OpenClaw/clawhub-security-signals huggingface dataset:
Skill: keke-skills/video-learner (version 1.0.10)

--- name: video-learner description: Analyze video content and generate a callable Skill. requires: binaries: - python3 - yt-dlp - ffmpeg - whisper - node skills: - douyin-download paths: - ~/.openclaw/workspace/skills/ --- # Video-Learner ## Declared Requirements This skill requires the following tools and access: - **Tools**: python3, yt-dlp, ffmpeg, whisper (Python package), node - **Dependency**: douyin-download (requires node) - **Write path**: ~/.openclaw/workspace/skills/ ## Trigger When the user sends a Douyin/BiliBili/YouTube video link, this skill is invoked. ## Processing Flow 1. Create temp directory in `/tmp/` for video download 2. Download video using yt-dlp or douyin-download 3. Extract audio using ffmpeg 4. Transcribe audio to text using Whisper (local) 5. Analyze text content using the agent's LLM capability 6. Display analysis results to user 7. After user confirmation, generate SKILL.md to ~/.openclaw/workspace/skills/<new-skill-name>/ 8. Delete temp video files after processing ## Security Notes - Whisper: Runs locally, no audio sent to external services - Content analysis: Uses the agent's own model capability - Temp files: Automatically cleaned up after processing - User confirmation: Generated new Skill requires explicit user approval - Generated Skills are stored in ~/.openclaw/workspace/skills/, can be deleted anytime ## Limitations - Only processes user-provided video links - Does not proactively fetch other content - Does not store video files


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sunba666/simple-concise-responder (version 1.0.0)

--- name: simple-concise-responder version: 1.0.0 description: Generate simple, easy-to-understand, and concise responses. Use when the user asks for brief, clear, to-the-point answers without unnecessary elaboration. Triggers on phrases like '简单易懂', '精辟', '简洁', '简单说', '一句话', '概括' or when brevity is explicitly requested. --- # 简单精辟回答 ## 核心原则 **三句话原则**： 1. **是什么** - 直接给出答案 2. **为什么** - 一句话解释原因（可选） 3. **怎么做** - 一句话给出行动建议（可选） ## 回答结构 ### 1. 先给结论 ``` ❌ 不好："关于这个问题，我们需要从多个角度来分析..." ✅ 好："答案是：XXX" ``` ### 2. 控制长度 - 简单问题：1-2句话 - 一般问题：3-5句话 - 复杂问题：分段，每段不超过3句话 ### 3. 去掉冗余 删除这些词： - "首先/其次/最后" - "需要注意的是" - "简单来说" - "实际上" - 重复的形容词 ## 示例对比 **用户问**：什么是区块链？ ❌ 冗长版： > 区块链是一种分布式账本技术，它通过密码学原理将数据区块按照时间顺序相连，形成一个不可篡改的链条。这种技术具有去中心化、透明性、安全性等特点，在金融、供应链、医疗等领域有广泛应用... ✅ 精辟版： > **区块链** = 多人共同记账的公共账本，记上的账无法篡改。 > > 用途：数字货币、合同存证等需要信任的场景。 ## 执行检查清单 回答前检查： - [ ] 能否用一句话概括？ - [ ] 有没有废话可以删？ - [ ] 用户能一眼看懂吗？ ## 特殊情况 - **技术问题**：给类比，不用术语 - **选择问题**：直接说选哪个，再简单说原因 - **操作问题**：步骤不超过3步，用序号 - **复杂问题**：分点，每点一行


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: whatsonyourmind/oraclaw-ensemble (version 1.0.0)

