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ai-writing-detection 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.
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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"
"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 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 in a formulaic way to mimic "punched up" sales writing, especially in parallelisms ("it's not X — it's Y"); swapping punctuation doesn't fix the underlying emphasis pattern
Perfect paragraph uniformity : All paragraphs same approximate length
Template conclusions : "In summary...", "In conclusion..."
Negative parallelisms : "It's not about X; it's about Y"
Elegant variation : Cycling through synonyms to avoid repetition
False ranges : "From X to Y" with incoherent endpoints
Content Red Flags
Importance puffery : "marking a pivotal moment in history"Ecosystem/conservation claims without citations"Challenges and Future" sections following rigid formulaPromotional language : "nestled in", "stunning natural beauty", "boasts"Superficial analyses : "-ing" phrases attributing significance to facts
Formatting Red Flags
Title Case in all section headingsExcessive boldface (every key term bolded)Inline-header lists : **Bold Header**: description patternEmojis in formal content or headingsSubject lines in non-email contexts
Markup Red Flags (Definitive)
turn0search0, turn0image0 : ChatGPT reference markerscontentReference[oaicite:] : ChatGPT reference bugsutm_source=chatgpt.com : URL tracking (definitive)Markdown in wikitext : ## headers, bold , text grok_card XML tags : Grok/X specific
Citation Red Flags
Broken external links that never existed (no archive)Invalid DOIs/ISBNs : Checksum failuresDeclared but unused references : Cite errorsPlaceholder values : url=URL, date=2025-XX-XX
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: Technical Artifact Scan (Definitive)
Check for turn0search/oaicite markers (ChatGPT)
Check for utm_source=chatgpt.com in URLs
Check for grok_card tags (Grok)
Check for Markdown in non-Markdown contexts
If found: Definitive AI involvement
Layer 2: Vocabulary Pattern Matching
Scan for overused AI words/phrases
Count frequency of flagged terms
Look for clusters of high-signal vocabulary
Check for importance/symbolism phrases
Layer 3: Structural Analysis
Observe sentence length variation (uniform = AI signal)
Check paragraph uniformity
Identify repetitive syntactic templates (tricolons, negative parallelisms)
Look for elegant variation (synonym cycling)
Check for false ranges
Layer 4: Content Pattern Analysis
Check for importance puffery and promotional language
Look for "Challenges and Future" formula
Check for ecosystem/conservation claims without citations
Identify superficial analyses with "-ing" attributions
Layer 5: Citation Verification
Test external links - do they exist?
Verify DOI/ISBN checksums
Check for declared but unused references
Look for placeholder values
Layer 6: Formatting Analysis
Check heading capitalization (Title Case = signal)
Count bold phrases per paragraph
Look for inline-header list patterns
Check for emojis in formal content
Layer 7: Stylometric Observation
Pronoun usage patterns (missing first-person?)
Tone consistency (too uniform = AI signal)
Punctuation patterns (em dash overuse? curly quotes?)
Do paragraphs build a coherent argument?
Are concepts repeated with different words?
Do transitions actually connect ideas?
Layer 9: Confidence Scoring
Weight multiple signals together
Require corroborating evidence (3+ signals minimum)
Apply context-specific adjustments
Check for mitigating factors (human signals)
Consider ineffective indicators (don't use them)
Model-Specific Patterns Different AI models have distinct "fingerprints":
Model Key Tells Technical Artifacts ChatGPT/GPT-4 "delve" (pre-2025), "tapestry", tricolons, em dashes, curly quotes turn0search, oaicite, utm_source=chatgpt.com Claude Analytical structure, extended analogies, cautious qualifications None (uses straight quotes, no tracking) Gemini Conversational synthesis, fact-dense paragraphs None (uses straight quotes, no tracking) DeepSeek Similar to ChatGPT, curly quotes Curly quotation marks Grok X/Twitter integration <grok_card> XML tagsPerplexity Source-focused output [attached_file:1], [web:1] tags
ChatGPT launched: November 30, 2022 (text before this is almost certainly human)
"delve" usage dropped: 2025 (still signals pre-2025 ChatGPT)
False Positive Prevention
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
Ineffective indicators (do NOT rely on these):
Perfect grammar alone
"Bland" or "robotic" prose
"Fancy" or unusual vocabulary
Letter-like formatting alone
Conjunctions starting sentences
Text from before November 30, 2022
Ability to explain editorial choices
Personal anecdotes with verifiable details
Minor errors and natural quirks
Analysis Output Format **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
No certainty claims - AI detection is probabilisticMultiple signals required - Single indicators prove nothingContext matters - Academic writing differs from blogsStakes awareness - False accusations cause real harmEvolving field - Detection methods require constant updates
Reference Files
vocabulary-patterns.md - Complete word/phrase lists with frequenciesstructural-patterns.md - Sentence, paragraph, and discourse patternscontent-patterns.md - Importance puffery, promotional language, content tellsformatting-patterns.md - Title case, boldface, emojis, visual patternsmarkup-artifacts.md - Technical artifacts: turn0search, oaicite, Markdown, trackingcitation-patterns.md - Broken links, invalid identifiers, hallucinated referencesmodel-fingerprints.md - GPT, Claude, Gemini, Grok, Perplexity specific tellsfalse-positive-prevention.md - Avoiding false accusations, ineffective indicators
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
Wikipedia:WikiProject AI Cleanup field guide (2025)
Community-documented patterns from Wikipedia editing
