Azure Compute Skill

SkillCloud & infra

azure-compute is a skill from Microsoft's official Azure collection. Once added, your AI can manage Azure cloud resources on your behalf, including virtual machines and services. It is built for common Azure scenarios, so everyday cloud tasks can be handled through your AI.

Connect your AI Use it in a pipeline

Then ask your AI: use the Azure Compute Skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.

No other account needed.

Add the skill, then ask your AI to manage the Azure resources you work with. It comes from Microsoft's official Azure skills collection.

What your AI can do with it

Manage Azure virtual machines
Manage Azure cloud services
Handle Azure resource tasks through your AI
Cover common Azure scenarios

What this skill tells your AI

The instructions your AI receives, as published by gbb-acelerators/awesome-harness-primitives in harness/claude-code/skills/azure-compute/SKILL.md and read by Ahel’s review.

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

When to invoke

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.

Workflow	File	Use when
VM Recommender	vm-recommender.md	User asks which VM/VMSS to choose, whether to use VMSS/autoscaling, wants pricing, or wants to compare options
VM Creator	vm-creator.md	User wants to create, provision, or deploy a bare VM or VMSS (not an app deployment)
Capacity Reservation	capacity-reservation.md	User needs to reserve / guarantee VM capacity (CRG create / associate / disassociate)
Essential Machine Management	essential-machine-management.md	User asks about EMM / machine enrollment / monitor

Output template

## Compute recommendation result

**Status:** recommended | provisioned | blocked
**Summary:** <one sentence covering scope and outcome>

### Details
VM or scale set size, image, orchestration mode, and cost basis.

### Validation
- <check performed>: <result and evidence>

Quality gate

Size and image availability were confirmed for the target region.
Quota was checked before recommending provisioning.
The output follows ## Output template exactly.
Every reported check was performed and its evidence is shown.
Irreversible Azure actions were confirmed with the user first.

Signals

GitHub stars: 20
Last commit: Sep 2026

Ahel review

S4info
community integration, published by gbb-acelerators, not azure

Automated review, not a security audit. Ruleset v1+k2.

Advanced

Item type: skill
Key: azure-compute
Source: github.com/gbb-acelerators/awesome-harness-primitives

github.com/gbb-acelerators/awesome-harness-primitives