SkillNet: Create, Evaluate, and Connect AI Skills
Current AI agents can flexibly invoke tools and execute complex tasks, yet their long-term advancement is hindered by the lack of systematic accumulation and transfer of skills. Without a unified mechanism for skill consolidation, agents frequently ``reinvent the wheel'', rediscovering solutions in isolated contexts without leveraging prior strategies. To overcome this limitation, we introduce SkillNet, an open infrastructure designed to create, evaluate, and organize AI skills at scale. SkillNet structures skills within a unified ontology that supports creating skills from heterogeneous sources, establishing rich relational connections, and performing multi-dimensional evaluation across Safety, Completeness, Executability, Maintainability, and Cost-awareness. Our infrastructure integrates a repository of over 200,000 skills, an interactive platform, and a versatile Python toolkit. Experimental evaluations on ALFWorld, WebShop, and ScienceWorld demonstrate that SkillNet significantly enhances agent performance, improving average rewards by 40% and reducing execution steps by 30% across multiple backbone models. By formalizing skills as evolving, composable assets, SkillNet provides a robust foundation for agents to move from transient experience to durable mastery.
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Yuan Liang
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Ruobin Zhong
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Haoming Xu
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Chen Jiang
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Yi Zhong
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Runnan Fang
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Jia-Chen Gu
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Shumin Deng
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Yunzhi Yao
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Mengru Wang
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Shuofei Qiao
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Xin Xu
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2026-03-09T10:01:52.730Z
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Xin Xu
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Tongtong Wu
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Kun Wang
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Yang Liu
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Zhen Bi
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Jungang Lou
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Yuchen Eleanor Jiang
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Hangcheng Zhu
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Gang Yu
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Haiwen Hong
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Longtao Huang
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Hui Xue
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Chenxi Wang
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Yijun Wang
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Zifei Shan
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Xi Chen
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Zhaopeng Tu
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Feiyu Xiong
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Xin Xie
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Peng Zhang
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Zhengke Gui
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Lei Liang
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Jun Zhou
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Chiyu Wu
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Jin Shang
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Yu Gong
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Junyu Lin
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Changliang Xu
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Hongjie Deng
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Wen Zhang
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Keyan Ding
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Qiang Zhang
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Fei Huang
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Ningyu Zhang
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2026-03-06T13:54:34.205Z
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Ningyu Zhang
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Jeff Z. Pan
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Guilin Qi
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Haofen Wang
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Huajun Chen
SkillNet: Create, Evaluate, and Connect AI Skills
Current AI agents can flexibly invoke tools and execute complex tasks, yet their long-term advancement is hindered by the lack of systematic accumulation and transfer of skills. Without a unified mechanism for skill consolidation, agents frequently ``reinvent the wheel'', rediscovering solutions in isolated contexts without leveraging prior strategies. To overcome this limitation, we introduce SkillNet, an open infrastructure designed to create, evaluate, and organize AI skills at scale. SkillNet structures skills within a unified ontology that supports creating skills from heterogeneous sources, establishing rich relational connections, and performing multi-dimensional evaluation across Safety, Completeness, Executability, Maintainability, and Cost-awareness. Our infrastructure integrates a repository of over 200,000 skills, an interactive platform, and a versatile Python toolkit. Experimental evaluations on ALFWorld, WebShop, and ScienceWorld demonstrate that SkillNet significantly enhances agent performance, improving average rewards by 40% and reducing execution steps by 30% across multiple backbone models. By formalizing skills as evolving, composable assets, SkillNet provides a robust foundation for agents to move from transient experience to durable mastery.
SkillNet introduces an open infrastructure for systematically accumulating and transferring AI skills through a unified ontology, significantly improving agent performance across multiple domains.
AI agents skill consolidation tool invocation long-term advancement unified mechanism skill organization ontology multi-dimensional evaluation ALFWorld WebShop ScienceWorld agent performance reward improvement execution steps