python-patterns
Python development principles and decision-making. Framework selection, async patterns, type hints, project structure. Teaches thinking, not copying.
Best use case
python-patterns is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Python development principles and decision-making. Framework selection, async patterns, type hints, project structure. Teaches thinking, not copying.
Teams using python-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/python-patterns/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How python-patterns Compares
| Feature / Agent | python-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?
Python development principles and decision-making. Framework selection, async patterns, type hints, project structure. Teaches thinking, not copying.
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.
Related Guides
SKILL.md Source
# Python Patterns
> Python development principles and decision-making for 2025.
> **Learn to THINK, not memorize patterns.**
---
## ⚠️ How to Use This Skill
This skill teaches **decision-making principles**, not fixed code to copy.
- ASK user for framework preference when unclear
- Choose async vs sync based on CONTEXT
- Don't default to same framework every time
---
## 1. Framework Selection (2025)
### Decision Tree
```
What are you building?
│
├── API-first / Microservices
│ └── FastAPI (async, modern, fast)
│
├── Full-stack web / CMS / Admin
│ └── Django (batteries-included)
│
├── Simple / Script / Learning
│ └── Flask (minimal, flexible)
│
├── AI/ML API serving
│ └── FastAPI (Pydantic, async, uvicorn)
│
└── Background workers
└── Celery + any framework
```
### Comparison Principles
| Factor | FastAPI | Django | Flask |
|--------|---------|--------|-------|
| **Best for** | APIs, microservices | Full-stack, CMS | Simple, learning |
| **Async** | Native | Django 5.0+ | Via extensions |
| **Admin** | Manual | Built-in | Via extensions |
| **ORM** | Choose your own | Django ORM | Choose your own |
| **Learning curve** | Low | Medium | Low |
### Selection Questions to Ask:
1. Is this API-only or full-stack?
2. Need admin interface?
3. Team familiar with async?
4. Existing infrastructure?
---
## 2. Async vs Sync Decision
### When to Use Async
```
async def is better when:
├── I/O-bound operations (database, HTTP, file)
├── Many concurrent connections
├── Real-time features
├── Microservices communication
└── FastAPI/Starlette/Django ASGI
def (sync) is better when:
├── CPU-bound operations
├── Simple scripts
├── Legacy codebase
├── Team unfamiliar with async
└── Blocking libraries (no async version)
```
### The Golden Rule
```
I/O-bound → async (waiting for external)
CPU-bound → sync + multiprocessing (computing)
Don't:
├── Mix sync and async carelessly
├── Use sync libraries in async code
└── Force async for CPU work
```
### Async Library Selection
| Need | Async Library |
|------|---------------|
| HTTP client | httpx |
| PostgreSQL | asyncpg |
| Redis | aioredis / redis-py async |
| File I/O | aiofiles |
| Database ORM | SQLAlchemy 2.0 async, Tortoise |
---
## 3. Type Hints Strategy
### When to Type
```
Always type:
├── Function parameters
├── Return types
├── Class attributes
├── Public APIs
Can skip:
├── Local variables (let inference work)
├── One-off scripts
├── Tests (usually)
```
### Common Type Patterns
```python
# These are patterns, understand them:
# Optional → might be None
from typing import Optional
def find_user(id: int) -> Optional[User]: ...
# Union → one of multiple types
def process(data: str | dict) -> None: ...
# Generic collections
def get_items() -> list[Item]: ...
def get_mapping() -> dict[str, int]: ...
# Callable
from typing import Callable
def apply(fn: Callable[[int], str]) -> str: ...
```
### Pydantic for Validation
```
When to use Pydantic:
├── API request/response models
├── Configuration/settings
├── Data validation
├── Serialization
Benefits:
├── Runtime validation
├── Auto-generated JSON schema
├── Works with FastAPI natively
└── Clear error messages
```
---
## 4. Project Structure Principles
### Structure Selection
```
Small project / Script:
├── main.py
├── utils.py
└── requirements.txt
Medium API:
├── app/
│ ├── __init__.py
│ ├── main.py
│ ├── models/
│ ├── routes/
│ ├── services/
│ └── schemas/
├── tests/
└── pyproject.toml
Large application:
├── src/
│ └── myapp/
│ ├── core/
│ ├── api/
│ ├── services/
│ ├── models/
│ └── ...
├── tests/
└── pyproject.toml
```
### FastAPI Structure Principles
```
Organize by feature or layer:
By layer:
├── routes/ (API endpoints)
├── services/ (business logic)
├── models/ (database models)
├── schemas/ (Pydantic models)
└── dependencies/ (shared deps)
By feature:
├── users/
│ ├── routes.py
│ ├── service.py
│ └── schemas.py
└── products/
└── ...
```
---
## 5. Django Principles (2025)
### Django Async (Django 5.0+)
```
Django supports async:
├── Async views
├── Async middleware
├── Async ORM (limited)
└── ASGI deployment
When to use async in Django:
├── External API calls
├── WebSocket (Channels)
├── High-concurrency views
└── Background task triggering
```
### Django Best Practices
```
Model design:
├── Fat models, thin views
├── Use managers for common queries
├── Abstract base classes for shared fields
Views:
├── Class-based for complex CRUD
├── Function-based for simple endpoints
├── Use viewsets with DRF
Queries:
├── select_related() for FKs
├── prefetch_related() for M2M
├── Avoid N+1 queries
└── Use .only() for specific fields
```
---
## 6. FastAPI Principles
### async def vs def in FastAPI
```
Use async def when:
├── Using async database drivers
├── Making async HTTP calls
├── I/O-bound operations
└── Want to handle concurrency
Use def when:
├── Blocking operations
├── Sync database drivers
├── CPU-bound work
└── FastAPI runs in threadpool automatically
```
### Dependency Injection
```
Use dependencies for:
├── Database sessions
├── Current user / Auth
├── Configuration
├── Shared resources
Benefits:
├── Testability (mock dependencies)
├── Clean separation
├── Automatic cleanup (yield)
```
### Pydantic v2 Integration
```python
# FastAPI + Pydantic are tightly integrated:
# Request validation
@app.post("/users")
async def create(user: UserCreate) -> UserResponse:
# user is already validated
...
