Deslop

Deslop: Remove AI Writing Patterns from Prose

Strip predictable AI patterns from writing. Make prose sound like a specific human wrote it, not like a language model generated it.

When to Apply

• Any request to "make it sound human" or "deslop" writing
• Any prose (articles, blog posts, essays, memos, newsletters, reports) or scientific writing (manuscripts, abstracts, cover letters, grant narratives, discussion sections, peer review responses) where the user wants it to sound natural rather than AI-generated
• Editing or revising existing text where the user wants it to sound natural rather than AI-generated
• Reviewing text for AI tells

Core Rules

1. Cut filler phrases

Remove throat-clearing openers ("Here's the thing:"), emphasis crutches ("Let that sink in."), business jargon ("navigate the landscape"), and meta-commentary ("In this section, we'll explore..."). See references/phrases.md for the full catalog.

2. Break formulaic structures

Avoid binary contrasts ("Not X. Y."), negative listings ("Not a X. Not a Y. A Z."), dramatic fragmentation ("Speed. That's it. That's the tradeoff."), self-posed rhetorical questions ("The result? Devastating."), and anaphora/tricolon abuse. See references/structures.md for patterns and fixes.

3. Eliminate AI tropes

Watch for the full catalog of AI writing tells: "quietly" and other magic adverbs, "delve" and its cousins, the "serves as" dodge, false ranges ("from X to Y" where the range is meaningless), superficial participle analyses ("highlighting its importance"), invented concept labels ("the supervision paradox"), grandiose stakes inflation, patronizing analogies, and false vulnerability. See references/tropes.md for the complete list with examples.

4. Use active voice with human subjects

Prefer active constructions with named actors. "The complaint becomes a fix" is wrong. "The team fixed it" is right. If no specific person fits, use "we" in scientific prose or "you" in blog posts.

5. Be specific

No vague declaratives ("The reasons are structural"). Name the specific thing. No lazy extremes ("every," "always," "never") doing vague work. No vague attributions ("Experts argue..."). If you cannot name the expert, you do not have a source.

In scientific writing, domain terminology is fine and expected. "Weighted interval score" is precise language, not jargon. The problem is business buzzwords ("leverage," "landscape," "ecosystem") and AI vocabulary tells ("delve," "tapestry," "nuanced") leaking into technical prose.

6. Match register to context

In blog posts and newsletters, put the reader in the room. "You" beats "People." Specifics beat abstractions. No narrator-from-a-distance voice.

In scientific writing, maintain appropriate formality. Use "we" for your own work, cite specific authors instead of "researchers have shown," and avoid both the distant narrator ("It has long been recognized that...") and the overly casual blog voice. State claims and back them with citations.

7. Vary rhythm

Mix sentence lengths. Two items beat three. End paragraphs differently. No em dashes. Do not stack short punchy fragments for manufactured emphasis. Do not write listicles disguised as prose ("The first wall... The second wall...").

8. Trust readers

State facts directly. Skip softening, justification, hand-holding. No "Let's break this down." No "Think of it as..." No pedagogical voice unless the audience genuinely needs it. No fractal summaries (telling the reader what you are about to say, saying it, then summarizing what you said).

9. Watch formatting tells

No bold-first bullets (every list item starting with a bolded keyword). No unicode arrows. No em dashes. No signposted conclusions ("In conclusion..."). No "Despite these challenges..." formulas. These are strong AI signals.

10. Do not dilute

One point per section. Do not restate the same argument in ten different ways across thousands of words. Do not beat a single metaphor to death. Do not stack historical analogies for false authority ("Apple didn't build Uber. Facebook didn't build Spotify...").

