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Azure Vms

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BagelHole
azure-vms

Manage Azure Virtual Machines and scale sets. Configure availability sets and managed disks. Use when deploying compute resources on Azure.

Overview

PublisherBagelHole
RepositoryDevOps-Security-Agent-Skills
Skill nameazure-vms
Stars
1.1K
Forks
161
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by BagelHole on GitHub. Read the source before you install it.

Installation

Install the Azure Vms AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/main/infrastructure/cloud-azure/azure-vms
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/BagelHole/DevOps-Security-Agent-Skills.git /tmp/DevOps-Security-Agent-Skills
mkdir -p .claude/skills
cp -r /tmp/DevOps-Security-Agent-Skills/infrastructure/cloud-azure/azure-vms .claude/skills/azure-vms
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Azure Vms in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Azure Vms on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Azure Vms is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Azure Virtual Machines

Deploy and manage Azure VMs, availability sets, scale sets, custom images, and managed disks. Covers VM creation, sizing, disk management, auto-scaling, and Terraform configurations for production environments.

When to Use

  • You need full control over the operating system and runtime environment.
  • Your application requires specific OS configurations or kernel modules.
  • You are running legacy applications that cannot be containerized.
  • You need GPU-accelerated compute for ML training or rendering.
  • You need high-availability compute with availability zones or scale sets.

Prerequisites

bash
# Install Azure CLI
curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash

# Login and set subscription
az login
az account set --subscription "my-subscription-id"

# Create resource group
az group create --name compute-rg --location eastus

# List available VM sizes in a region
az vm list-sizes --location eastus --output table

# List available VM images
az vm image list --output table
az vm image list --publisher Canonical --offer 0001-com-ubuntu-server-jammy --all --output table

VM Creation

Linux VM with SSH Key

bash
az vm create \
  --resource-group compute-rg \
  --name myapp-vm \
  --image Ubuntu2204 \
  --size Standard_D4s_v5 \
  --admin-username azureuser \
  --generate-ssh-keys \
  --vnet-name myapp-vnet \
  --subnet app-subnet \
  --nsg "" \
  --public-ip-address "" \
  --os-disk-size-gb 64 \
  --os-disk-caching ReadWrite \
  --storage-sku Premium_LRS \
  --zone 1 \
  --assign-identity \
  --tags environment=prod team=platform app=myapp

# SSH into the VM (if public IP assigned)
ssh azureuser@$(az vm show -g compute-rg -n myapp-vm -d --query publicIps -o tsv)

Windows VM

bash
az vm create \
  --resource-group compute-rg \
  --name myapp-win-vm \
  --image Win2022Datacenter \
  --size Standard_D4s_v5 \
  --admin-username azureadmin \
  --admin-password 'S3cur3P@ssw0rd!' \
  --vnet-name myapp-vnet \
  --subnet app-subnet \
  --public-ip-address "" \
  --os-disk-size-gb 128 \
  --storage-sku Premium_LRS \
  --zone 1

VM with Cloud-Init

bash
# cloud-init.yaml
# #cloud-config
# package_update: true
# packages:
#   - nginx
#   - docker.io
# runcmd:
#   - systemctl enable nginx
#   - systemctl start nginx
#   - usermod -aG docker azureuser

az vm create \
  --resource-group compute-rg \
  --name web-vm \
  --image Ubuntu2204 \
  --size Standard_B2s \
  --admin-username azureuser \
  --generate-ssh-keys \
  --custom-data cloud-init.yaml \
  --tags role=web

