Co-evolutionary algorithms have successfully trained agent teams for tasks such as autonomous exploration or robot soccer. However generally, such approaches seek a single strong team, whereas many real-world applications require agents to effectively cooperate across multiple teams. To adapt to different teammates, agents need to learn more general teamwork skills rather than a single team-specific role. Previous work primarily frames this as a fitness-shaping problem, providing high-quality but expensive evaluation methods to isolate an agent's contribution. In this work, we introduce Learned Evaluations for Robust Teaming (LERT), an approach that provides a local evaluation that leverages state trajectories of agents to better quantify their impact across multiple teams. The key insight of this work is that agent state trajectories and previous experiences carry sufficient information to map agent abilities to team performance. As a result, LERT cooperatively co-evolves agents to work together across arbitrary teams. While only using local information and significantly fewer team evaluations, LERT performs as well as-if not better than-current methods.