Python Patterns

This one's your copilot for writing Python that doesn't make other developers wince. It pushes you toward PEP 8, type hints, and idiomatic patterns like EAFP over LBYL. You get concrete examples of dataclasses, context managers, proper exception chaining, and when to use generators instead of building massive lists in memory. Activate it when writing new code, reviewing PRs, or refactoring legacy stuff. The guidance is opinionated in a good way, preferring explicit over clever and readability over showing off. It won't turn bad code into good code automatically, but it'll nudge you toward patterns that age well.

skills.sh installs: 6.7K
GitHub stars: 300.3K
last updated: Aug 21, 2026

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Install

$ npx -y skills add affaan-m/ecc --skill python-patterns --agent claude-code

Installs into .claude/skills of the current project.

Files

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?