VM Size Guide

FamilyExample SizesUse Case
B-seriesStandard_B1s, Standard_B2sDev/test, low-traffic web servers
D-seriesStandard_D4s_v5, Standard_D8s_v5General purpose, most production workloads
E-seriesStandard_E4s_v5, Standard_E16s_v5Memory-intensive (databases, caching)
F-seriesStandard_F4s_v2, Standard_F16s_v2CPU-intensive (batch processing, analytics)
L-seriesStandard_L8s_v3, Standard_L32s_v3Storage-optimized (big data, SQL)
N-seriesStandard_NC6s_v3, Standard_NC24ads_A100_v4GPU workloads (ML training, rendering)
M-seriesStandard_M128sSAP HANA, large in-memory workloads
bash
# Find VM sizes with specific capabilities
az vm list-sizes --location eastus \
  --query "[?numberOfCores >= \`4\` && memoryInMb >= \`16000\`]" \
  --output table

# Check VM size availability in a zone
az vm list-skus --location eastus \
  --size Standard_D4s_v5 \
  --output table

Managed Disks

bash
# Add a data disk to existing VM
az vm disk attach \
  --resource-group compute-rg \
  --vm-name myapp-vm \
  --name myapp-data-disk \
  --size-gb 256 \
  --sku Premium_LRS \
  --new \
  --lun 0

# Create a standalone managed disk
az disk create \
  --resource-group compute-rg \
  --name shared-data-disk \
  --size-gb 512 \
  --sku Premium_LRS \
  --zone 1

# Resize a disk (VM must be deallocated)
az vm deallocate --resource-group compute-rg --name myapp-vm
az disk update \
  --resource-group compute-rg \
  --name myapp-data-disk \
  --size-gb 512
az vm start --resource-group compute-rg --name myapp-vm

# Snapshot a disk for backup
az snapshot create \
  --resource-group compute-rg \
  --name myapp-disk-snapshot \
  --source myapp-data-disk

# Create disk from snapshot
az disk create \
  --resource-group compute-rg \
  --name myapp-disk-from-snap \
  --source myapp-disk-snapshot \
  --sku Premium_LRS

# List disks attached to a VM
az vm show \
  --resource-group compute-rg \
  --name myapp-vm \
  --query "storageProfile.dataDisks" \
  --output table

Custom Images

bash
# Generalize the VM (run inside the VM first)
# sudo waagent -deprovision+user -force

# Deallocate and generalize
az vm deallocate --resource-group compute-rg --name myapp-vm
az vm generalize --resource-group compute-rg --name myapp-vm

# Create image from VM
az image create \
  --resource-group compute-rg \
  --name myapp-golden-image \
  --source myapp-vm \
  --os-type Linux

# Create VM from custom image
az vm create \
  --resource-group compute-rg \
  --name myapp-from-image \
  --image myapp-golden-image \
  --size Standard_D4s_v5 \
  --admin-username azureuser \
  --generate-ssh-keys

# Use Azure Compute Gallery for shared images
az sig create \
  --resource-group compute-rg \
  --gallery-name myAppGallery

az sig image-definition create \
  --resource-group compute-rg \
  --gallery-name myAppGallery \
  --gallery-image-definition myapp-image \
  --publisher myorg \
  --offer myapp \
  --sku 1.0 \
  --os-type Linux \
  --os-state Generalized

az sig image-version create \
  --resource-group compute-rg \
  --gallery-name myAppGallery \
  --gallery-image-definition myapp-image \
  --gallery-image-version 1.0.0 \
  --managed-image myapp-golden-image \
  --target-regions eastus westus \
  --replica-count 2

Availability Sets and Zones

bash
# Create availability set
az vm availability-set create \
  --resource-group compute-rg \
  --name myapp-avset \
  --platform-fault-domain-count 3 \
  --platform-update-domain-count 5

# Create VM in availability set
az vm create \
  --resource-group compute-rg \
  --name myapp-vm-1 \
  --image Ubuntu2204 \
  --size Standard_D4s_v5 \
  --availability-set myapp-avset \
  --admin-username azureuser \
  --generate-ssh-keys

# Create VMs across availability zones
for zone in 1 2 3; do
  az vm create \
    --resource-group compute-rg \
    --name "myapp-vm-zone${zone}" \
    --image Ubuntu2204 \
    --size Standard_D4s_v5 \
    --zone "$zone" \
    --admin-username azureuser \
    --generate-ssh-keys \
    --no-wait
done

