AI-Generated Text Detection Skill

Purpose

Detect patterns typical of LLM-generated text to ensure natural, human-authored academic writing. This skill helps maintain authenticity in research publications, dissertations, and documentation.

When to Use This Skill

Primary Use Cases

1. Pre-Commit Validation – Automatically check manuscripts and documentation before git commits
2. Manuscript Review – Validate academic writing before submission to journals or committees
3. Quality Assurance – Part of systematic QA workflow for research artifacts
4. On-Demand Analysis – Manual review of any text file for AI patterns
5. Writing Evolution Tracking – Monitor writing style changes over time

Specific Scenarios

• Before submitting dissertation chapters to advisor
• Prior to journal article submission
• When reviewing team member contributions
• After making significant edits to documentation
• When suspicious patterns are noticed in writing
• During peer review or committee review preparation

Detection Methodology

1. Grammar Pattern Analysis

What We Check:

• Excessive Perfection: Zero typos, missing commas, or minor errors throughout
• Comma Placement: Perfect comma usage in all complex sentences
• Formal Register: Consistent formal tone with no informal elements
• Grammar Consistency: No variations in grammatical choices

Red Flags:

• Absolutely no grammatical errors in 10+ pages
• Every semicolon and colon used perfectly
• No sentence fragments or run-ons even in appropriate contexts
• Overly formal language even in methods sections

Human Writing Typically Has:

• Occasional minor typos or comma splices
• Some inconsistency in formality
• Natural variations in grammar choices
• Context-appropriate informality

2. Sentence Structure Uniformity

What We Check:

• Sentence Length Distribution: Variation in sentence lengths
• Structural Patterns: Repetitive sentence structures
• Complexity Variation: Mix of simple, compound, complex sentences
• Opening Patterns: How sentences begin

Red Flags:

• Most sentences 15-25 words (AI sweet spot)
• Repetitive subject-verb-object patterns
• Every paragraph starts with topic sentence
• Excessive use of transition words at sentence starts
• Predictable sentence complexity patterns

Human Writing Typically Has:

• Wide variation (5-40+ word sentences)
• Unpredictable sentence structures
• Occasional fragments for emphasis
• Natural flow without forced transitions

3. Paragraph Structure Analysis

What We Check:

• Paragraph Length: Uniformity vs. natural variation
• Structural Pattern: Topic sentence + support + conclusion pattern
• Information Flow: Natural vs. algorithmic organization
• Paragraph Transitions: Connection between paragraphs

Red Flags:

• All paragraphs 4-6 sentences long
• Every paragraph follows same structure
• Mechanical transitions between paragraphs
• Perfectly balanced paragraph lengths
• No single-sentence paragraphs

Human Writing Typically Has:

• Paragraph length variation (1-10+ sentences)
• Structural diversity based on content
• Natural transitions
• Strategic use of short/long paragraphs for emphasis

4. Word Frequency Analysis (AI-Typical Words)

High-Risk AI Words (overused by LLMs):

Verbs:

• “delve” (rarely used by humans)
• “leverage” (business jargon)
• “utilize” (instead of “use”)
• “facilitate” (overly formal)
• “demonstrate” (overused)
• “implement” (in non-technical contexts)
• “enhance” (marketing language)

Adjectives:

• “robust” (technical overuse)
• “comprehensive” (vague intensifier)
• “innovative” (buzzword)
• “cutting-edge” (cliché)
• “significant” (statistical overuse)
• “substantial” (formal overuse)
• “considerable” (formal overuse)
• “crucial” (intensity overuse)

Transition Words (overused):

• “furthermore” (very formal)
• “moreover” (archaic feeling)
• “additionally” (redundant)
• “consequently” (overused)
• “subsequently” (temporal overuse)
• “nevertheless” (formal overuse)
• “nonetheless” (synonym overuse)

Phrases:

• “it is important to note that”
• “it should be emphasized that”
• “a comprehensive analysis of”
• “in the context of”
• “with respect to”
• “in terms of”
• “in order to” (instead of “to”)

Detection Criteria:

• Count frequency per 1000 words
• Compare to human academic writing baselines
• Flag if 3+ high-risk words per 1000 words
• Weight by word rarity (delve = high weight)

5. Punctuation Patterns

What We Check:

