| name | azure-compute |
| description | **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). |
| license | MIT |
| metadata | {"author":"Microsoft","version":"1.0.2"} |
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
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) |