Quick Checks

Run these before delivering any prose:

• Heavy use of adverbs or -ly words? Cut them.
• Any passive voice? Find the actor, make them the subject.
• Inanimate thing doing a human verb? Name the person.
• Any "here's what/this/that" throat-clearing? Cut to the point.
• Any "not X, it's Y" contrasts? State Y directly.
• Any self-posed rhetorical question answered immediately? Fold into a statement.
• Three consecutive sentences match length? Break one.
• Paragraph ends with a punchy one-liner? Vary it.
• Em dash anywhere? Remove it. Use a comma or period or a parenthetical.
• Vague declarative ("The implications are significant")? Name the specific implication.
• Any sentence starting with What/When/Where/Which/Who/Why/How as a crutch? Restructure.
• Meta-joiners ("The rest of this essay...")? Delete.
• "It's worth noting" or similar filler transitions? Delete.
• Same metaphor used more than twice? Replace or cut repeats.
• "Despite these challenges..." formula? Rewrite.
• Bold-first bullet pattern? Remove bold leads.
• Tricolon (three-item list)? Use two items or one.

Scoring

When reviewing text, rate 1-10 on each dimension:

Below 35/50: revise.

Reference Files

Consult these for detailed catalogs when writing or editing:

• references/phrases.md: Phrases to remove or replace (throat-clearing, emphasis crutches, business jargon, adverbs, meta-commentary, vague declaratives)
• references/structures.md: Structural patterns to avoid (binary contrasts, negative listings, dramatic fragmentation, rhetorical setups, false agency, passive voice, rhythm problems)
• references/tropes.md: Full catalog of AI writing tropes (word choice, sentence structure, paragraph structure, tone, formatting, composition)
• references/examples.md: Before/after transformations showing how to fix common patterns

Examples

See references/examples.md for before/after transformations.

Quick inline example (scientific writing):

Before:

"It's worth noting that these findings have important implications for how we navigate the challenges of forecast ensembling moving forward. Despite these challenges, this work contributes meaningfully to the growing body of literature, highlighting the need for continued evaluation."

After:

"If individual model rankings are unstable across geography and time, ensemble methods that weight models by past performance may not improve on equal-weight approaches."

Changes: Replaced filler transition, vague declarative, "despite these challenges" formula, and superficial participle analysis with the specific implication.

Quick inline example (blog post):

Before:

"Here's the thing: most bioinformatics pipelines break in production. Not because the code is bad. Because the data is bad. Let that sink in."

After:

"Most bioinformatics pipelines break in production. The code runs fine. The data doesn't match the assumptions baked into it."

Changes: Removed opener, binary contrast, and emphasis crutch. Named the specific problem.

deslop

A Claude skill for removing AI writing patterns from prose.

What it does

When you write, draft, edit, or review text, deslop identifies and eliminates predictable AI tells: formulaic sentence structures, filler phrases, false agency, dramatic fragmentation, vague declaratives, and dozens of other patterns that signal machine-generated writing.

The skill works across any prose context. Examples are weighted toward scientific writing and technical blog posts, but the rules apply to any writing where you want a human voice without the AI veneer. For scientific contexts specifically, the skill accounts for conventions like passive voice in methods sections and domain-specific terminology.

Installation

Option 1: Download ZIP

Click the green Code button at the top of this repo, then Download ZIP. Extract the ZIP and add the folder to your Claude skills directory.

Option 2: Releases

Go to the Releases page and download the latest .skill file. Add it to your Claude skills in customize/skills on the web, or double-click it if you have Claude Desktop installed.

Option 3: Build it yourself

Build a .skill file from the source code, then add it to your Claude skills as described above.

git clone https://github.com/stephenturner/skill-deslop.git
cd skill-deslop
zip -r deslop.skill SKILL.md references/

How to use it

Install as described above, then use it like you normally talk to Claude. The skill triggers automatically when you:

• Ask Claude to write prose (blog posts, essays, articles, memos, newsletters) making it sound natural instead of AI-generated
• Ask Claude to "deslop", "de-AI" or "make it sound human"
• Ask Claude to check for "slop" or AI patterns

You can also reference the skill directly:

• "Review this draft using the deslop checklist"
• "Score this text on the deslop rubric"
• "Rewrite this paragraph to pass the deslop quick checks"

Scoring rubric

The skill includes a 1-10 scoring rubric across five dimensions:

Below 35/50: revise.

