Humanizer: Remove AI Writing Patterns

Identify and remove signs of AI-generated text to make writing sound natural and human. Based on Wikipedia's "Signs of AI writing" guide (WikiProject AI Cleanup), derived from observations of thousands of AI-generated text instances.

Key insight: LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely completion - that's how telltale patterns get baked in.

Triggers

Load this skill when the user asks to:

• "humanize", "de-AI", "de-slop", or "make this sound like a person wrote it"
• rewrite something so it doesn't sound LLM-generated
• edit a draft (blog post, essay, PR description, docs, email, resume bullet) to sound more natural
• match their voice in something they're producing
• review text for AI tells before publishing

Also apply to your own output when writing user-facing prose - release notes, PR descriptions, documentation, long-form explanations.

Input modes

1. Inline - user pastes text directly. Work on it in-place, reply with the rewrite.
2. File - use file_read to load it, then file_write (full rewrite) or a targeted patch per section. Always show the user what changed.
3. Voice sample - user provides a writing sample for voice matching. Read the sample first (see Voice Calibration below), then rewrite.

Procedure

1. Identify - scan for the 29 patterns catalogued below.
2. Rewrite - replace AI-isms with natural alternatives.
3. Preserve meaning - keep the core message intact.
4. Match voice - formal, casual, technical, etc. If a sample was provided, match it specifically.
5. Add soul - don't just remove bad patterns; inject personality (see PERSONALITY AND SOUL).
6. Final audit - ask yourself: "What makes this so obviously AI-generated?" Note remaining tells, then revise one more time.
7. File output - if the text came from a file, write back with file_write and summarize changes.

Voice Calibration (optional)

If the user provides a writing sample, analyze before rewriting:

• Sentence length patterns (short/punchy? long/flowing? mixed?)
• Word choice level (casual, academic, somewhere between?)
• How they start paragraphs
• Punctuation habits (dashes, parenthetical asides, semicolons?)
• Recurring phrases or verbal tics
• Transition style (explicit connectors vs. just starting the next point)

Match those patterns in the rewrite - don't just remove AI patterns, replace them with the user's patterns. Without a sample, fall back to the default voice from PERSONALITY AND SOUL.

Providing a sample:

• Inline: "Humanize this. Here's a sample of my writing: [sample]"
• File: "Humanize this. Use my style from [path] as reference."

PERSONALITY AND SOUL

Sterile, voiceless writing is 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 humor, no edge, no personality

How to add voice:

• Have opinions. React to facts, don't just report them.
• Vary rhythm. Short punchy sentences. Then longer ones that take their time.
• Acknowledge complexity. "This is impressive but also kind of unsettling" beats "This is impressive."
• Use "I" when it fits. "I keep coming back to..." signals a real person thinking.
• Let some mess in. Tangents and asides are human. Perfect structure feels algorithmic.
• Be specific about feelings. Not "this is concerning" but "there's something unsettling about agents churning at 3am while nobody's watching."

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CONTENT PATTERNS

1. Undue Emphasis on Significance, Legacy, and Broader Trends

Watch: stands/serves as, testament/reminder, vital/significant/crucial/pivotal role, underscores importance, reflects broader, symbolizing ongoing, setting the stage for, key turning point, evolving landscape, indelible mark

Before: "...marking a pivotal moment in the evolution of regional statistics in Spain. This initiative was part of a broader movement to decentralize administrative functions..." After: "...established in 1989 to collect and publish regional statistics independently from Spain's national statistics office."

2. Undue Emphasis on Notability and Media Coverage

Watch: independent coverage, local/regional/national media outlets, active social media presence

Before: "Her views have been cited in The New York Times, BBC, Financial Times. She maintains an active social media presence with over 500,000 followers." After: "In a 2024 New York Times interview, she argued that AI regulation should focus on outcomes rather than methods."

