humanize-ai-text

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 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."