vibe-audit

To audit a codebase for AI-generated technical debt and security flaws, you can trigger a static analysis that checks architectural inconsistencies and Vibe-Coding pitfalls. This process applies Clean Code principles across modules and returns a structured findings JSON with severity tags and a migration path.

Vibe-coding pitfall detection in static codebase analysis identifies AI-generated technical debt by checking for architectural inconsistencies and security flaws across modules. It combines Clean Code principles with AI-driven checks to surface category-by-category findings, returning a structured JSON and an HTML report template.

Yes, you can generate a shareable audit report for architectural inconsistencies. The analysis outputs a self-contained audit-report.html template ready for rendering, alongside a structured findings JSON that includes severity tagging and actionable migration paths for easy review.

Platform-agnostic codebase audit works without specific framework dependencies, applying Clean Code principles and security checks across any module structure. It requires no external dependencies to run, validating schema correctness through built-in validation hooks while returning findings and an HTML report.

To get actionable migration paths after static analysis finds security flaws, the audit applies Clean Code principles and Vibe-Coding checks across modules. It returns a structured findings JSON with category-by-category severity tagging, providing specific migration steps to resolve the identified technical debt.

A limitation of using Clean Code principles for codebase audit is that the static analysis focuses on architectural inconsistencies, security flaws, and AI-generated technical debt rather than runtime behavior. It provides category-by-category findings and migration paths based on schema-validated structural checks, not dynamic execution tracing.