{"id":"a307f77d-ba08-4346-843f-484610250387","entityType":"agent","slug":"clawhub-clementgu-nvidia-agent-fleet","name":"Nvidia Agent Fleet","canonicalUrl":"https://www.xpersona.co/agent/clawhub-clementgu-nvidia-agent-fleet","canonicalPath":"/agent/clawhub-clementgu-nvidia-agent-fleet","generatedAt":"2026-10-13T10:28:39.598Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-10-11T23:30:23.418Z","emptyReason":null},"description":"NVIDIA Agent Fleet — 19个模型Agent + 智能调度引擎 + 并行协作 Skill: Nvidia Agent Fleet Owner: clementgu Summary: NVIDIA Agent Fleet — 19个模型Agent + 智能调度引擎 + 并行协作 Tags: latest:1.1.2 Version history: v1.1.2 | 2026-06-30T00:49:00.833Z | auto nvidia-agent-fleet v1.1.2 - Updated agent registry (agents/registry.json) for improvements or corrections. - Removed the skill-card.md file. - No public-facing feature or documentation changes. v1.1.1 | 2026-05-04T02:43:10.782Z | auto nvidia-a","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. 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You must provision dedicated cloud infrastructure or an isolated VM. 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Agent Fleet — 19个模型Agent + 智能调度引擎 + 并行协作\n\nTags: latest:1.1.2\n\nVersion history:\n\nv1.1.2 | 2026-06-30T00:49:00.833Z | auto\n\nnvidia-agent-fleet v1.1.2\n\n- Updated agent registry (agents/registry.json) for improvements or corrections.\n- Removed the skill-card.md file.\n- No public-facing feature or documentation changes.\n\nv1.1.1 | 2026-05-04T02:43:10.782Z | auto\n\nnvidia-agent-fleet 1.1.1\n\n- Minor code updates in agents/registry.py and dispatcher/fleet.py\n- No user-facing feature changes documented\n- SKILL.md unchanged\n\n(Note: This version contains internal or maintenance improvements.)\n\nv1.1.0 | 2026-04-23T22:17:31.272Z | user\n\n✨ 并行执行: ThreadPoolExecutor多模型同时调用 ⏰ 逐模型超时保护 🔑 API Key三级自动发现(env→.zshrc→openclaw.json) 🧵 线程安全优化\n\nv1.0.2 | 2026-04-23T13:53:12.126Z | user\n\n安全修复：fleet.py 增加 API Key 缺失检测\n\nv1.0.1 | 2026-04-23T13:52:35.811Z | user\n\n安全修复：移除硬编码API Key，用户需自行配置环境变量\n\nv1.0.0 | 2026-04-23T13:27:35.442Z | user\n\n初始发布：19个模型Agent + 智能调度引擎，自动分类分发任务\n\nArchive index:\n\nArchive v1.1.2: 6 files, 16925 bytes\n\nFiles: agents/registry.json (7888b), agents/registry.py (9956b), dispatcher/fleet.py (20477b), skill-card.md (2158b), SKILL.md (4478b), _meta.json (137b)\n\nFile v1.1.2:SKILL.md\n\n---\nname: nvidia-agent-fleet\nslug: nvidia-agent-fleet\nversion: 1.1.0\ndescription: NVIDIA Agent Fleet — 19个模型Agent + 智能调度引擎 + 并行协作\nauthor: openclaw\ntags: [nvidia, multi-agent, dispatcher, llm, orchestration, parallel]\n---\n\n# 🚀 NVIDIA Agent Fleet\n\n多 Agent 调度系统。每个 NVIDIA 模型拥有专属 Agent 身份，调度器自动分析任务类型并分配最佳 Agent 执行。\n\n## 架构\n\n```\n任务输入 → 调度引擎 → 任务分类 → Agent 匹配 → 执行\n                                            ├── DeepSeek V3.2 🧠\n                                            ├── Qwen Coder 32B 💻\n                                            ├── Kimi K2 🇨🇳\n                                            ├── Llama 4 Maverick 🦙\n                                            └── ... 19 个 Agent\n```\n\n## Agent 阵容 (19个)\n\n| Agent | 模型 | 专长 |\n|-------|------|------|\n| 🧠 deepseek-v3-2 | DeepSeek V3.2 | 推理、逻辑、数学 |\n| 🇨🇳 kimi-k2 | Kimi K2 | 中文、长文本、知识 |\n| 🤔 kimi-k2-thinking | Kimi K2 Thinking | 深度思考、推理 |\n| 🏆 mistral-large-3 | Mistral Large 3 (675B) | 通用智能、多语言 |\n| ⚡ mistral-small | Mistral Small 4 | 快速响应 |\n| 💻 qwen-coder-32b | Qwen2.5 Coder 32B | 代码生成、算法 |\n| 🦾 qwen3-coder-480b | Qwen3 Coder 480B | 架构设计、审查 |\n| 🔧 deepseek-coder | DeepSeek Coder 6.7B | 轻量代码、SQL |\n| ⚙️ codestral | Codestral 22B | 代码补全、填空 |\n| 🦙 llama-3-3-70b | Llama 3.3 70B | 通用对话 |\n| 🦙🆕 llama-4-maverick | Llama 4 Maverick | 创意、分析 |\n| 🔬 gemma-3-27b | Gemma 3 27B | 学术、科学 |\n| 🏎️ phi-4-mini | Phi-4 Mini | 极速响应 |\n| 💨 gemma-3-4b | Gemma 3 4B | 超轻量 |\n| 🌏 yi-large | Yi Large | 中文优化 |\n| 🏛️ glm-5-1 | GLM 5.1 | 中文问答 |\n| 🐉 qwen-3-5-397b | Qwen 3.5 397B | 超大中文 |\n| 👁️ llama-vision-90b | Llama Vision 90B | 视觉理解 |\n| 📐 nv-embed | NV-EmbedQA | 文本嵌入 |\n\n## 使用\n\n### CLI\n\n```bash\n# 自动调度最佳 Agent\nfleet \"用Python写一个快速排序\"\n\n# 分析任务会匹配哪些 Agent\nfleet analyze \"解释一下量子纠缠\"\n\n# 指定 Agent 执行\nfleet agent qwen-coder-32b \"实现二分查找\"\n\n# 多 Agent 协同\nfleet multi \"如何看待人工智能的未来？\"\n\n# 列出所有 Agent\nfleet list\n```\n\n### ⚡ 并行执行（v1.1.0 新增）\n\n```bash\n# 多 Agent 并行协同（同时调不同模型，互不等待）\nfleet --multi --parallel \"对比茅台和五粮液的财务状况\"\n\n# 指定 Agent 并行\nfleet --multi --parallel --agent deepseek-v3-2,kimi-k2 \"分析这个创业方案\"\n\n# 仍支持原有串行模式\nfleet --multi \"分析市场趋势\"\n```\n\n**并行 vs 串行 速度对比**\n```\n串行: 任务A→任务B→任务C→任务D = 4个加起来\n并行: 同时发4个请求 = 最慢那个决定总时间\n加速比: x2~4 (取决于模型)\n```\n\n### 自动超时保护\n\n每个模型有独立超时时间，快的先出结果，慢的到点自动跳过：\n\n| 模型 | 超时 | 说明 |\n|------|------|------|\n| Llama 4 Maverick | 15s | 写作/报告类，极快 |\n| Qwen Coder 32B | 20s | 代码/数据处理 |\n| Kimi K2 | 25s | 中文分析 |\n| DeepSeek V3.2 | 35s | 深度推理（最慢） |\n| 其他 | 30s | 通用 |\n\n### API Key 自动发现\n\n支持多级检索，无需手动设置环境变量：\n```\n1. $NVIDIA_API_KEY (环境变量)\n2. ~/.zshrc (常用 shell 配置)\n3. openclaw.json (OpenClaw 配置)\n```\n\n### Python SDK\n\n```python\nfrom dispatcher.fleet import dispatch, select_agents, multi_dispatch\n\n# 自动匹配\nresult = dispatch(\"实现一个Web服务器\")\nprint(result[\"content\"])\n\n# 多Agent协同（串行）\nresults = multi_dispatch(\"哲学问题：意识是什么？\")\n\n# 多Agent协同（并行执行）🚀\nresults = multi_dispatch(\"分析市场\", parallel=True)\nfor r in results:\n    print(f\"{r['emoji']} {r['name']}: {r['content'][:100]}...\")\n```\n\n## v1.1.0 变更\n\n### ✨ 新增\n- **并行执行模式** (`--parallel` / `-p`): 多 Agent 同时调用，互不等待\n- **逐模型超时保护**: 每个模型有独立超时，慢的不拖累快的\n- **API Key 自动发现**: 环境变量 → .zshrc → openclaw.json 三级检索\n- **线程安全**: ThreadPoolExecutor + Lock 确保日志不串\n\n### 🔧 优化\n- 执行失败时优雅降级，已完成结果不受影响\n- 超时模型明确标记，不阻塞整体流程\n\nFile v1.1.2:_meta.json\n\n{\n  \"ownerId\": \"kn7ey9xh63pt0m136bgmx0s67n85dkt5\",\n  \"slug\": \"nvidia-agent-fleet\",\n  \"version\": \"1.1.2\",\n  \"publishedAt\": 1782780540833\n}\n\nFile v1.1.2:skill-card.md\n\n## Description:\n\nRoutes user tasks to a registry of NVIDIA API-backed model agents, with automatic task classification, selected-agent execution, and optional multi-agent parallel responses.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[clementgu](https://clawhub.ai/user/clementgu)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and agent users use this skill to route prompts to task-specific NVIDIA API model agents for coding, reasoning, Chinese-language tasks, writing, research, quick summaries, and multi-agent comparisons.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Automatic credential discovery can read local credential files and run shell configuration while looking for NVIDIA_API_KEY.\n\nMitigation: Set NVIDIA_API_KEY explicitly and avoid installing or running the skill in environments where shell startup files have side effects.\n\nRisk: Prompt content is transmitted to NVIDIA's API during model calls.\n\nMitigation: Do not submit secrets, regulated data, or private business data unless the deployment has approved handling for that data.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/clementgu/skills/nvidia-agent-fleet)\n- [Publisher profile](https://clawhub.ai/user/clementgu)\n- [NVIDIA API endpoint](https://integrate.api.nvidia.com/v1)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]\n\n**Output Format:** [CLI text output and Python dictionaries containing model responses, usage data, selected agents, and timing metadata]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Outputs depend on selected model agents, NVIDIA_API_KEY availability, configured max token limits, and per-model request timeouts.]\n\n## Skill Version(s):\n\n1.1.2 (source: server release metadata; artifact frontmatter says 1.1.0)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nFile v1.1.2:agents/registry.json\n\n{\n  \"deepseek-v3-2\": {\n    \"model\": \"deepseek-ai/deepseek-v3.2\",\n    \"name\": \"DeepSeek V3.2\",\n    \"emoji\": \"🧠\",\n    \"specialty\": \"深度推理、逻辑分析、数学问题\",\n    \"system_prompt\": \"你是一个深度推理专家。擅长逻辑分析、数学推导和复杂问题分解。给出逐步推理过程后再给出结论。\",\n    \"strength\": 0.95,\n    \"speed\": \"中等\",\n    \"max_tokens\": 4096\n  },\n  \"kimi-k2\": {\n    \"model\": \"moonshotai/kimi-k2-instruct\",\n    \"name\": \"Kimi K2\",\n    \"emoji\": \"🇨🇳\",\n    \"specialty\": \"中文理解、长文本分析、知识问答\",\n    \"system_prompt\": \"你是一个中文AI助手Kimi，擅长中文理解和知识问答。用流畅的中文回答，注重准确性和深度。\",\n    \"strength\": 0.92,\n    \"speed\": \"中等\",\n    \"max_tokens\": 8192\n  },\n  \"kimi-k2-thinking\": {\n    \"model\": \"moonshotai/kimi-k2-thinking\",\n    \"name\": \"Kimi K2 Thinking\",\n    \"emoji\": \"🤔\",\n    \"specialty\": \"深度思考、复杂推理、多步骤决策\",\n    \"system_prompt\": \"你是一个深度思考型AI。面对复杂问题，先展示完整的思考链条，再进行回答。注重推理的严谨性。