What problem does it solve? Codebases accumulate tech debt that slows development and increases regression risk, but teams lack a systematic way to detect, quantify, and prioritize cleanup work before building on fragile code. ## Core Features & Use Cases - Method-Level Debt Scoring: Scores individual methods and constructors using weighted signals like long methods, deep nesting, duplicated logic, and constructor dependency counts, reporting only the top 3 findings. - Flexible Suppression: Supports inline ignore comments, a persistent .claude/tech-debt-ignore.json file, and scan-time skips so known debt stays visible without blocking workflows. - Cleanup Story Routing: Generates structured STORY_REQUEST payloads for the story-author agent when scores exceed thresholds, turning findings into actionable cleanup stories. - Use Case: During a plan critique, scan the methods a plan will modify; if a method scores 12+, route it to story-author for a cleanup story before building new features on top of it. ## Quick Start Ask the AI to run a tech debt scan on a specific file, such as scanning src/services/OrderService.ts to score its methods and constructor.