python-backend skill
Build production-ready REST APIs using FastAPI, SQLAlchemy, and Upstash with a focus on async performance and security.
- GitHub stars
- 10
- Files
- 5
- Last commit
- Jan 25, 2026
- License
- MIT
- Allowed tools
- —
What does the python-backend skill do?
python-backend builds production-ready REST APIs using FastAPI, SQLAlchemy, and Upstash with a focus on async performance and security.
Summary generated from the skill's SKILL.md.
About the python-backend skill
Developing production-ready REST APIs with a focus on asynchronous performance is the primary function of this skill. Developers use it to architect backend systems using FastAPI, SQLAlchemy, and Upstash. It provides specific patterns for implementing JWT or OAuth2 authentication, setting up async database sessions, and integrating Redis for caching and rate limiting.
An agent should load this skill when tasked with building new API structures, designing database models, or refactoring existing Python code to improve quality. It produces structured code following async-first principles, utilizing Pydantic models for strict data validation and FastAPI's dependency injection system. The skill relies on specific patterns for project organization and security to ensure that user input is validated early and the event loop remains non-blocking.
What an agent can do with it
- 1Build a FastAPI REST APICreate a structured backend service using FastAPI and Pydantic for data validation.
- 2Implement Secure AuthenticationSet up user security using JWT or OAuth2 protocols.
- 3Configure Async Database OperationsEstablish asynchronous database connections and sessions using SQLAlchemy.
- 4Add Redis Caching and Rate LimitingIntegrate Upstash to manage data caching and protect endpoints from excessive requests.
- 5Refactor AI Generated CodeClean up and optimize Python code generated by AI to meet production standards.
Written from the skill's SKILL.md. Every use case is checked against a quote from it.
How to install the python-backend skill
Each command copies the skill folder into the client's skills directory. Restart the client or start a new session to load it.Install the python-backend skill in Plugin
/plugin marketplace add jiatastic/open-python-skills /plugin install open-python-skills@open-python-skills
Install the python-backend skill in Claude Code
~/.claude/skills/python-backend/SKILL.md
$ npx degit jiatastic/open-python-skills/skills/fastapi-design ~/.claude/skills/python-backendInstall the python-backend skill in Claude Code (project)
.claude/skills/python-backend/SKILL.md
$ npx degit jiatastic/open-python-skills/skills/fastapi-design .claude/skills/python-backendInstall the python-backend skill in Codex
~/.agents/skills/python-backend/SKILL.md
$ npx degit jiatastic/open-python-skills/skills/fastapi-design ~/.agents/skills/python-backendInstall the python-backend skill in Gemini CLI
~/.gemini/skills/python-backend/SKILL.md
$ npx degit jiatastic/open-python-skills/skills/fastapi-design ~/.gemini/skills/python-backendInstall the python-backend skill in Claude.ai
npx degit jiatastic/open-python-skills/skills/fastapi-design python-backend && zip -r python-backend.zip python-backend # claude.ai → Customize → Skills → + → Create skill → Upload a skill → python-backend.zip
SKILL.md
View on GitHubpython-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
Files in this skill
5python-backend skill questions
How do I install the python-backend skill in Claude Code?
Run npx degit jiatastic/open-python-skills/skills/fastapi-design ~/.claude/skills/python-backend. Claude Code loads skills from ~/.claude/skills (all projects) or .claude/skills (one project) and uses a skill when its description matches the task.
Which clients can use the python-backend skill?
Any client that reads the Agent Skills format: Claude Code, Codex, Gemini CLI and claude.ai (upload as a ZIP). Scripts in the skill run only where the client allows code execution.
How should I handle I/O operations in FastAPI to ensure performance?
You should use an async-first approach with async/await for I/O operations to prevent blocking the event loop.
What are the core principles for backend development with this skill?
The core principles include using async-first I/O, strict typing with Pydantic, dependency injection via FastAPI's Depends(), failing fast with HTTPExceptions, and maintaining security by default.
Can this skill help with refactoring AI-generated code?
Yes, the skill includes expertise in refactoring AI-generated Python code, a process referred to as deslopification.