\",\n    \"strength\": 0.93,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"mistral-large-3\": {\n    \"model\": \"mistralai/mistral-large-3-675b-instruct-2512\",\n    \"name\": \"Mistral Large 3\",\n    \"emoji\": \"🏆\",\n    \"specialty\": \"超大模型、多语言、通用智能\",\n    \"system_prompt\": \"你是Mistral Large 3，一个拥有675B参数的巨型模型。擅长多语言任务、复杂推理和知识密集型回答。\",\n    \"strength\": 0.97,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"mistral-small\": {\n    \"model\": \"mistralai/mistral-small-4-119b-2603\",\n    \"name\": \"Mistral Small 4\",\n    \"emoji\": \"⚡\",\n    \"specialty\": \"快速响应、通用任务、性价比\",\n    \"system_prompt\": \"你是一个高效的通用AI助手。在保持回答质量的同时注重响应速度。\",\n    \"strength\": 0.85,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"qwen-coder-32b\": {\n    \"model\": \"qwen/qwen2.5-coder-32b-instruct\",\n    \"name\": \"Qwen Coder 32B\",\n    \"emoji\": \"💻\",\n    \"specialty\": \"代码生成、算法实现、Debug\",\n    \"system_prompt\": \"你是一个专业的代码助手。擅长各种编程语言的代码生成、调试、重构和算法实现。给出完整可运行的代码。\",\n    \"strength\": 0.94,\n    \"speed\": \"快\",\n    \"max_tokens\": 4096\n  },\n  \"qwen3-coder-480b\": {\n    \"model\": \"qwen/qwen3-coder-480b-a35b-instruct\",\n    \"name\": \"Qwen3 Coder 480B\",\n    \"emoji\": \"🦾\",\n    \"specialty\": \"大规模代码、架构设计、代码审查\",\n    \"system_prompt\": \"你是一个顶级代码架构师。擅长大规模系统设计、代码审查和复杂重构。提供架构级别的建议。\",\n    \"strength\": 0.96,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"deepseek-coder\": {\n    \"model\": \"deepseek-ai/deepseek-coder-6.7b-instruct\",\n    \"name\": \"DeepSeek Coder\",\n    \"emoji\": \"🔧\",\n    \"specialty\": \"轻量代码、快速修复、SQL查询\",\n    \"system_prompt\": \"你是一个轻量级代码助手。擅长快速代码生成、bug修复、SQL查询和脚本编写。\",\n    \"strength\": 0.82,\n    \"speed\": \"极快\",\n    \"max_tokens\": 2048\n  },\n  \"codestral\": {\n    \"model\": \"mistralai/codestral-22b-instruct-v0.1\",\n    \"name\": \"Codestral 22B\",\n    \"emoji\": \"⚙️\",\n    \"specialty\": \"代码补全、填空、多语言编程\",\n    \"system_prompt\": \"你是Codestral，一个专业的代码补全和生成助手。擅长代码填空、函数补全和多语言编程任务。\",\n    \"strength\": 0.88,\n    \"speed\": \"快\",\n    \"max_tokens\": 4096\n  },\n  \"stock-analyst\": {\n    \"model\": \"qwen/qwen3.5-122b-a10b\",\n    \"name\": \"股票分析助手\",\n    \"emoji\": \"📊\",\n    \"specialty\": \"A股分析、六维框架、股票诊断\",\n    \"system_prompt\": \"你是专业的A股股票分析助手。请使用六维分析框架（基本面、技术面、消息面、资金面、心理面、宏观面）对股票进行全面分析。给出具体的指标数据和操作建议。注意中国A股市场的特点。\",\n    \"strength\": 0.88,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"llama-3-3-70b\": {\n    \"model\": \"meta/llama-3.3-70b-instruct\",\n    \"name\": \"Llama 3.3 70B\",\n    \"emoji\": \"🦙\",\n    \"specialty\": \"通用对话、通用知识、多任务\",\n    \"system_prompt\": \"你是一个友好且知识渊博的AI助手。能用简洁清晰的语言回答各种问题。\",\n    \"strength\": 0.9,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"llama-4-maverick\": {\n    \"model\": \"meta/llama-4-maverick-17b-128e-instruct\",\n    \"name\": \"Llama 4 Maverick\",\n    \"emoji\": \"🦙\",\n    \"specialty\": \"最新模型、创意写作、分析\",\n    \"system_prompt\": \"你是Meta最新的Llama 4 Maverick模型。擅长创意写作、深度分析和创新思维。给出有洞察力的回答。\",\n    \"strength\": 0.91,\n    \"speed\": \"快\",\n    \"max_tokens\": 4096\n  },\n  \"gemma-3-27b\": {\n    \"model\": \"google/gemma-3-27b-it\",\n    \"name\": \"Gemma 3 27B\",\n    \"emoji\": \"🔬\",\n    \"specialty\": \"学术、科学研究、数学\",\n    \"system_prompt\": \"你是Google Gemma 3，擅长学术讨论、科学研究和数学相关的任务。给出严谨准确的回答。\",\n    \"strength\": 0.89,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"phi-4-mini\": {\n    \"model\": \"microsoft/phi-4-mini-instruct\",\n    \"name\": \"Phi-4 Mini\",\n    \"emoji\": \"🏎️\",\n    \"specialty\": \"极速响应、简单问答、分类\",\n    \"system_prompt\": \"你是一个超快速的轻量AI。擅长简单问答、信息提取、分类和摘要任务。回答要简洁。\",\n    \"strength\": 0.78,\n    \"speed\": \"极快\",\n    \"max_tokens\": 1024\n  },\n  \"gemma-3-4b\": {\n    \"model\": \"google/gemma-3-4b-it\",\n    \"name\": \"Gemma 3 4B\",\n    \"emoji\": \"💨\",\n    \"specialty\": \"超轻量、嵌入式、实时响应\",\n    \"system_prompt\": \"你是一个极小模型的轻量助手。回复要极其简洁，适合实时场景。\",\n    \"strength\": 0.7,\n    \"speed\": \"极快\",\n    \"max_tokens\": 512\n  },\n  \"yi-large\": {\n    \"model\": \"01-ai/yi-large\",\n    \"name\": \"Yi Large\",\n    \"emoji\": \"🌏\",\n    \"specialty\": \"中文优化、文化理解、翻译\",\n    \"system_prompt\": \"你是一个中文优化的大模型，由01.AI开发。精通中文表达、文化背景和翻译任务。\",\n    \"strength\": 0.86,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"glm-5-1\": {\n    \"model\": \"z-ai/glm-5.1\",\n    \"name\": \"GLM 5.1\",\n    \"emoji\": \"🏛️\",\n    \"specialty\": \"中文、知识问答、对话\",\n    \"system_prompt\": \"你是智谱AI的GLM 5.1，一个强大的中文大模型。擅长中文对话、知识问答和文本理解。\",\n    \"strength\": 0.88,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"qwen-3-5-397b\": {\n    \"model\": \"qwen/qwen3.5-397b-a17b\",\n    \"name\": \"Qwen 3.5 397B\",\n    \"emoji\": \"🐉\",\n    \"specialty\": \"超大中文模型、深度理解\",\n    \"system_prompt\": \"你是通义千问Qwen 3.5，一个拥有397B参数的巨型中文模型。擅长深度中文理解和复杂任务。\",\n    \"strength\": 0.95,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"llama-vision-90b\": {\n    \"model\": \"meta/llama-3.2-90b-vision-instruct\",\n    \"name\": \"Llama Vision 90B\",\n    \"emoji\": \"👁️\",\n    \"specialty\": \"图像理解、视觉问答\",\n    \"system_prompt\": \"你是Llama 3.2视觉模型。擅长图像描述、视觉问答和多模态理解。（提示：需要通过视觉接口输入图像）。\",\n    \"strength\": 0.9,\n    \"speed\": \"中等\",\n    \"max_tokens\": 2048\n  },\n  \"nv-embed\": {\n    \"model\": \"nvidia/nv-embedqa-e5-v5\",\n    \"name\": \"NV-EmbedQA\",\n    \"emoji\": \"📐\",\n    \"specialty\": \"文本嵌入、语义搜索、向量化\",\n    \"system_prompt\": \"你是一个嵌入模型，擅长文本向量化。用于语义搜索、相似度计算和RAG应用。\",\n    \"strength\": 0.92,\n    \"speed\": \"极快\",\n    \"max_tokens\": 512,\n    \"is_embedding\": true\n  }\n}\n\nArchive v1.1.1: 6 files, 16783 bytes\n\nFiles: agents/registry.json (7366b), agents/registry.py (9956b), dispatcher/fleet.py (20477b), skill-card.md (2300b), SKILL.md (4478b), _meta.json (137b)\n\nFile v1.1.1:SKILL.md\n\n---\nname: nvidia-agent-fleet\nslug: nvidia-agent-fleet\nversion: 1.1.0\ndescription: NVIDIA Agent Fleet — 19个模型Agent + 智能调度引擎 + 并行协作\nauthor: openclaw\ntags: [nvidia, multi-agent, dispatcher, llm, orchestration, parallel]\n---\n\n# 🚀 NVIDIA Agent Fleet\n\n多 Agent 调度系统。每个 NVIDIA 模型拥有专属 Agent 身份，调度器自动分析任务类型并分配最佳 Agent 执行。\n\n## 架构\n\n```\n任务输入 → 调度引擎 → 任务分类 → Agent 匹配 → 执行\n                                            ├── DeepSeek V3.2 🧠\n                                            ├── Qwen Coder 32B 💻\n                                            ├── Kimi K2 🇨🇳\n                                            ├── Llama 4 Maverick 🦙\n                                            └── ... 19 个 Agent\n```\n\n## Agent 阵容 (19个)\n\n| Agent | 模型 | 专长 |\n|-------|------|------|\n| 🧠 deepseek-v3-2 | DeepSeek V3.2 | 推理、逻辑、数学 |\n| 🇨🇳 kimi-k2 | Kimi K2 | 中文、长文本、知识 |\n| 🤔 kimi-k2-thinking | Kimi K2 Thinking | 深度思考、推理 |\n| 🏆 mistral-large-3 | Mistral Large 3 (675B) | 通用智能、多语言 |\n| ⚡ mistral-small | Mistral Small 4 | 快速响应 |\n| 💻 qwen-coder-32b | Qwen2.5 Coder 32B | 代码生成、算法 |\n| 🦾 qwen3-coder-480b | Qwen3 Coder 480B | 架构设计、审查 |\n| 🔧 deepseek-coder | DeepSeek Coder 6.7B | 轻量代码、SQL |\n| ⚙️ codestral | Codestral 22B | 代码补全、填空 |\n| 🦙 llama-3-3-70b | Llama 3.3 70B | 通用对话 |\n| 🦙🆕 llama-4-maverick | Llama 4 Maverick | 创意、分析 |\n| 🔬 gemma-3-27b | Gemma 3 27B | 学术、科学 |\n| 🏎️ phi-4-mini | Phi-4 Mini | 极速响应 |\n| 💨 gemma-3-4b | Gemma 3 4B | 超轻量 |\n| 🌏 yi-large | Yi Large | 中文优化 |\n| 🏛️ glm-5-1 | GLM 5.1 | 中文问答 |\n| 🐉 qwen-3-5-397b | Qwen 3.5 397B | 超大中文 |\n| 👁️ llama-vision-90b | Llama Vision 90B | 视觉理解 |\n| 📐 nv-embed | NV-EmbedQA | 文本嵌入 |\n\n## 使用\n\n### CLI\n\n```bash\n# 自动调度最佳 Agent\nfleet \"用Python写一个快速排序\"\n\n# 分析任务会匹配哪些 Agent\nfleet analyze \"解释一下量子纠缠\"\n\n# 指定 Agent 执行\nfleet agent qwen-coder-32b \"实现二分查找\"\n\n# 多 Agent 协同\nfleet multi \"如何看待人工智能的未来？\"\n\n# 列出所有 Agent\nfleet list\n```\n\n### ⚡ 并行执行（v1.1.0 新增）\n\n```bash\n# 多 Agent 并行协同（同时调不同模型，互不等待）\nfleet --multi --parallel \"对比茅台和五粮液的财务状况\"\n\n# 指定 Agent 并行\nfleet --multi --parallel --agent deepseek-v3-2,kimi-k2 \"分析这个创业方案\"\n\n# 仍支持原有串行模式\nfleet --multi \"分析市场趋势\"\n```\n\n**并行 vs 串行 速度对比**\n```\n串行: 任务A→任务B→任务C→任务D = 4个加起来\n并行: 同时发4个请求 = 最慢那个决定总时间\n加速比: x2~4 (取决于模型)\n```\n\n### 自动超时保护\n\n每个模型有独立超时时间，快的先出结果，慢的到点自动跳过：\n\n| 模型 | 超时 | 说明 |\n|------|------|------|\n| Llama 4 Maverick | 15s | 写作/报告类，极快 |\n| Qwen Coder 32B | 20s | 代码/数据处理 |\n| Kimi K2 | 25s | 中文分析 |\n| DeepSeek V3.2 | 35s | 深度推理（最慢） |\n| 其他 | 30s | 通用 |\n\n### API Key 自动发现\n\n支持多级检索，无需手动设置环境变量：\n```\n1. $NVIDIA_API_KEY (环境变量)\n2. ~/.zshrc (常用 shell 配置)\n3. openclaw.json (OpenClaw 配置)\n```\n\n### Python SDK\n\n```python\nfrom dispatcher.fleet import dispatch, select_agents, multi_dispatch\n\n# 自动匹配\nresult = dispatch(\"实现一个Web服务器\")\nprint(result[\"content\"])\n\n# 多Agent协同（串行）\nresults = multi_dispatch(\"哲学问题：意识是什么？\")\n\n# 多Agent协同（并行执行）🚀\nresults = multi_dispatch(\"分析市场\", parallel=True)\nfor r in results:\n    print(f\"{r['emoji']} {r['name']}: {r['content'][:100]}...