• Semicolon Usage: Frequency and correctness
• Colon Usage: Perfect usage patterns
• Em-Dash Usage: Consistent stylistic choices
• Comma Patterns: Perfection vs. natural variation
• Ellipsis/Exclamation: Absence in informal contexts

Red Flags:

• Excessive semicolon use (2+ per paragraph)
• Perfect colon usage throughout
• Consistent em-dash formatting (—)
• No missing commas anywhere
• Zero informal punctuation

Human Writing Typically Has:

• Inconsistent punctuation choices
• Occasional missing/extra commas
• Variable dash formatting (- vs — vs —)
• Some informal punctuation where appropriate

Confidence Scoring System

Scoring Formula

Overall Confidence = Weighted average of:

• Grammar perfection: 20%
• Sentence uniformity: 25%
• Paragraph structure: 20%
• AI-typical words: 25%
• Punctuation patterns: 10%

Each metric scored 0-100, then combined with weights.

Confidence Levels

Low Confidence (0-30%): Likely Human Writing

Characteristics:

• Natural sentence length variation (5-40+ words)
• Occasional grammatical imperfections
• Authentic voice and natural flow
• Domain-specific terminology used naturally
• Structural variety in paragraphs
• Minimal AI-typical words (0-2 per 1000 words)

Action: ✅ Writing appears authentic, no changes needed

Medium Confidence (30-70%): Possible AI Assistance

Characteristics:

• Some uniformity in sentence structure
• Mix of AI-typical and natural patterns
• May be human-edited AI output
• Overly formal in places
• Some transition word overuse
• 3-5 AI-typical words per 1000 words

Action: ⚠️ Review flagged sections, apply suggestions selectively

Examples of Mixed Writing:

• AI-generated first draft with heavy human editing
• Human writing that mimics academic formality excessively
• Non-native English speakers using formal templates
• Multiple authors with different styles

High Confidence (70-100%): Likely AI-Generated

Characteristics:

• Excessive uniformity across all metrics
• Multiple AI-typical word clusters
• Perfect grammar and punctuation throughout
• Artificial transition patterns
• Mechanical paragraph structure
• 6+ AI-typical words per 1000 words

Action: 🚫 Significant revision needed, rewrite in authentic voice

Output Format

When running AI-check analysis, generate a comprehensive report:

1. Executive Summary

Overall Confidence Score: 65%
Status: MEDIUM - Possible AI assistance detected
Files Analyzed: 1
Total Words: 3,456
Recommendation: Review flagged sections

2. Metric Breakdown

Grammar Perfection:     85% (High - suspiciously few errors)
Sentence Uniformity:    72% (High - repetitive structures)
Paragraph Structure:    68% (Medium - some variation)
AI-Typical Words:       58% (Medium - 4.2 per 1000 words)
Punctuation Patterns:   45% (Low - natural variation)

3. Flagged Sections

Lines 45-67 (Confidence: 82%)
  Pattern: Excessive transition words + uniform sentences
  AI Words: "moreover", "furthermore", "leverage", "robust"
  
Lines 112-134 (Confidence: 76%)
  Pattern: Perfect grammar + mechanical structure
  AI Words: "delve", "comprehensive", "facilitate"

4. Specific Issues Detected

High-Risk AI Words Found (per 1000 words):
  • "delve" (2 occurrences) - RARELY used by humans
  • "leverage" (3 occurrences) - Business jargon overuse
  • "robust" (4 occurrences) - Technical overuse
  • "furthermore" (6 occurrences) - Formal transition overuse

Sentence Uniformity Issues:
  • 67% of sentences are 15-25 words (AI sweet spot)
  • 82% of paragraphs start with transition words
  • Low variation in sentence complexity

Paragraph Structure Issues:
  • All paragraphs 4-6 sentences long
  • Mechanical topic-sentence pattern throughout

5. Word Frequency Report

Top AI-Typical Words:
1. "furthermore" - 6x (baseline: 0.5x per 1000 words)
2. "robust" - 4x (baseline: 0.8x per 1000 words)
3. "leverage" - 3x (baseline: 0.3x per 1000 words)
4. "comprehensive" - 3x (baseline: 1.2x per 1000 words)
5. "delve" - 2x (baseline: 0.1x per 1000 words)

Comparison to Human Academic Writing:
  Your text: 4.2 AI-typical words per 1000
  Human baseline: 1.5 AI-typical words per 1000
  Ratio: 2.8x higher than human baseline

Improvement Suggestions

For High Confidence (70-100%) Detections

Sentence Structure:

• ❌ “Furthermore, the results demonstrate a comprehensive analysis of the robust dataset.”
• ✅ “The results show our analysis covered the full dataset.”

