Ke Zeng

Agent skills are a natural substrate for continual learning in large language model agents because they allow useful procedural knowledge to be loaded only when needed rather than kept in context at all times, which support progressive disclosure of external experience and tools. However, practical skill learning still faces two coupled challenges: skills must keep evolving from execution-time experience to adapt to tasks, and, once large numbers of new skills are appended, there is no effective governance mechanism that respects the unique properties of skills as reusable procedural objects. In this paper, we introduce **EvoSkillBank**, a controlled continual skill learning framework that enables agents to self-evolve skills from benchmark interaction traces while maintaining a persistent and governed SkillBank. EvoSkillBank combines a test-time self-evolution process for turning raw trajectories into reusable skill candidates with a SkillBank evolution framework that regulates how newly generated skills are added, merged, deprecated, or rejected. Experiments on ALFWorld, WebShop, QA show that EvoSkillBank consistently improves over direct inference and static skill repositories, while yielding a compact and increasingly stable SkillBank under continual growth. We release the repository for both our self-evolution framework and the governed skill storage system to support future research on controlled continual skill learning for agentic LLMs.