OR-Skill: Self Evolutionary Agent Skills for Optimization Modeling

Optimization modeling is crucial for decision-making. Constructing optimization models from natural language descriptions has long been a challenging research goal. Prior work has predominantly relied on fine-tuned large language models (LLMs) or LLM-based agentic workflows to automate this process. However, the rise of general-purpose coding agents, such as Claude Code, offers a new paradigm for tackling such complex tasks. In this paper, we present OR-Skill, a framework that equips a general-purpose agent with a specialized skill for optimization modeling, where the skill is evolved iteratively in an environment built from our curated benchmark. The evolution process operates in an iterative loop. In each iteration, the agent executes tasks with a candidate skill, receives feedback, attributes failures to their root causes, and summarizes them into consolidated diagnostic reports at different error levels. Based on these reports, we perform cascade evolution in reverse order of error severity to refine the skill. We conduct extensive experiments across seven optimization modeling benchmarks of varying difficulty, on which OR-Skill achieves state-of-the-art or comparable performance on all benchmarks. The improvements are particularly significant on challenging benchmarks: IndustryOR (+$21.5\%$ absolute accuracy) and ComplexLP (+$16.2\%$).