Skill Transfer: Adapting Agent Skills from Strong to Weak Agents

Agent skills are modular bundles of instructions, code, and resources that an agent loads on demand, increasingly authored and validated on frontier models and shared as an open standard. The same skill behaves differently across models: a strong model turns it into a large performance gain, while a weak model realizes little of that gain and sometimes does worse than with no skill at all. Cost and resource constraints, however, often restrict real deployments to exactly these weaker agents. We hypothesize that the strong model silently supplies procedural steps the skill leaves implicit, which the weak model omits, and formalize closing this gap as Skill Transfer: holding the skill's capability ceiling and the model's parameters fixed, we adapt the skill so a specific weak agent can execute it well. Our method abstracts strong and weak trajectories into typed execution structures, localizes their structural divergence with a graph edit distance (GED), and repairs the skill through an iterative diagnoser-patcher loop. Across three benchmarks and two model families, Skill Transfer raises pass rate over the unmodified skill significantly and well over other skill-evolution baselines. The adapted skill also improves the strong agent it was never adapted for, and transfers out of distribution to an unseen task.