tomevault-io 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-modelfor 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_useroraskQuestionstool whenever collecting information from the user - Use the
taskorrunSubagenttool 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
- Python
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