[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M69B0HB512ESRVEC3V07DH7R":3,"skill-same:01M69B0HB512ESRVEC3V07DH7R":802},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":26,"publishedAt":58,"linkOnly":59,"markdownContent":60,"markdownBody":61,"frontMatter":62,"refName":65,"commitSha":66,"skillDirectory":67,"forksCount":68,"repositoryHtmlUrl":69,"repositoryHomepageUrl":70,"ownerHtmlUrl":71,"ownerBlogUrl":72,"ownerDescription":73,"updatedAt":74,"install":75,"resources":88},"01M69B0HB512ESRVEC3V07DH7R","microsoft-foundry",null,"Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision\u002Fdeploy, hosted agent scaffold\u002Fdevelop\u002Frun\u002Fdeploy\u002Ftroubleshoot, prompt agent, managed harness agent, Copilot harness, voice agent, create\u002Fupdate\u002Finvoke\u002Fevaluate agent, add tool to agent, agent insights, batch\u002Fcontinuous eval, continuous monitoring, agent CI\u002FCD, optimize\u002Fimprove prompt, optimize agent instructions, Agent Optimizer, dataset curation from traces, deploy model, model fine-tuning (SFT\u002FDPO\u002FRFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).","microsoft\u002Fskills","microsoft","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6154722?v=4","01M698VYDXZ6JW5WH3CRV650R8","Microsoft","01M69AJYYW4E1RMSYCQDJ4FJSH",3199,"MIT","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fplugins\u002Fazure-skills\u002Fskills\u002Fmicrosoft-foundry\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000011","运维与部署","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[9,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57],"foundry","microsoft foundry","eval","run","update","permissions","batch","monitoring","ci","improve","knowledge","training","project","app","resources","distillation","invoke","create","ai agent","rbac","azure","add tool","customize","capacity","deploy model","copilot","azure deploy","scaffold","prepare","deploy","troubleshoot","2026-11-10 10:12:30",false,"---\nname: microsoft-foundry\ndescription: \"Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision\u002Fdeploy, hosted agent scaffold\u002Fdevelop\u002Frun\u002Fdeploy\u002Ftroubleshoot, prompt agent, managed harness agent, Copilot harness, voice agent, create\u002Fupdate\u002Finvoke\u002Fevaluate agent, add tool to agent, agent insights, batch\u002Fcontinuous eval, continuous monitoring, agent CI\u002FCD, optimize\u002Fimprove prompt, optimize agent instructions, Agent Optimizer, dataset curation from traces, deploy model, model fine-tuning (SFT\u002FDPO\u002FRFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).\"\nlicense: MIT\nmetadata:\n  author: Microsoft\n  version: \"1.2.34\"\n---\n\n# Microsoft Foundry Skill\n\nThis skill helps developers work with Microsoft Foundry resources, covering model discovery and deployment, complete dev lifecycle of AI agent, evaluation workflows, and troubleshooting.\n\n## Pre-Execution Requirements\n\nFollow each applicable subsection below before starting its corresponding action or workflow.\n\n### Dependency Check and Setup\n\n**MANDATORY:** As the first step after this skill loads, run the dependency check and setup script below from this skill's root and wait for it to finish before continuing. The script checks first and installs only missing dependencies; it does not reinstall dependencies that are already available.\n\n**You MUST complete this check before reading or entering any sub-skill, workflow, or workflow-specific reference.**\n\n```bash\n.\u002Fscripts\u002Fcheck-and-setup-dependencies.sh     # macOS \u002F Linux\n.\u002Fscripts\u002Fcheck-and-setup-dependencies.ps1    # Windows (pwsh)\n```\n\nStrictly follow the script output for subsequent actions.\n\n### Workflow Guidance\n\n**MANDATORY:** Before executing ANY workflow-specific steps, you MUST read the corresponding sub-skill document. Do not call workflow-specific 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.\n\n### Foundry MCP\n\n**MANDATORY:** Before using Foundry MCP operations, call the Azure MCP `foundry` tool and inspect the available Foundry MCP tools and related parameters. Treat this as the discovery\u002Fhelp step for MCP-based workflows.\n\n### azd\n\n**MANDATORY:** Before executing ANY azd command, you MUST read [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) and strictly follow the shared rules defined in it, especially the `AZURE_DEV_USER_AGENT` setting rules.\n\n## Sub-Skills\n\nThis skill includes specialized sub-skills for specific workflows. **When a sub-skill matches the task, strictly follow its workflow:**\n\n| Sub-Skill | When to Use | Reference |\n|-----------|-------------|-----------|\n| **deploy** | Deploy hosted agents to Foundry, smoke-test a deployment, create or update prompt agents, and manage agent versions and multi-environment deploys. | [deploy](foundry-agent\u002Fdeploy\u002Fdeploy.md) |\n| **cicd** | Set up a CI\u002FCD deployment pipeline for a Foundry agent. | [cicd](foundry-agent\u002Fcicd\u002Fcicd.md) |\n| **invoke** | Send messages to an agent, single or multi-turn conversations | [invoke](foundry-agent\u002Finvoke\u002Finvoke.md) |\n| **routine** | Schedule or event-trigger Foundry agents with routines; use `azd` for CRUD, enable\u002Fdisable, manual dispatch, and viewing past runs, or define routines in `azure.yaml`. | [routine](foundry-agent\u002Froutine\u002Froutine.md) |\n| **invocations-ws** | Build, deploy, and connect to custom Hosted agents that use developer-defined duplex real-time or media streams over `invocations_ws`. | [invocations-ws](foundry-agent\u002Finvocations-ws\u002Finvocations-ws.md) |\n| **observe** | Evaluate agent quality, run batch evals, analyze failures, optimize prompts, improve agent instructions, compare versions, set up CI\u002FCD monitoring, and enable continuous production evaluation | [observe](foundry-agent\u002Fobserve\u002Fobserve.md) |\n| **insights** | Pull generated agent insights, evidence, and recommendations from an existing monitor; read-only retrieval, not a new analysis run | [insights](foundry-agent\u002Finsights\u002Finsights.md) |\n| **trace** | Query traces, analyze latency\u002Ffailures, correlate eval results to specific responses via App Insights `customEvents` | [trace](foundry-agent\u002Ftrace\u002Ftrace.md) |\n| **troubleshoot** | View hosted agent logs, query telemetry, diagnose failures | [troubleshoot](foundry-agent\u002Ftroubleshoot\u002Ftroubleshoot.md) |\n| **validate** | Use only when the user explicitly asks to use this validation sub-skill or to validate Microsoft Foundry hosted-agent code against best practices. Never invoke it proactively or add it to another workflow. | [validate](foundry-agent\u002Fvalidate\u002Fvalidate.md) |\n| **create (quick start)** | Create a new hosted Foundry agent from scratch end-to-end — scaffold, provision or use an existing Foundry project, deploy, and smoke-test. Do not use for any work on existing code. For anything not covered by the quickstart, use **create**. | [create\u002Fquick-start-hosted.md](foundry-agent\u002Fcreate\u002Fquick-start-hosted.md) |\n| **create** | Use when the standard end-to-end happy path (quick start) doesn't fit. Create a new Foundry agent, update code of an existing agent, continue development of an existing agent, wire connections at scaffold time, or use advanced setup or A2A (Agent2Agent). | [create](foundry-agent\u002Fcreate\u002Fcreate-hosted.md) |\n| **create managed