End-to-end deployment, evaluation, and lifecycle management for Microsoft Foundry agents.
Works with
Covers the complete agent development lifecycle: create, deploy (Docker build, ACR push), invoke, evaluate, optimize prompts, and troubleshoot
Organized into specialized sub-skills for deploy, invoke, observe (evaluation and prompt optimization), trace, troubleshoot, and dataset curation from production traces
Supports both prompt-based agents (LLM-backed) and hosted agents (container-based cust
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmicrosoft-foundryExecute the skills CLI command in your project's root directory to begin installation:
Fetches microsoft-foundry from microsoft/GitHub-Copilot-for-Azure and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate microsoft-foundry. Access via /microsoft-foundry in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
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Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
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Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
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Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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This skill helps developers work with Microsoft Foundry resources, covering model discovery and deployment, complete dev lifecycle of AI agent, evaluation workflows, and troubleshooting.
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 |
| 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 |
💡 Tip: For a complete onboarding flow:
project/create→ agent workflows (deploy→invoke).
💡 Model Deployment: Use
models/deploy-modelfor all deployment scenarios — it intelligently routes between quick preset deployment, customized deployment with full control, and capacity discovery across regions.
💡 Prompt Optimization: For requests like "optimize my prompt" or "improve my agent instructions," load observe and use the
prompt_optimizeMCP tool through that eval-driven workflow.
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 → 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 |
| Optimize / improve agent prompt or instructions | observe (Step 4: Optimize) |
| Evaluate and optimize agent (full loop) | observe |
| Troubleshoot an agent issue | invoke → troubleshoot |
| Fix a broken agent (troubleshoot + redeploy) | invoke → troubleshoot → apply fixes → deploy → invoke |
| Start/stop agent container | deploy |
Every agent source folder should keep Foundry-specific state under .foundry/:
<agent-root>/
.foundry/
agent-metadata.yaml
datasets/
evaluators/
results/
agent-metadata.yaml is the required source of truth for environment-specific project settings, agent names, registry details, and evaluation test cases.datasets/ and evaluators/ are local cache folders. Reuse them when they are current, and ask before refreshing or overwriting them.Agent skills should run this step only when they need configuration values they don't already have. If a value (for example, agent root, environment, project endpoint, or agent name) is already known from the user's message or a previous skill in the same session, skip resolution for that value.
Search the workspace for .foundry/agent-metadata.yaml.
.foundry/ folder during setup; for all other workflows, stop and ask the user which agent source folder to initialize.Read .foundry/agent-metadata.yaml and resolve the environment in this order:
defaultEnvironment from metadataIf the metadata contains multiple environments and none of the rules above selects one, prompt the user to choose. Keep the selected agent root and environment visible in every workflow summary.
Use the selected environment in agent-metadata.yaml as the primary source:
| Metadata Field | Resolves To | Used By |
|---|---|---|
environments.<env>.projectEndpoint |
Project endpoint | deploy, invoke, observe, trace, troubleshoot |
environments.<env>.agentName |
Agent name | invoke, observe, trace, troubleshoot |
environments.<env>.azureContainerRegistry |
ACR registry name / image URL prefix | deploy |
environments.<env>.testCases[] |
Dataset + evaluator + threshold bundles | observe, eval-datasets |
If create/deploy is initializing a new .foundry workspace and metadata fields are still missing, check if azure.yaml exists in the project root. If found, run azd env get-values and use it to seed agent-metadata.yaml before continuing.
| azd Variable | Seeds |
|---|---|
AZURE_AI_PROJECT_ENDPOINT or AZURE_AIPROJECT_ENDPOINT |
environments.<env>.projectEndpoint |
AZURE_CONTAINER_REGISTRY_NAME or AZURE_CONTAINER_REGISTRY_ENDPOINT |
environments.<env>.azureContainerRegistry |
AZURE_SUBSCRIPTION_ID |
Azure subscription for trace/troubleshoot lookups |
Use the ask_user or askQuestions tool only for values not resolved from the user's message, session context, metadata, or azd bootstrap. Common values skills may need:
.foundry/agent-metadata.yamldev, prod, or another environment key from metadata💡 Tip: If the user already provides the agent path, environment, project endpoint, or agent name, extract it directly — do not ask again.
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.
ask_user or askQuestions tool whenever collecting information from the usertask or runSubagent tool to delegate long-running or independent sub-tasks (e.g., env var scanning, status polling, Dockerfile generation)Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
microsoft/GitHub-Copilot-for-Azure
microsoft/azure-skills
membranedev/application-skills
microsoft/azure-skills
aj-geddes/useful-ai-prompts
davila7/claude-code-templates
I recommend microsoft-foundry for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: microsoft-foundry is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added microsoft-foundry from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
microsoft-foundry fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
microsoft-foundry is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
microsoft-foundry has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for microsoft-foundry matched our evaluation — installs cleanly and behaves as described in the markdown.
microsoft-foundry is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
microsoft-foundry fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
microsoft-foundry reduced setup friction for our internal harness; good balance of opinion and flexibility.
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