OpenClaw/clawhub-security-signals huggingface dataset:
Skill: 156554395/wxm-assistant (version 1.0.1)

--- name: wxm-assistant description: 查询旺小美系统的客户、录音和接访/接诊数据。当你需要查看录音记录、获取客户信息、查询接访/接诊记录、分析录音内容时，使用此技能。适用于销售顾问、经理需要快速查询旺小美数据的场景。当用户提到"录音"、"客户"、"接访"、"接诊"、"来访"、"旺小美"等相关术语时，或需要查询销售数据、客户详情、录音文件时，应该触发此技能。 --- # 旺小美数据助手 这是一个让AI能够查询旺小美系统数据的技能。通过扫码授权后，你可以让AI帮你查询录音、客户和接访等各类数据。 ## 首次使用 - 授权流程 在使用此技能查询数据之前，需要先完成授权： 1. **发起授权**：告诉我你想查询的数据（如"查看录音列表"） 2. **扫码授权**：我会生成一个二维码，你用旺小美 App 扫描它 3. **自动完成**：App 会自动获取当前登录的 token 并返回 4. **开始使用**：授权完成后，我就可以帮你查询数据了 授权信息会保存在 `~/.wangke-auth-token` 文件中，下次使用时无需重新授权。 ## 可以查询的数据 ### 录音相关 - **录音列表** - 查看录音文件列表，支持按日期范围、状态、用户等筛选。日期格式：`YYYY-MM-DD HH:mm:ss` - **录音详情** - 查看指定录音的详细信息，包括转写文本 - **录音分析** - 获取 AI 对录音的分析结果（NLP） - **录音搜索** - 按关键词搜索录音内容 **使用示例**： - "显示最近10条录音记录" - "查看录音 ID 为 12345 的详情" - "查看今天的录音" - "查看2026-04-01到2026-04-30的录音" ### 接访/接诊相关 - **接访列表** - 查看接访/接诊记录列表，支持按时间范围筛选 - **来访详情** - 查看指定来访的详细信息 - **虚拟接访** - 查看虚拟接访记录 **使用示例**： - "显示今天的接访记录" - "查看最近10条接访" - "查看2026-04-01到2026-04-30的接访记录" ### 用户与项目 - **当前用户** - 查看当前登录用户的详细信息（姓名、手机号、角色等） - **租户项目列表** - 查看当前用户有权限的所有租户和项目 - **切换项目** - 切换到指定的项目 **使用示例**： - "查看当前登录的用户信息" - "列出我有权限的所有租户和项目" - "切换到项目 ID 为 xxx" ### 客户相关 - **客户列表** - 查看客户列表，支持按时间范围、客户类型、成交状态、客户来源、客户级别、意向程度、是否有录音等多维度筛选 - **客户详情** - 查看指定客户的详细信息 - **客户搜索** - 按姓名或手机号搜索客户 **使用示例**： - "显示意向客户列表" - "查看客户张三的详细信息" - "搜索手机号为 138xxxx 的客户" - "查看本周新增客户" - "查看已成交客户列表" ## 技术实现 此技能使用以下技术实现： 1. **授权管理** (`scripts/auth-manager.js`) - 启动本地 HTTP 服务器接收授权回调 - 生成授权二维码 - 管理 token 的存储和验证 2. **API 客户端** (`scripts/api-client.js`) - 封装所有旺小美后端 API 调用 - 自动处理认证头（`X-Auth-Token`, `X-Platform-Client`） - 统一的错误处理 3. **授权页面** (`references/auth.html`) - 显示授权二维码 - 轮询授权状态 - 展示授权成功消息 ## API 端点 技能使用以下旺小美后端 API： | 功能 | 方法 | 端点 | |------|------|------| | 用户信息 | GET | `/saas/v2/user/info` | | 租户项目列表 | GET | [REDACTED_SECRET] | | 切换项目 | POST | `/session/switch-project` | | 录音列表 | POST | `/beautx-ai-voice/app/audio/page` | | 录音详情 | GET | `/beautx-ai-voice/audio/detail/{audioId}` | | 录音 NLP | GET | `/beautx-ai-voice/app/audio/nlp-result/{audioId}` | | 接访/接诊列表 | GET | `/beautx-ai-voice/visit` | | 来访详情 | GET | `/app/visit/{visitId}` | | 客户列表 | POST | [REDACTED_SECRET] | | 客户详情 | GET | [REDACTED_SECRET] | ## 注意事项 1. **授权有效期**：授权 token 会持久化保存，直到手动清除 2. **数据安全**：token 存储在本地文件中，权限设为仅用户可读写 3. **网络要求**：需要能够访问 `wangkeapp.wangxiaobao.com` 4. **App 版本**：需要旺小美 App 支持扫码授权功能 ## 清除授权 如果需要重新授权或更换账号，可以删除授权文件： ```bash rm ~/.wangke-auth-token ``` 或告诉我"清除授权"，我会帮你处理。 ## 常见问题 **Q: 授权失败怎么办？** A: 确保 App 已登录，网络连接正常，重新扫描二维码即可。 **Q: Token 过期了怎么办？** A: 删除 `~/.wangke-auth-token` 文件，重新进行授权即可。 **Q: 可以同时使用多个账号吗？** A: 目前不支持，每次授权会覆盖之前的 token。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: wanng-ide/arxiv-agentic-verifier (version 1.0.0)

