AI

An Empirical Study of Agent Skills' Downstream Utility

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Briefings

An empirical study analyzed 87 SkillsBench tasks using LLM-assisted analysis of 37,596 marketplace Skills to evaluate downstream utility. The research found that Skills help some configurations and hurt others on 36.78% of tasks, with recommended procedures becoming an execution burden. Reranking by support for required operations increased first-choice pass rates by 4.35 to 5.80 percentage points across nine configurations.

Key points

• The study analyzed 87 SkillsBench tasks using LLM-assisted analysis of 37,596 marketplace Skills.
• Skills help some configurations and hurt others on 36.78% of tasks, with recommended procedures becoming an execution burden.

Context

This research addresses a gap in existing studies that characterize Skill content but lack explanations of how utility depends on configuration and organization.

Limitations

Evidence is limited to 87 SkillsBench tasks and 37,596 marketplace Skills; findings are based on LLM analysis and author review, not fully verified by human evaluation.

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