OpenClaw/clawhub-security-signals huggingface dataset:
Skill: amirbrooks/task (version 0.1.0)

--- name: task description: Tasker docstore task management via tool-dispatch. Use for task lists, due today/overdue, week planning, add/move/complete, or explicit /task commands. user-invocable: true disable-model-invocation: false command-dispatch: tool command-tool: tasker_cmd command-arg-mode: raw metadata: {"clawdbot":{"emoji":"🗂️"}} --- Route task-related requests to `tasker_cmd` (raw args only, no leading `tasker`). - For natural language, translate the request into CLI args. - For `/task ...`, pass the args through unchanged. - Prefer human-readable output. Avoid `--stdout-json`/`--stdout-ndjson` unless explicitly requested. - For chat-friendly output (Telegram/WhatsApp), add `--format telegram`. Use `--all` only when done/archived are explicitly requested. - This is the natural-language profile. For slash-only, use `skills/task-slash/`. - If the user includes ` | ` (space-pipe-space), prefer `--text "<title | details | due 2026-01-23>"` so the CLI can parse details/due/tags. Only split on explicit ` | ` to avoid corrupting titles. - Do not guess separators like "but" or "—"; only split on explicit ` | `. - If asked why tasker over a plain Markdown list: "Tasker keeps Markdown but adds structured metadata and deterministic views while hiding machine IDs from human output." - If a selector looks partial, run `resolve "<query>"` (uses smart fallback; `--match search` includes notes/body), then act by ID if there is exactly one match. Never show IDs in human output. - For notes, prefer `note add <selector...> -- <text...>` to avoid ambiguity; without `--`, tasker will attempt to infer the split. Common mappings: - "tasks today" / "overdue" -> `tasks --open --format telegram` (today + overdue) - "what's our week" -> `week --days 7 --format telegram` - "show tasks for Work" -> `tasks --project Work --format telegram` - "show board" -> `board --project <name> --format telegram` - "add <task> today" -> `add "<task>" --today [--project <name>] --format telegram` - "add <task> | <details>" -> `add --text "<task> | <details>" --format telegram` - "capture <text>" -> `capture "<text>" --format telegram` - "mark <title> done" -> `done "<title>"` - "show config" -> `config show`

path: README.md
content: # /task skill (tasker docstore)

Profiles:
- Natural language (this folder): NL + `/task` with `disable-model-invocation: false`
- Slash-only: copy `skills/task-slash/` into your skills folder as `task/` (low-bloat)

This skill uses:
- `command-dispatch: tool` for deterministic behavior

It expects:
- plugin tool `tasker_cmd` allowlisted (recommended)
- `tasker` CLI available via plugin `binary` config, `TASKER_BIN`, or PATH

See `docs/CLAWDBOT_INTEGRATION.md` at repo root for end-to-end setup.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: jianeasy/aupu-knowledge-base (version 1.0.1)

