What problem does it solve? AI-generated code carries recognizable tells—leftover chat artifacts, placeholder comments, swallowed errors, emoji, generic names like process_data, and structural issues like boilerplate and hallucinated APIs—that make it read as machine-written and sometimes ship real bugs. This Skill identifies those tells and guides their removal so code fits the surrounding project. ## Core Features & Use Cases - Multi-language scanner: A standalone Python script scans Python, JS/TS, Java, Go, Rust, Ruby, PHP, C/C++, C#, and more, reporting each finding with file, line, severity, bug/cosmetic class, verified data share, and a fix, plus a slop score and CI-friendly exit code. - Build and audit modes: Build mode establishes the surrounding code, real requirements, and verified APIs before generation; audit mode runs build/type-check first, then the scanner, then a human diff read for structural tells. - Evidence-ranked catalog: References document 19 tells ranked by verified share from an analysis of 11,906 Reddit posts and 11,306 comments, including which tells a regex cannot catch and which popular complaints failed verification. - Use Case: Before merging an AI-assisted pull request, run the scanner with --severity high in CI to fail on leftover chat artifacts and placeholder stubs, then manually review the diff for tutorial-shaped boilerplate and hallucinated API calls. ## Quick Start Ask the AI to audit a source directory for AI-written-code tells using the unslop-code scanner and report the highest-priority fixes.