bicep-diagrams

eraserlabs/eraser-io · updated Apr 8, 2026

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$npx skills add https://github.com/eraserlabs/eraser-io --skill bicep-diagrams
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summary

Generates architecture diagrams directly from Azure Bicep files. Bicep is a domain-specific language (DSL) for deploying Azure resources declaratively.

skill.md

Bicep Diagram Generator

Generates architecture diagrams directly from Azure Bicep files. Bicep is a domain-specific language (DSL) for deploying Azure resources declaratively.

When to Use

Activate this skill when:

  • User has Bicep files (.bicep) and wants to visualize the infrastructure
  • User asks to "diagram my Bicep" or "visualize this Bicep infrastructure"
  • User mentions Bicep or Azure Bicep
  • User wants to see the architecture of their Bicep-deployed resources

How It Works

This skill generates Bicep-specific diagrams by parsing Bicep code and calling the Eraser API directly:

  1. Parse Bicep Files: Identify resource declarations, modules, parameters, and outputs
  2. Extract Relationships: Map dependencies, resource references, and module hierarchies
  3. Generate Eraser DSL: Create Eraser DSL code from Bicep resources
  4. Call Eraser API: Use /api/render/elements with diagramType: "cloud-architecture-diagram"

Instructions

When the user provides Bicep code:

  1. Parse the Bicep

    • Identify all resource declarations (Microsoft.Compute/virtualMachines, etc.)
    • Extract module declarations and their configurations
    • Note param and output definitions
    • Identify var variables and their usage
  2. Map Relationships

    • Track resource dependencies (e.g., dependsOn or implicit dependencies)
    • Group resources by type (compute, networking, storage, etc.)
    • Identify VNets as containers for subnets and resources
    • Note Network Security Groups, Key Vaults, and other security resources
  3. Generate Eraser DSL Convert Bicep 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 VM sizes, 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
    
  4. 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
    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
      }'
    
  5. Track Sources During Analysis

    As you analyze Bicep files and resources to generate the diagram, track:

    • Internal files: Record each Bicep file path you read and what resources were extracted (e.g., infra/main.bicep - VNet and subnet definitions, infra/sql.bicep - SQL Database configuration)
    • External references: Note any documentation, examples, or URLs consulted (e.g., Azure Bicep documentation, Azure architecture best practices)
    • Annotations: For each source, note what it contributed to the diagram
  6. Handle the Response

    CRITICAL: Minimal Output Format

    Your response MUST always include these elements with clear headers:

    1. Diagram Preview: Display with a header

      ## Diagram
      ![{Title}]({imageUrl})
      

      Use the ACTUAL imageUrl from the API response.

    2. 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.

    3. Sources section: Brief list of files/resources analyzed (if applicable)

      ## Sources
      - `path/to/file` - What was extracted
      
    4. Diagram Code section: The Eraser DSL in a code block with eraser language tag

      ## Diagram Code
      ```eraser
      {DSL code here}
      
    5. 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.

  7. Handle Modules

    • If modules are used, show module boundaries
    • Include module parameters and outputs
    • Show how modules connect to main resources

Bicep-Specific Tips

  • Show Resource Groups: Bicep deployments target resource groups
  • VNets as Containers: Show VNets containing subnets and resources
  • Include Dependencies: Show dependsOn relationships
  • Module Structure: If modules are used, show their boundaries
  • Parameters: Note key parameters that affect resource configuration
  • Use Azure Icons: Request Azure-specific styling

Example: Bicep with Parameters and Modules

User Input

@description('The name of the Virtual Network')
param vnetName string = 'myVNet'
@description('The address prefix for the VNet')
param vnetAddressPrefix string = '10.0.0.0/16'
@description('The address prefix for the subnet')
param subnetAddressPrefix string = '10.0.1.0/24'
@description('VM size')
param vmSize string = 'Standard_B1s'

// Main VNet resource
resource virtualNetwork 'Microsoft.Network/virtualNetworks@2021-05-01' = {
  name: vnetName
  location: resourceGroup().location
  properties: {
    addressSpace: {
      addressPrefixes: [vnetAddressPrefix]
    }
    subnets: [
      {
        name: 'subnet1'
        properties: {
          addressPrefix: subnetAddressPrefix
        }
      }
    ]
  }
}

// VM resource with dependsOn
resource virtualMachine 'Microsoft.Compute/virtualMachines@2021-11-01' = {
  name: 'myVM'
  location: resourceGroup().location
  properties: {
    hardwareProfile: {
      vmSize: vmSize
    }
  }
  dependsOn: [virtualNetwork]
}

// Module usage
module storageModule './modules/storage.bicep' = {
  name: 'storage'
  params: {
    location: resourceGroup().location
  }
}

Expected Behavior

  1. Parses Bicep:

    • Parameters: vnetName, vnetAddressPrefix, subnetAddressPrefix, vmSize
    • Resources: VNet with subnet, VM with dependsOn relationship
    • Module: Storage module with parameters
  2. Generates DSL showing Bicep-specific features:

    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"]
      }
    }
    
    storage-module [label: "Storage Module"] {
      storage-account [icon: azure-storage]
    }
    
    myVNet -> myVM
    

    Important: All label text must be on a single line within quotes. Bicep-specific: Show modules as containers, include dependsOn relationships, note parameter usage in resource configuration.

  3. Calls /api/render/elements with diagramType: "cloud-architecture-diagram"

  4. Calls /api/render/elements with diagramType: "cloud-architecture-diagram"

Result

User receives a diagram showing:

  • VNet as a container
  • Subnet nested inside VNet
  • VM in the subnet
  • Dependency relationship shown
  • Proper Azure styling
how to use bicep-diagrams

How to use bicep-diagrams on Cursor

AI-first code editor with Composer

1

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 bicep-diagrams
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/eraserlabs/eraser-io --skill bicep-diagrams

The skills CLI fetches bicep-diagrams from GitHub repository eraserlabs/eraser-io and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/bicep-diagrams

Reload or restart Cursor to activate bicep-diagrams. Access the skill through slash commands (e.g., /bicep-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.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.639 reviews
  • Chaitanya Patil· Dec 16, 2024

    Keeps context tight: bicep-diagrams is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Henry Huang· Dec 12, 2024

    bicep-diagrams has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • James Thomas· Dec 4, 2024

    Useful defaults in bicep-diagrams — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Neel Sethi· Nov 23, 2024

    bicep-diagrams has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Henry Zhang· Nov 23, 2024

    bicep-diagrams fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Piyush G· Nov 7, 2024

    Registry listing for bicep-diagrams matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Charlotte Rahman· Nov 3, 2024

    Useful defaults in bicep-diagrams — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Shikha Mishra· Oct 26, 2024

    bicep-diagrams reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Charlotte Martinez· Oct 22, 2024

    I recommend bicep-diagrams for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • James Verma· Oct 14, 2024

    Solid pick for teams standardizing on skills: bicep-diagrams is focused, and the summary matches what you get after install.

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