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forgefabrik
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customize

Guide interactive Azure OpenAI model deployments with version, SKU, capacity, and RAI policy controls.

Updated Jun 5, 2026
Search Tags:#deployment#azure#quota#openai#sku#content-filter#ptu
One-click install
npx skills add https://github.com/forgefabrik/agent-bigbrother --skill customize-forgefabrik
Or copy as Structured Prompt for Agentâ–¼
Please help me install this Agent Skill.
Skill: customize
Source: https://github.com/forgefabrik/agent-bigbrother/tree/main/.agents/skills/microsoft-foundry/models/deploy-model/customize
Command: npx skills add https://github.com/forgefabrik/agent-bigbrother --skill customize-forgefabrik

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

The Customize Skill removes guesswork from Azure OpenAI deployments by guiding you through the exact choices needed for version, SKU, capacity, content filtering, and advanced deployment behavior.

Core Features & Use Cases

  • Model version selection, SKU selection, and capacity planning for precise deployments.
  • RAI policy choice, dynamic quota, priority processing, spillover, and version upgrade controls.
  • Useful for production launches, PTU reservations, development test deployments, and cross-region fallback when local capacity is unavailable.

Quick Start

Use the customize skill to guide me through a fully customized Azure OpenAI deployment for my target model and project.

Frequently Asked Questions about customize

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

FAQPage Schema
How do I deploy Azure OpenAI models with specific SKU and capacity settings?â–¼

To deploy Azure OpenAI models with specific SKU and capacity settings, use a guided process for interactive model deployment that validates capacity and applies model-provider-specific deployment rules. This ensures precise control over version selection and resource allocation.

Can I reserve PTU capacity for an Azure OpenAI deployment?â–¼

Yes, you can reserve PTU capacity for an Azure OpenAI deployment by using customized provisioning scenarios. The deployment process checks live quota availability and validates capacity reservations to ensure your PTU allocation succeeds.

How do I apply a custom RAI content filter policy to an Azure OpenAI deployment?â–¼

To apply a custom RAI content filter policy to an Azure OpenAI deployment, select your desired RAI policy during the interactive deployment workflow. This ensures your model adheres to specific content filtering rules before going live.

What Azure CLI checks are required before deploying a customized OpenAI model?â–¼

Before deploying a customized OpenAI model, live Azure CLI and REST checks are required for authentication, model catalog lookup, and capacity validation. These checks verify your environment and ensure deployment rules are met.

Does Azure OpenAI deployment support priority processing and spillover configuration?â–¼

Yes, Azure OpenAI deployment supports priority processing and spillover configuration through advanced deployment options. These controls allow you to manage traffic distribution and version upgrades for production launches.

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