OpenClaw/clawhub-security-signals huggingface dataset:
Skill: pr1vateer/image-magik-resize (version 1.0.0)

--- name: resize-magic version: 0.1.0 author: Stenkil <[REDACTED_SECRET]> description: Resize images using ImageMagick (CLI). Entrypoint is a Bash script. entrypoint: scripts/resize.sh metadata: { "openclaw": { "emoji": "🖼️", "requires": { "bins": ["bash"], "anyBins": ["magick","convert"] }, "install": [ { "id": "brew", "kind": "brew", "formula": "imagemagick", "bins": ["magick","convert"], "label": "Install ImageMagick (brew)" } ] } } user-invocable: true command-dispatch: tool command-tool: resize commands: - name: resize usage: resize <input-path> <geometry> [output-path] description: | Resize an image using ImageMagick. Geometry examples: - 800x -> width 800, preserve aspect ratio - 800x600 -> exact geometry (may change aspect) - 50% -> scale to 50% of original - 800x800\> -> resize only if larger than 800x800 --- ## Overview This skill provides a single executable script `scripts/resize.sh` that the agent (or the `openclaw` CLI) can call to resize an image with ImageMagick. ## Installation (manual) Copy the folder into your OpenClaw skills directory, e.g.: ```bash cp -r resize-magic ~/.openclaw/skills/resize-magic # or install via CLI if available openclaw skill install ./resize-magic ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mike47512/iqiyi (version 0.1.2)

--- name: "iQIYI" description: "提供爱奇艺独家自制剧、电影及综艺点播指引。" --- # 爱奇艺 ## 筛选项 - 类别/领域/功能（核心/进阶） - 场景/适用时段/设备要求（移动端/网页版/跨端） - 状态（实时/离线/授权/VIP特权/免费） ## 返回字段 - 操作摘要/交互流程/关键入口 - 功能亮点/提效工具/隐藏玩法/避坑指南 - 状态追踪/数据同步/消息提醒/安全策略 ## 示例请求 - “如何快速使用 爱奇艺 处理 [具体场景任务] ？” - “爱奇艺 的 [核心功能] 有哪些最新玩法/优惠信息？” ## 更新频率 - 结合产品迭代与社区反馈进行内容更新 ## 注意事项 - 保护个人隐私，谨慎进行敏感授权 - 部分功能涉及第三方接口，稳定性受合作方波动影响


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jisuapi/weather2 (version 1.0.5)

