LLM Agent Skill Consistency Analysis: Bridging Static Defects and Runtime Risks

Summary:This research presents a systematic empirical study on the runtime implications of skill inconsistencies in LLM agent frameworks. By conducting large-scale audits of 575 community and benchmark skills, the researchers identified 1,182 manually validated contract inconsistencies. They further built a controlled evaluation pipeline with 49 executable agent tasks and three representative LLMs across NoSkill, OriginalSkill, and FixedSkill settings. Through fine-grained behavioral analysis of 441 pai

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Background and Motivation

LLM agent frameworks rely on external skill packages to standardize tool usage, encode operational workflows, and provide executable runtime resources. However, multi-component skill packages often suffer from cross-artifact inconsistencies, such as conflicts between documentation, scripts, and configuration files. While static auditing techniques can uncover these contractual discrepancies, the volume of static defects cannot reliably predict the performance degradation of agents in real-world scenarios. This assessment gap leaves practitioners without rigorous evidence to evaluate the practical risk of flawed agent skills.

Methodology

The study employs the following steps for systematic empirical analysis:

1. Large-Scale Auditing: Auditing 575 community and benchmark skills, resulting in 1,182 manually validated contract inconsistencies.
2. Controlled Evaluation Pipeline: Constructing a pipeline with 49 executable tasks and testing with three representative LLMs across NoSkill, OriginalSkill, and FixedSkill settings.
3. Fine-Grained Behavioral Analysis: Conducting fine-grained behavioral analysis on 441 paired execution trajectories to quantify the impact of resolved defects on actual performance.

Key Findings

1. Relationship between Static Defects and Runtime Risks: The study finds that while the number of static defects indicates some issues, it does not fully predict the performance degradation of agents in real-world scenarios.
2. Effectiveness of Skill Repair: In the FixedSkill setting, the task resolution rate and efficiency of agents significantly improved, demonstrating the positive impact of skill repair on agent performance.
3. Quantification of Performance Impact: The fine-grained behavioral analysis allows the researchers to quantify the specific impact of different types of defects on agent performance, providing data support for subsequent skill optimization.

Engineering Trade-offs and Recommendations

1. Combine Static Auditing with Dynamic Testing: It is recommended to combine static auditing with dynamic testing in the development process to more comprehensively assess the quality of skill packages.
2. Incremental Skill Repair: Adopt an incremental approach to skill repair, first addressing defects that have a significant impact on performance, and then gradually optimizing other issues.
3. Develop Automated Tools: It is suggested to develop automated tools to detect and repair inconsistencies in skill packages, improving development efficiency and reducing human error.

Conclusion

This study, through empirical analysis, reveals the relationship between static defects and runtime risks in LLM agent skill packages, providing a scientific basis for the practical risk assessment of agent skills. Future research can further explore more efficient skill repair methods and more comprehensive evaluation metrics to enhance the reliability and performance of agents in real-world applications.

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Source: ArXiv AI (cs.AI) (2026-10-15)

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Tags: #LLMs & Foundation Models Follow #Agentic Follow #Skill Consistency Follow #Behavioral Analysis Follow #AI Research Follow