Skills Are Executor-Relative: Diagnosing and Bridging the Writer-Executor Gap

Language agents reuse skills written by other models, but these skills do not always transfer effectively. We identify the Writer–Executor Gap: different executors benefit from different skills, and skills produced by a stronger writer do not necessarily lead to better execution. Task success alone does not explain why this gap occurs or how to address it. We propose SKILLDIAG, a diagnostic method grounded in an information-theoretic view of skill transfer, to examine three sources of failure: insufficient evidence in the original experience, important information omitted during skill writing, and difficulty using information already present in the skill. We further introduce SKILLTUTOR, which uses the executor’s failure feedback to clarify and reorganize skill instructions, helping the executor use them more effectively. We conduct experiments with multiple language models across ALFWorld, WebShop and DAPO-Math. The results reveal the Writer–Executor Gap across diverse settings and show that SKILLTUTOR can improve skill use and task success. Our work provides a framework for understanding and improving skill transfer through diagnosis and adaptation to the executor.