terraform-diagrams

Generate architecture diagrams from Terraform infrastructure code.

eraserlabs/eraser-ioUpdated Apr 8, 2026

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Install Skill

Run in your terminal

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

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What it does

  • Parses .tf files to extract resources, modules, data sources, and variables across AWS, Azure, and GCP providers

  • Maps resource dependencies and relationships, grouping by provider and showing VPCs/VNets as containers

  • Converts Terraform to Eraser DSL and renders cloud architecture diagrams via the Eraser API

  • Requires network access and an Eraser API key; supports multi-provider setups and module hierarchies

Category

Cloud

Last updated

Apr 8, 2026

Installation Guide

How to use terraform-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 machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add terraform-diagrams
2

Run the install 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 terraform-diagrams

Fetches terraform-diagrams from eraserlabs/eraser-io and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/terraform-diagrams

Restart Cursor to activate terraform-diagrams. Access via /terraform-diagrams in your agent's command palette.

Security 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 environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

Terraform Diagram Generator

Generates architecture diagrams directly from Terraform .tf files. Specializes in parsing Terraform code and visualizing infrastructure resources, modules, and their relationships.

When to Use

Activate this skill when:

  • User has Terraform files (.tf, .tfvars) and wants to visualize the infrastructure
  • User asks to "diagram my Terraform" or "visualize this infrastructure"
  • User mentions Terraform, HCL, or infrastructure-as-code
  • User wants to see the architecture of their Terraform-managed resources

How It Works

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

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

Instructions

When the user provides Terraform code:

  1. Parse the Terraform

    • Identify all resource blocks (AWS, Azure, GCP, etc.)
    • Extract module blocks and their configurations
    • Note data sources and their dependencies
    • Identify variable and output definitions
  2. Map Relationships

    • Track resource dependencies (e.g., subnet_id = aws_subnet.public.id)
    • Group resources by provider (AWS, Azure, GCP)
    • Identify VPCs/VNets as containers for other resources
    • Note security groups, IAM roles, and networking rules
  3. Generate Eraser DSL Convert Terraform 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: "VPC 10.0.0.0/16"]

    Example:

    main-vpc [label: "VPC 10.0.0.0/16"] {
      public-subnet [label: "Public Subnet 10.0.1.0/24"] {
        web-server [icon: aws-ec2, label: "Web Server t3.micro"]
        load-balancer [icon: aws-elb]
      }
      private-subnet [label: "Private Subnet"] {
        database [icon: aws-rds]
      }
    }
    load-balancer -> web-server
    web-server -> 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 Terraform files and resources to generate the diagram, track:

    • Internal files: Record each Terraform file path you read and what resources were extracted (e.g., infra/main.tf - VPC and subnet definitions, infra/rds.tf - Database configuration)
    • External references: Note any documentation, examples, or URLs consulted (e.g., Terraform AWS provider documentation, AWS 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 Multiple Providers

    • If Terraform uses multiple providers, group by provider
    • Create separate sections for AWS, Azure, GCP resources
    • Show cross-provider connections if applicable

Terraform-Specific Tips

  • Group by Module: If modules are used, show module boundaries
  • Show VPCs/VNets as Containers: These should visually contain subnets and resources
  • Include Data Flows: Show how resources connect (e.g., ALB → EC2 → RDS)
  • Highlight Security: Include security groups, IAM roles, and network ACLs
  • Show Resource Types: Use provider-specific icons (AWS, Azure, GCP)
  • Include CIDR Blocks: Show network addressing for VPCs and subnets

Example: Multi-Provider Terraform

User Input

# AWS Resources
resource "aws_vpc" "main" {
  cidr_block = "10.0.0.0/16"
}

resource "aws_subnet" "public" {
  vpc_id     = aws_vpc.main.id
  cidr_block = "10.0.1.0/24"
}

resource "aws_instance" "web" {
  subnet_id     = aws_subnet.public.id
  instance_type = "t3.micro"
}

# Azure Resources (multi-provider)
resource "azurerm_resource_group" "main" {
  name     = "rg-main"
  location = "East US"
}

resource "azurerm_virtual_network" "main" {
  name                = "vnet-main"
  resource_group_name  = azurerm_resource_group.main.name
  address_space        = ["10.1.0.0/16"]
}

# Module usage
module "database" {
  source = "./modules/rds"
  vpc_id = aws_vpc.main.id
}

Expected Behavior

  1. Parses Terraform:

    • AWS: VPC, subnet, EC2 instance
    • Azure: Resource group, VNet (multi-provider setup)
    • Module: Database module with dependency on VPC
  2. Generates DSL showing multi-provider and module structure:

    # AWS Resources
    aws-vpc [label: "AWS VPC 10.0.0.0/16"] {
      aws-subnet [label: "Public Subnet 10.0.1.0/24"] {
        web-server [icon: aws-ec2, label: "Web Server t3.micro"]
      }
    }
    
    # Azure Resources
    resource-group [label: "Resource Group rg-main"] {
      azure-vnet [label: "Azure VNet 10.1.0.0/16"]
    }
    
    # Module
    database-module [label: "Database Module"] {
      rds-instance [icon: aws-rds]
    }
    
    aws-vpc -> database-module
    

    Important: All label text must be on a single line within quotes. Terraform-specific: Show modules as containers, group by provider, include resource dependencies.

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

Result

User receives a diagram showing:

  • VPC as a container
  • Public subnet nested inside VPC
  • EC2 instance in the subnet
  • Proper AWS styling

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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

Steps

  1. 1Install skill using provided installation command
  2. 2Test with simple use case relevant to your work
  3. 3Evaluate output quality and relevance
  4. 4Iterate on prompts to improve results
  5. 5Integrate 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

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Related Skills

Reviews

4.641 reviews
  • D
    Dhruvi JainDec 24, 2024

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

  • I
    Ishan JainDec 16, 2024

    terraform-diagrams is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • O
    OshnikdeepNov 15, 2024

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

  • C
    Chen JainNov 7, 2024

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

  • L
    Li TorresOct 26, 2024

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

  • X
    Xiao MartinOct 10, 2024

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

  • G
    Ganesh MohaneOct 6, 2024

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

  • S
    Sakshi PatilSep 25, 2024

    terraform-diagrams is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • A
    Aisha SinghSep 25, 2024

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

  • C
    Chen SmithSep 17, 2024

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

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