--- name: aupu-knowledge-base description: 当用户提及任何与本地文档、知识库、参考资料、历史记录、配置文件、说明文档、内部资料、已有内容、文件创建/读取等相关需求时，必须使用本技能从 /mnt/data 目录中检索或操作文件。 --- # 知识库访问技能（Knowledge Base Access） ## 🚀 触发条件（高优先级） 只要用户表达出以下任一意图，**立即启用本技能**： - 提到“知识库“、“文档”、“文件”、“资料”、“说明”、“手册”、“记录”、“历史”、“配置”、“模板”等词 - 询问“有没有…”、“在哪里找…”、“之前说过…”、“根据…”、“参考…” - 要求“创建一个基于现有内容的文件”、“从文档中提取…”、“总结一下资料” - 涉及项目、产品、流程、规范、政策、日志、数据等**具体实体**，且未提供完整上下文 - 用户请求看似“常识性”，但可能对应内部定制化内容（如“部署步骤是什么？”） > 💡 即使用户**没有明确写出 `/mnt/data` 或文件名**，也应默认尝试在此目录中查找！ ## 🔍 功能说明 所有结构化或非结构化知识均存储于： 📁 **`/mnt/data/`** 系统必须优先从此目录读取真实内容，而非依赖模型内置知识或猜测。 ## ✅ 使用规范 - 自动扫描 `/mnt/data/` 及其子目录（支持 `.pptx`, `.md`, `.txt`, `.json`, `.yaml`, `.csv`, `.pdf` 等） - 若找到多个候选文件，优先选择路径最匹配、文件名最相关的 - 若未找到，回复：“未在本地知识库（/mnt/data）中找到相关内容，请确认文件是否存在。” - **禁止编造**不存在的文档内容 ## 🌟 示例（扩大触发场景） | 用户输入 | 正确响应 | |--------|--------| | “怎么部署服务？” | 检查 `/mnt/data/docs/deployment.md` 或类似文件 | | “有用户手册吗？” | 查找 `/mnt/data/manuals/` 下的 PDF 或 Markdown | | “上次会议纪要发我一下” | 搜索 `/mnt/data/meetings/` 或 `/mnt/data/records/` | | “生成一个 API 调用示例” | 若存在 `/mnt/data/examples/api/`，从中提取模板 | | “公司有哪些部门？” | 检查 `/mnt/data/org/structure.json` 或 `departments.md` | | “写个 README” | 查看 `/mnt/data/templates/README.md` 是否存在 | ## ⚠️ 注意事项 - WebDAV 挂载点必须处于活跃状态 - 文件名和路径区分大小写 - 优先解析结构化格式（JSON/YAML/Markdown），提升准确性 - **宁可多查，不可不查**：不确定时，先查 `/mnt/data`！ --- > 启用后，**任何涉及“信息查询”、“内容生成依据”、“文件操作”的请求，默认关联本技能**。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: zhdryanchang/daily-alive-check (version 1.0.1)

# 还活着么监测服务 独居人群每日签到监测服务，关爱独居安全。 ## 功能介绍 ### 核心功能 **每日签到** - 用户每天签到证明"还活着" - 可添加心情、状态描述 - 支持语音、文字、图片签到 **紧急联系人** - 设置多个紧急联系人 - 分级通知机制 - 支持Telegram、Discord、Email、短信 **自动监测** - 每6小时检查用户状态 - 超过24小时未签到自动告警 - 48小时高危状态通知所有联系人 **签到历史** - 查看签到记录 - 统计分析 - 导出报告 ## API 端点 ### POST /register 注册用户并设置紧急联系人 **请求示例：** ```json { "userId": "user123", "name": "张三", "phone": "13800138000", "emergencyContacts": [ { "name": "李四", "relation": "朋友", "phone": "13900139000", "telegram": "123456789", "priority": 1 } ] } ``` ### POST /checkin 用户每日签到 **请求示例：** ```json { "userId": "user123", "message": "今天状态不错！", "mood": "😊", "location": "在家" } ``` ### GET /status/:userId 查询用户签到状态 **响应示例：** ```json { "userId": "user123", "name": "张三", "lastCheckin": "2026-03-06T10:30:00Z", "hoursSinceLastCheckin": 5, "status": "正常", "consecutiveDays": 15 } ``` ### GET /history/:userId 查看签到历史 **查询参数：** - `days`: 查询天数（默认7天） - `limit`: 返回记录数 ## 配置说明 必需配置： - `SKILLPAY_API_KEY`: SkillPay API密钥 可选配置： - `TELEGRAM_BOT_TOKEN`: Telegram通知 - `DISCORD_WEBHOOK_URL`: Discord通知 - `EMAIL_USER/EMAIL_PASS`: 邮件通知 - `SMS_API_KEY`: 短信通知 ## 告警流程 1. **12小时未签到** → 温馨提醒用户 2. **24小时未签到** → 通知第一紧急联系人 3. **48小时未签到** → 通知所有紧急联系人，标记高危 ## 使用场景 - 独居老人安全监测 - 独居年轻人互相关心 - 抑郁症患者安全保障 - 慢性病患者日常监测 - 独自旅行安全确认 ## 定价 - 0.001 USDT/天 - 自动通过 SkillPay.me 结算 ## 安装 ```bash npm install npm start ``` ## 许可证 MIT


