# Rodanthi Alexiou Sdc Infraops Hack Microsoft Foundry > Microsoft Foundry Skill - Skill: `tomevault-io/rodanthi-alexiou-sdc-infraops-hack-microsoft-foundry` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/rodanthi-alexiou-sdc-infraops-hack-microsoft-foundry` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/rodanthi-alexiou-sdc-infraops-hack-microsoft-foundry/raw - Safety review: pending (external: skill-scanner PASS, skillspector CAUTION) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/rodanthi-alexiou-sdc-infraops-hack-microsoft-foundry --- # 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](foundry-agent/deploy/deploy.md) | | **invoke** | Send messages to an agent, single or multi-turn conversations | [invoke](foundry-agent/invoke/invoke.md) | | **observe** | Eval-driven optimization loop: evaluate → analyze → optimize → compare → iterate | [observe](foundry-agent/observe/observe.md) | | **trace** | Query traces, analyze latency/failures, correlate eval results to specific responses via App Insights `customEvents` | [trace](foundry-agent/trace/trace.md) | | **troubleshoot** | View container logs, query telemetry, diagnose failures | [troubleshoot](foundry-agent/troubleshoot/troubleshoot.md) | | **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](foundry-agent/create/create.md) | | **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/eval-datasets/eval-datasets.md) | | **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](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](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](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](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](rbac/rbac.md) | | **faos-optimize** | Optimize agent configuration for FAOS eval sweeps: externalize instructions, model, and temperature for evaluator-driven tuning. Required for RAG accuracy improvement loops (WAF Reliability). | [faos-optimize](foundry-agent/faos-optimize/faos-optimize.md) | | **resource/private-network** | Deploy Foundry with VNet isolation: BYO VNet, Managed VNet, and hybrid topologies with private endpoints for AI Services, AI Search, Cosmos DB, and Storage. | [private-network-standard-agent-setup](references/private-network-standard-agent-setup.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 | 3. 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 | > [!CAUTION] > **Deprecated resource type:** The Azure AI Foundry Hub (`kind: Hub`, > `Microsoft.MachineLearningServices/workspaces`) was deprecated in 2025. > Do **not** generate it. The current pattern is: > > - **Resource:** `Microsoft.CognitiveServices/accounts` with `kind: AIServices` > - **Project:** Microsoft Foundry Project linked to the AI Services resource > > If existing IaC uses `kind: Hub`, replace it before deploying. ## 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](references/standard-agent-setup.md). | | **Standard + Private Network** | Azure AI Services | Standard setup with VNet isolation and private endpoints. See [private-network-standard-agent-setup](references/private-network-standard-agent-setup.md). | > **MANDATORY:** For standard setup, read the appropriate reference before proceeding: > > - **Public network:** [references/standard-agent-setup.md](references/standard-agent-setup.md) > - **Private network (VNet isolation):** [references/private-network-standard-agent-setup.md](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 - [Hosted Agents](https://learn.microsoft.com/azure/ai-foundry/agents/concepts/hosted-agents?view=foundry) - [Runtime Components](https://learn.microsoft.com/azure/ai-foundry/agents/concepts/runtime-components?view=foundry) - [Foundry Samples](https://github.com/azure-ai-foundry/foundry-samples) - [Python SDK](references/sdk/foundry-sdk-py.md) ## 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. ## Reference Index Load these on demand — do NOT read all at once: | Reference | When to Load | | ---------------------------------------------------- | -------------------------------------- | | `references/auth-best-practices.md` | Auth Best Practices | | `references/private-network-standard-agent-setup.md` | Private Network Standard Agent Setup | | `references/standard-agent-setup.md` | Standard Agent Setup | | `foundry-agent/faos-optimize/faos-optimize.md` | FAOS Optimization (eval-driven tuning) | --- > Source: [rodanthi-alexiou/sdc-infraops-hack](https://github.com/rodanthi-alexiou/sdc-infraops-hack) — distributed by [TomeVault](https://tomevault.io).