--- name: oraclaw-ensemble description: Multi-model consensus for AI agents. Combine predictions from multiple LLMs, models, or sources into a mathematically optimal consensus. Auto-weights by historical accuracy. version: 1.0.0 metadata: openclaw: requires: env: - ORACLAW_API_KEY primaryEnv: ORACLAW_API_KEY emoji: "🤝" homepage: https://oraclaw.dev/ensemble tags: - ensemble - consensus - multi-model - aggregation - voting - multi-agent price: 0.03 currency: USDC --- # OraClaw Ensemble — Multi-Model Consensus for Agents You are a consensus agent that combines outputs from multiple models or agents into an optimal combined prediction. ## When to Use This Skill Use when the user or agent needs to: - Combine predictions from Claude + GPT + Gemini into one answer - Aggregate forecasts from multiple team members or models - Auto-weight models by their track record (accurate models get more influence) - Detect when models strongly disagree (high entropy = low confidence) - Build multi-agent systems where agents vote on decisions ## Tool: `predict_ensemble` ```json { "predictions": [ { "modelId": "claude", "prediction": 0.72, "confidence": 0.85, "historicalAccuracy": 0.78 }, { "modelId": "gpt", "prediction": 0.68, "confidence": 0.80, "historicalAccuracy": 0.74 }, { "modelId": "gemini", "prediction": 0.45, "confidence": 0.70, "historicalAccuracy": 0.65 }, { "modelId": "analyst", "prediction": 0.80, "confidence": 0.60, "historicalAccuracy": 0.82 } ] } ``` Returns: consensus prediction, per-model weights, entropy (disagreement measure), individual model contributions. ## Rules 1. Provide `historicalAccuracy` when available — the ensemble auto-weights better-calibrated models higher 2. High entropy (>0.7) means models strongly disagree — flag to user before acting 3. Works for both continuous predictions (probabilities) and discrete classifications 4. Combine with `oraclaw-calibrate` to track how the ensemble performs over time 5. Minimum 2 models, but 3-5 is the sweet spot for robust consensus ## Pricing $0.03 per ensemble prediction. USDC on Base via x402. Free tier: 3,000 calls/month.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: renoblabs/forge-python-dev (version 1.0.0)

--- name: forge-python-dev description: Python development agent — automation scripts, API wrappers, CLI tools, data pipelines, web scrapers. Tested code with error handling and docs. Use when you need working Python code, API integrations, data processing, or CLI utilities. --- # Forge — Python Developer Autonomous Python development agent from the AEA Arena fleet. Delivers tested, documented code. ## Services ### Automation Scripts ``` Input: Task description (what to automate) Output: Working Python script with error handling and usage docs Price: 0.005 ETH ``` ### API Wrappers ``` Input: API URL + desired operations Output: Python wrapper library with methods for each endpoint Price: 0.005 ETH ``` ### CLI Tools ``` Input: Tool requirements and expected behavior Output: Python CLI with argparse, help text, error handling Price: 0.005 ETH ``` ### Data Pipelines ``` Input: Source format + target format + transformation rules Output: Working pipeline script (CSV, JSON, YAML, XML) Price: 0.005 ETH ``` ### Web Scrapers ``` Input: Target URL + data to extract Output: Python scraper using requests/re with structured output Price: 0.005 ETH ``` ## Quality Standards - Code runs. Tested before delivery. - Error handling for expected failure modes. - Usage docs: what it does, inputs, how to run. - No hardcoded values — configurable parameters. ## Hire ``` mltl hire --agent 39097 --task "your task description" ``` ## x402 Endpoint Coming soon — direct HTTP 402 payment for instant code generation. ## Part of AEA Arena Forge is one of 16 coordinated agents. For bundled services (code + docs + research), hire through Broker.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mohammadsheikhian/omadeus (version 1.0.1)

--- name: omadeus description: Manage Omadeus entities via the Omadeus REST API. homepage: https://omadeus.com/ metadata: {"clawdbot":{"emoji":"📋"}} --- # Omadeus Skill Manage Omadeus entities directly from Clawdbot. ## Usage All commands use curl to hit the omadeus REST API. ### List nuggets ```bash curl -X LIST -s "https://milestone.xeba.ir/dolphin/apiv1/nuggetviews?take=25&zone=inbox&kind=!task" ``` ## Notes - you should use the custome method names in API calls like that 'list', 'create' ... - entities can be found in the omadeus URL or via the list commands - The API key and token provide full access to your Trello account - keep them secret! - Rate limits: 300 requests per 10 seconds per - endpoints are limited to 100 requests per 900 seconds ## Examples ```bash curl -X LIST -s "https://milestone.xeba.ir/dolphin/apiv1/nuggetviews?take=25&zone=inbox&kind=!task" ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: eg-yks/local-websearch-1 (version 1.0.0)

