During November 2024, Di Wang developed core automated musical score analysis features for the music-computing/amads repository, focusing on monophonic scores. Leveraging Python and expertise in algorithmic composition and music information retrieval, Di implemented boundary detection and segmentgestalt algorithms to identify local boundaries and analyze pitch and temporal relationships within scores. Basic reliability tests were added to ensure the robustness of these new features. This work established a foundational layer for downstream processes such as transcription, segmentation, and analysis, enabling more efficient and accurate data extraction. The depth of the implementation supports broader analytics workflows in automated music analysis.