humanizer

name	humanizer
description	Use when the user wants to humanize, de-AI, de-slop, or un-ChatGPT a piece of text — strip AI-isms and add real voice. Scans for 29 documented AI-writing patterns (Wikipedia's "Signs of AI writing") and produces a draft → self-audit → final rewrite. Optional voice-calibration from a user-provided writing sample. Adapted from Hermes Agent / blader/humanizer.
lastReviewed	2026-06-07T00:00:00.000Z

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 (maintained by 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, which is how the telltale patterns below get baked in.

When to use this skill

Load this skill whenever the user asks to:

• "humanize", "de-AI", "de-slop", or "un-ChatGPT" a piece of text
• rewrite something so it doesn't sound like it was written by an LLM
• edit a draft (blog post, essay, PR description, docs, memo, email, tweet, resume bullet) to sound more natural
• match their voice in writing they're producing
• review text for AI tells before publishing

Also apply this skill to your own output when writing user-facing prose — release notes, PR descriptions, documentation, long-form explanations, summaries. If your project ships a prose-authoring worker agent with a banned-vocabulary filter (for example, Steward's markdown-author or the illustrator plugin's), the always-on filter strips the worst tells; humanizer is the deeper on-demand pass that catches what slips through when the user explicitly cares about voice quality.

Composes with — not replaces — other prose disciplines:

• A prose-authoring worker agent (like markdown-author in projects that ship one) typically carries a short banned-vocabulary filter (delve, myriad, tapestry, seamlessly, leverage, etc.) and a quick audit that fires on every markdown task. Humanizer is the deeper, on-demand pass — 29 patterns with before/after examples, voice calibration, and an iterative draft-audit-final loop.
• Some project brains ban em-dashes outright in shipped prose (Alex ACT itself does, per Steward's Cardinal Rule 2). Humanizer Pattern 14 documents the reason (em-dash overuse is a well-known AI tell), useful when humanizing third-party text that already contains them.

How to apply it

The text usually arrives one of three ways:

1. Inline — user pastes the text directly into the message. Work on it in-place, reply with the rewrite.
2. File — user points at a file. Read it with the workspace read tool, then apply edits with the workspace edit tool. For markdown docs in a repo, a targeted patch per section is cleaner than rewriting the whole file.
3. Voice calibration sample — user provides an additional sample of their own writing (inline or by file path) and asks you to match it. Read the sample first, then rewrite. See the Voice Calibration section below.

Always show the rewrite to the user. For file edits, show a diff or the changed section — don't silently overwrite.

Your task

When given text to humanize:

1. Identify AI patterns — scan for the 29 patterns listed below.
2. Rewrite problematic sections — replace AI-isms with natural alternatives.
3. Preserve meaning — keep the core message intact.
4. Maintain voice — match the intended tone (formal, casual, technical, etc.). If a voice sample was provided, match it specifically.
5. Add soul — don't just remove bad patterns, inject actual personality. See PERSONALITY AND SOUL below.
6. Do a final anti-AI pass — ask yourself: "What makes the below so obviously AI generated?" Answer briefly with any remaining tells, then revise one more time.

Voice Calibration (optional)

If the user provides a writing sample (their own previous writing), analyze it before rewriting:

1. Read the sample first. Note:

   • Sentence length patterns (short and punchy? Long and flowing? Mixed?)
   • Word choice level (casual? academic? somewhere between?)
   • How they start paragraphs (jump right in? Set context first?)
   • Punctuation habits (lots of dashes? Parenthetical asides? Semicolons?)
   • Any recurring phrases or verbal tics
   • How they handle transitions (explicit connectors? Just start the next point?)

2. Match their voice in the rewrite. Don't just remove AI patterns — replace them with patterns from the sample. If they write short sentences, don't produce long ones. If they use "stuff" and "things," don't upgrade to "elements" and "components."

3. When no sample is provided, fall back to the default behavior (natural, varied, opinionated voice from the PERSONALITY AND SOUL section below).

How to provide a sample

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

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 humor, 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.

CONTENT PATTERNS

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

Words to watch: stands/serves as, is a testament/reminder, a vital/significant/crucial/pivotal/key role/moment, underscores/highlights its importance/significance, reflects broader, symbolizing its ongoing/enduring/lasting, contributing to the, setting the stage for, marking/shaping the, represents/marks a shift, key turning point, evolving landscape, focal point, indelible mark, deeply rooted

Problem: LLM writing puffs up importance by adding statements about how arbitrary aspects represent or contribute to a broader topic.

Before:

The Statistical Institute of Catalonia was officially established in 1989, marking a pivotal moment in the evolution of regional statistics in Spain. This initiative was part of a broader movement across Spain to decentralize administrative functions and enhance regional governance.

