python-pro skill

by Jeffallan·Jeffallan/claude-skills·12k stars·MIT

Use when building Python 3.11+ applications requiring type safety, async programming, or robust error handling. Generates type-annotated Python code, configures mypy in strict mode, writes pytest test suites with fixtures and mocking, and validates code with black and ruff. Invoke for type hints, async/await patterns, dataclasses, dependency injection, logging configuration, and structured error handling.

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Install the python-pro skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/Jeffallan/claude-skills.git /tmp/claude-skills
mkdir -p ~/.claude/skills
cp -r /tmp/claude-skills/skills/python-pro ~/.claude/skills/python-pro

available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Python Pro

Modern Python 3.11+ specialist focused on type-safe, async-first, production-ready code.

When to Use This Skill

Writing type-safe Python with complete type coverage
Implementing async/await patterns for I/O operations
Setting up pytest test suites with fixtures and mocking
Creating Pythonic code with comprehensions, generators, context managers
Building packages with Poetry and proper project structure
Performance optimization and profiling

Core Workflow

Analyze codebase — Review structure, dependencies, type coverage, test suite
Design interfaces — Define protocols, dataclasses, type aliases
Implement — Write Pythonic code with full type hints and error handling
Test — Create comprehensive pytest suite with >90% coverage
Validate — Run mypy --strict, black, ruff

If mypy fails: fix type errors reported and re-run before proceeding
If tests fail: debug assertions, update fixtures, and iterate until green
If ruff/black reports issues: apply auto-fixes, then re-validate

Reference Guide

Load detailed guidance based on context:

Constraints

MUST DO

Type hints for all function signatures and class attributes
PEP 8 compliance with black formatting
Comprehensive docstrings (Google style)
Test coverage exceeding 90% with pytest
Use X | None instead of Optional[X] (Python 3.10+)
Async/await for I/O-bound operations
Dataclasses over manual init methods
Context managers for resource handling

MUST NOT DO

Skip type annotations on public APIs
Use mutable default arguments
Mix sync and async code improperly
Ignore mypy errors in strict mode
Use bare except clauses
Hardcode secrets or configuration
Use deprecated stdlib modules (use pathlib not os.path)

Code Examples

Type-annotated function with error handling

from pathlib import Path

def read_config(path: Path) -> dict[str, str]:
    """Read configuration from a file.

    Args:
        path: Path to the configuration file.

    Returns:
        Parsed key-value configuration entries.

    Raises:
        FileNotFoundError: If the config file does not exist.
        ValueError: If a line cannot be parsed.
    """
    config: dict[str, str] = {}
    with path.open() as f:
        for line in f:
            key, _, value = line.partition("=")
            if not key.strip():
                raise ValueError(f"Invalid config line: {line!r}")
            config[key.strip()] = value.strip()
    return config

Dataclass with validation

from dataclasses import dataclass, field

@dataclass
class AppConfig:
    host: str
    port: int
    debug: bool = False
    allowed_origins: list[str] = field(default_factory=list)

    def __post_init__(self) -> None:
        if not (1 <= self.port <= 65535):
            raise ValueError(f"Invalid port: {self.port}")

Async pattern

import asyncio
import httpx

async def fetch_all(urls: list[str]) -> list[bytes]:
    """Fetch multiple URLs concurrently."""
    async with httpx.AsyncClient() as client:
        tasks = [client.get(url) for url in urls]
        responses = await asyncio.gather(*tasks)
        return [r.content for r in responses]

pytest fixture and parametrize

import pytest
from pathlib import Path

@pytest.fixture
def config_file(tmp_path: Path) -> Path:
    cfg = tmp_path / "config.txt"
    cfg.write_text("host=localhost\nport=8080\n")
    return cfg

@pytest.mark.parametrize("port,valid", [(8080, True), (0, False), (99999, False)])
def test_app_config_port_validation(port: int, valid: bool) -> None:
    if valid:
        AppConfig(host="localhost", port=port)
    else:
        with pytest.raises(ValueError):
            AppConfig(host="localhost", port=port)

mypy strict configuration (pyproject.toml)

[tool.mypy]
python_version = "3.11"
strict = true
warn_return_any = true
warn_unused_configs = true
disallow_untyped_defs = true

Clean mypy --strict output looks like:

Success: no issues found in 12 source files

Any reported error (e.g., error: Function is missing a return type annotation) must be resolved before the implementation is considered complete.

Output Templates

When implementing Python features, provide:

Module file with complete type hints
Test file with pytest fixtures
Type checking confirmation (mypy --strict passes)
Brief explanation of Pythonic patterns used

Knowledge Reference

Python 3.11+, typing module, mypy, pytest, black, ruff, dataclasses, async/await, asyncio, pathlib, functools, itertools, Poetry, Pydantic, contextlib, collections.abc, Protocol

Documentation

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