Agent skill

Uv Package Manager

by Yikai-Liao in Yikai-Liao/symusic

Master the uv package manager for fast Python dependency management, virtual environments, and modern Python project workflows.

MITAuto-check: notesDevelopment

Install Uv Package Manager

skills CLI
$ npx skills add Yikai-Liao/symusic --skill uv-package-manager -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Yikai-Liao/symusic uv-package-manager --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Yikai-Liao/symusic.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/wshobson-agents-uv-package-manager .claude/skills/uv-package-manager && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
uv-package-manager
GitHub stars
189
Used in
16 other repos
Token cost
~4k tokens
SKILL.md length
519 words
Files
1
Skills in repo
11
Repo updated
First seen
Licence
MIT

At a glance

Master the uv package manager for fast Python dependency management, virtual environments, and modern Python project workflows.

  • Works in 3 steps: What is uv? → Key Features → UV vs Traditional Tools
  • Setting up Python projects
  • SKILL.md covers When to Use This Skill, Core Concepts, Installation and Quick Start, plus 5 more sections
  • Calls uv, poetry and pip; reaches github.com and astral.sh

What it does

Uv Package Manager is an agent skill from Yikai-Liao/symusic. 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.

Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Dependency management. It works with Python and Rust. The repository describes itself as: A swift and unified toolkit for symbolic music processing. The licence is MIT.

When your agent uses it

  • Setting up Python projects
  • Managing dependencies
  • Optimizing Python development workflows with uv

Example prompts

  • “/uv-package-manager”

Requirements

  • Python 3
  • Docker

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. What is uv?
  2. Key Features
  3. UV vs Traditional Tools

What it can do on your machine

Read from SKILL.md and the folder at commit 3cdd0ee. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • uv
    • poetry
    • pip
    • git
    • python
    • curl
    • sh
    • brew
    • cargo

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com
    • astral.sh

    Also links to:

    • docs.astral.sh

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Uv Package Manager loads about 4k tokens when it runs. Until then it costs about 65 tokens; SKILL.md has 519 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~65
When it runs · the whole SKILL.md, loaded when a task matches
~4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePipes a well-known installer script into a shellSKILL.md:58
    curl -LsSf https://astral.sh/uv/install.sh | sh
  • NotePipes a well-known installer script into a shellSKILL.md:61
    powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Yikai-Liao/symusic at commit 3cdd0ee, republished under its MIT licence (© Yikai-Liao). 519 words, ~4,013 tokens.

Download SKILL.mdSave it as .claude/skills/uv-package-manager/SKILL.md (or your agent's skills folder).
name
uv-package-manager
description
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.

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
bash
# 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
bash
uv --version
# uv 0.x.x

Quick Start

Create a New Project
bash
# 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
bash
# 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
bash
# 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
bash
# 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
bash
# 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
bash
# 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
bash
# Remove package
uv remove requests

# Remove dev dependency
uv remove --dev pytest

# Remove multiple packages
uv remove numpy pandas matplotlib
Pattern 6: Upgrading Dependencies
bash
# 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
bash
# 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
bash
# 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
bash
# 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
toml
[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
bash
# 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
bash
# 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
yaml
# .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
# 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:

dockerfile
# 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
bash
# 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
bash
# 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
bash
# 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
bash
# 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
bash
# 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
bash
# 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)
Show full SKILL.md (208 more words)Show less
uv vs pip-tools
bash
# 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
bash
# 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
bash
# 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
yaml
# .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
json
// .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
bash
# 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
bash
# 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
bash
# 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
bash
# 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
bash
# Before
pip-compile requirements.in
pip-sync requirements.txt

# After
uv lock
uv sync --frozen

Command Reference

Essential Commands
bash
# 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

© Yikai-Liao, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/wshobson-agents-uv-package-manager of Yikai-Liao/symusic.

Open the folder on GitHubat commit 3cdd0ee

Used in 16 other repositories

We found 33 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 16 other GitHub owners. This page covers the copy in Yikai-Liao/symusic, which our catalogue first saw on October 7, 2026.

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Works with

Questions about Uv Package Manager

What does Uv Package Manager do?

Master the uv package manager for fast Python dependency management, virtual environments, and modern Python project workflows. Uv Package Manager is an agent skill from Yikai-Liao/symusic. Master the uv package manager for fast Python dependency management, virtual environments, and modern Python project workflows.

When should I use Uv Package Manager?

Uv Package Manager fits situations like: setting up Python projects; managing dependencies; optimizing Python development workflows with uv.

How do I install Uv Package Manager in Claude Code?

Run `npx skills add Yikai-Liao/symusic --skill uv-package-manager -a claude-code`. Or copy the skill folder (.agents/skills/wshobson-agents-uv-package-manager in Yikai-Liao/symusic) into .claude/skills/uv-package-manager in your project. Claude Code loads it when a task matches its description.

How do I install Uv Package Manager in Codex?

Run `npx skills add Yikai-Liao/symusic --skill uv-package-manager -a codex`. Or copy the skill folder (.agents/skills/wshobson-agents-uv-package-manager in Yikai-Liao/symusic) into .agents/skills/uv-package-manager in your project. Codex loads it when a task matches its description.

Can I use Uv Package Manager in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Yikai-Liao/symusic --skill uv-package-manager -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/uv-package-manager, .gemini/skills/uv-package-manager, .github/skills/uv-package-manager and .opencode/skills/uv-package-manager in your project.

What does Uv Package Manager need to run?

Going by SKILL.md and its folder, Uv Package Manager needs the command-line tools its instructions call (uv, poetry, pip, git, python and curl). Our summary lists: Python 3; Docker.

Does Uv Package Manager access the network?

SKILL.md names 3 domains. In commands or code: github.com and astral.sh; the agent is likely to contact these when it follows the instructions. As links in the text: docs.astral.sh. This is read from the text; nothing was executed.

Is Uv Package Manager safe to install?

Our automated static check of SKILL.md found notes only (pipes a well-known installer script into a shell), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Uv Package Manager use?

Uv Package Manager is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Uv Package Manager use?

About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Uv Package Manager?

Skills that share tags, products or a category with Uv Package Manager: Flowfile Build and Environment Setup (Edwardvaneechoud/Flowfile, 375 stars), Update V8 Version (openinterpreter/openinterpreter, 69k stars), Next Python Stdlib Upgrade Picker (RustPython/RustPython, 22k stars) and Dep Auditor (laolaoshiren/claude-code-skills-zh, 879 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Uv Package Manager?

Yikai-Liao (a GitHub user) maintains it in Yikai-Liao/symusic, which has 189 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on August 11, 2026.

Source: Yikai-Liao/symusic on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.