# Alantushenko Python

> Use whenever writing, editing, reviewing, or refactoring Python code. Alex Lantushenko's Python conventions for readable, maintainable code.

- Skill: `lantushenko-alex/alantushenko-python` (Agent Skill)
- Install (CLI): `npx skillmds@latest add lantushenko-alex/alantushenko-python`
- Raw SKILL.md: https://api.skillmd.com/api/skills/lantushenko-alex/alantushenko-python/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: lantushenko-alex (https://skillmd.com/u/lantushenko-alex)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/lantushenko-alex/alantushenko-python

---


# alantushenko-python — Python Conventions

Apply these conventions whenever writing or modifying Python code. These are additive to the general `alantushenko` style; where they conflict, the more specific Python rule here wins over the general style, but repository conventions still win over both.

## 1. Prefer dataclasses over loosely-typed collections

Use Python dataclasses instead of `List[str]`, `Dict[int, Any]`, tuples, or nested dicts to carry structured data, because named fields provide better readability and let readers know what each value means.

```python
# Avoid: what do these positions and keys mean?
def make_user() -> Dict[str, Any]:
    return {"name": "Ada", "age": 36, "active": True}

# Prefer: fields document themselves and are type-checked
from dataclasses import dataclass

@dataclass
class User:
    name: str
    age: int
    active: bool
```

Use a dataclass when a value has more than one field, when a collection's elements share a fixed shape, or when a dict is passed between functions. Keep plain lists and dicts for genuinely homogeneous, unstructured data (e.g. a set of tags, a lookup table keyed by id).