Skill Tree: Hierarchy-Guided Experience Consolidation for Self-Evolving Agent Skills

Large language model (LLM) agents increasingly improve through reusable skills distilled from execution experience. Multi-trajectory skill evolution enables broader evidence aggregation, but many existing methods rely on LLM-generated consolidation, which can alter or omit useful details while leaving semantic redundancy unresolved. We propose Skill Tree, a framework that instead groups edits by their structural context, explicitly removes redundant guidance, and preserves useful content without generative rewriting. Skill Tree further organizes accumulated knowledge into a budgeted core skill and supplementary references, reorders core content under importance and workflow constraints, and retrieves task-relevant reference subtrees at inference time. Experiments across skill construction, out-of-distribution generalization, and cross-model reuse show substantial improvements in downstream task performance, with controlled ablations further demonstrating the importance of structure-aware consolidation, organization, and access.