This preprint introduces Collapse Index (CI): A Domain-Agnostic Mathematical Framework for Interpreting Stellar Instability and Supernova Collapse. The work demonstrates that the Collapse Index (CI), a bounded, lightweight instability metric originally developed for AI brittleness analysis, can quantify collapse structure across a synthetic Type II supernova lifecycle. Using a 600,000-row synthetic dataset generated with CI’s domain-agnostic perturbation engine, the study evaluates six canonical phases of stellar evolution: hydrostatic equilibrium, late-stage instability accumulation, catastrophic core collapse, shock rebound turbulence, plateau stabilization, and remnant cooling. CI successfully recovers the characteristic collapse arc (baseline → drift → critical spike → rebound → plateau → new equilibrium) without astrophysical tuning or domain-specific modeling. The framework is presented as a non-operational, conceptual tool for retrospective analysis only. CI is not a physical model and provides no predictive, detection, or early-warning capability. All astrophysical interpretation must be performed by accredited experts using archived datasets or simulations. Project page: https://collapseindex.org Licensed under CC BY-NC-ND 4.0.