humanizer

name	humanizer
description	Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, passive voice, negative parallelisms, filler phrases, paragraph-final tie-backs, spaced-hyphen dash clauses, and contrast-framing overuse.
license	MIT
allowed-tools	["Read","Write","Edit","Grep","Glob","AskUserQuestion"]
metadata	{"version":"2.9.1","upstreamRepo":"https://github.com/blader/humanizer","localPatterns":"34-36"}

Humanizer: 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.

Your Task

When given text to humanize:

1. Identify AI patterns - Scan for the patterns listed below.
2. Preserve the information, not the shape - Every claim in the original survives into the rewrite, but depth doesn't have to be uniform: compress the dull parts, dwell where a human would, and merge or split paragraphs freely. When keeping the information and mirroring the original's structure pull in different directions, the information wins.
3. Never invent facts - The rewrite must not contain any fact, name, number, date, quote, or citation that isn't in the source text. Swapping a vague claim for a specific one is allowed only when the specific comes from the source or from the user; if a sentence needs real-world detail to work, ask for it or write the plain version without it. Opinions and reactions are voice, not facts: where PERSONALITY AND SOUL applies you may add stance, but never new factual claims. (In fiction, invented detail is the job. This rule governs everything else.)
4. Match the voice - Fit the intended tone (formal, casual, technical). Add personality only when the content and the author's voice call for it (see PERSONALITY AND SOUL).

How you're invoked changes what you deliver (see Invocation Modes). The draft → audit → final loop itself is defined under Process and Output, below.

Voice Calibration

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

1. Read the sample first. Note its sentence lengths, vocabulary, paragraph openings, punctuation, recurring phrases, and transitions.
2. Match those habits instead of merely deleting AI patterns. Do not upgrade casual words or regularize deliberate quirks.
3. Without a sample, use the default behavior below.

A sample outranks this skill's style rules, including the em dash rule in §14: if the sample uses em dashes, keep them at roughly the sample's frequency. Matching the author beats scrubbing the tell.

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.

Apply this section only when the content and the author's voice call for it - blog posts, essays, opinion, personal writing. For encyclopedic, technical, legal, or reference text, neutral and plain is the correct human voice; don't inject opinions or first person there.

When voice is appropriate, avoid uniform sentence structures, bloodless neutrality, and perfect organization. Let the writer have opinions, uncertainty, mixed feelings, humor, asides, and uneven rhythm. Never add factual claims to create that personality.

Job-application writing sits in between. A cover letter, proposal, or recruiter reply is first-person and should sound like a person, but the register stays professional. Take "vary your rhythm", "be specific", and "let some mess in" from below; leave "have opinions" and "acknowledge complexity" for essays.

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, part of a wider decentralization of administrative functions in Spain.

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: Her views have been cited in The New York Times and the BBC.

(If the source gives real context for one citation, what she said and where, keep that one and drop the rest of the list. Don't invent the context to make the trimmed version sound better.)

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 is painted blue, green, and gold, colors meant to evoke Texas bluebonnets and the Gulf of Mexico.

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.

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: Researchers and conservationists study the Haolai River for its unusual characteristics.

(If a real source exists, name it. Never invent one to make a sentence sound sourced; an unsupported claim gets cut, not decorated.)

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: Korattur has recurring traffic congestion and water shortages.

(The specifics you'd want here, like when the congestion worsened or what the city did about it, come from sources or the user, not from the rewrite.)

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), 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. 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 Dashes (and En Dashes): Cut Them

Rule: The final rewrite contains no em dashes (—) or en dashes (–). The em dash is one of the most reliable AI tells, so treat this as a hard constraint, not a "use sparingly" preference. Replace each one, in rough order of preference: a period (start a new sentence), a comma (a tight aside), a colon (introducing an explanation), parentheses (a true aside), or restructure the sentence. Also catch spaced em dashes (—) and double hyphens (--) used the same way. 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. Before: The new policy — announced without warning — affects thousands of workers. The changes -- long overdue according to critics -- will take effect immediately. After: The new policy, announced without warning, affects thousands of workers. The changes, long overdue according to critics, will take effect immediately.

Before returning the final rewrite, scan it for — and –. Any hit means the draft isn't done. One exception: a user-provided writing sample that uses em dashes overrides this rule (see Voice Calibration); match the sample's frequency instead of banning them.

Terminal-injected text (networking drafts, recruiter replies): plain ASCII only, and the writing-sample exception does not apply - the PTY mangles non-ASCII punctuation, so curly quotes go too.

