fastapi-templates
Creates production-ready FastAPI projects with good practices. It helps build backend APIs quickly and efficiently.
What it does
This template creates the basic structure for an API project using FastAPI. It comes with good programming practices, like error handling and code organization, so you don't have to start from scratch.
When to use it
- ✓When starting a new FastAPI API project.
- ✓When you want a solid foundation for a fast web service.
- ✓To build APIs that need to respond quickly and handle many tasks concurrently.
Example of what to ask your AI
“Create a FastAPI project to manage users and items, with a PostgreSQL database.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
It organizes the code into logical folders (like 'api', 'models', 'services') and uses modern programming patterns to make the code more efficient and easier to maintain.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add wshobson/agents --skill fastapi-templatesOr download just the file:
curl -L https://raw.githubusercontent.com/wshobson/agents/main/plugins/api-scaffolding/skills/fastapi-templates/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
FastAPI Project Templates
Production-ready FastAPI project structures with async patterns, dependency injection, middleware, and best practices for building high-performance APIs.
When to Use This Skill
- Starting new FastAPI projects from scratch
- Implementing async REST APIs with Python
- Building high-performance web services and microservices
- Creating async applications with PostgreSQL, MongoDB
- Setting up API projects with proper structure and testing
Core Concepts
1. Project Structure
Recommended Layout:
app/
├── api/ # API routes
│ ├── v1/
│ │ ├── endpoints/
│ │ │ ├── users.py
│ │ │ ├── auth.py
│ │ │ └── items.py
│ │ └── router.py
│ └── dependencies.py # Shared dependencies
├── core/ # Core configuration
│ ├── config.py
│ ├── security.py
│ └── database.py
├── models/ # Database models
│ ├── user.py
│ └── item.py
├── schemas/ # Pydantic schemas
│ ├── user.py
│ └── item.py
├── services/ # Business logic
│ ├── user_service.py
│ └── auth_service.py
├── repositories/ # Data access
│ ├── user_repository.py
│ └── item_repository.py
└── main.py # Application entry
2. Dependency Injection
FastAPI's built-in DI system using Depends:
- Database session management
- Authentication/authorization
- Shared business logic
- Configuration injection
3. Async Patterns
Proper async/await usage:
- Async route handlers
- Async database operations
- Async background tasks
- Async middleware
4. Dependencies
The packages the examples import:
# requirements.txt
fastapi
uvicorn[standard]
pydantic-settings
sqlalchemy[asyncio]>=2.0 # DeclarativeBase; [asyncio] installs greenlet for the async engine
asyncpg # PostgreSQL driver: postgresql+asyncpg://...
PyJWT
pwdlib[argon2]
python-multipart # only for a login route that uses OAuth2PasswordRequestForm
# requirements-dev.txt
pytest
pytest-asyncio
httpx
aiosqlite # test database: sqlite+aiosqlite:///:memory:
Detailed worked examples and patterns
Detailed sections (starting with ## Implementation Patterns) live in references/details.md. Read that file when the navigation summary above is insufficient.
Testing
# tests/conftest.py
import pytest_asyncio
from httpx import ASGITransport, AsyncClient
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
from sqlalchemy.orm import sessionmaker
from app.main import app
from app.core.database import get_db, Base
TEST_DATABASE_URL = "sqlite+aiosqlite:///:memory:"
# Async fixtures need pytest_asyncio.fixture in pytest-asyncio's default strict mode
@pytest_asyncio.fixture
async def db_session():
engine = create_async_engine(TEST_DATABASE_URL, echo=True)
async with engine.begin() as conn:
await conn.run_sync(Base.metadata.create_all)
AsyncSessionLocal = sessionmaker(
engine, class_=AsyncSession, expire_on_commit=False
)
async with AsyncSessionLocal() as session:
yield session
await engine.dispose()
@pytest_asyncio.fixture
async def client(db_session):
async def override_get_db():
yield db_session
app.dependency_overrides[get_db] = override_get_db
# httpx 0.28 removed AsyncClient(app=...); use an ASGI transport
transport = ASGITransport(app=app)
async with AsyncClient(transport=transport, base_url="http://test") as client:
yield client
app.dependency_overrides.clear()
# tests/test_users.py
import pytest
@pytest.mark.asyncio
async def test_create_user(client):
response = await client.post(
"/api/v1/users/",
json={
"email": "test@example.com",
"password": "testpass123",
"name": "Test User"
}
)
assert response.status_code == 201
data = response.json()
assert data["email"] == "test@example.com"
assert "id" in data
FAQ
What is FastAPI?
FastAPI is a modern framework for building APIs (Application Programming Interfaces) in Python, known for its speed and ease of use.
What are 'async patterns'?
These are programming techniques that allow your program to do multiple things at once without freezing, like waiting for a database response while processing another request.
What is 'dependency injection'?
It's a way of organizing code where parts of the program receive the tools (dependencies) they need to function, instead of having to create them themselves. This makes the code more flexible and easier to test.
Details
Original description by the author
Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.
repo: wshobson/agents
Popularity
- GitHub stars
- 41k
- Ranking position
- #4800
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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