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This paper proposes SkillBoost, a three-stage constrained exploration-exploitation framework to mitigate skill overfitting in LLM agent self-evolution. It achieves state-of-the-art performance across 23 model-benchmark configurations and demonstrates that optimized skills transfer to other agents.