Detection Categories

The analyzer checks for 16 pattern categories from Wikipedia's guide:

Critical (Immediate AI Detection)

Category	Examples
Citation Bugs	`oaicite`, `turn0search`, `contentReference`
Knowledge Cutoff	"as of my last training", "based on available information"
Chatbot Artifacts	"I hope this helps", "Great question!", "As an AI"
Markdown	`bold`, `## headers`, `code blocks`

High Signal

Category	Examples
AI Vocabulary	delve, tapestry, landscape, pivotal, underscore, foster
Significance Inflation	"serves as a testament", "pivotal moment", "indelible mark"
Promotional Language	vibrant, groundbreaking, nestled, breathtaking
Copula Avoidance	"serves as" instead of "is", "boasts" instead of "has"

Medium Signal

Category	Examples
Superficial -ing	"highlighting the importance", "fostering collaboration"
Filler Phrases	"in order to", "due to the fact that", "Additionally,"
Vague Attributions	"experts believe", "industry reports suggest"
Challenges Formula	"Despite these challenges", "Future outlook"

Style Signal

Category	Examples
Curly Quotes	"" instead of "" (ChatGPT signature)
Em Dash Overuse	Excessive use of — for emphasis
Negative Parallelisms	"Not only... but also", "It's not just... it's"
Rule of Three	Forced triplets like "innovation, inspiration, and insight"

Scripts

detect.py — Scan for AI Patterns

python scripts/detect.py essay.txt
python scripts/detect.py essay.txt -j  # JSON output
python scripts/detect.py essay.txt -s  # score only
echo "text" | python scripts/detect.py

Output:

Issue count and word count
AI probability (low/medium/high/very high)
Breakdown by category
Auto-fixable patterns marked

transform.py — Rewrite Text

python scripts/transform.py essay.txt
python scripts/transform.py essay.txt -o output.txt
python scripts/transform.py essay.txt -a  # aggressive
python scripts/transform.py essay.txt -q  # quiet

Auto-fixes:

Citation bugs (oaicite, turn0search)
Markdown (**, ##, ```)
Chatbot sentences
Copula avoidance → "is/has"
Filler phrases → simpler forms
Curly → straight quotes

Aggressive (-a):

Simplifies -ing clauses
Reduces em dashes

compare.py — Before/After Analysis

python scripts/compare.py essay.txt
python scripts/compare.py essay.txt -a -o clean.txt

Shows side-by-side detection scores before and after transformation

Workflow

Scan for detection risk:
```
python scripts/detect.py document.txt
```

Transform with comparison:

python scripts/compare.py document.txt -o document_v2.txt

Verify improvement:

python scripts/detect.py document_v2.txt -s

Manual review for AI vocabulary and promotional language (requires judgment)

AI Probability Scoring

Rating	Criteria
Very High	Citation bugs, knowledge cutoff, or chatbot artifacts present
High	>30 issues OR >5% issue density
Medium	>15 issues OR >2% issue density
Low	<15 issues AND <2% density

Customizing Patterns

Edit scripts/patterns.json to add/modify:

ai_vocabulary — words to flag
significance_inflation — puffery phrases
promotional_language — marketing speak
copula_avoidance — phrase → replacement
filler_replacements — phrase → simpler form
chatbot_artifacts — phrases triggering sentence removal

Batch Processing

# Scan all files
for f in *.txt; do
  echo "=== $f ==="
  python scripts/detect.py "$f" -s
done

# Transform all markdown
for f in *.md; do
  python scripts/transform.py "$f" -a -o "${f%.md}_clean.md" -q
done

Reference

Based on Wikipedia's Signs of AI Writing, maintained by WikiProject AI Cleanup. Patterns documented from thousands of AI-generated text examples.

Key insight: "LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely result that applies to the widest variety of cases."

humanize-ai-text

Humanize AI Text

Quick Start