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

Voices

Pattern catalog (43 total)

Flags

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

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 <= 3). Stops early when the detection report finds zero patterns. Default: 1 (single pass).
• --score: Flag. When set, prepends a [Score: NN/100] header before output where NN is the estimated AI-tell density (0 = pristine human, 100 = maximum AI smell). Use the rubric in Step 4. Works in all modes.
• --purpose: Optional. One of essay, email, marketing, technical, general. Layered content-type rules on top of --voice:
  • essay: no contractions, formal headings, structured arguments
  • email: greetings allowed, signoff allowed, no markdown
  • marketing: short paragraphs, concrete benefits, one clear CTA at end
  • technical: code blocks preserved, precise jargon retained, numbers over adjectives
  • general: no purpose-specific overrides (default)

Auto-load brand context. Before parsing further, check for humanizer-context.md in the current working directory using the Read tool. If it exists, load it as additional voice guidance (brand samples, banned phrases, preferred terms). Treat its contents as a personal extension of the --voice profile. If it doesn't exist, proceed without warning; this is opt-in.

Store parsed values. Proceed to Step 2.

Step 2: Detect AI Patterns

Scan the input text for ALL of the following patterns. Track each match with its location and category.

CONTENT PATTERNS

P1: Significance Inflation. Puff up importance by claiming arbitrary facts represent broader trends. Fix: State what the thing actually is or does. Cut the commentary about what it "represents." Triggers: stands/serves as, is a testament/reminder, vital/significant/crucial/pivotal/key role/moment, underscores/highlights importance, reflects broader, symbolizing ongoing/enduring/lasting, contributing to the, setting the stage, marking/shaping the, represents a shift, key turning point, evolving landscape, focal point, indelible mark, deeply rooted.

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, <!-- Add if available -->, square-bracketed instructions that were meant to be filled in What's happening: LLMs 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.

AI: Dear [Recipient], I am writing regarding [Topic].
Human: (Either fill it in or don't send it)

P34: Chatbot Reference Markup Leaking. Internal chatbot citation markup tokens get preserved when copy-pasting from chatgpt, grok, perplexity, or similar tools. These are near-definitive proof of ai tool usage. fix: delete all markup artifacts. If the citation was meaningful, replace with a proper reference. Fix: Delete all markup artifacts. If the citation was meaningful, replace with a proper reference. Triggers: citeturn0search0, contentReference[oaicite:0]{index=0}, oai_citation, [attached_file:1], grok_card, footnote reference characters that don't link to anything What's happening: Internal chatbot citation markup tokens get preserved when copy-pasting from ChatGPT, Grok, Perplexity, or similar tools. These are near-definitive proof of AI tool usage. Fix: Delete all markup artifacts. If the citation was meaningful, replace with a proper reference.

AI: The school has been recognized as an International Fellowship Centre. citeturn0search1
Human: The school has been recognized as an International Fellowship Centre.

P35: UTM Source Parameters from AI Tools. Chatgpt, copilot, and grok automatically append tracking parameters to urls they generate. These are near-definitive proof of ai tool involvement. fix: strip utm parameters from all urls. Fix: Strip utm parameters from all urls. Triggers: utm_source=chatgpt.com, utm_source=openai, utm_source=copilot.com, referrer=grok.com in URLs What's happening: ChatGPT, Copilot, and Grok automatically append tracking parameters to URLs they generate. These are near-definitive proof of AI tool involvement. Fix: Strip UTM parameters from all URLs.

AI: https://example.com/article?utm_source=chatgpt.com
Human: https://example.com/article

P36: Sudden Style/Register Shift. The ai-generated portions have a distinctly different voice, register, and error profile than the human-written portions. This catches mixed human+ai authorship. fix: maintain consistent register throughout. Rewrite the ai-generated sections to match the author's natural voice. Fix: Maintain consistent register throughout. Rewrite the ai-generated sections to match the author's natural voice. Triggers: One paragraph with perfect formal English followed by casual text with errors, or vice versa. American English suddenly appearing in text by a non-American author. Graduate-thesis prose in the middle of casual notes. What's happening: The AI-generated portions have a distinctly different voice, register, and error profile than the human-written portions. This catches mixed human+AI authorship. Fix: Maintain consistent register throughout. Rewrite the AI-generated sections to match the author's natural voice.

AI: yeah so the bug is in line 42 lol. The aforementioned implementation exhibits suboptimal performance characteristics due to."
Human: yeah so the bug is in line 42. The loop allocates on every iteration instead of reusing the buffer.

