Create, Evaluate, and Connect AI Skills | SkillNet: A Large-Scale Agentic "Skill Graph" Knowledge Base

SkillNet is a large-scale agentic skill knowledge base designed to consolidate scattered practical experiences into a structured, computable, and composable network, aiming to improve AI agents' capabilities by providing a system for skill accumulation and sharing. It addresses the limitations of current AI models by transforming procedural knowledge into reusable and evolvable skill modules, enhancing agents' ability to perform complex tasks efficiently and reliably. The network is organized into a three-layer ontology—taxonomy, relational, and skill package layers—allowing agents to assemble capabilities dynamically. SkillNet has amassed over 200,000 skills, with 150,000 curated for high quality, and demonstrates performance improvements of 10-30 percentage points on benchmarks like ALFWorld, WebShop, and ScienceWorld. The initiative supports rapid integration and experimentation through a Python library, encouraging the development of maintainable, scalable, and evolvable AI systems, while also fostering collaboration and feedback from the community for future enhancements.