python-reproducibility-guide
Reproducible Python environments, notebooks, and literate programming
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill python-reproducibility-guide --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Python Reproducibility Guide Set up reproducible Python environments for research computing, using virtual environments, dependency management, Jupyter notebooks, and literate programming practices. ## Environment Management ### Virtual Environments ```bash # Option 1: venv (built-in, lightweight) python -m venv .venv source .venv/bin/activate # macOS/Linux # .venv\Scripts\activate # Windows pip install -r requirements.txt # Option 2: conda (includes non-Python dependencies) conda create -n myproject python=3.11 conda activate myproject conda install numpy pandas scipy matplotlib conda env export > environment.yml # Option 3: uv (fast, modern Python package manager) uv venv source .venv/bin/activate uv pip install -r requirements.txt ``` ### Dependency Pinning ```bash # requirements.txt with exact versions (pip freeze) pip freeze > requirements.txt # Better: use pip-tools for compiled dependencies pip install pip-tools # Create requirements.in (human-readable, loose constraints) cat > requirements.in << 'EOF' numpy>=1.24 pandas>=2.0 scipy>=1.11 matplotlib>=3.7 scikit-learn>=1.3 EOF # Compile to requirements.txt (pinned, reproducible) pip-compile requirements.in --output-file requ
- Environment Management
- Virtual Environments
- Dependency Pinning
- pyproject.toml (Modern Standard)
- Jupyter Notebooks for Research
- Best Practices
- Notebook Structure Template
- Converting Notebooks to Scripts
- Reproducible Random Seeds
- Containerization with Docker
- Dockerfile for Research
- Project Structure
- Makefile for Automation
- Logging and Experiment Tracking
Option 1: venv (built-in, lightweight) python -m venv .venv source .venv/bin/activate # macOS/Linux pip install -r requirements.txt Option 2: conda (includes non-Python dependencies) conda create -n myproject python=3.11 conda activate myproject conda install numpy pandas scipy matplotlib conda env export > environment.yml Option 3: uv (fast, modern Python package manager)
What does the python-reproducibility-guide skill do?
Reproducible Python environments, notebooks, and literate programming
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill python-reproducibility-guide --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.