What problem does it solve? Identifying which files in a codebase are decaying — frequently changed and structurally complex — requires manually correlating git history with code complexity, which is tedious and error-prone across large repositories. ## Core Features & Use Cases - Hotspot Ranking: Computes score = churn × complexity for every tracked file, using git log for churn and ast-grep (with a regex/indent proxy fallback) for complexity. - Heuristic Labeling: Flags outliers as God Class, Shotgun Surgery, or Dormant Complexity using percentiles of the repo's own distribution. - Optional Model Review: The --interpret flag sends top-scoring files to a model pass that confirms, corrects, or drops heuristic labels before rendering. - Use Case: Run /code-decay against a legacy repo to produce docs/code-decay/<repo>-<date>.md, a ranked table showing which files carry the highest maintenance risk and deserve refactoring attention first. ## Quick Start Ask the AI to run /code-decay on a target repository path to generate a ranked hotspot report inside that repo's docs/code-decay directory.