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References (40)
Founder's Pitch
"SkillsBench evaluates the effectiveness of procedural Skills in boosting LLM agent task performance."
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Why It Matters
SkillsBench addresses a critical gap in AI agent research by systematically evaluating the contribution of procedural skills to task performance, allowing developers to better understand when and how these skills can optimize AI behavior.
Product Angle
To productize SkillsBench, one could develop a SaaS platform offering a customizable set of Skills tailored to enhance various AI applications in industry-specific workflows, leveraging the benchmark's results for validation and improvement.
Disruption
SkillsBench could disrupt the AI model evaluation space by setting a new standard for assessing augmentation strategies, shifting focus from raw model capabilities to the strategic enhancement of tasks via skills.
Product Opportunity
Organizations deploying AI agents across industries such as healthcare, finance, and engineering could benefit from using a benchmarking service to improve and validate AI skill applicability, ensuring increased efficiency and accuracy, thereby justifying investment.
Use Case Idea
An enterprise AI toolkit that recommends and customizes procedural Skills for optimizing AI agent performance in specific domains like healthcare or software engineering.
Science
The paper introduces SkillsBench, a benchmark suite consisting of 86 tasks across 11 domains designed to evaluate the efficacy of procedural Skills in enhancing AI agent task performance. SkillsBench assesses task success in three configurations: without Skills, with curated Skills, and with self-generated Skills. The analysis shows that curated Skills notably increase task success rates, highlighting the utility of procedural knowledge in LLM operations.
Method & Eval
The benchmark involves testing AI agents on tasks across multiple configurations: without Skills, with curated Skills, and self-generated Skills. Performance is measured in 7,308 trajectories across varying model-agent setups, demonstrating that curated Skills boost pass rates, particularly in domains like healthcare.
Caveats
While the benchmark highlights the benefits of procedural Skills, it shows variability in efficacy across domains, and self-generated Skills often underperform, which can limit reliance on autonomous skill development by AI agents.
Author Intelligence
Xiangyi Li
BenchFlow
xiangyi@benchflow.ai
Wenbo Chen
Amazon
Yimin Liu
Ohio State University
Shenghan Zheng
Dartmouth College
Xiaokun Chen
Stanford University
Yifeng He
UC Davis
Yubo Li
Carnegie Mellon University
Bingran You
UC Berkeley
Haotian Shen
Independent
Jiankai Sun
Shuyi Wang
Qunhong Zeng
Beijing Institute of Technology
Di Wang
Foxconn
Xuandong Zhao
UC Berkeley
Yuanli Wang
Boston University
Roey Ben Chaim
Zenity
Zonglin Di
UC Santa Cruz
Yipeng Gao
USC
Junwei He
ByteDance
Yizhuo He
Carnegie Mellon University
Liqiang Jing
UT Dallas
Luyang Kong
Xin Lan
Michigan State University
Jiachen Li
UT Austin
Songlin Li
Yijiang Li
UC San Diego
Yueqian Lin
Duke University
Xinyi Liu
Xuanqing Liu
Haoran Lyu
Ze Ma
Columbia University
Bowei Wang
Runhui Wang
Tianyu Wang
Wengao Ye
University of Oxford
Yue Zhang
UT Dallas
Hanwen Xing
Yiqi Xue
USC
Steven Dillmann
Stanford University