Cyclic voltammetry is one of the most common electrochemical characterization techniques and it generates valuable mechanistic information for redox-active chemical systems. For decades, cyclic voltammetry has been indispensable for electrochemical applications in sensing, energystorage, chemical transformations, and beyond; however, the general protocol of initial mechanistic analysis after experiments has remained largely unchanged since its inception. Researchers manually inspect the shapes and variations of cyclic voltammograms under multiple different scan rates (v), sometimes with different reactant concentrations, and subsequently hypothesize a qualitative mechanism consisting of interfacial charge transfers (E step) and/or solution reactions (C steps), before sometimes extracting quantitative kinetic information via additional experiments and/or numerical simulations. However, such manual inspection demands extensive researcher training, potentially incurs human bias, and is not compatible with automated testing needed for high-throughput screening. An algorithm that automatically analyzes cyclic voltammograms and qualitatively categorizes electrochemical systems into mechanisms with a specific combination of E and/or C steps will help alleviate the aforementioned challenges in manual analysis of cyclic voltammograms.