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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

https://github.com/affaan-m/ECC/blob/main/pi/core/skills/python-patterns/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/affaan-m/ECC/blob/main/pi/core/skills/python-patterns/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
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-patterns

Or download just the file:

curl -L https://raw.githubusercontent.com/affaan-m/ECC/main/pi/core/skills/python-patterns/SKILL.md -o SKILL.md
Technical 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

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