Developed an enhancement for the music-computing/amads repository by implementing a new feature that calculates the relative mode of musical scores using pitch-class distribution analysis. This work involved designing and integrating a Python module, relative_mode.py, which processes musical data to improve the precision of mode analysis across diverse scores. The approach leveraged Python programming and data processing techniques to strengthen the library’s analytics capabilities, supporting more robust tonal analysis for both academic research and application development. Throughout the month, the developer focused on disciplined, commit-driven delivery, ensuring seamless integration of the new function into the existing music analysis workflow.