What problem does it solve? Domain-driven design models degrade silently: terminology drifts across artifacts, aggregate boundaries contradict context boundaries, and event catalogs fall out of sync with flows. This Skill provides a holistic quality gate that scores a DDD model across multiple dimensions and tells you exactly what to fix and where to backtrack. ## Core Features & Use Cases - Multi-dimension scoring: Rates terminology consistency, boundary reasonableness, invariant expression rate, event completeness, and coupling level on a 0-10 scale with evidence-backed deductions. - Feedback loop triggers: Maps specific failure conditions (e.g., invariant expression rate below 60%) to the upstream modeling skill that must be revisited, with infinite-loop prevention after 3 retries. - Industry benchmarking: Optionally compares your model against mature open-source reference implementations (Cargo, Broadleaf, Pyramid) to surface merge/split deviations and unmodeled domains. - Use Case: After completing tactical modeling with ddd-contexts, ddd-aggregates, and ddd-domain-interactions, run this review to get a score summary, prioritized issue list, and an implementation readiness verdict before writing code. ## Quick Start Ask the AI to perform a holistic quality assessment on your context directory, aggregate directory, domain event directory, and context relationship matrix, and to output per-dimension scores, an issue list, and backtrack recommendations.