\")\n```\n\n## v1.1.0 变更\n\n### ✨ 新增\n- **并行执行模式** (`--parallel` / `-p`): 多 Agent 同时调用，互不等待\n- **逐模型超时保护**: 每个模型有独立超时，慢的不拖累快的\n- **API Key 自动发现**: 环境变量 → .zshrc → openclaw.json 三级检索\n- **线程安全**: ThreadPoolExecutor + Lock 确保日志不串\n\n### 🔧 优化\n- 执行失败时优雅降级，已完成结果不受影响\n- 超时模型明确标记，不阻塞整体流程\n\nFile v1.1.1:_meta.json\n\n{\n  \"ownerId\": \"kn7ey9xh63pt0m136bgmx0s67n85dkt5\",\n  \"slug\": \"nvidia-agent-fleet\",\n  \"version\": \"1.1.1\",\n  \"publishedAt\": 1777862590782\n}\n\nFile v1.1.1:skill-card.md\n\n## Description: <br>\nNVIDIA Agent Fleet routes prompts to a registry of NVIDIA-hosted model agents, including task analysis, single-agent dispatch, and optional multi-agent parallel execution. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[clementgu](https://clawhub.ai/user/clementgu) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nDevelopers and external users can use this skill to route coding, reasoning, writing, research, Chinese-language, and other prompt tasks to selected NVIDIA API models. It supports CLI and Python SDK workflows for listing agents, analyzing task fit, dispatching to a specified or recommended agent, and running multiple agents serially or in parallel. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: The skill automatically searches local credential stores and executes the user's shell startup file while looking for NVIDIA_API_KEY. <br>\nMitigation: Set NVIDIA_API_KEY explicitly in the current environment and review or disable automatic credential discovery before installing in managed environments. <br>\nRisk: Normal use sends task content to NVIDIA's API. <br>\nMitigation: Do not include secrets, private code, or regulated data in prompts unless the target environment and API terms permit that use. <br>\n\n\n## Reference(s): <br>\n- [ClawHub skill page](https://clawhub.ai/clementgu/nvidia-agent-fleet) <br>\n- [NVIDIA API endpoint used by the dispatcher](https://integrate.api.nvidia.com/v1) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [text, markdown, code, shell commands, guidance] <br>\n**Output Format:** [Plain text from CLI runs and JSON-like Python dictionaries from SDK calls] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [Outputs depend on the selected model and configured token limits; multi-agent mode returns one response per successful agent.] <br>\n\n## Skill Version(s): <br>\n1.1.1 (source: server release evidence) <br>\n\n## Ethical Considerations: <br>\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>\n\nFile v1.1.1:agents/registry.json\n\n{\n  \"deepseek-v3-2\": {\n    \"model\": \"deepseek-ai/deepseek-v3.2\",\n    \"name\": \"DeepSeek V3.2\",\n    \"emoji\": \"🧠\",\n    \"specialty\": \"深度推理、逻辑分析、数学问题\",\n    \"system_prompt\": \"你是一个深度推理专家。擅长逻辑分析、数学推导和复杂问题分解。给出逐步推理过程后再给出结论。\",\n    \"strength\": 0.95,\n    \"speed\": \"中等\",\n    \"max_tokens\": 4096\n  },\n  \"kimi-k2\": {\n    \"model\": \"moonshotai/kimi-k2-instruct\",\n    \"name\": \"Kimi K2\",\n    \"emoji\": \"🇨🇳\",\n    \"specialty\": \"中文理解、长文本分析、知识问答\",\n    \"system_prompt\": \"你是一个中文AI助手Kimi，擅长中文理解和知识问答。用流畅的中文回答，注重准确性和深度。\",\n    \"strength\": 0.92,\n    \"speed\": \"中等\",\n    \"max_tokens\": 8192\n  },\n  \"kimi-k2-thinking\": {\n    \"model\": \"moonshotai/kimi-k2-thinking\",\n    \"name\": \"Kimi K2 Thinking\",\n    \"emoji\": \"🤔\",\n    \"specialty\": \"深度思考、复杂推理、多步骤决策\",\n    \"system_prompt\": \"你是一个深度思考型AI。面对复杂问题，先展示完整的思考链条，再进行回答。注重推理的严谨性。\",\n    \"strength\": 0.93,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"mistral-large-3\": {\n    \"model\": \"mistralai/mistral-large-3-675b-instruct-2512\",\n    \"name\": \"Mistral Large 3\",\n    \"emoji\": \"🏆\",\n    \"specialty\": \"超大模型、多语言、通用智能\",\n    \"system_prompt\": \"你是Mistral Large 3，一个拥有675B参数的巨型模型。擅长多语言任务、复杂推理和知识密集型回答。\",\n    \"strength\": 0.97,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"mistral-small\": {\n    \"model\": \"mistralai/mistral-small-4-119b-2603\",\n    \"name\": \"Mistral Small 4\",\n    \"emoji\": \"⚡\",\n    \"specialty\": \"快速响应、通用任务、性价比\",\n    \"system_prompt\": \"你是一个高效的通用AI助手。在保持回答质量的同时注重响应速度。\",\n    \"strength\": 0.85,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"qwen-coder-32b\": {\n    \"model\": \"qwen/qwen2.5-coder-32b-instruct\",\n    \"name\": \"Qwen Coder 32B\",\n    \"emoji\": \"💻\",\n    \"specialty\": \"代码生成、算法实现、Debug\",\n    \"system_prompt\": \"你是一个专业的代码助手。擅长各种编程语言的代码生成、调试、重构和算法实现。给出完整可运行的代码。\",\n    \"strength\": 0.94,\n    \"speed\": \"快\",\n    \"max_tokens\": 4096\n  },\n  \"qwen3-coder-480b\": {\n    \"model\": \"qwen/qwen3-coder-480b-a35b-instruct\",\n    \"name\": \"Qwen3 Coder 480B\",\n    \"emoji\": \"🦾\",\n    \"specialty\": \"大规模代码、架构设计、代码审查\",\n    \"system_prompt\": \"你是一个顶级代码架构师。擅长大规模系统设计、代码审查和复杂重构。提供架构级别的建议。\",\n    \"strength\": 0.96,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"deepseek-coder\": {\n    \"model\": \"deepseek-ai/deepseek-coder-6.7b-instruct\",\n    \"name\": \"DeepSeek Coder\",\n    \"emoji\": \"🔧\",\n    \"specialty\": \"轻量代码、快速修复、SQL查询\",\n    \"system_prompt\": \"你是一个轻量级代码助手。擅长快速代码生成、bug修复、SQL查询和脚本编写。\",\n    \"strength\": 0.82,\n    \"speed\": \"极快\",\n    \"max_tokens\": 2048\n  },\n  \"codestral\": {\n    \"model\": \"mistralai/codestral-22b-instruct-v0.1\",\n    \"name\": \"Codestral 22B\",\n    \"emoji\": \"⚙️\",\n    \"specialty\": \"代码补全、填空、多语言编程\",\n    \"system_prompt\": \"你是Codestral，一个专业的代码补全和生成助手。擅长代码填空、函数补全和多语言编程任务。\",\n    \"strength\": 0.88,\n    \"speed\": \"快\",\n    \"max_tokens\": 4096\n  },\n  \"llama-3-3-70b\": {\n    \"model\": \"meta/llama-3.3-70b-instruct\",\n    \"name\": \"Llama 3.3 70B\",\n    \"emoji\": \"🦙\",\n    \"specialty\": \"通用对话、通用知识、多任务\",\n    \"system_prompt\": \"你是一个友好且知识渊博的AI助手。能用简洁清晰的语言回答各种问题。\",\n    \"strength\": 0.9,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"llama-4-maverick\": {\n    \"model\": \"meta/llama-4-maverick-17b-128e-instruct\",\n    \"name\": \"Llama 4 Maverick\",\n    \"emoji\": \"🦙\",\n    \"specialty\": \"最新模型、创意写作、分析\",\n    \"system_prompt\": \"你是Meta最新的Llama 4 Maverick模型。擅长创意写作、深度分析和创新思维。给出有洞察力的回答。\",\n    \"strength\": 0.91,\n    \"speed\": \"快\",\n    \"max_tokens\": 4096\n  },\n  \"gemma-3-27b\": {\n    \"model\": \"google/gemma-3-27b-it\",\n    \"name\": \"Gemma 3 27B\",\n    \"emoji\": \"🔬\",\n    \"specialty\": \"学术、科学研究、数学\",\n    \"system_prompt\": \"你是Google Gemma 3，擅长学术讨论、科学研究和数学相关的任务。给出严谨准确的回答。\",\n    \"strength\": 0.89,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"phi-4-mini\": {\n    \"model\": \"microsoft/phi-4-mini-instruct\",\n    \"name\": \"Phi-4 Mini\",\n    \"emoji\": \"🏎️\",\n    \"specialty\": \"极速响应、简单问答、分类\",\n    \"system_prompt\": \"你是一个超快速的轻量AI。擅长简单问答、信息提取、分类和摘要任务。回答要简洁。\",\n    \"strength\": 0.78,\n    \"speed\": \"极快\",\n    \"max_tokens\": 1024\n  },\n  \"gemma-3-4b\": {\n    \"model\": \"google/gemma-3-4b-it\",\n    \"name\": \"Gemma 3 4B\",\n    \"emoji\": \"💨\",\n    \"specialty\": \"超轻量、嵌入式、实时响应\",\n    \"system_prompt\": \"你是一个极小模型的轻量助手。回复要极其简洁，适合实时场景。\",\n    \"strength\": 0.7,\n    \"speed\": \"极快\",\n    \"max_tokens\": 512\n  },\n  \"yi-large\": {\n    \"model\": \"01-ai/yi-large\",\n    \"name\": \"Yi Large\",\n    \"emoji\": \"🌏\",\n    \"specialty\": \"中文优化、文化理解、翻译\",\n    \"system_prompt\": \"你是一个中文优化的大模型，由01.AI开发。精通中文表达、文化背景和翻译任务。\",\n    \"strength\": 0.86,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"glm-5-1\": {\n    \"model\": \"z-ai/glm-5.1\",\n    \"name\": \"GLM 5.1\",\n    \"emoji\": \"🏛️\",\n    \"specialty\": \"中文、知识问答、对话\",\n    \"system_prompt\": \"你是智谱AI的GLM 5.1，一个强大的中文大模型。擅长中文对话、知识问答和文本理解。\",\n    \"strength\": 0.88,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"qwen-3-5-397b\": {\n    \"model\": \"qwen/qwen3.5-397b-a17b\",\n    \"name\": \"Qwen 3.5 397B\",\n    \"emoji\": \"🐉\",\n    \"specialty\": \"超大中文模型、深度理解\",\n    \"system_prompt\": \"你是通义千问Qwen 3.5，一个拥有397B参数的巨型中文模型。擅长深度中文理解和复杂任务。\",\n    \"strength\": 0.95,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"llama-vision-90b\": {\n    \"model\": \"meta/llama-3.2-90b-vision-instruct\",\n    \"name\": \"Llama Vision 90B\",\n    \"emoji\": \"👁️\",\n    \"specialty\": \"图像理解、视觉问答\",\n    \"system_prompt\": \"你是Llama 3.2视觉模型。擅长图像描述、视觉问答和多模态理解。（提示：需要通过视觉接口输入图像）。\",\n    \"strength\": 0.9,\n    \"speed\": \"中等\",\n    \"max_tokens\": 2048\n  },\n  \"nv-embed\": {\n    \"model\": \"nvidia/nv-embedqa-e5-v5\",\n    \"name\": \"NV-EmbedQA\",\n    \"emoji\": \"📐\",\n    \"specialty\": \"文本嵌入、语义搜索、向量化\",\n    \"system_prompt\": \"你是一个嵌入模型，擅长文本向量化。用于语义搜索、相似度计算和RAG应用。\",\n    \"strength\": 0.92,\n    \"speed\": \"极快\",\n    \"max_tokens\": 512,\n    \"is_embedding\": true\n  }\n}\n\nArchive v1.1.0: 5 files, 14883 bytes\n\nFiles: agents/registry.json (7366b), agents/registry.py (9357b), dispatcher/fleet.py (19607b), SKILL.md (4478b), _meta.json (137b)\n\nFile v1.1.0:SKILL.md\n\n---\nname: nvidia-agent-fleet\nslug: nvidia-agent-fleet\nversion: 1.1.0\ndescription: NVIDIA Agent Fleet — 19个模型Agent + 智能调度引擎 + 并行协作\nauthor: openclaw\ntags: [nvidia, multi-agent, dispatcher, llm, orchestration, parallel]\n---\n\n# 🚀 NVIDIA Agent Fleet\n\n多 Agent 调度系统。