# Response serialization
# Return type becomes response schema
```
---
## 7. Background Tasks
### Selection Guide
| Solution | Best For |
|----------|----------|
| **BackgroundTasks** | Simple, in-process tasks |
| **Celery** | Distributed, complex workflows |
| **ARQ** | Async, Redis-based |
| **RQ** | Simple Redis queue |
| **Dramatiq** | Actor-based, simpler than Celery |
### When to Use Each
```
FastAPI BackgroundTasks:
├── Quick operations
├── No persistence needed
├── Fire-and-forget
└── Same process
Celery/ARQ:
├── Long-running tasks
├── Need retry logic
├── Distributed workers
├── Persistent queue
└── Complex workflows
```
---
## 8. Error Handling Principles
### Exception Strategy
```
In FastAPI:
├── Create custom exception classes
├── Register exception handlers
├── Return consistent error format
└── Log without exposing internals
Pattern:
├── Raise domain exceptions in services
├── Catch and transform in handlers
└── Client gets clean error response
```
### Error Response Philosophy
```
Include:
├── Error code (programmatic)
├── Message (human readable)
├── Details (field-level when applicable)
└── NOT stack traces (security)
```
---
## 9. Testing Principles
### Testing Strategy
| Type | Purpose | Tools |
|------|---------|-------|
| **Unit** | Business logic | pytest |
| **Integration** | API endpoints | pytest + httpx/TestClient |
| **E2E** | Full workflows | pytest + DB |
### Async Testing
```python
# Use pytest-asyncio for async tests
import pytest
from httpx import AsyncClient
@pytest.mark.asyncio
async def test_endpoint():
async with AsyncClient(app=app, base_url="http://test") as client:
response = await client.get("/users")
assert response.status_code == 200
```
### Fixtures Strategy
```
Common fixtures:
├── db_session → Database connection
├── client → Test client
├── authenticated_user → User with token
└── sample_data → Test data setup
```
---
## 10. Decision Checklist
Before implementing:
- [ ] **Asked user about framework preference?**
- [ ] **Chosen framework for THIS context?** (not just default)
- [ ] **Decided async vs sync?**
- [ ] **Planned type hint strategy?**
- [ ] **Defined project structure?**
- [ ] **Planned error handling?**
- [ ] **Considered background tasks?**
---
## 11. Anti-Patterns to Avoid
### ❌ DON'T:
- Default to Django for simple APIs (FastAPI may be better)
- Use sync libraries in async code
- Skip type hints for public APIs
- Put business logic in routes/views
- Ignore N+1 queries
- Mix async and sync carelessly
### ✅ DO:
- Choose framework based on context
- Ask about async requirements
- Use Pydantic for validation
- Separate concerns (routes → services → repos)
- Test critical paths
---
> **Remember**: Python patterns are about decision-making for YOUR specific context. Don't copy code—think about what serves your application best.Related Skills
python-workflow
Python project workflow guidelines. Triggers: .py, pyproject.toml, uv, pip, pytest, Python. Covers package management, virtual environments, code style, type safety, testing, configuration, CQRS patterns, and Python-specific development tasks.
python-workflow-development
Develop Python scripts and modules for building AI workflows and integrations. Use when coding data ingestion, transformation, analysis, and automation pipelines in pilot projects requiring Python automation.
python-typing
Migrate Python codebases to strict type checking with pyright. Use when user wants to add types, fix type errors, set up strict mode, or run a typing migration. Provides setup automation, fix patterns, discipline enforcement, and optional iteration loop support.
python-testing
Use when implementing new Python code (follow TDD), designing test suites, reviewing test coverage, setting up pytest infrastructure, writing fixtures, mocking dependencies, or performing parametrized testing
python-testing-patterns
Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.
python-specialist
Deliver production-quality Python solutions with framework-aware patterns and tests.
python-setup-dev-environment
Set up and run a reproducible Python dev environment with uv, ruff, mypy, and VSCode.
Python Security Scan
Comprehensive security vulnerability scanner for Python projects including Flask, Django, and FastAPI applications. Detects OWASP Top 10 vulnerabilities, injection flaws, insecure deserialization, authentication issues, hardcoded secrets, and framework-specific security problems. Audits dependencies for known CVEs and generates actionable security reports.
python-project
Scaffold and harden Python projects using vpngw-aligned defaults (pyproject/setuptools-scm, src layout, Ruff, pytest, Typer, Pydantic) plus best practices for CLI tools, systemd services, APIs/UI apps, IaC/automation, security/networking, and AI/ML workflows.
python-programmer
Python programmer specialising in functional programming, clean code, documentation, and code quality using ruff and uv.
python-pro
Master Python 3.12+ with modern features, async programming,
python
Python coding conventions and guidelines Triggers on: **/*.py