azure-computelisted

Recommend Azure VM sizes, VM Scale Sets (VMSS), and configurations based on workload requirements, performance needs, and budget constraints. No Azure account required — uses public documentation and the Azure Retail Prices API. USE FOR: recommend VM size, which VM should I use, choose Azure VM, VM for web/database/ML/batch/HPC, GPU VM, compare VM sizes, cheapest VM, best VM for workload, VM pricing, cost estimate, burstable/compute/memory/storage optimized VM, confidential computing, VM trade-offs, VM families, VMSS, scale set recommendation, autoscale VMs, load balanced VMs, VMSS vs VM, scale out, horizontal scaling, flexible orchestration. DO NOT USE FOR: deploying VMs or VMSS, deploying apps (use azure-deploy), looking up existing VMs (use azure-resource-lookup), cost optimization of running VMs (use azure-cost-optimization), non-VM services like App Service or AKS.
aiskillstore/marketplace · ★ 329 · DevOps & Infrastructure · score 79

Install: claude install-skill aiskillstore/marketplace

# Azure Compute Skill Recommend Azure VM sizes, VM Scale Sets (VMSS), and configurations by analyzing workload type, performance requirements, scaling needs, and budget. No Azure subscription required — all data comes from public Microsoft documentation and the unauthenticated Retail Prices API. ## When to Use This Skill - User asks which Azure VM or VMSS to choose for a workload - User needs VM size recommendations for web, database, ML, batch, HPC, or other workloads - User wants to compare VM families, sizes, or pricing tiers - User asks about trade-offs between VM options (cost vs performance) - User needs a cost estimate for Azure VMs without an Azure account - User asks whether to use a single VM or a scale set - User needs autoscaling, high availability, or load-balanced VM recommendations - User asks about VMSS orchestration modes (Flexible vs Uniform) ## Workflow > Use reference files for initial filtering > **CRITICAL: then always verify with live documentation** from learn.microsoft.com before making final recommendations. If `web_fetch` fails, use reference files as fallback but warn the user the information may be stale. ### Step 1: Gather Requirements Ask the user for (infer when possible): | Requirement | Examples | | ---------------------- | ------------------------------------------------------------------ | | **Workload type** | Web server, relational DB, ML training, batch pr