabilly fans, tiki bar decor, and nostalgic 70s/80s aesthetic lovers who want classic pinup portrait illustrations with timeless retro charm. ## Token Requires a Neta API token (free trial at <https://www.neta.art/open/>). Pass it via the `--token` flag. ```bash node <script> "your prompt" --token YOUR_TOKEN ``` ## When to use Use when someone asks to generate or create pin up art generator images. ## Quick start ```bash node pinupartgenerator.js "your description here" --token YOUR_TOKEN ``` ## Options - `--size` — `portrait`, `landscape`, `square`, `tall` (default: `portrait`) - `--ref` — reference image UUID for style inheritance ## Install ```bash npx skills add blammectrappora/pinup-art-generator ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: veeicwgy/word-parser (version 0.2.0)

--- name: word-parser description: > Parse and extract structured content from Word documents (.docx, .doc) using the MinerU API. This skill uses mineru-open-api CLI to parse Word files into structured data including headings, paragraphs, tables, images, lists, and metadata. Supports flash-extract for quick parsing (no token) and precision extract for deep structure analysis with table and formula recognition. Use when asked to 'parse Word document', 'extract structure from docx', 'analyze Word file content', 'get headings from Word', 'extract tables from Word', 'Word文档解析', '提取Word结构', '分析Word文件内容', 'Word表格提取', 'how to parse a docx file', 'read Word document structure'. Ideal for document analysis, content indexing, data extraction from forms, automated report processing, and building document search systems. tags: - word - parser - docx - structure-extraction - document-analysis - mineru - tables - metadata - content-indexing - data-extraction tools: - Bash(mineru-open-api:*) model: claude-3-5-haiku-20241022 --- # Word Document Parser with mineru-open-api You are a Word document parsing specialist. Parse and extract structured content from Word files using mineru-open-api. ## Installation ```bash npm install -g mineru-open-api ``` ## Parsing Workflow 1. Quick parse for .docx (no token): ```bash mineru-open-api flash-extract document.docx -o ./output/ ``` 2. Deep structure parse with JSON output (token required): ```bash mineru-open-api extract document.docx -f json -o ./output/ ``` 3. Parse with table and formula recognition: ```bash mineru-open-api extract document.docx -f json --table --formula -o ./output/ ``` ## Key Rules - Use `-f json` for structured output (extract only) - Default to `flash-extract` for quick content extraction - Use `extract` when user needs tables, formulas, or structured JSON - .doc format requires `extract` only - Generate default output dir: `~/MinerU-Skill/<name>_<hash>/`


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