Beyond Skill Evolution: Navigating the Adaptation Path for Skill Libraries

A skill is a structured natural-language document that integrates tool descriptions and workflow examples, and can be reused across tasks. However, the performance of the same Large Language Model (LLM) agent models varies a lot on the same skill library. Existing reflective skill evolution methods answer only what to evolve: they edit contents along a single adaptation trajectory for a single skill, lacking an explicit mechanism for locating failures to evolve across a multi-skill library, and failing to break away from the evolution paths with accumulated suboptimal edits. In this paper, we reframe skill evolution as an agentic tree search problem over the two challenges and propose SkillPilot, a Meta-Optimizer-Guided Monte Carlo Evolution Tree Search (MCETS) framework that navigates where and which way to evolve for a skill library in four recurrent stages: selection, expansion, simulation, and backpropagation, flexibly navigating the evolution path, which prevents the agent from over-exploring the suboptimal evolution path. Extensive experiments have demonstrated SkillPilot's superior performance on agent-generated skills and human-curated skills on three benchmarks with various task types.