Enhancing the adaptive capabilities of large language models is a critical pursuit in both research and application.Traditional fine-tuning methods require substantial data, computational resources, and specific capabilities, while in-context learning is limited by the need for appropriate demonstrations and efficient token usage. Inspired by the expression of in-context learned capabilities through task vectors and the concept of modular capability or knowledge, we propose ELICIT, a framework consisting of two modules designed to effectively store and reuse task vectors to enhance the diverse adaptive capabilities of models without additional training or inference tokens. Our comprehensive experiments and analysis demonstrate that our pipeline is highly transferable across different input formats, tasks, and model architectures. Externally storing and reusing vectors that represent in-context learned capabilities not only shows the potential to extract modular capabilities but also significantly enhances the performance, versatility, adaptability, and scalability of large language models, paving the way for more efficient and effective use of these models in a wide range of applications.