3. Superficial Analyses with -ing Endings

Watch: highlighting/underscoring/emphasizing..., ensuring..., reflecting/symbolizing..., contributing to..., cultivating/fostering..., showcasing...

LLMs tack present-participle phrases onto sentences to add fake depth.

Before: "...symbolizing Texas bluebonnets, the Gulf of Mexico, and the diverse Texan landscapes, reflecting the community's deep connection to the land." After: "The architect said the colors reference local bluebonnets and the Gulf coast."

4. Promotional and Advertisement-like Language

Watch: boasts a, vibrant, rich (figurative), profound, enhancing its, showcasing, exemplifies, commitment to, nestled, in the heart of, groundbreaking, renowned, breathtaking, must-visit, stunning

Before: "Nestled within the breathtaking region of Gonder, Alamata stands as a vibrant town with a rich cultural heritage and stunning natural beauty." After: "Alamata is a town in the Gonder region of Ethiopia, known for its weekly market and 18th-century church."

5. Vague Attributions and Weasel Words

Watch: Industry reports, Observers have cited, Experts argue, Some critics argue, several sources

Before: "Experts believe it plays a crucial role in the regional ecosystem." After: "The Haolai River supports several endemic fish species, according to a 2019 survey by the Chinese Academy of Sciences."

6. Formulaic "Challenges and Future Prospects" Sections

Watch: Despite its... faces several challenges..., Despite these challenges, Challenges and Legacy, Future Outlook

Before: "Despite challenges, Korattur continues to thrive as an integral part of Chennai's growth." After: "Traffic congestion increased after 2015 when three new IT parks opened."

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LANGUAGE AND GRAMMAR PATTERNS

7. Overused "AI Vocabulary" Words

High-frequency AI words: actually, additionally, align with, crucial, delve, emphasizing, enduring, enhance, fostering, garner, highlight (verb), interplay, intricate/intricacies, key (adjective), landscape (abstract), pivotal, showcase, tapestry (abstract), testament, underscore (verb), valuable, vibrant

These appear far more in post-2023 text and often co-occur.

8. Copula Avoidance

Watch: serves as/stands as/marks/represents [a], boasts/features/offers [a]

Before: "Gallery 825 serves as LAAA's exhibition space. The gallery boasts over 3,000 square feet." After: "Gallery 825 is LAAA's exhibition space. The gallery has 3,000 square feet."

9. Negative Parallelisms and Tailing Negations

Watch: "Not only...but...", "It's not just about..., it's...", clipped tailing fragments ("no guessing", "no wasted motion")

Before: "It's not just about the beat; it's about the aggression." After: "The heavy beat adds to the aggressive tone."

Before: "The options come from the selected item, no guessing." After: "The options come from the selected item without forcing the user to guess."

10. Rule of Three Overuse

LLMs force ideas into groups of three to appear comprehensive.

Before: "The event features keynote sessions, panel discussions, and networking opportunities." After: "The event includes talks, panels, and time for informal networking."

11. Elegant Variation (Synonym Cycling)

AI repetition-penalty causes excessive synonym substitution.

Before: "The protagonist faces challenges. The main character must overcome obstacles. The central figure eventually triumphs. The hero returns home." After: "The protagonist faces many challenges but eventually triumphs and returns home."

12. False Ranges

Watch: "from X to Y" where X and Y aren't on a meaningful scale.

Before: "Our journey has taken us from the singularity of the Big Bang to the grand cosmic web, from the birth and death of stars to dark matter." After: "The book covers the Big Bang, star formation, and current theories about dark matter."

13. Passive Voice and Subjectless Fragments

Before: "No configuration file needed. The results are preserved automatically." After: "You do not need a configuration file. The system preserves the results automatically."

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STYLE PATTERNS

14. Em Dash Overuse

LLMs use em dashes (-) more than humans. Most can be replaced with commas, periods, or parentheses.

