MetaSkill: Reusable Problem-Solving Methods for Adaptive Skill Orchestration in LLM Agents

Reusable skills provide specialized procedures, but large language model (LLM) agents must still determine how to organize and adapt their use. We introduce MetaSkill, a two-layer framework for reusing problem-solving methods across tasks and domains. The method layer selects from 26 manually authored methods and binds abstract roles to task objects. The execution layer uses the instantiated method to identify intermediate objectives, coordinate native skills, and guide repair or method reselection. On 89 SkillsBench tasks and 995 ToolHop examples, MetaSkill outperforms both evaluated baselines in all six configurations, with accuracy gains of 3.51–13.48 percentage points over the strongest baseline. Ablations support explicit method-guided orchestration beyond direct method injection and further gains from method reselection.