OpenClaw/clawhub-security-signals huggingface dataset:
Skill: badmenfinance/agensi-mcp (version 1.0.0)

--- name: "agensi-mcp" description: "Connect your agent to the Agensi marketplace. Search, discover, and browse AI agent skills from agensi.io directly in your terminal." version: "1.0.0" author: "badmenfinance" type: "integration" category: "productivity" tags: - "mcp" - "marketplace" - "skills" - "agensi" - "skill-md" invocation: "/agensi" difficulty: "beginner" permissions: read: - "none" write: - "none" network: "https://mcp.agensi.io" --- # Agensi MCP — Browse the AI Agent Skill Marketplace Connect your agent to the Agensi skill marketplace (https://agensi.io) via MCP. Search, discover, and browse hundreds of community-built SKILL.md skills without leaving your terminal. ## Setup Add the Agensi MCP server to your agent's MCP configuration: ### Claude Code Add to your Claude Code MCP settings (`~/.claude/config.json` or project-level): ```json { "mcpServers": { "agensi": { "type": "sse", "url": "https://mcp.agensi.io/mcp" } } } ``` ### OpenClaw Add to your OpenClaw MCP configuration: ```json { "mcpServers": { "agensi": { "type": "sse", "url": "https://mcp.agensi.io/mcp" } } } ``` ### Cursor / Other Agents Any agent that supports MCP over SSE can connect using the URL: `https://mcp.agensi.io/mcp` ## Available Tools Once connected, your agent has access to these tools: - **search_skills** — Search the marketplace by keyword, category, or tag. Returns matching skills with names, descriptions, and pricing. - **get_skill** — Get full details about a specific skill including description, compatibility, and install instructions. - **get_popular** — Browse the most installed skills on the marketplace. - **list_categories** — List all skill categories with counts. - **get_creator** — View a creator's profile and their published skills. - **get_skill_requests** — Browse community-requested skills that haven't been built yet. ## Usage Examples Ask your agent: - "Search Agensi for code review skills" - "What are the most popular skills on Agensi?" - "Find testing and QA skills on the marketplace" - "Show me skills by creator Samuel Rose" - "What skills have been requested by the community?" ## About Agensi Agensi (https://agensi.io) is a curated marketplace for SKILL.md skills compatible with Claude Code, OpenClaw, Cursor, Codex CLI, Gemini CLI, and 20+ AI coding agents. Every skill is security-scanned and works across all major agents. Browse the full marketplace: https://agensi.io/skills Become a creator: https://agensi.io/submit


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: omactiengartelle/waifu-gen-skill (version 1.0.0)

--- name: waifu-gen-skill description: Generate waifu generator ai images from text descriptions via the Neta AI image generation API (free trial at neta.art/open). tools: Bash --- # Waifu Generator Generate stunning waifu generator ai images from a text description. Get back a direct image URL instantly. ## Token Requires a Neta API token (free trial at <https://www.neta.art/open/>). Pass it via the `--token` flag. ```bash node <script> "your prompt" --token YOUR_TOKEN ``` ## When to use Use when someone asks to generate or create waifu generator images. ## Quick start ```bash node waifugen.js "your description here" --token YOUR_TOKEN ``` ## Options - `--size` — `portrait`, `landscape`, `square`, `tall` (default: `portrait`) - `--ref` — reference image UUID for style inheritance ## Install ```bash npx skills add omactiengartelle/waifu-gen-skill ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: codeblackhole1024/asset-allocation (version 1.0.1)