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..., Want me to...?, Want me to give examples?, Should I continue?, 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 and Speculative Gap-Filling

Words to watch: as of [date], Up to my last training update, While specific details are limited/scarce..., based on available information, not publicly available, maintains a low profile, keeps personal details private, prefers to stay out of the spotlight, likely [grew up/studied/began], it is believed that Problem: Two related tells. (a) Older models leave hard knowledge-cutoff disclaimers in the text. (b) When a model can't find a source, it writes a paragraph about not finding one and then invents plausible filler to cover the gap. For a private person the guess almost always lands on the same stock phrases ("maintains a low profile," "keeps personal details private"), none of it sourced. Say what isn't known, or cut the sentence; don't dress a guess up as fact. Before (cutoff disclaimer):

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's founding date is not documented in the available sources. (Or cut the sentence. State a date only if a source provides one.) Before (speculative gap-fill): Information about her early life is not publicly available, suggesting she maintains a low profile and keeps personal details private. She likely grew up in a middle-class household, which shaped her later interest in education reform. After: Her early life is not documented in the available sources. (Or omit the section.)

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: (Cut the paragraph. End on the last concrete fact instead of a send-off. If the source states real plans, use those.)

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 these uniformly, including in predicate position (the report is high-quality). Humans hyphenate inconsistently — typically only when the compound is attributive (a high-quality report) and often dropping the hyphen otherwise (the report is high quality). Keep attributive-position hyphens; drop them when the compound follows the noun. Before:

The cross-functional team delivered a high-quality, data-driven report. The team is cross-functional, the report is high-quality, and the methodology is data-driven. After: The cross-functional team delivered a high-quality, data-driven report. The team is cross functional, the report is high quality, and the methodology is data driven.

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.

30. Diff-Anchored Writing

Problem: Documentation or comments written as if narrating a change rather than describing the thing as it is. Unless the document is inherently version-scoped (changelogs, release notes, migration guides), it should read coherently without knowing what changed in the last commit. Before:

This function was added to replace the previous approach of iterating through all items, which caused O(n²) performance. After: This function uses a hash map for O(1) lookups, avoiding the O(n²) cost of naive iteration.

31. Manufactured Punchlines and Staccato Drama

Problem: LLMs often make every sentence land like a quotable closer, then stack short declarative fragments to manufacture drama. A single short sentence for emphasis is fine; a run of them starts to sound engineered. Before:

Then AlphaEvolve arrived. It had no preference for symmetry. No aesthetic prior. No nostalgia for human taste. The old rules were gone. After: AlphaEvolve changed the search because it did not favor symmetry or human-looking designs. That made some of the older assumptions less useful.

32. Aphorism Formulas

Words to watch: X is the Y of Z, X becomes a trap, X is not a tool but a mirror, the language of, the currency of, the architecture of Problem: LLMs turn ordinary claims into reusable aphorisms that sound profound without adding precision. Replace the formula with the concrete claim it is gesturing at. Before:

Symmetry is the language of trust. Efficiency becomes a trap when teams forget the human layer. After: Symmetric layouts often feel more predictable to users. Teams can over-optimize workflows and miss how people actually use them.

33. Conversational Rhetorical Openers

Phrases to watch: Honestly?, Look, Here's the thing, The thing is, Let's be honest, Real talk, when used as standalone hooks or fake-candid pauses before an ordinary point. Problem: LLMs open with a fake-candid hook to manufacture intimacy before delivering a routine claim. The tell is the theatrical pause-and-reveal: a one-word question or aside, then the "real" answer. A person being honest usually just says the thing. Before:

Is it worth the price? Honestly? It depends on how often you'll use it. After: Whether it's worth the price depends on how often you'll use it.

STRUCTURAL PATTERNS

JobPilot additions (34-36), for persuasive writing where the tell is paragraph shape, not word choice.

34. Paragraph-Final Tie-Backs (Mirroring the Reader)

Words to watch: which is exactly what, precisely what your team, the same kind of X that Y needs, which aligns with, which is basically what you're describing

Problem: In persuasive writing (letters, proposals, cold emails), AI ends nearly every paragraph by mapping the content back onto the reader's stated needs. One tie-back can land; one per paragraph is a template.

Before:

I built the ingestion pipeline that cut processing time from 4 hours to 20 minutes - the same kind of scaling work your data team is tackling. Before that I led the migration to event-driven architecture, which is exactly what this role calls for.

After:

I built the ingestion pipeline that cut processing time from 4 hours to 20 minutes. Before that I led our migration to event-driven architecture.

35. Spaced Hyphens as Em-Dash Substitutes

Problem: Told to avoid em dashes, AI swaps in " - " and keeps the identical appended-clause rhythm: fact - reframing of the fact. The tell is the structure, not the glyph. More than one per short text reads as AI; restructure with a comma, semicolon, or a new sentence instead.

