$ loading_
帮助用户按意图在 Azure OpenAI 中部署模型并查询区域容量与可用性。
复制安装指令，让 AI 自动完成配置 · 推荐新手
请帮我安装 askskill 上的 "deploy-model" 技能： 1. 下载 https://raw.githubusercontent.com/microsoft/GitHub-Copilot-for-Azure/main/plugin/skills/microsoft-foundry/models/deploy-model/SKILL.md 2. 保存为 ~/.claude/skills/deploy-model/SKILL.md 3. 装好后重载技能，告诉我可以用了
请帮我在 Azure OpenAI 中快速部署一个 GPT 模型，用默认推荐配置完成，并告诉我部署名称、区域和后续可调用方式。
返回一个已完成或可执行的快速部署方案，包含模型、区域、部署配置和调用说明。
我要部署指定模型，请使用自定义参数：模型版本、SKU、容量和 RAI policy 都要可配置，并给出最终部署方案与关键参数摘要。
输出一份完整的自定义部署结果或计划，明确列出版本、SKU、容量、策略及部署细节。
请帮我分析这个模型在哪些区域和项目中更适合部署，检查容量与可用性，并推荐最佳部署区域及原因。
返回容量与可用性分析结果，并给出推荐区域、备选区域和选择依据。
Unified entry point for all Azure OpenAI model deployment workflows. Analyzes user intent and routes to the appropriate deployment mode.
| Mode | When to Use | Sub-Skill |
|---|---|---|
| Preset | Quick deployment, no customization needed | preset/SKILL.md |
| Customize | Full control: version, SKU, capacity, RAI policy | customize/SKILL.md |
| Capacity Discovery | Find where you can deploy with specific capacity | capacity/SKILL.md |
Analyze the user's prompt and route to the correct mode:
User Prompt
│
├─ Simple deployment (no modifiers)
│ "deploy gpt-4o", "set up a model"
│ └─> PRESET mode
│
├─ Customization keywords present
│ "custom settings", "choose version", "select SKU",
│ "set capacity to X", "configure content filter",
│ "PTU deployment", "with specific quota"
│ └─> CUSTOMIZE mode
│
├─ Capacity/availability query
│ "find where I can deploy", "check capacity",
│ "which region has X capacity", "best region for 10K TPM",
│ "where is this model available"
│ └─> CAPACITY DISCOVERY mode
│
└─ Ambiguous (has capacity target + deploy intent)
"deploy gpt-4o with 10K capacity to best region"
└─> CAPACITY DISCOVERY first → then PRESET or CUSTOMIZE
| Signal in Prompt | Route To | Reason |
|---|---|---|
| Just model name, no options | Preset | User wants quick deployment |
| "custom", "configure", "choose", "select" | Customize | User wants control |
| "find", "check", "where", "which region", "available" | Capacity | User wants discovery |
| Specific capacity number + "best region" | Capacity → Preset | Discover then deploy quickly |
| Specific capacity number + "custom" keywords | Capacity → Customize | Discover then deploy with options |
| "PTU", "provisioned throughput" | Customize | PTU requires SKU selection |
| "optimal region", "best region" (no capacity target) | Preset | Region optimization is preset's specialty |
Some prompts require two modes in sequence:
Pattern: Capacity → Deploy When a user specifies a capacity requirement AND wants deployment:
💡 Tip: If unsure which mode the user wants, default to Preset (quick deployment). Users who want customization will typically use explicit keywords like "custom", "configure", or "with specific settings".
Before any deployment, resolve which project to deploy to. This applies to all modes (preset, customize, and after capacity discovery).
PROJECT_RESOURCE_ID env var — if set, use it as the defaultAlways confirm the target before deploying. Show the user what will be used and give them a chance to change it:
Deploying to:
Project: <project-name>
Region: <region>
Resource: <resource-group>
Is this correct? Or choose a different project:
1. ✅ Yes, deploy here (default)
2. 📋 Show me other projects in this region
3. 🌍 Choose a different region
If user picks option 2, show top 5 projects in that region:
Projects in <region>:
1. project-alpha (rg-alpha)
2. project-beta (rg-beta)
3. project-gamma (rg-gamma)
...
⚠️ Never deploy without showing the user which project will be used. This prevents accidental deployments to the wrong resource.
…
分析并精简 Markdown 内容，降低 token 消耗并提升 AI 处理效率。
引导你逐步自定义 Azure OpenAI 模型部署参数与高级选项。