Cross-Layer Misalignment Detection in Agent Skills: A Progressive Loading-Aware Contrastive Learning Approach

Jul 16, 2026

Upload by Miles

Large language model (LLM) agents extend through Agent Skills—reusable artifacts that bundle natural-language metadata, procedural instructions, and execution-time resources. As skill marketplaces grow, short descriptions increasingly guide selection, yet inconsistencies between stated behavior and actual supported capabilities create “cross-layer misalignment.” Progressive Loading-Aware Hierarchical Contrastive Learning (PL-HCL) models Agent Skill layer structure and learns cross-layer consistency. On a normalized dataset of 264,000+ open-source skills with a human-verified challenge set, PL-HCL raises Macro-F1 from ~0.45 to 0.87–0.89 across evaluated LLM backbones.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

Abstract

Introduction

Cross-layer misalignment and progressive loading

Why pre-execution evaluation is necessary

Proposed PL-HCL framework overview

Cross-Layer Misalignment Detection in Agent Skills: A Progressive Loading-Aware Contrastive Learning Approach Chengjun Zhang [cz1@iu.edu](cz1@iu.edu) Indiana University Bloomington Bloomington, Indiana, USA Yang Gao [gaoyang@iu.edu](gaoyang@iu.edu)[ ](gaoyang@iu.edu)Indiana University Bloomington Bloomington, Indiana, USA Jianna Hur [jhur06@iu.edu](jhur06@iu.edu)[ ](jhur06@iu.edu)Indiana University Bloomington Bloomington, Indiana, USA Jingjing Zhang [jjzhang@iu.edu](jjzhang@iu.edu) Indiana University Bloomington Bloomington, Indiana, USA Sagar Samtani [ssamtani@iu.edu](ssamtani@iu.edu)[ ](ssamtani@iu.edu)Indiana University Bloomington Bloomington, Indiana, USA arXiv :2607 . 10534v 1 [ cs .AI] 12 Jul 2026 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 LoadingAware Hierarchical Contrastive Learning (PL-HCL), an LLMbased 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 anda 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. Keywords Agent Skills, agentic AI, trustworthiness, cross-layer misalignment, contrastive learning, continued pretraining 1 Introduction Large Language Models (LLMs) are evolving from single-turn text generators into agentic Artificial Intelligence (AI) systems capable of reasoning, tool use, and interaction with external environments [24, 32] . Agent Skills have become essential supply-side components of agentic AI systems to extend an agent’s capabilities [1, 17] . Agent Skills encapsulate reusable procedural knowledge into loadable modules, which typically include natural language instructions, scripts, and resources to support specific tasks or workflows [1] . In contrast to standard prompts or standalone tools, Agent Skills are hierarchical artifacts in which a surface metadata layer sits above layers of instructions, code, files, and executables. Users and agents access metadata before progressively accessing deeper components. This progressive loading mechanism introduces the risk that the metadata used for skill selection may not correspond to the behaviors actually supported or enabled by the underlying components [6, 7, 33] . For example, a skill may claim in its metadata to be a harmless productivity assistant while containing prompt injection, credential leakage, data exfiltration, or insecure command execution within its instruction or script layers [18, 19] . Conversely, a skill may claim to support a specific capability in its description, yet fail to implement it within its instructions or resources. This reflects a broader documentation-implementation consistency problem, where natural-language descriptions can diverge from the executable or procedural artifacts they are intended to summarize [20, 28, 36] . In Agent Skill packages, such inconsistency can be described as cross-layer misalignment. In this case, user-facing claims may diverge from the behaviors supported, requested, or enabled by the underlying instructions, resources, and executable components. This mismatch warrants attention because trust decisions regarding Agent Skills are frequently made before the package is ful

FAQs

What problem does cross-layer misalignment describe in Agent Skills?

Cross-layer misalignment occurs when user-facing metadata claims diverge from the actual behaviors supported by deeper instruction, resource, and executable components inside an Agent Skill package.

How does PL-HCL detect misalignment before execution?

PL-HCL evaluates skills in a pre-execution setting by comparing heterogeneous evidence across metadata, instructions, and resources using a progressive loading-aware hierarchical contrastive learning framework.

What performance improvement does the proposed approach achieve?

Using a normalized corpus of 264,000+ open-source skills and a human-verified challenge set, PL-HCL improves Macro-F1 from about 0.45 for unadapted baselines to 0.87–0.89 across evaluated LLM backbones.

Jul 16, 2026

Upload by Miles