harness** | Create or continue developing a Prompt Agent that explicitly uses the Foundry-managed Copilot harness. Uses `azd` and `azure.yaml`, not the ordinary Prompt Agent MCP workflow. | [create-managed-harness](foundry-agent\u002Fcreate\u002Fcreate-managed-harness.md) |\n| **create voice** | Create, configure, or extend a Foundry Voice Agent, defaulting generic requests to managed Prompt Voice and supporting GPT-Live Voice, self-deployed Prompt Voice, or two-agent Hosted Voice. | [create-voice](foundry-agent\u002Fcreate\u002Fcreate-voice.md) |\n| **agent-optimizer** | Make existing Python hosted-agent code optimization-ready, configure eval.yaml, run Agent Optimizer jobs, apply candidates locally, and deploy through azd after review. | [agent-optimizer](foundry-agent\u002Fagent-optimizer\u002Fagent-optimizer.md) |\n| **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](foundry-agent\u002Feval-datasets\u002Feval-datasets.md) |\n| **project\u002Fcreate** | Creating a new Microsoft Foundry project for hosting agents and models. Use when onboarding to Foundry or setting up new infrastructure. | [project\u002Fcreate\u002Fcreate-foundry-project.md](project\u002Fcreate\u002Fcreate-foundry-project.md) |\n| **resource\u002Fcreate** | Creating Azure AI Services multi-service resource (Foundry resource) using Azure CLI. Use when manually provisioning AI Services resources with granular control. | [resource\u002Fcreate\u002Fcreate-foundry-resource.md](resource\u002Fcreate\u002Fcreate-foundry-resource.md) |\n| **private-network** | Answer questions about Foundry network isolation **and** deploy Foundry with VNet isolation (BYO VNet, Managed VNet, hybrid). Covers architecture concepts, template selection, deployment, and post-deployment validation. | [resource\u002Fprivate-network\u002Fprivate-network.md](resource\u002Fprivate-network\u002Fprivate-network.md) |\n| **models\u002Fdeploy-model** | Unified model deployment with intelligent routing. Handles quick preset deployments, fully customized deployments (version\u002FSKU\u002Fcapacity\u002FRAI), and capacity discovery across regions. Routes to sub-skills: `preset` (quick deploy), `customize` (full control), `capacity` (find availability). | [models\u002Fdeploy-model\u002FSKILL.md](models\u002Fdeploy-model\u002FSKILL.md) |\n| **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\u002Fquota.md](quota\u002Fquota.md) |\n| **rbac** | Managing RBAC permissions, role assignments, managed identities, and service principals for Microsoft Foundry resources. Use for access control, auditing permissions, and CI\u002FCD setup. | [rbac\u002Frbac.md](rbac\u002Frbac.md) |\n| **finetuning** | Fine-tune models on Microsoft Foundry — SFT distillation, DPO preference optimization, RFT with graders and tool calling. Dataset preparation, grader calibration, training, checkpoint selection, deployment, evaluation. Use for: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, large file upload. | [finetuning\u002FSKILL.md](finetuning\u002FSKILL.md) |\n| **azd-guidance** | Provide shared azd knowledge and guidance for managing Foundry agents. Read this first for any workflows related to azd. | [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) |\n\n> 💡 **Tip:** For a complete onboarding flow: `project\u002Fcreate` (public) or `private-network` (VNet isolation) → `models\u002Fdeploy-model` → agent workflows (`create` → `deploy` → `invoke`).\n\n> 💡 **Fine-Tuning:** Use `finetuning` for all model customization — SFT distillation, DPO preference optimization, and RFT with graders. Includes quickstart, grader calibration, and training curve analysis.\n\n> 💡 **Model Deployment:** Use `models\u002Fdeploy-model` for all deployment scenarios — it intelligently routes between quick preset deployment, customized deployment with full control, and capacity discovery across regions.\n\n> 💡 **Prompt Optimization:** For requests like \"optimize my prompt\" or \"improve my agent instructions,\" load [observe](foundry-agent\u002Fobserve\u002Fobserve.md) and use the `prompt_optimize` MCP tool through that eval-driven workflow.\n\n## Infrastructure Lifecycle\n\nMatch user intent to the correct infrastructure workflow.\n\n| User Intent | Workflow |\n|-------------|---------|\n| \"Create Foundry\" \u002F \"Set up Foundry\" (ambiguous) | Use `AskUserQuestion`: (a) just an AI Services resource, (b) a project with public access, or (c) a project with network isolation? Route: (a) → [resource\u002Fcreate](resource\u002Fcreate\u002Fcreate-foundry-resource.md), (b) → [project\u002Fcreate](project\u002Fcreate\u002Fcreate-foundry-project.md), (c) → [private-network](resource\u002Fprivate-network\u002Fprivate-network.md) |\n| Set up Foundry with VNet isolation | [private-network](resource\u002Fprivate-network\u002Fprivate-network.md) |\n| Create a Foundry project (public) | [project\u002Fcreate](project\u002Fcreate\u002Fcreate-foundry-project.md) |\n| Create a bare Foundry resource | [resource\u002Fcreate](resource\u002Fcreate\u002Fcreate-foundry-resource.md) |\n\n## Agent Development Lifecycle\n\nMatch user intent to the correct agent workflow. Read each sub-skill in order before executing.\n\n| User Intent | Workflow (read in order) |\n|-------------|------------------------|\n| Create or develop a Voice Agent (GPT-Live Voice, Prompt Voice, or Hosted Voice) | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → [create-voice](foundry-agent\u002Fcreate\u002Fcreate-voice.md) → [deploy](foundry-agent\u002Fdeploy\u002Fdeploy.md) → [invoke](foundry-agent\u002Finvoke\u002Finvoke.md) |\n| Create or develop an explicitly requested Managed Harness Agent \u002F Copilot harness agent | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → [create-managed-harness](foundry-agent\u002Fcreate\u002Fcreate-managed-harness.md) → [deploy](foundry-agent\u002Fdeploy\u002Fdeploy.md) → [invoke](foundry-agent\u002Finvoke\u002Finvoke.md) |\n| Create a new hosted agent end-to-end (scaffold + deploy + test) | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → [quick-start-hosted](foundry-agent\u002Fcreate\u002Fquick-start-hosted.md) (self-contained end-to-end) |\n| Anything beyond the standard quickstart (existing code, migration, re-hosting, deployment customization, scaffold-time connections, A2A (Agent2Agent), recovery) | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → [create](foundry-agent\u002Fcreate\u002Fcreate-hosted.md) → [deploy](foundry-agent\u002Fdeploy\u002Fdeploy.md) → [invoke](foundry-agent\u002Finvoke\u002Finvoke.md) |\n| Optimize existing Python hosted agent | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → [agent-optimizer](foundry-agent\u002Fagent-optimizer\u002Fagent-optimizer.md) → scaffold\u002Freview → eval.yaml → optimize → apply candidate → deploy → invoke |\n| Deploy an agent (code already exists) | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → deploy (includes eval-suite setup) → invoke → observe (evaluate\u002Foptimize) |\n| Update\u002Fredeploy an agent after code changes | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → deploy (includes eval-suite setup) → invoke → observe (evaluate\u002Foptimize) |\n| Set up a CI\u002FCD deployment pipeline for a hosted agent | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → cicd |\n| Invoke\u002Ftest\u002Fchat with an agent | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → invoke |\n| Schedule\u002Fevent-trigger an agent, or