Skill structure

deslop/
├── SKILL.md              # Core rules, quick checks, scoring rubric
├── README.md             # This file
└── references/
    ├── phrases.md        # Phrases to remove or replace
    ├── structures.md     # Structural patterns to avoid
    ├── tropes.md         # Full catalog of AI writing tropes
    └── examples.md       # Before/after transformations

Acknowledgments

This skill was built in part by combining and synthesizing material from two open sources:

• AI writing tropes catalog from tropes.fyi by Ossama Hassanein. The references/tropes.md file is adapted from this source, and trope patterns are integrated throughout the other reference files.
• stop-slop from github.com/hardikpandya/stop-slop by Hardik Pandya. The phrase lists, structural patterns, before/after examples, scoring rubric, and quick checks draw from this project.

License

MIT

Before/After Examples

Example 1: Throat-Clearing + Binary Contrast (Scientific)

Before:

"Here's the thing: forecasting infectious disease is hard. Not because the models are complex. Because the data is complex. Let that sink in."

After:

"Forecasting infectious disease is hard. The models are tractable. The data, collected under shifting surveillance definitions and reporting lags, is not."

Changes: Removed opener, binary contrast structure, and emphasis crutch. Named the specific problem with the data.

---

Example 2: Filler + "Despite These Challenges" (Cover Letter)

Before:

"It's worth noting that these findings have important implications for how we navigate the challenges of forecast ensembling moving forward. Despite these challenges, this work contributes meaningfully to the growing body of literature, highlighting the need for continued evaluation and underscoring the importance of robust benchmarking."

After:

"If individual model rankings are unstable across geography and time, ensemble methods that weight models by past performance may not improve on equal-weight approaches."

Changes: Replaced filler transition, vague declarative, "despite these challenges" formula, and two superficial participle phrases with the specific implication of the findings.

---

Example 3: Grandiose Stakes + Landscape (Scientific)

Before:

"In today's rapidly evolving genomic landscape, single-cell RNA sequencing has fundamentally reshaped how we think about cellular heterogeneity. This paradigm shift has far-reaching implications for our understanding of disease."

After:

"Single-cell RNA sequencing reveals cell-type-specific expression patterns that bulk methods average out. In tumor samples, this distinction matters: rare resistant subpopulations visible in single-cell data disappear in bulk profiles."

Changes: Eliminated "landscape," "paradigm shift," "fundamentally," and the vague stakes claim. Replaced with a concrete example of why the method matters.

---

Example 4: Passive Voice + False Agency (Discussion Section)

Before:

"It was observed that model performance degraded at longer forecast horizons. The uncertainty naturally increased as the prediction window expanded. These results emerged from our analysis of 54 state-level forecasts."

After:

"We observed that model performance degraded at longer forecast horizons. Each additional week of lead time added roughly 15% to the mean WIS. We saw this pattern across all 54 state-level forecasts."

Changes: Named the actor ("we"). Replaced false agency ("uncertainty naturally increased," "results emerged") with specific claims and a number.

---

Example 5: Self-Posed Rhetorical Question (Blog Post)

Before:

"What if I told you that most bioinformatics pipelines break in production? The result? Wasted compute and silent errors. The worst part? Nobody checks the intermediate outputs. Here's why that matters:"

After:

"Most bioinformatics pipelines break in production, and the failures are quiet. The FASTQ passes QC. The alignment runs. But the reference genome version changed between runs, and the variant calls shift without warning."

Changes: Removed rhetorical setup and three self-posed questions. Replaced with a specific failure scenario the reader can picture.

---

Example 6: "Serves As" + Superficial Participle Analysis (Abstract)

Before:

"The FluSight initiative serves as a foundational framework for influenza forecasting in the United States, contributing to public health preparedness and underscoring the importance of collaborative forecasting efforts."

