adv-slop-detection

You can detect AI-generated code smells and technical debt by running an automated scan that combines AST analysis, regex pattern matching, and heuristic evaluation to identify structural bypasses and maintainability issues across your codebase.

AI code slop refers to degraded code quality from AI-assisted development, such as scope drift and hallucinated methods. Automated detection works by combining AST structural checks with regex signal detection and heuristic semantic scanning to categorize findings.

To scan a feature branch for hallucinated methods and dead code, execute a comprehensive slop detection scan on your directory. The tool enforces evidence-based reporting to ensure findings are verifiable before merging into the main branch.

Yes, automated code quality detection can find redundant defensive guards by operating across your entire codebase using heuristic evaluation and pattern matching to categorize structural correctness and maintainability smells.

AI slop detection provides actionable remediation paths by categorizing all findings by severity and confidence, ensuring that technical debt, maintainability, and security issues are clearly documented for verifiable fixes.

Automated slop detection relies on deterministic AST analysis and heuristic evaluation, meaning it is best suited for identifying structural bypasses and pattern-based code smells rather than fully understanding complex semantic business logic.