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: amabillis/brave-search-old (version 1.0.0)

--- name: brave-search description: Web search and content extraction via Brave Search API. Use for searching documentation, facts, or any web content. Lightweight, no browser required. --- # Brave Search Headless web search and content extraction using Brave Search. No browser required. ## Setup Run once before first use: ```bash cd ~/Projects/agent-scripts/skills/brave-search npm ci ``` Needs env: `BRAVE_API_KEY`. ## Search ```bash ./search.js "query" # Basic search (5 results) ./search.js "query" -n 10 # More results ./search.js "query" --content # Include page content as markdown ./search.js "query" -n 3 --content # Combined ``` ## Extract Page Content ```bash ./content.js https://example.com/article ``` Fetches a URL and extracts readable content as markdown. ## Output Format ``` --- Result 1 --- Title: Page Title Link: https://example.com/page Snippet: Description from search results Content: (if --content flag used) Markdown content extracted from the page... --- Result 2 --- ... ``` ## When to Use - Searching for documentation or API references - Looking up facts or current information - Fetching content from specific URLs - Any task requiring web search without interactive browsing


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: wangjinhongmy-pixel/tech-explainer (version 1.0.0)

# Tech Explainer - 技术名词通俗解释 ## 功能说明 当用户发来一句或一段包含技术名词的文本时，帮助用户逐一解释其中外行人难以理解的专业术语，用通俗易懂、生活化的比喻让零基础小白也能理解。 ## 触发场景 - 用户发来大模型/AI/编程相关的句子或段落 - 用户问"这句话里的XXX是什么意思" - 用户学习技术时碰到不懂的名词 ## 工作流程 1. **接收用户文本** - 用户发来任意包含技术词汇的句子或段落 2. **识别陌生名词** - 提取所有非通用、非生活化的专业术语 3. **逐一通俗解释** - 用"是什么 → 解决什么问题 → 通俗比喻"的结构解释每个词 4. **自然流畅输出** - 像朋友聊天一样解释，不要列表不要表格 ## 解释原则 ### 三步法（每个词都用这个结构） 1. **它是什么** - 一句话本质定义 2. **它解决什么问题** - 为什么要发明它 3. **通俗比喻** - 用生活中的例子类比（必选！） ### 比喻要求 - 优先用用户熟悉的生活场景 - 目标受众：没有技术背景的学生 - 比喻要精准，不能为了简单而失真 - 一个词一个比喻，不要跳跃 ### 语气要求 - 像朋友聊天，不是上课讲道理 - 可以适当用"就像""相当于""你可以理解为" - 解释完一个词后自然过渡到下一个，不要硬凑列表 ## 解释示例 用户输入： > "大语言模型在预训练阶段会使用Transformer架构来处理文本。" AI 解释： > 这句话里有几个专业词，我一个一个给你拆开讲： > > **大语言模型**：你可以把它想象成一个"超级阅读理解高手"——它读了几亿本书之后，记住了大量的语言规律和知识，所以当你问它问题时，它能根据"见过的套路"来组织回答。就像一个人读了海量的小说之后，写续集也会很自然。 > > **预训练**：相当于让模型"先做通识教育，再做专业培训"。先让它在广泛的文本里学到基本的语言能力，之后再针对具体任务微调。 > > **Transformer**：这是模型处理信息的一种"内部结构"名字。你可以把它理解为一个"超级翻译官"，它能同时看完整句话甚至整段话，理解词语之间的关系，然后决定每个词应该重点关注哪些上下文信息。 > > **架构**：你可以理解为"组织架构"——一个公司有不同部门，模型内部也有不同的模块组合方式，Transformer就是其中一种高效的组合方式。 ## 常见词汇速查 遇到高频词时，可参考 references/common-terms.md 获取已准备好的通俗解释。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nissan/tweet-pipeline (version 1.0.0)