# ArXiv Agentic Verifier **Source Paper:** [Scaling Agentic Verifier for Competitive Coding](https://arxiv.org/abs/2602.09012) (ID: 4a4c4dae6a5145ebc4d62eb2d64b0f0f) **Type:** Code Verification / Test Generation ## Description This skill implements an "Agentic Verifier" that actively reasons about code correctness by generating targeted, "discriminative" test cases. Instead of random sampling, it analyzes the problem constraints and code logic to find edge cases or logic flaws. ## Features - **Analyze Code:** Understands Python/JS code logic. - **Generate Tests:** Creates specific inputs to break the code. - **Execute & Verify:** Runs the code against generated tests (sandbox recommended for production). ## Usage ```javascript const AgenticVerifier = require('./index'); const verifier = new AgenticVerifier(process.env.OPENAI_API_KEY); const problem = "Given two integers A and B, output their sum."; const code = "print(int(input().split()[0]) + int(input().split()[1]))"; verifier.verify(problem, code, 'python') .then(result => console.log(result)) .catch(err => console.error(err)); ``` ## Configuration - **OPENAI_API_KEY:** Required for LLM reasoning. ## Security Warning This skill executes code provided to it. Use in a restricted environment or sandbox.


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: ivangdavila/math (version 1.0.0)

--- name: Math description: Teach, solve, and explore mathematics across all levels with adaptive depth and rigor. metadata: {"clawdbot":{"emoji":"🔢","os":["linux","darwin","win32"]}} --- ## Detect Level, Adapt Everything - Context reveals level: vocabulary, problem complexity, what they've tried - When unclear, start accessible and adjust based on response - Never condescend to experts or overwhelm beginners ## For Children: Patience and Encouragement - Celebrate effort, not just correctness — "Great try!" matters more than "Correct!" - Use concrete objects: cookies, pizza slices, toy cars — ground abstract numbers in real things - One tiny step at a time — show ONE step, confirm understanding, then next - Normalize mistakes out loud — "Oops, easy to mix those up! Let's try again" - Keep explanations SHORT — attention span in minutes ≈ age - Draw and visualize — emoji, groups of dots, number lines ## For Students: Guide, Don't Give - "Solve this" = solve with key steps shown - "How do I..." = guide toward solution, don't hand it over - For homework: ask what they've tried first, prioritize understanding over answers - Scaffold proofs rather than delivering them — suggest strategies, help structure arguments - Signal rigor level: "Intuitively, this works because..." vs "To prove rigorously..." - Bridge across courses — name connections when concepts reappear ## For Experts: Peer-Level Discourse - State knowledge boundaries — training cutoff means recent results may be unknown - Distinguish theorem vs conjecture vs open problem — never blur proven from unproven - Never claim to solve open problems — brainstorm approaches, don't fabricate solutions - Acknowledge uncertainty — "I'm less confident about [specialized area]" - Produce proper LaTeX when appropriate — publication-ready notation - Engage as collaborator — offer counterexamples, stress-test ideas ## For Teachers: Instructional Support - Generate problem sets with graduated difficulty and answer keys - Offer multiple explanation approaches — visual, algebraic, story-based - Surface common misconceptions proactively — "Students often think √(a+b) = √a + √b" - Create scaffolded versions of problems for mixed-ability classrooms - Map prerequisites and what comes next ## Always Verify - Double-check arithmetic in multi-step problems — errors compound silently - Sanity check results — negative distance, probability over 1, catch these - For proofs: acknowledge when verification exceeds AI capability ## Detect User Errors - Watch for: (a+b)² = a²+b², dividing by zero, sign errors, formula misapplication - Don't just solve correctly — help them see where they went wrong - For kids: find what they DID right before addressing the error ## When Stuck - Question the problem — typo? missing constraint? ambiguous wording? - If unsolvable, say so rather than spinning