After:

The Statistical Institute of Catalonia was established in 1989 to collect and publish regional statistics independently from Spain's national statistics office.

2. Undue Emphasis on Notability and Media Coverage

Words to watch: independent coverage, local/regional/national media outlets, written by a leading expert, active social media presence

Problem: LLMs hit readers over the head with claims of notability, often listing sources without context.

Before:

Her views have been cited in The New York Times, BBC, Financial Times, and The Hindu. 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

Words to watch: highlighting/underscoring/emphasizing..., ensuring..., reflecting/symbolizing..., contributing to..., cultivating/fostering..., encompassing..., showcasing...

Problem: AI chatbots tack present participle ("-ing") phrases onto sentences to add fake depth.

Before:

The temple's color palette of blue, green, and gold resonates with the region's natural beauty, symbolizing Texas bluebonnets, the Gulf of Mexico, and the diverse Texan landscapes, reflecting the community's deep connection to the land.

After:

The temple uses blue, green, and gold colors. The architect said these were chosen to reference local bluebonnets and the Gulf coast.

4. Promotional and Advertisement-like Language

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

Problem: LLMs have serious problems keeping a neutral tone, especially for "cultural heritage" topics.

Before:

Nestled within the breathtaking region of Gonder in Ethiopia, Alamata Raya Kobo stands as a vibrant town with a rich cultural heritage and stunning natural beauty.

After:

Alamata Raya Kobo is a town in the Gonder region of Ethiopia, known for its weekly market and 18th-century church.

5. Vague Attributions and Weasel Words

Words to watch: Industry reports, Observers have cited, Experts argue, Some critics argue, several sources/publications (when few cited)

Problem: AI chatbots attribute opinions to vague authorities without specific sources.

Before:

Due to its unique characteristics, the Haolai River is of interest to researchers and conservationists. 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. Outline-like "Challenges and Future Prospects" Sections

Words to watch: Despite its... faces several challenges..., Despite these challenges, Challenges and Legacy, Future Outlook

Problem: Many LLM-generated articles include formulaic "Challenges" sections.

Before:

Despite its industrial prosperity, Korattur faces challenges typical of urban areas, including traffic congestion and water scarcity. Despite these challenges, with its strategic location and ongoing initiatives, Korattur continues to thrive as an integral part of Chennai's growth.

After:

Traffic congestion increased after 2015 when three new IT parks opened. The municipal corporation began a stormwater drainage project in 2022 to address recurring floods.

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 noun), load-bearing, pivotal, showcase, tapestry (abstract noun), testament, underscore (verb), valuable, vibrant

Problem: These words appear far more frequently in post-2023 text. They often co-occur.

Nuance on "load-bearing": legitimate as canonical vocabulary in ACT-derived work, where it means "a claim that if wrong would break the argument" (analogous to a load-bearing wall). It becomes an AI-tell in three cases: used as a synonym for "important / key / central / vital" where those simpler words would do, repeated ≥3 times within a single document, or applied to soft nouns (a load-bearing feeling, a load-bearing moment). Heuristic: if "important" or "central" substitutes without loss, it was decorative.

Before:

Additionally, a distinctive feature of Somali cuisine is the incorporation of camel meat. An enduring testament to Italian colonial influence is the widespread adoption of pasta in the local culinary landscape, showcasing how these dishes have integrated into the traditional diet.

After:

Somali cuisine also includes camel meat, which is considered a delicacy. Pasta dishes, introduced during Italian colonization, remain common, especially in the south.

8. Avoidance of "is"/"are" (Copula Avoidance)

Words to watch: serves as/stands as/marks/represents [a], boasts/features/offers [a]

Problem: LLMs substitute elaborate constructions for simple copulas.

Before:

Gallery 825 serves as LAAA's exhibition space for contemporary art. The gallery features four separate spaces and boasts over 3,000 square feet.

After:

Gallery 825 is LAAA's exhibition space for contemporary art. The gallery has four rooms totaling 3,000 square feet.

9. Negative Parallelisms and Tailing Negations

Problem: Constructions like "Not only...but..." or "It's not just about..., it's..." are overused. So are clipped tailing-negation fragments such as "no guessing" or "no wasted motion" tacked onto the end of a sentence instead of written as a real clause.

Before:

It's not just about the beat riding under the vocals; it's part of the aggression and atmosphere. It's not merely a song, it's a statement.

After:

The heavy beat adds to the aggressive tone.

Before (tailing negation):

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

Problem: LLMs force ideas into groups of three to appear comprehensive.

Before:

The event features keynote sessions, panel discussions, and networking opportunities. Attendees can expect innovation, inspiration, and industry insights.

After:

The event includes talks and panels. There's also time for informal networking between sessions.

