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This paper introduces Agent Priors-guided Policy Learning (APPL), a framework where construction and runtime agents use structural priors—describing what a behavior depends on—as an interface between skill learning and skill composition, improving out-of-distribution skill generalization on MetaWorld and long-horizon ManiSkill tasks.