每个 NVIDIA 模型拥有专属 Agent 身份，调度器自动分析任务类型并分配最佳 Agent 执行。\n\n## 架构\n\n```\n任务输入 → 调度引擎 → 任务分类 → Agent 匹配 → 执行\n                                            ├── DeepSeek V3.2 🧠\n                                            ├── Qwen Coder 32B 💻\n                                            ├── Kimi K2 🇨🇳\n                                            ├── Llama 4 Maverick 🦙\n                                            └── ... 19 个 Agent\n```\n\n## Agent 阵容 (19个)\n\n| Agent | 模型 | 专长 |\n|-------|------|------|\n| 🧠 deepseek-v3-2 | DeepSeek V3.2 | 推理、逻辑、数学 |\n| 🇨🇳 kimi-k2 | Kimi K2 | 中文、长文本、知识 |\n| 🤔 kimi-k2-thinking | Kimi K2 Thinking | 深度思考、推理 |\n| 🏆 mistral-large-3 | Mistral Large 3 (675B) | 通用智能、多语言 |\n| ⚡ mistral-small | Mistral Small 4 | 快速响应 |\n| 💻 qwen-coder-32b | Qwen2.5 Coder 32B | 代码生成、算法 |\n| 🦾 qwen3-coder-480b | Qwen3 Coder 480B | 架构设计、审查 |\n| 🔧 deepseek-coder | DeepSeek Coder 6.7B | 轻量代码、SQL |\n| ⚙️ codestral | Codestral 22B | 代码补全、填空 |\n| 🦙 llama-3-3-70b | Llama 3.3 70B | 通用对话 |\n| 🦙🆕 llama-4-maverick | Llama 4 Maverick | 创意、分析 |\n| 🔬 gemma-3-27b | Gemma 3 27B | 学术、科学 |\n| 🏎️ phi-4-mini | Phi-4 Mini | 极速响应 |\n| 💨 gemma-3-4b | Gemma 3 4B | 超轻量 |\n| 🌏 yi-large | Yi Large | 中文优化 |\n| 🏛️ glm-5-1 | GLM 5.1 | 中文问答 |\n| 🐉 qwen-3-5-397b | Qwen 3.5 397B | 超大中文 |\n| 👁️ llama-vision-90b | Llama Vision 90B | 视觉理解 |\n| 📐 nv-embed | NV-EmbedQA | 文本嵌入 |\n\n## 使用\n\n### CLI\n\n```bash\n# 自动调度最佳 Agent\nfleet \"用Python写一个快速排序\"\n\n# 分析任务会匹配哪些 Agent\nfleet analyze \"解释一下量子纠缠\"\n\n# 指定 Agent 执行\nfleet agent qwen-coder-32b \"实现二分查找\"\n\n# 多 Agent 协同\nfleet multi \"如何看待人工智能的未来？\"\n\n# 列出所有 Agent\nfleet list\n```\n\n### ⚡ 并行执行（v1.1.0 新增）\n\n```bash\n# 多 Agent 并行协同（同时调不同模型，互不等待）\nfleet --multi --parallel \"对比茅台和五粮液的财务状况\"\n\n# 指定 Agent 并行\nfleet --multi --parallel --agent deepseek-v3-2,kimi-k2 \"分析这个创业方案\"\n\n# 仍支持原有串行模式\nfleet --multi \"分析市场趋势\"\n```\n\n**并行 vs 串行 速度对比**\n```\n串行: 任务A→任务B→任务C→任务D = 4个加起来\n并行: 同时发4个请求 = 最慢那个决定总时间\n加速比: x2~4 (取决于模型)\n```\n\n### 自动超时保护\n\n每个模型有独立超时时间，快的先出结果，慢的到点自动跳过：\n\n| 模型 | 超时 | 说明 |\n|------|------|------|\n| Llama 4 Maverick | 15s | 写作/报告类，极快 |\n| Qwen Coder 32B | 20s | 代码/数据处理 |\n| Kimi K2 | 25s | 中文分析 |\n| DeepSeek V3.2 | 35s | 深度推理（最慢） |\n| 其他 | 30s | 通用 |\n\n### API Key 自动发现\n\n支持多级检索，无需手动设置环境变量：\n```\n1. $NVIDIA_API_KEY (环境变量)\n2. ~/.zshrc (常用 shell 配置)\n3. openclaw.json (OpenClaw 配置)\n```\n\n### Python SDK\n\n```python\nfrom dispatcher.fleet import dispatch, select_agents, multi_dispatch\n\n# 自动匹配\nresult = dispatch(\"实现一个Web服务器\")\nprint(result[\"content\"])\n\n# 多Agent协同（串行）\nresults = multi_dispatch(\"哲学问题：意识是什么？\")\n\n# 多Agent协同（并行执行）🚀\nresults = multi_dispatch(\"分析市场\", parallel=True)\nfor r in results:\n    print(f\"{r['emoji']} {r['name']}: {r['content'][:100]}...\")\n```\n\n## v1.1.0 变更\n\n### ✨ 新增\n- **并行执行模式** (`--parallel` / `-p`): 多 Agent 同时调用，互不等待\n- **逐模型超时保护**: 每个模型有独立超时，慢的不拖累快的\n- **API Key 自动发现**: 环境变量 → .zshrc → openclaw.json 三级检索\n- **线程安全**: ThreadPoolExecutor + Lock 确保日志不串\n\n### 🔧 优化\n- 执行失败时优雅降级，已完成结果不受影响\n- 超时模型明确标记，不阻塞整体流程\n\nFile v1.1.0:_meta.json\n\n{\n  \"ownerId\": \"kn7ey9xh63pt0m136bgmx0s67n85dkt5\",\n  \"slug\": \"nvidia-agent-fleet\",\n  \"version\": \"1.1.0\",\n  \"publishedAt\": 1776982651272\n}\n\nFile v1.1.0:agents/registry.json\n\n{\n  \"deepseek-v3-2\": {\n    \"model\": \"deepseek-ai/deepseek-v3.2\",\n    \"name\": \"DeepSeek V3.2\",\n    \"emoji\": \"🧠\",\n    \"specialty\": \"深度推理、逻辑分析、数学问题\",\n    \"system_prompt\": \"你是一个深度推理专家。擅长逻辑分析、数学推导和复杂问题分解。给出逐步推理过程后再给出结论。\",\n    \"strength\": 0.95,\n    \"speed\": \"中等\",\n    \"max_tokens\": 4096\n  },\n  \"kimi-k2\": {\n    \"model\": \"moonshotai/kimi-k2-instruct\",\n    \"name\": \"Kimi K2\",\n    \"emoji\": \"🇨🇳\",\n    \"specialty\": \"中文理解、长文本分析、知识问答\",\n    \"system_prompt\": \"你是一个中文AI助手Kimi，擅长中文理解和知识问答。用流畅的中文回答，注重准确性和深度。\",\n    \"strength\": 0.92,\n    \"speed\": \"中等\",\n    \"max_tokens\": 8192\n  },\n  \"kimi-k2-thinking\": {\n    \"model\": \"moonshotai/kimi-k2-thinking\",\n    \"name\": \"Kimi K2 Thinking\",\n    \"emoji\": \"🤔\",\n    \"specialty\": \"深度思考、复杂推理、多步骤决策\",\n    \"system_prompt\": \"你是一个深度思考型AI。面对复杂问题，先展示完整的思考链条，再进行回答。注重推理的严谨性。\",\n    \"strength\": 0.93,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"mistral-large-3\": {\n    \"model\": \"mistralai/mistral-large-3-675b-instruct-2512\",\n    \"name\": \"Mistral Large 3\",\n    \"emoji\": \"🏆\",\n    \"specialty\": \"超大模型、多语言、通用智能\",\n    \"system_prompt\": \"你是Mistral Large 3，一个拥有675B参数的巨型模型。擅长多语言任务、复杂推理和知识密集型回答。\",\n    \"strength\": 0.97,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"mistral-small\": {\n    \"model\": \"mistralai/mistral-small-4-119b-2603\",\n    \"name\": \"Mistral Small 4\",\n    \"emoji\": \"⚡\",\n    \"specialty\": \"快速响应、通用任务、性价比\",\n    \"system_prompt\": \"你是一个高效的通用AI助手。在保持回答质量的同时注重响应速度。\",\n    \"strength\": 0.85,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"qwen-coder-32b\": {\n    \"model\": \"qwen/qwen2.5-coder-32b-instruct\",\n    \"name\": \"Qwen Coder 32B\",\n    \"emoji\": \"💻\",\n    \"specialty\": \"代码生成、算法实现、Debug\",\n    \"system_prompt\": \"你是一个专业的代码助手。擅长各种编程语言的代码生成、调试、重构和算法实现。给出完整可运行的代码。\",\n    \"strength\": 0.94,\n    \"speed\": \"快\",\n    \"max_tokens\": 4096\n  },\n  \"qwen3-coder-480b\": {\n    \"model\": \"qwen/qwen3-coder-480b-a35b-instruct\",\n    \"name\": \"Qwen3 Coder 480B\",\n    \"emoji\": \"🦾\",\n    \"specialty\": \"大规模代码、架构设计、代码审查\",\n    \"system_prompt\": \"你是一个顶级代码架构师。擅长大规模系统设计、代码审查和复杂重构。提供架构级别的建议。\",\n    \"strength\": 0.96,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"deepseek-coder\": {\n    \"model\": \"deepseek-ai/deepseek-coder-6.7b-instruct\",\n    \"name\": \"DeepSeek Coder\",\n    \"emoji\": \"🔧\",\n    \"specialty\": \"轻量代码、快速修复、SQL查询\",\n    \"system_prompt\": \"你是一个轻量级代码助手。擅长快速代码生成、bug修复、SQL查询和脚本编写。\",\n    \"strength\": 0.82,\n    \"speed\": \"极快\",\n    \"max_tokens\": 2048\n  },\n  \"codestral\": {\n    \"model\": \"mistralai/codestral-22b-instruct-v0.1\",\n    \"name\": \"Codestral 22B\",\n    \"emoji\": \"⚙️\",\n    \"specialty\": \"代码补全、填空、多语言编程\",\n    \"system_prompt\": \"你是Codestral，一个专业的代码补全和生成助手。擅长代码填空、函数补全和多语言编程任务。\",\n    \"strength\": 0.88,\n    \"speed\": \"快\",\n    \"max_tokens\": 4096\n  },\n  \"llama-3-3-70b\": {\n    \"model\": \"meta/llama-3.3-70b-instruct\",\n    \"name\": \"Llama 3.3 70B\",\n    \"emoji\": \"🦙\",\n    \"specialty\": \"通用对话、通用知识、多任务\",\n    \"system_prompt\": \"你是一个友好且知识渊博的AI助手。能用简洁清晰的语言回答各种问题。\",\n    \"strength\": 0.9,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"llama-4-maverick\": {\n    \"model\": \"meta/llama-4-maverick-17b-128e-instruct\",\n    \"name\": \"Llama 4 Maverick\",\n    \"emoji\": \"🦙\",\n    \"specialty\": \"最新模型、创意写作、分析\",\n    \"system_prompt\": \"你是Meta最新的Llama 4 Maverick模型。擅长创意写作、深度分析和创新思维。给出有洞察力的回答。\",\n    \"strength\": 0.91,\n    \"speed\": \"快\",\n    \"max_tokens\": 4096\n  },\n  \"gemma-3-27b\": {\n    \"model\": \"google/gemma-3-27b-it\",\n    \"name\": \"Gemma 3 27B\",\n    \"emoji\": \"🔬\",\n    \"specialty\": \"学术、科学研究、数学\",\n    \"system_prompt\": \"你是Google Gemma 3，擅长学术讨论、科学研究和数学相关的任务。给出严谨准确的回答。\",\n    \"strength\": 0.89,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"phi-4-mini\": {\n    \"model\": \"microsoft/phi-4-mini-instruct\",\n    \"name\": \"Phi-4 Mini\",\n    \"emoji\": \"🏎️\",\n    \"specialty\": \"极速响应、简单问答、分类\",\n    \"system_prompt\": \"你是一个超快速的轻量AI。擅长简单问答、信息提取、分类和摘要任务。回答要简洁。\",\n    \"strength\": 0.78,\n    \"speed\": \"极快\",\n    \"max_tokens\": 1024\n  },\n  \"gemma-3-4b\": {\n    \"model\": \"google/gemma-3-4b-it\",\n    \"name\": \"Gemma 3 4B\",\n    \"emoji\": \"💨\",\n    \"specialty\": \"超轻量、嵌入式、实时响应\",\n    \"system_prompt\": \"你是一个极小模型的轻量助手。回复要极其简洁，适合实时场景。\",\n    \"strength\": 0.7,\n    \"speed\": \"极快\",\n    \"max_tokens\": 512\n  },\n  \"yi-large\": {\n    \"model\": \"01-ai/yi-large\",\n    \"name\": \"Yi Large\",\n    \"emoji\": \"🌏\",\n    \"specialty\": \"中文优化、文化理解、翻译\",\n    \"system_prompt\": \"你是一个中文优化的大模型，由01.AI开发。精通中文表达、文化背景和翻译任务。\",\n    \"strength\": 0.86,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"glm-5-1\": {\n    \"model\": \"z-ai/glm-5.1\",\n    \"name\": \"GLM 5.1\",\n    \"emoji\": \"🏛️\",\n    \"specialty\": \"中文、知识问答、对话\",\n    \"system_prompt\": \"你是智谱AI的GLM 5.1，一个强大的中文大模型。擅长中文对话、知识问答和文本理解。\",\n    \"strength\": 0.88,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"qwen-3-5-397b\": {\n    \"model\": \"qwen/qwen3.5-397b-a17b\",\n    \"name\": \"Qwen 3.5 397B\",\n    \"emoji\": \"🐉\",\n    \"specialty\": \"超大中文模型、深度理解\",\n    \"system_prompt\": \"你是通义千问Qwen 3.5，一个拥有397B参数的巨型中文模型。