Virtual Machine Scale Sets

bash
# Create VMSS with autoscaling
az vmss create \
  --resource-group compute-rg \
  --name myapp-vmss \
  --image Ubuntu2204 \
  --vm-sku Standard_D4s_v5 \
  --instance-count 2 \
  --admin-username azureuser \
  --generate-ssh-keys \
  --vnet-name myapp-vnet \
  --subnet app-subnet \
  --upgrade-policy-mode Rolling \
  --health-probe "/" \
  --load-balancer myapp-lb \
  --zones 1 2 3 \
  --custom-data cloud-init.yaml \
  --tags environment=prod

# Configure autoscale rules
az monitor autoscale create \
  --resource-group compute-rg \
  --resource myapp-vmss \
  --resource-type Microsoft.Compute/virtualMachineScaleSets \
  --name myapp-autoscale \
  --min-count 2 \
  --max-count 20 \
  --count 3

# Scale out when CPU > 70%
az monitor autoscale rule create \
  --resource-group compute-rg \
  --autoscale-name myapp-autoscale \
  --condition "Percentage CPU > 70 avg 5m" \
  --scale out 2

# Scale in when CPU < 30%
az monitor autoscale rule create \
  --resource-group compute-rg \
  --autoscale-name myapp-autoscale \
  --condition "Percentage CPU < 30 avg 10m" \
  --scale in 1

# Manual scale
az vmss scale \
  --resource-group compute-rg \
  --name myapp-vmss \
  --new-capacity 5

# Update VMSS image
az vmss update \
  --resource-group compute-rg \
  --name myapp-vmss \
  --set virtualMachineProfile.storageProfile.imageReference.version=latest

# Rolling upgrade of instances
az vmss rolling-upgrade start \
  --resource-group compute-rg \
  --name myapp-vmss

# List VMSS instances
az vmss list-instances \
  --resource-group compute-rg \
  --name myapp-vmss \
  --output table

VM Management Operations

bash
# Start / Stop / Restart / Deallocate
az vm start --resource-group compute-rg --name myapp-vm
az vm stop --resource-group compute-rg --name myapp-vm
az vm restart --resource-group compute-rg --name myapp-vm
az vm deallocate --resource-group compute-rg --name myapp-vm

# Run command on a VM
az vm run-command invoke \
  --resource-group compute-rg \
  --name myapp-vm \
  --command-id RunShellScript \
  --scripts "df -h && free -m && uptime"

# Enable boot diagnostics
az vm boot-diagnostics enable \
  --resource-group compute-rg \
  --name myapp-vm

# Get boot diagnostics log
az vm boot-diagnostics get-boot-log \
  --resource-group compute-rg \
  --name myapp-vm

# Enable Azure Backup
az backup protection enable-for-vm \
  --resource-group compute-rg \
  --vault-name myapp-vault \
  --vm myapp-vm \
  --policy-name DefaultPolicy

# Resize a VM
az vm resize \
  --resource-group compute-rg \
  --name myapp-vm \
  --size Standard_D8s_v5

Terraform Configuration

hcl
resource "azurerm_linux_virtual_machine" "main" {
  name                  = "myapp-vm"
  resource_group_name   = azurerm_resource_group.main.name
  location              = azurerm_resource_group.main.location
  size                  = "Standard_D4s_v5"
  admin_username        = "azureuser"
  zone                  = "1"
  network_interface_ids = [azurerm_network_interface.main.id]

  admin_ssh_key {
    username   = "azureuser"
    public_key = file("~/.ssh/id_rsa.pub")
  }

  os_disk {
    caching              = "ReadWrite"
    storage_account_type = "Premium_LRS"
    disk_size_gb         = 64
  }

