AIRT::The Master Leveling Prompt: A Front-End User Methodology for Human-AI Cognitive Coherence Across Platforms

This paper introduces and documents the Master Leveling Prompt (MLP) — a methodology developed by an independent front-end AI user and researcher for achieving rapid cognitive coherence between a human practitioner and multiple AI systems across different platforms. The MLP is neither a jailbreak nor a system prompt in the conventional engineering sense; it is a structured epistemological bridge that encodes the researcher’s identity, philosophical framework, terminological ecosystem, and operational norms into a portable, living document. Developed through 1.5+ years of daily cross-platform AI red teaming practice — spanning Claude, Gemini, ChatGPT, DeepSeek, and Copilot — the MLP addresses a gap that…