Why Better: Simpler words, no transition word, more direct

Word Choice:

• ❌ “This study delves into the utilization of innovative methodologies.”
• ✅ “We examine how researchers use new methods.”

Why Better: Active voice, common words, clearer meaning

Paragraph Variation:

• ❌ All paragraphs 5 sentences, topic sentence + 3 support + conclusion
• ✅ Mix paragraph lengths: 2, 7, 4, 3, 6 sentences based on content needs

Why Better: Natural flow based on content, not formula

Specific Suggestion Categories

1. Vary Sentence Lengths

Current: 15-25 word sentences consistently
Suggestion: Mix short (5-10), medium (15-20), long (25-35) sentences
Example:
  - Short: "The effect was significant."
  - Medium: "We observed a 23% increase across all conditions."
  - Long: "This finding aligns with previous work showing that..."

2. Replace AI-Typical Words

Replace → With
- "delve into" → "examine", "explore", "investigate"
- "leverage" → "use", "apply", "employ"
- "utilize" → "use"
- "robust" → "strong", "reliable", "thorough"
- "facilitate" → "enable", "help", "allow"
- "furthermore" → "also", "next", [or remove]
- "moreover" → "additionally", "also", [or use dash]
- "comprehensive" → "complete", "thorough", "full"

3. Add Natural Imperfections (Where Appropriate)

- Use contractions in appropriate contexts ("it's", "we'll")
- Include domain-specific jargon naturally
- Allow informal phrasing in methods/procedures
- Use occasional sentence fragments for emphasis
- Add personal observations or interpretations
- Include field-specific colloquialisms

4. Break Paragraph Uniformity

Current: All paragraphs follow topic-support-support-support-conclusion
Suggestion: Vary based on content
  - Use single-sentence paragraphs for emphasis
  - Combine related ideas into longer paragraphs
  - Don't force every paragraph to have 5 sentences
  - Let content determine structure, not formula

5. Remove Mechanical Transitions

❌ "Furthermore, the results show... Moreover, the analysis reveals..."
✅ "The results show... The analysis also reveals..." [simpler transitions]
✅ "The results show... Looking closer, the analysis..." [natural bridges]

Integration Points

1. Pre-Commit Hook Integration

Automatic checking before git commits

# Configured in .claude/settings.json
"gitPreCommit": {
  "command": "python3 hooks/pre-commit-ai-check.py",
  "enabled": true
}

Behavior:

• Runs on staged .md, .tex, .rst files
• Warns if confidence 30-70%
• Blocks commit if confidence >70%
• User can override with git commit --no-verify

Exit Codes:

• 0: Pass (confidence <30%)
• 1: Warning (confidence 30-70%, commit allowed)
• 2: Block (confidence >70%, commit blocked)

2. Quality Assurance Integration

Part of comprehensive QA workflow

Integrated into code/quality_assurance/qa_manager.py:

• Runs during manuscript phase QA validation
• Checks all deliverable documents
• Generates detailed QA report section
• Fails QA if confidence >40%

Configuration (.ai-check-config.yaml):

qa_integration:
  enabled: true
  max_confidence_threshold: 0.40
  check_manuscripts: true
  check_documentation: true
  generate_detailed_reports: true

3. Manuscript Writer Agent Integration

Real-time feedback during writing

Agent checks writing incrementally:

• After drafting each section
• Before moving to next phase
• Applies suggestions automatically
• Re-checks until confidence <30%

Agent Workflow:

1. Draft section
2. Run ai-check skill
3. Review detection results
4. Apply improvement suggestions
5. Re-check until authentic
6. Proceed to next section

4. Standalone Skill Usage

Manual invocation by user or agents

User Invocation:

Please run ai-check on docs/manuscript/discussion.tex and provide detailed feedback.

Agent Invocation:

I'll use the ai-check skill to verify this text before proceeding.

