SkillNet is an open infrastructure that enables the creation, evaluation, and organization of AI skills at scale. It structures skills within a unified ontology, supports multi‑dimensional evaluation (Safety, Completeness, Executability, Maintainability, Cost‑awareness), and integrates a repository of over 600,000 skills, an interactive platform, and a Python toolkit. Experiments on ALFWorld, WebShop, and ScienceWorld demonstrate a 40% increase in average rewards and a 30% reduction in execution steps across multiple backbone models.
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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
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
arXiv:2606. 16774v1 Announce Type: new Abstract: Equipping Large Language Model (LLM) agents with effective skills is crucial for solving complex tasks in real-world systems like OpenClaw.
By Tianyi Lin, Chuanyu Sun, Jingyi Zhang, Changxu Wei, Huanjin Yao, Shunyu Liu, Xikun Zhang, Liu Liu, Jiaxing Huang
WikiSkill is a framework that separates raw execution experience, accumulated knowledge, and executable skills, continuously consolidating experience into a persistent knowledge base (wiki). By co‑evolving agent skills with this wiki, the method consistently outperforms state‑of‑the‑art skill‑evolution techniques across diverse benchmarks and models. The study shows that larger models benefit more from evolved skills, smaller models can outperform larger ones when equipped with skills, and that skills transfer effectively across model families, with the wiki’s persistent knowledge being critical for success.
By Liyan Tang, Cyrus Rashtchian, Chun-Sung Ferng, Andrew Tomkins, Da-Cheng Juan, Tu Vu
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
arXiv:2510. 15416v2 Announce Type: replace Abstract: We investigate a framework in which LoRA adapters are treated as callable tools that a base language model can dynamically select and invoke.