ai-writing-detector

Name: ai-writing-detector
Availability: InStock
Author: theycallmeholla

Analyze text for AI-generation patterns and produce a weighted quality scorecard.

Updated Jul 23, 2026

Search Tags:#writing #audit #copywriting #editorial #content-quality #ai-detection

Ready to run

npx skills add https://github.com/Theycallmeholla/skills --skill ai-writing-detector-theycallmeholla

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ai-writing-detector
Source: https://github.com/Theycallmeholla/skills/tree/main/skills/ai-writing-detector
Command: npx skills add https://github.com/Theycallmeholla/skills --skill ai-writing-detector-theycallmeholla

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires json, re, statistics, sys, and includes scripts (resource) components.

What problem does it solve?

This skill addresses the lack of transparency in content quality by identifying the specific linguistic patterns and structural tropes commonly associated with AI-generated text, helping editors ensure their content feels authentic and human-led.

Core Features & Use Cases

Mechanical Analysis: Uses script-based metrics to count lexicon hits, hedge density, and rhythm uniformity.
Judgment-Based Scoring: Evaluates stance, texture, and audience awareness to provide a comprehensive 0-100 scorecard.
Use Case: Before publishing a blog post or email, run this skill to identify if the text relies too heavily on generic AI signposts or lacks the specific evidence and judgment required to pass as high-quality human writing.

Quick Start

Use the ai-writing-detector skill to audit the text in the provided document and generate a scorecard with ranked fixes.

Frequently Asked Questions about ai-writing-detector

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I audit content for AI-generated patterns in marketing copy?▼

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.

What metrics are used to detect AI writing patterns in editorial workflows?▼

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.

Can I use this AI writing detector for blog posts and email content?▼

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.

Does Python-based AI content detection work without external API dependencies?▼

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.

What is the best way to score content quality for AI signposts?▼

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.

Why does AI generated text fail editorial quality checks?▼

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.