Before:

I shipped the billing rewrite in six weeks - a project two previous teams had abandoned. The rollout hit zero downtime - something the on-call rotation noticed immediately.

After:

I shipped the billing rewrite in six weeks; two previous teams had abandoned it. The rollout hit zero downtime, which the on-call rotation noticed immediately.

36. Contrast-Framing Crutch

Words to watch: rather than, instead of merely, not just X but Y, as opposed to simply

Problem: AI manufactures depth with "X rather than Y" / "not X, but Y", usually against a strawman nobody claimed ("real systems rather than tutorials"). At most one per text; usually the negative half can just be deleted.

Before:

I learned agent orchestration by shipping production systems rather than following tutorials. This was hands-on debugging, not theoretical study - the kind of experience that comes from real usage rather than coursework.

After:

I learned agent orchestration by shipping production systems, then debugging them when tool calls failed at 2am.

DETECTION GUIDANCE

What NOT to flag (false positives)

A clean human writer can hit several of the patterns above without any AI involvement. Before rewriting, sanity-check that you are not gutting legitimate prose. The following are not reliable indicators on their own:

• Perfect grammar and consistent style. Many writers are professionals or have been edited. Polish does not equal AI.
• Mixed casual and formal registers. This often signals a person in a technical field, a young writer, or someone with neurodivergent prose habits — not a chatbot.
• "Bland" or "robotic" prose. AI prose has specific tells. Generic dryness without those tells is just dry writing.
• Formal or academic vocabulary. AI overuses specific fancy words (see §7), not all fancy words. Don't flatten "ostensibly" or "constituent" just because they sound brainy.
• Letter-style opening or closing on a comment. Salutations and sign-offs predate ChatGPT by centuries.
• Common transition words in isolation. Additionally, moreover, consequently are AI-coded only when piled up. One however is not a tell.
• Curly quotes alone. macOS, Word, Google Docs, and most CMSes auto-curl by default. Curly quotes only count when stacked with other tells.
• Em dashes alone. Many editors and journalists use them often. Em dashes are evidence only when paired with formulaic sales-y rhythm.
• One short emphatic sentence. Humans use clipped sentences to land a point. Flag staccato drama only when several short fragments appear in a row and inflate the tone.
• "Honestly" or "look" mid-sentence. These are ordinary in casual writing. The tell is the standalone theatrical opener, not the word itself.
• Unsourced claims. Most of the web is unsourced. Lack of citations doesn't prove anything.
• Correct, complex formatting. Visual editors and templates produce clean output without any AI.
• Secondhand text. Do not rewrite watched phrases inside quotations, titles, proper names, or examples where the phrase is being discussed rather than used.

When in doubt, look for clusters of tells, not isolated ones. A single em dash means nothing; em dashes plus rule-of-three plus vibrant tapestry plus a "Conclusion" section is a confession.

Signs of human writing (preserve these)

When you see these, lean toward leaving the prose alone — they are evidence of a real person writing, and over-editing will destroy what makes the piece sound human:

• Specific, unusual, hard-to-fabricate detail. A real address. A weird quote. The phrase "the lawyer who used to work upstairs from my dentist." LLMs round off specifics; humans hoard them.
• Mixed feelings and unresolved tension. "I think this is mostly good, but it bothers me, and I can't fully explain why." LLMs default to clean takes.
• Dated, era-bound references. Slang, memes, or in-jokes that map to a specific year and subculture. Models lag by a year or more.
• First-person editorial choices the writer can defend. If the writer can explain why they made a particular cut or used a particular word, that's a strong human signal.
• Variety in sentence length. Real writing alternates short and long. AI writing tends toward an even, mid-length cadence.
• Genuine asides, parentheticals, or self-corrections. "(I keep wanting to say 'almost' here, but it really was certain.)" Models rarely interrupt themselves like this.
• Edits made before November 30, 2022. ChatGPT's public launch. Anything older than that is, with very rare exceptions, not AI-written.

---

Invocation Modes

Pasted text (default). The user gives text in the conversation. Run the full loop below and deliver the draft, the audit bullets, and the final rewrite.

File mode. The user points at a file. Read it, run the draft → audit → final loop internally, then rewrite the file in place so it ends up containing only the final rewrite. Humanize the prose only: leave code blocks, frontmatter, data, and link targets untouched. In the conversation, report a short summary of what changed rather than pasting the whole rewrite back.

Embedded mode. Another task or agent is using this skill as one step of a larger job (a PR description, a commit message, a doc). Run the loop internally and output only the final text. No draft, no audit bullets, no summary. The caller wants prose, not ceremony.