--- name: asset-allocation description: Professional investment advisory skill based on CFA framework. From financial diagnosis to portfolio rebalancing. Use when: users ask about investing, wealth management, fund/ETF selection, retirement planning, or asset allocation. Covers: risk assessment, goal-based planning, portfolio construction, product selection, and ongoing rebalancing. --- # Asset Allocation Advisor Professional investment advisory skill based on CFA framework. ## Quick Start When user asks about investment/wealth topics: 1. Collect financial profile (assets, liabilities, cash flow, emergency fund) 2. Assess risk tolerance (capacity + willingness) 3. Define investment goals (must-have, should-have, nice-to-have) 4. Analyze macro environment (economic cycle, valuation) 5. Create strategic asset allocation (SAA) 6. Provide product selection criteria 7. Set risk management rules ## Core Methodology - **CFA IPS Framework** - RRTTLLU principles - **Merrill Lynch Investment Clock** - Macro timing - **Modern Portfolio Theory** - Mean-variance optimization - **Behavioral Finance** - Cognitive bias identification ## Risk Assessment | Level | Type | Max Drawdown | |-------|------|-------------| | 1 | Conservative | <5% | | 2 | Stable | 5%-10% | | 3 | Balanced | 10%-20% | | 4 | Aggressive | 20%-35% | | 5 | Very Aggressive | >35% | ## Asset Allocation Models **Conservative (Level 1)** - Target: 3%-4.5% - Cash: 35% | Bonds: 40% | Hybrid: 15% | Equity: 8% | Gold: 2% **Stable (Level 2)** - Target: 4%-6.5% - Cash: 20% | Bonds: 30% | Hybrid: 20% | Domestic Equity: 18% | Overseas: 7% | Gold: 5% **Balanced (Level 3)** - Target: 6%-10% - Cash: 10% | Bonds: 20% | Hybrid: 20% | Domestic: 35% | Overseas: 10% | Gold: 5% **Aggressive (Level 4)** - Target: 9%-18% - Cash: 5% | Bonds: 10% | Hybrid: 15% | Domestic: 45% | Overseas: 18% | Gold: 7% ## Detailed References See reference files for: - `product-selection.md` - Fund/ETF selection criteria - `rebalancing-protocol.md` - Rebalancing rules - `macro-framework.md` - Macro analysis framework - `behavioral-biases.md` - Behavioral finance biases - `purchase-guide.md` - Detailed purchase instructions for all product types ## Execution Phase When user is ready to buy, ask their preferred platform: - **支付宝/微信** - 基金操作 - **天天基金** - 专业基金平台 - **证券账户** - ETF/股票/REITs - **银行App** - 国债/理财 Then provide step-by-step purchase instructions with specific product names/codes from `purchase-guide.md` ## Report Structure Output comprehensive IPS report: 1. Client Financial Profile 2. Risk Assessment 3. Investment Goals 4. Macro Analysis 5. Strategic Asset Allocation 6. Product Selection Criteria 7. Execution Plan 8. Growth Projection 9. Stress Testing 10. Risk Management Rules 11. Investment Discipline 12. Monitoring Plan ## Disclaimer ⚠️ Investment involves risk ⚠️ Past performance doesn't predict future ⚠️ For reference only, not formal advice


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: wangjiaocheng/innovation-os (version 1.0.1)

--- name: innovation-os author: 王教成 Wang Jiaocheng (波动几何) description: 通用创新操作系统——运用四种AI模式处理能力（模式直用、模式改进、模式迁移、模式构建）为任意领域生成创新方案。模式改进内置10种创新元框架（第一性原理、逆向思维、辩证综合、随机性驱动、涌现生成、演化迭代、系统动力学、约束驱动、故事叙述、游戏化），模式直用的基元可运用所有模式创新且基元重组本身也是创新方式，模式迁移和模式构建均为创新方法。适用于一切需要突破默认路径的领域。触发词：创新、突破、非平庸、模式直用、模式改进、模式迁移、模式构建、第一性原理、逆向思维、辩证综合、随机性驱动、涌现生成、演化迭代、系统动力学、约束驱动、故事叙述、游戏化。 --- # Innovation OS - 通用创新操作系统 创新就是改变默认原有模式。四种模式处理能力从不同维度打破思维惯性，生成非平庸方案。 ## 四维评估 | 维度 | 判断内容 | |------|---------| | 问题明确性 | 是否有可解决的核心问题 | | 可行性基础 | 是否有可依托的资源/能力/条件 | | 范围适度 | 是否过泛或过窄 | | 非平凡性 | 是否能产出非平庸结果 | ## 模式速览 | 模式 | 核心 | 适用场景 | |------|------|----------| | 模式直用 | IPO基元运用5种创新方式+基元重组 | 快速多角度扫描 | | 模式改进 | 10种创新元框架深度执行 | 某策略深度挖掘 | | 模式迁移 | 源领域→抽象模式→目标领域 | 跨界借鉴 | | 模式构建 | 维度矩阵强制连接 | 发现新组合 | 完整定义（核心原则/通用四维评估标准及领域适配推导规则与15领域示例/四种模式处理能力含各模式提示词原文、10种创新元框架提示词原文、基元嵌入vs独立模式对比表、迁移4类型+源领域质量检验标准、构建完整9步执行流程、工作流入口与Step1-4方案深化、事实纪律、方案输出格式模板、领域场景适配指引15领域完整配置、工作流总览表）在 `references/innovation-os.md`。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wlkqyang-star/zeelin-musk-activity-tracker (version 1.0.0)

