async def my_coroutine():
    result = await some_async_operation()
    return result

import time

def measure_time():
    """Simple timing me

...(truncated)

</details>

### python-testing-patterns

> Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.

<details>
<summary>View skill definition</summary>

# Python Testing Patterns

Comprehensive guide to implementing robust testing strategies in Python using pytest, fixtures, mocking, parameterization, and test-driven development practices.

## When to Use This Skill

- Writing unit tests for Python code
- Setting up test suites and test infrastructure
- Implementing test-driven development (TDD)
- Creating integration tests for APIs and services
- Mocking external dependencies and services
- Testing async code and concurrent operations
- Setting up continuous testing in CI/CD
- Implementing property-based testing
- Testing database operations
- Debugging failing tests

## Core Concepts

### 1. Test Types
- **Unit Tests**: Test individual functions/classes in isolation
- **Integration Tests**: Test interaction between components
- **Functional Tests**: Test complete features end-to-end
- **Performance Tests**: Measure speed and resource usage

### 2. Test Structure (AAA Pattern)
- **Arrange**: Set up test data and preconditions
- **Act**: Execute the code under test
- **Assert**: Verify the results

### 3. Test Coverage
- Measure what code is exercised by tests
- Identify untested code paths
- Aim for meaningful coverage, not just high percentages

### 4. Test Isolation
- Tests should be independent
- No shared state between tests
- Each test should clean up after itself

## Quick Start

```python
# test_example.py
def add(a, b):
    return a + b

def test_add():
    """Basic test example."""
    result = add(2, 3)
    assert 

...(truncated)

</details>

### uv-package-manager

> Master the uv package manager for fast Python dependency management, virtual environments, and modern Python project workflows. Use when setting up Python projects, managing dependencies, or optimizing Python development workflows with uv.

<details>
<summary>View skill definition</summary>

# UV Package Manager

Comprehensive guide to using uv, an extremely fast Python package installer and resolver written in Rust, for modern Python project management and dependency workflows.

## When to Use This Skill

- Setting up new Python projects quickly
- Managing Python dependencies faster than pip
- Creating and managing virtual environments
- Installing Python interpreters
- Resolving dependency conflicts efficiently
- Migrating from pip/pip-tools/poetry
- Speeding up CI/CD pipelines
- Managing monorepo Python projects
- Working with lockfiles for reproducible builds
- Optimizing Docker builds with Python dependencies

## Core Concepts

### 1. What is uv?
- **Ultra-fast package installer**: 10-100x faster than pip
- **Written in Rust**: Leverages Rust's performance
- **Drop-in pip replacement**: Compatible with pip workflows
- **Virtual environment manager**: Create and manage venvs
- **Python installer**: Download and manage Python versions
- **Resolver**: Advanced dependency resolution
- **Lockfile support**: Reproducible installations

### 2. Key Features
- Blazing fast installation speeds
- Disk space efficient with global cache
- Compatible with pip, pip-tools, poetry
- Comprehensive dependency resolution
- Cross-platform support (Linux, macOS, Windows)
- No Python required for installation
- Built-in virtual environment support

### 3. UV vs Traditional Tools
- **vs pip**: 10-100x faster, better resolver
- **vs pip-tools**: Faster, simpler, better UX
- **vs poe

...(truncated)

</details>

## Source

[View on GitHub](https://github.com/amurata/cc-tools)