Skill‑V is a verifiable self‑evolving skill library for interactive agents that represents skills as versioned, falsifiable contracts linking semantic intent to observable behavior. The system uses task failures to add new skills and contract‑outcome disagreements to revise existing skill boundaries, ensuring revisions preserve semantic constraints and pass non‑regression tests on historical evidence. An applicability‑aware filter excludes confidently inapplicable skill candidates, leading to high success rates on ALFWorld (95.3%) and WebShop (85.9%) while keeping the library more compact than growth‑only baselines.
By Jie Ma, Zhipeng Qian, Yufei Ma, Zihan Liang, Jiayi Ji, Qingpeng Cai, Ben Chen, Peng Jiang, Xiaoshuai Sun
Agent skills are the de facto mechanism for extending LLM agents with reusable guidance. A skill can shape the agent's task execution, including planning, tool use, problem-solving, and validation.
arXiv:2608. 11888v1 Announce Type: new Abstract: Agent skills are the de facto mechanism for extending LLM agents with reusable guidance.
By Gen Dong, Yanjie Gao, Liqun Li, Tianyin Xu, Yu Hua, Fan Yang
The paper introduces an online skill‑evolution framework that transforms interaction traces and evaluator feedback into a persistent, versioned library of reusable procedures for computer‑use agents. By executing each iteration against a frozen library snapshot, the system updates skills without altering the underlying model parameters. Experiments across four OSWorld domains show that the evolving library consistently outperforms an empty‑library baseline, with gains ranging from 5.7 to 18.6 percentage points, while also revealing domain‑specific temporal stability and challenges in skill retrieval and revision.
By Longtao Hu, Xiao Liang, Linchao Zhu
arXiv:2607. 01874v1 Announce Type: new Abstract: Skills are becoming a reusable operational layer for LLM agents, encoding SOPs, domain rules, tool workflows, scripts, and validation routines.
By Jiayin Zhu, Kelong Mao, Yudong Guo, Dengbo He, Sulong Xu, Simiu Gu, Yutao Yue
arXiv:2608. 06880v1 Announce Type: new Abstract: General-purpose skills promise reusable procedural knowledge for language agents, yet semantic relevance does not guarantee execution utility: a retrieved skill may encode assumptions that conflict with the current task, execution environment, or other retrieved skills.
By Qinfeng Li, Dalin He, Yuntai Bao, Ying Yang, Ruoxi Chen, Xinyan Yu, Lizhou Liang, Ge Su, Wenqi Zhang, Xuhong Zhang
arXiv:2605.08693v3 Announce Type: replace
Abstract: Skills provide an effective mechanism for improving LLM agents on complex tasks, yet in existing agent frameworks, their creation, refinement, and...
By Min Yang, Jinghua Piao, Xu Xia, Xiaochong Lan, Jiaju Chen, Yongshun Gong, Yong Li
arXiv:2606. 14239v1 Announce Type: new Abstract: Agent skills are structured procedural packages that guide frozen LLM agents in specialized workflows.
By Haowen Gao, Haoran Chen, Can Wang, Shasha Guo, Liang Pang, Zhaoyang Liu, Huawei Shen, Xueqi Cheng
arXiv:2606. 03692v1 Announce Type: new Abstract: Recent AI agents can flexibly invoke skills to solve complex tasks, but their long-term improvement is fundamentally constrained by a lack of systematic skill construction, accumulation, and transfer.
By Yuan Xiong, Ziqi Miao, Qian Chen, Lijun Li, Yequan Wang, Shizhu He, Jun Zhao, Kang Liu
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
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:2610.09430v1 Announce Type: new
Abstract: Internalizing external skills changes a language agent's capabilities and, with them, the value of its remaining guidance: rules can become redundant,...
By Ling Li, Qiuyu Shen, Zheng Jiang, Qinwei Ma, Yuxuan Liu, Zhidong Deng