| name | microsoft-foundry |
| description | Use this skill to work with Microsoft Foundry (Azure AI Foundry) and tools from Foundry MCP server: deploy AI models, manage AI agents (create, deploy, invoke, run, troubleshoot Foundry Agents), manage RBAC permissions and role assignments, manage quotas and capacity, create Foundry resources. USE FOR: Microsoft Foundry, AI Foundry, create agent, deploy agent, debug agent, invoke agent, run agent, agent chat, evaluate agent, agent monitoring, deploy model, model catalog, knowledge index, create Foundry project, new Foundry project, set up Foundry, onboard to Foundry, create Foundry resource, create AI Services, AIServices kind, register resource provider, enable Cognitive Services, setup AI Services account, create resource group for Foundry, RBAC, role assignment, quota, capacity, TPM, deployment failure, QuotaExceeded. DO NOT USE FOR: Azure Functions (use azure-functions), App Service (use azure-create-app), generic Azure resource creation (use azure-create-app). |
Microsoft Foundry Skill
This skill helps developers work with Microsoft Foundry resources, covering model discovery and deployment, complete dev lifecycle of AI agent, evaluation workflows, and troubleshooting.
Sub-Skills
MANDATORY: Before executing ANY workflow, you MUST read the corresponding sub-skill document. Do not call MCP tools for a workflow without reading its skill document. This applies even if you already know the MCP tool parameters — the skill document contains required workflow steps, pre-checks, and validation logic that must be followed. This rule applies on every new user message that triggers a different workflow, even if the skill is already loaded.
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 |
|---|
| deploy | Containerize, build, push to ACR, create/update/start/stop/clone agent deployments | deploy |
| invoke | Send messages to an agent, single or multi-turn conversations | invoke |
| troubleshoot | View container logs, query telemetry, diagnose failures | troubleshoot |
| create/agent-framework | Create agents and workflows using Microsoft Agent Framework SDK. Supports single-agent and multi-agent workflow patterns with HTTP server and F5/debug support. | create/agent-framework |
| 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 |
| 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 workflows (deploy → invoke).
💡 Model Deployment: Use models/deploy-model for all deployment scenarios — it intelligently routes between quick preset deployment, customized deployment with full control, and capacity discovery across regions.
Agent Development Lifecycle
Match user intent to the correct workflow. Read each sub-skill in order before executing.
| User Intent | Workflow (read in order) |
|---|
| Create a new agent from scratch | create/agent-framework → deploy → invoke |
| Deploy an agent (code already exists) | deploy → invoke |
| Update/redeploy an agent after code changes | deploy → invoke |
| Invoke/test/chat with an agent | invoke |
| Troubleshoot an agent issue | invoke → troubleshoot |
| Fix a broken agent (troubleshoot + redeploy) | invoke → troubleshoot → apply fixes → deploy → invoke |
| Start/stop agent container | deploy |
Agent: Project Context Resolution
Agent skills should run this step only when they need configuration values they don't already have. If a value (e.g., project endpoint, agent name) is already known from the user's message or a previous skill in the same session, skip resolution for that value.
Step 1: Detect azd Project
If any required configuration value is missing, check if azure.yaml exists in the project root (workspace root or user-specified project path). If found, run azd env get-values to load environment variables.
Step 2: Resolve Common Configuration
Match missing values against the azd environment:
| azd Variable | Resolves To | Used By |
|---|
AZURE_AI_PROJECT_ENDPOINT or AZURE_AIPROJECT_ENDPOINT | Project endpoint | deploy, invoke, troubleshoot |
AZURE_CONTAINER_REGISTRY_NAME or AZURE_CONTAINER_REGISTRY_ENDPOINT | ACR registry name / image URL prefix | deploy |
AZURE_SUBSCRIPTION_ID | Azure subscription | troubleshoot |
Step 3: Collect Missing Values
Use the ask_user or askQuestions tool only for values not resolved from the user's message, session context, or azd environment. Common values skills may need:
- Project endpoint — AI Foundry project endpoint URL
- Agent name — Name of the target agent
💡 Tip: If the user provides a project endpoint or agent name in their initial message, extract it directly — do not ask again.
Agent: Agent Types
All agent skills support two agent types:
| Type | Kind | Description |
|---|
| Prompt | "prompt" | LLM-based agents backed by a model deployment |
| Hosted | "hosted" | Container-based agents running custom code |
Use agent_get MCP tool to determine an agent's type when needed.
Tool Usage Conventions
- Use the
ask_user or askQuestions tool whenever collecting information from the user
- Use the
task or runSubagent tool to delegate long-running or independent sub-tasks (e.g., env var scanning, status polling, Dockerfile generation)
- Prefer Azure MCP tools over direct CLI commands when available
- Reference official Microsoft documentation URLs instead of embedding CLI command syntax
Additional Resources
SDK Quick Reference
