This paper presents our prompt-based approach for modeling mental health timelines from Reddit user posts. We address two tasks: identifying moments of change and generating summaries of clinically meaningful changes across post sequences. Our framework uses large language models with in-context learning to analyze self-states and mental health indicators without task-specific fine-tuning. We build an inference pipeline with vLLM and Qwen2.5-72B-Instruct-GPTQ-Int8, and experiment with few-shot prompting, and balanced few-shot sampling. We also examine how the number of visible posts affects the model’s ability to capture temporal changes. Our results suggest that prompt-based methods provide a practical and competitive baseline in low-resource and sensitive mental health settings, particularly for modeling self-state dynamics and generating summaries of psychological change over time.