sickn33/antigravity-awesome-skills/python-patterns

What are you building?
│
├── API-first / Microservices
│   └── FastAPI (async, modern, fast)
│
├── Full-stack web / CMS / Admin
│   └── Django (batteries-included)
│
├── Simple / Script / Learning
│   └── Flask (minimal, flexible)
│
├── AI/ML API serving
│   └── FastAPI (Pydantic, async, uvicorn)
│
└── Background workers
    └── Celery + any framework

async def is better when:
├── I/O-bound operations (database, HTTP, file)
├── Many concurrent connections
├── Real-time features
├── Microservices communication
└── FastAPI/Starlette/Django ASGI
 
def (sync) is better when:
├── CPU-bound operations
├── Simple scripts
├── Legacy codebase
├── Team unfamiliar with async
└── Blocking libraries (no async version)

I/O-bound → async (waiting for external)
CPU-bound → sync + multiprocessing (computing)
 
Don't:
├── Mix sync and async carelessly
├── Use sync libraries in async code
└── Force async for CPU work

Always type:
├── Function parameters
├── Return types
├── Class attributes
├── Public APIs
 
Can skip:
├── Local variables (let inference work)
├── One-off scripts
├── Tests (usually)

# These are patterns, understand them:
# Optional → might be None
from typing import Optional
def find_user(id: int) -> Optional[User]: ...
# Union → one of multiple types
def process(data: str | dict) -> None: ...
# Generic collections
def get_items() -> list[Item]: ...
def get_mapping() -> dict[str, int]: ...
# Callable
from typing import Callable
def apply(fn: Callable[[int], str]) -> str: ...

When to use Pydantic:
├── API request/response models
├── Configuration/settings
├── Data validation
├── Serialization
 
Benefits:
├── Runtime validation
├── Auto-generated JSON schema
├── Works with FastAPI natively
└── Clear error messages

Small project / Script:
├── main.py
├── utils.py
└── requirements.txt
 
Medium API:
├── app/
│   ├── __init__.py
│   ├── main.py
│   ├── models/
│   ├── routes/
│   ├── services/
│   └── schemas/
├── tests/
└── pyproject.toml
 
Large application:
├── src/
│   └── myapp/
│       ├── core/
│       ├── api/
│       ├── services/
│       ├── models/
│       └── ...
├── tests/
└── pyproject.toml

Organize by feature or layer:
 
By layer:
├── routes/ (API endpoints)
├── services/ (business logic)
├── models/ (database models)
├── schemas/ (Pydantic models)
└── dependencies/ (shared deps)
 
By feature:
├── users/
│   ├── routes.py
│   ├── service.py
│   └── schemas.py
└── products/
    └── ...

Django supports async:
├── Async views
├── Async middleware
├── Async ORM (limited)
└── ASGI deployment
 
When to use async in Django:
├── External API calls
├── WebSocket (Channels)
├── High-concurrency views
└── Background task triggering

Model design:
├── Fat models, thin views
├── Use managers for common queries
├── Abstract base classes for shared fields
 
Views:
├── Class-based for complex CRUD
├── Function-based for simple endpoints
├── Use viewsets with DRF
 
Queries:
├── select_related() for FKs
├── prefetch_related() for M2M
├── Avoid N+1 queries
└── Use .only() for specific fields

Use async def when:
├── Using async database drivers
├── Making async HTTP calls
├── I/O-bound operations
└── Want to handle concurrency
 
Use def when:
├── Blocking operations
├── Sync database drivers
├── CPU-bound work
└── FastAPI runs in threadpool automatically

Use dependencies for:
├── Database sessions
├── Current user / Auth
├── Configuration
├── Shared resources
 
Benefits:
├── Testability (mock dependencies)
├── Clean separation
├── Automatic cleanup (yield)

# FastAPI + Pydantic are tightly integrated:
# Request validation
@app.post("/users")
async def create(user: UserCreate) -> UserResponse:
    # user is already validated
    ...
# Response serialization
# Return type becomes response schema

FastAPI BackgroundTasks:
├── Quick operations
├── No persistence needed
├── Fire-and-forget
└── Same process
 
Celery/ARQ:
├── Long-running tasks
├── Need retry logic
├── Distributed workers
├── Persistent queue
└── Complex workflows

In FastAPI:
├── Create custom exception classes
├── Register exception handlers
├── Return consistent error format
└── Log without exposing internals
 
Pattern:
├── Raise domain exceptions in services
├── Catch and transform in handlers
└── Client gets clean error response

Include:
├── Error code (programmatic)
├── Message (human readable)
├── Details (field-level when applicable)
└── NOT stack traces (security)

# Use pytest-asyncio for async tests
import pytest
from httpx import ASGITransport, AsyncClient
@pytest.mark.asyncio
async def test_endpoint():
    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
        response = await client.get("/users")
        assert response.status_code == 200

Common fixtures:
├── db_session → Database connection
├── client → Test client
├── authenticated_user → User with token
└── sample_data → Test data setup