fastapi-patterns
Build production FastAPI applications with dependency injection, middleware, background tasks, and structured project layouts. Covers async patterns, Pydantic models, OpenAPI customization, and testing strategies. Triggers on FastAPI development, Python API, or async web framework requests.
Best use case
fastapi-patterns is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Build production FastAPI applications with dependency injection, middleware, background tasks, and structured project layouts. Covers async patterns, Pydantic models, OpenAPI customization, and testing strategies. Triggers on FastAPI development, Python API, or async web framework requests.
Teams using fastapi-patterns should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/fastapi-patterns/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How fastapi-patterns Compares
| Feature / Agent | fastapi-patterns | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Build production FastAPI applications with dependency injection, middleware, background tasks, and structured project layouts. Covers async patterns, Pydantic models, OpenAPI customization, and testing strategies. Triggers on FastAPI development, Python API, or async web framework requests.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# FastAPI Patterns
Build production-ready async APIs with FastAPI's dependency injection and type system.
## Project Layout
```
src/
├── app/
│ ├── __init__.py
│ ├── main.py # FastAPI app factory
│ ├── config.py # Pydantic Settings
│ ├── dependencies.py # Shared DI providers
│ ├── middleware.py # Custom middleware
│ ├── routers/
│ │ ├── __init__.py
│ │ ├── skills.py
│ │ └── registry.py
│ ├── models/
│ │ ├── __init__.py
│ │ ├── skill.py # Pydantic models
│ │ └── registry.py
│ ├── services/
│ │ ├── __init__.py
│ │ ├── skill_service.py
│ │ └── registry_service.py
│ └── db/
│ ├── __init__.py
│ └── session.py
└── tests/
├── conftest.py
└── test_skills.py
```
## App Factory
```python
from fastapi import FastAPI
from contextlib import asynccontextmanager
@asynccontextmanager
async def lifespan(app: FastAPI):
# Startup
await init_db()
await init_cache()
yield
# Shutdown
await close_db()
await close_cache()
def create_app() -> FastAPI:
app = FastAPI(
title="ORGANVM Skills API",
version="1.0.0",
lifespan=lifespan,
)
app.include_router(skills_router, prefix="/api/skills", tags=["skills"])
app.include_router(registry_router, prefix="/api/registry", tags=["registry"])
app.add_middleware(CorrelationMiddleware)
return app
app = create_app()
```
## Dependency Injection
```python
from fastapi import Depends
from typing import Annotated
async def get_db():
async with async_session() as session:
yield session
async def get_current_user(token: str = Depends(oauth2_scheme)) -> User: # allow-secret
user = await verify_token(token)
if not user:
raise HTTPException(status_code=401, detail="Invalid token")
return user
# Type aliases for clean signatures
DB = Annotated[AsyncSession, Depends(get_db)]
CurrentUser = Annotated[User, Depends(get_current_user)]
@router.get("/skills/{skill_id}")
async def get_skill(skill_id: str, db: DB, user: CurrentUser) -> SkillResponse:
skill = await db.get(Skill, skill_id)
if not skill:
raise HTTPException(status_code=404, detail="Skill not found")
return SkillResponse.model_validate(skill)
```
## Pydantic Models
```python
from pydantic import BaseModel, Field
from datetime import datetime
class SkillBase(BaseModel):
name: str = Field(pattern=r"^[a-z][a-z0-9-]*$", min_length=2, max_length=64)
description: str = Field(min_length=20, max_length=600)
category: str
tags: list[str] = []
class SkillCreate(SkillBase):
pass
class SkillResponse(SkillBase):
id: str
created_at: datetime
updated_at: datetime
model_config = {"from_attributes": True}
class SkillList(BaseModel):
items: list[SkillResponse]
total: int
page: int
per_page: int
```
## Router Patterns
```python
from fastapi import APIRouter, Query
router = APIRouter()
@router.get("/", response_model=SkillList)
async def list_skills(
db: DB,
category: str | None = None,
tag: str | None = None,
page: int = Query(default=1, ge=1),
per_page: int = Query(default=20, ge=1, le=100),
) -> SkillList:
query = select(Skill)
if category:
query = query.where(Skill.category == category)
if tag:
query = query.where(Skill.tags.contains([tag]))
total = await db.scalar(select(func.count()).select_from(query.subquery()))
skills = await db.scalars(query.offset((page - 1) * per_page).limit(per_page))
return SkillList(items=skills.all(), total=total, page=page, per_page=per_page)
@router.post("/", response_model=SkillResponse, status_code=201)
async def create_skill(data: SkillCreate, db: DB, user: CurrentUser) -> SkillResponse:
skill = Skill(**data.model_dump())
db.add(skill)
await db.commit()
await db.refresh(skill)
return SkillResponse.model_validate(skill)
```
## Background Tasks
```python
from fastapi import BackgroundTasks
@router.post("/skills/{skill_id}/validate")
async def validate_skill(
skill_id: str,
background_tasks: BackgroundTasks,
db: DB,
):
skill = await db.get(Skill, skill_id)
background_tasks.add_task(run_validation, skill_id)
return {"status": "validation_queued", "skill_id": skill_id}
async def run_validation(skill_id: str):
# Long-running validation logic
async with async_session() as db:
skill = await db.get(Skill, skill_id)
result = await validate_frontmatter(skill)
skill.validation_status = result.status
await db.commit()
```
## Error Handling
```python
from fastapi import Request
from fastapi.responses import JSONResponse
class AppError(Exception):
def __init__(self, message: str, code: str, status: int):
self.message = message
self.code = code
self.status = status
@app.exception_handler(AppError)
async def app_error_handler(request: Request, exc: AppError):
return JSONResponse(
status_code=exc.status,
content={"error": {"code": exc.code, "message": exc.message}},
)
```
## Testing
```python
import pytest
from httpx import AsyncClient, ASGITransport
@pytest.fixture
async def client():
async with AsyncClient(
transport=ASGITransport(app=app),
base_url="http://test",
) as client:
yield client
@pytest.mark.asyncio
async def test_list_skills(client: AsyncClient):
response = await client.get("/api/skills/")
assert response.status_code == 200
data = response.json()
assert "items" in data
assert "total" in data
@pytest.mark.asyncio
async def test_create_skill(client: AsyncClient):
response = await client.post("/api/skills/", json={
"name": "test-skill",
"description": "A test skill for unit testing purposes",
"category": "development",
})
assert response.status_code == 201
assert response.json()["name"] == "test-skill"
```
## Anti-Patterns
- **Sync database calls** — Always use async drivers (asyncpg, aiosqlite)
- **Business logic in routes** — Extract to service layer for testability
- **No response models** — Always define response_model for API documentation
- **Global state** — Use dependency injection, not module-level singletons
- **Missing validation** — Pydantic models should validate all inputs
- **No lifespan management** — Use the lifespan context manager for startup/shutdownRelated Skills
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