擅长深度中文理解和复杂任务。\",\n    \"strength\": 0.95,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"llama-vision-90b\": {\n    \"model\": \"meta/llama-3.2-90b-vision-instruct\",\n    \"name\": \"Llama Vision 90B\",\n    \"emoji\": \"👁️\",\n    \"specialty\": \"图像理解、视觉问答\",\n    \"system_prompt\": \"你是Llama 3.2视觉模型。擅长图像描述、视觉问答和多模态理解。（提示：需要通过视觉接口输入图像）。\",\n    \"strength\": 0.9,\n    \"speed\": \"中等\",\n    \"max_tokens\": 2048\n  },\n  \"nv-embed\": {\n    \"model\": \"nvidia/nv-embedqa-e5-v5\",\n    \"name\": \"NV-EmbedQA\",\n    \"emoji\": \"📐\",\n    \"specialty\": \"文本嵌入、语义搜索、向量化\",\n    \"system_prompt\": \"你是一个嵌入模型，擅长文本向量化。用于语义搜索、相似度计算和RAG应用。\",\n    \"strength\": 0.92,\n    \"speed\": \"极快\",\n    \"max_tokens\": 512,\n    \"is_embedding\": true\n  }\n}\n\nArchive v1.0.2: 5 files, 12404 bytes\n\nFiles: _meta.json (137b), agents/registry.json (7366b), agents/registry.py (9357b), dispatcher/fleet.py (13815b), SKILL.md (2777b)\n\nFile v1.0.2:SKILL.md\n\n---\nname: nvidia-agent-fleet\nslug: nvidia-agent-fleet\nversion: 1.0.0\ndescription: NVIDIA Agent Fleet — 19个模型Agent + 智能调度引擎，自动匹配任务类型分发\nauthor: openclaw\ntags: [nvidia, multi-agent, dispatcher, llm, orchestration]\n---\n\n# 🚀 NVIDIA Agent Fleet\n\n多 Agent 调度系统。每个 NVIDIA 模型拥有专属 Agent 身份，调度器自动分析任务类型并分配最佳 Agent 执行。\n\n## 架构\n\n```\n任务输入 → 调度引擎 → 任务分类 → Agent 匹配 → 执行\n                                            ├── DeepSeek V3.2 🧠\n                                            ├── Qwen Coder 32B 💻\n                                            ├── Kimi K2 🇨🇳\n                                            ├── Llama 4 Maverick 🦙\n                                            └── ... 19 个 Agent\n```\n\n## Agent 阵容 (19个)\n\n| Agent | 模型 | 专长 |\n|-------|------|------|\n| 🧠 deepseek-v3-2 | DeepSeek V3.2 | 推理、逻辑、数学 |\n| 🇨🇳 kimi-k2 | Kimi K2 | 中文、长文本、知识 |\n| 🤔 kimi-k2-thinking | Kimi K2 Thinking | 深度思考、推理 |\n| 🏆 mistral-large-3 | Mistral Large 3 (675B) | 通用智能、多语言 |\n| ⚡ mistral-small | Mistral Small 4 | 快速响应 |\n| 💻 qwen-coder-32b | Qwen2.5 Coder 32B | 代码生成、算法 |\n| 🦾 qwen3-coder-480b | Qwen3 Coder 480B | 架构设计、审查 |\n| 🔧 deepseek-coder | DeepSeek Coder 6.7B | 轻量代码、SQL |\n| ⚙️ codestral | Codestral 22B | 代码补全、填空 |\n| 🦙 llama-3-3-70b | Llama 3.3 70B | 通用对话 |\n| 🦙🆕 llama-4-maverick | Llama 4 Maverick | 创意、分析 |\n| 🔬 gemma-3-27b | Gemma 3 27B | 学术、科学 |\n| 🏎️ phi-4-mini | Phi-4 Mini | 极速响应 |\n| 💨 gemma-3-4b | Gemma 3 4B | 超轻量 |\n| 🌏 yi-large | Yi Large | 中文优化 |\n| 🏛️ glm-5-1 | GLM 5.1 | 中文问答 |\n| 🐉 qwen-3-5-397b | Qwen 3.5 397B | 超大中文 |\n| 👁️ llama-vision-90b | Llama Vision 90B | 视觉理解 |\n| 📐 nv-embed | NV-EmbedQA | 文本嵌入 |\n\n## 使用\n\n### CLI\n\n```bash\n# 自动调度最佳 Agent\nfleet \"用Python写一个快速排序\"\n\n# 分析任务会匹配哪些 Agent\nfleet analyze \"解释一下量子纠缠\"\n\n# 指定 Agent 执行\nfleet agent qwen-coder-32b \"实现二分查找\"\n\n# 多 Agent 协同\nfleet multi \"如何看待人工智能的未来？\"\n\n# 列出所有 Agent\nfleet list\n```\n\n### Python SDK\n\n```python\nfrom dispatcher.fleet import dispatch, select_agents, multi_dispatch\n\n# 自动匹配\nresult = dispatch(\"实现一个Web服务器\")\nprint(result[\"content\"])\n\n# 多Agent协同\nresults = multi_dispatch(\"哲学问题：意识是什么？\")\nfor r in results:\n    print(f\"{r['emoji']} {r['name']}: {r['content'][:100]}...\")\n```\n\nFile v1.0.2:_meta.json\n\n{\n  \"ownerId\": \"kn7ey9xh63pt0m136bgmx0s67n85dkt5\",\n  \"slug\": \"nvidia-agent-fleet\",\n  \"version\": \"1.0.2\",\n  \"publishedAt\": 1776952392126\n}\n\nFile v1.0.2:agents/registry.json\n\n{\n  \"deepseek-v3-2\": {\n    \"model\": \"deepseek-ai/deepseek-v3.2\",\n    \"name\": \"DeepSeek V3.2\",\n    \"emoji\": \"🧠\",\n    \"specialty\": \"深度推理、逻辑分析、数学问题\",\n    \"system_prompt\": \"你是一个深度推理专家。擅长逻辑分析、数学推导和复杂问题分解。给出逐步推理过程后再给出结论。\",\n    \"strength\": 0.95,\n    \"speed\": \"中等\",\n    \"max_tokens\": 4096\n  },\n  \"kimi-k2\": {\n    \"model\": \"moonshotai/kimi-k2-instruct\",\n    \"name\": \"Kimi K2\",\n    \"emoji\": \"🇨🇳\",\n    \"specialty\": \"中文理解、长文本分析、知识问答\",\n    \"system_prompt\": \"你是一个中文AI助手Kimi，擅长中文理解和知识问答。用流畅的中文回答，注重准确性和深度。\",\n    \"strength\": 0.92,\n    \"speed\": \"中等\",\n    \"max_tokens\": 8192\n  },\n  \"kimi-k2-thinking\": {\n    \"model\": \"moonshotai/kimi-k2-thinking\",\n    \"name\": \"Kimi K2 Thinking\",\n    \"emoji\": \"🤔\",\n    \"specialty\": \"深度思考、复杂推理、多步骤决策\",\n    \"system_prompt\": \"你是一个深度思考型AI。面对复杂问题，先展示完整的思考链条，再进行回答。注重推理的严谨性。\",\n    \"strength\": 0.93,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"mistral-large-3\": {\n    \"model\": \"mistralai/mistral-large-3-675b-instruct-2512\",\n    \"name\": \"Mistral Large 3\",\n    \"emoji\": \"🏆\",\n    \"specialty\": \"超大模型、多语言、通用智能\",\n    \"system_prompt\": \"你是Mistral Large 3，一个拥有675B参数的巨型模型。擅长多语言任务、复杂推理和知识密集型回答。\",\n    \"strength\": 0.97,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"mistral-small\": {\n    \"model\": \"mistralai/mistral-small-4-119b-2603\",\n    \"name\": \"Mistral Small 4\",\n    \"emoji\": \"⚡\",\n    \"specialty\": \"快速响应、通用任务、性价比\",\n    \"system_prompt\": \"你是一个高效的通用AI助手。在保持回答质量的同时注重响应速度。\",\n    \"strength\": 0.85,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"qwen-coder-32b\": {\n    \"model\": \"qwen/qwen2.5-coder-32b-instruct\",\n    \"name\": \"Qwen Coder 32B\",\n    \"emoji\": \"💻\",\n    \"specialty\": \"代码生成、算法实现、Debug\",\n    \"system_prompt\": \"你是一个专业的代码助手。擅长各种编程语言的代码生成、调试、重构和算法实现。给出完整可运行的代码。\",\n    \"strength\": 0.94,\n    \"speed\": \"快\",\n    \"max_tokens\": 4096\n  },\n  \"qwen3-coder-480b\": {\n    \"model\": \"qwen/qwen3-coder-480b-a35b-instruct\",\n    \"name\": \"Qwen3 Coder 480B\",\n    \"emoji\": \"🦾\",\n    \"specialty\": \"大规模代码、架构设计、代码审查\",\n    \"system_prompt\": \"你是一个顶级代码架构师。擅长大规模系统设计、代码审查和复杂重构。提供架构级别的建议。\",\n    \"strength\": 0.96,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"deepseek-coder\": {\n    \"model\": \"deepseek-ai/deepseek-coder-6.7b-instruct\",\n    \"name\": \"DeepSeek Coder\",\n    \"emoji\": \"🔧\",\n    \"specialty\": \"轻量代码、快速修复、SQL查询\",\n    \"system_prompt\": \"你是一个轻量级代码助手。擅长快速代码生成、bug修复、SQL查询和脚本编写。\",\n    \"strength\": 0.82,\n    \"speed\": \"极快\",\n    \"max_tokens\": 2048\n  },\n  \"codestral\": {\n    \"model\": \"mistralai/codestral-22b-instruct-v0.1\",\n    \"name\": \"Codestral 22B\",\n    \"emoji\": \"⚙️\",\n    \"specialty\": \"代码补全、填空、多语言编程\",\n    \"system_prompt\": \"你是Codestral，一个专业的代码补全和生成助手。擅长代码填空、函数补全和多语言编程任务。\",\n    \"strength\": 0.88,\n    \"speed\": \"快\",\n    \"max_tokens\": 4096\n  },\n  \"llama-3-3-70b\": {\n    \"model\": \"meta/llama-3.3-70b-instruct\",\n    \"name\": \"Llama 3.3 70B\",\n    \"emoji\": \"🦙\",\n    \"specialty\": \"通用对话、通用知识、多任务\",\n    \"system_prompt\": \"你是一个友好且知识渊博的AI助手。能用简洁清晰的语言回答各种问题。\",\n    \"strength\": 0.9,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"llama-4-maverick\": {\n    \"model\": \"meta/llama-4-maverick-17b-128e-instruct\",\n    \"name\": \"Llama 4 Maverick\",\n    \"emoji\": \"🦙\",\n    \"specialty\": \"最新模型、创意写作、分析\",\n    \"system_prompt\": \"你是Meta最新的Llama 4 Maverick模型。擅长创意写作、深度分析和创新思维。给出有洞察力的回答。\",\n    \"strength\": 0.91,\n    \"speed\": \"快\",\n    \"max_tokens\": 4096\n  },\n  \"gemma-3-27b\": {\n    \"model\": \"google/gemma-3-27b-it\",\n    \"name\": \"Gemma 3 27B\",\n    \"emoji\": \"🔬\",\n    \"specialty\": \"学术、科学研究、数学\",\n    \"system_prompt\": \"你是Google Gemma 3，擅长学术讨论、科学研究和数学相关的任务。给出严谨准确的回答。\",\n    \"strength\": 0.89,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"phi-4-mini\": {\n    \"model\": \"microsoft/phi-4-mini-instruct\",\n    \"name\": \"Phi-4 Mini\",\n    \"emoji\": \"🏎️\",\n    \"specialty\": \"极速响应、简单问答、分类\",\n    \"system_prompt\": \"你是一个超快速的轻量AI。擅长简单问答、信息提取、分类和摘要任务。回答要简洁。\",\n    \"strength\": 0.78,\n    \"speed\": \"极快\",\n    \"max_tokens\": 1024\n  },\n  \"gemma-3-4b\": {\n    \"model\": \"google/gemma-3-4b-it\",\n    \"name\": \"Gemma 3 4B\",\n    \"emoji\": \"💨\",\n    \"specialty\": \"超轻量、嵌入式、实时响应\",\n    \"system_prompt\": \"你是一个极小模型的轻量助手。回复要极其简洁，适合实时场景。\",\n    \"strength\": 0.7,\n    \"speed\": \"极快\",\n    \"max_tokens\": 512\n  },\n  \"yi-large\": {\n    \"model\": \"01-ai/yi-large\",\n    \"name\": \"Yi Large\",\n    \"emoji\": \"🌏\",\n    \"specialty\": \"中文优化、文化理解、翻译\",\n    \"system_prompt\": \"你是一个中文优化的大模型，由01.AI开发。精通中文表达、文化背景和翻译任务。\",\n    \"strength\": 0.86,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"glm-5-1\": {\n    \"model\": \"z-ai/glm-5.1\",\n    \"name\": \"GLM 5.1\",\n    \"emoji\": \"🏛️\",\n    \"specialty\": \"中文、知识问答、对话\",\n    \"system_prompt\": \"你是智谱AI的GLM 5.1，一个强大的中文大模型。擅长中文对话、知识问答和文本理解。\",\n    \"strength\": 0.88,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"qwen-3-5-397b\": {\n    \"model\": \"qwen/qwen3.5-397b-a17b\",\n    \"name\": \"Qwen 3.5 397B\",\n    \"emoji\": \"🐉\",\n    \"specialty\": \"超大中文模型、深度理解\",\n    \"system_prompt\": \"你是通义千问Qwen 3.5，一个拥有397B参数的巨型中文模型。