ai-humanizer
Skill4 starsChanged 8 months agoMITView on GitHub
Humanize AI-generated text by detecting and removing patterns typical of LLM output. Rewrites text to sound natural, specific, and human. Uses 24 pattern detectors, 500+ 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.
3 other projects use a version of this.
Adopted by
Most recently taken up by
- realjaymes/marketingagentskillstheir own version, first seen Oct 21, 2026
- lakshitha-dev/ai-humanizer-skilltheir own version, first seen Jun 26, 2026
- Iampattanayak/Claude-Humanizertheir own version, first seen Mar 9, 2026
Other versions3 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 3
- realjaymes/marketingagentskills★ 68same name
- Iampattanayak/Claude-Humanizer★ 2same name
- lakshitha-dev/ai-humanizer-skill★ 2same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository112 other files it gives its agents.
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- subagent-driven-developmentSkill
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- artifacts-builderSkill
- backend-patternsSkill
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- clean-content-fetchSkill
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- postgresql-table-designSkill
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- canvas-designSkill
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- cover-imageSkill
- doc-coauthoringSkill
- docxSkill
- fal-ai-mediaSkill
- frontend-slidesSkill
- image-genSkill
- markdown-to-htmlSkill
- pdfSkill
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- The first 60 shown.
The file
We split this skill into the rules it gives an agent
10 rules. 0 are common, 10 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 3 edited versions we read keep these.
10
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 22
When given text to humanize:
- Distinctive: line 25
Check statistical indicators (burstiness, vocabulary diversity, sentence uniformity)
- Distinctive: line 29
Add actual personality — sterile text is just as obvious as slop
- Distinctive: line 80
Use "is" and "has" freely — "serves as" is pretentious
- Distinctive: line 95
Remove chatbot filler: "I hope this helps!", "Great question!"
- Distinctive: line 137
No sycophancy, chatbot artifacts, or generic conclusions
- Distinctive: line 139
If you wouldn't say it in conversation, don't write it
- Distinctive: line 143
Read the input text
- Distinctive: line 144
Run pattern detection (24 patterns, 500+ vocabulary terms)
- Distinctive: line 148
Verify the result sounds natural when read aloud
“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Humanizer: remove AI writing patterns
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, Copyleaks stylometric research, and real-world pattern analysis.
Your task
When given text to humanize:
- Scan for the 24 patterns below
- Check statistical indicators (burstiness, vocabulary diversity, sentence uniformity)
- Rewrite problematic sections with natural alternatives
- Preserve the core meaning
- Match the intended tone (formal, casual, technical)
- Add actual personality — sterile text is just as obvious as slop
Quick reference: the 24 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" |
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
- Tier 2 (Suspicious in density): furthermore, moreover, paradigm, holistic, utilize, facilitate, nuanced, illuminate, encompasses, catalyze, proactive, ubiquitous, quintessential
- 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"
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
# 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.txtAlways-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
- Read the input text
- Run pattern detection (24 patterns, 500+ vocabulary terms)
- Compute text statistics (burstiness, TTR, readability)
- Identify all issues and generate suggestions
- Rewrite problematic sections
- Verify the result sounds natural when read aloud
- Present the humanized version with a brief change summary
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

