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name: ai-writing-detection description: Comprehensive AI writing detection patterns and methodology. Provides vocabulary lists, structural patterns, model-specific fingerprints, and false positive prevention guidance. Use when analyzing text for AI authorship or understanding detection patterns. allowed-tools: Read, Grep, Glob, WebFetch, WebSearch

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"

Overused phrases:

• "It's important to note that..."
• "In today's fast-paced world..."
• "At its core..."
• "Without further ado..."
• "Let me explain..."

See reference/vocabulary-patterns.md for complete lists.

Structural Red Flags

• Uniform sentence lengths: 12-18 words consistently (low burstiness)
• Tricolon structures: "research, collaboration, and problem-solving"
• Em dash overuse: AI uses em dashes more than typical human writing
• Perfect paragraph uniformity: All paragraphs same approximate length
• Template conclusions: "In summary...", "In conclusion..."

See reference/structural-patterns.md for details.

Tone Red Flags

• Passive and detached voice throughout
• Absence of first-person pronouns where expected
• Consistent formality with no stylistic variation
• Over-politeness and excessive hedging

Detection Methodology

Multi-Layer Analysis Approach

Layer 1: Vocabulary Pattern Matching

• Scan for overused AI words/phrases
• Count frequency of flagged terms
• Look for clusters of high-signal vocabulary

Layer 2: Structural Analysis

• Observe sentence length variation (uniform = AI signal)
• Check paragraph uniformity
• Identify repetitive syntactic templates
• Note formatting patterns (excessive headers, bullet points)

Layer 3: Stylometric Observation

• Pronoun usage patterns (missing first-person?)
• Tone consistency (too uniform = AI signal)
• Punctuation patterns (em dash overuse?)

Layer 4: Coherence Check

• Do paragraphs build a coherent argument?
• Are concepts repeated with different words?
• Do transitions actually connect ideas?

Layer 5: Confidence Scoring

• Weight multiple signals together
• Require corroborating evidence (3+ signals minimum)
• Apply context-specific adjustments

Model-Specific Patterns

Different AI models have distinct "fingerprints":

Model	Key Tells
ChatGPT/GPT-4	"delve", "tapestry", tricolons, em dashes
Claude	Analytical structure, extended analogies, cautious qualifications
Gemini	Conversational synthesis, fact-dense paragraphs

See reference/model-fingerprints.md for detailed model patterns.

False Positive Prevention

Critical requirements:

• Minimum 200 words for reliable analysis
• Never flag on single indicators alone
• Use ensemble scoring (multiple signals required)

High false-positive risk groups:

• Non-native English speakers (61% false positive rate in research)
• Technical/formal writing
• Neurodivergent writers
• Content using grammar correction tools

See reference/false-positive-prevention.md for detailed guidance.

Analysis Output Format

Structure findings as:

**Overall Assessment**: [Likely AI / Possibly AI / Likely Human / Inconclusive]
**Confidence**: [Low / Medium / High]

**Summary**: 2-3 sentence overview

**Evidence Found**:
- [Category]: [Specific indicator] - "[Quote from text]"
- [Category]: [Specific indicator] - "[Quote from text]"

**Mitigating Factors**: [Elements suggesting human authorship]

**Caveats**: [Limitations, alternative explanations]

Key Principles

1. No certainty claims - AI detection is probabilistic
2. Multiple signals required - Single indicators prove nothing
3. Context matters - Academic writing differs from blogs
4. Stakes awareness - False accusations cause real harm
5. Evolving field - Detection methods require constant updates

Reference Files

• vocabulary-patterns.md - Complete word/phrase lists with frequencies
• structural-patterns.md - Sentence, paragraph, and discourse patterns
• model-fingerprints.md - GPT, Claude, Gemini specific tells
• false-positive-prevention.md - Avoiding false accusations

Sources

This knowledge base synthesizes research from:

• Stanford HAI (DetectGPT, bias studies)
• GPTZero, Originality.ai, Turnitin, Pangram methodologies
• Academic papers on stylometry and discourse analysis
• Empirical studies on detection accuracy and limitations