REST and GraphQL API design principles for building scalable, developer-friendly APIs.
Works with
Covers resource-oriented REST patterns including HTTP method semantics, URL design, pagination, filtering, and error handling with consistent status codes
Includes GraphQL schema-first development with type definitions, resolver patterns, Relay-style pagination, and DataLoader implementation for N+1 prevention
Provides versioning strategies (URL, header, query parameter) and HATEOAS patterns for hy
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionapi-design-principlesExecute the skills CLI command in your project's root directory to begin installation:
Fetches api-design-principles from wshobson/agents 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 api-design-principles. Access via /api-design-principles 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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Automate repetitive workflows and reduce manual effort
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Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers and stand the test of time.
Resource-Oriented Architecture
HTTP Methods Semantics:
GET: Retrieve resources (idempotent, safe)POST: Create new resourcesPUT: Replace entire resource (idempotent)PATCH: Partial resource updatesDELETE: Remove resources (idempotent)Schema-First Development
Query Structure:
URL Versioning:
/api/v1/users
/api/v2/users
Header Versioning:
Accept: application/vnd.api+json; version=1
Query Parameter Versioning:
/api/users?version=1
# Good: Resource-oriented endpoints
GET /api/users # List users (with pagination)
POST /api/users # Create user
GET /api/users/{id} # Get specific user
PUT /api/users/{id} # Replace user
PATCH /api/users/{id} # Update user fields
DELETE /api/users/{id} # Delete user
# Nested resources
GET /api/users/{id}/orders # Get user's orders
POST /api/users/{id}/orders # Create order for user
# Bad: Action-oriented endpoints (avoid)
POST /api/createUser
POST /api/getUserById
POST /api/deleteUser
from typing import List, Optional
from pydantic import BaseModel, Field
class PaginationParams(BaseModel):
page: int = Field(1, ge=1, description="Page number")
page_size: int = Field(20, ge=1, le=100, description="Items per page")
class FilterParams(BaseModel):
status: Optional[str] = None
created_after: Optional[str] = None
search: Optional[str] = None
class PaginatedResponse(BaseModel):
items: List[dict]
total: int
page: int
page_size: int
pages: int
@property
def has_next(self) -> bool:
return self.page < self.pages
@property
def has_prev(self) -> bool:
return self.page > 1
# FastAPI endpoint example
from fastapi import FastAPI, Query, Depends
app = FastAPI()
@app.get("/api/users", response_model=PaginatedResponse)
async def list_users(
page: int = Query(1, ge=1),
page_size: int = Query(20, ge=1, le=100),
status: Optional[str] = Query(None),
search: Optional[str] = Query(None)
):
# Apply filters
query = build_query(status=status, search=search)
# Count total
total = await count_users(query)
# Fetch page
offset = (page - 1) * page_size
users = await fetch_users(query, limit=page_size, offset=offset)
return PaginatedResponse(
items=users,
total=total,
page=page,
page_size=page_size,
pages=(total + page_size - 1) // page_size
)
from fastapi import HTTPException, status
from pydantic import BaseModel
class ErrorResponse(BaseModel):
error: str
message: str
details: Optional[dict] = None
timestamp: str
path: str
class ValidationErrorDetail(BaseModel):
field: str
message: str
value: Any
# Consistent error responses
STATUS_CODES = {
"success": 200,
"created": 201,
"no_content": 204,
"bad_request": 400,
"unauthorized": 401,
"forbidden": 403,
"not_found": 404,
"conflict": 409,
"unprocessable": 422,
"internal_error": 500
}
def raise_not_found(resource: str, id: str):
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail={
"error": "NotFound",
"message": f"{resource} not found",
"details": {"id": id}
}
)
def raise_validation_error(errors: List[ValidationErrorDetail]):
raise HTTPExceptionPrerequisites
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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api-design-principles reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: api-design-principles is focused, and the summary matches what you get after install.
api-design-principles reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for api-design-principles matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in api-design-principles — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend api-design-principles for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added api-design-principles from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend api-design-principles for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
api-design-principles fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in api-design-principles — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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