CRUD\u002Fenable\u002Fdisable\u002Fdispatch a routine | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → routine |\n| Optimize \u002F improve agent prompt or instructions | observe (Step 4: Optimize) |\n| Evaluate and optimize agent (full loop) | observe |\n| Enable continuous evaluation monitoring | observe (Step 6: CI\u002FCD & Monitoring) |\n| Pull agent insights \u002F list generated issues and recommendations | [dependency check and setup](#dependency-check-and-setup) → [insights](foundry-agent\u002Finsights\u002Finsights.md) (all pages with expanded evidence; read-only) |\n| Troubleshoot an agent issue | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → invoke → troubleshoot |\n| Fix a broken agent (troubleshoot + redeploy) | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → invoke → troubleshoot → apply fixes → deploy → invoke |\n\n## Agent: .foundry Workspace Standard\n\nEvery agent source folder can keep Foundry-specific cache and overlay state under `.foundry\u002F`:\n\n```text\n\u003Cagent-root>\u002F\n  .foundry\u002F\n    agent-metadata.yaml\n    agent-metadata.prod.yaml\n    suites\u002F\n    datasets\u002F\n    evaluators\u002F\n    results\u002F\n```\n\n- In azd projects, derive deployment context (project endpoint, agent name\u002Fversion, ACR, App Insights) from `azure.yaml` plus `azd env get-values`; do not duplicate those values in metadata when azd already provides them.\n- `agent-metadata.yaml` is the preferred local\u002Fdev overlay for non-azd values, remote Foundry suite references, local cache paths, result summaries, and explicit overrides. Optional sidecar files such as `agent-metadata.prod.yaml` can hold a single prod or CI-targeted overlay without mixing multiple environments in one file.\n- `suites\u002F`, `datasets\u002F`, and `evaluators\u002F` are local cache folders. Reuse them when they are current, and ask before refreshing or overwriting them.\n- See [Agent Metadata Contract](references\u002Fagent-metadata-contract.md) for the canonical schema and workflow rules.\n\n## Agent: Setup References\n\n- [Standard Agent Setup](references\u002Fstandard-agent-setup.md) — advanced setup for production workloads that need data-residency control (bring-your-own Cosmos DB \u002F Storage \u002F AI Search via a Foundry capability host). The default `azd ai agent` flow uses **Basic Agent Setup** and does **not** provision `capabilityHosts\u002Fagents` — do not flag its absence as a bug. For default post-provision state, see the \"Expected env-var fingerprint\" section in [foundry-agent\u002Fcreate\u002Fcreate-hosted.md](foundry-agent\u002Fcreate\u002Fcreate-hosted.md).\n\n## Agent: Common Project Context Resolution\n\nAgent 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.\n\n### Step 1: Discover Agent Roots and azd Context\n\nFirst check whether the workspace has `azure.yaml` with services using `host: azure.ai.agent`.\n\n- **One azd agent service** -> classify its top-level agent type from `kind` and `harness`. For `kind: voice` or legacy `kind: prompt-voice`, read [Voice Agent subtypes](foundry-agent\u002Fcreate\u002Freferences\u002Fvoice-agent\u002Fsubtypes.md), then use the service's `project` folder or the `azure.yaml` directory as the agent root.\n- **Multiple azd agent services** -> classify Voice services with [Voice Agent subtypes](foundry-agent\u002Fcreate\u002Freferences\u002Fvoice-agent\u002Fsubtypes.md):\n  - **Selecting a Hosted Voice wrapper** includes its named `kind: hosted` target as an upstream dependency; deploy and verification must report both services separately.\n  - **Selecting the `kind: hosted` target alone** scopes changes\u002Fdebugging to the target container and does not automatically modify the wrapper.\n  - **Otherwise** -> require the user to choose the target service\u002Ffolder.\n- **No azd agent service** -> search the workspace for `.foundry\u002F` folders that contain `agent-metadata.yaml` or `agent-metadata.\u003Cenv>.yaml`.\n  - **One match** -> use that agent root.\n  - **Multiple matches** -> require the user to choose the target agent folder.\n  - **No matches** -> for [insights](foundry-agent\u002Finsights\u002Finsights.md), ask only for missing remote inputs without initializing local files; for create\u002Fdeploy workflows, seed a new `.foundry\u002F` folder during setup; for other workflows, stop and ask the user which agent source folder to initialize.\n\nAfter selecting an agent root, keep all local `.foundry` cache inspection, source inspection, evaluator suggestions, dataset suggestions, and prompt-optimization context inside that folder only. Do **not** scan sibling agent folders unless the user explicitly switches roots.\n\n### Step 2: Resolve Environment and Deployment Context\n\nIf `azure.yaml` is present, resolve the azd environment first:\n\n1. Environment explicitly named by the user\n2. `AZURE_ENV_NAME` from `azd env get-values`\n3. azd default environment from `.azure\u002Fconfig.json`\n4. Environment already selected earlier in the session\n\nRun `azd env get-values` for the selected environment when project\u002Fdeployment values are not already known. Prefer azd values for deployment context:\n\n| azd Variable | Resolves To |\n|-------------|-------------|\n| `AZURE_AI_PROJECT_ENDPOINT` or `AZURE_AIPROJECT_ENDPOINT` | Project endpoint |\n| `AGENT_\u003CSERVICE>_NAME` | Agent name for the selected azd service |\n| `AGENT_\u003CSERVICE>_VERSION` | Agent version for the selected azd service |\n| `AZURE_CONTAINER_REGISTRY_NAME` or `AZURE_CONTAINER_REGISTRY_ENDPOINT` | ACR registry name \u002F image URL prefix |\n| `APPLICATIONINSIGHTS_CONNECTION_STRING` | App Insights connection string for trace workflows |\n| `AZURE_SUBSCRIPTION_ID`, `AZURE_RESOURCE_GROUP`, `AZURE_AI_ACCOUNT_NAME`, `AZURE_AI_PROJECT_NAME` | Azure resource lookup and Playground links |\n\nWhen azd supplies these values, use them as the source of truth and do not copy them into `.foundry\u002Fagent-metadata*.yaml` on metadata writes.\n\n### Step 3: Select Metadata Overlay and Resolve Environment\n\nInside the selected agent root, choose the metadata file in this order:\n1. Metadata filename or path explicitly provided by the user or workflow\n2. If an explicit environment is already known and `.foundry\u002Fagent-metadata.\u003Cenv>.yaml` exists, use that file\n3. `.foundry\u002Fagent-metadata.yaml`\n4. If multiple metadata files remain and no rule above selects one, prompt the user to choose\n\nRead the selected metadata file and resolve any remaining environment choice in this order:\n1. Environment explicitly named by the user\n2. If the selected metadata file defines exactly one environment, use it\n3. Environment already selected earlier in the session\n4. `defaultEnvironment` from metadata\n\nIf the selected metadata file still contains multiple environments and none of the rules above selects one, prompt the user to choose. Keep the selected agent root, metadata file, environment, and whether context came from azd or metadata visible in every workflow summary.\n\nIf the selected environment exposes older `testSuites[]` metadata but not `evaluationSuites[]`, treat `testSuites[]` as the source for this session and normalize each entry in memory to the `evaluationSuites[]` shape before continuing. If the metadata is older still and only exposes legacy `testCases[]`, normalize that list the same way. Preserve dataset and evaluator fields, keep any existing `tags`, and map legacy `priority` to `tags.tier` only when `tags.tier` is missing: `P0` -> `smoke`, `P1` -> `regression`, `P2` -> `coverage`.