When identifying peptides and proteins in liquid-chromatography -tandem mass spectrometry (LC-MS/MS) data, proteomics search engines usually calculate a single score to represent the quality of each identification. The function that is used to generate this score is in most cases static and based on first principles and expert knowledge of peptide-spectrum matching. In addition to the search engine score, rescoring methods have emerged, with Percolator as one of the most prominent examples. By including additional information, such as peptide length, charge, missed cleavages, and mass accuracy, a dynamic scoring function is generated to better separate putatively correct and incorrect identifications with machine learning. In recent years, it has been repeatedly shown that extending this method by adding information from machine learning-based predictions of peptide LC-MS/MS behavior further improves peptide identification strategies. This is especially true for more challenging proteomics workflows, such as immunopeptidomics, proteogenomics, biopeptidomics, and metaproteomics. Unfortunately, implementing these advanced methods into routine proteomics data analysis workflows has remained difficult. Indeed, data-driven rescoring tools are often difficult to use due to complex installation procedures, specialized hardware requirements, the use of custom file formats, or even poorly documented code. Others are closed source or locked within a single proteomics data analysis pipeline.