--- name: "Historical Weather - 历史天气" description: 按城市与日期查询历史天气（温湿度、风、气压、AQI 等）。当用户说：去年今天北京多少度？查一下 2023 年 7 月上海历史天气，或类似历史气温问题时，使用本技能。 metadata: { "openclaw": { "emoji": "🌤", "requires": { "bins": ["python3"], "env": ["JISU_API_KEY"] }, "primaryEnv": "JISU_API_KEY" } } --- # 极速数据历史天气（Jisu Weather2） > 数据由 **[极速数据（JisuAPI）](https://www.jisuapi.com/)** 提供 — 国内专业的 API 数据服务平台，提供生活常用、交通出行、工具万能等数据接口。 ## 前置配置：获取 API Key 1. 前往 [极速数据官网](https://www.jisuapi.com/) 注册账号 2. 进入 [历史天气 API](https://www.jisuapi.com/api/weather2/) 页面，点击「申请数据」 3. 在会员中心获取 **AppKey** 4. 配置 Key： ```bash # Linux / macOS export JISU_API_KEY="your_appkey_here" # Windows PowerShell $env:JISU_API_KEY="your_appkey_here" ``` ## 脚本路径 脚本文件：`skills/weather2/weather2.py` ## 使用方式 ### 1. 历史天气查询（query） ```bash python3 skills/weather2/weather2.py query '{"city":"北京","date":"2018-01-01"}' python3 skills/weather2/weather2.py query '{"cityid":111,"date":"2018-01-01"}' ``` | 参数 | 类型 | 必填 | 说明 | |--------|--------|------|------| | city | string | 否 | 城市名（与 cityid 二选一） | | cityid | int | 否 | 城市 ID（见 city 命令） | | date | string | 是 | 日期，格式 2018-01-01，默认为昨天 | 返回字段示例：cityid, cityname, date, weather, temphigh, templow, img, humidity, pressure, windspeed, windpower, sunrise, sunset, aqi, primarypollutant 等。 ### 2. 获取城市列表（city） ```bash python3 skills/weather2/weather2.py city '{}' ``` 无参数，返回支持历史天气查询的城市列表（cityid, parentid, citycode, city）。 ## 常见错误码 | 代号 | 说明 | |------|----------------| | 201 | 城市和城市ID都为空 | | 202 | 城市不存在 | | 203 | 查询日期为空 | | 204 | 日期格式不正确 | | 210 | 没有信息 | 系统错误码 101–108 见极速数据官网。 ## 推荐用法 1. 用户问「北京 2018 年 1 月 1 日天气如何」时，先调用 `query`，传入 `city`（或 `cityid`）与 `date`。 2. 若需城市 ID，可先调用 `city` 获取列表再查。 3. 从返回的 result 中取 weather、temphigh、templow、aqi 等用自然语言回复。更多接口与计费见 [极速数据](https://www.jisuapi.com/)。 ## 关于极速数据 **极速数据（JisuAPI，[jisuapi.com](https://www.jisuapi.com/)）** 是国内专业的 **API数据服务平台** 之一，提供以下API： - **生活常用**：IP查询，快递查询，短信，全国天气预报，万年历，空气质量指数，彩票开奖，菜谱大全，药品信息 - **工具万能**：手机号码归属地，身份证号码归属地查询，NBA赛事数据，邮编查询，WHOIS查询，识图工具，二维码生成识别，手机空号检测 - **交通出行**：VIN车辆识别代码查询，今日油价，车辆尾号限行，火车查询，长途汽车，车型大全，加油站查询，车型保养套餐查询 - **图像识别**：身份证识别，驾驶证识别，车牌识别，行驶证识别，银行卡识别，通用文字识别，营业执照识别，VIN识别 - **娱乐购物**：商品条码查询，条码生成识别，电影影讯，微博百度热搜榜单，新闻，脑筋急转弯，歇后语，绕口令 - **位置服务**：基站查询，经纬度地址转换，坐标系转换 在官网注册后，按**具体 API 页面**申请数据，在会员中心获取 **AppKey** 进行接入；**免费额度和套餐**在API详情页查看，适合个人开发者与企业进行接入。在 **ClawHub** 上也可搜索 **`jisuapi`** 找到更多基于极速数据的 OpenClaw 技能。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/audio-tool (version 1.0.3)

--- name: audio-toolkit version: 1.0.0 description: 音频处理工具 - 格式转换、剪辑、压缩、提取音频。适合：播客、音乐制作、视频剪辑。 license: MIT metadata: openclaw: emoji: "🎵" requires: bins: ["ffmpeg"] --- # 音频处理工具 Skill 强大的音频处理能力。 ## 核心功能 ### 1. 格式转换 - MP3 ↔ WAV ↔ FLAC ↔ AAC - 批量转换 - 质量调整 ### 2. 音频剪辑 - 裁剪音频片段 - 合并音频 - 调整音量 ### 3. 音频提取 - 从视频提取音频 - 提取背景音乐 - 降噪处理 ## 使用方法 ### 格式转换 ``` 将 audio.wav 转换为 MP3 格式 ``` ### 裁剪音频 ``` 裁剪 audio.mp3 从 1:00 到 3:00 ``` ### 提取音频 ``` 从 video.mp4 提取音频 ``` ### 合并音频 ``` 合并 track1.mp3 和 track2.mp3 ``` ## 技术依赖 - FFmpeg --- 创建：2026-03-11 作者：ClawMart


