preset

Deploy Azure OpenAI models to optimal regions using Azure CLI and REST API.

Updated Mar 17, 2026
One-click install
npx skills add https://github.com/jaypatrick/skills --skill preset-jaypatrick
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: preset
Source: https://github.com/jaypatrick/skills/tree/main/microsoft-foundry/models/deploy-model/preset
Command: npx skills add https://github.com/jaypatrick/skills --skill preset-jaypatrick

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Automates end-to-end deployment of Azure OpenAI models to the optimal region by evaluating capacity across all regions and streamlining project setup.

Core Features & Use Cases

  • Automatic region selection: analyzes capacity across regions and deploys to the best available location.
  • Quick onboarding: creates a new AI Foundry project or uses an existing one, then deploys the model with GlobalStandard SKU.
  • Fast path and fallback: uses current-region capacity when available and gracefully handles region changes with multi-region capacity checks.
  • End-to-end deployment monitoring: tracks provisioning progress and surfaces deployment details.

Quick Start

Authenticate to Azure, ensure you have a project in your target region (or create one), then run the preset deployment workflow to deploy your OpenAI model to the optimal region.

Frequently Asked Questions about preset

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I deploy Azure OpenAI models to the region with the best capacity?

This skill automates OpenAI model deployment by analyzing regional capacity across multiple locations and routing deployments to the optimal available region. It evaluates capacity constraints and selects the best location for your models.

Can I automatically create an AI Foundry project during model deployment?

Yes, you can automatically create a new AI Foundry project or use an existing one during the deployment workflow. It deploys your OpenAI model using the GlobalStandard SKU and handles the project setup for you.

What happens if the current region lacks capacity for my OpenAI deployment?

If the current region lacks capacity, the deployment workflow triggers multi-region capacity checks and gracefully falls back to alternative regions. This ensures your OpenAI model deployment succeeds even if the initial region is full.

How do I monitor the provisioning progress of an Azure OpenAI deployment?

You can monitor the provisioning progress of an Azure OpenAI deployment using the end-to-end deployment monitoring feature. It tracks the deployment status and surfaces the final deployment details once the model is successfully provisioned.

Do I need Azure CLI to automate OpenAI model deployment across regions?

Yes, you need Azure CLI and REST API access to automate OpenAI model deployment across regions. The workflow leverages these prerequisites to check regional capacity, create projects, and execute the deployment.