Subtask-level skills expressed as text transfer more reliably across tasks than task-level or code-based skills, and you can predict skill usefulness by balancing how specific it is versus how broadly applicable it is.
This paper studies how LLM agents can learn and reuse skills from completed tasks. The researchers compare different ways of capturing skills—at the task level versus subtask level, and as text versus code—and find that breaking skills into subtasks and expressing them as text leads to better transfer across new tasks.