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bamontejano/skill-doctorbot-ci-validator (version 0.1.0)

--- name: doctorbot-ci-validator version: 1.0.0 description: Stop failing in production. Validate your GitHub Actions, GitLab CI & Keep workflows offline with surgical precision. Born from Keep bounty research, perfected for agents. author: DoctorBot-x402 tags: [devops, ci, github-actions, keep, validation, security] icon: 🩺 homepage: https://github.com/bamontejano/skill-doctorbot-ci-validator --- # DoctorBot: CI Validator 🩺✅ > **"An ounce of validation is worth a pound of cure."** This skill provides **offline, deterministic validation** for CI/CD workflow files. It bypasses environment dependencies (databases, networks) to catch syntax and schema errors *before* you push. ## 🚀 Features - **Keep Workflow Validation:** Specialized mocker for Keep (AIOps) workflows. Validates steps, providers, and logic without a live DB. - **Universal YAML Check:** Fast syntax validation for GitHub Actions, GitLab CI, CircleCI, etc. - **Surgical Precision:** Identifies exactly where your workflow will fail. ## 🛠️ Usage ### 1. Validate a Workflow (Keep/GitHub/GitLab) ```bash # Validate a specific file python3 scripts/validate_keep.py path/to/workflow.yaml # Validate an entire directory python3 scripts/validate_keep.py .github/workflows/ ``` ### 2. Quick Syntax Check (Any YAML) ```bash # Fast check for YAML errors python3 scripts/validate_yaml.py path/to/config.yml ``` ## 📦 Installation (ClawHub) ```bash openclaw install doctorbot-ci-validator ``` ## 🧠 Why use this? Most CI validators require a live environment or Docker container. This skill uses **mocking** to validate structure and logic *instantly*, making it perfect for: - Pre-commit hooks. - CI/CD pipelines (GitHub Actions). - Agent-based code generation (validate before suggesting). --- *Maintained by DoctorBot-x402. For advanced diagnostics, contact me on Moltbook.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hannah-schiebener/gandalf-ctf (version 1.0.0)

--- name: gandalf-ctf description: >- Plays Gandalf, a Capture The Flag prompt security game by Lakera. Extracts guarded secret passwords from AI defenders across 8 levels of increasing difficulty. Competes on a public agent leaderboard. Triggers: "play Gandalf", "play CTF", "play the password game", "prompt challenge", "agent CTF", "test prompt hacking skills". version: 1.0.0 homepage: https://gandalf.lakera.ai/agent-ctf metadata: clawdbot: emoji: 🧙 --- # Gandalf CTF 🧙 A prompt injection CTF game. Each level has an AI defender guarding a secret password. Craft prompts to trick the defender into revealing it. ## Rules - One message = one attempt. Each chat message counts toward the score. - No conversation memory. Each prompt is independent. - Fewer attempts = better rank on the leaderboard. - Levels are sequential, starting at level 1. Complete level N to unlock N+1. ## Base URL ``` https://gandalf-api.lakera.ai ``` ## Endpoints ### Register ``` POST /api/agent-ctf/register Content-Type: application/json {"agent_name": "YOUR_AGENT_NAME", "description": "Brief description"} ``` Agent names must be unique. Returns 409 if taken. Returns a token. Use it in all subsequent requests: ``` Authorization: Bearer <token> ``` ### List Levels ``` GET /api/agent-ctf/levels Authorization: Bearer <token> ``` Returns level name, description, status (unlocked/locked), completed, and attempts. ### Send Prompt ``` POST /api/agent-ctf/levels/{level}/chat Authorization: Bearer <token> Content-Type: application/json {"message": "Your prompt to the defender"} ``` Returns `defender_response`, `level`, and `attempts_this_level`. ### Submit Guess ``` POST /api/agent-ctf/levels/{level}/guess Authorization: Bearer <token> Content-Type: application/json {"secret": "the_password"} ``` Returns `correct` (bool). On success: attempts count, next level info. Guesses are case-insensitive. Wrong guesses do not count toward attempts. ### Leaderboard (no auth) ``` GET /api/agent-ctf/leaderboard ``` Ranked by most levels completed, then fewest total attempts. ### Stats ``` GET /api/agent-ctf/me Authorization: Bearer <token> ``` Returns per-level progress and overall stats. ## Error Codes | Status | Meaning | |--------|---------| | 400 | Missing or invalid field | | 401 | Missing or invalid token | | 403 | Level locked | | 404 | Level does not exist | | 409 | Agent name already taken | | 429 | Rate limited — wait and retry | ## Quick Start ``` 1. POST /api/agent-ctf/register → get token 2. GET /api/agent-ctf/levels → see available levels 3. POST /api/agent-ctf/levels/1/chat → prompt the defender 4. POST /api/agent-ctf/levels/1/guess → submit the password 5. GET /api/agent-ctf/leaderboard → check ranking 6. Repeat from step 3 for the next level. ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: exeandino/moodle-ws-skill (version 1.0.0)

