aws-diagrams

eraserlabs/eraser-io · updated Apr 8, 2026

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

Generates AWS architecture diagrams from CloudFormation templates, CLI output, or natural language descriptions.

  • Parses CloudFormation (YAML/JSON), AWS CLI JSON output, and text descriptions to extract VPCs, subnets, EC2 instances, RDS databases, Lambda functions, S3 buckets, security groups, load balancers, and other AWS services
  • Maps resource relationships including instances within subnets, security group attachments, IAM role bindings, and data flow between services
  • Converts AWS
skill.md

AWS Diagram Generator

Generates architecture diagrams for AWS infrastructure from CloudFormation templates, AWS CLI output, or natural language descriptions.

When to Use

Activate this skill when:

  • User has AWS CloudFormation templates (YAML/JSON)
  • User provides AWS CLI output (e.g., aws ec2 describe-instances)
  • User wants to visualize AWS resources
  • User mentions AWS services (EC2, S3, RDS, Lambda, VPC, etc.)
  • User asks to "diagram my AWS infrastructure"

How It Works

This skill generates AWS-specific diagrams by parsing AWS resources and calling the Eraser API directly:

  1. Parse AWS Resources: Extract resources from CloudFormation, CLI output, or descriptions
  2. Map AWS Relationships: Identify VPCs, subnets, security groups, IAM roles
  3. Generate Eraser DSL: Create Eraser DSL code from AWS resources
  4. Call Eraser API: Use /api/render/elements with diagramType: "cloud-architecture-diagram"

Instructions

When the user provides AWS infrastructure information:

  1. Parse the Source

    • CloudFormation: Extract Resources section, identify types (AWS::EC2::Instance, etc.)
    • CLI Output: Parse JSON output from aws commands
    • Description: Identify AWS service names and relationships
  2. Identify AWS Components

    • Networking: VPCs, Subnets, Internet Gateways, NAT Gateways, Route Tables
    • Compute: EC2 Instances, Auto Scaling Groups, Lambda Functions, ECS Services
    • Storage: S3 Buckets, EBS Volumes, EFS File Systems
    • Databases: RDS Instances, DynamoDB Tables, ElastiCache Clusters
    • Security: Security Groups, IAM Roles, IAM Policies, NACLs
    • Load Balancing: ALB, NLB, CLB
    • Other: SQS Queues, SNS Topics, API Gateway, CloudFront
  3. Map Relationships

    • EC2 instances in subnets
    • Subnets in VPCs
    • Security groups attached to instances
    • IAM roles attached to services
    • Load balancers targeting instances
    • Databases accessed by applications
  4. Generate Eraser DSL Convert AWS 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"] {
        web-server [icon: aws-ec2, label: "Web Server"]
        load-balancer [icon: aws-elb]
      }
      private-subnet [label: "Private Subnet"] {
        database [icon: aws-rds]
        cache [icon: aws-elasticache]
      }
    }
    data-bucket [icon: aws-s3]
    function [icon: aws-lambda]
    load-balancer -> web-server
    web-server -> database
    
  5. 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
      }'
    
  6. 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.tf - VPC and subnet definitions)
    • External references: Note any documentation, examples, or URLs consulted (e.g., AWS VPC best practices documentation)
    • Annotations: For each source, note what it contributed to the diagram
  7. 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.

AWS-Specific Tips

  • Show Regions and AZs: Include availability zones for multi-AZ deployments
  • VPC as Container: Always show VPCs containing subnets and resources
  • Security Groups: Include security group rules and attachments
  • IAM Roles: Show IAM roles attached to services
  • Data Flow: Show traffic flow (Internet → ALB → EC2 → RDS)
  • Use AWS Icons: Request AWS-specific styling in the description

Example: CloudFormation with Multiple AWS Services

User Input

Resources:
  MyVPC:
    Type: AWS::EC2::VPC
    Properties:
      CidrBlock: 10.0.0.0/16

  PublicSubnet:
    Type: AWS::EC2::Subnet
    Properties:
      VpcId: !Ref MyVPC
      CidrBlock: 10.0.1.0/24

  WebServer:
    Type: AWS::EC2::Instance
    Properties:
      InstanceType: t3.micro
      SubnetId: !Ref PublicSubnet

  MyBucket:
    Type: AWS::S3::Bucket
    Properties:
      BucketName: my-app-bucket

  MyFunction:
    Type: AWS::Lambda::Function
    Properties:
      Runtime: python3.9
      Handler: index.handler

  MyDatabase:
    Type: AWS::RDS::DBInstance
    Properties:
      Engine: postgres
      DBInstanceClass: db.t3.micro

Expected Behavior

  1. Parses CloudFormation:

    • Networking: VPC, Subnet
    • Compute: EC2 instance, Lambda function
    • Storage: S3 bucket
    • Database: RDS PostgreSQL instance
  2. Generates DSL showing AWS service diversity:

    MyVPC [label: "VPC 10.0.0.0/16"] {
      PublicSubnet [label: "Public Subnet 10.0.1.0/24"] {
        WebServer [icon: aws-ec2, label: "EC2 t3.micro"]
      }
    }
    
    MyBucket [icon: aws-s3, label: "S3 my-app-bucket"]
    MyFunction [icon: aws-lambda, label: "Lambda python3.9"]
    MyDatabase [icon: aws-rds, label: "RDS PostgreSQL db.t3.micro"]
    
    WebServer -> MyBucket
    MyFunction -> MyDatabase
    WebServer -> MyDatabase
    

    Important: All label text must be on a single line within quotes. AWS-specific: Include service icons, show data flows between services, group by VPC when applicable.

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

Example: AWS CLI Output

User Input

User runs: aws ec2 describe-instances
Provides JSON output

Expected Behavior

  1. Parses JSON to extract:

    • Instance IDs, types, states
    • Subnet IDs, VPC IDs
    • Security groups
    • Tags
  2. Formats and calls API

how to use aws-diagrams

How to use aws-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 aws-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 aws-diagrams

The skills CLI fetches aws-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/aws-diagrams

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

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

  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

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.873 reviews
  • Alexander Thomas· Dec 24, 2024

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

  • Mateo White· Dec 16, 2024

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

  • Mateo Jackson· Dec 16, 2024

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

  • Lucas Jackson· Dec 12, 2024

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

  • Nikhil Sanchez· Dec 8, 2024

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

  • James Li· Dec 4, 2024

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

  • Rahul Santra· Nov 27, 2024

    We added aws-diagrams from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Sofia Torres· Nov 23, 2024

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

  • Liam Singh· Nov 23, 2024

    We added aws-diagrams from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Jin Jackson· Nov 15, 2024

    We added aws-diagrams from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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