View PDF ↗
PDF Viewer

Loading PDF...

This may take a moment

Open Full PDF

BUILDER'S SANDBOX

Core Pattern

AI-generated implementation pattern based on this paper's core methodology.

Implementation pattern included in full analysis above.

MVP Investment

$10K - $14K
6-10 weeks
Engineering
$8,000
GPU Compute
$800
LLM API Credits
$500
SaaS Stack
$300
Domain & Legal
$100

6mo ROI

1-2x

3yr ROI

10-25x

Automation tools have long sales cycles but high retention. Expect $5K MRR by 6mo, accelerating to $500K+ ARR at 3yr as enterprises adopt.

Talent Scout

X

Xiangyi Li

BenchFlow

W

Wenbo Chen

Amazon

Y

Yimin Liu

Ohio State University

S

Shenghan Zheng

Dartmouth College

Find Similar Experts

Agents experts on LinkedIn & GitHub

Founder's Pitch

"SkillsBench evaluates the effectiveness of procedural Skills in boosting LLM agent task performance."

AgentsScore: 8View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

3/4 signals

7.5

Series A Potential

3/4 signals

7.5

🔭 Research Neighborhood

Generating constellation...

~3-8 seconds

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

Han-chung Lee

References (40)

[1]
Establishing Best Practices for Building Rigorous Agentic Benchmarks
2025Yuxuan Zhu, Tengjun Jin et al.
[2]
SWE-smith: Scaling Data for Software Engineering Agents
2025John Yang, Kilian Adriano Lieret et al.
[3]
MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
2024Jun Shern Chan, Neil Chowdhury et al.
[4]
Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models
2024Andy K. Zhang, Neil Perry et al.
[5]
AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents
2024H. Trivedi, Tushar Khot et al.
[6]
BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions
2024Terry Yue Zhuo, Minh Chien Vu et al.
[7]
τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains
2024Shunyu Yao, Noah Shinn et al.
[8]
SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
2024John Yang, Carlos E. Jimenez et al.
[9]
OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
2024Tianbao Xie, Danyang Zhang et al.
[10]
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
2024Wei-Lin Chiang, Lianmin Zheng et al.
[11]
VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks
2024Jing Yu Koh, Robert Lo et al.
[12]
SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
2023Carlos E. Jimenez, John Yang et al.
[13]
Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models
2023Andy Zhou, Kai Yan et al.
[14]
DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
2023O. Khattab, Arnav Singhvi et al.
[15]
Cognitive Architectures for Language Agents
2023T. Sumers, Shunyu Yao et al.
[16]
AgentBench: Evaluating LLMs as Agents
2023Xiao Liu, Hao Yu et al.
[17]
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
2023Yujia Qin, Shi Liang et al.
[18]
WebArena: A Realistic Web Environment for Building Autonomous Agents
2023Shuyan Zhou, Frank F. Xu et al.
[19]
InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback
2023John Yang, Akshara Prabhakar et al.
[20]
Voyager: An Open-Ended Embodied Agent with Large Language Models
2023Guanzhi Wang, Yuqi Xie et al.

Showing 20 of 40 references