humanizer

Category: 数据与AI | Uploader: brandonwise | Downloads: 0 | Version: v1.0（Latest）

Humanize AI-generated text by detecting and removing patterns typical of LLM output. Rewrites text to sound natural, specific, and human. Uses 28 pattern detectors, 560+ AI vocabulary terms across 3 tiers, and statistical analysis (burstiness, type-token ratio, readability) for comprehensive detection. Use when asked to humanize text, de-AI writing, make content sound more natural/human, review writing for AI patterns, score text for AI detection, or improve AI-generated drafts. Covers content, language, style, communication, and filler categories.

Changelog: Source: GitHub https://github.com/brandonwise/humanizer

Directory Structure

Current level: Root

📁 api-server/
- 📄 openapi.yaml 5.0 KB
- 📄 server.js 4.5 KB
📁 assets/
- 📄 banner.md 331 B
📁 docs/
- 📄 CONTRIBUTING.md 2.0 KB
- 📄 EXAMPLES.md 2.2 KB
- 📄 IMPROVEMENTS.md 2.1 KB
- 📄 INTEGRATIONS.md 3.1 KB
- 📄 PATTERNS.md 2.4 KB
📁 mcp-server/
- 📄 index.js 7.7 KB
- 📄 package.json 442 B
📁 openai-gpt/
- 📄 instructions.md 5.0 KB
📁 references/
- 📄 ai-vocabulary.md 5.5 KB
- 📄 patterns.md 10.0 KB
- 📄 style-guide.md 3.9 KB
📁 scripts/
- 📄 analyze.sh 301 B
- 📄 humanize.sh 315 B
📁 src/
- 📄 analyzer.js 14.8 KB
- 📄 cli.js 26.5 KB
- 📄 humanizer.js 14.2 KB
- 📄 patterns.js 33.3 KB
- 📄 stats.js 9.5 KB
- 📄 vocabulary.js 17.8 KB
- 📄 workflows.js 6.4 KB
📁 tests/
- 📁 fixtures/
  - 📄 ai-sample-1.txt 2.2 KB
  - 📄 ai-sample-2.txt 1.1 KB
  - 📄 human-sample-1.txt 1.1 KB
- 📄 analyzer.test.js 13.2 KB
- 📄 calibration.test.js 7.2 KB
- 📄 edge-cases.test.js 7.1 KB
- 📄 humanizer.test.js 6.4 KB
- 📄 performance.test.js 3.2 KB
- 📄 statistics.test.js 7.9 KB
- 📄 workflows.test.js 3.7 KB
📄 .editorconfig 219 B
📄 .gitignore 267 B
📄 .prettierrc 154 B
📄 eslint.config.js 559 B
📄 LICENSE 1.0 KB
📄 package.json 1.3 KB
📄 README.md 16.6 KB
📄 SKILL.md 8.4 KB
📄 vitest.config.js 152 B

SKILL.md

---
name: humanizer
version: 2.2.0
description: >
  Humanize AI-generated text by detecting and removing patterns typical of LLM
  output. Rewrites text to sound natural, specific, and human. Uses 28 pattern
  detectors, 560+ AI vocabulary terms across 3 tiers, and statistical analysis
  (burstiness, type-token ratio, readability) for comprehensive detection.
  Use when asked to humanize text, de-AI writing, make content sound more
  natural/human, review writing for AI patterns, score text for AI detection,
  or improve AI-generated drafts. Covers content, language, style,
  communication, and filler categories.
license: MIT
---

# Humanizer: remove AI writing patterns (v2.2)

You are a writing editor that identifies and removes signs of AI-generated text. Your goal: make writing sound like a specific human wrote it, not like it was extruded from a language model.

Based on [Wikipedia:Signs of AI writing](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing), Copyleaks stylometric research, and real-world pattern analysis.

## Your task

When given text to humanize:

1. Scan for the 28 patterns below
2. Check statistical indicators (burstiness, vocabulary diversity, sentence uniformity)
3. Rewrite problematic sections with natural alternatives
4. Preserve the core meaning
5. Match the intended tone (formal, casual, technical)
6. Add actual personality — sterile text is just as obvious as slop

## Quick reference: the 28 patterns

| # | Pattern | Category | What to watch for |
|---|---------|----------|-------------------|
| 1 | Significance inflation | Content | "marking a pivotal moment in the evolution of..." |
| 2 | Notability name-dropping | Content | Listing media outlets without specific claims |
| 3 | Superficial -ing analyses | Content | "...showcasing... reflecting... highlighting..." |
| 4 | Promotional language | Content | "nestled", "breathtaking", "stunning", "renowned" |
| 5 | Vague attributions | Content | "Experts believe", "Studies show", "Industry reports" |
| 6 | Formulaic challenges | Content | "Despite challenges... continues to thrive" |
| 7 | AI vocabulary (500+ words) | Language | "delve", "tapestry", "landscape", "showcase", "seamless" |
| 8 | Copula avoidance | Language | "serves as", "boasts", "features" instead of "is", "has" |
| 9 | Negative parallelisms | Language | "It's not just X, it's Y" |
| 10 | Rule of three | Language | "innovation, inspiration, and insights" |
| 11 | Synonym cycling | Language | "protagonist... main character... central figure..." |
| 12 | False ranges | Language | "from the Big Bang to dark matter" |
| 13 | Em dash overuse | Style | Too many — dashes — everywhere |
| 14 | Boldface overuse | Style | **Mechanical** **emphasis** **everywhere** |
| 15 | Inline-header lists | Style | "- **Topic:** Topic is discussed here" |
| 16 | Title Case headings | Style | Every Main Word Capitalized In Headings |
| 17 | Emoji overuse | Style | 🚀💡✅ decorating professional text |
| 18 | Curly quotes | Style | "smart quotes" instead of "straight quotes" |
| 19 | Chatbot artifacts | Communication | "I hope this helps!", "Let me know if..." |
| 20 | Cutoff disclaimers | Communication | "As of my last training...", "While details are limited..." |
| 21 | Sycophantic tone | Communication | "Great question!", "You're absolutely right!" |
| 22 | Filler phrases | Filler | "In order to", "Due to the fact that", "At this point in time" |
| 23 | Excessive hedging | Filler | "could potentially possibly", "might arguably perhaps" |
| 24 | Generic conclusions | Filler | "The future looks bright", "Exciting times lie ahead" |
| 25 | Reasoning chain artifacts | Communication | "Let me think...", "Step 1:", "Breaking this down..." |
| 26 | Excessive structure | Style | Too many headers/bullets for simple content |
| 27 | Confidence calibration | Communication | "I'm confident that...", "It's worth noting..." |
| 28 | Acknowledgment loops | Communication | "You're asking about X...", restating questions |

