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Azure Compute

MicrosoftOfficialCreated by Microsoft

  • Skill
  • Tool integration
  • Cloud and DevOps
  • MIT
  • Medium risk
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From the author

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.
— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The Azure Compute skill acts as a primary router for infrastructure-level tasks within Azure. It directs user requests to specialized workflows for recommending, provisioning, and managing bare virtual machine infrastructure. It is designed to handle the lifecycle of VMs and VMSS, including capacity planning through Capacity Reservation Groups and machine monitoring via Essential Machine Management.

What it does

  • Routes requests to specific workflows for VM and VMSS provisioning.
  • Provides guidance on selecting VM families based on workload requirements.
  • Facilitates capacity reservation and guarantee for critical infrastructure.
  • Supports Essential Machine Management for enrollment and monitoring tasks.
  • Enables cost estimation and pricing comparisons for various VM SKUs.
  • Distinguishes between infrastructure deployment and application-level service deployment.

When to use it

  • When you need to provision or deploy bare virtual machines or scale sets.
  • When you require assistance in selecting the appropriate VM family for a specific workload.
  • When you need to reserve or guarantee capacity for your Azure compute resources.
  • When you are setting up or monitoring machines using Essential Machine Management.
  • When you need to compare pricing for different VM SKUs or regions.

Advantages

  • Centralized entry point for all Azure compute-related infrastructure tasks.
  • Prevents confusion between infrastructure provisioning and application deployment.
  • Provides structured paths for complex tasks like capacity reservation.
  • Ensures consistent use of specialized tools for quota and SKU management.

Usage examples

“I need to deploy a set of web servers. Which VM family should I use?”
The skill routes the request to the VM Recommender to analyze your workload and suggest the appropriate D-series or B-series VMs.
“How can I reserve capacity for my production database VMs?”
The skill initiates the Capacity Reservation workflow to guide you through creating and associating a Capacity Reservation Group.
“Provision a new Ubuntu VM for testing.”
The skill routes the request to the VM Creator workflow to handle the infrastructure deployment.
“How do I enroll my machines in Essential Machine Management?”
The skill directs you to the Essential Machine Management workflow for enrollment and monitoring instructions.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/microsoft/azure-compute/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip azure-compute.zip -d .agents/skills

Documentation by Microsoft

Azure Compute Skill

Routes Azure VM and Virtual Machine Scale Set (VMSS) requests to the right workflow.

When to Use This Skill

  • User wants to recommend, compare, or price a VM or VMSS

  • User wants to create, provision, or deploy a VM or VMSS

  • User asks about Capacity Reservation Groups (CRG) — reserve, guarantee capacity, pre-provision

  • User asks about Essential Machine Management (EMM) — machine enrollment, monitor

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.

Routing

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.

WorkflowFileUse when
VM Recommendervm-recommender.mdUser asks which VM/VMSS to choose, whether to use VMSS/autoscaling, wants pricing, or wants to compare options
VM Creatorvm-creator.mdUser wants to create, provision, or deploy a bare VM or VMSS (not an app deployment)
Capacity Reservationcapacity-reservation.mdUser needs to reserve / guarantee VM capacity (CRG create / associate / disassociate)
Essential Machine Managementessential-machine-management.mdUser asks about EMM / machine enrollment / monitor

Files

workflows/vm-recommender/vm-recommender.md

Azure VM Recommender

Recommend Azure VM sizes, VM Scale Sets (VMSS), and configurations by analyzing workload type, performance requirements, scaling needs, and budget. No Azure subscription required — 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 wants to compare VM families, sizes, or pricing tiers

  • User asks about trade-offs (cost vs performance, single VM vs scale set, orchestration modes)

  • User needs a cost estimate without an Azure subscription

  • User asks "Needs autoscaling?" or wants to decide between a single VM and a scale set

Workflow

Use reference files for initial filtering. Then verify with live documentation via web_fetch before final recommendations. If web_fetch fails, fall back to the reference files and surface the staleness warning from web-fetch-policy.md.