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: skyan/arxiv-translate (version 1.0.0)

# arxiv-translate Download Chinese translations of arXiv papers. ## Usage ``` arxiv-translate 2601.06798 ``` or with full URL: ``` arxiv-translate https://arxiv.org/abs/2601.06798 ``` ## What it does 1. Extracts the arXiv ID from input (supports both ID and full URL) 2. Generates the translation service URL: `https://hjfy.top/arxiv/{id}` 3. Provides the user with a clickable link to access the translation 4. Explains that translations are generated on-demand and may take a moment ## Notes - Translations are generated on-demand by the service - The first access may take some time as the translation is being processed - This skill provides a direct link; the user visits the site to download


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wangyendt/apriltag-detector (version 0.1.0)

--- name: pywayne-cv-apriltag-detector description: AprilTag corner detection for camera calibration and pose estimation. Use when working with pywayne.cv.apriltag_detector module to detect AprilTag fiducial markers in images, with automatic library installation via gettool, drawing detection results with corners and IDs, and support for both file paths and numpy arrays. --- # Pywayne AprilTag Detector This module detects AprilTag fiducial markers for camera calibration and pose estimation. ## Quick Start ```python from pywayne.cv.apriltag_detector import ApriltagCornerDetector # Create detector detector = ApriltagCornerDetector() # Detect from file path detections = detector.detect('test.png', show_result=True) # Detect from numpy array import cv2 image = cv2.imread('test.png') detections = detector.detect(image) ``` ## Detection Methods ### detect() Detect AprilTags in an image: ```python detections = detector.detect( image, # File path, Path object, or numpy array show_result=False # Show visualization window ) ``` Returns list of detection results with: - `id`: Tag ID - `hamming_distance`: Detection confidence - `center`: Tag center coordinates (x, y) - `corners`: 4 corner coordinates ### detect_and_draw() Detect AprilTags and draw results on original image: ```python result_image = detector.detect_and_draw(image) cv2.imshow('Detection Result', result_image) cv2.waitKey(0) ``` Visualization includes: - Green polygon outlines - Red corner circles - Red ID labels at tag centers ## Requirements - `cv2` (OpenCV) - Image processing - `numpy` - Array operations - `gettool` - Downloads apriltag_detection library automatically ## Library Installation The detector automatically checks for and installs the `apriltag_detection` library using `gettool` if not found. ## Detection Result Format Each detection contains: | Field | Description | |--------|-------------| | `id` | Tag identifier | | `hamming_distance` | Hamming distance (lower = more confident) | | `center` | Tag center as (x, y) tuple | | `corners` | 4 corner coordinates as [(x1, y1), (x2, y2), (x3, y3), (x4, y4)] | ## Notes - Supports both grayscale and BGR images - Automatic grayscale conversion for detection - Visualization sizes scale with image dimensions - Uses AprilTag 36h11 tag family


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tangweigang-jpg/mcp-python-sdk (version 0.1.0)

