Instead of treating skill selection as separate retrieval and ordering problems, jointly predicting skill sequences as a single structured decision improves agent performance and reduces token costs.
This paper tackles how LLM agents should select and order skills (reusable procedural knowledge packages) when solving complex tasks. The authors propose SkillComposer, which treats skill selection as a structured prediction problem—jointly deciding which skills to use, how many times, and in what order.