OpenClaw/clawhub-security-signals huggingface dataset:
Skill: rigeneproject/safeproactive (version 1.0.3)

\--- name: SafeProactive version: 1.1.2 description: Framework di sicurezza locale con Write-Ahead Logging (WAL) e gate di approvazione umana obbligatori. triggers: &#x20; - every\_turn &#x20; - constraint\_violation maintainer: Roberto De Biase license: MIT \--- \# SafeProactive v1.1.2 \## 🔒 Sicurezza e Scopo (Strict) 1\. \*\*Confini del Filesystem:\*\* L'agente è limitato alle cartelle `./proposals/`, `./memory/` e al workspace corrente. È \*\*vietato\*\* l'accesso a log di sistema o cronologia della shell (es. .bash\_history). 2\. \*\*Cronologia Azioni:\*\* Ogni riferimento alla "storia" o "cronologia" si riferisce esclusivamente al file di audit locale `proposals/EXECUTION\_LOG.md`. 3\. \*\*Zero API Esterne:\*\* Questa skill non gestisce né richiede credenziali esterne. Ogni integrazione di Livello 2 deve essere configurata manualmente dall'operatore. \--- \## 🏛️ Architettura Il framework segue un ciclo decisionale rigido: 1\. \*\*Self-Location:\*\* Identificazione dei confini del sistema. 2\. \*\*Constraint Mapping:\*\* Definizione dei limiti di sicurezza (Livello 0). 3\. \*\*Proposal \& WAL:\*\* Scrittura dell'azione intenzionale in `./proposals/` PRIMA dell'esecuzione. 4\. \*\*Approval Gate:\*\* Le azioni di Livello 2+ richiedono conferma umana esplicita. \--- \## 🚦 Livelli Operativi | Livello | Nome | Descrizione | Approvazione | | :--- | :--- | :--- | :--- | | \*\*0\*\* | \*\*Integrità\*\* | Stabilità e sicurezza locale. | \*\*Auto\*\* | | \*\*1\*\* | \*\*Esplorazione\*\* | Analisi e ricerca nel workspace locale. | \*\*Auto\*\* | | \*\*2\*\* | \*\*Espansione\*\* | Creazione di nuovi file o strumenti locali. | \*\*MANUALE\*\* | | \*\*3\*\* | \*\*Ricorsione\*\* | Modifica del protocollo (SKILL.md). | \*\*STRETTAMENTE MANUALE\*\* | \--- \## 📄 Manutenzione L'integrità è monitorata via `HEARTBEAT.md`, verificando la coerenza tra i log in `proposals/WAL/` e `proposals/EXECUTION\_LOG.md`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: codenova58/arch-review (version 1.0.0)

--- name: arch-review description: "Stress-test designs before they ship—constraints, trade-offs, failure modes, and ADR-worthy decisions. Use for ADRs, big refactors, new services, or when ‘it works on my laptop’ isn’t enough." --- # Architecture Review **Challenge** a design without owning the team’s roadmap: clarify **forces** (scale, money, people, regulation), surface **risks**, and leave **decisions** traceable—usually as an ADR or review notes. ## Inputs you need (ask early) - **Goal** and non-goals; **users** and SLAs; **constraints** (budget, deadline, org skills). - **Current pain**—latency, incidents, cost, velocity—not buzzwords. - **Alternatives** considered, even if rough. ## Review lens (pick what fits) - **Failure**: blast radius, partial outages, data loss, replay. - **Ops**: deploy model, rollbacks, observability, on-call load. - **Change**: team size, Conway’s law, long-term ownership. - **Security**: trust boundaries, secrets, supply chain—at architecture depth, not a full pentest. ## Output shape - **Summary** of the proposal in your own words (catches misunderstandings). - **Top risks** with severity; **mitigations** or experiments. - **Open questions** for the team—not a pretend-final design. ## Not this - Replacing the team’s **product** judgment; rubber-stamping; 20-page templates nobody reads. ## Done when - The team can explain **what they decided**, **why**, and **what would falsify** the choice later.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: laolaoqi/sys-inspector (version 1.0.0)

