Claude Code Skills for Python Devs (2026)

Python developers get massive value from Claude Code, but generic skills miss Python-specific needs: virtual environments, type hints, Django/FastAPI patterns, data science workflows, and packaging. This guide covers the best skills tailored for Python workflows.

Find and install all of these from the Skill Finder where you can filter by language.

Essential Python Skills

1. Python Project Conventions Skill

This foundational skill teaches Claude your Python preferences:

# Python Project Conventions
## Style
- Follow PEP 8 strictly
- Use type hints on all function signatures
- Prefer f-strings over .format() or %
- Use pathlib.Path instead of os.path
- Prefer dataclasses or Pydantic models over plain dicts
## Imports
- Use absolute imports
- Group: stdlib, third-party, local (separated by blank lines)
- Sort alphabetically within groups (isort compatible)
## Error Handling
- Never use bare except
- Always specify the exception type
- Log exceptions with logger.exception(), not print()
- Use custom exception classes for domain errors
## Testing
- Use pytest (not unittest)
- Name tests: test_[function]_[scenario]_[expected]
- Use fixtures for setup, not setUp/tearDown
- Mock external services, never call real APIs in tests

Save this as .claude/skills/python-conventions.md and Claude will follow these rules in every interaction.

2. Virtual Environment Skill

Prevents the classic Python problem of Claude installing packages into the wrong environment:

# Virtual Environment Skill
Before running any pip install command:
1. Check if a virtual environment is active (look for VIRTUAL_ENV in env)
2. If not active, look for venv/, .venv/, or env/ directories
3. If found, activate it first: source .venv/bin/activate
4. If not found, create one: python -m venv .venv && source .venv/bin/activate
Always use `pip install` with the virtual environment active.
Never use `sudo pip install`.
Always add new dependencies to requirements.txt or pyproject.toml.

This skill prevents the most common Python environment issue: packages installed globally instead of in the project’s virtual environment.

3. Pytest Skill

A comprehensive testing skill for Python’s most popular test framework:

# Pytest Skill
When writing or running tests:
- Use pytest fixtures for dependency injection
- Use parametrize for testing multiple inputs
- Use conftest.py for shared fixtures
- Use tmp_path fixture for file system tests
- Use monkeypatch fixture for environment variables
- Run tests with: pytest -v --tb=short
- Check coverage with: pytest --cov=src --cov-report=term-missing
- Mark slow tests with @pytest.mark.slow
- Use pytest-asyncio for async test functions

4. Type Checking Skill

Python’s type system has matured significantly. This skill ensures Claude writes properly typed code:

# Type Checking Skill
## Rules
- Add type hints to all function parameters and return types
- Use Optional[X] for nullable parameters (or X | None for 3.10+)
- Use TypedDict for dictionary structures
- Use Protocol for structural subtyping (duck typing)
- Prefer Sequence over List for read-only parameters
- Use Generic[T] for generic classes
- Run mypy --strict as a quality check
## Common Patterns
- Factory functions: return the concrete type, not the protocol
- Decorators: use ParamSpec and Concatenate for typed decorators
- Context managers: annotate __enter__ and __exit__ properly

Framework-Specific Skills

5. Django Skill

# Django Skill
## Models
- Always define __str__ on models
- Use choices for fields with fixed options
- Add indexes on fields used in filters and ordering
- Use Django's built-in validators
## Views
- Prefer class-based views for CRUD operations
- Use get_object_or_404, never raw try/except
- Always validate form/serializer data before saving
- Return proper HTTP status codes (201 for create, 204 for delete)
## Queries
- Use select_related and prefetch_related to avoid N+1
- Use F() expressions for database-level operations
- Use Q() objects for complex filters
- Always use .exists() instead of len(queryset) > 0
## Security
- Never use raw SQL without parameterized queries
- Always use csrf_protect on form views
- Validate all user input with Django forms or DRF serializers