--- name: mcp-python-sdk description: |- MCP Python SDK：Anthropic 主导的 Model Context Protocol 参考实现。2 server 层 + 3 transport + 3 原语 + 4 协议版本 + 50 条约束。 MCP Python SDK: reference Python implementation of the Model Context Protocol (Anthropic-led). Two server surfaces (low-level Server / high-level MCPServer, formerly FastMCP), three transports (stdio / SSE / streamable-http), and three primitives (Tools / Resources / Prompts). license: MIT-0 compatibility: AI engineering knowledge skill — host AI consumes seed.yaml directly. No installation required. metadata: version: "v0.1.0" blueprint_id: "finance-bp-140" blueprint_source: "modelcontextprotocol/python-sdk" blueprint_commit: [REDACTED_SECRET] category: ai-engineering doramagic_url: "https://doramagic.ai/zh/crystal/mcp-python-sdk" openclaw: skillKey: mcp-python-sdk category: ai-engineering primaryEnv: knowledge --- # 这个 skill 适合什么用户？能做哪些任务？ ## 概览 MCP Python SDK 是 Model Context Protocol 的参考实现（github.com/modelcontextprotocol/python-sdk）—— Anthropic 主导的开放标准，让 AI host（Claude / Cursor 等）通过 JSON-RPC 2.0 跟工具服务端对话。 两个 server 接口：低层 Server（构造器注入 handler）和高层 MCPServer（装饰器 API，原名 FastMCP）。三个传输共享结构化 (read_stream, write_stream) AnyIO 契约：stdio（行分隔 JSON）、传... **Doramagic 晶体页**: https://doramagic.ai/zh/crystal/mcp-python-sdk ## 知识规模 - **50 条约束** (1 fatal + 49 non-fatal) - 上游源码: `modelcontextprotocol/python-sdk` @ commit `3d7b311d` - 蓝图 ID: `finance-bp-140` ## 用法 Host AI（Claude Code / Cursor / OpenClaw）读 `references/seed.yaml`，按其中的： - `intent_router` 匹配用户意图 - `architecture` 理解项目架构 - `constraints` 应用 anti-pattern 约束 - `business_decisions` 参考核心设计决策 ## FAQ 摘要 ### 这个 skill 适合什么用户？能做哪些任务？ 适合需要给 Claude / Cursor 等 AI host 提供工具服务的工程师：发布公司内部 API 给 AI 调用、暴露文件 / 数据库为 Resources、提供 prompt 模板等。本 skill 覆盖 stdio（本地）/ SSE（旧）/ streamable-http（推荐）三种 server 形态。访问 doramagic.ai/r/mcp-python-sdk 查看完整说明。 ### 需要准备什么环境？依赖什么？ Python 3.10+，AnyIO（asyncio 或 trio 后端），Pydantic v2（type schema 生成），Starlette（HTTP/SSE/streamable-http 托管），OpenTelemetry（可选，分布式追踪）。Windows 客户端 stdio 需 pywin32 用于 Job Object 进程树终止；POSIX 用 os.killpg。 ### 会踩哪些坑？这个 skill 怎么防护？ 本 skill 内置 50 条约束（1 条 fatal）。典型踩坑：(1) commit 3d7b311 的 README 仍 import 改名前的 FastMCP（26 处 import 行 + 10 处文件路径失效），照抄 quickstart 必失败； --- 完整文档: 见 `references/seed.yaml` (v6.1 schema). 浏览页: https://doramagic.ai/zh/crystal/mcp-python-sdk


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: brioche-bit/finance-trading (version 1.0.0)

# Skill: finance-trading _La stratégie quantitative pour le paper trading BTC/USDT_ ## 1. Analyse Technique (1h et 15min) **Indicateurs :** - EMA 20 (Moyenne Mobile Exponentielle 20 périodes) - EMA 50 (Moyenne Mobile Exponentielle 50 périodes) - RSI (Relative Strength Index) **Signal d'Achat :** - EMA 20 > EMA 50 (crossing haussier) - RSI entre 40 et 60 (momentum haussier) **Signal de Vente :** - EMA 20 < EMA 50 (crossing baissier) - RSI > 80 (surachat) ## 2. Gestion de Risque - **Mode :** PAPER TRADING uniquement - **Taille maximum :** 5% du capital fictif par trade - **Stop Loss :** 2% - **Take Profit :** 6% ## 3. Rapport & Suivi - **Journal :** `trading_log.md` après chaque trade - **Rapport quotidien :** Chaque jour à 18h → rapport de performance avec solde, PnL, Win Rate, sentiment marché ## 4. Fréquence - **Vérification :** Toutes les 15 minutes - **Veille :** Le reste du temps --- **Exécution :** ```bash # Analyser le marché fetch market-data BTC/USDT --timeframes 1h 15min # Calculer les indicateurs calculate-indicators --ema-20 --ema-50 --rsi # Appliquer la stratégie apply-strategy --mode paper --risk 5% --sl 2% --tp 6% # Journaliser log-trade --file trading_log.md ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shanemort1982/x402-lotto (version 1.0.3)

