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This article argues that the first AI strategy for companies should be creating a 'skill library' to capture the reusable working methods of top performers, so agents can learn the method behind tasks rather than just access data. It promotes a live webinar called Skills 101.
This MUSE-Autoskill paper focuses on how an Agent manages an entire skill library, placing skills into a complete lifecycle: creation, memory, management, evaluation, and re-optimization.
SkillGraph is a framework that represents reusable skills as nodes in a directed graph to enable large language model agents to handle compositional tasks more effectively through structured skill retrieval and continuous evolution.
Skill1 is a unified framework that trains a single policy to co-evolve skill selection, utilization, and distillation using a shared task-outcome objective. Experiments on ALFWorld and WebShop show it outperforms existing baselines in complex task environments.