uv-package-manager

Name: uv-package-manager - Agent Skill
Rating: 5.0 (26449 reviews)
Author: wshobson

26,449

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.

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CLI Install

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Use the skills CLI to install this skill with one command. Auto-detects all installed AI assistants.

Method 1 - skills CLI

npx skills i wshobson/agents/plugins/python-development/skills/uv-package-manager

Method 2 - openskills (supports sync & update)

npx openskills install wshobson/agents

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Installation Path

Download and extract to one of the following locations:

~/.claude/skills/uv-package-manager/

SKILL.md

references

Skill Instructions

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Claude Sonnet 4.5

$0.20/task

Best for coding tasks

Environment setup included

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 poetry: Faster, less opinionated, lighter
vs conda: Faster, Python-focused

Installation

Quick Install

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
 
# Windows (PowerShell)
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
 
# Using pip (if you already have Python)
pip install uv
 
# Using Homebrew (macOS)
brew install uv
 
# Using cargo (if you have Rust)
cargo install --git https://github.com/astral-sh/uv uv

Verify Installation

uv --version
# uv 0.x.x

Quick Start

Create a New Project

# Create new project with virtual environment
uv init my-project
cd my-project
 
# Or create in current directory
uv init .
 
# Initialize creates:
# - .python-version (Python version)
# - pyproject.toml (project config)
# - README.md
# - .gitignore

Install Dependencies

# Install packages (creates venv if needed)
uv add requests pandas
 
# Install dev dependencies
uv add --dev pytest black ruff
 
# Install from requirements.txt
uv pip install -r requirements.txt
 
# Install from pyproject.toml
uv sync

Virtual Environment Management

Pattern 1: Creating Virtual Environments

# Create virtual environment with uv
uv venv
 
# Create with specific Python version
uv venv --python 3.12
 
# Create with custom name
uv venv my-env
 
# Create with system site packages
uv venv --system-site-packages
 
# Specify location
uv venv /path/to/venv

Pattern 2: Activating Virtual Environments

# Linux/macOS
source .venv/bin/activate
 
# Windows (Command Prompt)
.venv\Scripts\activate.bat
 
# Windows (PowerShell)
.venv\Scripts\Activate.ps1
 
# Or use uv run (no activation needed)
uv run python script.py
uv run pytest

Pattern 3: Using uv run

# Run Python script (auto-activates venv)
uv run python app.py
 
# Run installed CLI tool
uv run black .
uv run pytest
 
# Run with specific Python version
uv run --python 3.11 python script.py
 
# Pass arguments
uv run python script.py --arg value

Package Management

Pattern 4: Adding Dependencies

# Add package (adds to pyproject.toml)
uv add requests
 
# Add with version constraint
uv add "django>=4.0,<5.0"
 
# Add multiple packages
uv add numpy pandas matplotlib
 
# Add dev dependency
uv add --dev pytest pytest-cov
 
# Add optional dependency group
uv add --optional docs sphinx

Pattern 5: Removing Dependencies

# Remove package
uv remove requests
 
# Remove dev dependency
uv remove --dev pytest
 
# Remove multiple packages
uv remove numpy pandas matplotlib

Pattern 6: Upgrading Dependencies

# Upgrade specific package
uv add --upgrade requests
 
# Upgrade all packages
uv sync --upgrade
 
# Upgrade package to latest
uv add --upgrade requests
 
# Show what would be upgraded
uv tree --outdated

Pattern 7: Locking Dependencies

# Generate uv.lock file
uv lock
 
# Update lock file
uv lock --upgrade
 
# Lock without installing
uv lock --no-install
 
# Lock specific package
uv lock --upgrade-package requests

Python Version Management

Pattern 8: Installing Python Versions

# Install Python version
uv python install 3.12
 
# Install multiple versions
uv python install 3.11 3.12 3.13
 
# Install latest version
uv python install
 
# List installed versions
uv python list
 
# Find available versions
uv python list --all-versions

Pattern 9: Setting Python Version

# Set Python version for project
uv python pin 3.12
 
# This creates/updates .python-version file
 
# Use specific Python version for command
uv --python 3.11 run python script.py
 
# Create venv with specific version
uv venv --python 3.12

Project Configuration

Pattern 10: pyproject.toml with uv

[project]
name = "my-project"
version = "0.1.0"
description = "My awesome project"
readme = "README.md"
requires-python = ">=3.8"
dependencies = [
    "requests>=2.31.0",
    "pydantic>=2.0.0",
    "click>=8.1.0",
]
 
[project.optional-dependencies

Pattern 11: Using uv with Existing Projects

# Migrate from requirements.txt
uv add -r requirements.txt
 
# Migrate from poetry
# Already have pyproject.toml, just use:
uv sync
 
# Export to requirements.txt
uv pip freeze > requirements.txt
 
# Export with hashes
uv pip freeze --require-hashes > requirements.txt

For advanced workflows including Docker integration, lockfile management, performance optimization, tool comparison, common workflows, tool integration, troubleshooting, best practices, migration guides, and command reference, see references/advanced-patterns.md