What does the preset do?

It intelligently deploys Azure OpenAI models by region capacity, automatically checks the current region, shows alternatives if needed, and deploys using the GlobalStandard SKU.

What are the prerequisites?

Azure CLI installed, active Azure subscription with Cognitive Services permissions, and a PROJECT_RESOURCE_ID for the target project.

Can I customize SKU or capacity?

Preset uses the GlobalStandard SKU by default and does not support custom SKU or capacity configurations; use the customize skill for those needs.

preset

npx machina-cli add skill microsoft/GitHub-Copilot-for-Azure/preset --openclaw

Files (1)

SKILL.md

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Deploy Model to Optimal Region

Automates intelligent Azure OpenAI model deployment by checking capacity across regions and deploying to the best available option.

What This Skill Does

Verifies Azure authentication and project scope
Checks capacity in current project's region
If no capacity: analyzes all regions and shows available alternatives
Filters projects by selected region
Supports creating new projects if needed
Deploys model with GlobalStandard SKU
Monitors deployment progress

Prerequisites

Azure CLI installed and configured
Active Azure subscription with Cognitive Services read/create permissions
Azure AI Foundry project resource ID (PROJECT_RESOURCE_ID env var or provided interactively)
- Format: /subscriptions/{sub-id}/resourceGroups/{rg}/providers/Microsoft.CognitiveServices/accounts/{account}/projects/{project}
- Found in: Azure AI Foundry portal → Project → Overview → Resource ID

Quick Workflow

Fast Path (Current Region Has Capacity)

1. Check authentication → 2. Get project → 3. Check current region capacity
→ 4. Deploy immediately

Alternative Region Path (No Capacity)

1. Check authentication → 2. Get project → 3. Check current region (no capacity)
→ 4. Query all regions → 5. Show alternatives → 6. Select region + project
→ 7. Deploy

Deployment Phases

Phase	Action	Key Commands
1. Verify Auth	Check Azure CLI login and subscription	`az account show`, `az login`
2. Get Project	Parse `PROJECT_RESOURCE_ID` ARM ID, verify exists	`az cognitiveservices account show`
3. Get Model	List available models, user selects model + version	`az cognitiveservices account list-models`
4. Check Current Region	Query capacity using GlobalStandard SKU	`az rest --method GET .../modelCapacities`
5. Multi-Region Query	If no local capacity, query all regions	Same capacity API without location filter
6. Select Region + Project	User picks region; find or create project	`az cognitiveservices account list`, `az cognitiveservices account create`
7. Deploy	Generate unique name, calculate capacity (50% available, min 50 TPM), create deployment	`az cognitiveservices account deployment create`

For detailed step-by-step instructions, see workflow reference.

Error Handling

Error	Symptom	Resolution
Auth failure	`az account show` returns error	Run `az login` then `az account set --subscription <id>`
No quota	All regions show 0 capacity	Defer to the quota skill for increase requests and troubleshooting; check existing deployments; try alternative models
Model not found	Empty capacity list	Verify model name with `az cognitiveservices account list-models`; check case sensitivity
Name conflict	"deployment already exists"	Append suffix to deployment name (handled automatically by `generate_deployment_name` script)
Region unavailable	Region doesn't support model	Select a different region from the available list
Permission denied	"Forbidden" or "Unauthorized"	Verify Cognitive Services Contributor role: `az role assignment list --assignee <user>`

Advanced Usage

# Custom capacity
az cognitiveservices account deployment create ... --sku-capacity <value>

# Check deployment status
az cognitiveservices account deployment show --name <acct> --resource-group <rg> --deployment-name <name> --query "{Status:properties.provisioningState}"

# Delete deployment
az cognitiveservices account deployment delete --name <acct> --resource-group <rg> --deployment-name <name>

Notes

SKU: GlobalStandard only — API Version: 2024-10-01 (GA stable)

Related Skills

microsoft-foundry - Parent skill for Azure AI Foundry operations
quota — For quota viewing, increase requests, and troubleshooting quota errors, defer to this skill
azure-quick-review - Review Azure resources for compliance
azure-cost-estimation - Estimate costs for Azure deployments
azure-validate - Validate Azure infrastructure before deployment

Source

git clone https://github.com/microsoft/GitHub-Copilot-for-Azure/blob/main/plugin/skills/microsoft-foundry/models/deploy-model/preset/SKILL.md

View on GitHub

Overview

Preset automates intelligent deployment of Azure OpenAI models by evaluating capacity across all regions and choosing the best option. It checks the current region first, then suggests alternatives if needed, enabling quick setup and high availability.

How This Skill Works

It authenticates to Azure, reads the target project via PROJECT_RESOURCE_ID, and checks capacity in the current region using the GlobalStandard SKU. If there’s no capacity, it queries all regions, surfaces alternatives, and proceeds to deploy to the selected region while monitoring progress.

When to Use It

Need to deploy quickly to the best available region based on capacity
Current region has no capacity and you want automatic alternatives
Require high-availability deployment across regions
Need automatic region selection with fast setup for a new project
Want to avoid manual SKU or capacity tuning and rely on defaults

Quick Start

Step 1: Ensure Azure CLI is installed and PROJECT_RESOURCE_ID is configured
Step 2: Authenticate to Azure and run the preset to check current region capacity
Step 3: If capacity exists, deployment starts automatically; if not, review alternatives and deploy

Best Practices

Confirm PROJECT_RESOURCE_ID format and environment variables before deployment
Ensure Azure CLI is installed and you are logged in (az login) with the correct subscription
Prefer allowing automatic project creation if needed and verify permissions
Use GlobalStandard SKU as defined by the skill for deployment
Monitor deployment progress and handle name conflicts with automatic suffixing

Example Use Cases

Auto-deploy a new model to the best region, creating a project if needed
Switch to an alternative region when the current region lacks capacity
Deploy with GlobalStandard SKU and monitor progress until completion
Automatically resolve deployment name conflicts with suffixes
Perform multi-region capacity checks to ensure high availability

Frequently Asked Questions

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