AtmoSkillRSI: Skill Formation and Recursive Refinement in Atmospheric Workflows

Recursive self-improvement (RSI) raises a central question for scientific agents: how can task experience be consolidated into persistent updates that participate in subsequent adaptation? In AtmoSkillRSI, we study this question in atmospheric workflow construction using Earth2Studio, where agents build executable workflows evaluated with objective scientific metrics. Agents convert task experience and evaluation feedback into persistent Skills containing reusable procedural guidance and supporting code; these Skills are retained across tasks, guide subsequent workflow exploration, and can themselves be revised using later experience. Across experiments, we hold the base coding model, execution environment, task interface, and evaluator fixed, while examining variations in experience organization, training-case coverage, synthesis evidence, and repeated Skill reuse. We compare Sequential and Task-Parallel experience organization, evaluate Skill effectiveness across atmospheric task families and learning conditions, and track repeated Skill refinement across successive stages. The available results show that both Sequential and Task-Parallel learning improve mean RPF performance over Base, that broader training coverage does not consistently yield larger gains, and that mean performance increases across successive refinement stages under a fixed learning rule. Because later stages also incorporate additional exploration and accumulated history, these stage-wise trends are descriptive rather than a causal estimate of prior-Skill feedback. Overall, AtmoSkillRSI provides a controlled execution setting for studying persistent Skill formation and recursive refinement in scientific agents.