ludo-technologies

coding-standards

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# Install this skill:
npx skills add ludo-technologies/python-best-practices --skill "coding-standards"

Install specific skill from multi-skill repository

# Description

Python coding standards and best practices for AI coding agents

# SKILL.md


name: coding-standards
description: Python coding standards and best practices for AI coding agents
globs:
- "/.py"
- "pyproject.toml"
- "setup.py"
- "requirements
.txt"


Python Coding Standards

A comprehensive collection of Python coding standards and best practices. Designed for AI agents and LLMs to generate high-quality, performant, and maintainable Python code.

Categories

Performance Optimization [CRITICAL]

Apply Python optimization patterns to improve processing speed and memory efficiency.

Rule Description
perf-list-comprehension Prefer list comprehensions over loops (1.5-2x faster)
perf-generator-expression Use generators for large datasets (O(1) memory)
perf-dict-get Use dict.get() for efficient default values
perf-set-lookup Use set for fast lookups (O(1) vs O(n))
perf-str-join Use join for string concatenation (O(n) vs O(nΒ²))

Async Processing [HIGH]

Efficient asynchronous programming patterns using asyncio.

Rule Description
async-gather Use asyncio.gather for independent tasks
async-create-task Proper background task creation
async-context-manager Resource management with async with
async-semaphore Limit concurrency with semaphores

Design Principles [HIGH]

Software design principles for maintainability and extensibility.

Rule Description
design-philosophy DRY, YAGNI, KISS principles
design-single-responsibility Single Responsibility Principle
design-dependency-injection Loose coupling with dependency injection
design-pure-functions Prefer pure functions without side effects
design-early-return Reduce nesting with early returns

Object-Oriented Programming [MEDIUM]

Best practices for Pythonic object-oriented programming.

Rule Description
oop-composition-over-inheritance Prefer composition over inheritance
oop-dataclass Use dataclass for data containers
oop-protocol Prefer Protocol over abstract base classes
oop-property Use property instead of getters

Quick Reference

Performance Patterns

# List comprehension (not loops)
result = [x * 2 for x in items]

# Generator for large data
total = sum(x * x for x in range(1_000_000))

# dict.get() with default
value = config.get("key", default_value)

# Set for fast lookup
valid_ids: set[int] = {1, 2, 3}
if item_id in valid_ids: ...

# join for strings
result = ",".join(values)

Async Patterns

# Concurrent execution
results = await asyncio.gather(task1(), task2(), task3())

# Resource management
async with aiohttp.ClientSession() as session:
    async with session.get(url) as response:
        data = await response.json()

# Concurrency limit
semaphore = asyncio.Semaphore(10)
async with semaphore:
    await do_work()

Design Patterns

# Dependency injection
class Service:
    def __init__(self, repository: Repository) -> None:
        self.repository = repository

# Early return
def process(data: Data | None) -> Result:
    if data is None:
        return Result.empty()
    # main logic here

OOP Patterns

# Dataclass
@dataclass
class User:
    name: str
    email: str

# Protocol for interfaces
class Repository(Protocol):
    def get(self, id: str) -> Entity: ...

# Property
@property
def full_name(self) -> str:
    return f"{self.first} {self.last}"

See Also

  • Recommended Tooling - Tools to enforce these standards automatically (ruff, mypy, pytest, pyscn, uv)

# Supported AI Coding Agents

This skill is compatible with the SKILL.md standard and works with all major AI coding agents:

Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.