api-designer
About
The api-designer skill helps developers create robust REST APIs and function contracts by defining clear request/response schemas, error handling, and authentication strategies. Use it when you need to design well-documented, maintainable APIs that follow industry best practices. It ensures your API designs are intuitive and comprehensively specified for consumers.
Documentation
Purpose
The api-designer skill provides comprehensive guidance for designing robust, RESTful APIs and function contracts that serve as clear interfaces for feature implementations. This skill helps the Architecture Designer agent create well-structured, documented, and maintainable API designs that follow industry best practices.
This skill emphasizes:
- REST Principles: Proper resource design, HTTP method usage, and status codes
- Clear Contracts: Well-defined request/response schemas
- Error Handling: Consistent error response formats
- Authentication: Security patterns and authorization strategies
- Documentation: Comprehensive API documentation for consumers
The api-designer skill ensures that APIs are intuitive, consistent, and provide excellent developer experience for both internal and external consumers.
When to Use
This skill auto-activates when the agent describes:
- "Design API endpoints for..."
- "Create REST API for..."
- "Define function contract for..."
- "Specify request/response schemas..."
- "Design authentication for..."
- "Plan API structure with..."
- "Define error responses for..."
- "Create API documentation for..."
Provided Capabilities
1. REST API Endpoint Design
What it provides:
- Resource identification and naming
- HTTP method selection (GET, POST, PUT, PATCH, DELETE)
- URL structure and path parameters
- Query parameter design
- Status code selection
- Idempotency considerations
REST Principles:
- Resources as nouns, not verbs
- HTTP methods for actions
- Stateless design
- Standard status codes
- HATEOAS (optional)
Example:
from pydantic import BaseModel, Field
from typing import List, Optional
from datetime import datetime
# ==================== RESOURCE: Users ====================
class UserCreate(BaseModel):
"""Request schema for creating user."""
username: str = Field(..., min_length=3, max_length=50)
email: str = Field(...)
full_name: str = Field(..., min_length=1, max_length=200)
class UserResponse(BaseModel):
"""Response schema for user."""
id: int
username: str
email: str
full_name: str
is_active: bool
created_at: datetime
class UserUpdate(BaseModel):
"""Request schema for updating user (all optional)."""
email: Optional[str] = None
full_name: Optional[str] = None
is_active: Optional[bool] = None
class UserList(BaseModel):
"""Response schema for user list with pagination."""
items: List[UserResponse]
total: int
page: int
page_size: int
total_pages: int
# API Endpoints
"""
POST /api/v1/users Create new user
GET /api/v1/users List users (with pagination)
GET /api/v1/users/{user_id} Get user by ID
PUT /api/v1/users/{user_id} Update user (full replace)
PATCH /api/v1/users/{user_id} Update user (partial)
DELETE /api/v1/users/{user_id} Delete user
Query Parameters for GET /api/v1/users:
- page: int = 1 (pagination)
- page_size: int = 20 (items per page)
- search: str = None (search filter)
- is_active: bool = None (status filter)
- sort_by: str = "created_at"
- sort_order: str = "desc"
Status Codes:
- 200 OK: Successful GET, PUT, PATCH
- 201 Created: Successful POST
- 204 No Content: Successful DELETE
- 400 Bad Request: Invalid input
- 401 Unauthorized: Authentication required
- 403 Forbidden: Insufficient permissions
- 404 Not Found: Resource not found
- 409 Conflict: Resource conflict (duplicate)
- 422 Unprocessable Entity: Validation error
- 500 Internal Server Error: Server error
"""
2. Request/Response Schema Design
What it provides:
- Input validation schemas
- Output serialization schemas
- Partial update schemas
- List/pagination schemas
- Error response schemas
Schema Patterns:
Create Request (POST):
class ResourceCreate(BaseModel):
"""All fields required for creation."""
name: str = Field(..., min_length=1, max_length=200)
description: Optional[str] = Field(None, max_length=1000)
category: str = Field(...)
Update Request (PUT - Full Replace):
class ResourceUpdate(BaseModel):
"""All fields required for full update."""
name: str = Field(..., min_length=1, max_length=200)
description: Optional[str] = Field(None, max_length=1000)
category: str = Field(...)
