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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).

Install with Tessl CLI

npx tessl i github:microsoft/azure-skills --skill microsoft-foundry
What are skills?

93

2.58x

Quality

92%

Does it follow best practices?

Impact

93%

2.58x

Average score across 3 eval scenarios

SKILL.md
Review
Evals

Microsoft Foundry Skill

MANDATORY: Read this skill and the relevant sub-skill BEFORE calling any Foundry MCP tool.

Sub-Skills

Sub-SkillWhen to UseReference
deployContainerize, build, push to ACR, create/update/start/stop/clone agent deploymentsdeploy
invokeSend messages to an agent, single or multi-turn conversationsinvoke
observeEval-driven optimization loop: evaluate → analyze → optimize → compare → iterateobserve
traceQuery traces, analyze latency/failures, correlate eval results to specific responses via App Insights customEventstrace
troubleshootView container logs, query telemetry, diagnose failurestroubleshoot
createCreate 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-datasetsHarvest 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/createCreating 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/createCreating 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-modelUnified 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
quotaManaging 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
rbacManaging 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

IntentWorkflow
New agent from scratchcreate → deploy → invoke
Deploy existing codedeploy → invoke
Test/chat with agentinvoke
Troubleshootinvoke → troubleshoot
Fix + redeploytroubleshoot → 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 VariableResolves To
AZURE_AI_PROJECT_ENDPOINT / AZURE_AIPROJECT_ENDPOINTProject endpoint
AZURE_CONTAINER_REGISTRY_NAME / AZURE_CONTAINER_REGISTRY_ENDPOINTACR registry
AZURE_SUBSCRIPTION_IDSubscription
  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

TypeKindDescription
Prompt"prompt"LLM-based, backed by model deployment
Hosted"hosted"Container-based, running custom code

Agent: Setup Types

SetupCapability HostDescription
BasicNoneDefault. All resources Microsoft-managed.
StandardAzure AI ServicesBring-your-own storage and search (public network). See standard-agent-setup.
Standard + Private NetworkAzure AI ServicesStandard setup with VNet isolation and private endpoints. See private-network-standard-agent-setup.

MANDATORY: For standard setup, read the appropriate reference before proceeding:

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.

Repository
microsoft/azure-skills
Commit

12d081d

Last updated
Created

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