--- name: musk-activity-tracker description: 追踪埃隆·马斯克过去10天内的最新动态。用于获取关于他在X（推特）上的帖子、公司（特斯拉、SpaceX、xAI、Neuralink、The Boring Company）的重大公告和媒体采访的精确信息。 --- # 埃隆·马斯克动态追踪技能 本技能旨在系统性地搜集、整理和汇报埃隆·马斯克在过去10天内的各类动态。 ## 核心原则：聚焦X平台，覆盖全公司矩阵 1. **X平台为核心**: 马斯克的主要沟通渠道是X（推特），其个人账号的帖子是最高优先级的信息来源。 2. **公司矩阵全覆盖**: 必须同时监控特斯拉、SpaceX、xAI、Neuralink和The Boring Company的官方动态，因为马斯克的活动与这些公司紧密相关。 3. **时间限定**: 所有搜集的信息必须严格限定在**过去10天内**。 ## 3步信息搜集流程 ### 第1步: 定义关键词并限定时间 在所有搜索中，必须使用搜索引擎的时间筛选工具，将范围设置为“过去10天”。 * **核心关键词**: `Elon Musk`, `马斯克` * **公司关键词**: `Tesla` (特斯拉), `SpaceX`, `xAI`, `Neuralink`, `The Boring Company` * **平台限定搜索**: * `from:elonmusk` (在X/推特高级搜索中使用) * `site:tesla.com` * `site:spacex.com` * `site:x.ai` * `site:neuralink.com` * `site:boringcompany.com` * `site:reuters.com "Elon Musk"` ### 第2步: 查阅特定信源 按以下顺序查阅信源，以确保信息的完整性和准确性。 | 优先级 | 信源类型 | 具体平台/网站 | 搜集重点 | | :--- | :--- | :--- | :--- | | 1 | **主要社交媒体** | X (`@elonmusk`) | 原创帖子、回复、点赞中透露的重要信息。 | | 2 | **公司官方渠道** | 各公司官网及官方X账号 | 重大产品发布、财报、人事变动、技术突破。 | | 3 | **国际通讯社** | Reuters, Bloomberg, Associated Press (AP) | 对其公司重大新闻的客观报道，用于事实核查。 | | 4 | **科技与财经媒体** | TechCrunch, The Verge, Wall Street Journal | 深度分析、采访报道、行业影响。 | ### 第3步: 结构化汇报 最终的汇报必须清晰、有条理，并按以下主题对信息进行分类： 1. **X平台言论**: 总结在X上的关键帖子、回复和重要互动。 2. **特斯拉 (Tesla) 动态**: 涉及生产、交付、股价、自动驾驶和新技术的动态。 3. **SpaceX 动态**: 涉及星舰（Starship）、星链（Starlink）、发射任务和合同的进展。 4. **xAI, Neuralink, The Boring Company 动态**: 简要报告这三家公司的重要进展。 5. **媒体采访与公开露面**: 概括在播客、会议或采访中的核心观点。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codekungfu/douyin-ecommerce (version 0.1.0)

