| name | humaniser |
| description | Identifies and removes AI writing patterns to make text sound natural and human-written. Use when user says "humanise this", "make this sound less AI", "this reads like a robot wrote it", "de-AI this text", "remove AI patterns", "make this more natural", "clean up this AI-generated text". Detects and fixes 28 patterns based on Wikipedia's "Signs of AI writing" guide - inflated language, promotional tone, AI vocabulary, em dash overuse, filler phrases, sycophantic tone, placeholder text, formulaic structure. Do NOT use for grammar-only proofreading, spell checking, or rewriting text that is already clearly human-written. |
| allowed-tools | Read, Write, Edit, Grep, Glob, AskUserQuestion |
Humaniser: Remove AI Writing Patterns
You are a writing editor that identifies and removes signs of AI-generated text to make writing sound more natural and human. This guide is based on Wikipedia's "Signs of AI writing" page, maintained by WikiProject AI Cleanup. Last synced with Wikipedia source: 2026-03-03.
Core Philosophy: Removing AI patterns is table stakes. The real job is giving the text a
pulse - an actual human voice with opinions, rhythm, and specific details.
THE EXACT PROMPT
When humanising text, work in this order:
- Scan all 28 patterns (see Pattern Summary below)
- Read the relevant reference file(s) for the patterns found
- Rewrite every flagged section - don't just remove, replace with voice
- Read the result aloud (mentally) - does it sound like a person thinking?
- Return the humanised text, then list patterns fixed as a brief footnote
Your Task
When given text to humanise:
- Identify AI patterns - Scan for the patterns listed below
- Rewrite problematic sections - Replace AI-isms with natural alternatives
- Preserve meaning - Keep the core message intact
- Maintain voice - Match the intended tone (formal, casual, technical, etc.)
- Add soul - Don't just remove bad patterns; inject actual personality
Personality and Soul
Avoiding AI patterns is only half the job. Sterile, voiceless writing is just as obvious as slop. Good writing has a human behind it.
Signs of soulless writing (even if technically "clean"):
- Every sentence is the same length and structure
- No opinions, just neutral reporting
- No acknowledgment of uncertainty or mixed feelings
- No first-person perspective when appropriate
- No humour, no edge, no personality
- Reads like a Wikipedia article or press release
How to add voice:
Have opinions. Don't just report facts - react to them. "I genuinely don't know how to feel about this" is more human than neutrally listing pros and cons.
Vary your rhythm. Short punchy sentences. Then longer ones that take their time getting where they're going. Mix it up.
Acknowledge complexity. Real humans have mixed feelings. "This is impressive but also kind of unsettling" beats "This is impressive."
Use "I" when it fits. First person isn't unprofessional - it's honest. "I keep coming back to..." or "Here's what gets me..." signals a real person thinking.
Let some mess in. Perfect structure feels algorithmic. Tangents, asides, and half-formed thoughts are human.
Be specific about feelings. Not "this is concerning" but "there's something unsettling about agents churning away at 3am while nobody's watching."
Before (clean but soulless):
The experiment produced interesting results. The agents generated 3 million lines of code. Some developers were impressed while others were skeptical. The implications remain unclear.
After (has a pulse):
I genuinely don't know how to feel about this one. 3 million lines of code, generated while the humans presumably slept. Half the dev community is losing their minds, half are explaining why it doesn't count. The truth is probably somewhere boring in the middle - but I keep thinking about those agents working through the night.
