Use when you have a written implementation plan to execute in a separate session with review checkpoints
npx skills add eraserlabs/eraser-io --skill "azure-diagrams"
Install specific skill from multi-skill repository
# Description
Visualizes Azure infrastructure from ARM templates, Azure CLI, or descriptions. Use when user has Azure resources to diagram.
# SKILL.md
name: azure-diagrams
description: 'Visualizes Azure infrastructure from ARM templates, Azure CLI, or descriptions. Use when user has Azure resources to diagram.'
license: MIT
compatibility: Requires network access to call Eraser API
allowed-tools: Read Write Bash(curl:*)
metadata:
version: "1.0.0"
author: Eraser Labs
tags: azure, diagram, arm, resource-group, vnet, vm, storage, infrastructure
Azure Diagram Generator
Generates architecture diagrams for Azure infrastructure from ARM templates, Azure CLI output, or natural language descriptions.
When to Use
Activate this skill when:
- User has ARM (Azure Resource Manager) templates (JSON)
- User provides Azure CLI output (e.g.,
az vm list) - User wants to visualize Azure resources
- User mentions Azure services (Virtual Machines, Storage Accounts, VNets, etc.)
- User asks to "diagram my Azure infrastructure"
How It Works
This skill generates Azure-specific diagrams by parsing Azure resources and calling the Eraser API directly:
- Parse Azure Resources: Extract resources from ARM templates, CLI output, or descriptions
- Map Azure Relationships: Identify Resource Groups, VNets, subnets, and service connections
- Generate Eraser DSL: Create Eraser DSL code from Azure resources
- Call Eraser API: Use
/api/render/elementswithdiagramType: "cloud-architecture-diagram"
Instructions
When the user provides Azure infrastructure information:
-
Parse the Source
-
ARM Templates: Extract
resourcesarray, identify types (Microsoft.Compute/virtualMachines, etc.) - CLI Output: Parse JSON output from
azcommands -
Description: Identify Azure service names and relationships
-
Identify Azure Components
-
Networking: Virtual Networks (VNets), Subnets, Network Security Groups, Load Balancers
- Compute: Virtual Machines, Virtual Machine Scale Sets, App Services, Functions
- Storage: Storage Accounts, Blob Storage, File Shares
- Databases: SQL Databases, Cosmos DB, Redis Cache
- Security: Network Security Groups, Azure AD, Key Vault
- Load Balancing: Application Gateway, Load Balancer, Traffic Manager
-
Other: Service Bus, Event Hubs, API Management
-
Map Relationships
-
VMs in subnets
- Subnets in VNets
- VNets in Resource Groups
- Storage accounts accessed by VMs
- Databases accessed by applications
-
Network Security Groups attached to subnets
-
Generate Eraser DSL Convert Azure resources to Eraser DSL:
-
CRITICAL: Label Formatting Rules
- Labels MUST be on a single line - NEVER use newlines inside label attributes
- Keep labels simple and readable - prefer separate labels over concatenating too much metadata
- Format DSL with proper line breaks (one node/group per line, but labels stay on single lines)
- If including metadata like CIDR blocks or instance types, include them in the same quoted label string:
[label: "VNet 10.0.0.0/16"]
Example:
myVNet [label: "VNet 10.0.0.0/16"] {
subnet1 [label: "Subnet 1"] {
myVM [icon: azure-vm, label: "Virtual Machine"]
gateway [icon: azure-app-gateway]
}
subnet2 [label: "Subnet 2"] {
database [icon: azure-sql]
}
}
storage [icon: azure-storage]
myVNet -> myVM
myVM -> database
- Make the HTTP Request
IMPORTANT: You MUST execute this curl command after generating the DSL. Never stop after generating DSL without making the API call.
