# python-best-practices

A skill with 75 Python rules drawn from real PR review patterns, covering dataclasses, service interfaces, error handling and type annotations.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 854 installs · ★ 3
- Author: python-best-practices
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/nathan-gage/python-skills/tree/HEAD/skills/python-best-practices/SKILL.md
- Updated: 2026-08-20
- HTML: https://affpapa.org/en/nevibecode/skills/python-best-practices

## What it does

The skill packs 75 rules into 8 categories, ordered by impact. They run from data modeling (`data-`) and error handling (`error-`) through type safety (`types-`), API design (`api-`), code simplification, performance, naming and imports. Every rule carries a level: `CRITICAL` means a real bug class, `HIGH` a meaningful correctness win, `MEDIUM` and below are mostly style.

Each rule lives in its own file with a quick-reference line. That line is a trigger, not a licence — the author advises opening the rule file and reading its counter-signal, the paragraph that says when not to apply it. A rule match is a signal, not a verdict. Most rules describe how to design new code rather than bugs to fix across a repo.

Rules assume Python 3.11+. Some depend on newer versions and say so inline: `warnings.deprecated()` needs 3.13+, PEP 695 generic syntax needs 3.12+, `assert_never` needs 3.11+ with a `typing_extensions` backport. Rules tagged `applicability:pydantic` are Pydantic-specific. For asyncio and concurrency you want the separate `python-async-best-practices` skill — this one does not cover it. And do not sweep the rules across stable code; the author warns the churn rarely pays off.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add nathan-gage/python-skills --skill python-best-practices -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/python-best-practices/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/python-best-practices/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add nathan-gage/python-skills --skill python-best-practices -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/python-best-practices/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add nathan-gage/python-skills --skill python-best-practices -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/python-best-practices/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install nathan-gage/python-skills python-best-practices
```

2. **Manually: put the files in place**
- File: `.github/skills/python-best-practices/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add nathan-gage/python-skills --skill python-best-practices -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/nathan-gage/python-skills --path skills/python-best-practices
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/python-best-practices/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add nathan-gage/python-skills --skill python-best-practices -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/nathan-gage/python-skills/tree/HEAD/skills/python-best-practices/SKILL.md

## When to use it
- check a new module for common mistakes before review
- pick between a dataclass and a dict for a data structure
- split variants with a discriminated union instead of optional-field bags
- spot swallowed exceptions and missing `raise ... from` chains
- move annotations to `X | None` and `list[str]` syntax

## Example prompts
- write a Python service and check it against python-best-practices
- review this module for error handling and typing
- refactor the data models in `models.py` using the skill rules

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 9b5f8d6e27f457be.

BEGIN THIRD-PARTY FILE 9b5f8d6e27f457be
```text
---
name: python-best-practices
description: Python software engineering guidelines from real PR review patterns. This skill should be used when writing, reviewing, or refactoring Python code — especially dataclasses, service interfaces, error handling, and type annotations. Triggers on tasks involving Python modules, API design, data modeling, type safety, exception handling, or refactoring for maintainability. For asyncio and concurrency guidance, see the python-async-best-practices skill.
license: MIT
metadata:
  author: python-best-practices
  version: "1.5.0"
  pythonVersion: ">=3.11"
---

# Python Best Practices

Guidelines for writing and reviewing Python. 75 rules across 8 categories, prioritized by impact.

A rule match is a signal, not a verdict. Most rules are design preferences for new code, not bugs to fix across the repo — check the rule's impact level before flagging in review or refactoring stable code.

Quick-reference lines are triggers, not licenses: before applying a rule as a review finding or a transformation, open the rule file and check its counter-signal — the marker-opened paragraph (`**When ...**` / `**Scope:**` / `**Preserve ...**`) saying when NOT to apply it.

## When to Apply

- Writing new Python modules, functions, classes, or data models
- Reviewing code for correctness or type safety
- Refactoring patterns in code that's being edited anyway

Avoid applying these rules as a blanket sweep across stable code — the churn rarely pays off.

## Impact Levels

- `CRITICAL` — prevents a real bug class (data corruption, swallowed cancellations, insecure defaults). Fix when found.
- `HIGH` — meaningful correctness or maintainability win. Worth fixing in most contexts.
- `MEDIUM` — good practice; clarity or drift prevention. Apply to new code; don't churn stable code.
- `LOW-MEDIUM` / `LOW` — style or micro-optimizations. Apply opportunistically.

## Python Version Baseline

Rules assume Python 3.11+. Rules depending on higher versions call it out inline:

- `warnings.deprecated()` — 3.13+
- `zoneinfo` — 3.9+
- Union types in `isinstance()` — 3.10+
- `assert_never` — 3.11+ (backport via `typing_extensions`)
- PEP 695 `type` statement and generic syntax — 3.12+ (noted inline in `types-modern-syntax`)

Rules tagged `applicability:pydantic` are Pydantic-specific.
```
END THIRD-PARTY FILE 9b5f8d6e27f457be

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