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。
该技能材料显示为纯提示词路由型 Skill,本身未声明需要密钥、远程端点或本机执行能力;结合 GitHub 开源来源与一定社区采用度,整体风险较低。需要注意的是,其业务目标涉及 Azure 模型部署与容量查询,实际联动的下游工具权限应单独核查。
材料明确标注“无”所需密钥/环境变量;作为 prompt-only 技能,文档中未见其直接处理、存储或请求凭证的描述,凭证滥用暴露面低。
系统检查项与材料均未声明任何远程端点,host 为“无”;该技能描述为意图分析与路由说明,未见自身向外部网络发送用户数据的事实依据。
已判定为 prompt-only,材料内容主要是根据用户意图选择 preset/customize/capacity 子技能的路由规则,未见本机起进程、执行脚本或调用系统命令的描述。
文档未声明可读写本地文件、数据库或其他用户数据资源;当前可见能力局限于对用户请求进行分类与流程编排,未发现过度数据访问迹象。
来源为 GitHub 上的微软相关开源仓库,源码可审计,且有 222 star 的社区采用度,这些都是降低风险的正面信号;许可证未声明、维护状态未知,建议持续关注,但不足以单独升高评级。
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.
…
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