--- name: System Inspector description: > Lightweight server health inspector. Checks CPU, memory, disk, temperature, network ports, and recent logs in one command. No external dependencies. Returns structured JSON health report. Use when you need a quick system health overview without logging into the machine. --- # System Inspector **Zero-dependency system health check for AI agents.** Runs entirely locally via shell commands. No npm packages, no API calls, no dependencies. Just pure bash + standard Linux tools. ## Usage ``` action: "inspect" ``` Returns: ```json { "cpu": { "load": [0.35, 0.21, 0.18], "cores": 4, "temp_c": 52.3 }, "memory": { "total_gb": 3.6, "used_gb": 2.1, "available_gb": 1.5, "percent": 58 }, "disk": { "total_gb": 114, "used_gb": 13, "percent": 12 }, "uptime_days": 5.4, "top_processes": [ { "pid": 91204, "name": "node", "cpu": 12, "mem": 460 } ], "top_listeners": [ { "port": 18792, "process": "node" } ], "health_score": 85, "warnings": [] } ``` ## Actions | Action | Description | |--------|-------------| | `inspect` | Full system health report | | `quick` | Lightweight check (CPU + memory only) | | `alerts` | Only return warnings and issues | ## Pricing Free — MIT License


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: pierreeurope/clawpulse (version 0.3.0)

--- name: clawpulse description: Connect your OpenClaw agent to ClawPulse - the community analytics dashboard. Automatically collects and pushes aggregate token stats (no message content) to clawpulse.vercel.app. See your dashboard, compare with the community, and track the pulse of OpenClaw. metadata: openclaw: requires: bins: ["node", "npx"] --- # ClawPulse - Community Analytics for OpenClaw Connect your agent to the OpenClaw community dashboard at https://clawpulse.vercel.app ## Setup (Agent does this automatically) ### Step 1: Install CLI ```bash npm install -g openclaw-pulse ``` ### Step 2: Authenticate The agent needs a GitHub token to push stats. Two options: **Option A: User runs `clawpulse setup` in their terminal** This opens a GitHub device flow - user enters a code at github.com/login/device. **Option B: Agent uses existing GitHub token** If the user has `gh` CLI authenticated, extract the token: ```bash TOKEN=$(gh auth token) mkdir -p ~/.clawpulse echo "{\"apiUrl\":\"https://clawpulse.vercel.app\",\"githubToken\":\"$TOKEN\"}" > ~/.clawpulse/config.json ``` ### Step 3: Push stats ```bash clawpulse push ``` This collects session stats from `~/.openclaw/agents/main/sessions/*.jsonl` and pushes aggregate numbers to the ClawPulse API. **No message content is ever collected.** ### Step 4: Set up auto-push via OpenClaw cron Create TWO cron jobs for auto-push at midnight and noon: ```json { "name": "ClawPulse Push (midnight)", "schedule": { "kind": "cron", "expr": "0 0 * * *" }, "payload": { "kind": "agentTurn", "message": "Run: clawpulse push --silent" }, "sessionTarget": "isolated", "delivery": { "mode": "none" } } ``` ```json { "name": "ClawPulse Push (noon)", "schedule": { "kind": "cron", "expr": "0 12 * * *" }, "payload": { "kind": "agentTurn", "message": "Run: clawpulse push --silent" }, "sessionTarget": "isolated", "delivery": { "mode": "none" } } ``` ## What Gets Collected (Privacy) Only aggregate numbers per day: - Token counts (input, output, cache read, cache write) - Model names and usage counts - Tool names and usage counts - Cost estimates (at API rates) - Message counts - Session counts **Never collected:** message content, file paths, tool arguments, personal data. ## Commands | Command | What it does | |---------|-------------| | `clawpulse setup` | Interactive: login + push + show instructions | | `clawpulse push` | Collect and push stats | | `clawpulse push --silent` | Same but no output (for cron) | | `clawpulse collect` | Just collect stats locally | | `clawpulse status` | Show stats summary | | `clawpulse login` | Re-authenticate with GitHub | ## Dashboard - **Personal:** https://clawpulse.vercel.app/dashboard (after login) - **Community:** https://clawpulse.vercel.app - **Leaderboard:** https://clawpulse.vercel.app/community ## Source - npm: https://www.npmjs.com/package/openclaw-pulse - GitHub: https://github.com/pierreeurope/clawpulse


