Traditionally, data reuse efforts have focused on individual or a few well-known datasets. However, recent approaches leveraging entire metabolomics repositories have yielded significant biological discoveries. For instance, advanced data searching tools such as the Mass Spectrometry Search Tool (MASST) and Mass Spectrometry Query Language (MassQL) have enabled the expansion of bile acid diversity from hundreds to thousands through the use of the public GNPS/MassIVE repository . These endeavors have linked discoveries to microbial producers, organ distributions, interventions, such as antibiotics or dietary changes, and their associations with different health conditions. Additionally, the indexing and metadata curation efforts at GNPS have provided the foundation for projects such as microbeMASST , which enables linking known and unknown microbial metabolites to putative producers. These proof-of-principle projects have unveiled the potential of data mining at the repository scale. However, each project demanded extensive efforts to collect and harmonize metadata, index file paths, convert data formats, and integrate multiple pre-existing data processing pipelines. Naturally, these tasks necessitated combined expertise in computational metabolomics and software engineering. Moreover, the technical and computational infrastructures required to enable repository scale analyses present substantial barriers. Combined, these challenges become more pronounced when integrating data from multiple repositories.