SkillBrew: Multi-Objective Curation of Skill Banks for LLM Agents

• URL: http://arxiv.org/abs/2605.29440v1
• Date: Thu, 28 May 2026 06:33:52 GMT
• Title: SkillBrew: Multi-Objective Curation of Skill Banks for LLM Agents
• Abstract summary: LLM agents increasingly rely on curated skill banks.<n>Existing approaches expand these banks in an append-only fashion.<n>We introduce SkillBrew, a multi-objective curation framework.
• Score: 71.54500730593263
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-augmented LLM agents increasingly rely on curated skill banks: collections of reusable textual principles that guide decision making on complex tasks. Existing approaches typically expand these banks in an append-only fashion, continuously adding new skills without removing redundant, outdated, or harmful ones, resulting in inefficient and poorly curated repositories. In this paper, we formulate the skill bank curation as a constrained multi-objective problem: a desirable bank must be useful for the agent, diverse in its content, and provide good coverage of the query distribution. To this end, we introduce SkillBrew, a multi-objective curation framework that formalizes skill bank curation as Pareto-aware optimization under a utility constraint, and solves it via a bi-level propose-then-verify loop. We evaluate our approach on two public benchmarks. Our findings suggest that treating skill banks as objects of principled curation, rather than ever-growing append-only logs, is an important step toward building self-improving LLM agents.

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