# Code Science

Scientific programming best practices including reproducible research, computational...

- Repository: https://github.com/beita6969/ScienceClaw (1024 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add beita6969/ScienceClaw --skill code-science -g -a claude-code -y`
Codex: `npx skills add beita6969/ScienceClaw --skill code-science -g -a codex -y`
Cursor: `npx skills add beita6969/ScienceClaw --skill code-science -g -a cursor -y`
Gemini CLI: `npx skills add beita6969/ScienceClaw --skill code-science -g -a gemini-cli -y`

## Description

Scientific programming best practices including reproducible research, computational notebooks, version control for research, data management, HPC/parallel computing, and research software engineering. Use when user needs help with research code organization, reproducibility, scientific Python/R workflows, or computational infrastructure. Triggers on "reproducible research", "research code", "scientific computing", "HPC", "parallel computing", "Jupyter", "notebook", "data management plan", "research software", "code review for science".

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Scientific Programming

Best practices for research software and reproducible computation.

## Project Structure

```
project/
├── README.md              # Project overview, how to reproduce
├── LICENSE                 # MIT, Apache 2.0, or GPL
├── requirements.txt       # or environment.yml (conda)
├── setup.py / pyproject.toml
├── data/
│   ├── raw/               # Never modify raw data
│   ├── processed/         # Cleaned/transformed data
│   └── external/          # Third-party data
├── src/ or scripts/
│   ├── data_processing.py
│   ├── analysis.py
│   ├── models.py
│   └── visualization.py
├── notebooks/             # Exploratory analysis
│   ├── 01_eda.ipynb
│   ├── 02_modeling.ipynb
│   └── 03_figures.ipynb
├── results/
│   ├── figures/
│   └── tables/
├── tests/
└── docs/
```

## Reproducibility Checklist

1. **Environment**: Pin all dependencies with versions
   ```bash
   pip freeze > requirements.txt
   # or conda
   conda env export > environment.yml
   ```

2. **Random seeds**: Set and document all random seeds
   ```python
   import numpy as np
   import random
   SEED = 42
   np.random.seed(SEED)
   random.seed(SEED)
   # torch.manual_seed(SEED)
   # tf.random.set_seed(SEED)
   ```

3. **Data versioning**: Use DVC or git-lfs for large data
   ```bash
   dvc init
   dvc add data/raw/dataset.csv
   git add data/raw/dataset.csv.dvc
   ```

4. **Configuration**: Separate config from code
   ```python
   # config.yaml
   # experiment:
   #   learning_rate: 0.001
   #   batch_size: 32
   #   epochs: 100
   import yaml
   with open('config.yaml') as f:
       config = yaml.safe_load(f)
   ```

5. **Logging**: Record all experiments
   ```python
   import logging
   logging.basicConfig(level=logging.INFO, 
                       format='%(asctime)s %(levelname)s: %(message)s',
                       filename='experiment.log')
   ```

## Parallel Computing

```python
# Multiprocessing (CPU-bound)
from multiprocessing import Pool
import numpy as np

The rest: https://github.com/beita6969/ScienceClaw/blob/HEAD/skills/code-science/SKILL.md