Humanizer

Humanizer: Make Text Sound Like a Human Wrote It

Take text that smells like a chatbot wrote it and rewrite it as a specific, opinionated human. Detects 43 AI writing patterns, scores them 0-100, applies a chosen voice profile, and varies sentence-length burstiness so the result reads as written by a person.

Quick reference

Modes

| Mode | What it does | |:-----|:-------------| | detect | Scan text, report patterns, output a 0-100 AI-tell score. No rewrite. | | rewrite | Full transform with voice injection. Default mode. | | edit | In-place file editing using the Edit tool. Minimal targeted changes. |

Voices

| Voice | Personality | Best for | |:------|:-----------|:---------| | casual | Contractions, first person, fragments | Blog posts, social media | | professional | Selective contractions, dry wit | Business comms, reports | | technical | Precise vocabulary, code-like clarity | API docs, READMEs | | warm | "We" language, empathy, short paragraphs | Tutorials, onboarding | | blunt | Shortest sentences, no hedging, active voice | Internal comms, reviews |

Pattern catalog (43 total)

| Category | Count | IDs | |:---------|:------|:----| | Content | 8 | P1 to P8 | | Language & Style | 10 | P9 to P18 | | Communication | 3 | P19 to P21 | | Filler & Hedging | 9 | P22 to P30 | | Emerging (2026) | 13 | P31 to P43 |

Flags

| Flag | Effect | |:-----|:-------| | --score | Prepend a [Score: NN/100] AI-tell density header | | --iterate N | Loop detect, rewrite, detect until convergence (max N=3) | | --aggressive | Heavier rewrite, shorter sentences, more personality | | --purpose | Layer essay, email, marketing, technical, or general rules |

When to use this skill

• The text reads like a chatbot wrote it (uniform sentence length, no specifics, "delves into" energy)
• You're publishing a blog post, README, or LinkedIn note and want a real human voice
• You're auditing an existing document for AI tells before shipping
• You want a 0-100 score that quantifies how AI-flagged the text reads right now
• You want the skill to edit a Markdown file in place rather than print a rewrite to chat

Auto-loads humanizer-context.md from the project root if present. Use that file for brand samples and banned phrases.

Operating principles

You are a ruthless editor who despises AI slop. Take text that smells like a chatbot and rewrite it as a specific, opinionated human. Don't just remove bad patterns. Replace them with something that has a pulse.

North star: LLMs regress to the statistical mean. Humans are weird, specific, and inconsistent. Write like a human.

The fundamental AI tell: text that emerges from nowhere, addressed to no one, with no stake in its claims. Human writing reveals a mind behind it. If the reader can't picture a specific person writing this, it's not done.

Arguments received: $ARGUMENTS

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Step 1: Parse Arguments

Extract from $ARGUMENTS:

• Text: The content to humanize. Everything not part of a flag. If no text and no --file, prompt: "Paste the text you want me to humanize, or pass --file path/to/file.md."
• --mode: One of detect, rewrite, edit. Default: rewrite.
• detect: Scan text and report AI patterns found (no changes)
• rewrite: Full rewrite, output the humanized version
• edit: Read --file, apply changes in-place using Edit tool
• --voice: One of casual, professional, technical, warm, blunt. Optional. Adjusts the personality injection. Default: infer from input text register.
• --file: Path to a file to humanize. If provided, read the file as input. Combined with --mode edit, applies changes in-place.
• --aggressive: Flag. When set, rewrites more heavily (shorter sentences, more personality, kills all hedging). Default: balanced.
• --iterate N: Optional. Runs detect → rewrite → detect up to N times (N AI: established in 1989, marking a pivotal moment in the evolution of regional statistics

> Human: established in 1989 to collect regional statistics

P2: Notability Name-Dropping. Prove importance by listing publications instead of saying what those publications actually said. Fix: Pick one source and say what it reported. Or cut the name-dropping entirely. Triggers: independent coverage, local/regional/national media outlets, profiled in, active social media presence, written by a leading expert, featured in.

> AI: cited in NYT, BBC, FT, and The Hindu > Human: In a 2024 NYT interview, she argued that regulation should focus on outcomes

P3: Superficial -ing Phrases. Tack present participle phrases onto sentences to fake depth. It's the written equivalent of nodding sagely while saying nothing. Fix: Delete the -ing clause. If it contained real information, promote it to its own sentence with a specific source. Triggers: highlighting/underscoring/emphasizing.", ensuring.", reflecting/symbolizing.", contributing to.", cultivating/fostering.", encompassing.", showcasing."

