aiskillstore/microsoft-foundry

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

• Foundry Hosted Agents
• Foundry Agent Runtime Components
• Foundry Samples

SDK Quick Reference

• Python