ai-writing-detection
This skill provides a systematic framework for identifying AI authorship through nine layers of analysis, from technical artifact detection to stylometric observation. It catalogs high-signal vocabulary, structural quirks, and model-specific fingerprints across ChatGPT, Claude, Gemini, and other systems, while emphasizing false-positive prevention for non-native speakers and formal writing. Use it to evaluate whether text originates from human or AI sources with confidence scoring and context-aware assessment.
AI Writing Detection helps you identify whether text was written by AI or humans using pattern analysis and technical markers.
AI-generated summary based on this skill's SKILL.md
Install
mike-coulbourn/claude-vibes/ai-writing-detection
git clone https://github.com/mike-coulbourn/claude-vibes
cp -r claude-vibes/plugins/vibes/skills/ai-writing-detection ~/.claude/skills/ai-writing-detectionnpx skillfed install mike-coulbourn/claude-vibes/ai-writing-detectionFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How to detect AI written text using ai-writing-detection?
ai-writing-detection provides a nine-layer analysis framework to identify AI authorship. Start by examining technical artifacts, vocabulary patterns, and structural quirks. The skill catalogs high-signal markers across ChatGPT, Claude, Gemini, and other systems. Combine stylometric observation with context-aware assessment to generate confidence scores. Always cross-reference findings against false-positive risk factors, particularly for non-native speakers and formal writing contexts.
What are model-specific AI fingerprints in ai-writing-detection?
ai-writing-detection identifies distinctive fingerprints from ChatGPT, Claude, Gemini, and other AI systems. Each model exhibits characteristic vocabulary choices, structural preferences, and formatting patterns. The skill documents these model-specific markers to help you pinpoint which AI system likely generated a given text. Understanding these fingerprints strengthens your ability to distinguish between different AI sources and recognize their unique stylistic signatures.
How does ai-writing-detection prevent false positives?
ai-writing-detection emphasizes false-positive prevention by accounting for non-native speakers, formal writing conventions, and legitimate stylistic variation. The framework includes context-aware assessment protocols that distinguish genuine human writing from AI-generated content without over-flagging legitimate edge cases. This prevents incorrect accusations and ensures your detection methodology remains reliable across diverse writing contexts and author backgrounds.
What vocabulary and structural patterns does ai-writing-detection teach?
ai-writing-detection catalogs high-signal vocabulary red flags, structural quirks, and formatting patterns characteristic of AI-generated text. You'll learn which word choices, sentence constructions, and organizational markers commonly appear in machine-generated content. The skill also covers citation verification techniques and technical artifacts that reveal AI authorship, enabling you to recognize these patterns across different writing samples and contexts.
Can ai-writing-detection identify ChatGPT generated content?
Yes. ai-writing-detection includes detection markers and artifacts specific to ChatGPT. The skill documents ChatGPT's characteristic vocabulary preferences, structural tendencies, and formatting patterns. By analyzing your text against these model-specific fingerprints alongside the broader nine-layer framework, you can identify whether ChatGPT likely generated the content and assess confidence levels for your conclusion.
What is the ai-writing-detection methodology for text analysis?
ai-writing-detection employs a systematic nine-layer methodology combining technical artifact detection, stylometric analysis, vocabulary assessment, structural pattern recognition, and citation verification. The approach integrates model-specific fingerprinting for ChatGPT, Claude, Gemini, and others with false-positive prevention protocols. This comprehensive framework enables confidence-scored evaluation of whether text originates from human or AI sources while accounting for legitimate writing variation.
SKILL.md
rendered from the published skill — quoted content, verbatim
AI Writing Detection Reference
Expert-level knowledge base for detecting AI-generated text, compiled from academic research, commercial detection tools, and empirical analysis.
Quick Reference: High-Confidence Signals
These indicators strongly suggest AI authorship when found together:
Vocabulary Red Flags
High-signal words (50-700x more common in AI text): - "delve", "tapestry", "nuanced", "multifaceted", "underscore" - "intricate interplay", "played a crucial role", "complex and multifaceted" - "paramount", "pivotal", "meticulous", "holistic", "robust" - "stands/serves as", "marking a pivotal moment", "underscores its importance"
Overused phrases: - "It's important to note that..." - "In today's fast-paced world..." - "At its core..." - "Without further ado..." - "Let me explain..."
See
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plugins/vibes/skills/ai-writing-detection/SKILL.md
plugins/vibes/skills/ai-writing-detection/reference/citation-patterns.md
plugins/vibes/skills/ai-writing-detection/reference/content-patterns.md
plugins/vibes/skills/ai-writing-detection/reference/false-positive-prevention.md
plugins/vibes/skills/ai-writing-detection/reference/formatting-patterns.md
plugins/vibes/skills/ai-writing-detection/reference/markup-artifacts.md
plugins/vibes/skills/ai-writing-detection/reference/model-fingerprints.md
plugins/vibes/skills/ai-writing-detection/reference/structural-patterns.md
plugins/vibes/skills/ai-writing-detection/reference/vocabulary-patterns.md