擅长深度中文理解和复杂任务。\",\n    \"strength\": 0.95,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"llama-vision-90b\": {\n    \"model\": \"meta/llama-3.2-90b-vision-instruct\",\n    \"name\": \"Llama Vision 90B\",\n    \"emoji\": \"👁️\",\n    \"specialty\": \"图像理解、视觉问答\",\n    \"system_prompt\": \"你是Llama 3.2视觉模型。擅长图像描述、视觉问答和多模态理解。（提示：需要通过视觉接口输入图像）。\",\n    \"strength\": 0.9,\n    \"speed\": \"中等\",\n    \"max_tokens\": 2048\n  },\n  \"nv-embed\": {\n    \"model\": \"nvidia/nv-embedqa-e5-v5\",\n    \"name\": \"NV-EmbedQA\",\n    \"emoji\": \"📐\",\n    \"specialty\": \"文本嵌入、语义搜索、向量化\",\n    \"system_prompt\": \"你是一个嵌入模型，擅长文本向量化。用于语义搜索、相似度计算和RAG应用。\",\n    \"strength\": 0.92,\n    \"speed\": \"极快\",\n    \"max_tokens\": 512,\n    \"is_embedding\": true\n  }\n}\n\nArchive v1.0.1: 5 files, 12282 bytes\n\nFiles: _meta.json (137b), agents/registry.json (7366b), agents/registry.py (9357b), dispatcher/fleet.py (13596b), SKILL.md (2777b)\n\nFile v1.0.1:SKILL.md\n\n---\nname: nvidia-agent-fleet\nslug: nvidia-agent-fleet\nversion: 1.0.0\ndescription: NVIDIA Agent Fleet — 19个模型Agent + 智能调度引擎，自动匹配任务类型分发\nauthor: openclaw\ntags: [nvidia, multi-agent, dispatcher, llm, orchestration]\n---\n\n# 🚀 NVIDIA Agent Fleet\n\n多 Agent 调度系统。每个 NVIDIA 模型拥有专属 Agent 身份，调度器自动分析任务类型并分配最佳 Agent 执行。\n\n## 架构\n\n```\n任务输入 → 调度引擎 → 任务分类 → Agent 匹配 → 执行\n                                            ├── DeepSeek V3.2 🧠\n                                            ├── Qwen Coder 32B 💻\n                                            ├── Kimi K2 🇨🇳\n                                            ├── Llama 4 Maverick 🦙\n                                            └── ... 19 个 Agent\n```\n\n## Agent 阵容 (19个)\n\n| Agent | 模型 | 专长 |\n|-------|------|------|\n| 🧠 deepseek-v3-2 | DeepSeek V3.2 | 推理、逻辑、数学 |\n| 🇨🇳 kimi-k2 | Kimi K2 | 中文、长文本、知识 |\n| 🤔 kimi-k2-thinking | Kimi K2 Thinking | 深度思考、推理 |\n| 🏆 mistral-large-3 | Mistral Large 3 (675B) | 通用智能、多语言 |\n| ⚡ mistral-small | Mistral Small 4 | 快速响应 |\n| 💻 qwen-coder-32b | Qwen2.5 Coder 32B | 代码生成、算法 |\n| 🦾 qwen3-coder-480b | Qwen3 Coder 480B | 架构设计、审查 |\n| 🔧 deepseek-coder | DeepSeek Coder 6.7B | 轻量代码、SQL |\n| ⚙️ codestral | Codestral 22B | 代码补全、填空 |\n| 🦙 llama-3-3-70b | Llama 3.3 70B | 通用对话 |\n| 🦙🆕 llama-4-maverick | Llama 4 Maverick | 创意、分析 |\n| 🔬 gemma-3-27b | Gemma 3 27B | 学术、科学 |\n| 🏎️ phi-4-mini | Phi-4 Mini | 极速响应 |\n| 💨 gemma-3-4b | Gemma 3 4B | 超轻量 |\n| 🌏 yi-large | Yi Large | 中文优化 |\n| 🏛️ glm-5-1 | GLM 5.1 | 中文问答 |\n| 🐉 qwen-3-5-397b | Qwen 3.5 397B | 超大中文 |\n| 👁️ llama-vision-90b | Llama Vision 90B | 视觉理解 |\n| 📐 nv-embed | NV-EmbedQA | 文本嵌入 |\n\n## 使用\n\n### CLI\n\n```bash\n# 自动调度最佳 Agent\nfleet \"用Python写一个快速排序\"\n\n# 分析任务会匹配哪些 Agent\nfleet analyze \"解释一下量子纠缠\"\n\n# 指定 Agent 执行\nfleet agent qwen-coder-32b \"实现二分查找\"\n\n# 多 Agent 协同\nfleet multi \"如何看待人工智能的未来？\"\n\n# 列出所有 Agent\nfleet list\n```\n\n### Python SDK\n\n```python\nfrom dispatcher.fleet import dispatch, select_agents, multi_dispatch\n\n# 自动匹配\nresult = dispatch(\"实现一个Web服务器\")\nprint(result[\"content\"])\n\n# 多Agent协同\nresults = multi_dispatch(\"哲学问题：意识是什么？\")\nfor r in results:\n    print(f\"{r['emoji']} {r['name']}: {r['content'][:100]}...\")\n```\n\nFile v1.0.1:_meta.json\n\n{\n  \"ownerId\": \"kn7ey9xh63pt0m136bgmx0s67n85dkt5\",\n  \"slug\": \"nvidia-agent-fleet\",\n  \"version\": \"1.0.1\",\n  \"publishedAt\": 1776952355811\n}\n\nFile v1.0.1:agents/registry.json\n\n{\n  \"deepseek-v3-2\": {\n    \"model\": \"deepseek-ai/deepseek-v3.2\",\n    \"name\": \"DeepSeek V3.2\",\n    \"emoji\": \"🧠\",\n    \"specialty\": \"深度推理、逻辑分析、数学问题\",\n    \"system_prompt\": \"你是一个深度推理专家。擅长逻辑分析、数学推导和复杂问题分解。给出逐步推理过程后再给出结论。\",\n    \"strength\": 0.95,\n    \"speed\": \"中等\",\n    \"max_tokens\": 4096\n  },\n  \"kimi-k2\": {\n    \"model\": \"moonshotai/kimi-k2-instruct\",\n    \"name\": \"Kimi K2\",\n    \"emoji\": \"🇨🇳\",\n    \"specialty\": \"中文理解、长文本分析、知识问答\",\n    \"system_prompt\": \"你是一个中文AI助手Kimi，擅长中文理解和知识问答。用流畅的中文回答，注重准确性和深度。\",\n    \"strength\": 0.92,\n    \"speed\": \"中等\",\n    \"max_tokens\": 8192\n  },\n  \"kimi-k2-thinking\": {\n    \"model\": \"moonshotai/kimi-k2-thinking\",\n    \"name\": \"Kimi K2 Thinking\",\n    \"emoji\": \"🤔\",\n    \"specialty\": \"深度思考、复杂推理、多步骤决策\",\n    \"system_prompt\": \"你是一个深度思考型AI。面对复杂问题，先展示完整的思考链条，再进行回答。注重推理的严谨性。\",\n    \"strength\": 0.93,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"mistral-large-3\": {\n    \"model\": \"mistralai/mistral-large-3-675b-instruct-2512\",\n    \"name\": \"Mistral Large 3\",\n    \"emoji\": \"🏆\",\n    \"specialty\": \"超大模型、多语言、通用智能\",\n    \"system_prompt\": \"你是Mistral Large 3，一个拥有675B参数的巨型模型。擅长多语言任务、复杂推理和知识密集型回答。\",\n    \"strength\": 0.97,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"mistral-small\": {\n    \"model\": \"mistralai/mistral-small-4-119b-2603\",\n    \"name\": \"Mistral Small 4\",\n    \"emoji\": \"⚡\",\n    \"specialty\": \"快速响应、通用任务、性价比\",\n    \"system_prompt\": \"你是一个高效的通用AI助手。在保持回答质量的同时注重响应速度。\",\n    \"strength\": 0.85,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"qwen-coder-32b\": {\n    \"model\": \"qwen/qwen2.5-coder-32b-instruct\",\n    \"name\": \"Qwen Coder 32B\",\n    \"emoji\": \"💻\",\n    \"specialty\": \"代码生成、算法实现、Debug\",\n    \"system_prompt\": \"你是一个专业的代码助手。擅长各种编程语言的代码生成、调试、重构和算法实现。给出完整可运行的代码。\",\n    \"strength\": 0.94,\n    \"speed\": \"快\",\n    \"max_tokens\": 4096\n  },\n  \"qwen3-coder-480b\": {\n    \"model\": \"qwen/qwen3-coder-480b-a35b-instruct\",\n    \"name\": \"Qwen3 Coder 480B\",\n    \"emoji\": \"🦾\",\n    \"specialty\": \"大规模代码、架构设计、代码审查\",\n    \"system_prompt\": \"你是一个顶级代码架构师。擅长大规模系统设计、代码审查和复杂重构。提供架构级别的建议。\",\n    \"strength\": 0.96,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"deepseek-coder\": {\n    \"model\": \"deepseek-ai/deepseek-coder-6.7b-instruct\",\n    \"name\": \"DeepSeek Coder\",\n    \"emoji\": \"🔧\",\n    \"specialty\": \"轻量代码、快速修复、SQL查询\",\n    \"system_prompt\": \"你是一个轻量级代码助手。擅长快速代码生成、bug修复、SQL查询和脚本编写。\",\n    \"strength\": 0.82,\n    \"speed\": \"极快\",\n    \"max_tokens\": 2048\n  },\n  \"codestral\": {\n    \"model\": \"mistralai/codestral-22b-instruct-v0.1\",\n    \"name\": \"Codestral 22B\",\n    \"emoji\": \"⚙️\",\n    \"specialty\": \"代码补全、填空、多语言编程\",\n    \"system_prompt\": \"你是Codestral，一个专业的代码补全和生成助手。擅长代码填空、函数补全和多语言编程任务。\",\n    \"strength\": 0.88,\n    \"speed\": \"快\",\n    \"max_tokens\": 4096\n  },\n  \"llama-3-3-70b\": {\n    \"model\": \"meta/llama-3.3-70b-instruct\",\n    \"name\": \"Llama 3.3 70B\",\n    \"emoji\": \"🦙\",\n    \"specialty\": \"通用对话、通用知识、多任务\",\n    \"system_prompt\": \"你是一个友好且知识渊博的AI助手。能用简洁清晰的语言回答各种问题。\",\n    \"strength\": 0.9,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"llama-4-maverick\": {\n    \"model\": \"meta/llama-4-maverick-17b-128e-instruct\",\n    \"name\": \"Llama 4 Maverick\",\n    \"emoji\": \"🦙\",\n    \"specialty\": \"最新模型、创意写作、分析\",\n    \"system_prompt\": \"你是Meta最新的Llama 4 Maverick模型。擅长创意写作、深度分析和创新思维。给出有洞察力的回答。\",\n    \"strength\": 0.91,\n    \"speed\": \"快\",\n    \"max_tokens\": 4096\n  },\n  \"gemma-3-27b\": {\n    \"model\": \"google/gemma-3-27b-it\",\n    \"name\": \"Gemma 3 27B\",\n    \"emoji\": \"🔬\",\n    \"specialty\": \"学术、科学研究、数学\",\n    \"system_prompt\": \"你是Google Gemma 3，擅长学术讨论、科学研究和数学相关的任务。给出严谨准确的回答。\",\n    \"strength\": 0.89,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"phi-4-mini\": {\n    \"model\": \"microsoft/phi-4-mini-instruct\",\n    \"name\": \"Phi-4 Mini\",\n    \"emoji\": \"🏎️\",\n    \"specialty\": \"极速响应、简单问答、分类\",\n    \"system_prompt\": \"你是一个超快速的轻量AI。擅长简单问答、信息提取、分类和摘要任务。回答要简洁。\",\n    \"strength\": 0.78,\n    \"speed\": \"极快\",\n    \"max_tokens\": 1024\n  },\n  \"gemma-3-4b\": {\n    \"model\": \"google/gemma-3-4b-it\",\n    \"name\": \"Gemma 3 4B\",\n    \"emoji\": \"💨\",\n    \"specialty\": \"超轻量、嵌入式、实时响应\",\n    \"system_prompt\": \"你是一个极小模型的轻量助手。回复要极其简洁，适合实时场景。\",\n    \"strength\": 0.7,\n    \"speed\": \"极快\",\n    \"max_tokens\": 512\n  },\n  \"yi-large\": {\n    \"model\": \"01-ai/yi-large\",\n    \"name\": \"Yi Large\",\n    \"emoji\": \"🌏\",\n    \"specialty\": \"中文优化、文化理解、翻译\",\n    \"system_prompt\": \"你是一个中文优化的大模型，由01.AI开发。精通中文表达、文化背景和翻译任务。\",\n    \"strength\": 0.86,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"glm-5-1\": {\n    \"model\": \"z-ai/glm-5.1\",\n    \"name\": \"GLM 5.1\",\n    \"emoji\": \"🏛️\",\n    \"specialty\": \"中文、知识问答、对话\",\n    \"system_prompt\": \"你是智谱AI的GLM 5.1，一个强大的中文大模型。擅长中文对话、知识问答和文本理解。\",\n    \"strength\": 0.88,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"qwen-3-5-397b\": {\n    \"model\": \"qwen/qwen3.5-397b-a17b\",\n    \"name\": \"Qwen 3.5 397B\",\n    \"emoji\": \"🐉\",\n    \"specialty\": \"超大中文模型、深度理解\",\n    \"system_prompt\": \"你是通义千问Qwen 3.5，一个拥有397B参数的巨型中文模型。