jupyter-notebook-guide

npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill jupyter-notebook-guide --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# Jupyter Notebook Guide A skill for using Jupyter notebooks effectively in research contexts. Covers notebook organization, reproducibility best practices, collaboration workflows, and integration with research computing infrastructure. ## Notebook Organization ### Recommended Structure Every research notebook should follow a consistent structure: ``` 01_data_collection.ipynb # Data acquisition and initial storage 02_data_cleaning.ipynb # Preprocessing, validation, transformations 03_exploratory_analysis.ipynb # EDA, descriptive statistics, initial plots 04_modeling.ipynb # Model training, evaluation, selection 05_results_visualization.ipynb # Publication-quality figures 06_supplementary.ipynb # Additional analyses, robustness checks ``` ### Cell Organization Within a Notebook ```python # === CELL 1: Header and metadata === """ # Analysis: Effect of Treatment on Outcome Variable Author: [Name] Date: 2026-03-09 Data: experiment_results_v2.csv Dependencies: pandas>=2.0, scipy>=1.11, matplotlib>=3.8 """ # === CELL 2: Imports and configuration === import pandas as pd import numpy as np import matplotlib.pyplot as plt from scipy import stats # Reproducibility np.random.seed(42) pd.set_

Create environment from scratch
conda create -n research python=3.11
conda activate research
Install and pin
pip install pandas==2.1.4 scipy==1.11.4 matplotlib==3.8.2 jupyterlab==4.0.9
Export for reproducibility
pip freeze > requirements.txt
Or use conda
conda env export --no-builds > environment.yml
Execute with different parameters