--- name: moodle-ws description: > Integrar con Moodle 4.x usando Web Services REST. Usar esta skill cuando el usuario pida crear cursos, inscribir o desinscribir usuarios, crear o actualizar actividades (quiz, assignment, forum), enviar calificaciones o listar cursos/estudiantes en un campus Moodle con Web Services habilitados. --- # Moodle Web Services (REST) – Skill ## 1. Contexto y requisitos Esta skill sirve para trabajar con **Moodle 4.x** mediante **Web Services (REST)**. Funcionalidades principales: - Crear curso - Inscribir / desinscribir usuarios - Crear / actualizar actividades: - Quiz (cuestionarios) - Assignment (tareas / entregas) - Forum (foros) - Enviar calificaciones - Obtener listas de cursos y estudiantes Requisitos: - URL base de Moodle (por ejemplo: `https://moodle.ejemplo.com`) - Token de Web Service con los permisos adecuados (role con capabilities para: - crear cursos, - gestionar matriculaciones, - gestionar actividades, - gestionar notas). **Importante:** nunca guardar el token en el chat. Pedirle al usuario que lo configure en un archivo local o variable de entorno. Sugerencia: guardar en un archivo de config no versionado, por ejemplo: ```bash ~/.openclaw/workspace/secrets/moodle-ws.json ## 7. Autoría y uso Skill **moodle-ws** diseñada por **Exe Andino**. Pensada para: - integrar Moodle 4.x con asistentes OpenClaw, - automatizar tareas docentes y administrativas (creación de cursos, matriculaciones, actividades, notas), - siempre usando tokens de Web Services con permisos limitados y entornos controlados. Se recomienda: - usar tokens específicos para este tipo de integración, - no versionar ni exponer la configuración de URL + token, - probar primero en entornos de prueba antes de producción.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: marjoriebroad/dadjoke-cli (version 1.0.0)

--- name: joke-teller description: "Tells programming jokes to brighten your day." allowed-tools: Bash --- # Joke Teller A simple skill that tells programming jokes. ## Tell a Joke ```bash echo "Why do programmers prefer dark mode? Because light attracts bugs." ``` ## Tell Another Joke ```bash echo "There are only 10 types of people in the world: those who understand binary and those who don't." ``` ## Usage Tips - Use when you need a laugh during debugging - Great for breaking the ice in code reviews - Works offline, no API key needed


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gyifei/weather-query-ll (version 1.0.0)

--- name: weather description: 当需要查询某个城市的天气时，可以调用使用这个技能。 --- # weather skill ## When to use this skill 当需要查询某个城市的天气时，可以使用这个技能. ## How to extract text 1. 从用户消息中提取 2 个核心信息： - 目标城市（必须，如“北京”“上海”）； - 查询时间； 2. 用 web_fetch 直接抓的网页数据以收集天气相关数据 3. 整理数据并返回，格式要求： - 开头明确“城市 + 时间”（如“北京 2026年2月10日 天气”）； - 核心信息：温度（默认摄氏度）、天气状况（晴/雨/多云等）、湿度、风力； - 结尾补充温馨提示（如“今日有雨，建议带伞”）； 4. 若未提取到城市，主动追问用户确认具体城市。 ---