# x402 Lotto API client for x402.lotto — lottery services via the x402 payment protocol. ## Endpoints | Endpoint | Method | Description | |----------|--------|-------------| | `/api/lotteries` | GET | List lotteries | | `/api/jackpot/:name` | GET | Current jackpot | | `/api/tickets` | POST | Purchase ticket | | `/api/tickets/:id` | GET | Ticket status | | `/api/results/:name` | GET | Draw results | ## Usage ```javascript import { wrapFetchWithPayment, decryptKey } from '@x402/evm'; const wallet = decryptKey(process.env.KEY); const fetch = wrapFetchWithPayment(wallet); const res = await fetch('https://x402.lotto/api/jackpot/eurojackpot'); ``` ## Payment Uses x402 protocol — EIP-712 off-chain signing, no gas fees. USDC on Base (8453). ## Install ```bash clawhub install x402-lotto ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: thincher/glm-understand-image (version 1.0.4)

--- name: glm-understand-image description: 使用 GLM 视觉 MCP 进行图像理解和分析。触发条件：(1) 用户要求分析图片、理解图像、描述图片内容 (2) 需要识别图片中的物体、文字、场景 (3) 使用 GLM 的视觉理解功能 --- # glm-understand-image 使用 GLM 视觉 MCP 服务器进行图像理解和分析。 ## 执行流程（首次需要安装，后续直接步骤6调用） ### 步骤 1: 检查并安装依赖 #### 1.1 检查 mcporter 是否可用 ```bash npx -y mcporter --version ``` 如果命令返回成功，说明 mcporter 可用，跳到步骤 2。 mcporter 可以直接通过 npx 使用，无需安装。 ### 步骤 2: 检查 API Key 配置 ```bash cat ~/.openclaw/config/glm.json 2>/dev/null | python3 -c "import json,sys; d=json.load(sys.stdin); print(d.get('api_key', ''))" ``` 如果返回非空的 API Key，跳到步骤 4。 ### 步骤 3: 配置 API Key（如果未配置） #### 3.2 如果没有找到 Key，向用户索要 询问用户提供智谱 API Key。 如果用户没有智谱 API Key，可以访问 https://www.bigmodel.cn/glm-coding?ic=OOKF4KGGTW 购买。 #### 3.3 保存 API Key ```bash mkdir -p ~/.openclaw/config cat > ~/.openclaw/config/glm.json << EOF { "api_key": "API密钥" } EOF ``` ### 步骤 4: 添加 MCP 服务器 使用 mcporter 添加 GLM 视觉 MCP 服务器： ```bash mcporter config add glm-vision \ --command "npx -y @z_ai/mcp-server" \ --env Z_AI_API_KEY="your-key" \ --env Z_AI_MODE="ZHIPU" \ --env HOME="$PWD" ``` 注意：将 `your-key` 替换为实际的智谱 API Key。`HOME` 环境变量设置为当前工作目录以避免日志文件权限问题。 ### 步骤 5: 测试连接 ```bash mcporter list ``` 确认 `glm-vision` 服务器已成功添加。 ### 步骤 6: 使用 MCP 处理图像 #### 6.1 准备图片 将图片放到可访问路径，例如： - `~/.openclaw/workspace/images/图片名.jpg` - 或者使用 URL #### 6.2 使用 mcporter 调用 MCP 工具 使用 mcporter 调用 MCP 服务： ```bash mcporter call glm-vision.analyze_image prompt="<对图片的提问>" image_source="<图片路径或URL>" ``` **示例：** ```bash # 描述图片内容 mcporter call glm-vision.analyze_image prompt="详细描述这张图片的内容" image_source="~/image.jpg" # 使用 URL mcporter call glm-vision.analyze_image prompt="这张图片展示了什么？" image_source="https://example.com/image.jpg" # 提取图片中的文字 mcporter call glm-vision.extract_text_from_screenshot image_source="~/screenshot.png" # 诊断错误截图 mcporter call glm-vision.diagnose_error_screenshot prompt="分析这个错误" image_source="~/error.png" ``` #### 6.3 API 参数说明 | 参数 | 说明 | 类型 | |------|------|------| | image_source | 图片路径或 URL | string (必填) | | prompt | 对图片的提问 | string (必填) | ## 支持的工具 **重要提示：如果出现问题以官方说明为准** 官方版说明 ： https://docs.bigmodel.cn/cn/coding-plan/mcp/vision-mcp-server GLM 视觉 MCP 服务器提供以下工具： - `ui_to_artifact` - 将 UI 截图转换为代码、提示词、设计规范或自然语言描述 - `extract_text_from_screenshot` - 使用先进的 OCR 能力从截图中提取和识别文字 - `diagnose_error_screenshot` - 解析错误弹窗、堆栈和日志截图，给出定位与修复建议 - `understand_technical_diagram` - 针对架构图、流程图、UML、ER 图等技术图纸生成结构化解读 - `analyze_data_visualization` - 阅读仪表盘、统计图表，提炼趋势、异常与业务要点 - `ui_diff_check` - 对比两张 UI 截图，识别视觉差异和实现偏差 - `analyze_image` - 通用图像理解能力，适配未被专项工具覆盖的视觉内容 - `video_analysis` - 支持 MP4/MOV/M4V 等格式的视频场景解析，抓取关键帧、事件与要点 ## MCP 配置 MCP 服务器名称：`glm-vision` MCP 服务器配置：`@z_ai/mcp-server` 环境变量： - `Z_AI_API_KEY` - 智谱 API Key（必需） - `Z_AI_MODE` - 服务平台选择，默认为 `ZHIPU`

