arXiv:2606. 06087v1 Announce Type: cross Abstract: Agent systems increasingly use textual skills to encode reusable task procedures, but injecting these skills into the prompt at every step incurs substantial context overhead and exposes skill content as plaintext.
By Aofan Yu, Chenyu Zhou, Tianyi Xu, Zihan Guo, Rong Shan, Zhihui Fu, Jun Wang, Weiwen Liu, Yong Yu, Weinan Zhang, Jianghao Lin
arXiv:2606. 09316v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) enables agents to access external knowledge at inference time, but it primarily retrieves fragmented declarative evidence, leaving agents to repeatedly infer task procedures from passages, manuals, examples, logs, or trajectories.
By Qianjun Pan, Yutao Yang, Junsong Li, Jie Zhou, Kai Chen, Xin Li, Qin Chen, Liang He
SkillGym is an automatic pipeline that generates verifiable environments for training skill-use agents. It crawls internet skills, filters for reproducible workflows, and uses a builder‑reviewer process to create difficulty‑controlled tasks with reference solutions and verifiers. The system builds 6.8k environments, collects 19k successful trajectories, and fine‑tunes LLMs from 2B to 122B parameters, improving performance and skill invocation rates.
By Renxi Wang, Mingshan Hee, Fajri Koto, Timothy Baldwin, Haonan Li
Agent Skills augment large language model (LLM) agents with procedural knowledge at inference time, but current benchmarks rarely distinguish what a Skill says from how it is organized. We study this distinction through Progressive Disclosure, where a concise root file points agents to supporting resources on demand, and compare it with a normalized flat baseline.
arXiv:2604. 01687v3 Announce Type: replace Abstract: Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks that simple tool invocations cannot address.
By Hanrong Zhang (Steve), Shicheng Fan (Steve), Henry Peng Zou (Steve), Yankai Chen (Steve), Zhenting Wang (Steve), Jiayu Zhou (Steve), Chengze Li (Steve), Wei-Chieh Huang (Steve), Yifei Yao (Steve), Kening Zheng (Steve), Xue (Steve), Liu, Xiaoxiao Li, Philip S. Yu
arXiv:2606. 11543v1 Announce Type: new Abstract: Agent Skills augment large language model (LLM) agents with procedural knowledge at inference time, but current benchmarks rarely distinguish what a Skill says from how it is organized.
By Zhiyu Chen, Zihan Guo, Bo Huang, Bingwei Lu, Jianghao Lin, Yuanjian Zhou, Weinan Zhang