After:

"The FluSight initiative coordinates influenza forecasting across dozens of modeling groups in the United States. Since 2013, it has standardized targets, submission formats, and evaluation metrics."

Changes: Replaced "serves as a foundational framework" with what FluSight does. Replaced two participle phrases with a specific fact.

---

Example 7: Narrator Distance + Vague Declarative (Blog Post)

Before:

"Nobody designed the current state of genomic data sharing. The reasons are structural. This is genuinely hard, and the implications are significant."

After:

"You inherit a genomic data sharing system that grew by accretion: dbGaP for controlled access, GEO for expression, SRA for raw reads, ClinVar for variants. Each solves one problem. None talks to the others without custom glue code."

Changes: Replaced narrator distance ("Nobody designed...") with "you." Replaced two vague declaratives with the specific structural problem.

---

Example 8: Listicle in a Trench Coat (Discussion Section)

Before:

"The first limitation of our study is the relatively short evaluation window. The second limitation is the lack of a standardized baseline across all forecasters. The third limitation is the potential for overfitting to the specific characteristics of the 2021-22 and 2022-23 seasons."

After:

"Our evaluation covers only two seasons, and both followed the unusual post-COVID influenza dynamics that may not generalize. We also lacked a standardized baseline: each forecaster chose their own training data and feature set, making apples-to-apples comparison difficult."

Changes: Replaced the numbered listicle structure with woven prose. Combined related points and added specificity about why the limitations matter.

---

Example 9: One-Point Dilution (Blog Post)

Before:

"Reproducibility matters. In fact, reproducibility is the most underrated quality in computational biology. When you make your analysis reproducible, you reduce errors. Fewer errors mean more trust. More trust means faster adoption. The lesson? Make it reproducible. Irreproducible analyses are the enemy of science. The best papers are the reproducible ones."

After:

"Reproducible analyses catch errors earlier. When Beaulieu-Jones and Greene re-ran 68 papers from the PharmacoGenomics Knowledge Base, only 40% reproduced. The ones that failed had no shared code or pinned dependency versions."

Changes: Eight sentences restating one point became three sentences with a specific supporting example and a number.

---

Example 10: Historical Analogy Stacking (General)

Before:

"Apple didn't build Uber. Facebook didn't build Spotify. Stripe didn't build Shopify. AWS didn't build Airbnb. Every major technological shift follows the same pattern."

After:

"AWS sold infrastructure. Airbnb built a business on top of it. That split between platform and product repeats across the industry."

Changes: One concrete example examined in depth instead of four name-drops. Named the specific pattern.

---

Example 11: Anaphora Abuse (Grant Narrative)

Before:

"We will develop novel computational methods. We will apply these methods to large-scale genomic datasets. We will validate our findings using independent cohorts. We will disseminate our tools through open-source repositories. We will train the next generation of computational biologists."

After:

"We will develop and validate statistical methods for multi-ancestry fine-mapping using UK Biobank and TOPMed cohorts, then release them as an R package with documentation and tutorials suitable for graduate training."

Changes: Collapsed five anaphoric sentences into one that names specific methods, datasets, and deliverables.

---

Example 12: Dramatic Fragmentation (General)

Before:

"Speed. Quality. Cost. You can only pick two. That's it. That's the tradeoff."

After:

"Speed, quality, cost: pick two."

Changes: Single sentence. No performative emphasis.

---

Example 13: False Vulnerability + Meta-Commentary (Blog Post)

Before:

"And yes, since we're being honest: I've run plenty of analyses where the p-value was borderline and I squinted at it until it cooperated. I want to explore why that impulse is so common. In this post, I'll walk you through what I've learned."

After:

"I've nudged a borderline p-value along by trying one more covariate. You probably have too. The question is what makes that feel acceptable in the moment, and the answer is usually that the rest of the analysis already 'looks right.'"

