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引导你逐步自定义 Azure OpenAI 模型部署参数与高级选项。
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 "customize" 技能： 1. 下载 https://raw.githubusercontent.com/microsoft/GitHub-Copilot-for-Azure/main/plugin/skills/microsoft-foundry/models/deploy-model/customize/SKILL.md 2. 保存为 ~/.claude/skills/customize/SKILL.md 3. 装好后重载技能，告诉我可以用了
请引导我部署 Azure OpenAI 模型到生产环境。我想自己选择模型版本、SKU、容量、内容过滤策略，并查看动态配额、优先处理和 spillover 等高级选项。
得到一个分步骤的部署引导，帮助用户逐项确认模型版本、SKU、容量、RAI 策略和高级配置。
我需要创建一个 Provisioned Managed 部署，请一步步帮我选择合适的模型版本、PTU 容量和相关高级设置，并说明每个选项的影响。
输出面向 PTU 场景的交互式部署流程，并说明容量与高级选项对吞吐和成本的影响。
帮我自定义一个 Azure OpenAI 部署，重点是选择合适的内容过滤策略、SKU 和容量设置，确保满足业务安全要求并兼顾性能。
生成围绕安全策略、SKU 与容量的定制化部署引导，便于在合规与性能之间做取舍。
Interactive guided workflow for deploying Azure OpenAI models with full customization control over version, SKU, capacity, content filtering, and advanced options.
| Property | Description |
|---|---|
| Flow | Interactive step-by-step guided deployment |
| Customization | Version, SKU, Capacity, RAI Policy, Advanced Options |
| SKU Support | GlobalStandard, Standard, ProvisionedManaged, DataZoneStandard |
| Best For | Precise control over deployment configuration |
| Authentication | Azure CLI (az login) |
| Tools | Azure CLI, MCP tools (optional) |
Use this skill when you need over deployment configuration:
Alternative: Use preset for quick deployment to the best available region with automatic configuration.
| Feature | customize | preset |
|---|---|---|
| Focus | Full customization control | Optimal region selection |
| Version Selection | User chooses from available | Uses latest automatically |
| SKU Selection | User chooses (GlobalStandard/Standard/PTU) | GlobalStandard only |
| Capacity | User specifies exact value | Auto-calculated (50% of available) |
| RAI Policy | User selects from options | Default policy only |
| Region | Current region first, falls back to all regions if no capacity | Checks capacity across all regions upfront |
| Use Case | Precise deployment requirements | Quick deployment to best region |
/subscriptions/{sub}/resourceGroups/{rg}/providers/Microsoft.CognitiveServices/accounts/{account}/projects/{project})az login)PROJECT_RESOURCE_ID environment variable1. Verify Authentication
2. Get Project Resource ID
3. Verify Project Exists
4. Get Model Name (if not provided)
5. List Model Versions → User Selects
6. List SKUs for Version → User Selects
7. Get Capacity Range → User Configures
7b. If no capacity: Cross-Region Fallback → Query all regions → User selects region/project
8. List RAI Policies → User Selects
9. Configure Advanced Options (if applicable)
10. Configure Version Upgrade Policy
11. Generate Deployment Name
12. Review Configuration
13. Execute Deployment & Monitor
If user accepts all defaults (latest version, GlobalStandard SKU, recommended capacity, default RAI policy, standard upgrade policy), deployment completes in ~5 interactions.
⚠️ MUST READ: Before executing any phase, load references/customize-workflow.md for the full scripts and implementation details. The summaries below describe what each phase does — the reference file contains the how (CLI commands, quota patterns, capacity formulas, cross-region fallback logic).
| Phase | Action | Key Details |
|---|---|---|
| 1. Verify Auth | Check az account show; prompt az login if needed | Verify correct subscription is active |
| 2. Get Project ID | Read PROJECT_RESOURCE_ID env var or prompt user | ARM resource ID format required |
| 3. Verify Project | Parse resource ID, call az cognitiveservices account show | Extracts subscription, RG, account, project, region |
…
分析并精简 Markdown 内容，降低 token 消耗并提升 AI 处理效率。
帮助你跨区域与项目查询 Azure OpenAI 容量配额并推荐最佳部署位置