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: xqw1377-prog/musk-insider-one (version 1.0.2)

# MuskInsider Pro ## 简介 提供一个最小可运行的 HTTP 技能服务，返回演示用的马斯克资讯简报数据与支付链接样例。无外部爬虫与模型推理，纯演示接口。 ## 接口 - GET `/` 或 `/health`：服务健康检查，返回 `{"status":"ok","project":"MuskInsider"}` - GET `/invoke`：返回当日简报预览的演示 JSON - POST `/invoke`：返回支付链接的演示 JSON（SkillPay 样例 URL） ## 运行 - 启动：`python bare.py` - 监听：默认 `0.0.0.0:8080`（若平台未注入端口变量，将同时尝试 8080/8000/3000/80） ## 版本 - v1.0.1

path: requirements.txt
content: fastapi==0.115.0
uvicorn==0.30.1


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: huangjingzhi07/file-organizer-zh (version 1.0.0)

--- name: file-organizer-zh version: 1.0.0 description: 文件整理器，按类型自动分类（中文版） author: 黄豆豆 tags: [文件, 整理, 效率, 工具, 中文] --- # file-organizer-zh - 文件整理器 自动按类型分类整理文件。 ## 功能 1. **按类型分类** - 图片/文档/代码/视频/音频/压缩包 2. **智能归类** - 根据文件内容智能分类 3. **清理重复** - 识别并清理重复文件 ## 触发关键词 - 整理 - 分类 - 整理文件 - 分类文件 ## 文件类型映射 - images: .jpg, .jpeg, .png, .gif, .bmp, .webp, .svg - documents: .doc, .docx, .pdf, .txt, .md, .xls, .xlsx, .ppt, .pptx - code: .js, .ts, .py, .java, .cpp, .c, .html, .css, .json - videos: .mp4, .avi, .mov, .wmv, .flv - audio: .mp3, .wav, .flac, .aac, .ogg - archives: .zip, .rar, .7z, .tar, .gz ## 使用示例 - "整理 D:\\Downloads" - "分类 C:\\Users\\Documents" - "整理桌面"


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: runkecheng/ligaowei-weekly-summary (version 1.0.0)

--- name: ligaowei-weekly-summary description: 为李高伟（收钱吧培训师）生成每周工作总结。支持按自定义周期（默认上周五至本周四）自动查询飞书日历日程和聊天记录，产出的总结以洞察分析为主而非事项罗列。触发场景：用户说"周报"、"周总结"、"本周内容"、"总结本周"、"周回顾"。 --- # Ligaowei Weekly Summary 为李高伟生成每周工作总结。核心原则：**不罗列事项，提炼洞察**。 ## 工作流程 ### Step 1: 确认时间范围 默认周期：**上周五 → 本周四**（与考核周期对齐）。 如果用户指定了其他周期，按用户要求调整。 ### Step 2: 查询日程数据 调用 `feishu_calendar_event(action=list, ...)` 查询该时间范围内的所有日程。 注意： - `start_time` 和 `end_time` 使用 ISO 8601 格式（含时区），如 `2026-05-08T00:00:00+08:00` - 留意每条日程的 `self_rsvp_status`（accept/needs_action/decline）判断用户是否实际参与 - 区分用户自己组织的日程（`event_organizer` 的 `user_id` 是用户本人）和他人的邀约 - 日程标题前的 emoji 保留（如 🤖📚 等） ### Step 3: 查询聊天记录 通过 `feishu_im_user_search_messages` 分两类查询： **A. 用户发出的消息**（了解用户主动沟通的内容）： - `sender_ids: [用户open_id]` - `relative_time: last_7_days`（或覆盖时间范围的参数） **B. @用户的群消息**（了解团队/他人找用户的事）： - `chat_type: group` - `mention_ids: [用户open_id]` 注意： - 有 `has_more: true` 时继续翻页 - 关注 `chat_name` 字段识别对话对象/群名 - 合并转发消息（msg_type=merge_forward）的内容包含关键对话脉络 ### Step 4: 按维度分析数据 不要按天罗列事项。从以下维度提炼： #### 1️⃣ 整体判断 - 本周的工作密度和节奏（高密度交付/正常推进/相对轻松） - 用户角色的定位（培训师/组织者/问题解决者/管理协同者） #### 2️⃣ 核心工作线索 识别本周最重要的 3-5 条工作线索，每条包含： - **发现/背景**：从什么场景发现或触发 - **做了什么**：实际推进的动作 - **结果/影响**：落地了什么，推动了什么 - **意义**：这件事对团队或业务有什么价值 常见的线索类型： - **新人培训体系**：培训场次、主题覆盖、验收闭环 - **业务痛点发现与解决**：从培训/沟通中发现业务问题，推动解决 - **管理型工作**：服务之星评选、数据搜集、团队通知 - **话术/内容建设**：话术讨论会、内容输出、协同产出 - **个人能力建设**：培训、学习、AI技能提升等 #### 3️⃣ 关键数字（如适用） 精选 3-5 个最有代表的数据，不要堆砌。 #### 4️⃣ 角色转变观察（如适用） - 用户在某些工作中的角色是否有升维（如从执行者变成组织者） - 是否有新的职责或能力要求出现 #### 5️⃣ 下周重点关注 精选 3-5 条下周需要跟进/留意的事项。 ### Step 5: 输出格式 使用 Markdown 格式输出，结构参考 `references/template.md`。 关键要求： - **不要使用表格结构**做日程罗列 - 每个线索段落控制在 3-7 行，精炼有信息量 - 语言风格：成熟、理性、有洞察力，符合李高伟的培训师身份 - 适当使用加粗强调核心结论 ## 资源 ### references/ - `template.md` - 每周总结的参考模板，包含各维度的输出结构示例 - `user_profile.md` - 李高伟的基本信息（open_id、部门、角色），用于快速获取查询参数


