Python Venv

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

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Tool Popularity Ranking

Priority	Tool	Best For
🥇	uv	New projects, fast installs
🥈	pip	Compatibility first
🥉	conda	Data science, specific versions
4	venv	Built-in, no extra install
5	poetry	Existing poetry.lock
6	pipenv	Existing Pipfile (declining)

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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

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1. Clear Decisions (Execute Directly, No Ask)

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

Detected File	Execute
uv.lock exists	uv sync or uv pip install -r requirements.txt
poetry.lock exists	poetry install
environment.yml exists	conda env create -f environment.yml
Pipfile.lock exists	pipenv install

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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>

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3. When Unclear (Assess Complexity)

Scenario	Action
Stdlib only, no 3rd party	System Python (python3)
Simple pip install test	System Python (temp)
Has requirements.txt	Recommend uv > pip > venv
Has pyproject.toml	Recommend uv > pip
Multi-file project, needs isolation	Recommend uv

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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

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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

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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

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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:

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Quick Command Reference

Action	uv	pip	conda	venv
Create env	uv venv	-	conda create	python3 -m venv
Install pkg	uv pip install	pip install	conda install	pip install
Install deps	uv sync	pip install -r	conda env create	pip install -r
Activate	(auto)	(auto)	conda activate	source venv/bin/activate

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Core Principle

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