6. FastAPI Skill

# FastAPI Skill
## Endpoints
- Use proper HTTP methods: GET (read), POST (create), PUT (replace), PATCH (update), DELETE (remove)
- Define Pydantic models for request and response bodies
- Use status_code parameter on decorators
- Use Depends() for dependency injection
- Use BackgroundTasks for non-blocking operations
## Validation
- Use Pydantic validators for complex validation logic
- Use Path(), Query(), and Body() for parameter validation
- Return 422 with validation error details (automatic with Pydantic)
## Async
- Use async def for I/O-bound endpoints
- Use regular def for CPU-bound endpoints (runs in threadpool)
- Never mix sync and async database calls in the same endpoint

7. Data Science Skill

# Data Science Skill
## Pandas
- Always specify dtypes when reading CSVs
- Use .loc and .iloc instead of chained indexing
- Avoid inplace=True (creates confusion, prefer reassignment)
- Use vectorized operations instead of apply() when possible
## Jupyter Integration
- Structure notebooks: imports, config, data loading, analysis, visualization
- Use markdown cells to explain each analysis step
- Set random seeds for reproducibility: np.random.seed(42)
- Display dataframe shapes after transformations
## Visualization
- Always label axes and add titles
- Use seaborn for statistical plots, matplotlib for custom layouts
- Save figures as both PNG and SVG
- Use constrained_layout=True to prevent label clipping

Advanced Python Skills

8. Packaging Skill

Ensures Claude creates properly structured Python packages:

# Packaging Skill

- Use pyproject.toml (PEP 621) as the single source of project metadata
- Include: name, version, description, requires-python, dependencies
- Use src/ layout: src/package_name/ with __init__.py
- Add py.typed marker for PEP 561 type stub support
- Include a LICENSE file
- Use semantic versioning
- Define entry points for CLI tools in [project.scripts]

9. Logging Skill

# Logging Skill

- Use the logging module, never print() for operational output
- Configure logging in one place (main.py or conftest.py)
- Use logger = logging.getLogger(__name__) per module
- Log levels: DEBUG (development), INFO (operations), WARNING (unexpected), ERROR (failures)
- Use structured logging (JSON) for production: python-json-logger
- Include context in log messages: logger.error("Payment failed", extra={"user_id": uid, "amount": amt})

10. Async Python Skill

# Async Skill

- Use asyncio for I/O-bound concurrency
- Use asyncio.gather() for parallel async operations
- Use asyncio.Semaphore to limit concurrent operations
- Use aiohttp or httpx for async HTTP requests
- Never call blocking functions in async code (use run_in_executor)
- Use async context managers for resource cleanup
- Test async code with pytest-asyncio

Try It Yourself

The Skill Finder has a Python category with all these skills and more. Filter by framework (Django, FastAPI, Flask) or use case (testing, data science, DevOps) to find exactly what you need. Each skill includes a one-command install.

Open Skill Finder

Combining Skills Effectively

Do not install all 10 skills at once. Start with:

  1. Python Project Conventions (always active)
  2. Your framework skill (Django, FastAPI, or Data Science)
  3. Testing skill (Pytest)

Add specialized skills only when you need them. Each active skill adds token overhead to every message.

FAQ

What is the best Claude Code skill for Python developers?

The Python Project Conventions skill is the highest impact starting point. It enforces PEP 8, type hints, proper imports, and error handling patterns across all your Claude Code interactions.

Does Claude Code support Django and FastAPI skills?

Yes. Framework-specific skills teach Claude your framework’s best practices: Django ORM optimization, FastAPI dependency injection, Flask blueprints. Install them as CLAUDE.md entries or skill files.

How do I make Claude Code use my virtual environment?

Install the Virtual Environment skill which instructs Claude to check for and activate your venv before running pip commands. This prevents accidental global package installs.

Can Claude Code skills help with Python data science workflows?

Yes. The Data Science skill covers pandas best practices, Jupyter notebook structure, visualization conventions, and reproducibility. It works alongside the Python Conventions skill.

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