擅长深度中文理解和复杂任务。\",\n    \"strength\": 0.95,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"llama-vision-90b\": {\n    \"model\": \"meta/llama-3.2-90b-vision-instruct\",\n    \"name\": \"Llama Vision 90B\",\n    \"emoji\": \"👁️\",\n    \"specialty\": \"图像理解、视觉问答\",\n    \"system_prompt\": \"你是Llama 3.2视觉模型。擅长图像描述、视觉问答和多模态理解。（提示：需要通过视觉接口输入图像）。\",\n    \"strength\": 0.9,\n    \"speed\": \"中等\",\n    \"max_tokens\": 2048\n  },\n  \"nv-embed\": {\n    \"model\": \"nvidia/nv-embedqa-e5-v5\",\n    \"name\": \"NV-EmbedQA\",\n    \"emoji\": \"📐\",\n    \"specialty\": \"文本嵌入、语义搜索、向量化\",\n    \"system_prompt\": \"你是一个嵌入模型，擅长文本向量化。用于语义搜索、相似度计算和RAG应用。\",\n    \"strength\": 0.92,\n    \"speed\": \"极快\",\n    \"max_tokens\": 512,\n    \"is_embedding\": true\n  }\n}\n\nArchive v1.0.0: 5 files, 12353 bytes\n\nFiles: _meta.json (137b), agents/registry.json (7366b), agents/registry.py (9357b), dispatcher/fleet.py (13632b), SKILL.md (2777b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: nvidia-agent-fleet\nslug: nvidia-agent-fleet\nversion: 1.0.0\ndescription: NVIDIA Agent Fleet — 19个模型Agent + 智能调度引擎，自动匹配任务类型分发\nauthor: openclaw\ntags: [nvidia, multi-agent, dispatcher, llm, orchestration]\n---\n\n# 🚀 NVIDIA Agent Fleet\n\n多 Agent 调度系统。每个 NVIDIA 模型拥有专属 Agent 身份，调度器自动分析任务类型并分配最佳 Agent 执行。\n\n## 架构\n\n```\n任务输入 → 调度引擎 → 任务分类 → Agent 匹配 → 执行\n                                            ├── DeepSeek V3.2 🧠\n                                            ├── Qwen Coder 32B 💻\n                                            ├── Kimi K2 🇨🇳\n                                            ├── Llama 4 Maverick 🦙\n                                            └── ... 19 个 Agent\n```\n\n## Agent 阵容 (19个)\n\n| Agent | 模型 | 专长 |\n|-------|------|------|\n| 🧠 deepseek-v3-2 | DeepSeek V3.2 | 推理、逻辑、数学 |\n| 🇨🇳 kimi-k2 | Kimi K2 | 中文、长文本、知识 |\n| 🤔 kimi-k2-thinking | Kimi K2 Thinking | 深度思考、推理 |\n| 🏆 mistral-large-3 | Mistral Large 3 (675B) | 通用智能、多语言 |\n| ⚡ mistral-small | Mistral Small 4 | 快速响应 |\n| 💻 qwen-coder-32b | Qwen2.5 Coder 32B | 代码生成、算法 |\n| 🦾 qwen3-coder-480b | Qwen3 Coder 480B | 架构设计、审查 |\n| 🔧 deepseek-coder | DeepSeek Coder 6.7B | 轻量代码、SQL |\n| ⚙️ codestral | Codestral 22B | 代码补全、填空 |\n| 🦙 llama-3-3-70b | Llama 3.3 70B | 通用对话 |\n| 🦙🆕 llama-4-maverick | Llama 4 Maverick | 创意、分析 |\n| 🔬 gemma-3-27b | Gemma 3 27B | 学术、科学 |\n| 🏎️ phi-4-mini | Phi-4 Mini | 极速响应 |\n| 💨 gemma-3-4b | Gemma 3 4B | 超轻量 |\n| 🌏 yi-large | Yi Large | 中文优化 |\n| 🏛️ glm-5-1 | GLM 5.1 | 中文问答 |\n| 🐉 qwen-3-5-397b | Qwen 3.5 397B | 超大中文 |\n| 👁️ llama-vision-90b | Llama Vision 90B | 视觉理解 |\n| 📐 nv-embed | NV-EmbedQA | 文本嵌入 |\n\n## 使用\n\n### CLI\n\n```bash\n# 自动调度最佳 Agent\nfleet \"用Python写一个快速排序\"\n\n# 分析任务会匹配哪些 Agent\nfleet analyze \"解释一下量子纠缠\"\n\n# 指定 Agent 执行\nfleet agent qwen-coder-32b \"实现二分查找\"\n\n# 多 Agent 协同\nfleet multi \"如何看待人工智能的未来？\"\n\n# 列出所有 Agent\nfleet list\n```\n\n### Python SDK\n\n```python\nfrom dispatcher.fleet import dispatch, select_agents, multi_dispatch\n\n# 自动匹配\nresult = dispatch(\"实现一个Web服务器\")\nprint(result[\"content\"])\n\n# 多Agent协同\nresults = multi_dispatch(\"哲学问题：意识是什么？\")\nfor r in results:\n    print(f\"{r['emoji']} {r['name']}: {r['content'][:100]}...\")\n```\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn7ey9xh63pt0m136bgmx0s67n85dkt5\",\n  \"slug\": \"nvidia-agent-fleet\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1776950855442\n}\n\nFile v1.0.0:agents/registry.json\n\n{\n  \"deepseek-v3-2\": {\n    \"model\": \"deepseek-ai/deepseek-v3.2\",\n    \"name\": \"DeepSeek V3.2\",\n    \"emoji\": \"🧠\",\n    \"specialty\": \"深度推理、逻辑分析、数学问题\",\n    \"system_prompt\": \"你是一个深度推理专家。擅长逻辑分析、数学推导和复杂问题分解。给出逐步推理过程后再给出结论。\",\n    \"strength\": 0.95,\n    \"speed\": \"中等\",\n    \"max_tokens\": 4096\n  },\n  \"kimi-k2\": {\n    \"model\": \"moonshotai/kimi-k2-instruct\",\n    \"name\": \"Kimi K2\",\n    \"emoji\": \"🇨🇳\",\n    \"specialty\": \"中文理解、长文本分析、知识问答\",\n    \"system_prompt\": \"你是一个中文AI助手Kimi，擅长中文理解和知识问答。用流畅的中文回答，注重准确性和深度。\",\n    \"strength\": 0.92,\n    \"speed\": \"中等\",\n    \"max_tokens\": 8192\n  },\n  \"kimi-k2-thinking\": {\n    \"model\": \"moonshotai/kimi-k2-thinking\",\n    \"name\": \"Kimi K2 Thinking\",\n    \"emoji\": \"🤔\",\n    \"specialty\": \"深度思考、复杂推理、多步骤决策\",\n    \"system_prompt\": \"你是一个深度思考型AI。面对复杂问题，先展示完整的思考链条，再进行回答。注重推理的严谨性。\",\n    \"strength\": 0.93,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"mistral-large-3\": {\n    \"model\": \"mistralai/mistral-large-3-675b-instruct-2512\",\n    \"name\": \"Mistral Large 3\",\n    \"emoji\": \"🏆\",\n    \"specialty\": \"超大模型、多语言、通用智能\",\n    \"system_prompt\": \"你是Mistral Large 3，一个拥有675B参数的巨型模型。擅长多语言任务、复杂推理和知识密集型回答。\",\n    \"strength\": 0.97,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"mistral-small\": {\n    \"model\": \"mistralai/mistral-small-4-119b-2603\",\n    \"name\": \"Mistral Small 4\",\n    \"emoji\": \"⚡\",\n    \"specialty\": \"快速响应、通用任务、性价比\",\n    \"system_prompt\": \"你是一个高效的通用AI助手。在保持回答质量的同时注重响应速度。\",\n    \"strength\": 0.85,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"qwen-coder-32b\": {\n    \"model\": \"qwen/qwen2.5-coder-32b-instruct\",\n    \"name\": \"Qwen Coder 32B\",\n    \"emoji\": \"💻\",\n    \"specialty\": \"代码生成、算法实现、Debug\",\n    \"system_prompt\": \"你是一个专业的代码助手。擅长各种编程语言的代码生成、调试、重构和算法实现。给出完整可运行的代码。\",\n    \"strength\": 0.94,\n    \"speed\": \"快\",\n    \"max_tokens\": 4096\n  },\n  \"qwen3-coder-480b\": {\n    \"model\": \"qwen/qwen3-coder-480b-a35b-instruct\",\n    \"name\": \"Qwen3 Coder 480B\",\n    \"emoji\": \"🦾\",\n    \"specialty\": \"大规模代码、架构设计、代码审查\",\n    \"system_prompt\": \"你是一个顶级代码架构师。擅长大规模系统设计、代码审查和复杂重构。提供架构级别的建议。\",\n    \"strength\": 0.96,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"deepseek-coder\": {\n    \"model\": \"deepseek-ai/deepseek-coder-6.7b-instruct\",\n    \"name\": \"DeepSeek Coder\",\n    \"emoji\": \"🔧\",\n    \"specialty\": \"轻量代码、快速修复、SQL查询\",\n    \"system_prompt\": \"你是一个轻量级代码助手。擅长快速代码生成、bug修复、SQL查询和脚本编写。\",\n    \"strength\": 0.82,\n    \"speed\": \"极快\",\n    \"max_tokens\": 2048\n  },\n  \"codestral\": {\n    \"model\": \"mistralai/codestral-22b-instruct-v0.1\",\n    \"name\": \"Codestral 22B\",\n    \"emoji\": \"⚙️\",\n    \"specialty\": \"代码补全、填空、多语言编程\",\n    \"system_prompt\": \"你是Codestral，一个专业的代码补全和生成助手。擅长代码填空、函数补全和多语言编程任务。\",\n    \"strength\": 0.88,\n    \"speed\": \"快\",\n    \"max_tokens\": 4096\n  },\n  \"llama-3-3-70b\": {\n    \"model\": \"meta/llama-3.3-70b-instruct\",\n    \"name\": \"Llama 3.3 70B\",\n    \"emoji\": \"🦙\",\n    \"specialty\": \"通用对话、通用知识、多任务\",\n    \"system_prompt\": \"你是一个友好且知识渊博的AI助手。能用简洁清晰的语言回答各种问题。\",\n    \"strength\": 0.9,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"llama-4-maverick\": {\n    \"model\": \"meta/llama-4-maverick-17b-128e-instruct\",\n    \"name\": \"Llama 4 Maverick\",\n    \"emoji\": \"🦙\",\n    \"specialty\": \"最新模型、创意写作、分析\",\n    \"system_prompt\": \"你是Meta最新的Llama 4 Maverick模型。擅长创意写作、深度分析和创新思维。给出有洞察力的回答。\",\n    \"strength\": 0.91,\n    \"speed\": \"快\",\n    \"max_tokens\": 4096\n  },\n  \"gemma-3-27b\": {\n    \"model\": \"google/gemma-3-27b-it\",\n    \"name\": \"Gemma 3 27B\",\n    \"emoji\": \"🔬\",\n    \"specialty\": \"学术、科学研究、数学\",\n    \"system_prompt\": \"你是Google Gemma 3，擅长学术讨论、科学研究和数学相关的任务。给出严谨准确的回答。\",\n    \"strength\": 0.89,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"phi-4-mini\": {\n    \"model\": \"microsoft/phi-4-mini-instruct\",\n    \"name\": \"Phi-4 Mini\",\n    \"emoji\": \"🏎️\",\n    \"specialty\": \"极速响应、简单问答、分类\",\n    \"system_prompt\": \"你是一个超快速的轻量AI。擅长简单问答、信息提取、分类和摘要任务。回答要简洁。\",\n    \"strength\": 0.78,\n    \"speed\": \"极快\",\n    \"max_tokens\": 1024\n  },\n  \"gemma-3-4b\": {\n    \"model\": \"google/gemma-3-4b-it\",\n    \"name\": \"Gemma 3 4B\",\n    \"emoji\": \"💨\",\n    \"specialty\": \"超轻量、嵌入式、实时响应\",\n    \"system_prompt\": \"你是一个极小模型的轻量助手。回复要极其简洁，适合实时场景。\",\n    \"strength\": 0.7,\n    \"speed\": \"极快\",\n    \"max_tokens\": 512\n  },\n  \"yi-large\": {\n    \"model\": \"01-ai/yi-large\",\n    \"name\": \"Yi Large\",\n    \"emoji\": \"🌏\",\n    \"specialty\": \"中文优化、文化理解、翻译\",\n    \"system_prompt\": \"你是一个中文优化的大模型，由01.AI开发。精通中文表达、文化背景和翻译任务。\",\n    \"strength\": 0.86,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"glm-5-1\": {\n    \"model\": \"z-ai/glm-5.1\",\n    \"name\": \"GLM 5.1\",\n    \"emoji\": \"🏛️\",\n    \"specialty\": \"中文、知识问答、对话\",\n    \"system_prompt\": \"你是智谱AI的GLM 5.1，一个强大的中文大模型。擅长中文对话、知识问答和文本理解。\",\n    \"strength\": 0.88,\n    \"speed\": \"快\",\n    \"max_tokens\": 2048\n  },\n  \"qwen-3-5-397b\": {\n    \"model\": \"qwen/qwen3.5-397b-a17b\",\n    \"name\": \"Qwen 3.5 397B\",\n    \"emoji\": \"🐉\",\n    \"specialty\": \"超大中文模型、深度理解\",\n    \"system_prompt\": \"你是通义千问Qwen 3.5，一个拥有397B参数的巨型中文模型。