python-patterns
Helps your agent write Python code following best practices. It ensures code is clear, efficient, and maintainable using patterns and style guides.
What it does
This guide teaches how to write Python code that is clear, efficient, and easy to understand by following best practices. It covers how to organize code, use type hints for clarity, and handle errors intelligently.
When to use it
- ✓When writing new Python code.
- ✓When reviewing someone else's code.
- ✓When trying to improve existing code.
Example of what to ask your AI
“How can I write a Python function that is easy to read and returns only active users from a list?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
It shows examples of good and bad code to illustrate principles. It explains the use of 'type hints' to indicate expected data types and how to use 'context managers' to manage resources like files.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add affaan-m/ECC --skill python-patternsOr download just the file:
curl -L https://raw.githubusercontent.com/affaan-m/ECC/main/pi/core/skills/python-patterns/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Python Development Patterns
Idiomatic Python patterns and best practices for building robust, efficient, and maintainable applications.
When to Activate
- Writing new Python code
- Reviewing Python code
- Refactoring existing Python code
- Designing Python packages/modules
Core Principles
1. Readability Counts
Python prioritizes readability. Code should be obvious and easy to understand.
# Good: Clear and readable
def get_active_users(users: list[User]) -> list[User]:
"""Return only active users from the provided list."""
return [user for user in users if user.is_active]
# Bad: Clever but confusing
def get_active_users(u):
return [x for x in u if x.a]
2. Explicit is Better Than Implicit
Avoid magic; be clear about what your code does.
# Good: Explicit configuration
import logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
# Bad: Hidden side effects
import some_module
some_module.setup() # What does this do?
3. EAFP - Easier to Ask Forgiveness Than Permission
Python prefers exception handling over checking conditions.
# Good: EAFP style
def get_value(dictionary: dict, key: str, default_value: Any = None) -> Any:
try:
return dictionary[key]
except KeyError:
return default_value
# Bad: LBYL (Look Before You Leap) style
def get_value(dictionary: dict, key: str, default_value: Any = None) -> Any:
if key in dictionary:
return dictionary[key]
else:
return default_value
Type Hints
Basic Type Annotations
from typing import Optional, List, Dict, Any
def process_user(
user_id: str,
data: Dict[str, Any],
active: bool = True
) -> Optional[User]:
"""Process a user and return the updated User or None."""
if not active:
return None
return User(user_id, data)
Modern Type Hints (Python 3.9+)
# Python 3.9+ - Use built-in types
def process_items(items: list[str]) -> dict[str, int]:
return {item: len(item) for item in items}
# Python 3.8 and earlier - Use typing module
from typing import List, Dict
def process_items(items: List[str]) -> Dict[str, int]:
return {item: len(item) for item in items}
Type Aliases and TypeVar
from typing import TypeVar, Union
# Type alias for complex types
JSON = Union[dict[str, Any], list[Any], str, int, float, bool, None]
def parse_json(data: str) -> JSON:
return json.loads(data)
# Generic types
T = TypeVar('T')
def first(items: list[T]) -> T | None:
"""Return the first item or None if list is empty."""
return items[0] if items else None
Protocol-Based Duck Typing
from typing import Protocol
class Renderable(Protocol):
def render(self) -> str:
"""Render the object to a string."""
def render_all(items: list[Renderable]) -> str:
"""Render all items that implement the Renderable protocol."""
return "\n".join(item.render() for item in items)
Error Handling Patterns
Specific Exception Handling
# Good: Catch specific exceptions
def load_config(path: str) -> Config:
try:
with open(path) as f:
return Config.from_json(f.read())
except FileNotFoundError as e:
raise ConfigError(f"Config file not found: {path}") from e
except json.JSONDecodeError as e:
raise ConfigError(f"Invalid JSON in config: {path}") from e
# Bad: Bare except
def load_config(path: str) -> Config:
try:
with open(path) as f:
return Config.from_json(f.read())
except:
return None # Silent failure!
Exception Chaining
def process_data(data: str) -> Result:
try:
parsed = json.loads(data)
except json.JSONDecodeError as e:
# Chain exceptions to preserve the traceback
raise ValueError(f"Failed to parse data: {data}") from e
Custom Exception Hierarchy
class AppError(Exception):
"""Base exception for all application errors."""
pass
class ValidationError(AppError):
"""Raised when input validation fails."""
pass
class NotFoundError(AppError):
"""Raised when a requested resource is not found."""
pass
# Usage
def get_user(user_id: str) -> User:
user = db.find_user(user_id)
if not user:
raise NotFoundError(f"User not found: {user_id}")
return user
Context Managers
…
FAQ
What are 'type hints'?
'Type hints' are like annotations in code that say what kind of information a variable or function expects (e.g., text, number) and what type it returns. This helps prevent errors and makes code easier to understand.
What does 'EAFP' mean?
'EAFP' stands for 'Easier to Ask Forgiveness Than Permission'. In programming, this means it's better to try doing something and handle an error if it occurs, rather than checking all possible conditions before trying.
What are 'context managers'?
'Context managers' are tools that help manage resources, like automatically opening and closing files. They ensure resources are released correctly, even if errors happen.
Details
Original description by the author
Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust, efficient, and maintainable Python applications. Use when writing or reviewing Python code and idiomatic structure, typing, or PEP 8 is in question.
repo: affaan-m/ECC
Popularity
- GitHub stars
- 280k
- Ranking position
- #103
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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