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preset

Deploy Azure OpenAI models to optimal regions by analyzing capacity and automating deployment.

Updated Apr 12, 2026
Search Tags:#azure#anthropic#model-deployment#ai-foundry#region-selection#capacity-check
One-click install
npx skills add https://github.com/davidrrowley/CortexYouV3 --skill preset-davidrrowley
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: preset
Source: https://github.com/davidrrowley/CortexYouV3/tree/main/.agents/skills/microsoft-foundry/models/deploy-model/preset
Command: npx skills add https://github.com/davidrrowley/CortexYouV3 --skill preset-davidrrowley

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Many Azure AI Foundry projects fail or stall when the current region lacks capacity or when deployment workflows require manual region selection and project setup. This Skill removes the friction of checking regional availability, selecting alternate regions, creating projects, and initiating deployments so teams can get models running quickly and reliably.

Core Features & Use Cases

  • Capacity-aware deployment: Checks the current project's region first and queries multi-region capacities to find the best available location.
  • Project handling and creation: Filters projects by region and can create AI Services hubs and new AI Foundry projects when none exist.
  • Robust deployment flows: Generates unique deployment names, calculates GlobalStandard SKU capacity, supports Anthropic deployments via modelProviderData, and monitors provisioning until completion.
  • Use Case: Quickly deploy an OpenAI model to an alternate region when the production region is out of quota or create a new project in a compliant region for data residency.

Quick Start

Deploy gpt-4o to the best available Azure region for my project and create a new project if none exists.

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 alternate regions when my current region lacks capacity?▼

To deploy Azure OpenAI models when a region lacks capacity, you can analyze regional capacity and automate deployment to an optimal alternate region. This checks the current project first and queries multi-region capacities to find the best available location.

What is the best way to check GlobalStandard SKU capacity before an Azure AI Foundry deployment?▼

Checking GlobalStandard SKU capacity involves calculating available quotas across regions before initiating deployment. This ensures your Azure AI Foundry workflows target regions with sufficient resources, preventing stalls.

Can I automate AI Foundry project creation and region selection for data residency?▼

Yes, you can automate AI Foundry project creation and region selection for data residency. This filters projects by region and creates AI Services hubs and new projects in compliant regions when none exist.

Does Azure AI Foundry support deploying Anthropic models via ARM REST API?▼

Yes, Azure AI Foundry supports deploying Anthropic models via ARM REST API and Azure CLI. It handles Anthropic provider requirements using modelProviderData and monitors provisioning until completion.

Why does my Azure model deployment stall and how do I monitor provisioning status?▼

Azure model deployments stall when regional capacity is exhausted. You can monitor provisioning status through automated Azure CLI or ARM REST API calls, which track deployment progress until success or failure.

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