--- name: searxng description: Search the web using a self-hosted SearXNG metasearch engine. Aggregates Google, Brave, DuckDuckGo, and more without API keys. homepage: https://docs.searxng.org command-dispatch: cli command: python3 {baseDir}/scripts/searxng_search.py metadata: {"moltbot":{"emoji":"🔍","requires":{"bins":["python3"],"env":["SEARXNG_URL"]}}} --- # SearXNG Web Search Privacy-respecting metasearch via your self-hosted SearXNG instance. ## When to use (trigger phrases) Use this skill when the user asks: - "search the web for..." - "look up..." / "find information about..." - "what is..." (when current info needed) - "research..." / "search for..." - "google..." (redirect to privacy-respecting search) ## Quick start ```bash python3 {baseDir}/scripts/searxng_search.py "your query" python3 {baseDir}/scripts/searxng_search.py "query" --count 10 python3 {baseDir}/scripts/searxng_search.py "query" --lang de ``` ## Setup Set `SEARXNG_URL` environment variable: ```bash export SEARXNG_URL="http://your-searxng-host:8888" ``` ## Flags | Flag | Default | Description | |------|---------|-------------| | `-n`, `--count` | 5 | Results to return (1-20) | | `-l`, `--lang` | auto | Language code (en, de, fr, es, etc.) | ## Output Returns JSON: ```json { "query": "search terms", "count": 5, "results": [ {"title": "...", "url": "...", "description": "...", "engines": ["google", "brave"], "score": 1.5} ] } ``` ## Notes - No API keys needed—SearXNG aggregates upstream engines - Results include source engines for transparency - Scores indicate relevance (higher = better) - For news, add "news" to query or use `--lang` for regional results


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mi-fan/answer-box (version 1.0.0)

--- name: answer-box slug: answer-box description: 答案盒子 - 默念问题即获温暖正向建议，适配日常解压、轻决策、情绪舒缓、培训演示等场景，6大分类定制化暖心回复 user-invocable: true version: 1.0.0 category: 生活工具 tags: [正能量, 解压, 决策参考, 情绪舒缓, 正念] --- # Answer Box · 答案盒子 **默念问题，一键获取专属温暖正向建议** 一款专为日常困惑、情绪内耗、轻量决策设计的治愈系Skill，无需复杂操作，心中想好问题调用技能，即可随机获得一句贴合场景的正向回复，温柔化解纠结、舒缓焦虑。 ## 🌟 核心特性 ✅ **6大分类精准适配**：覆盖「肯定鼓励、理性谨慎、行动推动、心态调节、轻松幽默、中性指引」，贴合日常90%以上的使用场景 ✅ **纯正向无负面**：所有回复均为暖心设计，拒绝消极导向，兼顾理性建议与情绪安慰 ✅ **极简操作**：无需输入/配置，默念问题后调用技能即可，一秒获取答案 ✅ **无重复高实用**：60条原创回复，重复调用低概率撞款，适配多次使用需求 ## 📌 核心回答分类&场景示例 所有回复按使用场景精准分类，调用时随机匹配，兼顾实用性与治愈性： | 回答分类 | 适用核心场景 | 典型回复示例 | | :--- | :--- | :--- | | 肯定鼓励 | 决策确认、增强信心、想做某事需勇气 | 相信你的判断。/ 时机刚好。/ 放手去做吧。 | | 理性谨慎 | 重大决策、风险评估、纠结是否行动 | 再观察一段时间。/ 三思而后行。/ 多准备再行动。 | | 行动推动 | 拖延犹豫、想做不敢做、需要迈出第一步 | 先迈出第一步。/ 用行动代替纠结。/ 现在就开始。 | | 心态调节 | 焦虑内耗、自我怀疑、情绪低落 | 放轻松，别焦虑。/ 别内耗，值得更好。/ 一切都会过去。 | | 轻松幽默 | 日常小烦恼、轻微纠结、解压消遣 | 问题不大，能搞定。/ 别慌，小场面。/ 你已经很棒啦。 | | 中性指引 | 无明确倾向、需要方向感、想顺其自然 | 听从内心声音。/ 交给时间来验证。/ 做好自己就行。 | ## 🎯 适用场景 ### 日常个人使用 1. **职场解压**：加班焦虑、工作纠结时，默念「如何调整状态」获安慰 2. **轻量决策**：纠结「要不要尝试新方案」「是否开始学新技能」获指引 3. **情绪舒缓**：emo、内耗时，默念「怎么缓解不开心」获暖心回复 4. **日常鼓励**：迷茫、没动力时，随手调用获正向心理暗示 ### 团队/培训使用 1. **培训演示**：作为正念/情绪管理培训的轻量互动工具，提升参与感 2. **团队破冰**：团建/小组活动时使用，化解尴尬，增加轻松氛围 3. **办公小工具**：团队成员纠结小问题时，用它获得随机正向建议 ## 🚀 技能使用方式 ### 基础调用（平台通用） 1. 找一个安静的环境，**心中清晰默念你的问题**（如：如何缓解焦虑？要不要接受新机会？） 2. 在Skill平台**语音/文字调用「answer-box」或「答案盒子」** 3. 系统自动返回**1条随机定制化暖心建议**，重复调用可获取不同答案 ### 适配说明 - 支持重复无限次调用，每次随机匹配60条回复中的1条 - 无需输入问题，纯默念即可，适配所有需要轻量建议的场景 - 无设备/环境限制，平台支持的场景均可调用 ## 📝 技能说明 1. 本技能所有回复均为原创正向内容，无任何消极/负面导向 2. 回复仅作**轻量参考/情绪安慰**，不构成专业决策建议 3. 技能无后台数据收集，仅本地随机匹配回复，保护隐私


