SkillsWiki: Taming Ever-Growing LLM Agent Skills with a Self-Evolving Hierarchical Wiki

Large language model agents increasingly rely on a large and diverse repertoire of skills to execute long-horizon tasks, yet existing frameworks manage these skills as a flat, isolated, and static list retrieved by embedding similarity. This design scales poorly as the skill library grows, ignores the relational structure among skills, and discards the experience accumulated across tasks. We propose SkillsWiki, a unified framework that organizes agent skills into a three-layer wiki. A skill taxonomy constructed by large language models materializes skill categories as a hierarchical directory tree with summarization nodes, enabling coarse-to-fine retrieval. A skill relation graph, autonomously discovered by the agent through wiki exploration, links related skills and couples a shared cloud-side wiki with a local workspace wiki. A skill package library distills proven skill compositions from historical execution traces and surfaces them only when an associated skill is activated, enabling context-triggered reuse. The entire structure is maintained as a lightweight, file-based wiki that remains readable and editable by human developers. Experiments on three agent benchmarks with multiple backbone models show that SkillsWiki consistently improves task success rate and reduces execution steps over strong baselines.