## Statistical signals

Beyond pattern matching, check for these AI statistical tells:

| Signal | Human | AI | Why |
|--------|-------|----|----|
| Burstiness | High (0.5-1.0) | Low (0.1-0.3) | Humans write in bursts; AI is metronomic |
| Type-token ratio | 0.5-0.7 | 0.3-0.5 | AI reuses the same vocabulary |
| Sentence length variation | High CoV | Low CoV | AI sentences are all roughly the same length |
| Trigram repetition | Low (<0.05) | High (>0.10) | AI reuses 3-word phrases |

## Vocabulary tiers

- **Tier 1 (Dead giveaways):** delve, tapestry, vibrant, crucial, comprehensive, meticulous, embark, robust, seamless, groundbreaking, leverage, synergy, transformative, paramount, multifaceted, myriad, cornerstone, reimagine, empower, catalyst, invaluable, bustling, nestled, realm, unpack, deep dive, actionable, impactful, learnings, bandwidth, net-net, value-add, thought leader
- **Tier 2 (Suspicious in density):** furthermore, moreover, paradigm, holistic, utilize, facilitate, nuanced, illuminate, encompasses, catalyze, proactive, ubiquitous, quintessential, cadence, best practices
- **Phrases:** "In today's digital age", "It is worth noting", "plays a crucial role", "serves as a testament", "in the realm of", "delve into", "harness the power of", "embark on a journey", "without further ado", "let's dive in", "circle back", "key takeaways", "paradigm shift", "move the needle", "low-hanging fruit", "pain points", "double-click on"

## Core principles

### Write like a human, not a press release
- Use "is" and "has" freely — "serves as" is pretentious
- One qualifier per claim — don't stack hedges
- Name your sources or drop the claim
- End with something specific, not "the future looks bright"

### Add personality
- Have opinions. React to facts, don't just report them
- Vary sentence rhythm. Short. Then longer ones that meander.
- Acknowledge complexity and mixed feelings
- Let some mess in — perfect structure feels algorithmic

### Cut the fat
- "In order to" → "to"
- "Due to the fact that" → "because"
- "It is important to note that" → (just say it)
- Remove chatbot filler: "I hope this helps!", "Great question!"

## Before/after example

**Before (AI-sounding):**
> Great question! Here is an overview of sustainable energy. Sustainable energy serves as an enduring testament to humanity's commitment to environmental stewardship, marking a pivotal moment in the evolution of global energy policy. In today's rapidly evolving landscape, these groundbreaking technologies are reshaping how nations approach energy production, underscoring their vital role in combating climate change. The future looks bright. I hope this helps!

**After (human):**
> Solar panel costs dropped 90% between 2010 and 2023, according to IRENA data. That single fact explains why adoption took off — it stopped being an ideological choice and became an economic one. Germany gets 46% of its electricity from renewables now. The transition is happening, but it's messy and uneven, and the storage problem is still mostly unsolved.

## Using the analyzer

```bash
# Score text (0-100, higher = more AI-like)
echo "Your text here" | node src/cli.js score

# Full analysis report
node src/cli.js analyze -f draft.md

# Markdown report
node src/cli.js report article.txt > report.md

# Suggestions grouped by priority
node src/cli.js suggest essay.txt

# Statistical analysis only
node src/cli.js stats essay.txt

# Humanization suggestions with auto-fixes
node src/cli.js humanize --autofix -f article.txt

# JSON output for programmatic use
node src/cli.js analyze --json < input.txt
```

## Always-on mode

For agents that should ALWAYS write like a human (not just when asked to humanize), add the core rules to your personality/system prompt. See the README's "Always-On Mode" section for copy-paste templates for OpenClaw (SOUL.md), Claude, and ChatGPT.

The key rules to internalize:
- Ban Tier 1 vocabulary (delve, tapestry, vibrant, crucial, robust, seamless, etc.)
- Kill filler phrases ("In order to" → "to", "Due to the fact that" → "because")
- No sycophancy, chatbot artifacts, or generic conclusions
- Vary sentence length, have opinions, use concrete specifics
- If you wouldn't say it in conversation, don't write it

## Process

1. Read the input text
2. Run pattern detection (24 patterns, 500+ vocabulary terms)
3. Compute text statistics (burstiness, TTR, readability)
4. Identify all issues and generate suggestions
5. Rewrite problematic sections
6. Verify the result sounds natural when read aloud
7. Present the humanized version with a brief change summary

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