\n\n### Step 4: Resolve eval.yaml Local Evaluation Intent\n\nIf `eval.yaml` exists in the selected agent root, parse it before generating new suites:\n\n- `agent.name` -> target agent candidate; verify it matches the selected azd\u002Fmetadata agent before using it.\n- `dataset.local_uri` -> local seed dataset candidate; legacy `dataset_file` may be normalized in memory.\n- `dataset.name` \u002F `dataset.version` -> registered dataset candidate.\n- `validation_dataset` -> optional validation dataset candidate.\n- `evaluators[]` -> candidate Foundry evaluator names; verify with `evaluator_catalog_get` before treating them as remote evaluators.\n- `name` -> local eval\u002Fsuite candidate; verify remotely before persisting as `suiteName`.\n- `options.eval_model`, `options.optimization_model`, `options.max_candidates`, `options.optimization_config.model_search_space`, `options.pass_threshold`, `max_samples`, `trace_days`, and `generation_instruction` -> setup defaults.\n\nTreat `eval.yaml` as local evaluation intent, not proof that a Foundry suite exists. Persist synced suite\u002Fdataset\u002Fevaluator references to `.foundry` only after remote lookup or registration succeeds.\n\n### Step 5: Resolve Common Configuration\n\nLayer sources in this order:\n\n1. Explicit user input and values already selected in the session\n2. azd environment values for deployment context\n3. `.foundry\u002Fagent-metadata*.yaml` overlay values and remote suite\u002Fcache references\n4. `azure.yaml` and `eval.yaml` local source configuration\n5. User prompts for anything still missing\n\nIf azd and metadata both provide the same value and they differ, stop and ask which source is authoritative. If they match, use the azd value and avoid rewriting the duplicate on future metadata writes.\n\n| Effective Value | Preferred Source | Used By |\n|-----------------|------------------|---------|\n| Project endpoint | azd env | deploy, invoke, observe, trace, troubleshoot |\n| Agent name\u002Fversion | azd agent variables, then `azure.yaml` | invoke, observe, trace, troubleshoot |\n| ACR | azd env | deploy |\n| Evaluation suites and cache paths | `.foundry\u002Fagent-metadata*.yaml` | observe, eval-datasets |\n| Local seed dataset\u002Fevaluator intent | `eval.yaml` | observe, eval-datasets |\n\n### Step 6: Write Metadata Overlay (Create\u002FDeploy\u002FObserve Only)\n\nOn any metadata write (deploy, auto-setup, dataset refresh, or trace-to-dataset update), persist only non-derivable overlay\u002Fcache state in the selected metadata file:\n\n- azd binding (`azd.environmentName`, `azd.service`) when useful for future resolution\n- `evaluationSuites[]` with remote suite\u002Fdataset\u002Fevaluator references and local cache paths\n- `lastEval`, result files, comparison summaries, or explicit non-azd overrides\n\nDo not copy azd-owned deployment values into metadata when azd already provides them. If the selected file is a preferred single-environment file, rewrite only that one environment block. If the selected file is a legacy multi-environment file, rewrite only the selected environment block. Never copy or merge environments across sibling metadata files automatically. If the selected environment still uses older `testSuites[]` or legacy `testCases[]`, rewrite it to `evaluationSuites[]` and remove migrated `priority` fields from the rewritten entries.\n\n### Step 7: Collect Missing Values\n\nUse 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:\n- **Agent root** — Target azd service project folder or folder containing `.foundry\u002Fagent-metadata*.yaml`\n- **Metadata file** — `agent-metadata.yaml` for local\u002Fdev, or an explicit sidecar such as `agent-metadata.prod.yaml`\n- **Environment** — azd environment, `dev`, `prod`, or another environment key from metadata\n- **Project endpoint** — Microsoft Foundry project endpoint URL\n- **Agent name** — Name of the target agent\n\n> 💡 **Tip:** If the user already provides the agent path, environment, project endpoint, or agent name, extract it directly — do not ask again.\n\n## Agent: Agent Types\n\nAgent workflows distinguish four first-class agent types:\n\n| Type | Kind | Description |\n|------|------|-------------|\n| **Prompt** | `\"prompt\"` | LLM-based agents backed by a model deployment; managed via Foundry MCP or SDK |\n| **Hosted** | `\"hosted\"` | Container-based agents running custom code; developed and deployed via `azd` |\n| **Managed Harness Agent** | `\"prompt\"` with `harness.type: \"microsoft_copilot_preview\"` for new authoring | Prompt Agent running on the Foundry-managed Copilot harness; developed and deployed via `azd` |\n| **Voice** | `\"voice\"` | Managed real-time Voice Agent; read [Voice Agent subtypes](foundry-agent\u002Fcreate\u002Freferences\u002Fvoice-agent\u002Fsubtypes.md) to distinguish GPT-Live Voice, Prompt Voice, and Hosted Voice |\n\nClassify an `azure.yaml` service by its definition in this exact order, not by `host` alone:\n\n1. `kind: voice` -> **Voice**. For compatibility, silently classify existing `kind: prompt-voice` services as Voice and normalize them when editing. Then read [Voice Agent subtypes](foundry-agent\u002Fcreate\u002Freferences\u002Fvoice-agent\u002Fsubtypes.md).\n2. `kind: hosted` -> **Hosted**. If Voice subtype classification identifies it as a wrapper's named target, treat it as that dependency; otherwise a standalone `invocations_ws` service is a **custom Hosted real-time\u002Faudio agent**.\n3. `kind: prompt` plus `harness.type: microsoft_copilot_preview` or legacy `github_copilot_preview` -> **Managed Harness Agent**.\n4. `kind: prompt` without that harness -> ordinary **Prompt Agent**.\n\nUse `agent_get` only when the type cannot be resolved from project context. Preserve the harness discriminator already authored on an existing Managed Harness Agent; do not migrate it during ordinary edits or redeploys. Do not convert an existing Agent between Prompt, Hosted, Managed Harness Agent, and Voice types; create a new Agent instead. Before creating any **Voice Agent**, determine the model strategy first per [create-voice](foundry-agent\u002Fcreate\u002Fcreate-voice.md).\n\n## Tool Usage Conventions\n\n- Use the `ask_user` or `askQuestions` tool whenever collecting information from the user\n- Use the `task` or `runSubagent` tool to delegate long-running or independent sub-tasks (e.g., env var scanning, status polling, Dockerfile generation)\n- Prefer azd for Hosted Agents, Managed Harness Agents, and Voice Agents; prefer Foundry MCP for ordinary Prompt Agents.\n- Reference official Microsoft documentation URLs instead of embedding CLI command syntax\n\n## Azure Authentication\n\n- [Azure Authentication Best Practices](references\u002Fauth-best-practices.md)\n\n## Additional Resources\n\n- [Foundry Hosted Agents](https:\u002F\u002Flearn.microsoft.com\u002Fazure\u002Fai-foundry\u002Fagents\u002Fconcepts\u002Fhosted-agents?view=foundry)\n- [Foundry Agent Runtime Components](https:\u002F\u002Flearn.microsoft.com\u002Fazure\u002Fai-foundry\u002Fagents\u002Fconcepts\u002Fruntime-components?view=foundry)\n\n## Network Isolation Errors\n\nApplies to **any** call against a Foundry project or its parent Foundry account — Foundry MCP tools, `azd`, `az` CLI, `curl`, REST, or SDK.