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 |
| 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 |
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