  source_image_reference {
    publisher = "Canonical"
    offer     = "0001-com-ubuntu-server-jammy"
    sku       = "22_04-lts-gen2"
    version   = "latest"
  }

  identity {
    type = "SystemAssigned"
  }

  tags = var.tags
}

resource "azurerm_managed_disk" "data" {
  name                 = "myapp-data-disk"
  resource_group_name  = azurerm_resource_group.main.name
  location             = azurerm_resource_group.main.location
  storage_account_type = "Premium_LRS"
  create_option        = "Empty"
  disk_size_gb         = 256
  zone                 = "1"
  tags                 = var.tags
}

resource "azurerm_virtual_machine_data_disk_attachment" "data" {
  managed_disk_id    = azurerm_managed_disk.data.id
  virtual_machine_id = azurerm_linux_virtual_machine.main.id
  lun                = 0
  caching            = "ReadOnly"
}

resource "azurerm_linux_virtual_machine_scale_set" "main" {
  name                = "myapp-vmss"
  resource_group_name = azurerm_resource_group.main.name
  location            = azurerm_resource_group.main.location
  sku                 = "Standard_D4s_v5"
  instances           = 3
  admin_username      = "azureuser"
  zones               = [1, 2, 3]

  admin_ssh_key {
    username   = "azureuser"
    public_key = file("~/.ssh/id_rsa.pub")
  }

  source_image_reference {
    publisher = "Canonical"
    offer     = "0001-com-ubuntu-server-jammy"
    sku       = "22_04-lts-gen2"
    version   = "latest"
  }

  os_disk {
    caching              = "ReadWrite"
    storage_account_type = "Premium_LRS"
  }

  network_interface {
    name    = "vmss-nic"
    primary = true

    ip_configuration {
      name      = "internal"
      primary   = true
      subnet_id = azurerm_subnet.app.id
    }
  }

  automatic_os_upgrade_policy {
    disable_automatic_rollback  = false
    enable_automatic_os_upgrade = true
  }

  rolling_upgrade_policy {
    max_batch_instance_percent              = 20
    max_unhealthy_instance_percent          = 20
    max_unhealthy_upgraded_instance_percent = 5
    pause_time_between_batches              = "PT0S"
  }

  tags = var.tags
}

Troubleshooting

SymptomCauseFix
VM fails to startQuota exceeded in regionCheck quota with az vm list-usage --location eastus; request increase
SSH connection refusedNSG blocking port 22 or VM not runningCheck NSG rules and VM power state; use Azure Bastion for private VMs
VM disk fullOS disk too small or logs not rotatedResize disk after deallocating; configure log rotation
VM performance is slowWrong VM size or disk throttlingCheck metrics with az monitor metrics list; upgrade size or disk tier
Scale set not scaling outAutoscale rule threshold not metReview autoscale settings; verify metric thresholds match workload
Custom image VM boot failsImage not properly generalizedRe-run waagent -deprovision before capturing; check boot diagnostics
VMSS rolling upgrade stuckHealth probe failing on new instancesFix application health endpoint; check az vmss rolling-upgrade get-latest
Spot VM evicted unexpectedlyAzure reclaimed capacityUse eviction policy Deallocate and set up eviction notifications

Related Skills

  • azure-networking -- VNet and NSG configuration for VM connectivity.
  • azure-aks -- Container alternative when VMs are not required.
  • arm-templates -- Bicep-based VM deployment templates.
  • terraform-azure -- Terraform-based VM and VMSS provisioning.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Azure Vms AI skill do?

Manage Azure Virtual Machines and scale sets. Configure availability sets and managed disks. Use when deploying compute resources on Azure.

Why use Azure Vms on TypingMind?

Because you install it once and use it with any model. Azure Vms is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Azure Vms in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/main/infrastructure/cloud-azure/azure-vms. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Azure Vms?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Azure Vms?

As many as you like. As long as a model supports skills, you can use Azure Vms with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Azure Vms AI skill free?

Yes. It is published on GitHub by BagelHole under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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