Cross-Layer Misalignment Detection in Agent Skills: A Progressive Loading-Aware Contrastive Learning Approach
2026-07-12 • Artificial Intelligence
Artificial IntelligenceCryptography and SecurityMachine Learning
AI summaryⓘ
The authors studied how large language model agents use reusable components called Agent Skills, which come with short descriptions but may not always behave as described. They call the problem of mismatch between a skill’s description and real behavior cross-layer misalignment. To fix this, they developed a method named PL-HCL that checks consistency between different parts of a skill by learning how they relate to each other. Testing on a large set of skills showed their method greatly improved detection of mismatches. This helps users avoid unreliable skills and guides better design of layered software components.
Large Language ModelsAgent SkillsCross-Layer MisalignmentContrastive LearningHierarchical ModelingNatural Language MetadataSkill MarketplacesMacro-F1 Score
Authors
Chengjun Zhang, Yang Gao, Jianna Hur, Jingjing Zhang, Sagar Samtani
Abstract
Large language model (LLM) agents are increasingly extended through Agent Skills, reusable artifacts that package natural-language metadata, procedural instructions, and execution-time resources for runtime use. As open-source skill marketplaces expand, users and agents increasingly rely on brief metadata to select third-party skills, making it difficult to detect inconsistencies between a skill's description and its true behavior, a problem we call cross-layer misalignment. To address this issue, we propose Progressive Loading-Aware Hierarchical Contrastive Learning (PL-HCL), an LLM-based framework that detects misalignment by modeling the layered structure of Agent Skills and learning cross-layer consistency. Using a normalized corpus of over 264,000 open-source skills and a human-verified challenge set, PL-HCL improves Macro-F1 from approximately 0.45 for unadapted baselines to 0.87-0.89 across evaluated LLM backbones. This approach offers an effective screening tool for users and operators, as well as design principles for detecting inconsistencies in layered digital artifacts.