Depression is a common mental illness in modern society. However, incorrect and missed diagnoses of this illness still widely exist, making timely and accurate depression detection based on artificial intelligence (AI) technology urgent. Despite recent progress in depression detection, current models often struggle to analyze large amounts of data containing ambiguous content. The emergence of large language models (LLMs) sheds light on solving this issue. This paper proposes a Cascade Large Language Model via In-Context Learning (CLLM-ICL) for detecting depression. CLLM-ICL is a cascade model that includes an in-context learning prompt for GPT-3.5-Turbo-1106 and a small-sized linear neural network, subtly combining the strengths of the small-sized model and LLM in our application. We conduct experiments on the Weibo user depression detection dataset (WU3D) to evaluate the effectiveness of our model. The results show that our cascade model achieves better accuracy and F1-score than other recently proposed models.