code-smell-detective

Code smell detection analyzes your repository to identify maintainability issues like long functions and god classes, returning a ranked list of findings with file paths, line numbers, severity levels, and suggested refactors.

Yes, static analysis applies language-specific thresholds pulled from local configuration files including .eslintrc, .pylintrc, rubocop.yml, and clippy.toml to classify code smells according to your project standards.

Prioritizing code quality issues involves generating a ranked report categorized by severity and smell type, which helps teams tackle the most critical maintainability problems first. The report includes a verdict on overall quality using a defined confidence model.

Yes, code smell detection works on file, directory, or whole repository scopes and can be integrated into pull request reviews, CI checks, or pre-commit workflows to catch maintainability issues early.

Code smells are classified by fetching live smell taxonomies to identify issues like deep nesting, primitive obsession, duplicated code, and data clumps. Each finding includes tool corroboration and citations to authoritative sources.

No, code smell detection does not modify source code. It returns a prioritized report with actionable refactoring suggestions and structured evidence blocks, leaving the actual code changes to the developer.