/python-dev
Python development guidance with code quality standards, error handling, testing practices, and environment management. Use when writing, reviewing, or modifying Python code (.py files) or Jupyter notebooks (.ipynb files).
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Python development guidance with code quality standards, error handling, testing practices, and environment management. Use when writing, reviewing, or modifying Python code (.py files) or Jupyter notebooks (.ipynb files).
SKILL.md
python-dev.SKILL.mdname: python-dev
description: "Python development guidance with code quality standards, error handling, testing practices, and environment management. Use when writing, reviewing, or modifying Python code (.py files) or Jupyter notebooks (.ipynb files)."
Python Development Rules
Overview
Python development guidance focused on code quality, error handling, testing, and environment management. Apply when working with Python code or Jupyter notebooks.
When to Use This Skill
Use this skill when:
- Writing new Python code or modifying existing Python files
- Creating or updating Jupyter notebooks
- Setting up Python development environments
- Writing or updating tests
- Reviewing Python code for quality and best practices
Code Quality
Principles
- **DRY (Don't Repeat Yourself)**: Avoid code duplication
- **Composition over inheritance**: Prefer composition patterns
- **Pure functions when possible**: Functions without side effects
- **Simple solutions over clever ones**: Prioritize readability and maintainability
- **Design for common use cases first**: Solve the primary problem before edge cases
Style & Documentation
- **Type hints required**: All functions must include type annotations
- **snake_case naming**: Use snake_case for variables, functions, and modules
- **Google-style docstrings**: Document functions, classes, and modules using Google-style docstrings
- **Keep functions small**: Single responsibility principle - one function, one purpose
- **Preserve existing comments**: Maintain and update existing code comments
Example
def calculate_total(items: list[dict[str, float]], tax_rate: float = 0.08) -> float:
"""Calculate total cost including tax.
Args:
items: List of items with 'price' key
tax_rate: Tax rate as decimal (default 0.08)
Returns:
Total cost including tax
Raises:
ValueError: If tax_rate is negative or items list is empty
"""
if not items:
raise ValueError("Items list cannot be empty")
if tax_rate < 0:
raise ValueError("Tax rate cannot be negative")
subtotal = sum(item['price'] for item in items)
return subtotal * (1 + tax_rate)Error Handling & Efficiency
Error Handling
- **Specific exception types**: Catch specific exceptions, not bare `except`
- **Validate inputs early**: Check inputs at function entry
- **No bare except**: Always specify exception types
Efficiency Patterns
- **f-strings**: Use f-strings for string formatting
- **Comprehensions**: Prefer list/dict/set comprehensions over loops when appropriate
- **Context managers**: Use `with` statements for resource management
Example
def process_file(file_path: str) -> list[str]:
"""Process file and return lines.
Args:
file_path: Path to file
Returns:
List of non-empty lines
Raises:
FileNotFoundError: If file doesn't exist
PermissionError: If file cannot be read
"""
if not file_path:
raise ValueError("File path cannot be empty")
try:
with open(file_path, 'r', encoding='utf-8') as f:
return [line.strip() for line in f if line.strip()]
except FileNotFoundError:
raise FileNotFoundError(f"File not found: {file_path}")
except PermissionError:
raise PermissionError(f"Permission denied: {file_path}")Testing (Critical)
Framework & Structure
- **pytest only**: Use pytest exclusively (no unittest)
- **Test location**: All tests in `./tests/` directory
- **Test package**: Include `__init__.py` in tests directory
- **TDD approach**: Write/update tests for all new/modified code
- **All tests must pass**: Ensure all tests pass before task completion
Test Structure Example
project/
├── src/
│ └── my_module.py
└── tests/
├── __init__.py
└── test_my_module.pyExample Test
# tests/test_calculations.py
import pytest
from src.calculations import calculate_total
def test_calculate_total_basic():
"""Test basic total calculation."""
items = [{'price': 10.0}, {'price': 20.0}]
result = calculate_total(items, tax_rate=0.1)
assert result == 33.0
def test_calculate_total_empty_list():
"""Test error handling for empty list."""
with pytest.raises(ValueError, match="Items list cannot be empty"):
calculate_total([])
def test_calculate_total_negative_tax():
"""Test error handling for negative tax rate."""
items = [{'price': 10.0}]
with pytest.raises(ValueError, match="Tax rate cannot be negative"):
calculate_total(items, tax_rate=-0.1)Environment Management
Dependency Management
- **Use uv exclusively**: All packaging, environment, and script execution via [uv](https://github.com/astral-sh/uv)
- **No pip/venv/conda**: Do not use `pip`, `python3 -m venv`, or `conda` — `uv` handles all of this
- **pyproject.toml is the source of truth**: Define all dependencies in `pyproject.toml` (not `requirements.txt`)
Environment Setup Example
# Install dependencies from pyproject.toml
uv sync
# Install with optional dev dependencies
uv sync --extra dev
# Run a script (no activation needed)
uv run python script.py
# Run pytest
uv run pytest
# Add a new dependency
uv add requests
# Remove a dependency
uv remove requests
Running Python Code
- Use `uv run` to execute scripts — no manual venv activation needed
- Use `uv run <tool>` for dev tools (pytest, ruff, etc.)
- Dependencies are defined in `pyproject.toml` (not requirements.txt)
Linting & Formatting (Ruff)
- **Ruff**: Use Ruff for linting AND formatting (replaces flake8, black, isort)
# Lint code
uv run ruff check .
# Lint and auto-fix
uv run ruff check --fix .
# Format code
uv run ruff format .