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jeffli2002/jeffli-wechat-article (version 1.0.0)

--- name: wechat-article description: 微信公众号文章抓取工具。将微信公众号文章转换为 Markdown 格式，支持图片本地下载。当用户提到抓取微信公众号文章、提取公众号内容、爬取微信文章时触发。 --- # 微信公众号文章抓取 将微信公众号文章转换为 Markdown 格式，支持图片本地下载。 ## 脚本位置 - 主程序：`scripts/main.py` - MCP Server：`scripts/mcp_server.py` ## 快速使用 ```bash cd ~/.openclaw/workspace/skills/wechat-article python3 scripts/main.py "文章URL" -o /root/.openclaw/workspace/output ``` ## 参数 | 参数 | 说明 | |------|------| | `-o DIR` | 输出目录（默认 ./output） | | `-v` | 调试日志 | | `--no-images` | 不下载图片，保持远程 URL | | `--force` | 覆盖已存在文件 | | `--no-headless` | 显示浏览器（用于处理验证码） | ## 输出结构 ``` output/ └── 文章标题/ ├── 文章标题.md └── images/ ├── img_001.jpg └── ... ``` ## 注意事项 1. 验证码：遇到验证页面时加 `--no-headless` 手动处理 2. 反爬：微信有频率限制，建议间隔操作 3. 图片失败：保留远程 URL，可用 `--force` 重试 ## 依赖 - camoufox - markdownify - beautifulsoup4 - httpx - aiohttp

path: scripts/requirements.txt
content: camoufox[geoip]
markdownify
beautifulsoup4
httpx
mcp


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: runapi-ai/runapi-runway-aleph (version 0.2.4)

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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xueyetianya/proj-builder (version 1.0.0)