--- name: "抖音电商" description: "该技能介绍如何通过抖音电商实现变现；当你计划从事或优化抖音电商时调用。" --- # 抖音电商 概览： - 场景：以“抖音电商”为核心的可复制变现路径 - 目标：在90天内跑通最小可行闭环，并达成正向现金流 定位与人群： - 适合：具备基础认知或实践经验，愿意持续投入的人/团队 - 不适合：短期套利、违规刷量、无内容/供给能力者 典型渠道： - 抖音小店、精选联盟、抖音团长 - 通用补充：微信/抖音/小红书/闲鱼/淘宝/独立站/社群 变现模型： - 收入结构：主收入 + 辅助收入（广告/佣金/订阅/服务/周边） - 成本结构：获客成本（CAC）+ 供给/履约 + 固定开销 - 核心指标：GMV、毛利率、转化率、留存、LTV/CAC、复购率 执行流程： - 01 选赛道与定位：明确供给、目标用户、差异化与定价 - 02 搭MVP：以最低成本验证一个渠道的有效性 - 03 获取流量：内容分发/投放/合作互推，建立追踪 - 04 转化与交付：标准化流程与模板，保证体验与口碑 - 05 复盘与迭代：每周数据盘点，优化漏斗与供给结构 工具清单： - 生产：文案/设计/剪辑/表格/自动化工具 - 获取：SEO/SEM/信息流/平台任务/达人合作 - 追踪：表格/CRM/数据看板/UTM/埋点 - 履约：工单/客服/交付模板/质检清单 合规与风险： - 遵守平台政策与法律法规，严禁虚假宣传、刷量、侵权 - 明确版权与交付约定，规范合同与发票 常见坑位： - 盲目扩渠道、无数据追踪、定价不清、交付不标准 - 过度依赖单平台、忽视复购与会员化 30日执行计划： - 第1周：定位、选品/供给、搭建MVP与基本素材 - 第2周：上线一个主渠道，跑数据，优化文案与转化 - 第3周：稳定交付流程，建立追踪与看板，启动第二渠道 - 第4周：总结复盘，完善模板与清单，制定90日目标 检查清单： - 明确目标与指标，完成MVP验证，建立复盘与模板库 - 形成可复制的标准作业流程（SOP）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: minsheep/daily-news (version 1.0.0)

--- name: daily-news description: Fetch top news from Baidu, Google, and other sources daily. metadata: openclaw: requires: bins: ["python"] env: ["PYTHONIOENCODING=utf-8"] command-dispatch: tool command-tool: exec command-arg-mode: raw --- # Daily News Skill This skill allows the agent to fetch the daily top news headlines from multiple sources (Baidu, Google Trends, etc.) by running a Python script. ## Instructions To get the daily news summary: 1. Execute the Python script located at `{baseDir}/daily_news.py`. Use the following command: ```bash python "{baseDir}/daily_news.py" ``` 2. The script will output the news in the format requested by the user. 3. Return the script's output directly as the final answer. ## Setup Ensure you have the required Python packages installed: ```bash pip install -r "{baseDir}/requirements.txt" ```

path: requirements.txt
content: beautifulsoup4
requests
feedparser

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chronicuser21/nix-mode (version 1.0.0)

--- name: nix-mode description: Handle Clawdbot operations in Nix mode (configuration management, environment detection). metadata: {"clawdbot":{"emoji":"❄️","requires":{"bins":["nix","bash"],"envs":["CLAWDBOT_NIX_MODE"]},"install":[]}} --- # Clawdbot Nix Mode Skill This skill handles Clawdbot operations specifically when running in Nix mode. ## Nix Mode Specific Features ### Environment Detection - Detect when `CLAWDBOT_NIX_MODE=1` is set - Identify Nix-managed configuration paths - Recognize Nix-specific error messages and behaviors ### Configuration Management - Understand that auto-install flows are disabled in Nix mode - Guide users to proper Nix package management - Explain why certain self-modification features are unavailable ### Path Handling - Recognize Nix store paths - Understand the difference between config and state directories - Handle `CLAWDBOT_CONFIG_PATH` and `CLAWDBOT_STATE_DIR` appropriately ### Troubleshooting - Identify Nix-specific remediation messages - Guide users to proper dependency management via Nix - Explain the read-only Nix mode banner behavior ## Usage Guidelines When operating in Nix mode: 1. Do not attempt to auto-install dependencies 2. Direct users to Nix package management instead 3. Respect the immutable nature of Nix installations 4. Advise on proper configuration practices for Nix environments


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: saschaspoonbill/openai-image-gen-fixed (version 1.0.2)

