Claude Code already knows Python well. Skills make it better at your Python — your frameworks, your testing patterns, your code style.

Quick Answer: Claude Code skills for Python improve code quality by enforcing project-specific standards for testing (e.g., pytest, type hints), code quality (e.g., docstrings, Pydantic), and framework usage (e.g., FastAPI, Django).

Why Python developers need skills

Without a skill, Claude Code writes generic Python. It might use unittest when you use pytest. It might skip type hints when your codebase requires them. It might generate Django patterns when you're using FastAPI.

A skill that says "use pytest with fixtures, always add type hints, we use FastAPI with Pydantic v2" produces code that fits your project without cleanup.

Testing skills for Python

Python testing skills are among the highest-value additions. A good one:

• Detects whether you use pytest, unittest, or nose from your project config
• Matches your fixture patterns from existing test files
• Uses your assertion style (plain asserts vs pytest.raises vs hypothesis)
• Generates parametrized tests for edge cases
• Checks for missing test coverage on new functions

Browse testing skills on Agensi.

Type checking and code quality

Skills can enforce type hint standards that go beyond what mypy catches. A code quality skill for Python might require:

• Type hints on all function signatures (no bare def process(data):)
• Docstrings on public functions using Google or NumPy style
• No Any types except in explicitly typed exceptions
• Pydantic models for data validation instead of raw dicts

Framework-specific skills

The most impactful Python skills are framework-specific:

FastAPI: Generate endpoints with proper Pydantic models, dependency injection, background tasks, and OpenAPI annotations.

Django: Follow Django patterns for models, views, serializers, URL routing, and admin configuration. Handle migrations correctly.

Flask: Structure blueprints, use application factories, handle extensions properly.

Data science: Pandas/NumPy code that avoids common performance pitfalls — vectorized operations instead of loops, proper memory management, correct merge strategies.

Building a Python-specific skill

If no existing skill matches your stack, create one. Here's a starting point:

Python Standards

Style

• Type hints on all function signatures
• Google-style docstrings on public functions
• Use pathlib instead of os.path
• f-strings for string formatting, never .format() or %

Testing

• pytest with fixtures, no unittest
• Parametrize edge cases with @pytest.mark.parametrize
• Use pytest.raises for exception testing
• Minimum one test per public function

Dependencies

• Use poetry for dependency management
• Pin all dependency versions
• Separate dev dependencies from production

Customize this to match your actual standards and drop it in ~/.claude/skills/.

Find Python-ready skills at Agensi.