Back to the 1920s, the Sabatier principle was proposed which had revolutionized the way how scientists evaluated the performance of a catalyst: 2 a good heterogeneous catalyst should neither bind the adsorbate too strongly, nor too weakly, to reach the optimal activity (Figure ). In the 2000s, Jens K. Nørskov, 3 a pioneer of modern catalysis theory development, successfully quantified and visualized the Sabatier principle by volcano activity models coupling the identified scaling relations and microkinetic modeling based upon density functional theory (DFT) calculations. This type of models can depict the intrinsic activity optima and trends of a catalytic reaction as a function of the reaction descriptors, i.e., the bonding strengths of key reaction species, which can reduce the high-dimension catalytic problem into one-or twodimension. Over the past two decades, tremendous follow-up works have corroborated that as long as it is judiciously derived (e.g., with rational computational methods and parameters, well-defined mechanism, and precise linear scaling relations), a catalytic volcano model can be precisely developed and can exhibit strong concordance with experimental measurements. Therefore, when designing an active catalytic site, one can only provide the information about the bonding strength of the key adsorbate(s) over the site, and the model can predict the corresponding activity in the form of turnover frequency (TOF), Gmax (i.e., the maximum free energy needs to be overcome along the favorable reaction pathway), or overpotential. This offers a substantial advantage for researchers by obviating the need for resource-intensive experimental trial-and-error process, thereby enabling a more streamlined approach for catalyst design. Nonetheless, a significant challenge arises in that many of these volcano activity models are derived via a complicated process, which could be a hard task for beginners in catalysis theory research and experimentalists. Although there are some available packages for microkinetic modeling, e.g., the CatMap package, 30 these tools often necessitate a degree of proficiency in programming languages (e.g., Python), thereby requiring users to undergo preliminary training prior to effective utilization. This significantly hampers the widespread employment of rational catalyst design and understanding before/after experiments.