Reconfiguring Procedural Knowledge for Compositional Robot Skill Adaptation

General-purpose behavior models increasingly internalize broad procedural knowledge at training time, making adaptation less a problem of learning entirely new behaviors than of reconfiguring existing competence for new goals, object relations, and skill interfaces. We study this as reconfigurability, the ability to realize new behaviors by restructuring components and relations cost-efficiently, under sparse governing information, where a task specification provides high-level goals or abstract directives but omits the concrete bindings and compositions required for execution. We propose RecPo, a framework that represents skills task-agnostically through multiple procedure views and materializes a new task by composing only the transition relations consistent with the task-relevant views. RecPo maintains a procedure knowledge base of local transition relations within each view. At deployment time, it interprets a task demonstration through the same views, converts observed view-level changes into a compositional query, and resolves the query into an executable skill sequence. On an extended Franka Kitchen benchmark with held-out long-horizon tasks outside the stored compositions, RecPo achieves 91.7\% ordered subtask completion, substantially outperforming non-reconfigurable skill retrieval baselines. Real-robot case studies further show deployment-time adaptation through recomposition rather than task-specific retraining.