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Agent Skills Can Be Harmful: An Empirical Study of Skill-Induced Failures in LLM Agents

arXiv cs.AI LLM Agents Gen Dong, Yanjie Gao, Liqun Li, Tianyin Xu, Yu Hua, Fan Yang 2026-08-12

TL;DR - This paper analyzes how reusable skills can cause LLM-agent failures and higher execution costs. Across two benchmarks, it identifies 307 skill-induced regressions and introduces SkillTriage for evidence-based failure attribution.

  • The study finds 125 functional failures and 182 efficiency regressions.
  • Seemingly relevant skills can cause agents to implement requirements incorrectly or omit them.
  • Efficiency regressions are not attributable to prompt length alone.
  • Excessive verification and heavy implementation pipelines account for 67 and 30 excessive-procedure cases, respectively.

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