\n\nIf an error matches `Public access is disabled` \u002F `PublicNetworkAccessDisabled` \u002F `403 Forbidden` from a private endpoint \u002F connection timeout \u002F the project endpoint FQDN resolves to a public IP, this typically means the parent Foundry account has `publicNetworkAccess=Disabled` or `Enabled from selected IP addresses`, and the current shell is outside its VNet.\n\nOnly if the error is ambiguous, confirm against the Foundry account using a management-plane call (works from anywhere with reader access):\n\n```bash\naz cognitiveservices account show \\\n  --name \u003Caccount> --resource-group \u003Crg> \\\n  --query \"properties.{publicNetworkAccess:publicNetworkAccess, networkAcls:networkAcls, privateEndpointConnections:privateEndpointConnections[].properties.privateLinkServiceConnectionState.status}\"\n```\n\n`publicNetworkAccess: \"Disabled\"` — or `\"Enabled\"` together with non-empty `networkAcls.ipRules` \u002F `virtualNetworkRules` — confirms isolation. If `publicNetworkAccess: \"Enabled\"` and `networkAcls` is empty, the failure is a caller-side network issue (e.g. Private DNS resolving the FQDN to a public IP from inside a VNet with a private endpoint), not an account-config issue.\n\nIf it's indeed a network isolation issue, supported connection options are documented in [Choose a secure connection method to Foundry](https:\u002F\u002Flearn.microsoft.com\u002Fazure\u002Ffoundry\u002Fhow-to\u002Fconfigure-private-link#choose-a-secure-connection-method-to-foundry).\n\n> ℹ️ Foundry MCP tools cannot reach a VNet-isolated project even from inside the VNet.\n","# Microsoft Foundry Skill\n\nThis skill helps developers work with Microsoft Foundry resources, covering model discovery and deployment, complete dev lifecycle of AI agent, evaluation workflows, and troubleshooting.\n\n## Pre-Execution Requirements\n\nFollow each applicable subsection below before starting its corresponding action or workflow.\n\n### Dependency Check and Setup\n\n**MANDATORY:** As the first step after this skill loads, run the dependency check and setup script below from this skill's root and wait for it to finish before continuing. The script checks first and installs only missing dependencies; it does not reinstall dependencies that are already available.\n\n**You MUST complete this check before reading or entering any sub-skill, workflow, or workflow-specific reference.**\n\n```bash\n.\u002Fscripts\u002Fcheck-and-setup-dependencies.sh     # macOS \u002F Linux\n.\u002Fscripts\u002Fcheck-and-setup-dependencies.ps1    # Windows (pwsh)\n```\n\nStrictly follow the script output for subsequent actions.\n\n### Workflow Guidance\n\n**MANDATORY:** Before executing ANY workflow-specific steps, you MUST read the corresponding sub-skill document. Do not call workflow-specific 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.\n\n### Foundry MCP\n\n**MANDATORY:** Before using Foundry MCP operations, call the Azure MCP `foundry` tool and inspect the available Foundry MCP tools and related parameters. Treat this as the discovery\u002Fhelp step for MCP-based workflows.\n\n### azd\n\n**MANDATORY:** Before executing ANY azd command, you MUST read [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) and strictly follow the shared rules defined in it, especially the `AZURE_DEV_USER_AGENT` setting rules.\n\n## Sub-Skills\n\nThis skill includes specialized sub-skills for specific workflows. **When a sub-skill matches the task, strictly follow its workflow:**\n\n| Sub-Skill | When to Use | Reference |\n|-----------|-------------|-----------|\n| **deploy** | Deploy hosted agents to Foundry, smoke-test a deployment, create or update prompt agents, and manage agent versions and multi-environment deploys. | [deploy](foundry-agent\u002Fdeploy\u002Fdeploy.md) |\n| **cicd** | Set up a CI\u002FCD deployment pipeline for a Foundry agent. | [cicd](foundry-agent\u002Fcicd\u002Fcicd.md) |\n| **invoke** | Send messages to an agent, single or multi-turn conversations | [invoke](foundry-agent\u002Finvoke\u002Finvoke.md) |\n| **routine** | Schedule or event-trigger Foundry agents with routines; use `azd` for CRUD, enable\u002Fdisable, manual dispatch, and viewing past runs, or define routines in `azure.yaml`. | [routine](foundry-agent\u002Froutine\u002Froutine.md) |\n| **invocations-ws** | Build, deploy, and connect to custom Hosted agents that use developer-defined duplex real-time or media streams over `invocations_ws`. | [invocations-ws](foundry-agent\u002Finvocations-ws\u002Finvocations-ws.md) |\n| **observe** | Evaluate agent quality, run batch evals, analyze failures, optimize prompts, improve agent instructions, compare versions, set up CI\u002FCD monitoring, and enable continuous production evaluation | [observe](foundry-agent\u002Fobserve\u002Fobserve.md) |\n| **insights** | Pull generated agent insights, evidence, and recommendations from an existing monitor; read-only retrieval, not a new analysis run | [insights](foundry-agent\u002Finsights\u002Finsights.md) |\n| **trace** | Query traces, analyze latency\u002Ffailures, correlate eval results to specific responses via App Insights `customEvents` | [trace](foundry-agent\u002Ftrace\u002Ftrace.md) |\n| **troubleshoot** | View hosted agent logs, query telemetry, diagnose failures | [troubleshoot](foundry-agent\u002Ftroubleshoot\u002Ftroubleshoot.md) |\n| **validate** | Use only when the user explicitly asks to use this validation sub-skill or to validate Microsoft Foundry hosted-agent code against best practices. Never invoke it proactively or add it to another workflow. | [validate](foundry-agent\u002Fvalidate\u002Fvalidate.md) |\n| **create (quick start)** | Create a new hosted Foundry agent from scratch end-to-end — scaffold, provision or use an existing Foundry project, deploy, and smoke-test. Do not use for any work on existing code. For anything not covered by the quickstart, use **create**. | [create\u002Fquick-start-hosted.md](foundry-agent\u002Fcreate\u002Fquick-start-hosted.md) |\n| **create** | Use when the standard end-to-end happy path (quick start) doesn't fit. Create a new Foundry agent, update code of an existing agent, continue development of an existing agent, wire connections at scaffold time, or use advanced setup or A2A (Agent2Agent). | [create](foundry-agent\u002Fcreate\u002Fcreate-hosted.md) |\n| **create managed harness** | Create or continue developing a Prompt Agent that explicitly uses the Foundry-managed Copilot harness. Uses `azd` and `azure.yaml`, not the ordinary Prompt Agent MCP workflow. | [create-managed-harness](foundry-agent\u002Fcreate\u002Fcreate-managed-harness.md) |\n| **create voice** | Create, configure, or extend a Foundry Voice Agent, defaulting generic requests to managed Prompt Voice and supporting GPT-Live Voice, self-deployed Prompt Voice, or two-agent Hosted Voice. | [create-voice](foundry-agent\u002Fcreate\u002Fcreate-voice.md) |\n| **agent-optimizer** | Make existing Python hosted-agent code optimization-ready, configure eval.yaml, run Agent Optimizer jobs, apply candidates locally, and deploy through azd after review. | [agent-optimizer](foundry-agent\u002Fagent-optimizer\u002Fagent-optimizer.md) |\n| **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](foundry-agent\u002Feval-datasets\u002Feval-datasets.md) |\n| **project\u002Fcreate** | Creating a new Microsoft Foundry project for hosting agents and models. Use when onboarding to Foundry or setting up new