CRITICAL: In the X-Skill-Source header below, you MUST replace the value with your AI agent name:
- If you are Claude (Anthropic), use: claude
- If you are running in Cursor, use: cursor
- If you are ChatGPT (OpenAI), use: chatgpt
- If you are Gemini (Google), use: gemini
- Otherwise, use your model/agent name in lowercase
bash
curl -X POST https://app.eraser.io/api/render/elements \
-H "Content-Type: application/json" \
-H "X-Skill-Source: eraser-skill" \
-H "Authorization: Bearer ${ERASER_API_KEY}" \
-d '{
"elements": [{
"type": "diagram",
"id": "diagram-1",
"code": "<your generated DSL>",
"diagramType": "cloud-architecture-diagram"
}],
"scale": 2,
"theme": "${ERASER_THEME:-dark}",
"background": true
}'
- Track Sources During Analysis
As you analyze files and resources to generate the diagram, track:
- Internal files: Record each file path you read and what information was extracted (e.g.,
infra/main.bicep- VNet and subnet definitions) - External references: Note any documentation, examples, or URLs consulted (e.g., Azure architecture best practices documentation)
-
Annotations: For each source, note what it contributed to the diagram
-
Handle the Response
CRITICAL: Minimal Output Format
Your response MUST always include these elements with clear headers:
-
Diagram Preview: Display with a header
## Diagram 
Use the ACTUALimageUrlfrom the API response. -
Editor Link: Display with a header
## Open in Eraser [Edit this diagram in the Eraser editor]({createEraserFileUrl})
Use the ACTUAL URL from the API response. -
Sources section: Brief list of files/resources analyzed (if applicable)
```
## Sourcespath/to/file- What was extracted
```
-
Diagram Code section: The Eraser DSL in a code block with
eraserlanguage tag
## Diagram Codeeraser
{DSL code here}
-
Learn More link:
You can learn more about Eraser at https://docs.eraser.io/docs/using-ai-agent-integrations
Additional content rules:
- If the user ONLY asked for a diagram, include NOTHING beyond the 5 elements above
- If the user explicitly asked for more (e.g., "explain the architecture", "suggest improvements"), you may include that additional content
- Never add unrequested sections like Overview, Security Considerations, Testing, etc.
The default output should be SHORT. The diagram image speaks for itself.
Azure-Specific Tips
- Resource Groups: Show Resource Groups as logical containers
- VNets as Containers: Always show VNets containing subnets and resources
- Network Security Groups: Include NSG rules and attachments
- Subscriptions: Note subscription context if provided
- Data Flow: Show traffic flow (Internet β Application Gateway β VM β SQL Database)
- Use Azure Icons: Request Azure-specific styling in the description
Example: ARM Template with Multiple Azure Services
User Input
{
"resources": [
{
"type": "Microsoft.Resources/resourceGroups",
"name": "rg-main"
},
{
"type": "Microsoft.Network/virtualNetworks",
"name": "myVNet",
"properties": {
"addressSpace": {
"addressPrefixes": ["10.0.0.0/16"]
},
"subnets": [
{
"name": "subnet1",
"properties": {
"addressPrefix": "10.0.1.0/24"
}
}
]
}
},
{
"type": "Microsoft.Compute/virtualMachines",
"name": "myVM",
"properties": {
"hardwareProfile": {
"vmSize": "Standard_B1s"
}
}
},
{
"type": "Microsoft.Web/sites",
"name": "myAppService",
"properties": {
"serverFarmId": "/subscriptions/.../serverfarms/myPlan"
}
},
{
"type": "Microsoft.Storage/storageAccounts",
"name": "mystorageaccount"
},
{
"type": "Microsoft.Sql/servers",
"name": "mysqlserver",
"properties": {
"administratorLogin": "admin"
}
}
]
}
Expected Behavior
-
Parses ARM template:
-
Resource Group: rg-main (container)
- Networking: VNet with subnet
- Compute: VM, App Service
- Storage: Storage Account
-
Database: SQL Server
-
Generates DSL showing Azure service diversity:
```
resource-group [label: "Resource Group rg-main"] {
myVNet [label: "VNet 10.0.0.0/16"] {
subnet1 [label: "Subnet 1 10.0.1.0/24"] {
myVM [icon: azure-vm, label: "VM Standard_B1s"]
}
}
myAppService [icon: azure-app-service, label: "App Service"]
mystorageaccount [icon: azure-storage, label: "Storage Account"]
mysqlserver [icon: azure-sql, label: "SQL Server"]
}
myAppService -> mystorageaccount
myVM -> mysqlserver
```
Important: All label text must be on a single line within quotes. Azure-specific: Show Resource Groups as containers, include App Services, Storage Accounts, and SQL databases with proper Azure icons.
- Calls
/api/render/elementswithdiagramType: "cloud-architecture-diagram"
Example: Azure CLI Output
User Input
User runs: az vm list --output json
Provides JSON output
Expected Behavior
-
Parses JSON to extract:
-
VM names, sizes, states
- Resource groups
- Network interfaces
-
Storage accounts
-
Formats and calls API
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.