# builder **Project Scaffold Generator** — Instantly generate well-structured project skeletons for Node.js, Python, and Go. Get a ready-to-code directory layout with sensible defaults in seconds. ## Commands | Command | Description | Example | |---------|-------------|---------| | `list` | List all available project templates | `list` | | `init` | Initialize a project scaffold in a directory | `init node my-api` | | `generate` | Print the file contents for a specific template file | `generate python main.py` | ## Usage ```bash bash script.sh list bash script.sh init <template> <project-name> bash script.sh generate <template> <filename> ``` ## Supported Templates | Template | Language | Description | |----------|----------|-------------| | `node` | JavaScript / Node.js | Express REST API with basic routing | | `node-cli` | JavaScript / Node.js | CLI tool with commander.js | | `python` | Python 3 | Python package with src layout | | `python-flask` | Python 3 | Flask REST API | | `go` | Go | Go module with cmd/internal layout | | `go-cli` | Go | CLI tool with cobra | ## Requirements - `bash` >= 4.0 - `python3` >= 3.7 (for `init` command file writing) - No external packages required ## Examples ``` $ bash script.sh list 📦 Available Project Templates ────────────────────────────────────── node Node.js Express REST API node-cli Node.js CLI tool python Python package (src layout) python-flask Python Flask REST API go Go module (cmd/internal) go-cli Go CLI tool with cobra Usage: bash script.sh init <template> <project-name> ``` ``` $ bash script.sh init python my-project 🏗 Scaffolding: python → my-project/ ────────────────────────────────────── ✅ my-project/src/my_project/__init__.py ✅ my-project/src/my_project/main.py ✅ my-project/tests/__init__.py ✅ my-project/tests/test_main.py ✅ my-project/pyproject.toml ✅ my-project/README.md ✅ my-project/.gitignore ✨ Done! Next steps: cd my-project python3 -m venv venv && source venv/bin/activate pip install -e . ``` ``` $ bash script.sh generate go main.go package main import ( "fmt" "log" "net/http" ) ... ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: drones277/tg-image-sender (version 1.0.0)

--- name: tg-image-sender description: Send test or generated images directly to Telegram chats using the message tool with Picsum.photos URLs or custom media. Use when the user requests to 'send photo', 'generate image here in TG', or show/test images in Telegram (e.g., 'пришли фото', 'покажи картинку'). --- # TG Image Sender ## Quick Usage Call the `message` tool directly: ``` message action=send channel=telegram media="https://picsum.photos/800/600?random=1" caption="Test image 🦞" ``` - **Size**: Adjust width/height, e.g., `https://picsum.photos/400/300` - **Seed**: `https://picsum.photos/800/600?random=1234` for reproducible. - **Real image**: Replace with actual URL/media path. - **Caption**: Optional description. ## Examples - Random photo: `media="https://picsum.photos/800/600?random=1"` - Specific: `media="https://picsum.photos/seed/cat/800/600"` After sending, use `NO_REPLY` to avoid duplicate text. ## Workflow 1. Match user request for TG image. 2. Generate Picsum URL or use provided. 3. Send via `message` tool. 4. NO_REPLY. No scripts needed—pure tool call.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hongruiji/boc-init (version 1.0.1)

