ai-writing-analyzer

name	ai-writing-analyzer
description	Detect AI writing patterns in text. Use when: (1) need to check if text sounds AI-generated, (2) reviewing content before publication, (3) analyzing writing for Wikipedia-style AI markers, (4) generating report-only analysis without changes.
author	cajias
version	1.0.0
date	"2025-01-27T00:00:00.000Z"
tags	["writing","ai-detection","analysis"]

AI Writing Analyzer

Detect AI writing patterns in text using Wikipedia's "Signs of AI Writing" guidelines. For fixing detected issues, see ai-writing-fixer.

Prerequisites

Access to the pattern database in ../ai-writing-humanizer/patterns/patterns.json

Pattern Categories (15 Total)

Priority	Category	Examples
Critical	Chatbot Artifacts	"I hope this helps", "As an AI..."
High	Buzzwords	"leverage", "utilize", "delve into"
High	Inflated Symbolism	"stands as a testament", "watershed moment"
High	Promotional Language	"breathtaking", "revolutionary"
High	Editorializing	"It's important to note", "Worth mentioning"
Medium	Negative Parallelism	"It's not just X, but Y"
Medium	Participle Endings	", ensuring...", ", highlighting..."
Medium	Filler Phrases	"In today's ever-evolving world"
Medium	Weasel Words	"Some experts say", "It is believed"
Medium	Transition Overuse	"Furthermore", "Moreover" (frequency-based)
Low	Em Dash Overuse	More than 2 per 500 words
Low	Hedge Words	"somewhat", "relatively", "arguably"
Low	Rule of Three	Repeated triadic structures
Low	Formatting Patterns	Excessive bullet lists, headers
Low	Sentence Starters	Repetitive patterns

Quick Analysis

# Provide text directly
echo "Your text here" | analyze_for_ai_patterns

# Or analyze a file
cat document.md | analyze_for_ai_patterns

Analysis Process

Step 1: Load Pattern Database

import json
patterns = json.load(open('patterns/patterns.json'))

Step 2: Run Detection

For each category, apply appropriate detection method:

Simple Matching (buzzwords, filler phrases):

• Case-insensitive exact match
• Record location and context

Regex Matching (negative parallelism, participle endings):

• Apply regex patterns
• Validate in context

Frequency Detection (transitions, em dashes):

• Count occurrences per 500 words
• Flag if exceeds threshold

Structural Analysis (rule of three, formatting):

• Detect repeated patterns
• Count structural elements

Step 3: Generate Report

Output format:

{
  "issues": [
    {
      "id": 1,
      "category": "Buzzwords",
      "priority": "high",
      "pattern": "leverage",
      "location": "paragraph 2, sentence 3",
      "context": "...can leverage advanced algorithms..."
    }
  ],
  "summary": {
    "total_issues": 15,
    "critical": 1,
    "high": 9,
    "medium": 3,
    "low": 2,
    "ai_likelihood": "high"
  }
}

Output Format

Console Report

## AI Writing Analysis

**AI Likelihood:** High (15 patterns detected)

### Critical (1)

- **Chatbot Artifact** [para 1]: "I hope this helps"

### High Priority (9)

- **Buzzword** [para 2]: "leverage" → consider "use"
- **Inflated Symbolism** [para 3]: "stands as a testament"
  ...

### Summary

| Category | Count |
| -------- | ----- |
| Critical | 1     |
| High     | 9     |
| Medium   | 3     |
| Low      | 2     |

Thresholds

Pattern	Threshold	Flagged When
Em dashes	2 per 500 words	Exceeds
Transitions	3 per 500 words	Exceeds
Hedge words	4 per 500 words	Exceeds
Rule of three	2 instances	Exceeds

AI Likelihood Scoring

Score	Likelihood	Criteria
0-2 issues	Low	Likely human-written
3-7 issues	Medium	Some AI markers
8-15 issues	High	Likely AI-assisted
16+ issues	Very High	Almost certainly AI

See Also

• ai-writing-fixer - Fix detected AI patterns
• ai-writing-humanizer - Full iterative workflow (analyze + fix)