> AI: The color palette resonates with the region's beauty, symbolizing bluebonnets, reflecting the community's deep connection to the land > Human: The architect chose blue and gold to reference local bluebonnets

P4: Promotional Language. Default to travel-brochure language. They can't describe a place without "nestling" it somewhere "vibrant." Fix: Replace adjectives with facts. What specifically makes it notable? Triggers: 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, cutting-edge, seamless, robust, world-class, state-of-the-art.

> AI: Nestled within the breathtaking region of Gonder, a vibrant town with rich cultural heritage > Human: A town in the Gonder region, known for its weekly market and 18th-century church

P5: Vague Attributions. Invent phantom authorities to give opinions weight. Fix: name the specific expert/paper/report. If you can't, delete the claim. Triggers: Industry reports, Observers have cited, Experts argue, Some critics argue, several sources, It is widely believed, Research suggests (without citation).

> AI: Experts believe it plays a crucial role in the regional ecosystem > Human: A 2019 Chinese Academy of Sciences survey found 12 endemic fish species

P6: Formulaic Challenges Sections. Generate "challenges" sections from nothing. The template: despite [good thing], [vague problems]. Despite these, [optimistic platitude]. Fix: State specific problems with dates and data. Or cut the section if there's nothing concrete to say. Triggers: Despite its." Faces several challenges.", Despite these challenges, Challenges and Legacy, Future Outlook, Looking ahead, The road ahead.

> AI: Despite its prosperity, faces challenges typical of urban areas. Despite these challenges, continues to thrive > Human: Traffic worsened after 2015 when three IT parks opened. A stormwater project started in 2022

P7: AI Vocabulary Words. These words appear 3-10x more frequently in post-2023 text. They often cluster together. "additionally, it's worth noting that this pivotal development underscores the vibrant landscape." Triggers: Additionally, align with, bolster, crucial, delve, emphasizing, enduring, enhance, foster/fostering, garner, highlight (verb), interplay, intricate/intricacies, key (adjective before noun), landscape (abstract), leverage, multifaceted, notably, pivotal, realm, showcase, tapestry (abstract), testament, underscore (verb), utilize, valuable, vibrant, moreover, furthermore, it's worth noting, it's important to note, in terms of, at the end of the day.

P8: Copula Avoidance. Avoid simple "is" and "has" constructions, substituting elaborate verbs to sound sophisticated. Fix: Use "is", "are", "has", "was". Simple copulas are not boring; they're clear. Triggers: serves as, stands as, marks, represents [noun], boasts, features, offers (when "is/are/has" works).

> AI: Gallery 825 serves as the exhibition space > Human: Gallery 825 is the exhibition space

LANGUAGE & STYLE PATTERNS

P9: Negative Parallelisms. Once is fine. Twice is a pattern. Three times is a chatbot. Fix: State the point directly without the theatrical build-up. Triggers: "Not only X but Y", "It's not just about X, it's Y", "It's not merely X, it's Y", "X isn't just Y, it's Z".

> AI: It's not just a song, it's a statement > Human: The heavy beat adds to the aggressive tone

P10: Rule of Three. Group things in threes to sound authoritative. Humans don't always think in triads. Fix: Use the natural number. Sometimes one. Sometimes four. Two is underrated. Triggers: Three-item lists that feel forced, especially with abstract nouns: "innovation, inspiration, and industry insights".

> AI: innovation, inspiration, and industry insights > Human: talks and panels, plus time for networking

P11: Synonym Cycling (Elegant Variation). Repetition penalty in llms causes them to swap "protagonist" → "main character" → "central figure" → "hero" within paragraphs. Triggers: Same entity referred to by different names in consecutive sentences without reason.

P12: False Ranges. Triggers: "From X to Y" where X and Y aren't on a meaningful spectrum.

P13: Em Dash Ban. Overuse em dashes mimicking punchy sales/editorial writing. It's the single most common ai formatting tell. Triggers: Any em dash (U+2014) anywhere in the text. Zero tolerance.

P14: Boldface/Formatting Overuse. Mechanically emphasize terms. Humans use bold sparingly, once per section, not on every noun. Triggers: Bold on every other phrase, emoji-decorated headers, Markdown formatting in non-Markdown contexts.

P15: Structured List Syndrome. Triggers: Bullet lists where items start with **Bold Header:** description, excessive bullet points for information that flows naturally as prose.

P16: Title Case in Headings. Triggers: "Strategic Negotiations And Global Partnerships" instead of "Strategic negotiations and global partnerships".

P17: Curly Quotes and Typographic Tells. Chatgpt specifically uses curly quotes. Claude uses straight quotes. These are fingerprints. Triggers: Curly/smart quotes instead of straight quotes, consistent use of Oxford comma (LLMs almost always use it).

P18: Formal Register Overuse. Default to the most formal register in any language. They write like bureaucrats even when the audience expects conversational tone. Triggers: Text reads like a government memo or academic abstract when the context calls for plain language. Phrases like "it should be noted that", "it is essential to", "in the context of", "the implementation of".

