To evaluate the fit of a confirmatory factor analysis model, researchers often rely on fit indices such as SRMR, RMSEA, and CFI. These indices are frequently compared to benchmark values of 0.08, 0.06, and 0.96, respectively, established by Hu and Bentler (1999). However, these indices are affected by model characteristics and their sensitivity to misfit can change across models. Decisions about model fit can therefore be improved by tailoring cutoffs to each model. The methodological literature has proposed methods for deriving customized cutoffs, although it can require knowledge of linear algebra and Monte Carlo simulation. Given that many empirical researchers do not have training in these technical areas, empirical studies largely continue to rely on fixed benchmarks even though they are known to generalize poorly and can be poor arbiters of fit. To address this, this paper introduces the R package, "dynamic," to make computation of dynamic fit index cutoffs (which are tailored to the user's model) more accessible to empirical researchers. "dynamic" heavily automatizes this process and only requires a "lavaan" object to automatically conduct several custom Monte Carlo simulations and output fit index cutoffs designed to be sensitive to misfit with the user's model characteristics. [This paper was published in "Multivariate Behavioral Research" v58 n1 p189-194 2023.]