Abstract: 【Purpose/significance】This study aims to construct a competency scale for academic library staff in generative artificial in⁃ telligence, providing a scientific assessment of their generative AI literacy and application capabilities. The scale serves as a theoreti⁃ cal basis for enhancing staff core competencies and optimizing service quality.【Method/process】Based on competency theory and the iceberg model, this study employs methods such as literature analysis and interviews to identify key competency elements and develop an initial assessment scale. The scale structure was refined through item analysis and factor analysis, ultimately resulting in a finalized scale, which was then subjected to empirical validation.【Result/conclusion】The scale encompasses five dimensions: knowledge and cognition, technology and application, service and attitude, thinking and literacy, and communication and collaboration. It effectively evaluates the comprehensive competency of library staff in the context of generative artificial intelligence, offering scientific support and practical insights for service innovation, staff capacity building, and AI application promotion in academic libraries.【Innovation/ limitation】This study presents the first competency scale for academic library staff in generative AI, validated through systematic sta⁃ tistical methods. The scale demonstrates strong discriminative power, reliability, and validity, making it a valuable tool for practical ap⁃ plication.