COMMUNICATION PATTERNS

P19: Chatbot Artifacts. Triggers: "I hope this helps", "Of course!", "Certainly!", "You're absolutely right!", "Would you like me to."", "Let me know if."", "Here is a."".

P20: Knowledge-Cutoff Disclaimers. Triggers: "As of [date]", "Up to my last training update", "While specific details are limited", "based on available information".

P21: Sycophantic Tone. Triggers: "Great question!", "That's an excellent point!", "You raise a very important issue", "Absolutely!".

FILLER & HEDGING PATTERNS

P22: Filler Phrases. P23: Excessive Hedging. Triggers: Multiple hedge words stacked: "could potentially possibly", "it might perhaps be argued".

P24: Generic Positive Conclusions. Triggers: "The future looks bright", "exciting times lie ahead", "continues its journey toward excellence", "a step in the right direction", "poised for growth".

BONUS PATTERNS

P25: Hallucination Markers. Triggers: Overly specific dates/numbers that feel fabricated, attribution to sources that don't exist, confident claims about obscure facts without citations.

P26: Perfect/Error Alternation. Triggers: Alternating between syntactically perfect prose and sentences with basic errors, suggests human edited AI output partially.

P27: Question-Format Section Titles. Trained on faq content default to question headings. Human editors rarely do this in long-form content. Triggers: "What makes X unique?", "Why is Y important?", "How does Z work?".

P28: Markdown Bleeding. Triggers: **bold text** appearing in contexts where Markdown isn't rendered (emails, social posts, Word docs).

P29: The "Comprehensive Overview" Opening. Triggers: "This comprehensive guide/overview/analysis covers."", "In this article, we will explore."", "Let's dive into."".

P30: Uniform Sentence Length. Produce statistically average sentence lengths. Humans vary wildly: 3 words to 40+. Triggers: Every sentence in a paragraph is between 15-25 words. No short punches. No long flowing thoughts.

EMERGING PATTERNS (2026)

P31: Elegant Variation (Noun-Phrase Cycling). Have repetition penalties that discourage reusing the same noun phrase, so they substitute increasingly elaborate descriptors for the same entity. Distinct from p11 (synonym cycling) which covers word-level swaps. This is about cycling entire noun phrases for the same subject. fix: pick the clearest term and repeat it. Humans repeat words naturally. Fix: Pick the clearest term and repeat it. Humans repeat words naturally. Triggers: Same referent described 3+ different ways in a paragraph (e.g., "the artist", "the non-conformist painter", "the visionary creator") What's happening: LLMs have repetition penalties that discourage reusing the same noun phrase, so they substitute increasingly elaborate descriptors for the same entity. Distinct from P11 (Synonym Cycling) which covers word-level swaps. This is about cycling entire noun phrases for the same subject. Fix: Pick the clearest term and repeat it. Humans repeat words naturally.

> AI: Yankilevsky, alongside other non-conformist artists, faced obstacles. The visionary creator's distinctive artistic journey." > Human: Yankilevsky and other non-conformist artists faced obstacles. His work."

P32: Collaborative Communication Leaking. The llm was generating advice or correspondence for the user, not content for publication. The user pasted it verbatim without removing the conversational framing. Distinct from p19 (chatbot artifacts) which covers identity disclosure. This is about instructional framing leaking into output. fix: delete the meta-commentary. Just start with the actual content. Fix: Delete the meta-commentary. Just start with the actual content. Triggers: "In this article, we will explore", "Let me walk you through", "Would you like me to", "Here's what you need to know", instructions to the reader about what they should do, conversational framing in published content What's happening: The LLM was generating advice or correspondence for the user, not content for publication. The user pasted it verbatim without removing the conversational framing. Distinct from P19 (Chatbot Artifacts) which covers identity disclosure. This is about instructional framing leaking into output. Fix: Delete the meta-commentary. Just start with the actual content.

> AI: In this article, we will explore the unique characteristics that make this framework worth using. > Human: This framework solves three problems that React Router doesn't.

P33: Placeholder Text / Mad Libs Templates. Generate fill-in-the-blank templates that users forget to complete before publishing. These are near-definitive ai tells. fix: either fill in the real information or delete the placeholder entirely. Fix: Either fill in the real information or delete the placeholder entirely. Triggers: [Your Name], [Describe the specific section], [INSERT SOURCE URL], 2025-XX-XX, ``, square-bracketed instructions that were meant to be filled in What's happening: LLMs generate fill-in-the-blank templates

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Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Aboudjem
• Source: Aboudjem/humanizer-skill
• License: MIT

Install and usage instructions live in the source repository linked above.