SkillNet：创建、评估与连接AI技能

摘要

当前AI智能体已能灵活调用工具并执行复杂任务，但其长远发展仍受制于技能缺乏系统性积累与迁移的瓶颈。由于缺少统一的技能整合机制，智能体常常陷入"重复造轮子"的困境，在孤立场景中重复发现解决方案而无法有效利用既有策略。为突破这一局限，我们推出SkillNet——一个面向大规模AI技能创建、评估与管理的开放基础设施。SkillNet通过统一本体论框架构建技能体系，支持异构来源的技能创建、建立丰富的关联关系，并开展涵盖安全性、完备性、可执行性、可维护性与成本意识的多维评估。该基础设施整合了包含20万余技能的资源库、交互式平台及多功能Python工具包。在ALFWorld、WebShop和ScienceWorld上的实验表明，SkillNet能显著提升智能体性能，在多种骨干模型上平均奖励提升40%，执行步骤减少30%。通过将技能形式化为可进化、可组合的资产，SkillNet为智能体从短暂经验积累迈向持久能力精通奠定了坚实基础。

English

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：创建、评估与连接AI技能

SkillNet: Create, Evaluate, and Connect AI Skills

摘要

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