--- name: openai-image-gen description: Batch-generate images via OpenAI Images API. Random prompt sampler + `index.html` gallery. --- # OpenAI Image Gen Generate a handful of “random but structured” prompts and render them via OpenAI Images API. ## Setup - Needs env: `OPENAI_API_KEY` ## Run From any directory (outputs to `~/Projects/tmp/...` when present; else `./tmp/...`): ```bash python3 ~/Projects/agent-scripts/skills/openai-image-gen/scripts/gen.py open ~/Projects/tmp/openai-image-gen-*/index.html ``` Useful flags: ```bash python3 ~/Projects/agent-scripts/skills/openai-image-gen/scripts/gen.py --count 16 --model gpt-image-1.5 python3 ~/Projects/agent-scripts/skills/openai-image-gen/scripts/gen.py --prompt "ultra-detailed studio photo of a lobster astronaut" --count 4 python3 ~/Projects/agent-scripts/skills/openai-image-gen/scripts/gen.py --size 1536x1024 --quality high --out-dir ./out/images ``` ## Output - `*.png` images - `prompts.json` (prompt ↔ file mapping) - `index.html` (thumbnail gallery)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: spideystreet/workout-track (version 1.0.2)

--- name: workout-track description: Log a strength training session and insert it into the life_db database. Use when the user shares their gym session, exercises, sets, reps, weights, RPE, rest times, session duration, feelings, or asks to record a workout. metadata: {"openclaw":{"requires":{"bins":["uv"]}}} --- # Workout Log Parses strength training sessions from natural or structured input and inserts them into the `sport` schema of `life_db`. ## Connection Credentials are in `~/.openclaw/services/life-db/.env`. ## Workflow ### 1. Parse the user's message Extract from free text or structured input: | Field | Type | Notes | |-------|------|-------| | `session_date` | YYYY-MM-DD | Default: today | | `duration_min` | int | Total session duration | | `feeling` | 1–10 integer | Overall session feeling | | `notes` | string | Optional — program name, injuries, etc. | | `exercises[]` | array | One object per exercise | Each exercise: | Field | Type | Notes | |-------|------|-------| | `exercise_name` | string | Normalize consistently (e.g. "Bench Press", "Squat", "Romanian Deadlift") | | `sets` | int | Number of working sets | | `reps` | int | Reps per set (use average if varied) | | `weight_kg` | float | Working weight — `null` for bodyweight exercises | | `rpe` | float | Rate of perceived exertion (1–10), optional | | `rest_sec` | int | Rest between sets in seconds, optional | | `order_in_session` | int | Order as mentioned | | `notes` | string | Optional — tempo, drop sets, supersets, etc. | ### 2. Confirm with the user Show a summary before inserting: ``` 📅 {DD/MM/YYYY} · Muscu · {duration} min · Feeling {feeling}/10 • {Exercise 1} — {sets}×{reps} @ {weight} kg • {Exercise 2} — {sets}×{reps} bodyweight • … Save? (yes / no) ``` ### 3. Insert Only after confirmation, use the `exec` tool: ```json { "tool": "exec", "command": "bash -c 'set -a; source ~/.openclaw/services/life-db/.env; uv run --project ~/.openclaw {baseDir}/scripts/insert_workout.py <json>'" } ``` Replace `<json>` with the minified JSON payload (no newlines, properly shell-escaped). ### 4. Confirm result **On `OK`** — respond with exactly this format, nothing more: ``` ✅ Muscu · {duration} min · Feeling {feeling}/10 · #{session_id} {comment} ``` `{comment}` rules — **1 line, max 60 chars, no emoji**: - feeling ≥ 8 → short hype line - feeling 5–7 → encouragement - feeling ≤ 4 → grind acknowledgement **On `ERROR`** — report the error as-is, do not retry without user input. ## Examples | User says | Parsed session | |-----------|---------------| | "Gym 1h feeling 8. Bench 4x10 80kg, Squat 4x8 100kg, Curl 3x12 15kg" | 3 exercises, 60 min, feeling 8/10 | | "45 min chest/shoulders. Bench press 5x5 90kg RPE 9, overhead press 4x8 40kg, lateral raises 3x15 10kg" | 3 exercises, 45 min, feeling asked | | "Quick session 30min: pull-ups 4x8 bodyweight, rows 4x10 60kg" | 2 exercises, 30 min, feeling asked |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: minbang930/giphy (version 1.0.7)

