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uv-package-manager

Set up Python projects and manage packages, isolated environments, and Python versions with uv. Includes migration guidance and lockfiles that record dependency versions, plus workflows for consistent installations in CI and Docker.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

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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설치 방법 보기

이럴 때 유용합니다

  • Setting up a new Python project
  • Migrating from pip or Poetry to uv
  • Keeping dependency versions consistent across environments
  • Adding uv to CI and Docker workflows

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  • SKILL.md15.7 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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

# Add from git
uv add git+https://github.com/user/repo.git

# Add from git with specific ref
uv add git+https://github.com/user/repo.git@v1.0.0

# Add from local path
uv add ./local-package

# Add editable local package
uv add -e ./local-package

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]
dev = [
    "pytest>=7.4.0",
    "pytest-cov>=4.1.0",
    "black>=23.0.0",
    "ruff>=0.1.0",
    "mypy>=1.5.0",
]
docs = [
    "sphinx>=7.0.0",
    "sphinx-rtd-theme>=1.3.0",
]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[tool.uv]
dev-dependencies = [
    # Additional dev dependencies managed by uv
]

[tool.uv.sources]
# Custom package sources
my-package = { git = "https://github.com/user/repo.git" }

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

Advanced Workflows

Pattern 12: Monorepo Support

# Project structure
# monorepo/
#   packages/
#     package-a/
#       pyproject.toml
#     package-b/
#       pyproject.toml
#   pyproject.toml (root)

# Root pyproject.toml
[tool.uv.workspace]
members = ["packages/*"]

# Install all workspace packages
uv sync

# Add workspace dependency
uv add --path ./packages/package-a

Pattern 13: CI/CD Integration

# .github/workflows/test.yml
name: Tests

on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v4

      - name: Install uv
        uses: astral-sh/setup-uv@v2
        with:
          enable-cache: true

      - name: Set up Python
        run: uv python install 3.12

      - name: Install dependencies
        run: uv sync --all-extras --dev

      - name: Run tests
        run: uv run pytest

      - name: Run linting
        run: |
          uv run ruff check .
          uv run black --check .

Pattern 14: Docker Integration

# Dockerfile
FROM python:3.12-slim

# Install uv
COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv

# Set working directory
WORKDIR /app

# Copy dependency files
COPY pyproject.toml uv.lock ./

# Install dependencies
RUN uv sync --frozen --no-dev

# Copy application code
COPY . .

# Run application
CMD ["uv", "run", "python", "app.py"]

Optimized multi-stage build:

# Multi-stage Dockerfile
FROM python:3.12-slim AS builder

# Install uv
COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv

WORKDIR /app

# Install dependencies to venv
COPY pyproject.toml uv.lock ./
RUN uv sync --frozen --no-dev --no-editable

# Runtime stage
FROM python:3.12-slim

WORKDIR /app

# Copy venv from builder
COPY --from=builder /app/.venv .venv
COPY . .

# Use venv
ENV PATH="/app/.venv/bin:$PATH"

CMD ["python", "app.py"]

Pattern 15: Lockfile Workflows

# Create lockfile (uv.lock)
uv lock

# Install from lockfile (exact versions)
uv sync --frozen

# Update lockfile without installing
uv lock --no-install

# Upgrade specific package in lock
uv lock --upgrade-package requests

# Check if lockfile is up to date
uv lock --check

# Export lockfile to requirements.txt
uv export --format requirements-txt > requirements.txt

# Export with hashes for security
uv export --format requirements-txt --hash > requirements.txt

Performance Optimization

Pattern 16: Using Global Cache

# UV automatically uses global cache at:
# Linux: ~/.cache/uv
# macOS: ~/Library/Caches/uv
# Windows: %LOCALAPPDATA%\uv\cache

