SkillNet: Create, Evaluate, and Connect AI Skills

2026-02-26

arXiv.org

AI Summary

为解决AI技能缺乏系统积累和转移的问题，提出SkillNet，一个创建、评估和组织AI技能的开放基础设施。

Abstract

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.

Problem

• AI Agents
• Skill Accumulation
• Transfer of Skills
• Unified Mechanism

Innovation

• unified ontology
• multi-dimensional evaluation
• skill consolidation
• large-scale skills repository
• task-specific skill routing