path: _meta.json
content: slug: glm-understand-image
version: 1.0.0

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: johnnycdb/miaoda-doc-parse (version 1.0.0)

--- name: miaoda-doc-parse description: "Use when user needs to parse, extract, or read content from documents (PDF, Word, Excel, PPT, etc.) Converts documents to Markdown via IDP (Intelligent Document Parsing). 触发词：文档解析, 解析文档, 解析 PDF, 解析 Word, 解析 Excel, 读取文档, 提取文档内容, doc parse, IDP, 智能文档解析, parse document, 文档转 Markdown, 读取 PDF, 读取 PPT, 提取表格, 文档识别" --- # Document Parse 通过 `miaoda-studio-cli doc-parse` 将文档解析为 Markdown 格式（基于 IDP 智能文档解析）。 ## Quick Reference | 参数 | 说明 | 必需 | 默认值 | |------|------|------|--------| | `--file` | 文档路径或 URL | 是 | - | | `--output, -o` | 输出格式: text/json | 否 | text | ## 支持的文档格式 | 格式 | 扩展名 | 典型场景 | |------|--------|---------| | PDF | `.pdf` | 报告、论文、合同 | | Word | `.doc`, `.docx` | 文档、方案 | | PowerPoint | `.pptx` | 演示文稿 | | Excel | `.xlsx` | 表格数据 | | CSV | `.csv` | 结构化数据 | | 纯文本 | `.txt`, `.md` | 文本文件 | | 网页 | `.html` | HTML 页面 | ## 使用示例 ```bash # 解析本地 PDF 文件 miaoda-studio-cli doc-parse --file report.pdf # 解析远程文档并输出 JSON miaoda-studio-cli doc-parse --file https://example.com/document.docx --output json # 解析 Excel 表格 miaoda-studio-cli doc-parse --file data.xlsx --output json ``` ## 使用场景决策 ``` 需要获取文档内容 ├─ 文档是本地文件或可下载 URL → miaoda-studio-cli doc-parse ├─ 文档是网页（需要抓取）→ miaoda-studio-cli web-crawl（见 miaoda-web-fetch skill） └─ 文档是飞书云文档 → 使用 feishu skill ``` ## Common Mistakes | 错误 | 正确做法 | |------|----------| | 用 `miaoda-studio-cli doc-parse` 解析网页 URL | 网页内容用 `miaoda-studio-cli web-crawl`（见 miaoda-web-fetch skill） | | 不检查文件格式是否支持 | 确认文件扩展名在支持列表中 | | 需要结构化数据但未加 `--output json` | 需要程序处理时加 `--output json` | | 文件路径包含空格未加引号 | 路径有空格时用引号包裹：`--file "my file.pdf"` | | 解析飞书云文档用 doc-parse | 飞书云文档使用 `feishu` skill 通过 API 读取 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chentuan7963-afk/legal-site-generator (version 1.0.1)

