Model-Generated Agent Skills (paper)

Systematic Study of Model-Generated Agent Skills Across the Full Skill Lifecycle

This paper presents a utility-grounded evaluation framework for model-generated agent skills, covering the full lifecycle of experience generation, skill extraction, and skill consumption across five agentic task domains. The authors find that while such skills are beneficial on average, they exhibit non-trivial negative transfer, and that skill utility is independent of model scale or baseline task strength. A key finding is that strong extractors are not necessarily strong consumers and vice versa. The work culminates in a 'meta-skill' that guides extraction toward utility-correlated features, consistently improving skill quality and reducing negative transfer.