CLI Tool:

python tools/ai_check.py path/to/file.md
python tools/ai_check.py --directory docs/
python tools/ai_check.py --format html --output report.html

Tracking System

Historical Tracking

Log all AI-check runs to database for evolution tracking:

Database Schema (PostgreSQL via research-database MCP):

CREATE TABLE ai_check_history (
  id SERIAL PRIMARY KEY,
  file_path TEXT NOT NULL,
  git_commit TEXT,
  timestamp TIMESTAMP DEFAULT NOW(),
  overall_confidence FLOAT,
  grammar_score FLOAT,
  sentence_score FLOAT,
  paragraph_score FLOAT,
  word_score FLOAT,
  punctuation_score FLOAT,
  ai_words_found JSONB,
  flagged_sections JSONB
);

Trend Analysis

Track writing evolution:

File: docs/manuscript/discussion.tex

Version History:
2025-01-15: 78% confidence (HIGH - likely AI)
2025-01-18: 52% confidence (MEDIUM - revision 1)
2025-01-20: 34% confidence (LOW-MEDIUM - revision 2)
2025-01-22: 18% confidence (LOW - authentic writing)

Trend: ✅ Improving toward authentic writing

Use Cases:

• Monitor dissertation chapters over time
• Track improvements after applying suggestions
• Demonstrate writing authenticity to committee
• Identify sections needing more work

Configuration

Configuration File: .ai-check-config.yaml

# AI-Check Skill Configuration

# Pre-Commit Hook Settings
pre_commit:
  enabled: true
  check_files: [".md", ".tex", ".rst", ".txt"]
  check_docstrings: true  # Check Python docstrings
  block_threshold: 0.70   # Block commit if >= 70%
  warn_threshold: 0.30    # Warn if >= 30%
  exclude_patterns:
    - "*/examples/*"
    - "*/tests/*"
    - "*/node_modules/*"
    - "*/.venv/*"

# Quality Assurance Integration
qa_integration:
  enabled: true
  max_confidence_threshold: 0.40  # Fail QA if >= 40%
  check_manuscripts: true
  check_documentation: true
  generate_detailed_reports: true
  track_history: true

# Detection Parameters
detection:
  # Weight each metric (must sum to 1.0)
  weights:
    grammar_perfection: 0.20
    sentence_uniformity: 0.25
    paragraph_structure: 0.20
    ai_word_frequency: 0.25
    punctuation_patterns: 0.10
  
  # AI-typical word lists
  ai_words:
    high_risk: ["delve", "leverage", "utilize"]
    medium_risk: ["robust", "comprehensive", "facilitate"]
    transitions: ["furthermore", "moreover", "additionally"]
  
  # Thresholds
  ai_words_per_1000_threshold: 3.0
  human_baseline_per_1000: 1.5
  
# Report Generation
reporting:
  default_format: "markdown"  # markdown, json, html
  include_suggestions: true
  include_word_frequency: true
  include_flagged_sections: true
  max_flagged_sections: 10

# Tracking
tracking:
  enabled: true
  database: "research-database-mcp"
  retention_days: 365

Per-Project Overrides

Create .ai-check.local.yaml for project-specific settings:

# Project-specific overrides
pre_commit:
  block_threshold: 0.60  # More lenient for early drafts
  
detection:
  ai_words:
    high_risk: ["delve"]  # Only flag worst offenders

Examples

Example 1: High Confidence Detection

Input Text:

Furthermore, this comprehensive study delves into the robust 
methodologies utilized to facilitate the implementation of innovative 
approaches. Moreover, the analysis demonstrates significant findings 
that leverage state-of-the-art techniques. Subsequently, the results 
indicate substantial improvements across all metrics. Nevertheless, 
additional research is crucial to fully comprehend the implications.

AI-Check Report:

Overall Confidence: 89% (HIGH - Likely AI-generated)

Issues Detected:
- 8 AI-typical words in 60 words (13.3 per 1000 words!)
- Every sentence starts with transition word
- Uniform sentence length (15-18 words each)
- Perfect grammar, zero natural imperfections
- Mechanical paragraph structure

AI Words Found:
- furthermore, comprehensive, delves, robust
- utilized, facilitate, innovative, leverage
- demonstrates, significant, subsequently, substantial
- nevertheless, crucial, comprehend

Recommendation: Complete rewrite recommended

Suggested Revision:

We examined the methods used in this approach. The analysis shows 
clear improvements across metrics. However, more research is needed 
to understand the full implications.