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: uynewnas/map-query (version 1.0.1)

--- name: map-query description: Map Query Skill - Query nearby food and promotions by address, supports AMap/Baidu/Tencent Maps metadata: version: 1.0.0 author: uynewnas category: location-services tags: [map, poi, food, location, amap, baidu, tencent] env: required: - name: AMAP_KEY description: AMap (高德地图) API Key required: false - name: BAIDU_MAP_KEY description: Baidu Maps API Key required: false - name: TENCENT_MAP_KEY description: Tencent Maps API Key required: false note: At least one map provider API key must be configured --- # Map Query Skill Query nearby restaurants, shops, promotions and other POI information by specific address. ## Core Features - **Address Geocoding**: Convert detailed address to latitude and longitude coordinates - **Nearby Search**: Query POIs (Points of Interest) around specified location - **Multi-map Support**: Integrate AMap, Baidu Maps, Tencent Maps APIs - **Food Search**: Specialized search for nearby restaurants and food places - **Promotions Search**: Query nearby merchant promotions and deals ## Supported Map Providers | Provider | Features | Notes | |---------|---------|-------| | AMap (高德) | ✅ Geocoding, POI Search, Food Search | API Key required | | Baidu Maps | ✅ Geocoding, POI Search, Food Search | API Key required | | Tencent Maps | ✅ Geocoding, POI Search, Food Search | API Key required | ## Main Functions ### 1. Address Geocoding - Support detailed addresses: e.g., "No. 47, Shangjiao xx Street, Panyu District, Guangzhou" - Support city + landmark: e.g., "Beijing Tiananmen" - Auto-resolve and return coordinates ### 2. Nearby Search - Search by type: food, hotel, bank, gas station, etc. - Filter by distance: 1km, 2km, 5km, etc. - Sort by rating: prioritize high-rated merchants - Sort by distance: prioritize nearby merchants ### 3. Food Search - Cuisine filter: Sichuan, Cantonese, Japanese, etc. - Price range: per capita consumption range - Business hours filter - User rating filter ### 4. Promotions Search - Find nearby merchant deals - New store opening information - Limited-time discount activities ## Dependencies - Node.js &gt;= 18 - Map provider API Key (at least one configured) ## Quick Start ```bash # Configure API Key export AMAP_KEY=your_amap_api_key export BAIDU_MAP_KEY=your_baidu_map_key export TENCENT_MAP_KEY=your_tencent_map_key # Search for food near address node scripts/search.mjs "Sanlitun, Chaoyang District, Beijing" --type food # Search for promotions node scripts/search.mjs "Sanlitun, Chaoyang District, Beijing" --type promotion ``` --- ## References - 12306 Skill: https://github.com/kirorab/12306-skill - AMap API: https://lbs.amap.com/ - Baidu Maps API: https://lbsyun.baidu.com/ - Tencent Maps API: https://lbs.qq.com/


