name: python-venv description: "Python environment management skill. Automatically detect project type and existing environments, recommend based on popularity. Minimize interruptions, only ask when necessary."

Python Environment Management Skill

Core Principles

1. Reuse Existing Environments - Don't recreate, reuse existing virtual environments
2. Use Project-Type Decision - Auto-select based on lock files
3. Recommend by Popularity - uv > pip > conda > venv
4. Minimize Interruption - Only ask when necessary

   7w4.net收錄了海量優質技能外掛。

Tool Popularity Ranking

Decision Flow

┌─────────────────────────────────────┐
│  Detect project dependency files     │
└─────────────────────────────────────┘
              ↓
    ┌─────────┴─────────┐
    ↓                   ↓
  Clear decision       Unclear
    ↓                   ↓
  Use directly     Detect existing env
                        ↓
                  ┌─────┴─────┐
                  ↓           ↓
              Has env        No env
                  ↓           ↓
              Reuse      Assess complexity
                            ↓
                  ┌─────────┴─────────┐
                  ↓                   ↓
              Simple task       Needs deps
                  ↓                   ↓
            System Python      Recommend uv/conda

1. Clear Decisions (Execute Directly, No Ask)

When these files are detected, use the corresponding tool directly:

2. Detect Existing Environments (Reuse First)

# Priority: uv venv > conda > venv

# 2.1 Detect uv virtual environment
ls -la .venv/ 2>/dev/null && uv pip list 2>/dev/null | head -3

# 2.2 Detect conda environment
conda info --envs 2>/dev/null | grep "*" || echo $CONDA_PREFIX

# 2.3 Detect standard venv
ls -la venv/ .venv/ env/ 2>/dev/null

# 2.4 If exists → Reuse (activate and run commands)

Reuse Example:

Detected existing .venv/ directory
→ Activate: source .venv/bin/activate
→ Run: uv pip install <package>

3. When Unclear (Assess Complexity)

4. When to Ask User (Only These Cases)

✅ Ask: 1. Empty project + first dependency install → Ask which tool 2. Both requirements.txt + pyproject.toml → Ask which to use 3. User explicitly wants different tool → e.g., "I want conda"

❌ Don't Ask: - Has uv.lock but user didn't specify - Has .venv/ directory - Regular pip install task

5. Recommended Tool (No Clear Directive)

First: uv
  ├── uv venv (create)
  ├── uv pip install (install)
  └── uv sync (sync)

Backup: pip
  ├── python3 -m venv .venv
  └── pip install

Special: conda
  ├── conda create -n envname python=x.x
  └── conda env create

Detection Commands

# Check available tools
which uv
which conda
which pip
which python3

# Check project files
ls -la *.lock pyproject.toml requirements.txt environment.yml Pipfile 2>/dev/null

# Check existing environments
ls -la .venv/ venv/ env/ 2>/dev/null
conda info --envs 2>/dev/null

# Check current environment
echo $VIRTUAL_ENV
echo $CONDA_PREFIX

Interaction Examples (Only When Needed)

🔍 Detection result:
- Project file: pyproject.toml
- Existing env: None
- Recommended: uv (fastest)

Running: uv pip install <package>

🔍 Detection result:
- Project file: requirements.txt
- Existing env: None
- Recommended: uv

Available options:
1) uv (recommended) - faster
2) pip - better compatibility
3) venv - uses stdlib
4) conda - if specific version needed

Enter option or press Enter to use recommended:

Quick Command Reference

Core Principle

"Do more, ask less" - Execute directly when you can determine, only ask when truly unclear.

質量較好，這個 Skill 提供了一套清晰的 Python 環境管理方法，能自動檢測專案型別並推薦合適工具，減少使用者決策負擔。文件結構完善，跨平臺支援良好，故障排除指南實用。但目前只有文件說明，缺少可直接執行的自動化程式碼，實際使用仍需手動執行命令。