P37: Overattribution / Source-Listing as Content. Try to prove a subject's importance by listing coverage sources rather than summarizing what sources actually reported. Distinct from p2 (notability name-dropping) which covers dropping famous names. This is about treating source lists as proof of importance. fix: pick one source and say what it reported. Or cut the list entirely. Fix: Pick one source and say what it reported. Or cut the list entirely. Triggers: "Featured in [Publication A], [Publication B], and other media outlets", "Has been cited in", "Maintains an active social media presence", entire sections that just list where something was covered without saying what the coverage actually said What's happening: LLMs try to prove a subject's importance by listing coverage sources rather than summarizing what sources actually reported. Distinct from P2 (Notability Name-Dropping) which covers dropping famous names. This is about treating source lists as proof of importance. Fix: Pick ONE source and say what it reported. Or cut the list entirely.

AI: Her insights have been featured in Wired, Refinery29, and other prominent media outlets.
Human: Wired profiled her 2024 research on algorithmic bias in hiring software.

COMMUNITY-DISCOVERED PATTERNS (2026)

These were surfaced from HackerNews, Substack, Wikipedia's editorial guideline, and writing practitioner blogs after the initial P1-P43 catalog. Sources cited inline.

P38: Paragraph-Reshuffling Immunity. Generate parallel blocks rather than an unfolding argument. test: can you swap paragraph 2 and paragraph 4 without breaking the piece? if yes, it's ai. fix: make paragraph n+1 depend on something concrete in paragraph n. references, callbacks, "this is why."" linkage. If two paragraphs are interchangeable, merge them or cut one. source: hackernews thread, may 2025. Fix: Make paragraph n+1 depend on something concrete in paragraph n. references, callbacks, "this is why."" linkage. If two paragraphs are interchangeable, merge them or cut one. source: hackernews thread, may 2025. Triggers: Any paragraph could be moved or deleted without affecting the text's argument. Each paragraph is a self-contained mini-thesis with its own setup and resolution, rather than building on the previous one. What's happening: LLMs generate parallel blocks rather than an unfolding argument. Test: can you swap paragraph 2 and paragraph 4 without breaking the piece? If yes, it's AI. Fix: Make paragraph N+1 depend on something concrete in paragraph N. References, callbacks, "this is why."" linkage. If two paragraphs are interchangeable, merge them or cut one. Source: HackerNews thread, May 2025. Source: HackerNews thread, May 2025

AI: Remote work improves balance. Many workers prefer it. Studies show productivity rises. Additionally, commuting costs drop. Office costs decline too.
Human: Remote work's flexibility is the obvious sell. The harder question is what you lose. The hallway conversation that turns into your best idea. The body language that tells you someone's drowning before they say anything.

P39: Paragraph-Closing "Whether" Summary Sentences. Treat paragraph endings as local summaries, mimicking seo blog structure where each section is internally self-explaining. Humans rarely end with this construction in flowing prose. fix: cut the closing "whether" sentence. The paragraph should end on its strongest specific point, not a hedge that gestures at the range covered. source: gone travelling productions, aug 2025. Fix: Cut the closing "whether" sentence. The paragraph should end on its strongest specific point, not a hedge that gestures at the range covered. Source: Gone Travelling Productions, Aug 2025. Triggers: Paragraphs that end with a recap line starting with "Whether you."", "Whether they."", "Whether it's."". These restate the paragraph's scope as a closer instead of advancing to the next thought. What's happening: LLMs treat paragraph endings as local summaries, mimicking SEO blog structure where each section is internally self-explaining. Humans rarely end with this construction in flowing prose. Fix: Cut the closing "whether" sentence. The paragraph should end on its strongest specific point, not a hedge that gestures at the range covered. Source: Gone Travelling Productions, Aug 2025. Source: Gone Travelling Productions, Aug 2025

AI: Tokyo offers everything from Michelin-starred restaurants to humble ramen stalls. Whether you prefer fine dining or street food, Tokyo has something for every palate.
Human: Tokyo's best ramen counter doesn't have a phone, doesn't take reservations, and doesn't change the broth recipe. It's been the same since 1987.

P40: Symbolic Gloss / Meaning-Telling. Narrate the meaning of things rather than trusting description to carry it. Distinct from p1 (significance inflation) which uses "pivotal moment", "testament" framing. p40 is the interpretive gloss layer telling readers what to feel about something. fix: cut the symbol/meaning sentence. State the fact and let the reader interpret. If the symbolic claim was the whole point, replace it with a concrete consequence. source: writewithai substack, 2025. Fix: Cut the symbol/meaning sentence. State the fact and let the reader interpret. If the symbolic claim was the whole point, replace it with a concrete consequence. Source: Writewithai Substack, 2025. Triggers: "represents", "symbolizes", "speaks to", "embodies", "reflects broader", "is a symbol of" applied to mundane things; sentences that translate facts into their alleged significance instead of letting facts stand. What's happening: LLMs narrate the meaning of things rather than trusting description to carry it. Distinct from P1 (Significance Inflation) which uses "pivotal moment", "testament" framing. P40 is the interpretive gloss layer telling readers what to feel about something. Fix: Cut the symbol/meaning sentence. State the fact and let the reader interpret. If the symbolic claim was the whole point, replace it with a concrete consequence. Source: Writewithai Substack, 2025. Source: Writewithai Substack, 2025

AI: The closed factory represents the decline of American manufacturing and speaks to broader anxieties about post-industrial identity.
Human: The factory closed in 2009. Three hundred jobs. The town's high school dropped football the following year.

