Vision-language models like CLIP have gained popularity due to their zero-shot ability. To improve performance on specific downstream tasks, fine-tuning methods like prompt tuning and feature adapter have shown notable progress. However, these methods inevitably encounter two challenges when deployed in the wild: (1) task-specific adaptation gains come at the cost of generalization, and (2) the training and deployment costs need to be considered. To address these issues, we propose GateLIP-X, a simple yet effective framework, comprising three key components: AutoClick-SA (AC-SA), Slim Information Enhancement (SlimInfoX), and GateModule (GM). Specifically, combined with SlimInfoX, GateLIP-X introduces GM to predict whether the test data belongs to seen or unseen classes and utilizes prior refined feature information from SlimInfoX to predict the class independently from each other. To avoid reliance on unseen-class data, AC-SA extracts seen-class-irrelevant nuisances (e.g., background regions) as pseudo-unseen samples. This design allows GateLIP-X to generalize effectively in a training-free and unseen-classes-data-free way, offering a lightweight, efficient, and low-cost solution. Extensive experiments on 11 benchmark datasets demonstrate that GateLIP-X surpasses existing state-of-the-art methods, achieving accuracy improvements ranging from 5.50% to 23.69%, while maintaining high computational efficiency. These results underscore the practical value of GateLIP-X for scalable and low-cost open-world recognition.