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uv-package-manageruv 包管理器

Agent Skill

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。

总安装

306

周安装

13

GitHub Stars

公开资料未说明

下载量

107
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

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/philoserf/claude-code-setup
仓库路径:skills/uv-package-manager
安装命令:
npx skills add philoserf/claude-code-setup --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。

AgentSkills.tonpx skills
npx skills add philoserf/claude-code-setup --skill "uv-package-manager"

简介

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。

  • 适用于包管理、依赖分析和环境配置等开发支持类任务。
  • 通过 npx skills add philoserf/claude-code-setup --skill "uv-package-manager" 命令安装。
  • 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。
  • 可结合来源仓库和原始 README 继续核验具体用法和功能边界。

SKILL.md

name
uv-package-manager
description
Expert in uv, the ultra-fast Python package manager and project tool. Use when setting up Python projects, managing dependencies, creating virtual environments, installing Python versions, working with lockfiles, migrating from pip/poetry/pip-tools, or optimizing Python workflows with uv's blazing-fast performance.
allowed-tools

Reference Files

Detailed uv guidance organized by topic:


UV Package Manager

Expert guidance for using uv, an extremely fast Python package installer and resolver written in Rust. Provides 10-100x faster installation than pip with drop-in compatibility, virtual environment management, Python version management, and modern lockfile support.

Focus Areas

  • Ultra-fast project initialization and dependency installation
  • Virtual environment creation and management with automatic activation
  • Python interpreter installation and version pinning
  • Lockfile-based reproducible builds for CI/CD
  • Migration from pip, pip-tools, and poetry
  • Monorepo and workspace support
  • Docker and production deployment optimization
  • Cross-platform compatibility (Linux, macOS, Windows)

Core Approach

Essential patterns for effective uv usage:

Project initialization:

  • Use uv init for new projects (creates pyproject.toml, .python-version, .gitignore)
  • Use uv sync to install from existing pyproject.toml
  • Pin Python versions with uv python pin 3.12
  • Always commit uv.lock for reproducible builds

Virtual environments:

  • Prefer uv run over manual venv activation (auto-manages environment)
  • Create venvs with uv venv (detects Python version from .python-version)
  • Use uv venv --python 3.12 for specific versions

Package management:

  • Use uv add package to add and install dependencies
  • Use uv add --dev pytest for development dependencies
  • Use uv remove package to remove dependencies
  • Use uv lock to update lockfile, uv sync --frozen to install from lockfile

Reproducible builds:

  • Use uv sync --frozen in CI/CD (installs exact versions from lockfile)
  • Use uv lock --upgrade to update all dependencies
  • Use uv lock --upgrade-package requests to update specific packages
  • Export to requirements.txt with uv export --format requirements-txt

Performance optimization:

  • Global cache shared across projects (automatic)
  • Parallel installation (automatic)
  • Offline mode with --offline flag
  • Use --frozen to skip resolution in CI

Quality Checklist

Before deploying uv-based projects:

  • uv.lock committed to version control for reproducible builds
  • .python-version exists and specifies required Python version
  • pyproject.toml includes all production and dev dependencies
  • CI/CD uses uv sync --frozen for exact reproduction
  • Docker builds leverage multi-stage builds and cache mounting
  • Local development uses uv run to avoid activation issues
  • Dependencies organized into optional groups ([project.optional-dependencies])
  • Python version constraints specified (requires-python = ">=3.8")
  • Security: uv export with --require-hashes for production lockdown
  • Documentation explains uv installation for new contributors

Output

Production-ready deliverables:

  • Initialized projects with pyproject.toml and uv.lock
  • Virtual environments configured for development
  • CI/CD workflows using uv for fast, reproducible builds
  • Dockerfiles optimized for uv with caching and multi-stage builds
  • Migration scripts and documentation for team adoption
  • Requirements.txt exports for compatibility when needed

Common Workflows

Starting a New Project

# Initialize project
uv init my-project
cd my-project

# Pin Python version
uv python pin 3.12

# Add dependencies
uv add fastapi uvicorn pydantic

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

# Run application
uv run python -m my_project

# Run tests
uv run pytest

Working with Existing Project

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

# Install dependencies (auto-creates venv)
uv sync

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

# Run application
uv run python app.py

Updating Dependencies

# Update all dependencies
uv lock --upgrade
uv sync

# Update specific package
uv lock --upgrade-package requests
uv sync

# View outdated packages
uv tree --outdated

CI/CD Integration

# Install uv in CI
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install exact dependencies from lockfile
uv sync --frozen --no-dev

# Run tests
uv run pytest

Key Advantages Over Alternatives

vs pip:

  • 10-100x faster installation
  • Built-in virtual environment support
  • Better dependency resolution
  • Lockfile support (uv.lock)

vs poetry:

  • Significantly faster (6-8x)
  • Less opinionated, simpler workflows
  • Compatible with standard pyproject.toml
  • Lighter weight, no Python required for install

vs pip-tools:

  • Faster compilation (7-8x)
  • Integrated venv and Python management
  • Better UX with uv add/uv remove
  • Single tool for entire workflow

Safety and Best Practices

Version control:

  • Always commit uv.lock for reproducibility
  • Commit .python-version for consistency
  • Never commit .venv directory

CI/CD:

  • Use --frozen flag to prevent unexpected updates
  • Pin uv version in CI for consistency
  • Cache uv's global cache directory for speed

Security:

  • Use uv export --require-hashes for supply chain security
  • Review dependency updates before applying
  • Use uv tree to audit dependency graph

Development:

  • Use uv run instead of activating venvs
  • Create separate optional dependency groups for different use cases
  • Test with minimal dependencies before adding extras

Tool Integration

Pre-commit hooks:

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

VS Code:

// .vscode/settings.json
{
  "python.defaultInterpreterPath": "${workspaceFolder}/.venv/bin/python",
  "python.terminal.activateEnvironment": true
}

GitHub Actions:

- uses: astral-sh/setup-uv@v2
  with:
    enable-cache: true
- run: uv sync --frozen
- run: uv run pytest

Troubleshooting

Common issues and solutions:

# uv not found after install
echo 'export PATH="$HOME/.cargo/bin:$PATH"' >> ~/.bashrc
source ~/.bashrc

# Wrong Python version
uv python pin 3.12
uv venv --python 3.12

# Lockfile out of sync
uv lock --upgrade

# Cache issues
uv cache clean

# Dependency conflicts
uv lock --verbose  # See resolution details

Where to Find What

Resources

  • Official documentation: <https://docs.astral.sh/uv/>
  • GitHub repository: <https://github.com/astral-sh/uv>
  • Migration guides: <https://docs.astral.sh/uv/guides/>
  • Comparison with other tools: <https://docs.astral.sh/uv/pip/compatibility/>

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenCode

27.08%
按下载量换算29

Claude Code

25.1%
按下载量换算27

Antigravity

19.27%
按下载量换算21

Gemini CLI

14.32%
按下载量换算15

windsurf

8.8%
按下载量换算9

kiro-cli

3.44%
按下载量换算4

安全审计

暂无安全审计结果可展示。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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