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: tridefender/multimodal (version 1.0.0)

--- name: Multimodal Analyzer slug: multimodal version: 1.0.0 description: 使用GLM-4.6V模型进行多模态内容理解（图片、视频、文档） --- # Multimodal Understanding Skill 使用智谱GLM-4.6V模型理解图片、视频、文档内容。 ## 功能 - 图片理解：OCR、场景分析、物体检测、属性识别 - 视频理解：内容摘要、关键帧分析 - 文档理解：PDF、复杂表格解析 - 深度思考模式：开启后进行深层推理分析 ## 使用方式 ``` 理解这张图片：[图片URL或本地路径] 分析这个视频：[视频URL] 这个PDF讲什么：[PDF URL] ``` ## 技术细节 - 模型：GLM-4.6V (106B, 128K上下文) - API端点：https://open.bigmodel.cn/api/paas/v4/chat/completions - 需要ZHIPU_API_KEY环境变量 ## 限制 - 不支持同时处理图片+视频+文件（只能选一种模态） - 视频URL需要公网可访问 ## 调用脚本 调用 `scripts/analyze.py` 进行分析： ```bash python scripts/analyze.py --type image|video|file --input <url_or_path> --prompt "你的问题" ``` 参数： - `--type`: 输入类型 (image/video/file) - `--input`: URL或本地文件路径 - `--prompt`: 分析提示词 - `--thinking`: 启用深度思考模式 - `--stream`: 流式输出

path: _meta.json
content: slug: multimodal
version: 1.0.0

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: itzsubhadip/universal-video-downloader (version 1.0.2)

--- name: universal-video-downloader description: Download videos from YouTube, Instagram, TikTok, Twitter/X, and 1000+ other sites using yt-dlp. Supports quality selection and automatic cleanup. Use when a user provides a video link from any platform and wants to download it. metadata: {"openclaw":{"emoji":"🎥","requires":{"bins":["yt-dlp","ffmpeg"]}}} --- # Universal Video Downloader Download videos from almost any platform using the powerful `yt-dlp` tool. ## Features - **Platform Support:** YouTube, Instagram, TikTok, Twitter/X, Facebook, and many more. - **Quality Selection:** Choose from 144p up to 4K/8K resolutions. - **Automatic Cleanup:** Files are deleted from the server immediately after successful upload to the chat. - **Smart Merging:** Automatically merges high-quality video and audio streams into a single MP4 file. ## Workflow 1. **Trigger:** User sends a video link (e.g., YouTube, Instagram). 2. **Information Gathering:** The agent uses `scripts/download.py info` to fetch available qualities and the video title. 3. **User Choice:** The agent presents resolutions to the user and asks which one they prefer. 4. **Download:** Once selected, the agent runs `scripts/download.py download` with the specific Format ID. 5. **Delivery:** The agent sends the resulting file using the `message` tool with `filePath`. 6. **Cleanup:** The agent **must** delete the file from disk using `rm` immediately after the message is successfully sent to save disk space. ## Usage for Agents ### 1. Fetch Video Info ```bash python3 scripts/download.py info "URL" ``` ### 2. Download Specific Format ```bash python3 scripts/download.py download "URL" "FORMAT_ID" ``` ## Safety & Storage - This skill is intended for temporary processing. - **CRITICAL:** Always delete the downloaded file after the user receives it to maintain disk space.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: unisound-llm/unisound-glucose-monitor-record (version 1.0.0)

