SkillNet
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AI agents repeatedly rediscover solutions across separate scenarios instead of systematically reusing what they have already learned. SkillNet introduces an open infrastructure designed to create, evaluate, and organize AI skills at scale, enabling agents to transition from transient experience to durable mastery.
Unified skill ontology: Skills are structured within a unified ontology that supports creation from heterogeneous sources, including code libraries, prompt templates, and tool compositions. Rich relational connections between skills enable discovery and composition that would be impossible with flat skill stores.
Multi-dimensional evaluation: Every skill is assessed across five dimensions: Safety, Completeness, Executability, Maintainability, and Cost-awareness. This systematic evaluation ensures that skills entering the repository meet quality thresholds before agents rely on them in production.
Massive skill repository: SkillNet includes a repository of over 200,000 skills, an interactive platform for skill browsing and management, and a Python toolkit for programmatic access. This scale enables meaningful skill retrieval and composition across diverse task domains.
Consistent agent improvements: 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.
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