AI RESEARCH
SkillMOO: Multi-Objective Optimization of Agent Skills for Software Engineering
arXiv CS.AI
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ArXi:2604.09297v1 Announce Type: cross Agent skills provide modular, task-specific guidance for LLM- based coding agents, but manually tuning skill bundles to balance success rate, cost, and runtime is expensive and fragile. We present SkillMOO, a multi-objective optimization framework that automatically evolves skill bundles using LLM-proposed edits and NSGA-II survivor selection: a solver agent evaluates candidate skill bundles on coding tasks and an optimizer agent proposes bundle edits based on failure analysis.