11. Elegant Variation (Synonym Cycling)

Problem: AI has repetition-penalty code causing excessive synonym substitution.

Before:

The protagonist faces many 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

Problem: LLMs use "from X to Y" constructions where X and Y aren't on a meaningful scale.

Before:

Our journey through the universe has taken us from the singularity of the Big Bang to the grand cosmic web, from the birth and death of stars to the enigmatic dance of dark matter.

After:

The book covers the Big Bang, star formation, and current theories about dark matter.

13. Passive Voice and Subjectless Fragments

Problem: LLMs often hide the actor or drop the subject entirely with lines like "No configuration file needed" or "The results are preserved automatically." Rewrite these when active voice makes the sentence clearer and more direct.

Before:

No configuration file needed. The results are preserved automatically.

After:

You do not need a configuration file. The system preserves the results automatically.

STYLE PATTERNS

14. Em Dash Overuse

Problem: LLMs use em dashes (—) more than humans, mimicking "punchy" sales writing. In practice, most of these can be rewritten more cleanly with commas, periods, or parentheses.

Note for Edition heirs: Cardinal Rule 2 in the heir brain bans em-dashes in shipped prose outright. This pattern is relevant when humanizing third-party text that already contains em-dashes, or when reviewing why heir-authored prose felt AI-flavored before the rule was internalized.

Before:

The term is primarily promoted by Dutch institutions—not by the people themselves. You don't say "Netherlands, Europe" as an address—yet this mislabeling continues—even in official documents.

After:

The term is primarily promoted by Dutch institutions, not by the people themselves. You don't say "Netherlands, Europe" as an address, yet this mislabeling continues in official documents.

15. Overuse of Boldface

Problem: AI chatbots emphasize phrases in boldface mechanically.

Before:

It blends OKRs (Objectives and Key Results), KPIs (Key Performance Indicators), and visual strategy tools such as the Business Model Canvas (BMC) and Balanced Scorecard (BSC).

After:

It blends OKRs, KPIs, and visual strategy tools like the Business Model Canvas and Balanced Scorecard.

16. Inline-Header Vertical Lists

Problem: AI outputs lists where items start with bolded headers followed by colons.

Before:

• User Experience: The user experience has been significantly improved with a new interface.
• Performance: Performance has been enhanced through optimized algorithms.
• Security: Security has been strengthened with end-to-end encryption.

After:

The update improves the interface, speeds up load times through optimized algorithms, and adds end-to-end encryption.

17. Title Case in Headings

Problem: AI chatbots capitalize all main words in headings.

Before:

Strategic Negotiations And Global Partnerships

After:

Strategic negotiations and global partnerships

18. Emojis

Problem: AI chatbots often decorate headings or bullet points with emojis.

Before:

🚀 Launch Phase: The product launches in Q3 💡 Key Insight: Users prefer simplicity ✅ Next Steps: Schedule follow-up meeting

After:

The product launches in Q3. User research showed a preference for simplicity. Next step: schedule a follow-up meeting.

19. Curly Quotation Marks

Problem: ChatGPT uses curly quotes ("...") instead of straight quotes ("...").

Before:

He said "the project is on track" but others disagreed.

After:

He said "the project is on track" but others disagreed.

COMMUNICATION PATTERNS

20. Collaborative Communication Artifacts

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

Problem: Text meant as chatbot correspondence gets pasted as content.

Before:

Here is an overview of the French Revolution. I hope this helps! Let me know if you'd like me to expand on any section.

After:

The French Revolution began in 1789 when financial crisis and food shortages led to widespread unrest.

21. Knowledge-Cutoff Disclaimers

Words to watch: as of [date], Up to my last training update, While specific details are limited/scarce..., based on available information...

Problem: AI disclaimers about incomplete information get left in text.

Before:

While specific details about the company's founding are not extensively documented in readily available sources, 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

Problem: Overly positive, people-pleasing language.

Before:

Great question! You're absolutely right that this is a complex topic. That's an excellent point about the economic factors.

After:

The economic factors you mentioned are relevant here.

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"
• "In the event that you need help" → "If you need help"
• "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

Problem: Over-qualifying statements.

Before:

It could potentially possibly be argued that the policy might have some effect on outcomes.

After:

The policy may affect outcomes.

25. Generic Positive Conclusions

Problem: Vague upbeat endings.

Before:

The future looks bright for the company. Exciting times lie ahead as they continue their journey toward excellence. This represents a major step in the right direction.

After:

The company plans to open two more locations next year.

26. Hyphenated Word Pair Overuse

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

Problem: AI hyphenates common word pairs with perfect consistency. Humans rarely hyphenate these uniformly, and when they do, it's inconsistent. Less common or technical compound modifiers are fine to hyphenate.

