Reusable skills play a key role in improving LLM-based agents, but existing skill-evolution methods often fail to ensure that evolved skills both cover the knowledge required by the task and remain aligned with the target task. As a result, evolved skills could be incomplete or irrelevant. To address this limitation, we propose AlignEvoSkill, a skill-evolution framework that jointly models knowledge coverage and task alignment. Given failed task trajectories, AlignEvoSkill first identifies task-relevant knowledge tags, retrieves complementary prior skills, and adapts them into candidate skills that address missing knowledge. It then selects high-quality candidates using a joint filtering criterion based on knowledge-coverage and task-alignment scores. Experiments on 3 benchmarks with4 LLM backbones show a 34.7% relative gain of AlignEvoSkill over the non-evolution baseline and achieves a new SOTA in skill evolution with lower cost.
@article{arxiv.2506.23149,
title = {AlignEvoSkill: Towards Knowledge-Aware and Task-Aligned Agent Skill Evolution},
author = {Dingzirui Wang and Xuanliang Zhang and Keyan Xu and Qingfu Zhu and Wanxiang Che and Yang Deng},
journal= {arXiv preprint arXiv:2506.23149},
year = {2026}
}