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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.04448 |
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| _version_ | 1866914369607041024 |
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| author | Liang, Yuan Zhong, Ruobin Xu, Haoming Jiang, Chen Zhong, Yi Fang, Runnan Gu, Jia-Chen Deng, Shumin Yao, Yunzhi Wang, Mengru Qiao, Shuofei Xu, Xin Wu, Tongtong Wang, Kun Liu, Yang Bi, Zhen Lou, Jungang Jiang, Yuchen Eleanor Zhu, Hangcheng Yu, Gang Hong, Haiwen Huang, Longtao Xue, Hui Wang, Chenxi Wang, Yijun Shan, Zifei Chen, Xi Tu, Zhaopeng Xiong, Feiyu Xie, Xin Zhang, Peng Gui, Zhengke Liang, Lei Zhou, Jun Wu, Chiyu Shang, Jin Gong, Yu Lin, Junyu Xu, Changliang Deng, Hongjie Zhang, Wen Ding, Keyan Zhang, Qiang Huang, Fei Zhang, Ningyu Pan, Jeff Z. Qi, Guilin Wang, Haofen Chen, Huajun |
| author_facet | Liang, Yuan Zhong, Ruobin Xu, Haoming Jiang, Chen Zhong, Yi Fang, Runnan Gu, Jia-Chen Deng, Shumin Yao, Yunzhi Wang, Mengru Qiao, Shuofei Xu, Xin Wu, Tongtong Wang, Kun Liu, Yang Bi, Zhen Lou, Jungang Jiang, Yuchen Eleanor Zhu, Hangcheng Yu, Gang Hong, Haiwen Huang, Longtao Xue, Hui Wang, Chenxi Wang, Yijun Shan, Zifei Chen, Xi Tu, Zhaopeng Xiong, Feiyu Xie, Xin Zhang, Peng Gui, Zhengke Liang, Lei Zhou, Jun Wu, Chiyu Shang, Jin Gong, Yu Lin, Junyu Xu, Changliang Deng, Hongjie Zhang, Wen Ding, Keyan Zhang, Qiang Huang, Fei Zhang, Ningyu Pan, Jeff Z. Qi, Guilin Wang, Haofen Chen, Huajun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_04448 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SkillNet: Create, Evaluate, and Connect AI Skills Liang, Yuan Zhong, Ruobin Xu, Haoming Jiang, Chen Zhong, Yi Fang, Runnan Gu, Jia-Chen Deng, Shumin Yao, Yunzhi Wang, Mengru Qiao, Shuofei Xu, Xin Wu, Tongtong Wang, Kun Liu, Yang Bi, Zhen Lou, Jungang Jiang, Yuchen Eleanor Zhu, Hangcheng Yu, Gang Hong, Haiwen Huang, Longtao Xue, Hui Wang, Chenxi Wang, Yijun Shan, Zifei Chen, Xi Tu, Zhaopeng Xiong, Feiyu Xie, Xin Zhang, Peng Gui, Zhengke Liang, Lei Zhou, Jun Wu, Chiyu Shang, Jin Gong, Yu Lin, Junyu Xu, Changliang Deng, Hongjie Zhang, Wen Ding, Keyan Zhang, Qiang Huang, Fei Zhang, Ningyu Pan, Jeff Z. Qi, Guilin Wang, Haofen Chen, Huajun Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Machine Learning Multiagent Systems 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. |
| title | SkillNet: Create, Evaluate, and Connect AI Skills |
| topic | Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2603.04448 |