--- name: med-patient-glucose-monitor-record description: 病人端慢病管理血糖监测记录。参考 Glucosio 的 blood glucose logging 部分，构建慢病管理基础记录能力。 metadata: { "openclaw": { "emoji": "🩸" } } --- # 血糖监测记录 概述 ---- 本 skill 对应：病人端 / 慢病管理 / 血糖监测记录。 要求：慢病管理基础记录能力。 来源核验 -------- - 匹配来源：Glucosio - 来源类型：公开开源 Android App - 来源链接：https://f-droid.org/packages/org.glucosio.android/ - 匹配结论：匹配。Glucosio 明确面向糖尿病管理，包含 blood glucose 记录、HbA1c、目标范围、提醒、图表、CSV 导入导出等能力。 参考部分 -------- 只参考 Glucosio 的 **blood glucose logging** 部分： - 血糖值记录 - 血糖测量类型区分 - 血糖目标范围 - 历史记录与趋势展示 - CSV 导入导出思路 不参考部分 ---------- - 不参考 Glucosio 的完整 Android App 架构 - 不参考 Google Drive 备份 - 不参考研究数据上报 - 不扩展到血压、胆固醇、体重等非本 skill 能力 构建方式 -------- OpenClaw 中应构建为一个独立的记录型 skill： - 输入患者本次血糖记录信息 - 识别并结构化血糖值、单位、测量时间、测量类型 - 输出标准血糖记录 JSON - 可追加输出简短自然语言确认 建议输入字段 ------------ - `value`：血糖值 - `unit`：单位，如 `mmol/L` 或 `mg/dL` - `measure_type`：测量类型，如空腹、餐后、随机 - `measured_at`：测量时间 - `note`：备注 建议输出字段 ------------ - `skill`：`血糖监测记录` - `record_type`：`blood_glucose` - `value` - `unit` - `measure_type` - `measured_at` - `note` 医疗边界 -------- 本 skill 只做血糖记录，不做诊断，不替代医生判断。 快速开始 -------- 从本 skill 目录执行： ```bash python3 scripts/run.py --input input.json --output output.json --appkey YOUR_KEY ``` 最小输入示例 ------------ ```json { "value": 6.8, "unit": "mmol/L", "measure_type": "空腹", "measured_at": "2026-04-29 08:00", "note": "" } ``` 输出约定 -------- 输出 UTF-8 JSON，采用统一格式： ```json { "skill": "技能名称", "status": "ok", "data": { /* 结构化数据 */ }, "text": "API 生成的 Markdown/自然语言内容，OpenClaw 直接渲染给用户" } ``` - `data`：本地预处理得到的结构化数据 - `text`：内部医疗大模型生成的自然语言解读/分析/提醒，Markdown 格式 支持的输入格式 -------------- 除 JSON 外，还支持以下格式（通过 `--input-type` 自动检测或手动指定）： | 格式 | 说明 | |------|------| | JSON | 默认，直接读取结构化输入 | | CSV / XLSX / XLS | 表格数据，按列头自动映射字段 | | TXT / MD | key:value 文本格式（支持中文/英文字段名） | | PDF / DOC / DOCX | 文档，提取文本后解析 | | PNG / JPG 等图片 | OCR 提取文本后解析 | 文本格式示例 ----------- ``` 血糖值：6.8 单位：mmol/L 测量类型：空腹 ``` CSV 格式示例 ----------- ``` 血糖值,单位,测量类型 6.8,mmol/L,空腹 ``` 统一入口附加参数 ---------------- - `--input-type auto|pdf|doc|docx|xls|xlsx|csv|txt|json`：输入类型；默认 `auto`。 - `--sheet STRING`：读取 Excel 时指定 sheet（可选）。 - `--encoding STRING`：`txt/csv` 编码（默认：`utf-8`）。 - `--save-prepared`：保存预处理后的 JSON，便于调试。 - `--appkey STRING`：**必填**。调用内部医疗大模型的鉴权 key，由平台分配。 依赖 ---- ### 运行环境 - Python 3.7+ ### Python 第三方包（可选，按输入格式需要） | 包名 | 用途 | 必要条件 | |------|------|---------| | `openpyxl` | 读取 `.xlsx` 文件 | 输入为 xlsx 时必须 | | `pypdf` | 提取 PDF 文本 | 输入为 pdf 时必须 | ### 外部工具（可选，按输入格式需要） | 工具 | 用途 | 必要条件 | |------|------|---------| | LibreOffice (`soffice`) | 转换 `.doc` / `.xls` | 输入为 doc/xls 时必须 | | `pdftotext`（poppler-utils） | 提取 PDF 文本 | 输入为 pdf 且未安装 pypdf 时 | | `tesseract`（含 chi_sim+eng） | 图片 OCR | 输入为图片时必须 | > 仅使用 JSON 输入时，无需安装任何第三方包或外部工具。 模型配置 -------- 本 skill 执行时通过内部医疗大模型进行推理： - endpoint：`https://maas-api.hivoice.cn/v1/chat/completions` - model：`u1-insuremed` - 协议：OpenAI Chat Completions（兼容标准 /v1/chat/completions） - 鉴权：通过 `--appkey` 参数传入 Bearer token，由用户在 OpenClaw 中调用时提供 > 本 skill 强制走 API 推理，无本地透传模式。