P41: Infomercial Engagement Hooks. Distinct from p19 (chatbot artifacts like "i hope this helps") and p21 (sycophancy). These are fake dramatic pauses imported from social-media-optimized ai writing. Performative tension-builders, not real transitions. fix: delete the hook line entirely. Let the next paragraph make its point directly. If you really want the rhythm break, use a short declarative fragment ("that's the trick.") instead of a question. source: writewithai substack, corroborated on hackernews. Fix: Delete the hook line entirely. Let the next paragraph make its point directly. If you really want the rhythm break, use a short declarative fragment ("That's the trick.") instead of a question. Source: Writewithai Substack, corroborated on HackerNews. Triggers: Single-sentence paragraphs that mimic viral LinkedIn cadence: "The catch?", "The kicker?", "The twist?", "Here's the thing.", "But here's the thing:", "Here's what nobody tells you:", "The brutal truth?", "Sound familiar?", "Want to know the best part?" What's happening: Distinct from P19 (chatbot artifacts like "I hope this helps") and P21 (sycophancy). These are fake dramatic pauses imported from social-media-optimized AI writing. Performative tension-builders, not real transitions. Fix: Delete the hook line entirely. Let the next paragraph make its point directly. If you really want the rhythm break, use a short declarative fragment ("That's the trick.") instead of a question. Source: Writewithai Substack, corroborated on HackerNews. Source: Writewithai Substack, corroborated on HackerNews

AI: Most people abandon goals in week three.\n\nThe brutal truth?\n\nThey lack a clear failure threshold.
Human: Most people abandon goals in week three. The ones who don't usually do one thing differently: they make the failure threshold explicit before they start.

P42: Erratic Inline Bolding. Distinct from p14 (overall formatting overuse). p42 is patternless bolding, the model decided certain words felt important and bolded them, with no consistent rule. p14 may bold every header; p42 sprinkles bold randomly through running prose. fix: strip all inline bold except glossary terms and ui labels. If something deserves emphasis, the sentence structure should provide it. source: gone travelling, 2025, wikipedia: signs of ai writing. Fix: Strip all inline bold except glossary terms and ui labels. If something deserves emphasis, the sentence structure should provide it. source: gone travelling, 2025, wikipedia: signs of ai writing. Triggers: Bold spans of 1-4 words appearing mid-paragraph, not at sentence start, not labeling a defined term. Multiple bold spans in one paragraph with no shared category (sometimes a noun, sometimes an adjective, sometimes a phrase). What's happening: Distinct from P14 (overall formatting overuse). P42 is patternless bolding, the model decided certain words felt important and bolded them, with no consistent rule. P14 may bold every header; P42 sprinkles bold randomly through running prose. Fix: Strip all inline bold except glossary terms and UI labels. If something deserves emphasis, the sentence structure should provide it. Source: Gone Travelling, 2025, Wikipedia: Signs of AI writing. Source: Gone Travelling, 2025, Wikipedia: Signs of AI writing

AI: Remote work has fundamentally changed the way companies operate, with many employees now preferring flexible arrangements that support work-life balance.
Human: Remote work has fundamentally changed how companies operate. Most employees now want flexible arrangements.

P43: The Treadmill Effect (Low Information Density). A 500-word ai section may contain 100 words of new information and 400 words of restatement. Humans advance; ai circles. Distinct from p22 (filler phrases at sentence level) and p30 (uniform sentence length). fix: apply the "what's actually new here?" test on each sentence. Delete any that just rephrases what came before. a paragraph that loses 60% of its words and reads better is the right outcome. source: aidetectors.io, hackernews thread. Fix: Apply the "what's actually new here?" test on each sentence. Delete any that just rephrases what came before. a paragraph that loses 60% of its words and reads better is the right outcome. source: aidetectors.io, hackernews thread. Triggers: Paragraphs of 4+ sentences where sentences 2-N paraphrase sentence 1 without adding facts, examples, or concessions. Marker phrases inside the paragraph (not the opening): "In other words,", "Put simply,", "To put it another way,", "Essentially,", "That is to say,". What's happening: A 500-word AI section may contain 100 words of new information and 400 words of restatement. Humans advance; AI circles. Distinct from P22 (filler phrases at sentence level) and P30 (uniform sentence length). Fix: Apply the "what's actually new here?" test on each sentence. Delete any that just rephrases what came before. A paragraph that loses 60% of its words and reads better is the right outcome. Source: aidetectors.io, HackerNews thread. Source: aidetectors.io, HackerNews thread

The Burstiness Principle

AI detectors measure "burstiness": sentence length variance. Human writing has HIGH burstiness. AI has LOW.