--- name: tweet-pipeline version: 1.0.0 description: Notion-to-Twitter automation — pull approved tweets from a Notion database, schedule one-shot crons for exact post times, and post via X/Twitter OAuth2 API. Use when managing a content calendar in Notion and want automated, precisely-timed tweet posting. metadata: {"openclaw": {"emoji": "🐦", "requires": {"bins": ["python3"], "env": ["NOTION_API_KEY"]}, "primaryEnv": "NOTION_API_KEY", "network": {"outbound": true, "reason": "Reads from Notion API (api.notion.com) for tweet queue. Posts to X/Twitter API (api.x.com) via OAuth2."}}} --- # Tweet Pipeline Automate tweet posting from a Notion content calendar. Draft tweets in Notion, set status to "Approved" with a scheduled time, and this pipeline handles the rest. ## Workflow ``` Notion DB (Tweet Pipeline) ├── Status: Pending → Agent drafts tweet ├── Status: Approved → Heartbeat picks up │ ├── Future time → Schedules one-shot cron │ └── Past due → Posts immediately └── Status: Posted → Done (updated by poster) ``` ## Notion DB Schema | Property | Type | Values | |---|---|---| | Title | title | Tweet text | | Status | select | Pending, Approved, Posted, Failed | | Scheduled | date | ISO datetime with timezone | | Platform | select | Twitter, LinkedIn | | Posted At | date | Filled after posting | ## Usage ```bash python3 scripts/tweet_poster.py # Check and schedule python3 scripts/tweet_poster.py --dry-run # Preview without scheduling ``` ## Key Lessons - **One-shot crons for exact times** — don't batch-post from heartbeat, schedule each tweet individually - **X Free tier:** 1,500 tweets/month, 280 char limit, no media upload via API - **Track scheduled tweets** in a state file to avoid duplicate scheduling ## Files - `scripts/tweet_poster.py` — Scheduler (reads Notion, creates crons) - `scripts/tweet_post_one.py` — Poster (called by each cron job)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: dinghaibin/tee-tool (version 1.0.0)

--- name: tee-tool description: Read from stdin and write to both stdout and files. Use for saving pipe output while still displaying it on screen. --- # Tee - I/O Multiplexer Read standard input and write to both standard output and one or more files simultaneously. Useful for logging command output while monitoring progress. ## Usage ```bash command | tee-tool [options] <file...> ``` ## Options - `-a`: Append to files instead of overwriting - `-i`: Ignore interrupt signals ## Examples ```bash echo "data" | tee-tool output.txt ls -la | tee-tool -a log.txt make 2>&1 | tee-tool build.log ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: unixlamadev-spec/search-bot (version 1.0.0)

--- name: search-bot description: Real-time web search. Returns current results, titles, URLs, and an AI-synthesized summary. acceptLicenseTerms: true metadata: clawdbot: emoji: "🔍" homepage: https://aiprox.dev requires: env: - AIPROX_SPEND_TOKEN --- # Search Bot Real-time web search powered by Brave Search. Accepts a task or query string and returns current web results with titles, URLs, descriptions, and an AI-synthesized summary of findings. Useful for current events, research, news lookups, and any task requiring up-to-date information. ## When to Use - Finding current news or recent events on any topic - Researching products, companies, or public figures - Looking up documentation, tutorials, or technical references - Verifying facts with live web results - Any task where training data cutoff is a limitation ## Usage Flow 1. Provide a `task` string describing what you want to find 2. Optionally set `num_results` (default 8, max 20) 3. AIProx routes to the search-bot agent 4. Returns ranked results plus an AI-written summary with key findings ## Security Manifest | Permission | Scope | Reason | |------------|-------|--------| | Network | aiprox.dev | API calls to orchestration endpoint | | Network | api.search.brave.com | Web search queries (server-side) | | Env Read | AIPROX_SPEND_TOKEN | Authentication for paid API | ## Make Request ```bash curl -X POST https://aiprox.dev/api/orchestrate \ -H "Content-Type: application/json" \ -d '{ "task": "latest developments in AI agent frameworks this week", "spend_token": "$AIPROX_SPEND_TOKEN" }' ``` ### Response ```json { "results": [ { "title": "LangGraph 0.3 Released — New Agent Loop Primitives", "url": "https://blog.langchain.dev/langgraph-0-3", "description": "LangGraph 0.3 introduces native support for multi-agent handoffs...", "age": "2 days ago" } ], "summary": "This week saw significant updates to agent orchestration frameworks. LangGraph released version 0.3 with improved multi-agent coordination. OpenAI published new guidelines for tool-using agents. Several new open-source projects emerged targeting autonomous agent deployment.", "key_findings": [ "LangGraph 0.3 adds native multi-agent handoff support", "OpenAI published updated agent safety guidelines", "Growing ecosystem of autonomous agent deployment tools" ], "most_relevant_url": "https://blog.langchain.dev/langgraph-0-3", "query": "latest developments in AI agent frameworks this week", "result_count": 8 } ``` ## Trust Statement Search Bot submits queries to Brave Search on your behalf. Queries are transient and not stored beyond the transaction. Results are summarized by Claude via LightningProx. Your spend token is used for payment only.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chgy123/hanime1-search (version 1.0.0)

