Fastapi design
Python backend development expertise for FastAPI, security patterns, database operations, Upstash integrations, and code quality. Use when: (1) Building REST APIs with FastAPI, (2) Implementing JWT/OAuth2 authentication, (3) Setting up SQLAlchemy/async databases, (4) Integrating Redis/Upstash caching, (5) Refactoring AI-generated Python code (deslopification), (6) Designing API patterns, or (7) Optimizing backend performance.From its SKILL.md
npx -y skills add jiatastic/open-python-skills --skill fastapi-designAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
4.2 KB, 931 tokens by cl100k_base, as published. Nobody here has run it
python-backend
Production-ready Python backend patterns for FastAPI, SQLAlchemy, and Upstash.
When to Use This Skill
- Building REST APIs with FastAPI
- Implementing JWT/OAuth2 authentication
- Setting up SQLAlchemy async databases
- Integrating Redis/Upstash caching and rate limiting
- Refactoring AI-generated Python code
- Designing API patterns and project structure
Core Principles
- Async-first - Use async/await for I/O operations
- Type everything - Pydantic models for validation
- Dependency injection - Use FastAPI's Depends()
- Fail fast - Validate early, use HTTPException
- Security by default - Never trust user input
Quick Patterns
Project Structure
src/
├── auth/
│ ├── router.py # endpoints
│ ├── schemas.py # pydantic models
│ ├── models.py # db models
│ ├── service.py # business logic
│ └── dependencies.py
├── posts/
│ └── ...
├── config.py
├── database.py
└── main.py
Async Routes
# BAD - blocks event loop
@router.get("/")
async def bad():
time.sleep(10) # Blocking!
# GOOD - runs in threadpool
@router.get("/")
def good():
time.sleep(10) # OK in sync function
# BEST - non-blocking
@router.get("/")
async def best():
await asyncio.sleep(10) # Non-blocking
Pydantic Validation
from pydantic import BaseModel, EmailStr, Field
class UserCreate(BaseModel):
email: EmailStr
username: str = Field(min_length=3, max_length=50, pattern="^[a-zA-Z0-9_]+$")
age: int = Field(ge=18)
Dependency Injection
async def get_current_user(token: str = Depends(oauth2_scheme)) -> User:
payload = decode_token(token)
user = await get_user(payload["sub"])
if not user:
raise HTTPException(401, "User not found")
return user
@router.get("/me")
async def get_me(user: User = Depends(get_current_user)):
return user
SQLAlchemy Async
from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
engine = create_async_engine(DATABASE_URL, pool_pre_ping=True)
SessionLocal = async_sessionmaker(engine, expire_on_commit=False)
async def get_session() -> AsyncGenerator[AsyncSession, None]:
async with SessionLocal() as session:
yield session
Redis Caching
from upstash_redis import Redis
redis = Redis.from_env()
@app.get("/data/{id}")
def get_data(id: str):
cached = redis.get(f"data:{id}")
if cached:
return cached
data = fetch_from_db(id)
redis.setex(f"data:{id}", 600, data)
return data
Rate Limiting
from upstash_ratelimit import Ratelimit, SlidingWindow
ratelimit = Ratelimit(
redis=Redis.from_env(),
limiter=SlidingWindow(max_requests=10, window=60),
)
@app.get("/api/resource")
def protected(request: Request):
result = ratelimit.limit(request.client.host)
if not result.allowed:
raise HTTPException(429, "Rate limit exceeded")
return {"data": "..."}
Reference Documents
For detailed patterns, see:
| Document | Content |
|---|---|
references/fastapi_patterns.md | Project structure, async, Pydantic, dependencies, testing |
references/security_patterns.md | JWT, OAuth2, password hashing, CORS, API keys |
references/database_patterns.md | SQLAlchemy async, transactions, eager loading, migrations |
references/upstash_patterns.md | Redis, rate limiting, QStash background jobs |
Resources
What ships with it: 4 files
30.1 KB alongside SKILL.md
references/
- database_patterns.md9.3 KB
- fastapi_patterns.md10.0 KB
- security_patterns.md2.9 KB
- upstash_patterns.md7.9 KB
Gives 0 of the 12 instructions most databases sql skills give in 931 tokens
Counted across 609 of the 712 authors here whose files we hold, read 2026-09-06
- Index all foreign key columnsin 26 of 609
- Use cursor pagination instead of offsetin 25 of 609, across 20 files
- Use timestamptz for timestampsin 21 of 609
- Specify columns instead of using select starin 20 of 609, across 10 files
- Use parameterized queries for all database interactionsin 20 of 609, across 19 files
- Use Enum for categorical datain 17 of 609, across 7 files
- Order by frequently filtered columnsin 17 of 609, across 7 files
- Batch data insertsin 17 of 609, across 7 files
- Use expand-contract pattern for schema changesin 17 of 609
- Use materialized views for real-time aggregationsin 16 of 609, across 6 files
- Partition tables by timein 16 of 609, across 6 files
- Use smallest appropriate data typesin 16 of 609, across 6 files
Said here and by no other author read
- Use FastAPI dependency injection for shared logic
- Validate user input early
- Use async SQLAlchemy sessions
- Implement rate limiting with Redis
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.