How presenting skills differently improves language model agent success
SkillAlign: Aligning Skill Interfaces for LLM-based Agents
Artificial Intelligence
Summary
Large language model agents use skills to help them solve problems and interact with tools. This paper shows that how these skills are presented to the agent—like full instructions or short hints—greatly affects how well the agent performs. The authors created SkillAlign, a way to try different presentations of skills while keeping everything else the same. They found that sometimes giving less detailed skill information can actually help more than showing everything. Their work suggests that AI agents should choose not only which skills to use but also the best way to show those skills.
language model agentsskillsprocedural knowledgeskill interfaceexposure interfacetask successALFWorldSkillsBenchpolicy learningadaptive exposure
Authors
Shuo Ren, Xiaomian Kang, Jiajun Zhang
Abstract
Language-model agents increasingly rely on skills: reusable procedural knowledge for reasoning, tool use, and interaction. Existing work studies how skills are acquired, retrieved, compressed, or composed, but often assumes that once a skill is selected, its interface to the agent is fixed. We argue that this overlooks a key source of skill utility: the same skill can help, distract, or mislead depending on how it is exposed. We propose SkillAlign, a provider-agnostic framework that represents candidate skills as multi-view procedural cards and renders them through alternative exposure interfaces, including full instructions, hints, compressed summaries, workflows, or no exposure. This enables counterfactual evaluation where the task, agent, and candidate skills are fixed while only the exposure interface varies. Across ALFWorld and SkillsBench, we show that exposure form substantially affects task success and rendered context cost, and that compact top-k exposure can outperform full-library injection. We further conduct a replay-based policy-learning analysis on ALFWorld, showing that adaptive exposure contains learnable signal but remains far from oracle selection. Our results suggest that skill-augmented agents should optimize not only which skills to use, but also how those skills are presented.