arXiv AI October 2, 2026 By Zongrui Yang, Li Xintong, Runchen Xu, Zhongsheng Wang, Zhedong Lin, Haoyuan Li, Jiamou Liu

MCRI: A Four-Dimensional Framework for Analyzing and Evaluating Agent Skills

The paper introduces MCRI, a four‑dimensional framework for analyzing and evaluating agent skills, and implements it as MCRI‑Eval, a large‑language‑model‑based evaluation method. Using 63,812 public skills from OpenClaw and 58,275 skill‑conditioned executions across BigCodeBench, BFCL‑Fundamental, and Mind2Web, MCRI‑Eval’s scores correlate with community popularity and outperform other methods in downstream ranking agreement. The evaluation also shows that MCRI‑Eval improves top‑1 skill selection by 17.7, 22.8, and 19.6 percentile points on the three benchmarks, offering a valuable pre‑execution signal for prioritizing promising skills before costly evaluation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 17

A Framework for Evaluating Agentic Skills at Scale

arXiv:2606. 17819v1 Announce Type: cross Abstract: Agent skills -- structured, reusable knowledge artifacts that augment LLM agent capabilities -- have been rapidly adopted in industry, yet their cross-domain impact and use across commercial and open-source models remain under-studied, and no reusable methodology exists for evaluating an individual skill.

By Maksim Shaposhnikov, Nicolas Fortuin, Simon Stipcich, Maria I. Gorinova, Amy Heineike, Rob Willoughby

llms agents

arXiv AI

Jun 16

SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks

arXiv:2602. 12670v4 Announce Type: replace Abstract: Agent Skills are structured packages of procedural knowledge that augment large language model (LLM) agents at inference time.

By Xiangyi Li, Yimin Liu, Wenbo Chen, Bingran You, Zonglin Di, Yifeng He, Shenghan Zheng, Kyoung Whan Choe, Jiankai Sun, Shuyi Wang, Chujun Tao, Binxu Li, Xuandong Zhao, Hejia Geng, Xiaojun Wu, Junwei Zhou, Xiaokun Chen, Hanwen Xing, Yubo Li, Qunhong Zeng, Di Wang, Yuanli Wang, Roey Ben Chaim, Penghao Jiang, Haotian Shen, Luyang Kong, Xinyi Liu, Runhui Wang, Xuanqing Liu, Jiachen Li, Xin Lan, Yueqian Lin, Wengao Ye, Junwei He, Songlin Li, Yue Zhang, Yipeng Gao, Yijiang Li, Ze Ma, Liqiang Jing, Tianyu Wang, Kaixin Li, Yiqi Xue, Haoran Lyu, Yizhuo He, Yuchen Tian, Shutong Wu, Bowei Wang, Yixuan Gao, Bo Chen, Litong Liu, Sikai Cheng, Jiajun Bao, Shuaicheng Tong, Shuwen Xu, Terry Yue Zhuo, Tinghan Ye, Qi Qi, Miao Li, Longtai Liao, Zelin Tan, Chang Shi, Xilin Tang, Srinath Tankasala, Boqin Yuan, Yaoyao Qian, Jianhong Tu, Chenguang Wang, Yizhou Sun, Wei Wang, Aaron Taylor, Ziyue Yang, Changkun Guan, Zhikang Dong, Xinyu Zhang, Steven Dillmann, Han-chung Lee, Dawn Song

llms agents benchmarks

arXiv AI

Jun 16

SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution

arXiv:2605. 18401v2 Announce Type: replace-cross Abstract: Long-horizon LLM agents generate traces that could become reusable experience, but raw trajectories are noisy, local, and hard to govern.

By Hongyi Liu, Haoyan Yang, Tao Jiang, Bo Tang, Feiyu Xiong, Yuyu Luo, Zhiyu Li

llms agents benchmarks

Hugging Face Trending Papers

Aug 11

SKILLER: Language-Level Reinforcement Learning for Reusable Skill Extraction in Small Language Models

Agent skills represent a standardized format for packaging procedural knowledge and domain expertise, serving within agent harness systems as an essential mechanism to continually constrain a language model's behavior space for repeatable, high-quality task execution. However, because strong closed-source models entail high inference costs, current popular agent harnesses, such as Codex and OpenClaw, remain prohibitively expensive when deploying these skills to accomplish real-world tasks.

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 3

Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward

arXiv:2602. 12430v4 Announce Type: replace-cross Abstract: The transition from monolithic language models to modular, skill-equipped agents marks a defining shift in how large language models (LLMs) are deployed in practice.

By Renjun Xu, Yang Yan

llms agents reinforcement-learning benchmarks

arXiv AI

Aug 12

SKILLER: Language-Level Reinforcement Learning for Reusable Skill Extraction in Small Language Models

arXiv:2608. 10538v1 Announce Type: new Abstract: Agent skills represent a standardized format for packaging procedural knowledge and domain expertise, serving within agent harness systems as an essential mechanism to continually constrain a language model's behavior space for repeatable, high-quality task execution.

By Chenhao Dang, Siyuan Xiong, Conghui He, Weijia Li

llms agents reinforcement-learning benchmarks

One email a morning, machine-written

Related stories

A Framework for Evaluating Agentic Skills at Scale

SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks

SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution

SKILLER: Language-Level Reinforcement Learning for Reusable Skill Extraction in Small Language Models

Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward

SKILLER: Language-Level Reinforcement Learning for Reusable Skill Extraction in Small Language Models