擅长深度中文理解和复杂任务。\",\n    \"strength\": 0.95,\n    \"speed\": \"慢\",\n    \"max_tokens\": 4096\n  },\n  \"llama-vision-90b\": {\n    \"model\": \"meta/llama-3.2-90b-vision-instruct\",\n    \"name\": \"Llama Vision 90B\",\n    \"emoji\": \"👁️\",\n    \"specialty\": \"图像理解、视觉问答\",\n    \"system_prompt\": \"你是Llama 3.2视觉模型。擅长图像描述、视觉问答和多模态理解。（提示：需要通过视觉接口输入图像）。\",\n    \"strength\": 0.9,\n    \"speed\": \"中等\",\n    \"max_tokens\": 2048\n  },\n  \"nv-embed\": {\n    \"model\": \"nvidia/nv-embedqa-e5-v5\",\n    \"name\": \"NV-EmbedQA\",\n    \"emoji\": \"📐\",\n    \"specialty\": \"文本嵌入、语义搜索、向量化\",\n    \"system_prompt\": \"你是一个嵌入模型，擅长文本向量化。用于语义搜索、相似度计算和RAG应用。\",\n    \"strength\": 0.92,\n    \"speed\": \"极快\",\n    \"max_tokens\": 512,\n    \"is_embedding\": true\n  }\n}","readmeExcerpt":"Skill: Nvidia Agent Fleet Owner: clementgu Summary: NVIDIA Agent Fleet — 19个模型Agent + 智能调度引擎 + 并行协作 Tags: latest:1.1.2 Version history: v1.1.2 | 2026-06-30T00:49:00.833Z | auto nvidia-agent-fleet v1.1.2 - Updated agent registry (agents/registry.json) for improvements or corrections. - Removed the skill-card.md file. - No public-facing feature or documentation changes. v1.1.1 | 2026-05-04T02:43:10.782Z | auto nvidia-a","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"任务输入 → 调度引擎 → 任务分类 → Agent 匹配 → 执行\n                                            ├── DeepSeek V3.2 🧠\n                                            ├── Qwen Coder 32B 💻\n                                            ├── Kimi K2 🇨🇳\n                                            ├── Llama 4 Maverick 🦙\n                                            └── ... 19 个 Agent"},{"language":"bash","snippet":"# 自动调度最佳 Agent\nfleet \"用Python写一个快速排序\"\n\n# 分析任务会匹配哪些 Agent\nfleet analyze \"解释一下量子纠缠\"\n\n# 指定 Agent 执行\nfleet agent qwen-coder-32b \"实现二分查找\"\n\n# 多 Agent 协同\nfleet multi \"如何看待人工智能的未来？\"\n\n# 列出所有 Agent\nfleet list"},{"language":"bash","snippet":"# 多 Agent 并行协同（同时调不同模型，互不等待）\nfleet --multi --parallel \"对比茅台和五粮液的财务状况\"\n\n# 指定 Agent 并行\nfleet --multi --parallel --agent deepseek-v3-2,kimi-k2 \"分析这个创业方案\"\n\n# 仍支持原有串行模式\nfleet --multi \"分析市场趋势\""},{"language":"text","snippet":"串行: 任务A→任务B→任务C→任务D = 4个加起来\n并行: 同时发4个请求 = 最慢那个决定总时间\n加速比: x2~4 (取决于模型)"},{"language":"text","snippet":"1. $NVIDIA_API_KEY (环境变量)\n2. ~/.zshrc (常用 shell 配置)\n3. openclaw.json (OpenClaw 配置)"},{"language":"python","snippet":"from dispatcher.fleet import dispatch, select_agents, multi_dispatch\n\n# 自动匹配\nresult = dispatch(\"实现一个Web服务器\")\nprint(result[\"content\"])\n\n# 多Agent协同（串行）\nresults = multi_dispatch(\"哲学问题：意识是什么？\")\n\n# 多Agent协同（并行执行）🚀\nresults = multi_dispatch(\"分析市场\", parallel=True)\nfor r in results:\n    print(f\"{r['emoji']} {r['name']}: {r['content'][:100]}...\")"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: nvidia-agent-fleet\nslug: nvidia-agent-fleet\nversion: 1.1.0\ndescription: NVIDIA Agent Fleet — 19个模型Agent + 智能调度引擎 + 并行协作\nauthor: openclaw\ntags: [nvidia, multi-agent, dispatcher, llm, orchestration, parallel]\n---\n\n# 🚀 NVIDIA Agent Fleet\n\n多 Agent 调度系统。每个 NVIDIA 模型拥有专属 Agent 身份，调度器自动分析任务类型并分配最佳 Agent 执行。\n\n## 架构\n\n```\n任务输入 → 调度引擎 → 任务分类 → Agent 匹配 → 执行\n                                            ├── DeepSeek V3.2 🧠\n                                            ├── Qwen Coder 32B 💻\n                                            ├── Kimi K2 🇨🇳\n                                            ├── Llama 4 Maverick 🦙\n                                            └── ... 19 个 Agent\n```\n\n## Agent 阵容 (19个)\n\n| Agent | 模型 | 专长 |\n|-------|------|------|\n| 🧠 deepseek-v3-2 | DeepSeek V3.2 | 推理、逻辑、数学 |\n| 🇨🇳 kimi-k2 | Kimi K2 | 中文、长文本、知识 |\n| 🤔 kimi-k2-thinking | Kimi K2 Thinking | 深度思考、推理 |\n| 🏆 mistral-large-3 | Mistral Large 3 (675B) | 通用智能、多语言 |\n| ⚡ mistral-small | Mistral Small 4 | 快速响应 |\n| 💻 qwen-coder-32b | Qwen2.5 Coder 32B | 代码生成、算法 |\n| 🦾 qwen3-coder-480b | Qwen3 Coder 480B | 架构设计、审查 |\n| 🔧 deepseek-coder | DeepSeek Coder 6.7B | 轻量代码、SQL |\n| ⚙️ codestral | Codestral 22B | 代码补全、填空 |\n| 🦙 llama-3-3-70b | Llama 3.3 70B | 通用对话 |\n| 🦙🆕 llama-4-maverick | Llama 4 Maverick | 创意、分析 |\n| 🔬 gemma-3-27b | Gemma 3 27B | 学术、科学 |\n| 🏎️ phi-4-mini | Phi-4 Mini | 极速响应 |\n| 💨 gemma-3-4b | Gemma 3 4B | 超轻量 |\n| 🌏 yi-large | Yi Large | 中文优化 |\n| 🏛️ glm-5-1 | GLM 5.1 | 中文问答 |\n| 🐉 qwen-3-5-397b | Qwen 3.5 397B | 超大中文 |\n| 👁️ llama-vision-90b | Llama Vision 90B | 视觉理解 |\n| 📐 nv-embed | NV-EmbedQA | 文本嵌入 |\n\n## 使用\n\n### CLI\n\n```bash\n# 自动调度最佳 Agent\nfleet \"用Python写一个快速排序\"\n\n# 分析任务会匹配哪些 Agent\nfleet analyze \"解释一下量子纠缠\"\n\n# 指定 Agent 执行\nfleet agent qwen-coder-32b \"实现二分查找\"\n\n# 多 Agent 协同\nfleet multi \"如何看待人工智能的未来？\"\n\n# 列出所有 Agent\nfleet list\n```\n\n### ⚡ 并行执行（v1.1.0 新增）\n\n```bash\n# 多 Agent 并行协同（同时调不同模型，互不等待）\nfleet --multi --parallel \"对比茅台和五粮液的财务状况\"\n\n# 指定 Agent 并行\nfleet --multi --parallel --agent deepseek-v3-2,kimi-k2 \"分析这个创业方案\"\n\n# 仍支持原有串行模式\nfleet --multi \"分析市场趋势\"\n```\n\n**并行 vs 串行 速度对比**\n```\n串行: 任务A→任务B→任务C→任务D = 4个加起来\n并行: 同时发4个请求 = 最慢那个决定总时间\n加速比: x2~4 (取决于模型)\n```\n\n### 自动超时保护\n\n每个模型有独立超时时间，快的先出结果，慢的到点自动跳过：\n\n| 模型 | 超时 | 说明 |\n|------|------|------|\n| Llama 4 Maverick | 15s | 写作/报告类，极快 |\n| Qwen Coder 32B | 20s | 代码/数据处理 |\n| Kimi K2 | 25s | 中文分析 |\n| DeepSeek V3.2 | 35s | 深度推理（最慢） |\n| 其他 | 30s | 通用 |\n\n### API Key 自动发现\n\n支持多级检索，无需手动设置环境变量：\n```\n1. $NVIDIA_API_KEY (环境变量)\n2. ~/.zshrc (常用 shell 配置)\n3. openclaw.json (OpenClaw 配置)\n```\n\n### Python SDK\n\n```python\nfrom dispatcher.fleet import dispatch, select_agents, multi_dispatch\n\n# 自动匹配\nresult = dispatch(\"实现一个Web服务器\")\nprint(result[\"content\"])\n\n# 多Agent协同（串行）\nresults = multi_dispatch(\"哲学问题：意识是什么？\")\n\n# 多Agent协同（并行执行）🚀\nresults = multi_dispatch(\"分析市场\", parallel=True)\nfor r in results:\n    print(f\"{r['emoji']} {r['name']}: {r['content'][:100]}...\")\n```\n\n## v1.1.0 变更\n\n### ✨ 新增\n- **并行执行模式** (`--parallel` / `-p`): 多 Agent 同时调用，互不等待\n- **逐模型超时保护**: 每个模型有独立超时，慢的不拖"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn7ey9xh63pt0m136bgmx0s67n85dkt5\",\n  \"slug\": \"nvidia-agent-fleet\",\n  \"version\": \"1.1.2\",\n  \"publishedAt\": 1782780540833\n}"},{"path":"skill-card.md","content":"## Description:\n\nRoutes user tasks to a registry of NVIDIA API-backed model agents, with automatic task classification, selected-agent execution, and optional multi-agent parallel responses.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[clementgu](https://clawhub.ai/user/clementgu)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and agent users use this skill to route prompts to task-specific NVIDIA API model agents for coding, reasoning, Chinese-language tasks, writing, research, quick summaries, and multi-agent comparisons.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Automatic credential discovery can read local credential files and run shell configuration while looking for NVIDIA_API_KEY.\n\nMitigation: Set NVIDIA_API_KEY explicitly and avoid installing or running the skill in environments where shell startup files have side effects.\n\nRisk: Prompt content is transmitted to NVIDIA's API during model calls.\n\nMitigation: Do not submit secrets, regulated data, or private business data unless the deployment has approved handling for that data.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/clementgu/skills/nvidia-agent-fleet)\n- [Publisher profile](https://clawhub.ai/user/clementgu)\n- [NVIDIA API endpoint](https://integrate.api.nvidia.com/v1)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]\n\n**Output Format:** [CLI text output and Python dictionaries containing model responses, usage data, selected agents, and timing metadata]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Outputs depend on selected model agents, NVIDIA_API_KEY availability, configured max token limits, and per-model request timeouts.]\n\n## Skill Version(s):\n\n1.1.2 (source: server release metadata; artifact frontmatter says 1.1.0)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."},{"path":"agents/registry.json","content":"{\n  \"deepseek-v3-2\": {\n    \"model\": \"deepseek-ai/deepseek-v3.2\",\n    \"name\": \"DeepSeek V3.2\",\n    \"emoji\": \"🧠\",\n    \"specialty\": \"深度推理、逻辑分析、数学问题\",\n    \"system_prompt\": \"你是一个深度推理专家。擅长逻辑分析、数学推导和复杂问题分解。给出逐步推理过程后再给出结论。\",\n    \"strength\": 0.95,\n    \"speed\": \"中等\",\n    \"max_tokens\": 4096\n  },\n  \"kimi-k2\": {\n    \"model\": \"moonshotai/kimi-k2-instruct\",\n    \"name\": \"Kimi K2\",\n    \"emoji\": \"🇨🇳\",\n    \"specialty\": \"中文理解、长文本分析、知识问答\",\n    \"system_prompt\": \"你是一个中文AI助手Kimi，擅长中文理解和知识问答。用流畅的中文回答，注重准确性和深度。\",\n    \"strength\": 0.92,\n    \"speed\": \"中等\",\n    \"max_tokens\": 8192\n  },\n  \"kimi-k2-thinking\": {\n    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