Use this skill to work with Microsoft Foundry (Azure AI Foundry) and tools from Foundry MCP server: deploy AI models, manage hosted agent and prompt agent (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, hosted agent, 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).
Manages Microsoft Foundry AI agents and models including deployment, invocation, troubleshooting, and resource provisioning.
npx claudepluginhub microsoft/github-copilot-for-azureThis skill inherits all available tools. When active, it can use any tool Claude has access to.
foundry-agent/create/create-prompt.mdfoundry-agent/create/create.mdfoundry-agent/create/references/agent-tools.mdfoundry-agent/create/references/agentframework.mdfoundry-agent/create/references/sdk-operations.mdfoundry-agent/create/references/tool-azure-ai-search.mdfoundry-agent/create/references/tool-bing-grounding.mdfoundry-agent/create/references/tool-file-search.mdfoundry-agent/create/references/tool-mcp.mdfoundry-agent/create/references/tool-memory.mdfoundry-agent/create/references/tool-web-search.mdfoundry-agent/deploy/deploy.mdfoundry-agent/invoke/invoke.mdfoundry-agent/observe/observe.mdfoundry-agent/observe/references/analyze-results.mdfoundry-agent/observe/references/cicd-monitoring.mdfoundry-agent/observe/references/compare-iterate.mdfoundry-agent/observe/references/deploy-and-setup.mdfoundry-agent/observe/references/evaluate-step.mdfoundry-agent/observe/references/optimize-deploy.mdThis skill helps developers work with Microsoft Foundry resources, covering model discovery and deployment, complete dev lifecycle of AI agent, evaluation workflows, and troubleshooting.
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 | 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 |
| 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-modelfor all deployment scenarios — it intelligently routes between quick preset deployment, customized deployment with full control, and capacity discovery across regions.
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 |
| Troubleshoot an agent issue | invoke → troubleshoot |
| Fix a broken agent (troubleshoot + redeploy) | invoke → troubleshoot → apply fixes → deploy → invoke |
| Start/stop agent container | deploy |
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.
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.
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 |
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:
💡 Tip: If the user provides a project endpoint or agent name in their initial message, extract it directly — do not ask again.
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.
ask_user or askQuestions tool whenever collecting information from the usertask or runSubagent tool to delegate long-running or independent sub-tasks (e.g., env var scanning, status polling, Dockerfile generation)Activates when the user asks about AI prompts, needs prompt templates, wants to search for prompts, or mentions prompts.chat. Use for discovering, retrieving, and improving prompts.
Search, retrieve, and install Agent Skills from the prompts.chat registry using MCP tools. Use when the user asks to find skills, browse skill catalogs, install a skill for Claude, or extend Claude's capabilities with reusable AI agent components.
This skill should be used when the user asks to "create a slash command", "add a command", "write a custom command", "define command arguments", "use command frontmatter", "organize commands", "create command with file references", "interactive command", "use AskUserQuestion in command", or needs guidance on slash command structure, YAML frontmatter fields, dynamic arguments, bash execution in commands, user interaction patterns, or command development best practices for Claude Code.