Generates architecture diagrams for Azure infrastructure from ARM templates, Azure CLI output, or natural language descriptions.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionazure-diagramsExecute the skills CLI command in your project's root directory to begin installation:
Fetches azure-diagrams from eraserlabs/eraser-io and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate azure-diagrams. Access via /azure-diagrams in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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Generates architecture diagrams for Azure infrastructure from ARM templates, Azure CLI output, or natural language descriptions.
Activate this skill when:
az vm list)This skill generates Azure-specific diagrams by parsing Azure resources and calling the Eraser API directly:
/api/render/elements with diagramType: "cloud-architecture-diagram"When the user provides Azure infrastructure information:
Parse the Source
resources array, identify types (Microsoft.Compute/virtualMachines, etc.)az commandsIdentify Azure Components
Map Relationships
Generate Eraser DSL Convert Azure resources to Eraser DSL:
[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:
claudecursorchatgptgeminicurl -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:
infra/main.bicep - VNet and subnet definitions)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 ACTUAL imageUrl from 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)
## Sources
- `path/to/file` - What was extracted
Diagram Code section: The Eraser DSL in a code block with eraser language tag
## Diagram Code
```eraser
{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:
The default output should be SHORT. The diagram image speaks for itself.
{
"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"
}
}
]
}
Parses ARM template:
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/elements with diagramType: "cloud-architecture-diagram"
User runs: az vm list --output json
Provides JSON output
Parses JSON to extract:
Formats and calls API
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
microsoft/GitHub-Copilot-for-Azure
microsoft/azure-skills
microsoft/azure-skills
microsoft/GitHub-Copilot-for-Azure
hoodini/ai-agents-skills
davila7/claude-code-templates
azure-diagrams reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend azure-diagrams for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for azure-diagrams matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: azure-diagrams is focused, and the summary matches what you get after install.
azure-diagrams fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: azure-diagrams is focused, and the summary matches what you get after install.
azure-diagrams has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added azure-diagrams from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: azure-diagrams is the kind of skill you can hand to a new teammate without a long onboarding doc.
azure-diagrams fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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