azure-diagrams▌
eraserlabs/eraser-io · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Generates architecture diagrams for Azure infrastructure from ARM templates, Azure CLI output, or natural language descriptions.
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
- ARM Templates: Extract
-
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 - CRITICAL: Label Formatting Rules
-
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-Sourceheader 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
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 }' - If you are Claude (Anthropic), use:
-
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
- Internal files: Record each file path you read and what information was extracted (e.g.,
-
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
imageUrlfrom 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
eraserlanguage 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:
- 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 → Application Gateway → VM → 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 -> mysqlserverImportant: 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
How to use azure-diagrams on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add azure-diagrams
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches azure-diagrams from GitHub repository eraserlabs/eraser-io and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate azure-diagrams. Access the skill through slash commands (e.g., /azure-diagrams) or your agent's skill management interface.
Security & Verification Notice
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.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ 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.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★57 reviews- ★★★★★Henry Martinez· Dec 16, 2024
azure-diagrams reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Daniel Thomas· Dec 16, 2024
I recommend azure-diagrams for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Olivia Martinez· Dec 8, 2024
Registry listing for azure-diagrams matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Sophia Park· Dec 8, 2024
Solid pick for teams standardizing on skills: azure-diagrams is focused, and the summary matches what you get after install.
- ★★★★★Anaya Sethi· Nov 27, 2024
azure-diagrams fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Anaya Reddy· Nov 15, 2024
Solid pick for teams standardizing on skills: azure-diagrams is focused, and the summary matches what you get after install.
- ★★★★★Sophia Choi· Nov 15, 2024
azure-diagrams has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Anika Smith· Nov 7, 2024
We added azure-diagrams from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Daniel White· Nov 7, 2024
Keeps context tight: azure-diagrams is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Anaya Zhang· Oct 26, 2024
azure-diagrams fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
showing 1-10 of 57