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Agent Skill · 研究检索

uv-package-manager(uv 包管理器)

uv-package-manager 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

uv-package-manager 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 36,940。

总安装 1,835周安装 78GitHub Stars 3,492下载 643核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:uv-package-manager(uv 包管理器)
来源仓库:https://github.com/galaxy-dawn/claude-scholar
仓库路径:skills/uv-package-manager
安装命令:
npx skills add https://github.com/galaxy-dawn/claude-scholar --skill uv-package-manager
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/galaxy-dawn/claude-scholar --skill uv-package-manager

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息,支持基于关键词快速定位候选结果。适用于需要根据任务场景或来源线索在 Codex、Claude 等平台中获取信息时。安装方式:GitHub 仓库,命令为 npx skills add galaxy-dawn/claude-scholar --skill uv-package-manager。

使用前建议核对原始 README 以确认具体用法和功能边界。注意权限范围和维护状态,避免触发联网、命令执行或文件读写操作。

来源仓库内路径:skills/uv-package-manager

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/galaxy-dawn/claude-scholar --skill uv-package-…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

uv-package-manager 怎么安装?

在终端执行 npx skills add https://github.com/galaxy-dawn/claude-scholar --skill uv-package-manager。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

uv-package-manager 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

uv-package-manager 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

uv-package-manager 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

uv-package-manager 用的人多吗?

公开数据是累计安装 1,835 次,近一周安装 78 次,下载 643 次,GitHub 3,492 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

uv-package-manager 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 来源仓库有 3,492 星,维护活跃度相对可查。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
uv-package-manager

uv 包管理器 · 本页

1,8353,492需要联网未明确未通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

平台分布

Codex

36.22%
按下载量换算233

Claude

29.11%
按下载量换算187

Cursor

16.46%
按下载量换算106

Gemini CLI

9.3%
按下载量换算60

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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