# Clear cache
uv cache clean

# Check cache size
uv cache dir

Pattern 17: Parallel Installation

# UV installs packages in parallel by default

# Control parallelism
uv pip install --jobs 4 package1 package2

# No parallel (sequential)
uv pip install --jobs 1 package

Pattern 18: Offline Mode

# Install from cache only (no network)
uv pip install --offline package

# Sync from lockfile offline
uv sync --frozen --offline

Comparison with Other Tools

uv vs pip

# pip
python -m venv .venv
source .venv/bin/activate
pip install requests pandas numpy
# ~30 seconds

# uv
uv venv
uv add requests pandas numpy
# ~2 seconds (10-15x faster)

uv vs poetry

# poetry
poetry init
poetry add requests pandas
poetry install
# ~20 seconds

# uv
uv init
uv add requests pandas
uv sync
# ~3 seconds (6-7x faster)

uv vs pip-tools

# pip-tools
pip-compile requirements.in
pip-sync requirements.txt
# ~15 seconds

# uv
uv lock
uv sync --frozen
# ~2 seconds (7-8x faster)

Common Workflows

Pattern 19: Starting a New Project

# Complete workflow
uv init my-project
cd my-project

# Set Python version
uv python pin 3.12

# Add dependencies
uv add fastapi uvicorn pydantic

# Add dev dependencies
uv add --dev pytest black ruff mypy

# Create structure
mkdir -p src/my_project tests

# Run tests
uv run pytest

# Format code
uv run black .
uv run ruff check .

Pattern 20: Maintaining Existing Project

# Clone repository
git clone https://github.com/user/project.git
cd project

# Install dependencies (creates venv automatically)
uv sync

# Install with dev dependencies
uv sync --all-extras

# Update dependencies
uv lock --upgrade

# Run application
uv run python app.py

# Run tests
uv run pytest

# Add new dependency
uv add new-package

# Commit updated files
git add pyproject.toml uv.lock
git commit -m "Add new-package dependency"

Tool Integration

Pattern 21: Pre-commit Hooks

# .pre-commit-config.yaml
repos:
  - repo: local
    hooks:
      - id: uv-lock
        name: uv lock
        entry: uv lock
        language: system
        pass_filenames: false

      - id: ruff
        name: ruff
        entry: uv run ruff check --fix
        language: system
        types: [python]

      - id: black
        name: black
        entry: uv run black
        language: system
        types: [python]

Pattern 22: VS Code Integration

// .vscode/settings.json
{
  "python.defaultInterpreterPath": "${workspaceFolder}/.venv/bin/python",
  "python.terminal.activateEnvironment": true,
  "python.testing.pytestEnabled": true,
  "python.testing.pytestArgs": ["-v"],
  "python.linting.enabled": true,
  "python.formatting.provider": "black",
  "[python]": {
    "editor.defaultFormatter": "ms-python.black-formatter",
    "editor.formatOnSave": true
  }
}

Troubleshooting

Common Issues

# Issue: uv not found
# Solution: Add to PATH or reinstall
echo 'export PATH="$HOME/.cargo/bin:$PATH"' >> ~/.bashrc

# Issue: Wrong Python version
# Solution: Pin version explicitly
uv python pin 3.12
uv venv --python 3.12

# Issue: Dependency conflict
# Solution: Check resolution
uv lock --verbose

# Issue: Cache issues
# Solution: Clear cache
uv cache clean

# Issue: Lockfile out of sync
# Solution: Regenerate
uv lock --upgrade

Best Practices

Project Setup

  1. Always use lockfiles for reproducibility
  2. Pin Python version with .python-version
  3. Separate dev dependencies from production
  4. Use uv run instead of activating venv
  5. Commit uv.lock to version control
  6. Use --frozen in CI for consistent builds
  7. Leverage global cache for speed
  8. Use workspace for monorepos
  9. Export requirements.txt for compatibility
  10. Keep uv updated for latest features

Performance Tips

# Use frozen installs in CI
uv sync --frozen

# Use offline mode when possible
uv sync --offline

# Parallel operations (automatic)
# uv does this by default

# Reuse cache across environments
# uv shares cache globally

# Use lockfiles to skip resolution
uv sync --frozen  # skips resolution

Migration Guide

From pip + requirements.txt

# Before
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# After
uv venv
uv pip install -r requirements.txt
# Or better:
uv init
uv add -r requirements.txt

