ai-writing-detector

To audit content for AI-generated patterns, this skill analyzes text for lexicon density, rhythm uniformity, and structural tropes, providing a weighted 0-100 scorecard to verify human-centric authenticity before publishing.

AI writing detection uses script-based mechanical metrics like lexicon hits, hedge density, and rhythm uniformity, combined with qualitative judgment of stance and texture to evaluate editorial content quality.

Yes, you can use this AI writing detector for blog posts and emails. It applies mechanical analysis and judgment-based scoring to ensure marketing copy passes as high-quality human writing.

Yes, Python-based AI content detection works offline using built-in libraries like json, re, statistics, and sys to mechanically analyze text construction and calculate density metrics without external API calls.

The best way to score content quality for AI signposts is to run a comprehensive audit that evaluates audience awareness and generic trope reliance, generating a ranked scorecard with specific fixes.

AI generated text fails editorial quality checks when it relies too heavily on generic signposts, hedge words, and rhythm uniformity, lacking the specific evidence and judgment required to pass as authentic human writing.