Generate architecture diagrams from Terraform infrastructure code.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionterraform-diagramsExecute the skills CLI command in your project's root directory to begin installation:
Fetches terraform-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 terraform-diagrams. Access via /terraform-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.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Generates architecture diagrams directly from Terraform .tf files. Specializes in parsing Terraform code and visualizing infrastructure resources, modules, and their relationships.
Activate this skill when:
.tf, .tfvars) and wants to visualize the infrastructureThis skill generates Terraform-specific diagrams by parsing Terraform code and calling the Eraser API directly:
/api/render/elements with diagramType: "cloud-architecture-diagram"When the user provides Terraform code:
Parse the Terraform
resource blocks (AWS, Azure, GCP, etc.)module blocks and their configurationsdata sources and their dependenciesvariable and output definitionsMap Relationships
subnet_id = aws_subnet.public.id)Generate Eraser DSL Convert Terraform resources to Eraser DSL:
[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
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 Terraform files and resources to generate the diagram, track:
infra/main.tf - VPC and subnet definitions, infra/rds.tf - Database configuration)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.
Handle Multiple Providers
# 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
}
Parses Terraform:
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.
Calls /api/render/elements with diagramType: "cloud-architecture-diagram"
User receives a diagram showing:
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.
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terraform-diagrams reduced setup friction for our internal harness; good balance of opinion and flexibility.
terraform-diagrams is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend terraform-diagrams for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: terraform-diagrams is focused, and the summary matches what you get after install.
terraform-diagrams has been reliable in day-to-day use. Documentation quality is above average for community skills.
terraform-diagrams reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in terraform-diagrams — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
terraform-diagrams is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for terraform-diagrams matched our evaluation — installs cleanly and behaves as described in the markdown.
terraform-diagrams fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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