Worked on the Smithsonian/layup repository to enhance the reliability and usability of Jupyter notebooks by refining code block formatting and improving notebook hygiene. Focused on converting Python code from markdown to executable cells, tightening numeric formatting for consistent execution, and removing unused cells to streamline demonstration notebooks. Addressed a formatting regression in the Orbit Visualizer notebook, restoring the intended structure between markdown and code cells. Leveraged Python, Markdown, and Jupyter Notebook skills to improve maintainability, reproducibility, and onboarding for contributors. The work reduced execution errors, improved readability, and ensured that customer-facing data visualization demos remained robust and easy to maintain.