Agent skill

microsoft-foundry

Deploy, evaluate, and manage Foundry agents end-to-end: Docker build, ACR push, hosted/prompt agent create, container start, batch eval, prompt optimization, agent.yaml, dataset curation from traces. USE FOR: deploy agent to Foundry, hosted agent, create agent, invoke agent, evaluate agent, run batch eval, optimize prompt, deploy model, Foundry project, RBAC, role assignment, permissions, quota, capacity, region, troubleshoot agent, deployment failure, create dataset from traces, dataset versioning, eval trending, create AI Services, Cognitive Services, create Foundry resource, provision resource, knowledge index, agent monitoring, customize deployment, onboard, availability, standard agent setup, capability host. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

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Install this agent skill to your Project

npx add-skill https://github.com/autohandai/community-skills/tree/main/microsoft-foundry-ms

Metadata

Additional technical details for this skill

author
Microsoft
version
1.0.3

SKILL.md

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.

  1. Check for azure.yaml; if found, run azd env get-values
  2. 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
  1. Ask user only for unresolved values (project endpoint, agent name)

Validation

After each workflow step, validate before proceeding:

  1. Run the operation
  2. Check output for errors or unexpected results
  3. If failed → diagnose using troubleshoot sub-skill → fix → retry
  4. 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_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

References

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.

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