Step 1: Gather Requirements

Ask the user (infer when possible):

RequirementExamples
Workload typeWeb server, relational DB, ML training, batch, dev/test
vCPU / RAM needs"4 cores, 16 GB" or "lightweight" / "heavy"
GPU needed?Yes → GPU families; No → general / compute / memory
Storage needsHigh IOPS, large temp disk, premium SSD
Budget priorityCost-sensitive, performance-first, balanced
OSLinux or Windows (affects pricing)
RegionAffects availability and price
Instance countSingle, fixed count, or variable
Scaling needsNone, manual, autoscale (metrics / schedule)
Availability needsBest-effort, fault-domain, cross-zone HA
Load balancingNone, Azure Load Balancer (L4), Application Gateway (L7)
Step 2: Determine VM vs VMSS

Review VMSS Guide. Decision shortcut — start by asking Needs autoscaling? then walk the table:

SignalPick
Autoscale on CPU, memory, or scheduleVMSS
Stateless web/API tier behind a load balancerVMSS
Batch / parallel processing across many nodesVMSS
Mixed VM sizes in one groupVMSS (Flexible)
Single long-lived server (jumpbox, AD DC)VM
Unique per-instance configVM
Stateful, tightly-coupled clusterVM (or VMSS case-by-case)

If recommending VMSS, verify with web_fetch per web-fetch-policy.md. When in doubt, default to a single VM.

Step 3: Select VM Family

Review VM Family Guide and pick 2–3 candidate families. Verify each candidate's specs with web_fetch against:

https://learn.microsoft.com/en-us/azure/virtual-machines/sizes/<family-category>/<series-name>

For Spot eligibility, also fetch https://learn.microsoft.com/en-us/azure/virtual-machine-scale-sets/use-spot. If any fetch fails, follow web-fetch-policy.md. Same SKUs apply to single VMs and VMSS.

Step 4: Look Up Pricing

Query the Azure Retail Prices API per Retail Prices API Guide.

VMSS: no extra charge — pricing is per-VM. Multiply per-instance price × expected count. For autoscale, estimate at both min and max.

Step 5: Validate Quota Availability

GATE — do not present recommendations until quota is validated.

If the user has a subscription + region, review and run the checks from VM Quota Validation Guide. Without a subscription, note quota must be checked before deployment.

OutcomeAction
✅ SufficientProceed to Step 6
⚠️ Near limit (>80%)Proceed but warn; suggest quota increase
❌ InsufficientRequest increase, swap family, or try another region

Include a "Quota Status" column (✅/⚠️/❌) in the table.

Step 6: Present Recommendations

Provide 2–3 options with trade-offs:

ColumnPurpose
Hosting ModelVM or VMSS (with orchestration mode if VMSS)
VM SizeARM SKU name (e.g., Standard_D4s_v5)
vCPUs / RAMCore specs
Instance Count1 for VM; min–max for VMSS with autoscale
Estimated $/hrPer-instance pay-as-you-go
WhyWorkload fit
Trade-offWhat the user gives up

Always explain why a family fits and the Trade-off (cost vs cores, burstable vs dedicated, VM simplicity vs VMSS scale).

For VMSS, also mention orchestration mode (default Flexible), autoscale strategy (metric / schedule / both), and load balancer type.

Step 7: Offer Next Steps
Step 8: Hand Off to VM Creator (Optional)

If the user wants to actually provision what was recommended, hand off to vm-creator. See handoff-to-creator.md for the required Plan Card render and routing rules.

Error Handling

ScenarioAction
API returns empty resultsBroaden filters — check armRegionName, serviceName, armSkuName spelling
User unsure of workload typeAsk clarifying questions; default to General Purpose D-series
Region not specifiedUse eastus as default; note prices vary by region
Unclear if VM or VMSS neededAsk about scaling + instance count; default to single VM if still unsure
User asks VMSS pricing directlySame VM pricing API; VMSS has no extra charge — multiply by instance count

References

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

What our scanner noticed· Medium risk

  • References to credential files

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Microsoft
Publisher
Microsoft (Official)
Source
microsoft/skills · .github/plugins/azure-skills/skills/azure-compute · commit 0696600
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from .github/plugins/azure-skills/LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Also distributed in
microsoft/azure-skills

AutoGineer is not affiliated with or endorsed by Microsoft.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download azure-compute.zip