From Poetry

# Before
poetry install
poetry add requests

# After
uv sync
uv add requests

# Keep existing pyproject.toml
# uv reads [project] and [tool.poetry] sections

From pip-tools

# Before
pip-compile requirements.in
pip-sync requirements.txt

# After
uv lock
uv sync --frozen

Command Reference

Essential Commands

# Project management
uv init [PATH]              # Initialize project
uv add PACKAGE              # Add dependency
uv remove PACKAGE           # Remove dependency
uv sync                     # Install dependencies
uv lock                     # Create/update lockfile

# Virtual environments
uv venv [PATH]              # Create venv
uv run COMMAND              # Run in venv

# Python management
uv python install VERSION   # Install Python
uv python list              # List installed Pythons
uv python pin VERSION       # Pin Python version

# Package installation (pip-compatible)
uv pip install PACKAGE      # Install package
uv pip uninstall PACKAGE    # Uninstall package
uv pip freeze               # List installed
uv pip list                 # List packages

# Utility
uv cache clean              # Clear cache
uv cache dir                # Show cache location
uv --version                # Show version

Resources

Best Practices Summary

  1. Use uv for all new projects - Start with uv init
  2. Commit lockfiles - Ensure reproducible builds
  3. Pin Python versions - Use .python-version
  4. Use uv run - Avoid manual venv activation
  5. Leverage caching - Let uv manage global cache
  6. Use --frozen in CI - Exact reproduction
  7. Keep uv updated - Fast-moving project
  8. Use workspaces - For monorepo projects
  9. Export for compatibility - Generate requirements.txt when needed
  10. Read the docs - uv is feature-rich and evolving

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같은 저장소의 스킬

a1

무료

Turn a broad research idea into clear, testable questions. Compare familiar and less conventional approaches, structure the selected question around its population and methods, and assess variables, feasibility, and potential theoretical contributions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Turn a research topic into testable questions
  • Narrow an overly broad research question
  • Compare alternative research angles
brycewang-stanford/Auto-Empirical-Research-Skills4.7천2026년 10월 20일 업데이트

a2

무료

Helps build a research framework by comparing theories, mapping relationships between variables, and deriving testable hypotheses. Explores less conventional options, examines weaknesses and competing explanations, and produces diagrams with anticipated reviewer objections.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Comparing theories for a research question
  • Deriving hypotheses from relationships between variables
  • Examining weaknesses and anticipated reviewer objections
brycewang-stanford/Auto-Empirical-Research-Skills4.7천2026년 10월 20일 업데이트

a5

무료

Clarify the philosophical assumptions behind a research project and check whether they fit the proposed methods. Get a reasoned research stance, draft proposal wording, and guidance for explaining how the researcher's background and values shape the study.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Clarifying philosophical foundations for a research proposal
  • Explaining why the chosen research methods fit the study
  • Drafting a statement about the researcher's position
brycewang-stanford/Auto-Empirical-Research-Skills4.7천2026년 10월 20일 업데이트

Plan and draft academic papers through specialized agent roles covering literature, arguments, citations, and review. Get chapter guidance, revision roadmaps, Traditional Chinese and English abstracts, and documents formatted as LaTeX, DOCX, PDF, or Markdown.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Planning a paper chapter by chapter
  • Drafting a paper from research materials
  • Turning reviewer comments into a revision roadmap
brycewang-stanford/Auto-Empirical-Research-Skills4.7천2026년 10월 20일 업데이트

Turn a detailed outline into a philosophy or interdisciplinary manuscript. Draft chapters with repeated quality reviews, check terminology and arguments across sections, and prepare the manuscript, evaluation reports, abstract, and metadata for submission.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Draft a full paper from a detailed outline
  • Review argument and citation quality chapter by chapter
  • Check terminology and argument flow across chapters
brycewang-stanford/Auto-Empirical-Research-Skills4.7천2026년 10월 20일 업데이트

Simulate academic peer review through five reviewer roles tailored to the paper's field. Receive separate critiques, an editorial decision, and a prioritized revision roadmap, or check whether a revised manuscript addresses earlier comments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Review a manuscript before journal submission
  • Examine research methods and statistical validity
  • Challenge core arguments and alternative explanations
brycewang-stanford/Auto-Empirical-Research-Skills4.7천2026년 10월 20일 업데이트

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