More Skills, Worse Agents? Skill Shadowing Degrades Performance When Expanding Skill Libraries
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...
| Authors | Hongwen Song, Song Wei |
|---|---|
| Primary category | cs.SE |
| Published | 21 May 2026 |
| Last updated at source | 23 June 2026 |
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