/deploy-model

name: deploy-model
description: "Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create)."
license: MIT
metadata:
  author: Microsoft
  version: "1.0.0"

Deploy Model

> **Scope — read this first.** This skill creates model deployments **out-of-band** via Azure CLI / MCP / portal. For azd-managed Foundry projects (those scaffolded from `azd ai agent init`), declare deployments in `azure.yaml services.ai-project.deployments[]` instead — `azd ai agent init` writes the entry from the sample manifest and `azd provision` creates the deployment through Bicep. See [foundry-agent/create/create-hosted.md](../../foundry-agent/create/create-hosted.md) for the Golden Path. Use this skill only for: (a) Foundry projects not managed by an azd project, (b) ad-hoc deployments outside the azd lifecycle.

Unified entry point for all Azure OpenAI model deployment workflows. Analyzes user intent and routes to the appropriate deployment mode.

Quick Reference

| Mode | When to Use | Sub-Skill | |------|-------------|-----------| | **Preset** | Quick deployment, no customization needed | [preset/SKILL.md](preset/SKILL.md) | | **Customize** | Full control: version, SKU, capacity, RAI policy | [customize/SKILL.md](customize/SKILL.md) | | **Capacity Discovery** | Find where you can deploy with specific capacity | [capacity/SKILL.md](capacity/SKILL.md) |

Intent Detection

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

Routing Rules

| 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 |

Multi-Mode Chaining

Some prompts require two modes in sequence:

**Pattern: Capacity → Deploy** When a user specifies a capacity requirement AND wants deployment: 1. Run **Capacity Discovery** to find regions/projects with sufficient quota 2. Present findings to user 3. Ask: "Would you like to deploy with **quick defaults** or **customize settings**?" 4. Route to **Preset** or **Customize** based on answer

> 💡 **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".

Project Selection (All Modes)

Before any deployment, resolve which project to deploy to. This applies to **all** modes (preset, customize, and after capacity discovery).

Resolution Order

1. **Check `PROJECT_RESOURCE_ID` env var** — if set, use it as the default 2. **Check user prompt** — if user named a specific project or region, use that 3. **If neither** — query the user's projects and suggest the current one

Confirmation Step (Required)

**Always 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.

Pre-Deployment Validation (All Modes)

Before presenting any deployment options (SKU, capacity), always validate both of these:

1. **Model supports the SKU** — query the model catalog to confirm the selected model+version supports the target SKU:

   az cognitiveservices model list --location <region> --subscription <sub-id> -o json

Filter for the model, extract `.model.skus[].name` to get supported SKUs.

2. **Subscription has available quota** — check that the user's subscription has unallocated quota for the SKU+model combination:

   az cognitiveservices usage list --location <region> -