python-backend▌
jiatastic/open-python-skills · updated Apr 8, 2026
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Production-ready Python backend patterns for FastAPI, SQLAlchemy, and Upstash integrations.
- ›Covers async-first REST API development with FastAPI, including dependency injection, Pydantic validation, and structured project organization
- ›Implements authentication patterns for JWT/OAuth2, password hashing, CORS, and API key management
- ›Provides SQLAlchemy async database setup with transactions, eager loading, and migration strategies
- ›Includes Redis/Upstash caching, rate limiting with s
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
How to use python-backend on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add python-backend
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches python-backend from GitHub repository jiatastic/open-python-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate python-backend. Access the skill through slash commands (e.g., /python-backend) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.4★★★★★73 reviews- ★★★★★Ishan Chen· Dec 28, 2024
python-backend reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Aditi Diallo· Dec 20, 2024
Keeps context tight: python-backend is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Chen Kapoor· Dec 16, 2024
We added python-backend from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Noor Anderson· Dec 16, 2024
python-backend fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Dhruvi Jain· Dec 4, 2024
I recommend python-backend for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Charlotte Bhatia· Dec 4, 2024
We added python-backend from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Oshnikdeep· Nov 23, 2024
Solid pick for teams standardizing on skills: python-backend is focused, and the summary matches what you get after install.
- ★★★★★Charlotte Ghosh· Nov 23, 2024
python-backend fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Mei Reddy· Nov 19, 2024
python-backend has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Rahul Santra· Nov 15, 2024
Useful defaults in python-backend — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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