As AI agents become more sophisticated, the quality of their underlying skills becomes critical. This article proposes a five-layer evidence chain for evaluating agent skills: format compliance, semantic correctness, engineering relationships, dynamic effects, and delivery closure. The core insight is that a skill can be perfectly formatted and still fail in real-world tasks. The framework encourages developers to validate skills not just in isolation but within the context of an entire agent system, considering how skills interact and perform under dynamic conditions. This holistic approach helps identify weaknesses that static checks miss, leading to more reliable and effective AI agents. For engineering teams building agent-based solutions, adopting such an evaluation framework can significantly improve the quality and reliability of their deployments.
A structured approach to evaluating AI agent skills, moving from static format checks to dynamic real-task validation across five layers of evidence.