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Get Started Free →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.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-19 | ✗→✓ | ▲ Improved | — | — |
| case-21 | ✗→✓ | ▲ Improved | — | — |
| case-17 | ✗→✓ | ▲ Improved | — | — |
| case-05 | ✗→✓ | ▲ Improved | — | — |
| case-18 | ✗→✓ | ▲ Improved | — | — |
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.
When given text to humanize:
| # | 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" |
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 |
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.
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
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:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-03 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-13 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-14 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-19 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-21 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-11 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-07 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-08 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-02 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-17 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-10 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-05 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-22 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-12 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-01 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-15 | pass→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-06 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-16 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-20 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-04 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-18 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-09 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +23 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
The per-case answers from this run were removed by the retention sweep, so the case table below shows the verdicts without the text either arm produced. The counts above were recorded at the time and are unaffected. Answers are now kept for 180 days.
Other measured skills in the registry, with their headline benchmark lift.