RASO retrieves from an external skill corpus and rewrites it across harnesses, beating retrieval-free skill optimization on four agent benchmarks and two models
The work proposes Retrieval-Augmented Skill Optimization (RASO), a framework that retrieves section-level knowledge from a public skill corpus and adapts it to the target task and harness through Cross-Harness Adaptation, where RASI builds an initial skill without any agent rollouts and RASU iteratively refines the skill by retrieving missing knowledge guided by execution feedback; across four benchmarks (OfficeQA, SpreadsheetBench, ALFWorld, WebShop) and two models (GPT-5.6-Luna, Qwen-3.5-9B), RASO consistently outperforms baselines without retrieval-augmented initialization and updating.