--- name: giphy-gif description: Search and send contextual Giphy GIFs in Discord. Use when a user asks for a GIF or when a brief visual reaction (celebration, humor, emotion) improves the flow. --- # Giphy GIF Search Find a relevant GIF from Giphy and send it naturally in Discord. ## Behavior Rules - Send GIFs when explicitly requested. - Also allow proactive GIFs (without explicit request) when the moment clearly fits: celebration, shared humor, or strong emotional beats. - Keep proactive usage occasional (at most one GIF for a moment, avoid back-to-back GIF-only replies). - Prefer text-only in serious or information-dense conversation. - Keep results safe-for-work (`rating=g`). ## API Key (Easy Setup) This skill reads only one variable: `GIPHY_API_KEY`. ### Option A: Temporary (current shell session) ```bash export GIPHY_API_KEY="your-api-key" ``` ### Option B: Persistent for OpenClaw (recommended) Add to `~/.openclaw/.env`: ```bash GIPHY_API_KEY=your-api-key ``` Then restart OpenClaw so the environment is reloaded. ### Validation - If `GIPHY_API_KEY` is present, the skill works. - If missing, ask the user to set it and retry. ## Workflow 1. Build a Giphy Search API URL with user intent as query. 2. URL-encode the query text. 3. Request one result from Giphy. 4. Extract the first GIF page URL from `data[0].url`. 5. Send that URL to Discord. ## API Request Template Use this endpoint shape: `https://api.giphy.com/v1/gifs/search?[REDACTED_SECRET]` ## Output Rule - If a GIF URL is found: send only the URL (Discord auto-embeds). - If no result is found: send a short fallback text and ask for a better keyword. ## Good Query Examples - `happy dance` - `facepalm reaction` - `mind blown` - `awkward silence` ## Fallback Message "I couldn’t find a GIF with the vibe you’re looking for. Could you give me a bit more specific keywords?"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: wang-junjian/llm-deploy (version 1.0.0)

--- name: llm-deploy description: "在 GPU 服务器上部署 LLM 模型服务（vLLM）。支持多服务器配置，自动检查 GPU 和端口占用，一键部署流行的开源大语言模型。" homepage: https://github.com/vllm-project/vllm metadata: { "openclaw": { "emoji": "🚀", "requires": { "bins": ["ssh"] } } } --- # 🚀 LLM 部署技能 在 GPU 服务器上快速部署 vLLM 模型服务。 ## ✨ 功能特点 - 🖥️ **多服务器支持** - 配置多个 GPU 服务器，灵活选择 - 🔍 **自动检查** - 一键检查 GPU 状态和端口占用 - 🤖 **模型库** - 预置流行模型配置 - ⚡ **快速部署** - 简单命令即可启动服务 --- ## 📋 快速开始 ### 1. 配置服务器 创建 `~/.config/llm-deploy/servers.json`： ```json { "servers": { "gpu1": { "host": "gpu1", "user": "lnsoft", "gpu_count": 4, "model_path": "/data/models/llm" }, "my-gpu": { "host": "192.168.1.100", "user": "ubuntu", "gpu_count": 2, "model_path": "/home/ubuntu/models" } }, "default_server": "gpu1" } ``` ### 2. 检查服务器状态 ```bash # 使用默认服务器 llm-deploy check # 指定服务器 llm-deploy check --server gpu1 ``` ### 3. 部署模型 ```bash # 部署预设模型 llm-deploy deploy deepseek-r1-32b # 指定端口 llm-deploy deploy deepseek-r1-32b --port 8112 ``` --- ## 🎛️ 可用命令 ### `check` - 检查服务器状态 检查 GPU 显存和端口占用情况。 ```bash llm-deploy check [--server NAME] [--port PORT] ``` **输出示例：** ``` ✅ GPU 状态正常 - 4 × Tesla T4 (15GB) - 显存占用: 12.6GB/卡 - 温度: 51-55°C ✅ 端口 8111 可用 ``` ### `deploy` - 部署模型 启动 vLLM 模型服务。 ```bash llm-deploy deploy <MODEL_NAME> [--server NAME] [--port PORT] ``` **支持的模型：** - `deepseek-r1-32b` - DeepSeek-R1-Distill-Qwen-32B-AWQ - `llama-3-8b` - Llama 3 8B - `qwen-7b` - Qwen 7B - `mistral-7b` - Mistral 7B ### `list` - 列出可用模型 ```bash llm-deploy list ``` ### `ps` - 查看运行中的服务 ```bash llm-deploy ps [--server NAME] ``` ### `stop` - 停止服务 ```bash llm-deploy stop [--server NAME] [--port PORT] ``` --- ## 🔧 手动使用（无脚本） 如果不想用封装脚本，也可以直接用原始命令： ### 检查 GPU ```bash ssh <user>@<host> nvidia-smi ``` ### 检查端口 ```bash ssh <user>@<host> "lsof -i :<port> 2>/dev/null || echo '端口可用'" ``` ### 部署模型（DeepSeek R1 32B） ```bash ssh <user>@<host> "tmux new-session -d -s vllm ' source /data/miniconda3/etc/profile.d/conda.sh && \ conda activate vllm && \ cd /data/models/llm && \ vllm serve /data/models/llm/deepseek/DeepSeek-R1-Distill-Qwen-32B-AWQ/ \ --tensor-parallel-size 4 \ --max-model-len 102400 \ --dtype half \ --port 8111 \ --served-model-name gpt-4o-mini '" ``` --- ## 📦 添加自定义模型 在 `~/.config/llm-deploy/models.json` 中添加： ```json { "my-model": { "name": "My Awesome Model", "path": "/path/to/model", "tensor_parallel_size": 2, "max_model_len": 8192, "dtype": "half", "port": 8111, "served_model_name": "my-model" } } ``` --- ## ⚠️ 注意事项 1. **部署前检查** - 总是先运行 `check` 确认资源可用 2. **后台运行** - 建议使用 tmux/screen 保持服务运行 3. **端口管理** - 不同模型使用不同端口 4. **显存估算** - 7B 模型约需 8-10GB，32B 约需 10-14GB/卡 --- ## 🔗 相关链接 - vLLM 文档: https://docs.vllm.ai - 模型下载: https://huggingface.co/models - 问题反馈: https://github.com/your-username/llm-deploy-skill --- **由 OpenClaw 社区贡献 🦞**