infrastructure. | [project\u002Fcreate\u002Fcreate-foundry-project.md](project\u002Fcreate\u002Fcreate-foundry-project.md) |\n| **resource\u002Fcreate** | Creating Azure AI Services multi-service resource (Foundry resource) using Azure CLI. Use when manually provisioning AI Services resources with granular control. | [resource\u002Fcreate\u002Fcreate-foundry-resource.md](resource\u002Fcreate\u002Fcreate-foundry-resource.md) |\n| **private-network** | Answer questions about Foundry network isolation **and** deploy Foundry with VNet isolation (BYO VNet, Managed VNet, hybrid). Covers architecture concepts, template selection, deployment, and post-deployment validation. | [resource\u002Fprivate-network\u002Fprivate-network.md](resource\u002Fprivate-network\u002Fprivate-network.md) |\n| **models\u002Fdeploy-model** | Unified model deployment with intelligent routing. Handles quick preset deployments, fully customized deployments (version\u002FSKU\u002Fcapacity\u002FRAI), and capacity discovery across regions. Routes to sub-skills: `preset` (quick deploy), `customize` (full control), `capacity` (find availability). | [models\u002Fdeploy-model\u002FSKILL.md](models\u002Fdeploy-model\u002FSKILL.md) |\n| **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\u002Fquota.md](quota\u002Fquota.md) |\n| **rbac** | Managing RBAC permissions, role assignments, managed identities, and service principals for Microsoft Foundry resources. Use for access control, auditing permissions, and CI\u002FCD setup. | [rbac\u002Frbac.md](rbac\u002Frbac.md) |\n| **finetuning** | Fine-tune models on Microsoft Foundry — SFT distillation, DPO preference optimization, RFT with graders and tool calling. Dataset preparation, grader calibration, training, checkpoint selection, deployment, evaluation. Use for: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, large file upload. | [finetuning\u002FSKILL.md](finetuning\u002FSKILL.md) |\n| **azd-guidance** | Provide shared azd knowledge and guidance for managing Foundry agents. Read this first for any workflows related to azd. | [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) |\n\n> 💡 **Tip:** For a complete onboarding flow: `project\u002Fcreate` (public) or `private-network` (VNet isolation) → `models\u002Fdeploy-model` → agent workflows (`create` → `deploy` → `invoke`).\n\n> 💡 **Fine-Tuning:** Use `finetuning` for all model customization — SFT distillation, DPO preference optimization, and RFT with graders. Includes quickstart, grader calibration, and training curve analysis.\n\n> 💡 **Model Deployment:** Use `models\u002Fdeploy-model` for all deployment scenarios — it intelligently routes between quick preset deployment, customized deployment with full control, and capacity discovery across regions.\n\n> 💡 **Prompt Optimization:** For requests like \"optimize my prompt\" or \"improve my agent instructions,\" load [observe](foundry-agent\u002Fobserve\u002Fobserve.md) and use the `prompt_optimize` MCP tool through that eval-driven workflow.\n\n## Infrastructure Lifecycle\n\nMatch user intent to the correct infrastructure workflow.\n\n| User Intent | Workflow |\n|-------------|---------|\n| \"Create Foundry\" \u002F \"Set up Foundry\" (ambiguous) | Use `AskUserQuestion`: (a) just an AI Services resource, (b) a project with public access, or (c) a project with network isolation? Route: (a) → [resource\u002Fcreate](resource\u002Fcreate\u002Fcreate-foundry-resource.md), (b) → [project\u002Fcreate](project\u002Fcreate\u002Fcreate-foundry-project.md), (c) → [private-network](resource\u002Fprivate-network\u002Fprivate-network.md) |\n| Set up Foundry with VNet isolation | [private-network](resource\u002Fprivate-network\u002Fprivate-network.md) |\n| Create a Foundry project (public) | [project\u002Fcreate](project\u002Fcreate\u002Fcreate-foundry-project.md) |\n| Create a bare Foundry resource | [resource\u002Fcreate](resource\u002Fcreate\u002Fcreate-foundry-resource.md) |\n\n## Agent Development Lifecycle\n\nMatch user intent to the correct agent workflow. Read each sub-skill in order before executing.\n\n| User Intent | Workflow (read in order) |\n|-------------|------------------------|\n| Create or develop a Voice Agent (GPT-Live Voice, Prompt Voice, or Hosted Voice) | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → [create-voice](foundry-agent\u002Fcreate\u002Fcreate-voice.md) → [deploy](foundry-agent\u002Fdeploy\u002Fdeploy.md) → [invoke](foundry-agent\u002Finvoke\u002Finvoke.md) |\n| Create or develop an explicitly requested Managed Harness Agent \u002F Copilot harness agent | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → [create-managed-harness](foundry-agent\u002Fcreate\u002Fcreate-managed-harness.md) → [deploy](foundry-agent\u002Fdeploy\u002Fdeploy.md) → [invoke](foundry-agent\u002Finvoke\u002Finvoke.md) |\n| Create a new hosted agent end-to-end (scaffold + deploy + test) | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → [quick-start-hosted](foundry-agent\u002Fcreate\u002Fquick-start-hosted.md) (self-contained end-to-end) |\n| Anything beyond the standard quickstart (existing code, migration, re-hosting, deployment customization, scaffold-time connections, A2A (Agent2Agent), recovery) | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → [create](foundry-agent\u002Fcreate\u002Fcreate-hosted.md) → [deploy](foundry-agent\u002Fdeploy\u002Fdeploy.md) → [invoke](foundry-agent\u002Finvoke\u002Finvoke.md) |\n| Optimize existing Python hosted agent | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → [agent-optimizer](foundry-agent\u002Fagent-optimizer\u002Fagent-optimizer.md) → scaffold\u002Freview → eval.yaml → optimize → apply candidate → deploy → invoke |\n| Deploy an agent (code already exists) | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → deploy (includes eval-suite setup) → invoke → observe (evaluate\u002Foptimize) |\n| Update\u002Fredeploy an agent after code changes | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → deploy (includes eval-suite setup) → invoke → observe (evaluate\u002Foptimize) |\n| Set up a CI\u002FCD deployment pipeline for a hosted agent | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → cicd |\n| Invoke\u002Ftest\u002Fchat with an agent | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → invoke |\n| Schedule\u002Fevent-trigger an agent, or CRUD\u002Fenable\u002Fdisable\u002Fdispatch a routine | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → routine |\n| Optimize \u002F improve agent prompt or instructions | observe (Step 4: Optimize) |\n| Evaluate and optimize agent (full loop) | observe |\n| Enable continuous evaluation monitoring | observe (Step 6: CI\u002FCD & Monitoring) |\n| Pull agent insights \u002F list generated issues and recommendations | [dependency check and setup](#dependency-check-and-setup) → [insights](foundry-agent\u002Finsights\u002Finsights.md) (all pages with expanded evidence; read-only) |\n| Troubleshoot an agent issue | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → invoke → troubleshoot |\n| Fix a broken agent (troubleshoot + redeploy) | [dependency check and setup](#dependency-check-and-setup) → [azd-guidance](foundry-agent\u002Fazd-guidance\u002Fazd-guidance.md) → invoke → troubleshoot → apply fixes → deploy → invoke |\n\n## Agent: .foundry Workspace Standard\n\nEvery agent source folder can keep Foundry-specific cache and overlay state under `.foundry\u002F`:\n\n```text\n\u003Cagent-root>\u002F\n  .foundry\u002F\n    agent-metadata.yaml\n    agent-metadata.prod.yaml\n    suites\u002F\n    datasets\u002F\n    evaluators\u002F\n    results\u002F\n```\n\n- In azd projects, derive deployment context (project endpoint, agent name\u002Fversion, ACR, App Insights) from `azure.yaml` plus `azd env get-values`; do not duplicate those values in metadata when azd already provides them.