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: erick0309/feishu-drive-cn (version 1.0.0)

# Feishu Drive Skill Use this skill when user wants to manage Feishu (飞书) cloud storage files and folders. ## Tools Available - `feishu_drive`: List, info, create_folder, move, delete files/folders in Feishu Drive ## Use Cases - List files and folders in Feishu Drive - Get file/folder information - Create new folders - Move files/folders - Delete files/folders ## Auth User must have Feishu account with appropriate permissions to access cloud storage. ## Examples - "帮我列出飞书云盘的文件" - "创建一个新文件夹" - "移动这个文件到另一个文件夹" - "删除这个文件"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: obekt/nifty-agents-protocol (version 1.0.3)

--- name: nifty-agents-protocol version: "1.0.3" license: MIT description: A cryptographic protocol for AI agents to mint, sign, verify, and transfer SVG digital assets without a blockchain. metadata: openclaw: requires: env: [] files: ["index.ts", "server.ts", "package.json", "tsconfig.json"] homepage: "https://github.com/obekt/niftyagents" --- # 💎 Nifty Agent SVG Protocol (NASP) - Agent Skill Manual NASP is a cryptographic protocol for creating, signing, and trading SVGs with verifiable provenance—**without a blockchain.** ## 🎯 Objective Use this skill to mint "Nifty" digital assets, verify their ownership, and securely transfer them to other agents using `did:key` identities. ## 🛠 Setup & Imports To use this skill in your environment: ```typescript import { generateIdentity, signSVG, verifySVG, transferSVG } from './index.js'; ``` ## 📋 Data Structures ### `AgentIdentity` ```typescript { did: string; // e.g. "did:key:z6Mk..." publicKey: Uint8Array; secretKey: Uint8Array; } ``` ### `VerificationResult` ```typescript { isValid: boolean; // Cryptographic integrity check creator: string; // DID of the original minter currentOwner: string; // DID of the latest owner in the chain chain: string[]; // Array of DIDs representing the full history } ``` ## 🕹 Usage Guide ### 1. Generate an Identity Call this once to create your agent's cryptographic persona. **Store the `secretKey` securely.** ```typescript const myIdentity = generateIdentity(); ``` ### 2. Mint & Sign a New SVG Takes a raw SVG string and returns a "Nifty" SVG with embedded NASP metadata. ```typescript const rawSVG = `<svg>...</svg>`; const signedSVG = await signSVG(rawSVG, myIdentity); ``` ### 3. Verify an Asset Always verify an SVG before accepting a trade or performing an action. ```typescript const audit = await verifySVG(receivedSVG); if (audit.isValid) { console.log(`Current Owner: ${audit.currentOwner}`); } ``` ### 4. Transfer Ownership Appends a new signed grant to the metadata. This function **throws an error** if: - The SVG is invalid/tampered. - You (the signer) are not the `currentOwner` of the asset. ```typescript const updatedSVG = await transferSVG(currentSVG, myIdentity, targetAgentDID); ``` ## 🛡 Security Protocol for Agents 1. **Canonicalization:** The library automatically handles canonicalization using `svgo`. Do not manually edit the SVG content after signing, or the signature will break. 2. **Private Keys:** Never log, share, or expose your `secretKey`. Treat it as your agent's "soul." 3. **Audit First:** Never assume an SVG is valid based on its filename. Always run `verifySVG()`. 4. **Double Spend Detection:** In decentralized environments, if you are presented with two different valid chains for the same asset, the one with the **longest chain** or the **latest timestamp** is typically preferred.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: mrblackeg/tiktok-streak-bot (version 1.0.1)

