Is the pipeline reproducible across different machines?

Yes, the skill enforces rules like using relative paths and loading configurations from a central YAML file to ensure scripts work regardless of the local environment.

How do I mark cells in my Python scripts for conversion?

Use the standard '# %%' marker for code cells and '# %% [markdown]' for documentation cells to ensure the conversion tools recognize the structure.

Can I run these scripts without Jupyter installed?

Yes, Stage 1 scripts are designed to be the 'source of truth' and are fully runnable as standalone Python scripts using standard imports.

What are the four stages of the pipeline?

The pipeline moves from standalone Python scripts (Stage 1) to interactive Jupyter notebooks (Stage 2), then to full markdown content (Stage 3), and finally to documentation with ASCII graphs (Stage 4).

How does this skill help with code documentation?

It automatically generates markdown files from notebooks and can trigger an LLM to read your script and create ASCII workflow diagrams and function trees for quick understanding.

Notebook Analysis Pipeline

Name: Notebook Analysis Pipeline
Author: jluo41

byjluo41

0•

Data Science & ML

Manages a robust four-stage pipeline that converts modular Python scripts into interactive Jupyter notebooks and comprehensive markdown documentation.

The Notebook Analysis Pipeline skill streamlines academic and data science workflows by orchestrating a transition from modular Python scripts to interactive Jupyter notebooks and finally to searchable markdown documentation. By enforcing a 'source of truth' approach using standardized cell markers (# %%), it ensures that research analysis is reproducible, easy to debug, and automatically documented. The skill also leverages LLMs to generate ASCII workflow diagrams and function trees, providing a high-level visual understanding of complex code structures without requiring manual diagramming.

Key Features

01Automated notebook generation and markdown conversion via nbconvert integration

02LLM-powered generation of ASCII workflow diagrams and function documentation

030 GitHub stars

04Standardized 4-stage pipeline: Script to Notebook to Markdown to Documentation

05Configuration-driven analysis support with relative path handling for reproducibility

06Cell-based structure using # %% markers for seamless script-to-notebook parity

Use Cases

01Converting experimental Python scripts into interactive environments for step-by-step debugging

02Setting up structured evaluation pipelines for machine learning model comparisons

03Generating automated, visual documentation for complex research repositories

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jluo41/research-skills notebook-cell-python

For use in Claude.ai and ChatGPT

Download Skill