.github/plugins/azure-skills/skills/azure-compute/SKILL.md
Azure VM and VMSS router for recommendations, pricing, autoscale, orchestration, and connectivity troubleshooting. WHEN: Azure VM, VMSS, scale set, recommend, compare, server, website, burstable, lightweight, VM family, workload, GPU, learning, simulation, dev/test, backend, autoscale, load balancer, Flexible orchestration, Uniform orchestration, cost estimate, connect, refused, Linux, black screen, reset password, reach VM, port 3389, NSG, troubleshoot.
npx skillsauth add microsoft/azure-skills azure-computeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Routes Azure VM requests to the appropriate workflow based on user intent.
Activate this skill when the user:
User intent?
├─ Recommend / choose / compare / price a VM or VMSS
│ └─ Route to [VM Recommender](workflows/vm-recommender/vm-recommender.md)
│
├─ Can't connect / RDP / SSH / troubleshoot a VM
│ └─ Route to [VM Troubleshooter](workflows/vm-troubleshooter/vm-troubleshooter.md)
│
└─ Unclear
└─ Ask: "Are you looking for a VM recommendation, or troubleshooting a connectivity issue?"
| Signal | Workflow | | ----------------------------------------------------------------------------- | ------------------------------------------------------------------ | | "recommend VM", "which VM", "VM size", "VM pricing", "VMSS", "scale set" | VM Recommender | | "can't connect", "RDP", "SSH", "NSG blocking", "reset password", "black screen" | VM Troubleshooter |
| Workflow | Purpose | References | | --------------------- | -------------------------------------------------------- | ---------------------------------------------------------------------------- | | VM Recommender | Recommend VM sizes, VMSS, pricing using public APIs/docs | vm-families, retail-prices-api, vmss-guide | | VM Troubleshooter | Diagnose and resolve VM connectivity failures (RDP/SSH) | cannot-connect-to-vm |
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).