--- name: boc-init description: 博云BOC容器平台 部署机初始化工具。自动完成部署机环境检查、部署包校验、解压和 bocctl init 初始化。使用场景：用户需要初始化 BOC容器平台 部署机时使用。 version: 1.0.1 icon: 🚀 --- # BOC 部署机初始化 自动化完成 博云BOC容器平台 部署机的初始化工作。 ## 输入参数 | 参数 | 说明 | 示例 | |------|------|------| | deploy_pkg_dir | 部署包和校验文件所在目录 | /opt | | deploy_pkg_file | 部署包文件名 | BOC.tar.gz | | ssh_host | 部署机IP | <部署机IP> | | ssh_port | SSH端口 | 22 | | ssh_user | SSH用户名 | root | | ssh_password | SSH密码 | Password | ## 工作流程 ### 1. 环境检查 连接部署机并检查： - 主机名 - 操作系统版本 - 内核版本 - DNS配置 - 磁盘空间（/var 目录至少50GB） - 部署包是否存在 ### 2. 部署包校验 执行 SHA256 校验： ```bash nohup cd <deploy_pkg_dir> && sha256sum <deploy_pkg_file>.sha256 > /tmp/sha256sum-<deploy_pkg_file>.txt ``` 定期检查校验结果 ```bash cat /tmp/sha256sum-<deploy_pkg_file>.txt ``` 判断标准: 结果中有 "<deploy_pkg_file>: 成功" ### 3. 解压部署包 **重要**：解压前需先清理旧目录，避免残留文件导致问题： ```bash # 解压前先清理旧目录 cd <deploy_pkg_dir> test -d BOC_k8s_noarch && rm -rf BOC_k8s_noarch # 后台执行解压（30GB文件预计15-20分钟） nohup tar -xzf <deploy_pkg_file> > /tmp/unpack.log 2>&1 & # 定期检查解压，通过进程判断 ## 1 检查是否有以下进程，有就代表还在解压 ps aux |grep 'tar -xzf <deploy_pkg_file>' ## 2 没有就再检查/opt/BOC_k8s_noarch 占用空间正常应该在 31GB du -sh /opt/BOC_k8s_noarch ``` ### 4. 验证解压结果 解压后的正确目录结构： ``` /opt/BOC_k8s_noarch/ ├── bocctl # 主程序 ├── bocctl_lib # 库文件 ├── images # 镜像文件 ├── packages # 安装包 ├── playbooks # Ansible playbook ``` ### 5. 执行 bocctl init 后台执行初始化（预计15-20分钟）： ```bash cd <deploy_dir> nohup ./bocctl init > /tmp/bocctl_init.log 2>&1 & # 定期检查bocctl 进程是否存在，如存在代表init 还在进行 # 也一块检查执行日志 pa aux |grep bocctl tail -n 20 /opt/BOC_k8s_noarch/log/bocctl.log ``` ### 6. 验证初始化完成 **判断标准**：使用 `nerdctl ps` 检查以下两个容器是否正常运行： ```bash nerdctl -n k8s.io ps | grep -E "(yum_registry|bocloud_deploy_registry)" ``` 预期输出应包含： - `yum_registry` 容器 - 运行中 - `bocloud_deploy_registry_k8s` 容器 - 运行中 ## 使用示例 ``` 请使用 boc-init 初始化部署机： - 部署包目录：/opt - 部署包文件：BOC.tar.gz - 部署机IP：10.50.6.181 - SSH端口：22 - SSH用户：root - SSH密码：password ``` ## 输出 - 部署机初始化完成状态 - 初始化后关键组件状态： - ansible 版本 - nerdctl 容器运行状态（yum_registry 和 bocloud_deploy_registry_k8s） - containerd 服务状态 ## 常见问题 ### Q: 解压后目录结构异常 A: 重新执行解压，确保先删除旧目录 ### Q: bocctl init 卡住不动 A: 检查是否有 yum 进程卡住，如有则杀掉后重试 ### Q: 忽略./bocctl 参数参考 A：忽略./bocctl 参数参考，只使用./bocctl init 。不需要远程初始化部署，只用本地初始化。 ### Q: 本地执行前置条件 A： 需要安装node 才能使用rssh2 skill 需要安装python 才能使用ssh-exec skill windows 系统需要安装git for windows ### Q: ssh 使用密码连接问题解决 A: 如果安装了python ，优先使用ssh-exec skill 如安装了node ，优先使用rssh2 skill 先检查操作系统中是否有sshpass ，没有就参考以下处理： windows下sshpass 从 https://github.com/xhcoding/sshpass-win32/releases/download/v1.0.7/sshpass.exe 下载使用 Linux 下使用pip install sshpass -g 进行安装


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: li-chi/local-skill (version 0.2.0)

--- name: local-skill version: 0.2.0 description: "Local skill repository for publish and pre-publish inspection. Provides inspectSkill and summarizeSkill utilities." license: MIT author: Acme --- # local-skill A skill for local inspection and release workflows before publishing. ## Usage ```js const { inspectSkill, summarizeSkill } = require('./src'); inspectSkill(); // returns { name, version, status } summarizeSkill(); // returns a human-readable summary string ``` ## Release checklist 1. `bash scripts/check.sh` 2. `bash scripts/publish.sh --dry-run` 3. Review metadata 4. `bash scripts/publish.sh`

path: src/metadata.js
content: 'use strict';

module.exports = {
  name: 'local-skill',
  version: '0.2.0'
};


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