Importantly, we emphasize that library data can be dynamically regenerated with different spectral processing parameters and filters to enable precise control of the generated library entries. This capability is expected to improve model training and enable the customization of libraries for specific machine learning models. All necessary information for regenerating the MS n library, namely the data, compound metadata, and mzmine workflow configuration, is openly shared. This is a paradigm shift in the generation of spectral libraries, leading to greater traceability of the library entries to raw data using the Universal Spectrum Identifier (USI) with transparent processing parameters. A recent study validated our library quality by checking compound and spectral metadata in all public spectral libraries. These checks removed over 31,000 spectra each from GNPS, MoNA and MassBankEU, while MS n Lib failed checks for 58 entries. This shows that automatically curated libraries may improve the overall quality compared to historically grown libraries that relied on manual curation by contributors and often lost links to the processing history and original raw data.