# python

Gives the agent one set of Python conventions: typing, dependency injection, module and test layout. Useful when edits land in an existing codebase.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 349 installs · ★ 22
- Author: siviter-xyz
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/siviter-xyz/dot-agent/tree/HEAD/skills/python/SKILL.md
- Updated: 2026-03-21
- HTML: https://affpapa.org/en/nevibecode/skills/siviter-xyz-python

## What it does

The skill loads as a set of guidelines the agent reads before touching Python code. It covers design, style, type annotations, architecture, testing and the order of implementation. On design it asks for DRY, KISS and SOLID, functional methods where they fit, classes for stateful behaviour, and composition through Protocol classes instead of inheritance.

The typing section is concrete: `dict` and `list` over `typing.Dict` and `typing.List`, `str | None` over `Optional[str]`, and `from __future__ import annotations` at the top of annotated files. Dependencies go in through constructors or methods, one service class per module, and environment access lives in `environment.py` as one method per variable, like `api_key()` for `API_KEY`.

For data models it points at Pydantic v2 — schemas, validation, configuration. Tests run on `pytest` with `pytest-mock`, shared fixtures in `conftest.py`, a directory layout mirroring `src/`, and a `TestFoo` class for `def foo()`. If your repo already has a workflow, spec-first or TDD, the skill says to follow that instead of overriding it. Two reference files ship alongside: `references/uv-scripts.md` and `references/uv-monorepo.md`, covering uv and Hatch setups.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add siviter-xyz/dot-agent --skill python -a claude-code
```

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

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add siviter-xyz/dot-agent --skill python -a codex
```

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

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add siviter-xyz/dot-agent --skill python -a cursor
```

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

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install siviter-xyz/dot-agent python
```

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

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add siviter-xyz/dot-agent --skill python -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/siviter-xyz/dot-agent --path skills/python
```

### Windsurf (Devin Desktop)

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

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add siviter-xyz/dot-agent --skill python -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/siviter-xyz/dot-agent/tree/HEAD/skills/python/SKILL.md

## When to use it
- make type hints consistent across a codebase
- move dependencies into constructors instead of globals
- collect environment variable access in one file
- rewrite tests around pytest fixtures in conftest.py
- define request and response schemas with Pydantic v2

## Example prompts
- add a new FastAPI endpoint and define the schemas with Pydantic v2
- refactor this module to inject dependencies through the constructor and define the interface as a Protocol
- write pytest tests for this class and move the fixtures into conftest.py

## 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: caefc27f4da218fd.

BEGIN THIRD-PARTY FILE caefc27f4da218fd
```text
---
name: python
description: Python development guidelines and best practices. Use when working with Python code.
---

# Python Guidelines

Standards and best practices for Python development. Follow these guidelines when writing or modifying Python code.

## Design Principles

Apply DRY, KISS, and SOLID consistently. Prefer functional methods where relevant; use classes for stateful behavior. Use composition with Protocol classes for interfaces rather than inheritance. Each module should have a single responsibility. Use dependency injection for class dependencies.

## Code Style

- **Naming**: Descriptive yet concise names for variables, methods, and classes
- **Documentation**: Docstrings for all classes, functions, enums, enum values
- **Type hints**: Use consistently; avoid `Any` unless necessary
- **Imports**: Avoid barrel exports in `__init__.py`; prefer blank files

## Type Annotations

- Use `dict`, `list` instead of `typing.Dict`, `typing.List`
- Use `str | None` instead of `Optional[str]`
- Include `from __future__ import annotations` at top of files with type hints
- Prefer built-in types over typing module equivalents

## Architecture

### Dependency Injection

- Always inject dependencies via constructors or methods when using classes
- One service class per module (interface and class models allowed in addition)
- Use Protocol classes to define interfaces for dependency injection and testing

### Module Organization

- Each module focuses on one concern with clear boundaries
- Extract reusable methods to avoid duplication
- Design for reusability across contexts

### Environment Variables

- Use an `environment.py` file with individual methods per variable (e.g., `api_key()` for `API_KEY`, `database_url()` for `DATABASE_URL`)
- Co-locate all environment access in one place per package for easier mocking in tests

### Data Models

- Use Pydantic v2 for schemas, validation, and data models
- Leverage Pydantic's type validation, serialization, and configuration management
- Use Pydantic models for API request/response schemas, configuration objects, and data transfer objects

## Testing

### Structure

- Tests mirror `src/` directory structure
- Test methods start with `test_`
- Use test class suites: for `def foo()` create `class TestFoo`
- Keep names concise, omit class suite name from method
- Always check for appropriate unit tests when changing code

### Quality

- Use AAA (Arrange, Act, Assert) pattern
- Tests should be useful, readable, concise, maintainable
- Avoid tests that create massive diffs or become burdensome

### Tools
```
END THIRD-PARTY FILE caefc27f4da218fd

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