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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 magnitude","title":"More Skills, Worse Agents? 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