Partial Update Request (PATCH):
class ResourcePatch(BaseModel):
"""All fields optional for partial update."""
name: Optional[str] = Field(None, min_length=1, max_length=200)
description: Optional[str] = Field(None, max_length=1000)
category: Optional[str] = None
Response Schema:
class ResourceResponse(BaseModel):
"""Response includes ID and audit fields."""
id: int
name: str
description: Optional[str]
category: str
created_at: datetime
updated_at: Optional[datetime]
class Config:
orm_mode = True # Enable ORM integration
List Response with Pagination:
class PaginatedResponse(BaseModel):
"""Generic paginated response."""
items: List[ResourceResponse]
total: int = Field(..., description="Total number of items")
page: int = Field(..., description="Current page number", ge=1)
page_size: int = Field(..., description="Items per page", ge=1, le=100)
total_pages: int = Field(..., description="Total number of pages")
@property
def has_next(self) -> bool:
"""Check if there's a next page."""
return self.page < self.total_pages
@property
def has_previous(self) -> bool:
"""Check if there's a previous page."""
return self.page > 1
3. Error Response Formats
What it provides:
- Consistent error structure
- Error codes and types
- Detailed validation errors
- User-friendly messages
- Debug information (optional)
Standard Error Response:
from typing import Optional, List, Dict, Any
class ValidationError(BaseModel):
"""Individual validation error."""
field: str = Field(..., description="Field name with error")
message: str = Field(..., description="Error message")
code: str = Field(..., description="Error code")
class ErrorResponse(BaseModel):
"""Standard error response."""
error: str = Field(..., description="Error type (e.g., 'validation_error')")
message: str = Field(..., description="Human-readable error message")
details: Optional[List[ValidationError]] = Field(None, description="Validation errors")
request_id: Optional[str] = Field(None, description="Request ID for tracking")
timestamp: datetime = Field(default_factory=datetime.utcnow)
class Config:
schema_extra = {
"example": {
"error": "validation_error",
"message": "Request validation failed",
"details": [
{
"field": "email",
"message": "Invalid email format",
"code": "invalid_format"
}
],
"request_id": "req_abc123",
"timestamp": "2025-10-29T10:00:00Z"
}
}
# Error Types
"""
validation_error: Request validation failed (400)
authentication_error: Authentication failed (401)
authorization_error: Insufficient permissions (403)
not_found_error: Resource not found (404)
conflict_error: Resource conflict (409)
rate_limit_error: Rate limit exceeded (429)
internal_error: Internal server error (500)
"""
4. Authentication and Authorization
What it provides:
- Authentication patterns (JWT, OAuth2, API Key)
- Authorization strategies (RBAC, ABAC)
- Token validation
- Permission checking
- Security headers
JWT Authentication Example:
from pydantic import BaseModel, Field
from typing import Optional, List
class LoginRequest(BaseModel):
"""Login request schema."""
username: str = Field(..., min_length=1)
password: str = Field(..., min_length=1)
class TokenResponse(BaseModel):
"""Token response schema."""
access_token: str = Field(..., description="JWT access token")
token_type: str = Field(default="bearer", description="Token type")
expires_in: int = Field(..., description="Token expiration in seconds")
refresh_token: Optional[str] = Field(None, description="Refresh token")
class TokenPayload(BaseModel):
"""JWT token payload."""
sub: int = Field(..., description="User ID (subject)")
username: str = Field(..., description="Username")
roles: List[str] = Field(default_factory=list, description="User roles")
exp: int = Field(..., description="Expiration timestamp")
# API Endpoints
"""
POST /api/v1/auth/login Login and get token
POST /api/v1/auth/refresh Refresh access token
POST /api/v1/auth/logout Logout (invalidate token)
Authentication Header:
Authorization: Bearer <access_token>
Example:
Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...
"""
Role-Based Access Control (RBAC):
from enum import Enum
class UserRole(str, Enum):
"""User roles for RBAC."""
ADMIN = "admin"
MANAGER = "manager"
USER = "user"
GUEST = "guest"
class Permission(str, Enum):
"""Permissions for resources."""
CREATE = "create"
READ = "read"
UPDATE = "update"
DELETE = "delete"
# Permission Matrix
"""
Resource: Users
- ADMIN: create, read, update, delete
- MANAGER: read, update
- USER: read (own profile only)
- GUEST: read (public profiles only)
Endpoint Protection:
POST /api/v1/users Requires: admin
GET /api/v1/users Requires: admin, manager
GET /api/v1/users/{user_id} Requires: authenticated
PUT /api/v1/users/{user_id} Requires: admin OR owner
DELETE /api/v1/users/{user_id} Requires: admin
"""
5. Rate Limiting
What it provides:
- Rate limit strategies
- Rate limit headers
- Error responses for exceeded limits
- Quota management
Rate Limit Design:
class RateLimitInfo(BaseModel):
"""Rate limit information."""
