BackgroundComputational models of cell signaling networks are extremely useful tools for the exploration of underlying system behavior and prediction of response to various perturbations. By representing signaling cascades as executable Boolean networks, the previously developedrxncon(“reaction-contingency”) formalism and associated Python package enable accurate and scalable modeling of signal transduction even in large (thousands of components) biological systems. The models are split into reactions, which generate states, and contingencies, that impinge on reactions; this avoids the so-called “combinatorial explosion” of system size. Boolean description of the biological system compensates for the poor availability of kinetic parameters which are necessary for quantitative models. Unfortunately, few tools are available to supportrxnconmodel development, especially for large, intricate systems.ResultsWe present thekboolnettoolkit (https://github.com/Kufalab-UCSD/kboolnet, complete documentation athttps://github.com/Kufalab-UCSD/kboolnet/wiki), an R package and a set of scripts that seamlessly integrate with the python-basedrxnconsoftware and collectively provide a complete workflow for the verification, validation, and visualization ofrxnconmodels. The verification scriptVerifyModel.Rchecks for responsiveness to repeated stimulations as well as consistency of steady state behavior. The validation scriptsTruthTable.R,SensitivityAnalysis.R, andScoreNet.Rprovide various readouts for the comparison of model predictions to experimental data. In particular,ScoreNet.Rcompares model predictions to a cloud-storedMIDAS-format experimental database to provide a numerical score for tracking model accuracy. Finally, the visualization scripts allow for graphical representations of model topology and behavior. The entirekboolnettoolkit is cloud-enabled, allowing for easy collaborative development; most scripts also allow for the extraction and analysis of individual user-defined “modules”.ConclusionThekboolnettoolkit provides a modular, cloud-enabled workflow for the development ofrxnconmodels, as well as their verification, validation, and visualization. This will enable the creation of larger, more comprehensive, and more rigorous models of cell signaling using therxnconformalism in the future.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12859-023-05329-6.