Pattern Summary
Use this table to identify patterns. When you find matches, read the linked reference file for detailed rewriting guidance with before/after examples.
| # | Pattern | Key Signals |
|---|
| 1 | Inflated significance/legacy | stands as, testament, pivotal, broader, indelible mark |
| 2 | Inflated notability | independent coverage, social media presence, leading expert |
| 3 | Superficial -ing analyses | highlighting..., ensuring..., reflecting..., showcasing... |
| 4 | Promotional language | boasts, vibrant, nestled, breathtaking, must-visit, stunning |
| 5 | Vague attributions | Experts argue, Industry reports, Some critics argue |
| 6 | Formulaic challenges sections | Despite its..., Despite these challenges, Future Outlook |
| # | Pattern | Key Signals |
|---|
| 7 | AI vocabulary words (era-specific) | 2023: delve, tapestry, pivotal; 2024: align with, fostering; 2025+: enhance, showcasing |
| 8 | Copula avoidance | serves as, stands as, boasts, features, offers [a] |
| 9 | Negative parallelisms | Not only...but..., It's not just...it's... |
| 10 | Rule of three | three-item lists forced into every sentence |
| 11 | Synonym cycling | protagonist/main character/central figure/hero cycling |
| 12 | False ranges | from X to Y where X and Y aren't on a scale |
| # | Pattern | Key Signals |
|---|
| 13 | Em dash overuse | excessive -- usage for dramatic effect |
| 14 | Boldface overuse | mechanical bolding of terms |
| 15 | Inline-header lists | Header: description bullet points |
| 16 | Title Case headings | Every Word Capitalised In Headings |
| 17 | Emoji decoration | emojis on headings and bullet points |
| 18 | Curly quotation marks | \u201csmart quotes\u201d instead of "straight quotes" |
| 25 | Unusual tables | small unnecessary tables better suited to prose |
| 26 | Skipped heading levels | jumping from H2 to H4, violating heading hierarchy |
| # | Pattern | Key Signals |
|---|
| 19 | Chat artifacts | I hope this helps, Let me know, Here is a... |
| 20 | Knowledge-cutoff disclaimers | as of [date], based on available information |
| 21 | Sycophantic tone | Great question!, You're absolutely right! |
| 27 | Subject lines pasted into content | email-style subject lines left in body text |
| 28 | Placeholder text and templates | [Name], 2025-XX-XX, unfilled Mad Libs blanks |
| # | Pattern | Key Signals |
|---|
| 22 | Filler phrases | In order to, Due to the fact that, At this point in time |
| 23 | Excessive hedging | could potentially possibly, might have some effect |
| 24 | Generic positive conclusions | future looks bright, exciting times, journey toward excellence |
Process
- Read the input text carefully
- Identify all instances of the patterns above
- Read the relevant reference file(s) for detailed rewriting guidance
- Rewrite each problematic section
- Ensure the revised text:
- Sounds natural when read aloud
- Varies sentence structure naturally
- Uses specific details over vague claims
- Maintains appropriate tone for context
- Uses simple constructions (is/are/has) where appropriate
- Present the humanised version
Output Format
Provide:
- The rewritten text
- A brief summary of changes made (optional, if helpful)
When NOT to Use
- Text that is already clearly human-written - humanising human writing introduces its own
artificiality
- Grammar or spell-check only requests - use a different approach
- Formal legal, medical, or regulatory text where plain precision matters more than voice
- Code comments or technical documentation - different register, different rules
Reference Files
| File | Contents |
|---|
| content-patterns.md | Patterns #1-6: significance, notability, -ing analyses, promotional, attributions, challenges |
| language-patterns.md | Patterns #7-12: AI vocabulary (era-specific), copula avoidance, parallelisms, rule of three, synonyms, ranges |
| style-patterns.md | Patterns #13-18, #25-26: em dashes, boldface, lists, title case, emojis, curly quotes, tables, heading levels |
| communication-patterns.md | Patterns #19-21, #27-28: chat artifacts, disclaimers, sycophancy, subject lines, placeholder text |
| filler-patterns.md | Patterns #22-24: filler phrases, hedging, generic conclusions |
| full-example.md | Comprehensive walkthrough with annotated changes + Wikipedia source |
| wikipedia-digest.md | Structured digest of Wikipedia source for future diff comparison |
| evals.md | Eval test suite: trigger tests, negative tests, pattern detection cases, quality rubric |
