Skill libraries allow LLM agents to load task-specific instructions on demand, letting non-expert users solve domain-specific tasks through natural language without knowing which skills exist or how they work. However, performance degrades as libraries grow -- by up to 21\% when scaling from a small set of helpful skills to a 202-skill library. In this work, we formulate this performance degradation as the pass rate drop between loading a library of known-helpful skills and the full library. Moreover, we propose to decompose the pass rate drop by conditioning on the skill(s) invocation -- which skills the agent selects during a trajectory -- into two effects: \emph{skill shadowing}, where the agent selects wrong skills more often as the library expands, and \emph{context overhead}, where the enlarged context degrades execution even when selection is correct. We derive upper bounds on both effects to characterize their magnitudes of impacts to the pass rate drop. Our empirical estimates of the effects and their upper bounds both show that the \emph{skill shadowing} effect grows with library size and significantly contributes to the performance degradation, whereas the \emph{context overhead} effect remains small and indistinguishable from zero. This observed asymmetry establishes that the skill selection failure, not the enlarged context, is the primary bottleneck when expanding the skill libraries.

翻译：技能库允许大语言模型智能体按需加载特定任务的指令，使非专业用户能够通过自然语言解决领域特定任务，而无需了解技能的存在方式或运作机制。然而，随着技能库规模扩大，性能会下降——从一组少量有效技能扩展到202项技能时，性能降幅高达21%。本研究将这种性能退化定义为在加载已知有效技能库与完整技能库之间的通过率下降。此外，我们提出通过条件化技能调用（即智能体在轨迹中选用的技能组合）将通过率下降分解为两种效应：技能遮蔽效应（随着技能库扩展，智能体更频繁地选择错误技能）和上下文开销效应（即使选择正确，扩大的上下文也会降低执行效果）。我们推导出两种效应的上界，以表征它们对通过率下降的影响程度。对两种效应及其上界的经验估计均表明：技能遮蔽效应随技能库规模增长而增强，是导致性能退化的主要因素；而上下文开销效应始终微小且不显著。这种观测到的不对称性证实：在扩展技能库时，技能选择失败而非上下文扩大才是主要瓶颈。