An Empirical Study of Agent Skills' Downstream Utility

2026-10-06

AI Summary

This study addresses the disconnect between agent skill relevance and task utility, along with the lack of mechanistic explanations, through an empirical investigation across 87 tasks. Methodologically, we define a downstream utility metric based on pass-rate differentials and employ both large language models and human review to analyze skill content, execution trajectories, and artifacts. Furthermore, we propose a re-ranking strategy supporting essential operations alongside a DAG-based dependency organization method. Results reveal that in 36.78% of tasks, identical skills exhibit opposite utility under different configurations. The proposed re-ranking strategy improves preferred pass rates by 4.35 to 5.80 percentage points. Additionally, this work distills 17 practical guidelines for effective skill authoring.

Abstract

Agent Skills package procedural guidance and resources for reuse, but a relevant Skill does not necessarily improve task performance. Existing studies characterize Skill content and evaluate downstream performance, yet provide limited explanations of how utility depends on content, execution configuration, and multi-Skill organization. We conduct an empirical study on 87 SkillsBench tasks, defining downstream utility as the pass-rate difference from No-Skill on the same tasks under the same model--harness configuration. We compare the same Skills across nine configurations, then examine alternative published Skills and organizations of fixed Skill sets under three selected configurations. We retrieve marketplace candidates from a curated corpus of 37,596 Skills. LLM-assisted analysis of content, execution traces, and final artifacts, followed by author review, relates provided support to actual use and task outcomes. The same Skills help some configurations and hurt others on 36.78\% of tasks, with trajectories showing that recommended procedures can become an execution burden. Relevance rankings overlook more useful candidates. Within the evaluated candidate sets, reranking by support for required operations raises first-choice pass rates by 4.35--5.80 percentage points across the three configurations. We derive 17 authoring practices linking executable procedures to recovery, preservation of task requirements, and checks on final artifacts. Stage Plan and Dependency DAG outperform use order alone, with DAG's additional benefits concentrated in tasks supplied with five or six Skills. These findings guide developers to assess usable operation support, allow procedure adaptation while preserving task requirements, and make artifact dependencies explicit when organizing Skills.

Problem

• Agent Skills
• Downstream Utility
• Skill Organization
• Execution Configuration
• Task Performance

Innovation

• Agent Skills
• Downstream Utility
• Empirical Study
• Reranking
• Dependency DAG