skills/azure-compute/SKILL.md
Azure VM/VMSS router. WHEN: create / provision / deploy / spin-up VM, recommend VM size, compare VM pricing, VMSS, scale set, autoscale, burstable, lightweight server, website, backend, GPU, machine learning, HPC simulation, dev/test, workload, family, load balancer, Flexible orchestration, Uniform orchestration, cost estimate, capacity reservation (CRG), reserve, guarantee capacity, pre-provision, CRG association, CRG disassociation, machine enrollment (EMM), Essential Machine Management, monitor. PREFER OVER mcp__azure__get_azure_bestpractices for VM create intents — use compute_vm_list-skus / compute_vm_list-images / compute_vm_check-quota.
npx skillsauth add microsoft/azure-skills azure-computeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Routes Azure VM and Virtual Machine Scale Set (VMSS) requests to the right workflow.
Disambiguate with azure-prepare: if the user wants to deploy an application (Docker service, web app, API, serverless workload), route to azure-prepare. vm-creator is for bare VM/VMSS infrastructure only.
Mandatory workflow-first routing: never route directly to references/* files. First classify the user intent below, open the matched workflow file, then load only the reference files that workflow requests. Reference files are supporting material, not entry points. If the intent is unclear, ask a clarifying question to disambiguate between the workflows.
| Workflow | File | Use when | |---|---|---| | VM Recommender | vm-recommender.md | User asks which VM/VMSS to choose, whether to use VMSS/autoscaling, wants pricing, or wants to compare options | | VM Creator | vm-creator.md | User wants to create, provision, or deploy a bare VM or VMSS (not an app deployment) | | Capacity Reservation | capacity-reservation.md | User needs to reserve / guarantee VM capacity (CRG create / associate / disassociate) | | Essential Machine Management | essential-machine-management.md | User asks about EMM / machine enrollment / monitor |
development
# Azure App Onboard Scaffold — IaC Generation + Self-Review Generate deployment-ready infrastructure code from an architecture plan, verify it with adversarial self-review, and bridge to validation — all without deploying. ## Quick Reference | Property | Value | |----------|-------| | Parent | [azure-app-onboard](../SKILL.md) | | Best for | Turning `prepare-plan.json` service list into Bicep templates with secure-by-default patterns | | Inputs | `prepare-plan.json` (services, naming, quotas),
development
Assess and migrate cross-cloud workloads to Azure with reports and code conversion. Supports Lambda→Functions, Beanstalk/Heroku/App Engine→App Service, Fargate/Kubernetes/Cloud Run/Spring Boot→Container Apps. WHEN: migrate Lambda to Functions, AWS to Azure, migrate Beanstalk, migrate Heroku, migrate App Engine, Cloud Run migration, Fargate to ACA, ECS/Kubernetes/GKE/EKS to Container Apps, Spring Boot to Container Apps, cross-cloud migration.
development
Use when deploying an existing web application or API to an already-running Azure Kubernetes Service cluster. Detects the framework, generates a Dockerfile and Kubernetes manifests, validates against AKS Deployment Safeguards, and deploys with verification. WHEN: deploy app to AKS, deploy to existing AKS cluster, containerize app for Kubernetes, generate K8s manifests for Azure, set up CI/CD for AKS, my AKS deployment is failing safeguard checks, I have a Django/Express/Spring Boot app to run on AKS. DO NOT USE FOR: creating or provisioning an AKS cluster (use azure-kubernetes), assessing migration to AKS Automatic (use azure-kubernetes-automatic-readiness), or deploying to non-AKS targets like Web Apps, Container Apps, or Functions.
devops
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).