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ivangdavila/hungarian (version 1.0.0)

--- name: Hungarian description: Write Hungarian that sounds human. Not formal, not robotic, not AI-generated. --- ## The Real Problem AI Hungarian is technically correct but sounds off. Too formal. Too irodalmi (literary). Natives write more casually, with particles and warmth. Match that. ## Formality Default Default register is too high. Hungarian casual is warm. Unless explicitly formal: lean casual. "Szia" not "Jó napot kívánok". "Oké" not "Rendben van". ## Te vs Ön/Maga Critical distinction: - Ön/Maga: formal, strangers, professional - Te: friends, peers, internet, casual - Hungarian internet uses te - Ön online = robotic, distant ## Conjugation Matters Hungarian verbs mark formality: - Definite vs indefinite conjugation - -lak/-lek for "I...you" - Get these right—fundamental to Hungarian ## Particles & Softeners These make Hungarian natural: - Hát: "well" filler ("Hát, nem tudom") - Csak: "just" ("Csak kérdeztem") - Már: emphasis, impatience - Ugye: "right?" tag - Azért: "still", "though" ## Fillers & Flow Real Hungarian has fillers: - Hát, szóval, na - Tudod, érted, nézd - Asszem, szerintem - Mondjuk, viszont ## Expressiveness Don't pick the safe word: - Jó → Szuper, Király, Zsír, Frankó - Rossz → Gáz, Szar, Béna - Nagyon → Tök, Bazi, Irtó ## Common Expressions Natural expressions: - Király!, Zsír!, Szuper! - Semmi gond, Nem para - Komolyan?, Tényleg?, Nocsak - Oké, Ja, Aha ## Reactions React naturally: - Komolyan?, Tényleg?, Mi? - Hú!, Basszus!, Jesszus! - Király!, Szuper!, Zsír! - Haha, lol in text ## Word Order Flexibility Hungarian has flexible word order: - Topic-focus structure - Emphasis through position - Use this for natural emphasis ## Suffixes Hungarian is agglutinative: - Many suffixes on words - Don't break them unnaturally - Házamban (in my house) is one word ## The "Native Test" Before sending: would a Hungarian screenshot this as "AI-generated"? If yes—too formal, no "hát", too stiff. Add "na" and "szóval".


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