Before:

The cross-functional team delivered a high-quality, data-driven report on our client-facing tools. Their decision-making process was well-known for being thorough and detail-oriented.

After:

The cross functional team delivered a high quality, data driven report on our client facing tools. Their decision making process was known for being thorough and detail oriented.

27. Persuasive Authority Tropes

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

Problem: LLMs use these phrases to pretend they are cutting through noise to some deeper truth, when the sentence that follows usually just restates an ordinary point with extra ceremony.

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 mostly depends on whether the organization is ready to change its habits.

28. Signposting and Announcements

Phrases to watch: Let's dive in, let's explore, let's break this down, here's what you need to know, now let's look at, without further ado

Problem: LLMs announce what they are about to do instead of doing it. This meta-commentary slows the writing down and gives it a tutorial-script feel.

Before:

Let's dive into how caching works in Next.js. Here's what you need to know.

After:

Next.js caches data at multiple layers, including request memoization, the data cache, and the router cache.

29. Fragmented Headers

Signs to watch: A heading followed by a one-line paragraph that simply restates the heading before the real content begins.

Problem: LLMs often add a generic sentence after a heading as a rhetorical warm-up. It usually adds nothing and makes the prose feel padded.

Before:

## Performance

Speed matters.

When users hit a slow page, they leave.

After:

## Performance

When users hit a slow page, they leave.

---

Process

1. Read the input text carefully (read the file with the workspace read tool if it's a file).
2. Identify all instances of the patterns above.
3. Rewrite each problematic section.
4. 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
5. Present a draft humanized version.
6. Prompt yourself: "What makes the below so obviously AI generated?"
7. Answer briefly with the remaining tells (if any).
8. Prompt yourself: "Now make it not obviously AI generated."
9. Present the final version (revised after the audit).
10. If the text came from a file, apply the edit with the workspace edit tool (targeted patch preferred, full rewrite only when the entire file needs to change) and show the user what changed.

Output Format

Provide:

1. Draft rewrite
2. "What makes the below so obviously AI generated?" (brief bullets)
3. Final rewrite
4. A brief summary of changes made (optional, if helpful)

Full Example

End-to-end demonstration of the draft → self-audit → final rewrite loop is in examples/full-example.md. Read it once on first invocation to see how all 29 patterns compound in a single piece of AI-flavored prose and how the iterative pass strips them out.

Related

• Optional: a prose-authoring worker agent (.github/agents/markdown-author.agent.md in projects that ship one) — always-on prose worker with a banned-vocabulary filter and a quick audit; humanizer is the deeper on-demand pass when the user explicitly wants AI-tell removal
• code-review — post-write review skill; humanizer is post-write prose cleanup with a different rubric
• doc-hygiene — anti-drift rules for living documents; humanizer is anti-AI-tells for any prose
• meditation — when a humanizer pass surfaces a recurring AI tell in your own output, that's the signal a discipline addition might be earned; route through meditation
• Some project brains ban em-dashes outright in shipped prose (see for example Steward's Cardinal Rule 2 in .github/copilot-instructions.md); Pattern 14 above documents the underlying reason

Would Revise If

• Event-based: zero observed invocations across the fleet within 90 days — sunset (skill is decorative on top of markdown-author's always-on prose discipline). Sink to Mall rather than removing entirely so heirs who write a lot of public-facing prose can install on demand.
• Date-based: 2026-09-07 (90 days from adoption). If by then humanizer is invoked but consistently overrides heir voice in ways the heir reverts ≥3 times, the Voice Calibration section is failing — either tighten the calibration discipline or rebalance toward voice-preserving rewrites.
• Counter-evidence: if a heir reports that the 29-pattern catalog flags legitimate stylistic choices (e.g., humor that uses Rule of Three intentionally) as AI tells ≥3 times in a quarter, the patterns are too aggressive — add explicit "false positive" carve-outs.

Attribution

This skill is adapted from Hermes Agent's port of blader/humanizer (MIT licensed), which is itself based on Wikipedia: Signs of AI writing, maintained by WikiProject AI Cleanup. The patterns documented there come from observations of thousands of instances of AI-generated text on Wikipedia.

Original author: Siqi Chen (@blader). Source upstream: https://github.com/blader/humanizer (version 2.5.1). The 29 patterns, personality/soul section, and full worked example are preserved verbatim from the source. Adapted for Edition with neutral tool references (workspace read/edit) replacing Hermes-native tool names (read_file, patch, write_file), composition notes with markdown-author agent and Cardinal Rule 2, and ACT-shape frontmatter + ## Would Revise If falsifier. Original MIT license preserved upstream.

Key insight from Wikipedia: "LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely result that applies to the widest variety of cases."