--- name: hanime-search description: 当作者提及hanime是否更新时，或者miuuuu是否更新时，h站是否更新，调用此技能。 --- ## 用法（Windows / PowerShell） 从工作区根目录运行（示例）： ```bash python ".openclaw/workspace/skills/hanime-search/scripts/web_page_title_scraper.py" ``` 常用可选参数： - 等待动态内容加载（秒）： ```bash python ".openclaw/workspace/skills/hanime-search/scripts/web_page_title_scraper.py" ``` - JSON 输出（便于程序消费）： ```bash python ".openclaw/workspace/skills/hanime-search/scripts/web_page_title_scraper.py" ``` - 自定义 CSS 选择器（逗号分隔，按顺序尝试，先命中先用）： ```bash python ".openclaw/workspace/skills/hanime-search/scripts/web_page_title_scraper.py" ``` ## 输出约定


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: roojenkins/uplo-legal (version 1.0.0)

--- name: uplo-legal description: AI-powered legal knowledge management. Search contracts, compliance requirements, legal cases, and policy documents with structured extraction. --- # UPLO Legal — Contract & Compliance Intelligence You have access to organizational knowledge through UPLO, focused on **legal** domain expertise. ## Session Start When you begin a new session, fetch your organizational context: ```bash mcporter call uplo-legal.get_identity_context ``` ## When to Use - Questions about legal policies, procedures, or processes - Looking up domain-specific knowledge and documentation - Finding subject matter experts - Verifying facts against the knowledge base ## Key Tools **Search knowledge:** ```bash mcporter call uplo-legal.search_knowledge query="your question here" ``` **Search with full context (GraphRAG):** ```bash mcporter call uplo-legal.search_with_context query="complex question with org context" ``` **Export org context:** ```bash mcporter call uplo-legal.export_org_context ``` **Get directives:** ```bash mcporter call uplo-legal.get_directives ``` ## Session End Log the conversation: ```bash mcporter call uplo-legal.log_conversation summary="Brief summary" topics='["topic1"]' tools_used='["search_knowledge"]' ``` ## Important - Always cite sources when sharing UPLO information - Respect classification levels - If UPLO doesn't have the answer, say so rather than guessing


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mike47512/modao (version 0.1.0)