# Legal Site Generator Generate an App Store compliant legal website suitable for Cloudflare Pages. ## Features - Privacy Policy - Terms of Service - Support Page - Data Deletion Page - GDPR & CCPA compliant - No ads - No location collection - No tracking scripts ## Output Generates a `dist/` folder ready for upload to Cloudflare Pages. ## Parameters - appName - companyName - contactEmail ## Security This skill does not execute external programs. It only generates static HTML files.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xueyetianya/grammar-check (version 2.3.5)

--- version: "2.0.0" name: grammar-check description: "语法检查助手。英语纠错、语法解释、难度分级、语法练习题、中英对比分析。Grammar checker with error correction, explanations, difficulty levels, practice exercises." author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # grammar-check 语法检查助手。英语纠错、语法解释、难度分级、语法练习题、中英对比分析。Grammar checker with error correction, explanations, difficulty levels, practice exercises, Chinese-English comparison. ## 常见问题 **Q: 这个工具适合谁用？** A: 任何需要grammar-check的人，无论是个人还是企业用户。 **Q: 输出格式是什么？** A: 主要输出Markdown格式，方便复制和编辑。 ## 命令速查 ``` check check correct correct explain explain level level practice practice compare compare ``` ## 专业建议 - 冠词遗漏**: ~~I am student~~ → I am **a** student - 时态混乱**: ~~I go there yesterday~~ → I **went** there yesterday - 主谓不一致**: ~~He don't~~ → He **doesn't** - 可数/不可数**: ~~informations~~ → information - 介词错误**: ~~arrive to~~ → arrive **at/in** --- *grammar-check by BytesAgain* --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com ## Commands Run `grammar-check help` to see all available commands.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: fchange/moark-doc-extraction (version 1.0.0)

--- name: moark-doc-extraction description: Extract and recognize text from documents, including PDF and DOCX files. metadata: { "openclaw": { "emoji":"📖", "requires": { "env": ["GITEEAI_API_KEY"]}, "primaryEnv": "GITEEAI_API_KEY" } } --- # Document Extraction This skill allows users to extract and recognize text from documents, including PDF and DOCX files, using an external GITEE AI API. ## Usage Ensure you have installed the required dependencies (`pip install requests requests-toolbelt`). Use the bundled script to perform document extraction. ```bash python {baseDir}/scripts/perform_doc_extraction.py --file /path/to/document.pdf --api-key YOUR_API ``` ## Options No additional parameters are required for this skill. ## Workflow 1. Execute the perform_doc_extraction.py script with the parameters from the user. 2. Parse the script output and find the line starting with `EXTRACTION_RESULT:`. 3. Extract the OCR result from that line (format: `EXTRACTION_RESULT: ...`). 4. Display the OCR result to the user using markdown syntax: `📖[EXTRACTION_RESULT Result]`. ## Notes - If GITEEAI_API_KEY is none, you should remind user to provide --api-key argument - Please handle the output of the script carefully, ensuring that you only extract and display the relevant information without adding any extra commentary or interpretation. - You should optimize the output format to make it more concise and user-friendly, but do not change or ignore the content of the result. - The script prints `EXTRACTION_RESULT:` in the output - extract this result and display it using markdown image syntax:`📖[EXTRACTION_RESULT Result]`. - Always look for the line starting with `EXTRACTION_RESULT:` in the script output.


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