terraform-diagrams▌
eraserlabs/eraser-io · updated Apr 8, 2026
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Generate architecture diagrams from Terraform infrastructure code.
- ›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
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:
- Parse Terraform Files: Identify resources, modules, data sources, and variables
- Extract Relationships: Map dependencies, resource connections, and module hierarchies
- Generate Eraser DSL: Create Eraser DSL code from Terraform resources
- Call Eraser API: Use
/api/render/elementswithdiagramType: "cloud-architecture-diagram"
Instructions
When the user provides Terraform code:
-
Parse the Terraform
- Identify all
resourceblocks (AWS, Azure, GCP, etc.) - Extract
moduleblocks and their configurations - Note
datasources and their dependencies - Identify
variableandoutputdefinitions
- Identify all
-
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
- Track resource dependencies (e.g.,
-
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 - 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 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
- Internal files: Record each Terraform file path you read and what resources were 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.
-
-
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
-
Parses Terraform:
- AWS: VPC, subnet, EC2 instance
- Azure: Resource group, VNet (multi-provider setup)
- Module: Database module with dependency on VPC
-
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-moduleImportant: 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/elementswithdiagramType: "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
How to use terraform-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 terraform-diagrams
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches terraform-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 terraform-diagrams. Access the skill through slash commands (e.g., /terraform-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
4.6★★★★★41 reviews- ★★★★★Dhruvi Jain· Dec 24, 2024
terraform-diagrams reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Ishan Jain· Dec 16, 2024
terraform-diagrams is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Oshnikdeep· Nov 15, 2024
I recommend terraform-diagrams for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Chen Jain· Nov 7, 2024
Solid pick for teams standardizing on skills: terraform-diagrams is focused, and the summary matches what you get after install.
- ★★★★★Li Torres· Oct 26, 2024
terraform-diagrams has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Xiao Martin· Oct 10, 2024
terraform-diagrams reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Ganesh Mohane· Oct 6, 2024
Useful defaults in terraform-diagrams — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Sakshi Patil· Sep 25, 2024
terraform-diagrams is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Aisha Singh· Sep 25, 2024
Registry listing for terraform-diagrams matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Chen Smith· Sep 17, 2024
terraform-diagrams fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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