Initial commit with translated description
This commit is contained in:
182
SKILL.md
Normal file
182
SKILL.md
Normal file
@@ -0,0 +1,182 @@
|
||||
---
|
||||
name: humanize-ai-text
|
||||
description: "人性化AI生成的文本以绕过检测。"
|
||||
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](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
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 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
|
||||
|
||||
```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
|
||||
|
||||
---
|
||||
|
||||
## 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)
|
||||
|
||||
---
|
||||
|
||||
## 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
|
||||
|
||||
```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
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 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."
|
||||
Reference in New Issue
Block a user