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-starter-basic or via 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 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
Customize	Full control: version, SKU, capacity, RAI policy	customize/SKILL.md
Capacity Discovery	Find where you can deploy with specific capacity	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> --subscription <sub-id> -o json
   

   Match by usage name pattern OpenAI.<SKU>.<model-name> (e.g., OpenAI.GlobalStandard.gpt-4o). Compute available = limit - currentValue.

⚠️ Warning: Only present options that pass both checks. Do NOT show hardcoded SKU lists — always query dynamically. SKUs with 0 available quota should be shown as ❌ informational items, not selectable options.

💡 Quota management: For quota increase requests, usage monitoring, and troubleshooting quota errors, defer to the quota skill instead of duplicating that guidance inline.

Prerequisites

All deployment modes require:

• Azure CLI installed and authenticated (az login)
• Active Azure subscription with deployment permissions
• Azure AI Foundry project resource ID (or agent will help discover it via PROJECT_RESOURCE_ID env var)

Sub-Skills

• preset/SKILL.md — Quick deployment to optimal region with sensible defaults
• customize/SKILL.md — Interactive guided flow with full configuration control
• capacity/SKILL.md — Discover available capacity across regions and projects