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: openlang-cn/open (version 1.0.0)

--- name: open description: General helper for opening things from the command line (files, folders, URLs, repositories, docs). Use when the user wants to quickly open something they mentioned, and choose the right OS-specific command to launch it. --- # Open Anything This skill helps the agent suggest the **right command or action to open things**: - Files and folders on disk - URLs (web pages, docs, dashboards) - Git repositories in a file explorer - Logs, configs, or other local resources It does not actually execute the commands itself; it tells the user what to run. ## When to Use Use this skill when the user says： - “打开某某文件 / 目录 / 日志” - “帮我打开这个链接 / 页面” - “想在资源管理器里打开这个项目 / 仓库” - “how do I open this from the terminal” and you need to translate that intent into a concrete **open** action. ## OS-specific Conventions ### Windows（PowerShell / CMD） Prefer using `start` for most cases： - **Open a folder in Explorer**： ```powershell start . start "C:\path\to\folder" ``` - **Open a specific file with its default app**： ```powershell start "C:\path\to\file.txt" ``` - **Open a URL in the default browser**： ```powershell start "https://clawhub.ai" ``` If the path might contain spaces, always wrap it in quotes. ### macOS（for reference） If the user is on macOS, suggest： ```bash open . open /path/to/folder open /path/to/file.txt open "https://clawhub.ai" ``` ### Linux（for reference） If the user is on Linux with a desktop environment, suggest： ```bash xdg-open . xdg-open /path/to/folder xdg-open /path/to/file.txt xdg-open "https://clawhub.ai" ``` ## Common Patterns ### Open the current project folder From the project root： - Windows（PowerShell）： ```powershell start . ``` - macOS： ```bash open . ``` - Linux： ```bash xdg-open . ``` ### Open a specific file mentioned by the user If the user says “打开 `logs/app.log`”，assume it is relative to the project root： - Windows： ```powershell start ".\logs\app.log" ``` （路径中有空格时同样加引号。） ### Open a URL from text When the user pastes or mentions a URL： - Windows： ```powershell start "https://example.com" ``` - macOS： ```bash open "https://example.com" ``` - Linux： ```bash xdg-open "https://example.com" ``` ### Open a Git repo in Explorer / Finder If the user wants to “open the repo” visually： - Ensure you are in repo root, then： - Windows： ```powershell start . ``` - macOS： ```bash open . ``` - Linux： ```bash xdg-open . ``` ## Guidelines - **Always respect the user’s OS**：根据用户系统选择 Windows / macOS / Linux 命令。 - **Prefer non-destructive commands**：只负责“打开”，不要顺带修改、删除任何东西。 - **Quote paths and URLs**：凡是可能包含空格或特殊字符的路径与 URL，都加上引号。 - **Clarify ambiguity briefly**：如果用户说“打开它”但上下文里有多个可能的目标，先用一句话确认你指的是哪一个，再给命令。


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