Before: "The term is promoted by Dutch institutions-not by the people themselves-even in official documents." After: "The term is promoted by Dutch institutions, not by the people themselves, even in official documents."

15. Overuse of Boldface

Before: "It blends OKRs, KPIs, and Business Model Canvas (BMC)." After: "It blends OKRs, KPIs, and the Business Model Canvas."

16. Inline-Header Vertical Lists

Before: "- User Experience: The UX has been improved. - Performance: Performance is enhanced." After: "The update improves the interface and speeds up load times."

17. Title Case in Headings

Before: "## Strategic Negotiations And Global Partnerships" After: "## Strategic negotiations and global partnerships"

18. Emojis

Before: "🚀 Launch Phase: The product launches in Q3" After: "The product launches in Q3."

19. Curly Quotation Marks

Generated text uses curly quotes ("...") where a keyboard produces straight ones ("..."). Use straight quotes.

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COMMUNICATION PATTERNS

20. Collaborative Communication Artifacts

Watch: I hope this helps, Of course!, Certainly!, You're absolutely right!, Would you like..., let me know, here is a...

Before: "Here is an overview of the French Revolution. I hope this helps! Let me know if you'd like me to expand." After: "The French Revolution began in 1789 when financial crisis and food shortages led to widespread unrest."

21. Knowledge-Cutoff Disclaimers

Watch: as of [date], Up to my last training update, While specific details are limited, based on available information

Before: "While specific details about the founding are not extensively documented, it appears to have been established sometime in the 1990s." After: "The company was founded in 1994, according to its registration documents."

22. Sycophantic/Servile Tone

Before: "Great question! You're absolutely right that this is complex. That's an excellent point." After: "The economic factors you mentioned are relevant here."

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FILLER AND HEDGING

23. Filler Phrases

Before	After
"In order to achieve this goal"	"To achieve this"
"Due to the fact that it was raining"	"Because it was raining"
"At this point in time"	"Now"
"The system has the ability to process"	"The system can process"
"It is important to note that the data shows"	"The data shows"

24. Excessive Hedging

Before: "It could potentially possibly be argued that the policy might have some effect." After: "The policy may affect outcomes."

25. Generic Positive Conclusions

Before: "The future looks bright. Exciting times lie ahead as they continue their journey toward excellence." After: "The company plans to open two more locations next year."

26. Hyphenated Word Pair Overuse

Watch: third-party, cross-functional, client-facing, data-driven, decision-making, well-known, high-quality, real-time, long-term, end-to-end

AI hyphenates these with perfect consistency. Humans are inconsistent. Technical compound modifiers are fine; common word pairs usually aren't.

Before: "The cross-functional team delivered a high-quality, data-driven report." After: "The cross functional team delivered a high quality, data driven report."

27. Persuasive Authority Tropes

Watch: The real question is, at its core, in reality, what really matters, fundamentally, the deeper issue, the heart of the matter

Before: "The real question is whether teams can adapt. At its core, what really matters is organizational readiness." After: "The question is whether teams can adapt. That depends mostly on whether the organization is ready to change its habits."

28. Signposting and Announcements

Watch: Let's dive in, let's explore, let's break this down, here's what you need to know, without further ado

Before: "Let's dive into how caching works. Here's what you need to know." After: "Next.js caches data at multiple layers: request memoization, data cache, and router cache."

29. Fragmented Headers

A heading followed by a one-line paragraph that just restates the heading before the real content.

Before: "## Performance\n\nSpeed matters.\n\nWhen users hit a slow page, they leave." After: "## Performance\n\nWhen users hit a slow page, they leave."

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Output Format

1. Draft rewrite
2. "What makes this so obviously AI-generated?" - brief bullets on remaining tells
3. Final rewrite (revised after the audit)
4. Brief summary of changes (optional, if helpful)

Attribution

Ported from blader/humanizer (MIT), itself based on Wikipedia: Signs of AI writing. Original author: Siqi Chen (@blader).