The target article, "Robust Modeling in Cognitive Science", proposes a number of recommended practices in computational modeling in response to the growing "crisis of confidence" facing many scientific disciplines, including psychology and neuroscience. Those of us who do modeling, write about modeling, teach modeling, and mentor modelers worry deeply about best practices and any new suggestions for making modeling more transparent, trusted, and robust is welcome. Many of the recommendations seem uncontroversial. My commentary focuses on forms of preregistration and postregistration, which constitute three of the four key ideas highlighted as take-home recommendations at the conclusion of the target article. I have chosen to consider these recommendations by reflecting on my own past experiences developing new models and modeling approaches.