Decades of studies identify personality traits as an important predictor of life outcomes. However, previous investigations of personality-outcome associations have not taken a principled approach to covariate use or other sampling strategies to ensure the robustness of personality-outcome associations. The result is that it is unclear (1) whether personality predicts important outcomes after accounting for a range of background variables, (2) for whom and when personality predictions hold, and 3) which background variables are most important to account for. The present study examines the robustness and boundary conditions of personality prediction using the Big Five to predict 14 health, social, education/work, and societal outcomes across eight different person- and study-level moderators using individual participant data from 171,395 individuals across 10 longitudinal panel studies in a mega-analytic framework. Robustness and boundary conditions were systematically tested using two approaches: propensity score matching and specification curve analysis. Three findings emerged: First, personality traits remain a robust predictor of life outcomes. Second, the effects generalize, as there are few moderators of personality-outcome associations. Third, robustness was differential across covariate choice in nearly half of the tested models, with the inclusion or exclusion of some of these flipping the direction of association. In sum, personality is a powerful predictor of life outcomes with few moderated associations. However, researchers need to be careful in their choices of covariates. We discuss how these findings can inform personality prediction, as well as recommendations for covariate inclusion.