\n- `agent-metadata.yaml` is the preferred local\u002Fdev overlay for non-azd values, remote Foundry suite references, local cache paths, result summaries, and explicit overrides. Optional sidecar files such as `agent-metadata.prod.yaml` can hold a single prod or CI-targeted overlay without mixing multiple environments in one file.\n- `suites\u002F`, `datasets\u002F`, and `evaluators\u002F` are local cache folders. Reuse them when they are current, and ask before refreshing or overwriting them.\n- See [Agent Metadata Contract](references\u002Fagent-metadata-contract.md) for the canonical schema and workflow rules.\n\n## Agent: Setup References\n\n- [Standard Agent Setup](references\u002Fstandard-agent-setup.md) — advanced setup for production workloads that need data-residency control (bring-your-own Cosmos DB \u002F Storage \u002F AI Search via a Foundry capability host). The default `azd ai agent` flow uses **Basic Agent Setup** and does **not** provision `capabilityHosts\u002Fagents` — do not flag its absence as a bug. For default post-provision state, see the \"Expected env-var fingerprint\" section in [foundry-agent\u002Fcreate\u002Fcreate-hosted.md](foundry-agent\u002Fcreate\u002Fcreate-hosted.md).\n\n## Agent: Common Project Context Resolution\n\nAgent 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.\n\n### Step 1: Discover Agent Roots and azd Context\n\nFirst check whether the workspace has `azure.yaml` with services using `host: azure.ai.agent`.\n\n- **One azd agent service** -> classify its top-level agent type from `kind` and `harness`. For `kind: voice` or legacy `kind: prompt-voice`, read [Voice Agent subtypes](foundry-agent\u002Fcreate\u002Freferences\u002Fvoice-agent\u002Fsubtypes.md), then use the service's `project` folder or the `azure.yaml` directory as the agent root.\n- **Multiple azd agent services** -> classify Voice services with [Voice Agent subtypes](foundry-agent\u002Fcreate\u002Freferences\u002Fvoice-agent\u002Fsubtypes.md):\n  - **Selecting a Hosted Voice wrapper** includes its named `kind: hosted` target as an upstream dependency; deploy and verification must report both services separately.\n  - **Selecting the `kind: hosted` target alone** scopes changes\u002Fdebugging to the target container and does not automatically modify the wrapper.\n  - **Otherwise** -> require the user to choose the target service\u002Ffolder.\n- **No azd agent service** -> search the workspace for `.foundry\u002F` folders that contain `agent-metadata.yaml` or `agent-metadata.\u003Cenv>.yaml`.\n  - **One match** -> use that agent root.\n  - **Multiple matches** -> require the user to choose the target agent folder.\n  - **No matches** -> for [insights](foundry-agent\u002Finsights\u002Finsights.md), ask only for missing remote inputs without initializing local files; for create\u002Fdeploy workflows, seed a new `.foundry\u002F` folder during setup; for other workflows, stop and ask the user which agent source folder to initialize.\n\nAfter selecting an agent root, keep all local `.foundry` cache inspection, source inspection, evaluator suggestions, dataset suggestions, and prompt-optimization context inside that folder only. Do **not** scan sibling agent folders unless the user explicitly switches roots.\n\n### Step 2: Resolve Environment and Deployment Context\n\nIf `azure.yaml` is present, resolve the azd environment first:\n\n1. Environment explicitly named by the user\n2. `AZURE_ENV_NAME` from `azd env get-values`\n3. azd default environment from `.azure\u002Fconfig.json`\n4. Environment already selected earlier in the session\n\nRun `azd env get-values` for the selected environment when project\u002Fdeployment values are not already known. Prefer azd values for deployment context:\n\n| azd Variable | Resolves To |\n|-------------|-------------|\n| `AZURE_AI_PROJECT_ENDPOINT` or `AZURE_AIPROJECT_ENDPOINT` | Project endpoint |\n| `AGENT_\u003CSERVICE>_NAME` | Agent name for the selected azd service |\n| `AGENT_\u003CSERVICE>_VERSION` | Agent version for the selected azd service |\n| `AZURE_CONTAINER_REGISTRY_NAME` or `AZURE_CONTAINER_REGISTRY_ENDPOINT` | ACR registry name \u002F image URL prefix |\n| `APPLICATIONINSIGHTS_CONNECTION_STRING` | App Insights connection string for trace workflows |\n| `AZURE_SUBSCRIPTION_ID`, `AZURE_RESOURCE_GROUP`, `AZURE_AI_ACCOUNT_NAME`, `AZURE_AI_PROJECT_NAME` | Azure resource lookup and Playground links |\n\nWhen azd supplies these values, use them as the source of truth and do not copy them into `.foundry\u002Fagent-metadata*.yaml` on metadata writes.\n\n### Step 3: Select Metadata Overlay and Resolve Environment\n\nInside the selected agent root, choose the metadata file in this order:\n1. Metadata filename or path explicitly provided by the user or workflow\n2. If an explicit environment is already known and `.foundry\u002Fagent-metadata.\u003Cenv>.yaml` exists, use that file\n3. `.foundry\u002Fagent-metadata.yaml`\n4. If multiple metadata files remain and no rule above selects one, prompt the user to choose\n\nRead the selected metadata file and resolve any remaining environment choice in this order:\n1. Environment explicitly named by the user\n2. If the selected metadata file defines exactly one environment, use it\n3. Environment already selected earlier in the session\n4. `defaultEnvironment` from metadata\n\nIf the selected metadata file still contains multiple environments and none of the rules above selects one, prompt the user to choose. Keep the selected agent root, metadata file, environment, and whether context came from azd or metadata visible in every workflow summary.\n\nIf the selected environment exposes older `testSuites[]` metadata but not `evaluationSuites[]`, treat `testSuites[]` as the source for this session and normalize each entry in memory to the `evaluationSuites[]` shape before continuing. If the metadata is older still and only exposes legacy `testCases[]`, normalize that list the same way. Preserve dataset and evaluator fields, keep any existing `tags`, and map legacy `priority` to `tags.tier` only when `tags.tier` is missing: `P0` -> `smoke`, `P1` -> `regression`, `P2` -> `coverage`.\n\n### Step 4: Resolve eval.yaml Local Evaluation Intent\n\nIf `eval.yaml` exists in the selected agent root, parse it before generating new suites:\n\n- `agent.name` -> target agent candidate; verify it matches the selected azd\u002Fmetadata agent before using it.\n- `dataset.local_uri` -> local seed dataset candidate; legacy `dataset_file` may be normalized in memory.\n- `dataset.name` \u002F `dataset.version` -> registered dataset candidate.\n- `validation_dataset` -> optional validation dataset candidate.\n- `evaluators[]` -> candidate Foundry evaluator names; verify with `evaluator_catalog_get` before treating them as remote evaluators.\n- `name` -> local eval\u002Fsuite candidate; verify remotely before persisting as `suiteName`.\n- `options.eval_model`, `options.optimization_model`, `options.max_candidates`, `options.optimization_config.model_search_space`, `options.pass_threshold`, `max_samples`, `trace_days`, and `generation_instruction` -> setup defaults.