limit: int = Field(..., description="Requests allowed per window")
remaining: int = Field(..., description="Requests remaining")
reset: int = Field(..., description="Unix timestamp when limit resets")
window: int = Field(..., description="Time window in seconds")
# Rate Limit Headers
"""
X-RateLimit-Limit: 100
X-RateLimit-Remaining: 95
X-RateLimit-Reset: 1730203200
X-RateLimit-Window: 3600
Rate Limit Tiers:
- Anonymous: 10 requests/hour
- Authenticated: 100 requests/hour
- Premium: 1000 requests/hour
- Admin: Unlimited
Status Code: 429 Too Many Requests
Response:
{
"error": "rate_limit_exceeded",
"message": "Rate limit exceeded. Try again in 3600 seconds.",
"limit": 100,
"window": 3600,
"reset": 1730203200
}
"""
6. API Versioning
What it provides:
- Versioning strategies
- Version migration paths
- Backward compatibility
- Deprecation notices
Versioning Strategies:
URL Path Versioning (Recommended):
/api/v1/users
/api/v2/users
Pros: Clear, explicit, easy to route
Cons: URLs change between versions
Header Versioning:
GET /api/users
Accept-Version: v1
GET /api/users
Accept-Version: v2
Pros: Clean URLs
Cons: Less visible, harder to test in browser
Query Parameter Versioning:
/api/users?version=1
/api/users?version=2
Pros: Flexible
Cons: Easy to forget, pollutes query params
Deprecation Example:
class DeprecationWarning(BaseModel):
"""Deprecation warning in response header."""
deprecated: bool = True
sunset_date: str = "2026-01-01"
replacement_url: str = "/api/v2/users"
documentation: str = "https://api.example.com/docs/migration/v1-to-v2"
# Response Headers for Deprecated Endpoint
"""
X-API-Deprecated: true
X-API-Sunset: 2026-01-01
X-API-Replacement: /api/v2/users
Link: <https://api.example.com/docs/migration/v1-to-v2>; rel="deprecation"
"""
Usage Guide
Step 1: Identify Resources
Requirements → Identify nouns → Define resources → Name endpoints
Step 2: Design Endpoints
Resources → HTTP methods → URL structure → Path/query params
Step 3: Define Schemas
Create schemas → Update schemas → Response schemas → Error schemas
Step 4: Plan Authentication
Identify auth needs → Choose strategy → Define tokens → Permission model
Step 5: Error Handling
Identify error cases → Standard format → Status codes → Error messages
Step 6: Rate Limiting
Define tiers → Set limits → Response headers → Exceeded handling
Step 7: Documentation
OpenAPI spec → Examples → Authentication guide → Error reference
Step 8: Versioning Strategy
Choose approach → Migration plan → Deprecation policy → Documentation
Best Practices
-
Use Proper HTTP Methods
- GET: Retrieve resources (idempotent, safe)
- POST: Create resources (non-idempotent)
- PUT: Full replace (idempotent)
- PATCH: Partial update (idempotent)
- DELETE: Remove resource (idempotent)
-
Consistent Naming
- Use plural nouns:
/users,/posts - Use kebab-case:
/user-profiles - Avoid verbs:
/usersnot/getUsers
- Use plural nouns:
-
Status Codes
- 2xx: Success
- 4xx: Client errors
- 5xx: Server errors
- Be specific: 201 for created, 204 for no content
-
Pagination
- Always paginate lists
- Provide total count
- Include next/previous links (HATEOAS)
-
Filtering and Sorting
- Use query params:
?status=active&sort=created_at - Document available filters
- Provide defaults
- Use query params:
-
Security
- Always use HTTPS
- Validate all input
- Rate limit requests
- Use proper authentication
Resources
api-design-guide.md
Comprehensive API design guide including:
- REST principles and best practices
- GraphQL patterns (if applicable)
- Request/response schema design
- Error response formats
- Authentication/authorization patterns
- Rate limiting strategies
- API versioning approaches
- Documentation standards
function-design-patterns.md
Function contract design patterns:
- Function signature design
- Parameter patterns (required, optional, defaults)
- Return type patterns
- Error handling in functions
- Async function patterns
- Type hints for functions
- Docstring standards
Example Usage
Input (from Architecture Designer agent):
"Design REST API for a task management system with tasks, projects, users, and comments."
Output (api-designer skill provides):
# Complete API design with endpoints and schemas
from pydantic import BaseModel, Field
from typing import List, Optional
from datetime import datetime
from enum import Enum
# ==================== ENUMS ====================
class TaskStatus(str, Enum):
"""Task status options."""
TODO = "todo"
IN_PROGRESS = "in_progress"
DONE = "done"
class TaskPriority(str, Enum):
"""Task priority levels."""
