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Microsoft Foundry

microsoft/azure-skills
335
microsoft

The Microsoft Foundry skill enables developers to manage AI agents within the Foundry environment, including resource discovery, deployment, testing, evaluation, and troubleshooting. It offers specialized sub-skills for tasks such as creating, deploying, invoking, optimizing, and troubleshooting agents, making it suitable for AI developers and operations teams. The skill supports the full lifecycle of AI agents, from setup and deployment to continuous evaluation and capacity management.

npx skills add https://github.com/microsoft/azure-skills --skill 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 observe Evaluate agent quality, run batch evals, analyze failures, optimize prompts, improve agent instructions, compare versions, and set up CI/CD monitoring observe trace Query traces, analyze latency/failures, correlate eval results to specific responses via App Insights customEvents trace troubleshoot View container logs, query telemetry, diagnose failures troubleshoot create Create new hosted agent applications. Supports Microsoft Agent Framework, LangGraph, or custom frameworks in Python or C#. Downloads starter samples from foundry-samples repo. create eval-datasets Harvest production traces into evaluation datasets, manage dataset versions and splits, track evaluation metrics over time, detect regressions, and maintain full lineage from trace to deployment. Use for: create dataset from traces, dataset versioning, evaluation trending, regression detection, dataset comparison, eval lineage. eval-datasets 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. 💡 Prompt Optimization: For requests like "optimize my prompt" or "improve my agent instructions," load observe and use the prompt_optimize MCP tool through that eval-driven workflow.

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 → 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 Optimize / improve agent prompt or instructions observe (Step 4: Optimize) Evaluate and optimize agent (full loop) observe Troubleshoot an agent issue invoke → troubleshoot Fix a broken agent (troubleshoot + redeploy) invoke → troubleshoot → apply fixes → deploy → invoke Start/stop agent container deploy

Agent: .foundry Workspace Standard

Every agent source folder should keep Foundry-specific state under .foundry/:

<agent-root>/
  .foundry/
    agent-metadata.yaml
    datasets/
    evaluators/
    results/
  • agent-metadata.yaml is the required source of truth for environment-specific project settings, agent names, registry details, and evaluation test cases.
  • datasets/ and evaluators/ are local cache folders. Reuse them when they are current, and ask before refreshing or overwriting them.
  • See Agent Metadata Contract for the canonical schema and workflow rules.

Agent: Setup References

Agent: Project Context Resolution

Agent skills should run this step only when they need configuration values they don't already have. If a value (for example, agent root, environment, project endpoint, or 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: Discover Agent Roots

Search the workspace for .foundry/agent-metadata.yaml.

  • One match → use that agent root.
  • Multiple matches → require the user to choose the target agent folder.
  • No matches → for create/deploy workflows, seed a new .foundry/ folder during setup; for all other workflows, stop and ask the user which agent source folder to initialize.

Step 2: Resolve Environment

Read .foundry/agent-metadata.yaml and resolve the environment in this order:

  1. Environment explicitly named by the user
  2. Environment already selected earlier in the session
  3. defaultEnvironment from metadata If the metadata contains multiple environments and none of the rules above selects one, prompt the user to choose. Keep the selected agent root and environment visible in every workflow summary.

Step 3: Resolve Common Configuration

Use the selected environment in agent-metadata.yaml as the primary source: Metadata Field Resolves To Used By environments.<env>.projectEndpoint Project endpoint deploy, invoke, observe, trace, troubleshoot environments.<env>.agentName Agent name invoke, observe, trace, troubleshoot environments.<env>.azureContainerRegistry ACR registry name / image URL prefix deploy environments.<env>.testCases[] Dataset + evaluator + threshold bundles observe, eval-datasets

Step 4: Bootstrap Missing Metadata (Create/Deploy Only)

If create/deploy is initializing a new .foundry workspace and metadata fields are still missing, check if azure.yaml exists in the project root. If found, run azd env get-values and use it to seed agent-metadata.yaml before continuing. azd Variable Seeds AZURE_AI_PROJECT_ENDPOINT or AZURE_AIPROJECT_ENDPOINT environments.<env>.projectEndpoint AZURE_CONTAINER_REGISTRY_NAME or AZURE_CONTAINER_REGISTRY_ENDPOINT environments.<env>.azureContainerRegistry AZURE_SUBSCRIPTION_ID Azure subscription for trace/troubleshoot lookups

Step 5: Collect Missing Values

Use the ask_user or askQuestions tool only for values not resolved from the user's message, session context, metadata, or azd bootstrap. Common values skills may need:

  • Agent root — Target folder containing .foundry/agent-metadata.yaml
  • Environment — dev, prod, or another environment key from metadata
  • Project endpoint — AI Foundry project endpoint URL
  • Agent name — Name of the target agent

💡 Tip: If the user already provides the agent path, environment, project endpoint, or agent name, 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

GitHub Owner

Owner: microsoft

GitHub Links

Files

deploy

invoke

observe

trace

troubleshoot

create

eval-datasets

create-foundry-project.md

create-foundry-resource.md

SKILL.md

quota.md

rbac.md

observe

create

deploy

invoke

Agent Metadata Contract

Standard Agent Setup

Private Network Standard Agent Setup

Python

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