COBRA-Skills: Contextual Bandit-Guided Evolution for Agent Skill Optimization

TL;DR - COBRA-Skills optimizes reusable LLM-agent skills by combining contextual-bandit prioritization with execution-feedback-driven skill evolution. It reports leading average performance across multiple benchmarks while cutting optimization costs and data requirements.

• Treats skill optimization as budgeted sequential optimization over a dynamically evolving candidate pool.
• Selectively evaluates promising or informative skills rather than exhaustively testing candidates.
• Across six agent benchmarks and three target models, it achieves the strongest average performance among compared methods.
• Reduces optimization cost by 55–58% versus SkillOpt while using 50 unique optimization examples per benchmark.