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deploy-model

Routes Azure OpenAI model deployment requests by detecting user intent and selecting workflows.

Updated Jul 3, 2025
Search Tags:#deployment automation#resource provisioning#azure openai#capacity discovery#intent routing#quota validation
One-click install
npx skills add https://github.com/isakawar/crmKvitkovaPovnya --skill deploy-model-isakawar
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deploy-model
Source: https://github.com/isakawar/crmKvitkovaPovnya/tree/main/.agents/skills/microsoft-foundry/models/deploy-model
Command: npx skills add https://github.com/isakawar/crmKvitkovaPovnya --skill deploy-model-isakawar

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires azure-cli, jq, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Users need a reliable way to deploy Azure OpenAI models across regions while respecting model availability and quota constraints, avoiding trial-and-error that wastes time.

Core Features & Use Cases

  • Intent-based deployment routing: Automatically selects Preset (fast), Customize (full control), or Capacity Discovery (where you can deploy) based on what the user asks.
  • Safe project targeting: Resolves the target project via PROJECT_RESOURCE_ID or prompt context, then forces confirmation before any deployment.
  • Pre-deployment validation: Verifies the selected model supports the SKU and that the subscription has available quota for the SKU+model in the target region.
  • Use cases: Deploy gpt-4o quickly, configure custom deployments with chosen version/SKU/capacity/RAI, or answer “where can I deploy with X capacity?” before creating a deployment.

Quick Start

Ask for a fast deployment by saying: deploy gpt-4o to the best available region, using the current project when possible.

Frequently Asked Questions about deploy-model

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

FAQPage Schema
How do I deploy an Azure OpenAI model to a region with available quota?▼

Azure OpenAI model deployment is automated by detecting user intent and routing to preset, customization, or capacity-discovery workflows. It validates subscription quota and model SKU support in the target region before provisioning.

How can I check which Azure regions have capacity for a specific OpenAI model?▼

Capacity discovery for Azure OpenAI models identifies available regions for your requested SKU and capacity. This workflow answers where you can deploy before executing any actual provisioning.

Do I need to install azure-cli and jq to provision Azure AI Foundry deployments?▼

Yes, azure-cli and jq are required dependencies to provision Azure AI Foundry deployments. These command-line tools execute the scripts necessary for project resolution, quota validation, and deployment automation.

What is the safest way to target an Azure OpenAI deployment project?▼

Safe project targeting for Azure OpenAI deployments is achieved by resolving the target project via PROJECT_RESOURCE_ID or prompt context. The system enforces mandatory user confirmation of the target project prior to any deployment.

Can I customize Azure OpenAI deployment version and content filtering settings?▼

You can customize Azure OpenAI deployments through the customization workflow, which provides full control over model version, SKU, capacity, and RAI content filtering settings during the provisioning process.

Why does my Azure OpenAI deployment fail with a quota validation error?▼

Azure OpenAI deployment fails quota validation when the subscription lacks available quota for the requested SKU and model in the target region. Pre-deployment validation verifies this constraint to prevent failed provisioning attempts.

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