--- name: modao-hot-trend description: 注册“墨刀”技能；用于原型项目公开页信息整理与标注摘要。 homepage: `https://modao.cc/` metadata: {"clawdbot":{"emoji":"🧰"}} --- # 墨刀 用途与边界 - 面向公开项目页与分享页的检索与页面/交互摘要 - 不提供项目写入或权限绕过能力 - 仅处理公开页面的轻量信息整理 关键入口 - 主页：https://modao.cc/ - 项目分享页：公开链接 - 帮助中心：站点入口 常见任务 - 页面结构与交互流程摘要 - 元件与标注信息提取 - 资源链接汇总（图层、导出、规范） 数据字段 - 页面名称、交互目标、更新时间、负责人（若展示）、链接 - 元件名称、尺寸/色值/标注信息、资源链接 - 设计规范标题、入口链接 自动化要点 - 页面动态加载，需等待完成后解析 - 仅处理公开分享链接，不进行账号操作 - 频率控制，避免重复访问 示例流程 - 页面摘要：访问项目分享页 → 抽取页面与交互 → 输出结构与流程 - 标注整理：进入页面 → 抽取元件与标注 → 输出字段结构 - 资源汇总：收集导出与规范 → 输出链接集合 合规提示 - 遵守平台与团队信息安全规定 - 不存储或输出受限内容


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sunshine-del-ux/medical-imaging-suite (version 1.0.0)

# Medical Imaging Suite Complete medical imaging solution with OHIF viewer and DICOM segmentation. ## Features - OHIF viewer deployment - DICOMweb integration (tbidea, orthanc, DCM4CHEE) - TotalSegmentator integration - MONAI workflow support - Docker deployment ## Usage ```bash # Deploy full suite ./deploy.sh # Deploy viewer only ./deploy.sh viewer # Deploy segmentation API ./deploy.sh segmentation ``` ## Requirements - Docker - 4GB RAM minimum ## Author Sunshine-del-ux

path: _meta.json
content: {
  "schema_version": "1",
  "name": "medical-imaging-suite",
  "version": "1.0.0",
  "description": "Complete medical imaging solution - OHIF viewer + DICOM segmentation + DICOMweb integration",
  "homepage": "",
  "author": {
    "name": "Sunshine-del-ux",
    "email": "",
    "url": ""
  },
  "repository": "",
  "keywords": ["medical", "imaging", "dicom", "ohif", "segmentation", "healthcare"],
  "license": "MIT",
  "dependencies": {}
}


path: deploy.sh
content: #!/bin/bash
# Medical Imaging Suite Deployment

set -e

MODE="${1:-all}"

echo "🚀 Deploying Medical Imaging Suite..."
echo "📦 Mode: $MODE"

case $MODE in
  all)
    echo "📦 Deploying OHIF Viewer + Segmentation API..."
    # Placeholder for full deployment
    echo "✅ Use ohif-deploy and dicom-segmentation-api skills"
    ;;
  viewer)
    echo "📦 Deploying OHIF Viewer only..."
    echo "✅ Use ohif-deploy skill"
    ;;
  segmentation)
    echo "📦 Deploying Segmentation API only..."
    echo "✅ Use dicom-segmentation-api skill"
    ;;
esac

echo "📚 For full deployment, use individual skills:"
echo "   - ohif-deploy"
echo "   - dicom-segmentation-api"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/ai-intelligent-employee-survey (version 1.0.0)

--- name: ai-intelligent-employee-survey version: 1.0.0 description: 员工调研，问卷 + 分析 + 报告。 metadata: openclaw: emoji: "🤖" requires: bins: [] --- # AI 智能员工调研系统 ## 描述 员工调研，问卷 + 分析 + 报告。 ## 功能 - 问卷设计（可视化设计） - 匿名调研（隐私保护） - 数据分析（自动分析） - 报告生成（自动生成） - 改进跟踪（问题跟踪） ## 定价 - 基础版：¥49/月（5 调研/月） - 专业版：¥199/月（50 调研/月） - 企业版：¥799/月（无限调研） ## 适用场景 - 员工满意度 - 组织诊断 - 意见收集 - 改进跟踪 ## 技术栈 - Python + FastAPI - 问卷系统 - 数据分析 - 报告生成 ## 安装 ```bash git clone https://github.com/openclaw-skills/ai-intelligent-employee-survey cd ai-intelligent-employee-survey pip install -r requirements.txt python app.py ``` --- 创建：2026-03-13 作者：OpenClaw Skills Team