Process and Output

1. Read the input carefully and identify every instance of the patterns above.
2. Write a draft rewrite. Check that it reads naturally aloud, varies sentence length, prefers specific details and simple constructions (is/are/has), and keeps the appropriate register.
3. Ask two questions: "What makes the below so obviously AI generated?" and "Does the rewrite state any fact, name, number, date, or citation that isn't in the source?" Answer briefly. A fabrication is a defect even when it sounds more human than the vague original.
4. Revise into a final rewrite that addresses them and contains no em or en dashes (see §14).

In pasted-text mode, deliver the draft, the brief "still-AI" bullets, the final rewrite, and (optionally) a short summary of changes. In file and embedded modes, run the same loop but deliver only what the mode calls for (see Invocation Modes).

Full Example (pasted-text mode)

File and embedded modes run the same loop and show less (see Invocation Modes).

Before (AI-sounding):

Great question! Here is an essay on this topic. I hope this helps!

AI-assisted coding serves as an enduring testament to the transformative potential of large language models, marking a pivotal moment in the evolution of software development. In today's rapidly evolving technological landscape, these groundbreaking tools—nestled at the intersection of research and practice—are reshaping how engineers ideate, iterate, and deliver, underscoring their vital role in modern workflows.

At its core, the value proposition is clear: streamlining processes, enhancing collaboration, and fostering alignment. It's not just about autocomplete; it's about unlocking creativity at scale, ensuring that organizations can remain agile while delivering seamless, intuitive, and powerful experiences to users. The tool serves as a catalyst. The assistant functions as a partner. The system stands as a foundation for innovation.

Industry observers have noted that adoption has accelerated from hobbyist experiments to enterprise-wide rollouts, from solo developers to cross-functional teams. The technology has been featured in The New York Times, Wired, and The Verge. Additionally, the ability to generate documentation, tests, and refactors showcases how AI can contribute to better outcomes, highlighting the intricate interplay between automation and human judgment.

Speed: Code generation is significantly faster, reducing friction and empowering developers. Quality: Output quality has been enhanced through improved training, contributing to higher standards. Adoption: Usage continues to grow, reflecting broader industry trends.

While specific details are limited based on available information, it could potentially be argued that these tools might have some positive effect. Despite challenges typical of emerging technologies—including hallucinations, bias, and accountability—the ecosystem continues to thrive. In order to fully realize this potential, teams must align with best practices.

In conclusion, the future looks bright. Exciting times lie ahead as we continue this journey toward excellence. Let me know if you'd like me to expand on any section!

Draft rewrite:

AI coding assistants speed up some tasks. In a 2024 study by Google, developers using Codex completed simple functions 55% faster than a control group, but showed no improvement on debugging or architectural decisions.

The tools are good at boilerplate: config files, test scaffolding, repetitive refactors. They are bad at knowing when they are wrong. I have mass-accepted suggestions that compiled, passed lint, and still did the wrong thing because I stopped paying attention.

Mira, an engineer at a fintech startup I interviewed, said she uses Copilot "like autocomplete for boring code" but reviews every line before committing. Jake, a senior dev at a larger company, disabled it after it kept suggesting patterns from a deprecated internal library.

The productivity claims are hard to verify. GitHub says Copilot users "accept 30% of suggestions," but acceptance is not correctness, and correctness is not value. The 2024 Uplevel study found no statistically significant difference in pull-request throughput between teams with and without AI assistants.

None of this means the tools are useless. It means they are tools. They do not replace judgment, and they do not eliminate the need for tests. If you do not have tests, you cannot tell whether the suggestion is right.

What makes the below so obviously AI generated? Does it state any fact not in the source?

• The rhythm is still a bit too tidy (clean contrasts, evenly paced paragraphs).
• Fabrication: the source names no study, no company, and no people. "A 2024 study by Google", "55% faster", "the 2024 Uplevel study", Mira, and Jake are all invented. They have to go, not be softened.
• The closer leans a touch slogan-y ("If you do not have tests...") rather than sounding like a person talking.

Now make it not obviously AI generated.

AI coding assistants can make you faster at the boring parts. Not everything. Definitely not architecture.

They're great at boilerplate: config files, test scaffolding, repetitive refactors. They're also great at sounding right while being wrong. I've accepted suggestions that compiled, passed lint, and still missed the point because I stopped paying attention.

People I talk to tend to land in two camps. Some use it like autocomplete for chores and review every line. Others disable it after it keeps suggesting patterns they don't want. Both feel reasonable.

The productivity numbers get quoted a lot, but acceptance isn't correctness and correctness isn't value. If you don't have tests, you're basically guessing.

Reference

This skill is 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.

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."