python-env

name	python-env
description	Create and maintain Python environments and dependencies with uv. Use when installing packages, creating a virtual environment, resolving Python dependency state, or migrating away from pip. Not for general Python coding.
allowed-tools	Bash(uv*), Bash(uv:*), Bash(mkdir*), Bash(ls*)

Python Environment Management

CRITICAL RULE: Never use pip directly. Always use uv. This applies to all Python package management.

Golden Rule

ALWAYS use uv for Python package and environment management. Never use pip directly.

Commands

Task	Command
Create venv	uv venv
Install package	uv pip install <package>
Install from requirements	uv pip install -r requirements.txt
Run script in project	uv run python script.py
Run with dependencies	uv run --with pandas python script.py
Install CLI tool globally	uv tool install <tool>
Sync project deps	uv sync
Add dependency	uv add <package>

Project Setup

For new projects:

uv init
uv add <dependencies>
uv sync

For existing projects with pyproject.toml:

uv sync
uv run python main.py

Rules

1. Never use pip install — always uv pip install or uv add
2. Never install globally — use uv tool install for CLI tools
3. Always work in a venv — created by uv venv or uv sync
4. Use uv run — to execute scripts within the project environment

Typical Project

For a project with a Python entry point:

cd <project>
uv sync                           # Install dependencies
uv run python scripts/task.py     # Run a project script

On [HPC cluster] HPC

Avon uses Miniconda3 + Lmod (not uv) because cluster users need to compose with module load CUDA/12.6.0 and other pre-built modules. The project-specific pattern is hpc/env-setup.sh (conda create + pip install) — see docs/guides/hpc.md and reference implementations under Projects/NLP/{example-project-a,benchmark-gaming-llm-safety}/hpc/env-setup.sh. The local dev env still uses uv; HPC gets its own conda env with identical pins. Don't try to port uv to Avon — the module system assumes conda.