Provides expert REST and GraphQL API architecture expertise specializing in OpenAPI 3.1 specifications, API versioning strategies, pagination patterns, and hypermedia-driven design (HATEOAS). Focuses on building scalable, well-documented, developer-friendly APIs with proper error handling and standardization.
Fetches api-designer from 404kidwiz/claude-supercode-skills and configures it for Cursor.
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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.
Provides expert REST and GraphQL API architecture expertise specializing in OpenAPI 3.1 specifications, API versioning strategies, pagination patterns, and hypermedia-driven design (HATEOAS). Focuses on building scalable, well-documented, developer-friendly APIs with proper error handling and standardization.
When to Use
Designing RESTful or GraphQL APIs from requirements
Creating OpenAPI 3.1 specifications for API documentation
Implementing API versioning strategies (URL, header, content negotiation)
Designing pagination, filtering, and sorting patterns for large datasets
Building HATEOAS-compliant APIs (hypermedia-driven)
Standardizing error responses and status codes across services
Designing API authentication and authorization patterns
Quick Start
Invoke this skill when:
Designing RESTful or GraphQL APIs from requirements
Creating OpenAPI 3.1 specifications for API documentation
Implementing API versioning strategies (URL, header, content negotiation)
Designing pagination, filtering, and sorting patterns for large datasets
Building HATEOAS-compliant APIs (hypermedia-driven)
Standardizing error responses and status codes across services
Do NOT invoke when:
Only implementing pre-designed API endpoints (use backend-developer)
Database schema design without API context (use database-administrator)
Frontend API integration (use frontend-developer)
API security implementation (use security-engineer for authentication/authorization)
API performance optimization (use performance-engineer)
Core Workflows
Workflow 1: Design RESTful API with OpenAPI 3.1
Use case: E-commerce platform needs product catalog API
Step 1: Resource Modeling
# Resources identified:# - Products (CRUD)# - Categories (read-only, hierarchical)# - Reviews (nested under products)# - Inventory (separate resource, linked to products)# URL Structure Design:GET /v1/products # List products (paginated)POST /v1/products # Create productGET /v1/products/{id}# Get product detailsPUT /v1/products/{id}# Update product (full replacement)PATCH /v1/products/{id}# Partial updateDELETE /v1/products/{id}# Delete productGET /v1/products/{id}/reviews # Get reviews for productPOST /v1/products/{id}/reviews # Create reviewGET /v1/products/{id}/reviews/{reviewId}# Get specific reviewGET /v1/categories # List categoriesGET /v1/categories/{id}# Get category + subcategories# Query parameters (filtering, pagination, sorting):GET /v1/products?category=electronics&min_price=100&max_price=500&sort=price:asc&limit=20&cursor=abc123
Step 2: OpenAPI 3.1 Specification
# openapi.yamlopenapi: 3.1.0
info:title: E-commerce Product API
version: 1.0.0
description: RESTful API for product catalog management
contact:name: API Support
email:[email protected]servers:-url: https://api.ecommerce.com/v1
description: Production server
-url: https://staging-api.ecommerce.com/v1
description: Staging server
paths:/products:get:summary: List products
operationId: listProducts
tags:[Products]parameters:-name: category
in: query
description: Filter by category slug
schema:type: string
example: electronics
-name: min_price
in: query
description: Minimum price filter
schema:type: number
format: float
minimum:0-name: max_price
in: query
description: Maximum price filter
schema:type: number
format: float
minimum:0-name: sort
in: query
description: Sort order (field:direction)
schema:type: string
enum:[price:asc, price:desc, created_at:asc, created_at:desc]default: created_at:desc
-name: limit
in: query
description: Number of results per page
schema:type: integer
minimum:1maximum:100default:20-name: cursor
in: query
description: Pagination cursor (opaque token)
schema:type: string
responses:'200':description: Successful response
content:application/json:schema:type: object
required:[data, meta, links]properties:data:type: array
items:$ref:'#/components/schemas/Product'meta:type: object
properties:total_count:type: integer
description: Total number of products matching filters
has_more:type: boolean
description: Whether more results exist
links:type: object
properties:self:type: string
format: uri
next:type: string
format: uri
nullable:trueprev:type: string
format: uri
nullable:trueexamples:success:value:data:-id:"prod_123"name:"Wireless Headphones"description:"Premium noise-cancelling headphones"price:299.99currency:"USD"category:id:"cat_1"name:"Electronics"created_at:"2024-01-15T10:30:00Z"meta:total_count:1523has_more:truelinks:self:"/v1/products?limit=20"next:"/v1/products?limit=20&cursor=eyJpZCI6InByb2RfMTIzIn0="prev:null'400':$ref:'#/components/responses/BadRequest'
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
1Install skill using provided installation command
2Test with simple use case relevant to your work
3Evaluate output quality and relevance
4Iterate on prompts to improve results
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
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