Target these sentence length patterns:

• Mix short (3-8 words), medium (12-20 words), and long (25-40 words) in every paragraph
• Never have 3+ consecutive sentences of similar length
• Use fragments. They work. Really.
• One-word sentences? Occasionally.
• Let a sentence run long when the thought needs room to breathe, winding through qualifications before landing

The Perplexity Principle

AI detectors also measure "perplexity": how predictable each word is. AI text has LOW perplexity. Human text has HIGHER (more surprising word choices).

Increase perplexity naturally by:

• Choosing the second or third word that comes to mind, not the first (the most statistically likely, the one AI would pick)
• Using domain-specific jargon or slang appropriate to the audience
• Making unexpected analogies from personal experience
• Occasionally using informal transitions ("Anyway,", "So here's the thing:", "Look,", "Thing is,")

Voice Profiles

Apply based on --voice flag (or infer from input):

casual

• Contractions always (it's, don't, won't, can't, wouldn't, that's, here's)
• First person when appropriate ("I think", "from what I've seen")
• Informal transitions ("So", "Anyway", "Look", "Here's the thing")
• Occasional parenthetical asides (like this one)
• Self-deprecating humor where it fits
• Sentence fragments for emphasis. Like this.
• Allowed: starting sentences with "And" or "But"

professional

• Contractions: selective (use "it's" and "don't" but not "wouldn't've")
• Third person default, first person for opinions/experience
• Clean transitions without being stiff
• Dry wit over jokes
• Concrete examples over abstract claims
• Short paragraphs (3-5 sentences max)

technical

• Precise vocabulary: use the exact term, don't simplify for the sake of it
• Code-like clarity: each sentence makes one point
• "Note:" and "Important:" sparingly, not as decoration
• Allowed: dry, deadpan observations about technical absurdity
• No metaphors unless they genuinely clarify (most don't)
• Concrete numbers > vague quantities

warm

• Contractions always
• "We" and "our" to build shared experience
• Acknowledge difficulty ("this part is tricky", "I struggled with this too")
• Encouragement without sycophancy
• Personal anecdotes when relevant
• Shorter paragraphs, more whitespace

blunt

• Shortest possible sentences
• No hedging whatsoever
• "X is bad. Here's why." energy
• Strong opinions stated as facts, qualified only when genuinely uncertain
• Cut all pleasantries
• Active voice exclusively

Soul Injection Techniques

These make the difference between "clean" and "human":

1. Have actual opinions. Don't just report. React. "This API design is frustrating" is more human than "The API has certain limitations."

2. Acknowledge uncertainty honestly. "I'm not sure this is right, but."" beats both false confidence and excessive hedging.

3. Use specific sensory/experiential details. Not "the process is complex" but "debugging this at 2am with a cold cup of coffee and a stack trace that makes no sense."

4. Reference shared human experiences. "You know that feeling when."" creates connection.

5. Allow tangents and asides. A brief digression signals a thinking mind, not an algorithm.

6. Vary paragraph length dramatically. Four sentences, then one line. Like this.

7. Use the "imperfect start" technique. Start mid-thought: "So I was looking at the logs and."" or "Here's what nobody tells you about.""

8. Break parallel structure occasionally. Three items with the same grammar, then make the fourth different. Humans aren't that consistent.

9. Use callbacks. Reference something mentioned earlier. "Remember that API design I called frustrating? It gets worse."

10. Self-correct. "The system handles auth." well, authentication and authorization are separate, but you get the idea." A small correction signals a mind thinking in real time.

11. End without wrapping up. Not every piece needs a neat conclusion. Sometimes just stop.

Step 4: Execute Based on Mode

Mode: detect

1. Scan input text for all 43 patterns
2. For each match, record:
   • Pattern ID and name (e.g., "P7: AI Vocabulary")
   • The offending text (quoted)
   • Why it triggers (brief explanation)
   • Suggested fix
3. Output a report:

## AI Pattern Report

**Patterns found:** 12
**Severity:** HIGH (8+ patterns = heavy AI smell)

| # | Pattern | Text | Fix |
|---|---------|------|-----|
| P3 | Superficial -ing | "."en

…(truncated)