Introduction
# Humanize AI Text
Comprehensive CLI for detecting and transforming AI-generated text to bypass detectors. Based on [Wikipedia's Signs of AI Writing](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing).
## Quick Start
```bash # 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 ```
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## 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" |
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## Scripts
### detect.py — Scan for AI Patterns
```bash 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
```bash 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
```bash 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
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## Workflow
1. **Scan** for detection risk: ```bash python scripts/detect.py document.txt ```
2. **Transform** with comparison: ```bash python scripts/compare.py document.txt -o document_v2.txt ```
3. **Verify** improvement: ```bash python scripts/detect.py document_v2.txt -s ```
4. **Manual review** for AI vocabulary and promotional language (requires judgment)
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## 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 |
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## 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
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## Batch Processing
```bash # 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 ```
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## Reference
Based on Wikipedia's [Signs of AI Writing](https://en.wikipedia.org/wiki/Wikipedia: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."