\n\nTreat `eval.yaml` as local evaluation intent, not proof that a Foundry suite exists. Persist synced suite\u002Fdataset\u002Fevaluator references to `.foundry` only after remote lookup or registration succeeds.\n\n### Step 5: Resolve Common Configuration\n\nLayer sources in this order:\n\n1. Explicit user input and values already selected in the session\n2. azd environment values for deployment context\n3. `.foundry\u002Fagent-metadata*.yaml` overlay values and remote suite\u002Fcache references\n4. `azure.yaml` and `eval.yaml` local source configuration\n5. User prompts for anything still missing\n\nIf azd and metadata both provide the same value and they differ, stop and ask which source is authoritative. If they match, use the azd value and avoid rewriting the duplicate on future metadata writes.\n\n| Effective Value | Preferred Source | Used By |\n|-----------------|------------------|---------|\n| Project endpoint | azd env | deploy, invoke, observe, trace, troubleshoot |\n| Agent name\u002Fversion | azd agent variables, then `azure.yaml` | invoke, observe, trace, troubleshoot |\n| ACR | azd env | deploy |\n| Evaluation suites and cache paths | `.foundry\u002Fagent-metadata*.yaml` | observe, eval-datasets |\n| Local seed dataset\u002Fevaluator intent | `eval.yaml` | observe, eval-datasets |\n\n### Step 6: Write Metadata Overlay (Create\u002FDeploy\u002FObserve Only)\n\nOn any metadata write (deploy, auto-setup, dataset refresh, or trace-to-dataset update), persist only non-derivable overlay\u002Fcache state in the selected metadata file:\n\n- azd binding (`azd.environmentName`, `azd.service`) when useful for future resolution\n- `evaluationSuites[]` with remote suite\u002Fdataset\u002Fevaluator references and local cache paths\n- `lastEval`, result files, comparison summaries, or explicit non-azd overrides\n\nDo not copy azd-owned deployment values into metadata when azd already provides them. If the selected file is a preferred single-environment file, rewrite only that one environment block. If the selected file is a legacy multi-environment file, rewrite only the selected environment block. Never copy or merge environments across sibling metadata files automatically. If the selected environment still uses older `testSuites[]` or legacy `testCases[]`, rewrite it to `evaluationSuites[]` and remove migrated `priority` fields from the rewritten entries.\n\n### Step 7: Collect Missing Values\n\nUse 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:\n- **Agent root** — Target azd service project folder or folder containing `.foundry\u002Fagent-metadata*.yaml`\n- **Metadata file** — `agent-metadata.yaml` for local\u002Fdev, or an explicit sidecar such as `agent-metadata.prod.yaml`\n- **Environment** — azd environment, `dev`, `prod`, or another environment key from metadata\n- **Project endpoint** — Microsoft Foundry project endpoint URL\n- **Agent name** — Name of the target agent\n\n> 💡 **Tip:** If the user already provides the agent path, environment, project endpoint, or agent name, extract it directly — do not ask again.\n\n## Agent: Agent Types\n\nAgent workflows distinguish four first-class agent types:\n\n| Type | Kind | Description |\n|------|------|-------------|\n| **Prompt** | `\"prompt\"` | LLM-based agents backed by a model deployment; managed via Foundry MCP or SDK |\n| **Hosted** | `\"hosted\"` | Container-based agents running custom code; developed and deployed via `azd` |\n| **Managed Harness Agent** | `\"prompt\"` with `harness.type: \"microsoft_copilot_preview\"` for new authoring | Prompt Agent running on the Foundry-managed Copilot harness; developed and deployed via `azd` |\n| **Voice** | `\"voice\"` | Managed real-time Voice Agent; read [Voice Agent subtypes](foundry-agent\u002Fcreate\u002Freferences\u002Fvoice-agent\u002Fsubtypes.md) to distinguish GPT-Live Voice, Prompt Voice, and Hosted Voice |\n\nClassify an `azure.yaml` service by its definition in this exact order, not by `host` alone:\n\n1. `kind: voice` -> **Voice**. For compatibility, silently classify existing `kind: prompt-voice` services as Voice and normalize them when editing. Then read [Voice Agent subtypes](foundry-agent\u002Fcreate\u002Freferences\u002Fvoice-agent\u002Fsubtypes.md).\n2. `kind: hosted` -> **Hosted**. If Voice subtype classification identifies it as a wrapper's named target, treat it as that dependency; otherwise a standalone `invocations_ws` service is a **custom Hosted real-time\u002Faudio agent**.\n3. `kind: prompt` plus `harness.type: microsoft_copilot_preview` or legacy `github_copilot_preview` -> **Managed Harness Agent**.\n4. `kind: prompt` without that harness -> ordinary **Prompt Agent**.\n\nUse `agent_get` only when the type cannot be resolved from project context. Preserve the harness discriminator already authored on an existing Managed Harness Agent; do not migrate it during ordinary edits or redeploys. Do not convert an existing Agent between Prompt, Hosted, Managed Harness Agent, and Voice types; create a new Agent instead. Before creating any **Voice Agent**, determine the model strategy first per [create-voice](foundry-agent\u002Fcreate\u002Fcreate-voice.md).\n\n## Tool Usage Conventions\n\n- Use the `ask_user` or `askQuestions` tool whenever collecting information from the user\n- Use the `task` or `runSubagent` tool to delegate long-running or independent sub-tasks (e.g., env var scanning, status polling, Dockerfile generation)\n- Prefer azd for Hosted Agents, Managed Harness Agents, and Voice Agents; prefer Foundry MCP for ordinary Prompt Agents.\n- Reference official Microsoft documentation URLs instead of embedding CLI command syntax\n\n## Azure Authentication\n\n- [Azure Authentication Best Practices](references\u002Fauth-best-practices.md)\n\n## Additional Resources\n\n- [Foundry Hosted Agents](https:\u002F\u002Flearn.microsoft.com\u002Fazure\u002Fai-foundry\u002Fagents\u002Fconcepts\u002Fhosted-agents?view=foundry)\n- [Foundry Agent Runtime Components](https:\u002F\u002Flearn.microsoft.com\u002Fazure\u002Fai-foundry\u002Fagents\u002Fconcepts\u002Fruntime-components?view=foundry)\n\n## Network Isolation Errors\n\nApplies to **any** call against a Foundry project or its parent Foundry account — Foundry MCP tools, `azd`, `az` CLI, `curl`, REST, or SDK.\n\nIf an error matches `Public access is disabled` \u002F `PublicNetworkAccessDisabled` \u002F `403 Forbidden` from a private endpoint \u002F connection timeout \u002F the project endpoint FQDN resolves to a public IP, this typically means the parent Foundry account has `publicNetworkAccess=Disabled` or `Enabled from selected IP addresses`, and the current shell is outside its VNet.\n\nOnly if the error is ambiguous, confirm against the Foundry account using a management-plane call (works from anywhere with reader access):\n\n```bash\naz cognitiveservices account show \\\n  --name \u003Caccount> --resource-group \u003Crg> \\\n  --query \"properties.{publicNetworkAccess:publicNetworkAccess, networkAcls:networkAcls, privateEndpointConnections:privateEndpointConnections[].properties.privateLinkServiceConnectionState.status}\"\n```\n\n`publicNetworkAccess: \"Disabled\"` — or `\"Enabled\"` together with non-empty `networkAcls.ipRules` \u002F `virtualNetworkRules` — confirms isolation. 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