  This study introduces a novel software tool leveraging large language model
(LLM) prompts, designed to automate the generation of academic articles from
Python code a significant advancement in the fields of biomedical informatics
and computer science. Selected for its widespread adoption and analytical
versatility, Python served as a foundational proof of concept; however, the
underlying methodology and framework exhibit adaptability across various GitHub
repo's underlining the tool's broad applicability (Harper 2024). By mitigating
the traditionally time-intensive academic writing process, particularly in
synthesizing complex datasets and coding outputs, this approach signifies a
monumental leap towards streamlining research dissemination. The development
was achieved without reliance on advanced language model agents, ensuring high
fidelity in the automated generation of coherent and comprehensive academic
content. This exploration not only validates the successful application and
efficiency of the software but also projects how future integration of LLM
agents which could amplify its capabilities, propelling towards a future where
scientific findings are disseminated more swiftly and accessibly.
