stephschofield Microsoft Foundry Skill
MANDATORY: Read this skill and the relevant sub-skill BEFORE calling any Foundry MCP tool.
Sub-Skills
| 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 | Eval-driven optimization loop: evaluate → analyze → optimize → compare → iterate | 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 |
Onboarding flow: project/create → deploy → invoke
Agent Lifecycle
| Intent | Workflow |
|---|---|
| New agent from scratch | create → deploy → invoke |
| Deploy existing code | deploy → invoke |
| Test/chat with agent | invoke |
| Troubleshoot | invoke → troubleshoot |
| Fix + redeploy | troubleshoot → fix → deploy → invoke |
Project Context Resolution
Resolve only missing values. Extract from user message first, then azd, then ask.
- Check for
azure.yaml; if found, runazd env get-values - Map azd variables:
| azd Variable | Resolves To |
|---|---|
AZURE_AI_PROJECT_ENDPOINT / AZURE_AIPROJECT_ENDPOINT |
Project endpoint |
AZURE_CONTAINER_REGISTRY_NAME / AZURE_CONTAINER_REGISTRY_ENDPOINT |
ACR registry |
AZURE_SUBSCRIPTION_ID |
Subscription |
- Ask user only for unresolved values (project endpoint, agent name)
Validation
After each workflow step, validate before proceeding:
- Run the operation
- Check output for errors or unexpected results
- If failed → diagnose using troubleshoot sub-skill → fix → retry
- Only proceed to next step when validation passes
Agent Types
| Type | Kind | Description |
|---|---|---|
| Prompt | "prompt" |
LLM-based, backed by model deployment |
| Hosted | "hosted" |
Container-based, running custom code |
Agent: Setup Types
| Setup | Capability Host | Description |
|---|---|---|
| Basic | None | Default. All resources Microsoft-managed. |
| Standard | Azure AI Services | Bring-your-own storage and search (public network). See standard-agent-setup. |
| Standard + Private Network | Azure AI Services | Standard setup with VNet isolation and private endpoints. See private-network-standard-agent-setup. |
MANDATORY: For standard setup, read the appropriate reference before proceeding:
- Public network: references/standard-agent-setup.md
- Private network (VNet isolation): references/private-network-standard-agent-setup.md
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
References
- Hosted Agents
- Runtime Components
- Foundry Samples
- Python SDK
Dependencies
Scripts in sub-skills require: Azure CLI (az) ≥2.0, jq (for shell scripts). Install via pip install azure-ai-projects azure-identity for Python SDK usage.