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/deploy-model

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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).

Use this Skill: https://skilld.dev/gh/microsoft/github-copilot-for-azure/deploy-model

This session only. Nothing lands on disk.

presetEXAMPLES.md

≈748 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Examples: preset

Example 1: Fast Path — Current Region Has Capacity

Scenario: Deploy gpt-4o to project in East US, which has capacity. Result: Deployed in ~45s. No region selection needed. 100K TPM default, GlobalStandard SKU.

Example 2: Alternative Region — No Capacity in Current Region

Scenario: Deploy gpt-4-turbo to dev project in West US 2 (no capacity). Result: Queried all regions → user selected East US 2 (120K available) → deployed in ~2 min.

Example 3: Create New Project in Optimal Region

Scenario: Deploy gpt-4o-mini in Europe for data residency; no existing European project. Result: Created AI Services hub + project in Sweden Central → deployed in ~4 min with 150K TPM.

Example 4: Insufficient Quota Everywhere

Scenario: Deploy gpt-4 but all regions have exhausted quota. Result: Graceful failure with actionable guidance:

  1. Request quota increase via the quota skill
  2. List existing deployments consuming quota
  3. Suggest alternative models (gpt-4o, gpt-4o-mini)

Example 5: First-Time User — No Project

Scenario: Deploy gpt-4o with no existing Microsoft Foundry project. Result: Full onboarding in ~5 min — created resource group, AI Services hub, project, then deployed.

Example 6: Deployment Name Conflict

Scenario: Auto-generated deployment name already exists. Result: Appended random hex suffix (e.g., -7b9e) and retried automatically.

Example 7: Multi-Version Model Selection

Scenario: Deploy "latest gpt-4o" when multiple versions exist. Result: Latest stable version auto-selected. Capacity aggregated across versions.

Example 8: Anthropic Model (claude-sonnet-4-6)

Scenario: Deploy claude-sonnet-4-6 (Anthropic model requiring modelProviderData). Result: User prompted for industry selection → tenant country code and org name fetched automatically → deployed via ARM REST API with modelProviderData payload in ~2 min. Capacity set to 1 (MaaS billing).


Summary of Scenarios

Scenario Duration Key Features
1: Fast Path ~45s Current region has capacity, direct deploy
2: Alt Region ~2m Region selection, project switch
3: New Project ~4m Project creation in optimal region
4: No Quota N/A Graceful failure, actionable guidance
5: First-Time ~5m Complete onboarding
6: Name Conflict ~1m Auto-retry with suffix
7: Multi-Version ~1m Latest version auto-selected
8: Anthropic ~2m Industry prompt, tenant info, REST API deploy

Common Patterns

A: Quick Deploy     Auth → Get Project → Check Region (✓) → Deploy
B: Region Select    Auth → Get Project → Region (✗) → Query All → Select → Deploy
C: Full Onboarding  Auth → No Projects → Create Project → Deploy
D: Error Recovery   Deploy (✗) → Analyze → Fix → Retry

Source: SKILL.md on GitHub

No alerts6mo3 checks · Risk SAFE
  • Gen Agent Trust Hub6mo

    This skill provides a unified workflow for deploying Azure OpenAI models using the Azure CLI. It includes intelligent routing, capacity discovery across regions, and support for both standard OpenAI models and Anthropic models on Azure while maintaining security best practices like mandatory project confirmation.

  • Socket6mo

    No alerts

  • Snyk6mo

    Risk: LOW · No issues

Signed by skilld at 54c2a28. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 18 hours ago.

Activeupdated 2 months ago
metadata
{
  "author": "Microsoft",
  "version": "1.0.0"
}
  • azure
  • openai
  • deployment
  • model
  • capacity
  • sku
  • foundry
  • azure-cli
  • provisioning

README badge

README badge for microsoft/github-copilot-for-azure/deploy-model

Deploys Azure OpenAI models with intent-based routing to preset deployment, customized configuration, or capacity discovery workflows. Routes user requests to the appropriate mode based on whether they want quick defaults, custom SKU/capacity/RAI settings, or to find available capacity across regions and projects.

Generated from the current SKILL.md.

Does this skill handle deployments in azd-managed Foundry projects?
No. For azd projects (those scaffolded from azd-ai-starter-basic or via azd ai agent init), declare deployments in azure.yaml instead — azd provision will create them through Bicep. Use this skill only for standalone Foundry projects or ad-hoc deployments outside the azd lifecycle.
What happens if I don't specify a project?
The skill checks the PROJECT_RESOURCE_ID environment variable first, then looks for clues in your prompt. If neither exists, it queries your projects and suggests the current one, with a confirmation step before deploying.
Can this skill list or delete existing deployments?
No. This skill creates deployments only. Use the foundry_models_deployments_list MCP tool to list existing deployments, or the Azure portal to delete them.
Does this skill validate quota and SKU support before deploying?
Yes. It queries the model catalog to confirm the model supports your chosen SKU, and checks your subscription's available quota via Azure CLI before presenting any deployment options.
What should I do if I hit a quota limit?
Defer to the quota skill (quota/quota.md) for quota increase requests, usage monitoring, and troubleshooting quota errors.

Generated from the current SKILL.md. These answers refresh after source changes.