# Jongio Azd Copilot Microsoft Foundry > Microsoft Foundry Skill - Skill: `tomevault-io/jongio-azd-copilot-microsoft-foundry` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/jongio-azd-copilot-microsoft-foundry` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/jongio-azd-copilot-microsoft-foundry/raw - Safety review: pending (external: skill-scanner PASS, skillspector CAUTION) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/jongio-azd-copilot-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](foundry-agent/deploy/deploy.md) | | **invoke** | Send messages to an agent, single or multi-turn conversations | [invoke](foundry-agent/invoke/invoke.md) | | **troubleshoot** | View container logs, query telemetry, diagnose failures | [troubleshoot](foundry-agent/troubleshoot/troubleshoot.md) | | **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](foundry-agent/create/agent-framework/SKILL.md) | | **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](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](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](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](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](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](https://learn.microsoft.com/azure/ai-foundry/agents/concepts/hosted-agents?view=foundry) - [Foundry Agent Runtime Components](https://learn.microsoft.com/azure/ai-foundry/agents/concepts/runtime-components?view=foundry) - [Foundry Samples](https://github.com/azure-ai-foundry/foundry-samples) ## SDK Quick Reference - [Python](references/sdk/foundry-sdk-py.md) --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/jongio) — claim your Tome and manage your conversions.