MAS-Skill: Skill-Guided Multi-Agent Systems via Collaborative Topology Transfer

Large language model (LLM)-based multi-agent systems (MAS) benefit from structured collaboration, yet existing approaches either use a fixed topology for all queries or perform costly per-query topology search without reusing successful interaction patterns. We propose MAS-Skill, a framework that models multi-agent collaboration as a reusable skill. During offline learning, MAS-Skill conducts controlled A/B tests over topology and agent-count configurations, selects accuracy-cost Pareto-optimal configurations as base skills, and evolves them through genetic recombination and mutation guided by successful and failed trajectories. Each skill records a collaboration topology, agent roles, tool assignments, synthesis strategy, applicability conditions, and execution experience. At inference time, a main agent retrieves a type-aware, high-confidence skill and instantiates its collaboration plan; when no reliable match exists, it constructs a new plan autonomously, thereby avoiding per-query topology search. Across six benchmarks covering mathematical reasoning, code generation, and multi-hop question answering, MAS-Skill achieves 92.8% on GSM8K, 52.9% on MATH, 99.0% on MultiArith, 84.2% on MBPP, 94.7% on HumanEval, and 87.5% on HotpotQA, yielding an average score of 85.17% and outperforming the strongest compared baseline by 1.58 percentage points. On a multi-agent collaboration benchmark, MAS-Skill achieves an average Overall Goal Success Rate of 0.444 while using fewer online inference tokens than AFlow and MaAS. These results demonstrate that reusable collaboration skills provide an effective alternative to repeated runtime topology search.