microsoft-foundry
Microsoft Foundry Skill
This skill helps developers work with Microsoft Foundry resources, covering model discovery and deployment, RAG (Retrieval-Augmented Generation) applications, AI agent creation, evaluation workflows, and troubleshooting.
Sub-Skills
This skill includes specialized sub-skills for specific workflows. Use these instead of the main skill when they match your task:
| Sub-Skill | When to Use | Reference |
|---|---|---|
| project/create | Creating a new Azure AI Foundry project for hosting agents and models. Use when onboarding to Foundry or setting up new infrastructure. | project/create/create-foundry-project.md |
| resource/create | Creating Azure AI Services multi-service resource (Foundry resource) using Azure CLI. Use when manually provisioning AI Services resources with granular control. | resource/create/create-foundry-resource.md |
| models/deploy-model | Unified model deployment with intelligent routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI), and capacity discovery across regions. Routes to sub-skills: preset (quick deploy), customize (full control), capacity (find availability). |
models/deploy-model/SKILL.md |
| agent/create/agent-framework | Creating AI agents and workflows using Microsoft Agent Framework SDK. Supports single-agent and multi-agent workflow patterns with HTTP server and F5/debug support. | agent/create/agent-framework/SKILL.md |
| quota | Managing quotas and capacity for Microsoft Foundry resources. Use when checking quota usage, troubleshooting deployment failures due to insufficient quota, requesting quota increases, or planning capacity. | quota/quota.md |
| rbac | Managing RBAC permissions, role assignments, managed identities, and service principals for Microsoft Foundry resources. Use for access control, auditing permissions, and CI/CD setup. | rbac/rbac.md |
💡 Tip: For a complete onboarding flow:
project/create→agent/create→agent/deploy. If the user wants to create AND deploy an agent, start withagent/createwhich can optionally invokeagent/deployautomatically.
💡 Model Deployment: Use
models/deploy-modelfor all deployment scenarios — it intelligently routes between quick preset deployment, customized deployment with full control, and capacity discovery across regions.