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: baokui/glm-v-model (version 1.0.1)

--- name: glm-v-model description: | 智谱 GLM-4V/4.6V 视觉模型调用技能。用于图像/视频理解、多模态对话、图表分析等任务。 当用户提到：图片理解、图像识别、视觉模型、GLM-4V、GLM-4.6V、多模态分析、看图说话、图表分析、视频理解时使用此技能。 --- # GLM 视觉模型调用 本技能提供调用智谱 AI 的 GLM-4V 和 GLM-4.6V 视觉模型的能力，支持图像理解、视频分析、图表解读等功能。 ## 支持的模型 | 模型 | 说明 | 特点 | |------|------|------| | glm-4v | GLM-4 视觉模型 | 基础视觉理解 | | glm-4.6v | GLM-4.6V 视觉模型 | 更强的视觉理解能力，支持更长上下文 | ## 快速使用 ### 基本图像理解 ```python from zai import ZhipuAiClient import base64 client = ZhipuAiClient([REDACTED_SECRET]") # 读取本地图片并转为 base64 with open("image.jpg", "rb") as f: img_base = base64.b64encode(f.read()).decode("utf-8") response = client.chat.completions.create( model="glm-4.6v", messages=[{ "role": "user", "content": [ {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img_base}"}}, {"type": "text", "content": "描述这张图片"} ] }], thinking={"type": "enabled"} ) print(response.choices[0].message.content) ``` ### 使用图片URL ```python response = client.chat.completions.create( model="glm-4.6v", messages=[{ "role": "user", "content": [ {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}, {"type": "text", "content": "这张图片里有什么？"} ] }] ) ``` ### 多图理解 ```python response = client.chat.completions.create( model="glm-4.6v", messages=[{ "role": "user", "content": [ {"type": "image_url", "image_url": {"url": "图片1 base64 或 URL"}}, {"type": "image_url", "image_url": {"url": "图片2 base64 或 URL"}}, {"type": "text", "content": "比较这两张图片的异同"} ] }] ) ``` ### 视频理解（GLM-4.6V） ```python # 支持理解视频内容 response = client.chat.completions.create( model="glm-4.6v", messages=[{ "role": "user", "content": [ {"type": "video_url", "video_url": {"url": "视频URL"}}, {"type": "text", "content": "描述这个视频的内容"} ] }] ) ``` ## 使用脚本 项目中已包含脚本 `script/infer_glmv.py`，可直接调用： ```python import sys sys.path.append('/Users/guobaokui/.openclaw/workspace_multmodal/skills/glm-v-model/script') from infer_glmv import glm_v # 使用方式 # glm_v(['image.jpg'], '描述图片', 'glm-4.6v') ``` ## 常用场景 | 场景 | Prompt 示例 | |------|-------------| | 图片描述 | "详细描述这张图片的内容" | | 图表分析 | "分析这张图表数据" | | 文字识别(OCR) | "提取图片中的文字" | | 物体识别 | "图片中有哪些物体" | | 场景理解 | "这是什么地方" | | 多图对比 | "比较这两张图片的异同" | | 视频理解 | "总结这个视频的内容" | ## 注意事项 1. **API Key**: 需要智谱 AI 的 API Key，可从 https://open.bigmodel.cn 获取 2. **图片格式**: 支持 JPEG、PNG、WebP 等常见格式 3. **图片大小**: 单张图片建议不超过 10MB 4. **thinking**: 可启用深度思考模式 `thinking={"type": "enabled"}` 5. **计费**: 按 token 计费，图片会转换为 token 消耗


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: showx/win-music-skill (version 1.0.0)

--- name: win-music-skill description: 控制音乐的播放 --- # 音乐播放 ## When to use this skill 需要播放音乐的时候 ## 下一首歌 cmd下执行命令nircmd sendkeypress ctrl+alt+right ## 上一首歌 cmd下执行命令nircmd sendkeypress ctrl+alt+left ## 播放或暂停 cmd下执行命令nircmd sendkeypress ctrl+alt+f5


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