name: humanize-ai-text description: Humanize AI-generated text to bypass detection. This humanizer rewrites ChatGPT, Claude, and GPT content to sound natural and pass AI detectors like GPTZero, Turnitin, and Originality.ai. Based on Wikipedia's comprehensive "Signs of AI Writing" guide. Makes robotic AI writing undetectable and human-like. allowed-tools:
- Read
- Write
- StrReplace
- Glob
Humanize AI Text
Comprehensive CLI for detecting and transforming AI-generated text to bypass detectors. Based on Wikipedia's Signs of AI Writing.
Quick Start
# Detect AI patterns
python scripts/detect.py text.txt
# Transform to human-like
python scripts/transform.py text.txt -o clean.txt
# Compare before/after
python scripts/compare.py text.txt -o clean.txt
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 flagsignificance_inflation— puffery phrasespromotional_language— marketing speakcopula_avoidance— phrase → replacementfiller_replacements— phrase → simpler formchatbot_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."