microsoft-foundryby Azure

Use this skill to work with Microsoft Foundry (Azure AI Foundry): deploy AI models, manage hosted agent and prompt agent, manage RBAC permissions and role assignments, manage quotas and capacity, create Foundry resources.

npx skills add https://github.com/microsoft/GitHub-Copilot-for-Azure --skill microsoft-foundry

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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

Standard Agent Setup - Standard capability-host setup with customer-managed data, search, and AI Services resources.
Private Network Standard Agent Setup - Standard setup with VNet isolation and private endpoints.

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:

Environment explicitly named by the user
Environment already selected earlier in the session
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