azure-compute

**ANALYSIS SKILL** — Recommend Azure VM sizes and VMSS for workload, performance, and budget. Uses public docs and the Azure Retail Prices API. WHEN: "recommend VM size", "choose Azure VM", "GPU VM", "compare VM sizes", "VMSS vs VM", "autoscale VMs". DO NOT USE FOR: provisioning VMs (azure-prepare), VM pricing for budgets (azure-pricing MCP).

Quality

85%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

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)

Rules

Always verify against live docs — call web_fetch against learn.microsoft.com before finalizing recommendations; warn the user when web_fetch fails
Default to General Purpose D-series when workload type is unclear
Default region follows the canonical declaration in copilot-instructions.md; prices vary by region
Default to single VM when scaling needs are unclear; recommend VMSS only when autoscale, fleet, or mixed-size requirements are explicit
VMSS pricing = VM pricing × instance count (no extra VMSS charge)
Reservation pricing is recommended for long-lived production VMs (1y/3y commitments)
No deployment — this skill recommends sizes; for provisioning use azure-prepare

Steps

The full 6-step procedure (with all decision tables, dichotomy tree, and web_fetch URLs) lives in references/recommendation-workflow.md. Load it on demand. Summary:

Gather requirements — workload type, vCPU/RAM, GPU, storage, budget, OS, region, instance count, scaling, HA, load balancing
Determine VM vs VMSS — VMSS for autoscale / fleet / mixed sizes (Flexible orchestration); VM for single long-lived servers, jumpboxes, AD DCs. Default to single VM when unsure
Select VM family — pick 2–3 candidates from vm-families.md, then verify specs via web_fetch against learn.microsoft.com
Look up pricing — Azure Retail Prices API per retail-prices-api.md; for VMSS multiply by instance count
Present 2–3 recommendations — include hosting model, VM size, vCPU/RAM, instance count, $/hr, fit, trade-off
Offer next steps — reservation pricing, Azure Pricing Calculator, VMSS autoscale + networking docs

Critical: always verify recommendations against live learn.microsoft.com docs via web_fetch. If web_fetch fails, proceed with reference-file guidance and warn the user data may be stale.

Error Handling

Scenario	Action
API returns empty results	Broaden filters — check `armRegionName`, `serviceName`, `armSkuName` spelling
User unsure of workload type	Ask clarifying questions; default to General Purpose D-series
Region not specified	Use the canonical default from copilot-instructions.md; prices vary by region
Unclear if VM or VMSS needed	Ask about scaling and instance count; default to single VM if unsure
User asks VMSS pricing directly	Use same VM pricing API — VMSS has no extra charge; multiply by instance count

References

Recommendation Workflow — Full 6-step procedure with decision tables and web_fetch URLs
VM Family Guide — Family-to-workload mapping and selection
Retail Prices API Guide — Query patterns, filters, and examples
VMSS Guide — When to use VMSS, orchestration modes, and autoscale patterns

Reference Index

Load these on demand — do NOT read all at once:

Reference	When to Load
`references/recommendation-workflow.md`	Full Steps 1–6 (decision tables, web_fetch URLs)
`references/retail-prices-api.md`	Pricing queries (Step 4)
`references/vm-families.md`	VM family selection (Step 3)
`references/vmss-guide.md`	VMSS vs VM decision (Step 2)

Repository: jonathan-vella/azure-agentic-infraops
Commit: 05d7617

Last updated: 5 days ago
Created: 5 days ago

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