用多目标优化提升编程智能体技能，兼顾成功率与成本。
SkillMOO: Multi-Objective Optimization of Agent Skills for Software Engineering
- 通过大模型生成修改、NSGA-II算法优化，动态进化技能组合。
- 12个非零通过任务中11个排名第一，成本降低31.7%，成功率最高提升21个百分点。
- 揭示剪枝和替换是有效优化手段，适合关注效率的工程团队。
编程智能体的技能正被广泛用于配置软件工程任务，但当前做法将技能视为静态手工设计资产，或仅以通过率进行演化。这不够充分：一个技能可能提高任务成功率，却大幅增加令牌消耗或引入误导性指导。我们提出将技能包视为多目标优化对象，构建SkillMOO框架，通过大模型提议的修改和NSGA-II帕累托选择，在通过率与推理成本之间进行优化。在SkillsBench全部16个软件工程任务上评估，SkillMOO在12个非零通过任务中取得11项最高通过率排名，相比静态技能包成本降低最高达31.7%，通过率最高提升21个百分点。对38次技能修改的分析显示，剪枝与替换是成功操作的主要方式，为技能包设计提供了可操作原则。当前未考虑成本的技能部署会遗漏更优配置，因此亟需一种基于搜索的成本感知技能工程范式。
原文摘要 · Abstract (English)
Agent skills are increasingly used to configure coding agents for software engineering (SE) tasks, yet current practice treats them as static, hand-crafted assets, or evolved on pass rate alone. This is insufficient: a skill can improve task success while substantially raising token cost, or introducing misleading guidance. We argue that SE agent skill bundles can be treated as multi-objective search objects and present SkillMOO, a framework that evolves skill bundles through LLM-proposed edits and NSGA-II Pareto selection on pass rate and inference cost. Evaluated across all 16 SkillsBench SE tasks, SkillMOO achieves the top pass rate rank on 11 of 12 non-zero-pass tasks while achieving cost reductions of up to 31.7% over static bundles, with pass rate gains up to 21 percentage points. Analysis of 38 skill edits shows that pruning and substitution dominate successful operations, offering actionable principles for skill bundle design. Thereby, the current practice of deploying skills without cost-aware validation leaves better skill configurations unexplored, motivating a new class of cost-aware, search-based skill engineering.
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