LOW = "low"
MEDIUM = "medium"
HIGH = "high"
# ==================== REQUEST SCHEMAS ====================
class TaskCreate(BaseModel):
"""Create task request."""
title: str = Field(..., min_length=1, max_length=200)
description: Optional[str] = Field(None, max_length=2000)
project_id: int = Field(..., gt=0)
assignee_id: Optional[int] = Field(None, gt=0)
priority: TaskPriority = TaskPriority.MEDIUM
due_date: Optional[datetime] = None
class TaskUpdate(BaseModel):
"""Update task request (partial)."""
title: Optional[str] = Field(None, min_length=1, max_length=200)
description: Optional[str] = Field(None, max_length=2000)
assignee_id: Optional[int] = Field(None, gt=0)
status: Optional[TaskStatus] = None
priority: Optional[TaskPriority] = None
due_date: Optional[datetime] = None
class CommentCreate(BaseModel):
"""Create comment request."""
content: str = Field(..., min_length=1, max_length=1000)
# ==================== RESPONSE SCHEMAS ====================
class TaskResponse(BaseModel):
"""Task response schema."""
id: int
title: str
description: Optional[str]
project_id: int
assignee_id: Optional[int]
status: TaskStatus
priority: TaskPriority
due_date: Optional[datetime]
created_at: datetime
updated_at: Optional[datetime]
created_by: int
class Config:
orm_mode = True
class TaskListResponse(BaseModel):
"""Paginated task list response."""
items: List[TaskResponse]
total: int
page: int
page_size: int
total_pages: int
# ==================== API ENDPOINTS ====================
"""
Base URL: /api/v1
Authentication: Bearer token
Rate Limit: 100 requests/hour per user
# Tasks
POST /tasks Create new task
GET /tasks List tasks (paginated, filtered)
GET /tasks/{task_id} Get task by ID
PATCH /tasks/{task_id} Update task
DELETE /tasks/{task_id} Delete task
# Comments on Tasks
POST /tasks/{task_id}/comments Add comment to task
GET /tasks/{task_id}/comments List task comments
DELETE /comments/{comment_id} Delete comment
# Query Parameters for GET /tasks:
- page: int = 1
- page_size: int = 20
- project_id: int (filter by project)
- assignee_id: int (filter by assignee)
- status: TaskStatus (filter by status)
- priority: TaskPriority (filter by priority)
- search: str (search in title/description)
- sort_by: str = "created_at" (sort field)
- sort_order: str = "desc" (asc or desc)
# Status Codes:
- 200 OK: Successful GET, PATCH
- 201 Created: Successful POST
- 204 No Content: Successful DELETE
- 400 Bad Request: Invalid input
- 401 Unauthorized: Not authenticated
- 403 Forbidden: Insufficient permissions
- 404 Not Found: Task not found
- 422 Unprocessable Entity: Validation error
- 429 Too Many Requests: Rate limit exceeded
- 500 Internal Server Error: Server error
# Permissions:
- Create task: Authenticated user
- List tasks: Authenticated user (filtered by access)
- Get task: Task assignee, project member, or admin
- Update task: Task assignee, project owner, or admin
- Delete task: Task creator, project owner, or admin
"""
Integration
Used By:
- @architecture-designer (Primary) - Phase 2 sub-agent for architecture design
Integrates With:
- architecture-planner skill - API contracts defined after component structure
- data-modeler skill - Uses data models for request/response schemas
Workflow Position:
- Analysis Specialist completes requirements analysis
- Architecture Designer receives analysis
- architecture-planner skill designs component structure (Step 3)
- data-modeler skill designs data models (Step 4)
- api-designer skill designs API contracts (Step 5)
- Results synthesized into PRP
Version: 2.0.0 Auto-Activation: Yes Phase: 2 - Design & Planning Created: 2025-10-29
Quick Install
/plugin add https://github.com/matteocervelli/llms/tree/main/api-designerCopy and paste this command in Claude Code to install this skill
GitHub 仓库
Related Skills
evaluating-llms-harness
TestingThis Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.
langchain
MetaLangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.
Algorithmic Art Generation
MetaThis skill helps developers create algorithmic art using p5.js, focusing on generative art, computational aesthetics, and interactive visualizations. It automatically activates for topics like "generative art" or "p5.js visualization" and guides you through creating unique algorithms with features like seeded randomness, flow fields, and particle systems. Use it when you need to build reproducible, code-driven artistic patterns.
webapp-testing
TestingThis Claude Skill provides a Playwright-based toolkit for testing local web applications through Python scripts. It enables frontend verification, UI debugging, screenshot capture, and log viewing while managing server lifecycles. Use it for browser automation tasks but run scripts directly rather than reading their source code to avoid context pollution.