# Check formatting without changes
uv run ruff format --check .
Type Checking (Pyright)
# Check types
uv r
Read more
name: python-dev description: "Python development guidance with code quality standards, error handling, testing practices, and environment management. Use when writing, reviewing, or modifying Python code (.py files) or Jupyter notebooks (.ipynb files)."
Python Development Rules
Overview
Python development guidance focused on code quality, error handling, testing, and environment management. Apply when working with Python code or Jupyter notebooks.
When to Use This Skill
Use this skill when:
- Writing new Python code or modifying existing Python files
- Creating or updating Jupyter notebooks
- Setting up Python development environments
- Writing or updating tests
- Reviewing Python code for quality and best practices
Code Quality
Principles
- **DRY (Don't Repeat Yourself)**: Avoid code duplication
- **Composition over inheritance**: Prefer composition patterns
- **Pure functions when possible**: Functions without side effects
- **Simple solutions over clever ones**: Prioritize readability and maintainability
- **Design for common use cases first**: Solve the primary problem before edge cases
Style & Documentation
- **Type hints required**: All functions must include type annotations
- **snake_case naming**: Use snake_case for variables, functions, and modules
- **Google-style docstrings**: Document functions, classes, and modules using Google-style docstrings
- **Keep functions small**: Single responsibility principle - one function, one purpose
- **Preserve existing comments**: Maintain and update existing code comments
Example
def calculate_total(items: list[dict[str, float]], tax_rate: float = 0.08) -> float:
"""Calculate total cost including tax.
Args:
items: List of items with 'price' key
tax_rate: Tax rate as decimal (default 0.08)
Returns:
Total cost including tax
Raises:
ValueError: If tax_rate is negative or items list is empty
"""
if not items:
raise ValueError("Items list cannot be empty")
if tax_rate < 0:
raise ValueError("Tax rate cannot be negative")
subtotal = sum(item['price'] for item in items)
return subtotal * (1 + tax_rate)Error Handling & Efficiency
Error Handling
- **Specific exception types**: Catch specific exceptions, not bare `except`
- **Validate inputs early**: Check inputs at function entry
- **No bare except**: Always specify exception types
Efficiency Patterns
- **f-strings**: Use f-strings for string formatting
- **Comprehensions**: Prefer list/dict/set comprehensions over loops when appropriate
- **Context managers**: Use `with` statements for resource management
Example
def process_file(file_path: str) -> list[str]:
"""Process file and return lines.
Args:
file_path: Path to file
Returns:
List of non-empty lines
Raises:
FileNotFoundError: If file doesn't exist
PermissionError: If file cannot be read
"""
if not file_path:
raise ValueError("File path cannot be empty")
try:
with open(file_path, 'r', encoding='utf-8') as f:
return [line.strip() for line in f if line.strip()]
except FileNotFoundError:
raise FileNotFoundError(f"File not found: {file_path}")
except PermissionError:
raise PermissionError(f"Permission denied: {file_path}")Testing (Critical)
Framework & Structure
- **pytest only**: Use pytest exclusively (no unittest)
- **Test location**: All tests in `./tests/` directory
- **Test package**: Include `__init__.py` in tests directory
- **TDD approach**: Write/update tests for all new/modified code
- **All tests must pass**: Ensure all tests pass before task completion
Test Structure Example
project/
├── src/
│ └── my_module.py
└── tests/
├── __init__.py
└── test_my_module.pyExample Test
# tests/test_calculations.py
import pytest
from src.calculations import calculate_total
def test_calculate_total_basic():
"""Test basic total calculation."""
items = [{'price': 10.0}, {'price': 20.0}]
result = calculate_total(items, tax_rate=0.1)
assert result == 33.0
def test_calculate_total_empty_list():
"""Test error handling for empty list."""
with pytest.raises(ValueError, match="Items list cannot be empty"):
calculate_total([])
def test_calculate_total_negative_tax():
"""Test error handling for negative tax rate."""
items = [{'price': 10.0}]
with pytest.raises(ValueError, match="Tax rate cannot be negative"):
calculate_total(items, tax_rate=-0.1)Environment Management
Dependency Management
- **Use uv exclusively**: All packaging, environment, and script execution via [uv](https://github.com/astral-sh/uv)
- **No pip/venv/conda**: Do not use `pip`, `python3 -m venv`, or `conda` — `uv` handles all of this
- **pyproject.toml is the source of truth**: Define all dependencies in `pyproject.toml` (not `requirements.txt`)
Environment Setup Example
# Install dependencies from pyproject.toml uv sync # Install with optional dev dependencies uv sync --extra dev # Run a script (no activation needed) uv run python script.py # Run pytest uv run pytest # Add a new dependency uv add requests # Remove a dependency uv remove requests
Running Python Code
- Use `uv run` to execute scripts — no manual venv activation needed
- Use `uv run <tool>` for dev tools (pytest, ruff, etc.)
- Dependencies are defined in `pyproject.toml` (not requirements.txt)
Linting & Formatting (Ruff)
- **Ruff**: Use Ruff for linting AND formatting (replaces flake8, black, isort)
# Lint code uv run ruff check . # Lint and auto-fix uv run ruff check --fix . # Format code uv run ruff format . # Check formatting without changes uv run ruff format --check .
Type Checking (Pyright)
# Check types uv r
📣 A big step forward: AI Dev Kit skills are now official Databricks AI Tools The skills from this AI Dev Kit repository are now delivered as part of Databricks AI Tools, a Databricks engineering-owned repository, built and maintained in close collaboration
Repo: databricks-solutions/ai-dev-kit

