As people use large language models (LLMs) over time, they develop their own ways of prompting to achieve desired results. These useful experiences often remain scattered and unorganized across conversations. Yet existing personalization approaches primarily capture user preferences or explicitly defined workflows. Through a formative study, we found that users develop prompting strategies that range from implicit instructions to explicit templates from these experiences. We further find that interaction behaviors provide evidence for these strategies, which can be decomposed and recombined for reuse across tasks. Building on these findings, we present Promptile, a framework that identifies and validates users' prompting strategies from interaction histories. Promptile organizes these strategies into reusable components that can be retrieved, combined, and adapted to users' current tasks. Our evaluation shows that Promptile accurately recovers and reuses these strategies, better aligning model outputs with users' expectations while reducing repetitive prompting effort.

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