SkillDAG introduces a novel approach for selecting skills in large language model (LLM) agents by modeling inter-skill relationships as a dynamic directed graph, enhancing retrieval accuracy and adaptability during execution. This innovation has shown significant performance improvements over existing methods, indicating a strong potential for advancing LLM capabilities in complex task environments.