Why Static Skill Inconsistencies Do Not Equate to Runtime Risks: A Behavioral Analysis of LLM Agent Execution

arXiv cs.AIOctober 15, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2610.18927v1 Announce Type: new Abstract: LLM agent frameworks heavily rely on external skill assets to standardize tool usage, encode operational workflows, and supply executable runtime resources. Despite their widespread adoption, multi-component skill packages frequently suffer from cross-artifact inconsistencies, where documentation, scripts, and configuration files specify conflicting behavioral constraints. Although static auditing techniques can uncover such contractual discrepancies, static defect volume cannot reliably reflect real-world agent performance degradation. This gap leaves practitioners without rigorous evidence to assess the practical risk of flawed agent skills.

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