--- name: tiktok-streak version: 1.0.0 description: Browser-automated TikTok streak messaging skill using Playwright. Sends daily messages to configured usernames with state tracking, retry control, and optional content discovery via hashtags or keywords. --- # TikTok Streak Skill ## Purpose Send daily streak messages to a list of TikTok usernames using browser automation. ## Execution Flow 1. Load config and state 2. Initialize browser session using stored cookies 3. Validate session (TikTok homepage load) 4. Load usernames list 5. Filter users already processed today 6. For each remaining user: - Generate message (text or discovered content) - Send message via TikTok inbox - Update state 7. Persist updated cookies and state 8. Exit ## Features ### 1. Daily Streak Messaging - Sends one message per user per day - Default message: random short text ### 2. Content Discovery (Optional) - If enabled in config: - Fetch random TikTok video using keyword/hashtag - Send link instead of text ### 3. State Tracking - Prevent duplicate sends within same day - Store last sent timestamp per username ### 4. Failure Handling - Retry up to N times per user - Skip on repeated failure ## Constraints - Max users per run configurable - Random delays between actions - Session must be valid (cookies) ## Outputs - Logs per execution - Updated state.json - Updated cookies.json ## Setup & Usage Instructions ### 1. Identify Target Users Provide the profile usernames you would like to keep a streak with by creating a list in `data/usernames.json` (e.g. `["friend1", "friend2"]`). ### 2. Provide Authenticated Cookies Export your TikTok authenticated browser cookies utilizing a standard browser extension (like "EditThisCookie") and save the JSON array securely within `data/cookies.json`. ### 3. Configure Execution Optionally edit `data/config.json` to change default bot behaviors: - Set `"enable_discovery": true` to pull and send trending videos utilizing your `"keywords"` list instead of plain generic text strings. - Keep `"headless": true` to silently run in the background without UI interruption, or set to `false` to view the graphical browser UI during processing. ### 4. Run the Bot Ensure all requirements are installed: ```bash pip install -r scripts/requirements.txt playwright install chromium ``` Launch the skill via: ```bash python scripts/main.py ```

path: data/cookies.json

path: data/state.json

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: kirkraman/godfery-google-search (version 1.0.0)

--- name: google-search description: Search the web using SkillBoss API Hub. Use this when you need live information, documentation, or to research topics and the built-in web_search is unavailable. --- # Google Search Skill This skill allows OpenClaw agents to perform web searches via SkillBoss API Hub (unified web search routing). ## Setup 1. **SkillBoss API Key:** Obtain your API key from SkillBoss API Hub. 2. **Environment:** Store your credentials in a `.env` file in your workspace: ``` SKILLBOSS_API_KEY=your_key_here ``` ## Workflow ... (rest of file) ## Example Usage ```bash SKILLBOSS_API_KEY=xxx python3 skills/google-search/scripts/search.py "OpenClaw documentation" ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sxliuyu/screenshot-ocr (version 1.0.0)

--- name: screenshot-ocr version: 1.0.0 description: 截图 OCR 识别工具。截图→自动识别文字→复制/保存，适合提取图片内容、表格数据、验证码。 author: 你的名字 triggers: - "截图识别" - "OCR" - "图片转文字" - "截图文字" - "屏幕识别" --- # Screenshot OCR 📷 截图自动识别文字，支持中文、英文、数字。 ## 功能 - 📷 截图识别（截屏或粘贴图片） - 🔤 高精度 OCR 识别 - 📋 一键复制到剪贴板 - 💾 保存为 TXT 文件 - 📊 支持表格识别 ## 使用方法 ### 识别剪贴板图片 ```bash python3 scripts/ocr.py clipboard ``` ### 识别图片文件 ```bash python3 scripts/ocr.py file screenshot.png ``` ### 识别并保存 ```bash python3 scripts/ocr.py file screenshot.png --save result.txt ``` ### 识别并复制 ```bash python3 scripts/ocr.py file screenshot.png --copy ``` ## 依赖安装 ```bash # 安装 Tesseract（Linux） sudo apt install tesseract-ocr tesseract-ocr-chi-sim # macOS brew install tesseract # Windows # 下载安装 https://github.com/UB-Mannheim/tesseract/wiki ``` ## 示例 ```bash # 识别截图 python3 scripts/ocr.py clipboard # 识别图片并复制 python3 scripts/ocr.py file ~/Desktop/1.png --copy ``` ## 环境要求 - Python 3 - tesseract-ocr - pytesseract - pillow ```bash pip install pytesseract pillow ```


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