Humanize AI text

Low
by moltbro | Audited: 2026-02-26T09:59:20.936Z | Ruleset: 0.2.0

Quick Install

Add this skill to your agent

clawhub install humanize-ai-text

About This Skill

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 Wi…

Issue count and word count
AI probability (low/medium/high/very high)
Breakdown by category
Auto-fixable patterns marked
Citation bugs (oaicite, turn0search)
Chatbot sentences
Copula avoidance → "is/has"
Filler phrases → simpler forms

Use Cases

1 Simplifies -ing clauses
2 significance_inflation — puffery phrases
3 chatbot_artifacts — phrases triggering sentence removal

Documentation (Original)

Source: SKILL.md
The following is the author's original documentation (often English). For installation, follow “Quick Install” above.

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

  1. Scan for detection risk:

    python scripts/detect.py document.txt
    
  2. Transform with comparison:

    python scripts/compare.py document.txt -o document_v2.txt
    
  3. Verify improvement:

    python scripts/detect.py document_v2.txt -s
    
  4. 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."

Security Audit

Low

Summary

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.

Risk Profile Toxicity Privacy Scope Reputation Quality

ToxicSkills Analysis

Blocklist
Not matched
Prompt Injection
Not detected

No Toxic signals detected by current static checks.

Key Risks 0 items

No LLM risk bullets (LLM disabled or not cached).

Deterministic Findings (Evidence)

No findings detected.

Scoring Criteria

Each skill is scored across 5 dimensions. The weighted total determines the star rating.

Code Toxicity 100/100 (weight 30%)
Privacy Risk 100/100 (weight 25%)
Permission Scope 100/100 (weight 20%)
Author Reputation 75/100 (weight 15%)
Code Quality 70/100 (weight 10%)

Star Rating Scale

5★ Safe — Score ≥ 80
4★ Good — Score 70–79
3★ Caution — Score 60–69
2★ Risky — Score 40–59
1★ Dangerous — Score < 40

Why This Score?

All dimensions scored above 60. This skill passed the safety baseline.

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