slop-detector

To detect unimplemented stubs and phantom imports, you run a static scan using the AI-SLOP Detector CLI. It analyzes project files to identify structural-risk defects and misleading logic before code reaches production.

4D scoring evaluates structural risk by quantifying logic density, inflation, dependency coupling, and critical-pattern severity across analyzed files. It uses the metrics LDR, ICR, DDC, and Purity to produce actionable evidence for prioritized fixes.

Yes, you can enforce a hard CI gate for Python, JS, and Go repositories. The analyzer routes results by confidence thresholds to make automated PASS or FAIL merge decisions based on detected structural failure modes.

The best way to verify improvements is by running a before and after delta comparison. This workflow analyzes the scan output differences to confirm that structural risk patterns and dependency coupling have actually decreased.

Yes, the AI-SLOP Detector supports single-file diagnostics alongside full-project scans. It produces a session baseline and routes investigation by confidence status to decide when to analyze deeply or quickly verify.

You should reconsider relying solely on automated review when dealing with complex disconnected pipelines requiring contextual logic validation. While it detects structural risk, adversarial validation workflows are recommended for critical pipelines.