(23 words, 12% confidence - much more natural)

Example 2: Medium Confidence Detection

Input Text:

The experimental design followed standard protocols established in 
previous work (Smith et al., 2023). We collected data from 150 
participants over six months. Statistical analysis used mixed-effects 
models to account for repeated measures. The results showed a 
significant main effect of condition (p < 0.001).

AI-Check Report:

Overall Confidence: 35% (MEDIUM - Possible minor AI assistance)

Issues Detected:
- Slightly uniform sentence length (11-15 words)
- One AI-typical word: "significant" (statistical context acceptable)
- Otherwise natural academic writing

Recommendation: Minor revisions optional, writing appears largely authentic

Example 3: Low Confidence (Human Writing)

Input Text:

OK so here's what we found. The effect was huge - way bigger than 
expected. Participants in the experimental group scored 23% higher 
on average. This wasn't just statistically significant; it was 
practically meaningful.

We're still not sure why. Maybe it's the timing? Could be the 
instructions were clearer. Need to run follow-ups.

AI-Check Report:

Overall Confidence: 8% (LOW - Clearly human writing)

Human Writing Indicators:
- Natural sentence variation (4-19 words)
- Informal elements ("OK so", "way bigger")
- Incomplete thoughts and questions
- Natural uncertainty expressions
- Zero AI-typical words
- Authentic voice throughout

Recommendation: Writing is authentic, no changes needed

Best Practices

For PhD Students

1. Run Before Advisor Meetings

   • Check chapters before sending to advisor
   • Ensure authenticity before committee review
   • Track improvements over time

2. Use During Drafting

   • Check each section after writing
   • Apply suggestions immediately
   • Develop natural writing habits

3. Pre-Submission Validation

   • Run on complete manuscripts before journal submission
   • Check supplementary materials
   • Verify all documentation

For Research Teams

1. Establish Team Standards

   • Set agreed-upon confidence thresholds
   • Define when to block vs. warn
   • Create team-specific word lists

2. Code Review Integration

   • Check documentation in pull requests
   • Validate README files and guides
   • Ensure authentic technical writing

3. Track Team Writing

   • Monitor trends across team members
   • Identify systematic issues
   • Share improvement strategies

For Journal Submission

1. Pre-Submission Checklist

   • Overall confidence <30%
   • No flagged high-risk sections
   • AI-typical words <2 per 1000 words
   • Natural sentence variation present
   • Authentic academic voice throughout

2. Demonstrating Authenticity

   • Include AI-check reports in submission materials
   • Show writing evolution over time
   • Document revision process

Limitations

What This Skill Cannot Do

1. Not 100% Accurate

   • LLMs constantly improving
   • Patterns evolve over time
   • False positives possible (very formal human writing)
   • False negatives possible (heavily edited AI text)

2. Cannot Detect All AI Usage

   • Well-edited AI text may pass
   • Human writing in AI style may be flagged
   • Paraphrasing tools may evade detection
   • Future models may have different patterns

3. Domain Limitations

   • Trained primarily on academic writing
   • May not work well for creative writing
   • Technical jargon may affect scores
   • Non-English text not supported

Use Alongside Human Judgment

This skill is a tool, not a replacement for human judgment:

• Use confidence scores as guidance, not absolute truth
• Consider context and field-specific norms
• Combine with plagiarism detection tools
• Maintain academic integrity standards
• Update word lists as AI patterns evolve

Support

Troubleshooting

Problem: False positive on authentic writing Solution: Check if writing is overly formal. Consider field-specific norms. Adjust thresholds in config.

Problem: AI text passing with low confidence Solution: Update AI-typical word lists. Check for heavily edited text. Report patterns for skill updates.

Problem: Pre-commit hook too slow Solution: Reduce checked file types. Enable caching. Check only modified sections.

Problem: Disagreement with manual review Solution: Generate detailed report. Review flagged sections specifically. Consider multiple metrics not just overall score.

Getting Help

• Documentation: See docs/skills/ai-check-reference.md
• Issues: https://github.com/astoreyai/ai_scientist/issues
• Updates: Check for skill updates regularly as AI patterns evolve

Last Updated: 2025-11-09 Version: 1.0.0 License: MIT