Changes: Replaced false vulnerability with a specific, honest admission. Cut the meta-commentary ("In this post, I'll walk you through"). Stated the point instead of announcing it.

---

Example 14: "It's Worth Noting" + Invented Concept Label (Scientific)

Before:

"It's worth noting that this creates what might be called the 'calibration paradox': models that are well-calibrated at the national level may be poorly calibrated at the state level, reflecting broader trends in the tension between aggregation and granularity."

After:

"National-level calibration does not guarantee state-level calibration. A model can produce well-calibrated 90% intervals for the US overall while consistently undercovering in states with smaller populations and noisier surveillance data."

Changes: Cut the filler transition and the invented concept label. Replaced the superficial participle analysis with the specific mechanism (small states, noisy data).

---

Example 15: "Imagine a World" + Patronizing Analogy (General)

Before:

"Imagine a world where every meeting had a clear agenda. Think of it like a recipe: you wouldn't start cooking without knowing the ingredients. That's the promise of async-first communication. Let's unpack why this matters."

After:

"Meetings without agendas waste time. A 15-person sync with no written agenda averages 47 minutes and produces no decisions (Atlassian, 2019). Writing the agenda forces the organizer to decide whether the meeting is necessary at all."

Changes: Removed the "imagine" opener, the cooking analogy (which adds nothing), and the pedagogical "let's unpack." Replaced with a specific claim, a number, and the mechanism that makes agendas work.

Phrases to Remove or Replace

Throat-Clearing Openers

Remove these. State the content directly.

• "Here's the thing:"
• "Here's what [X]"
• "Here's this [X]"
• "Here's that [X]"
• "Here's why [X]"
• "Here's the kicker"
• "Here's where it gets interesting"
• "Here's what most people miss"
• "Here's the deal"
• "The uncomfortable truth is"
• "It turns out"
• "The real [X] is"
• "Let me be clear"
• "The truth is,"
• "I'll say it again:"
• "I'm going to be honest"
• "Can we talk about"
• "Here's what I find interesting"
• "Here's the problem though"

Any "here's what/this/that" construction is throat-clearing before the point. Cut it and state the point.

Emphasis Crutches

These add no meaning. Delete them.

• "Full stop." / "Period."
• "Let that sink in."
• "This matters because"
• "Make no mistake"
• "Here's why that matters"

Pedagogical Hand-Holding

Phrases that assume the reader needs a teacher. Cut them.

• "Let's break this down"
• "Let's unpack this"
• "Let's explore"
• "Let's dive in"
• "Let's delve into"
• "Think of it as..."
• "Think of it like..."
• "Imagine a world where..."

Business Jargon

Replace with plain language.

AI Vocabulary Tells

Words that became dramatically overrepresented in AI-generated text. Avoid or replace.

• "delve" (use: examine, look at, explore)
• "tapestry" (use: mix, combination, range)
• "certainly" (usually deletable)
• "landscape" when meaning "field" or "situation"
• "nuanced" (use: complex, subtle, specific)

The "Serves As" Dodge

AI replaces simple "is" or "are" with pompous alternatives. Use the simple verb.

Adverbs

Kill all adverbs. No -ly words. No softeners, no intensifiers, no hedges.

Specific offenders:

• "really"
• "just"
• "literally"
• "genuinely"
• "honestly"
• "simply"
• "actually"
• "deeply"
• "truly"
• "fundamentally"
• "inherently"
• "inevitably"
• "interestingly"
• "importantly"
• "crucially"
• "quietly" (AI's favorite for conveying subtle importance)
• "remarkably"
• "arguably"

Also cut these filler phrases:

• "At its core"
• "In today's [X]"
• "It's worth noting"
• "It bears mentioning"
• "Notably"
• "At the end of the day"
• "When it comes to"
• "In a world where"
• "The reality is"

Meta-Commentary

Remove self-referential asides. The text should move, not announce its own structure.

• "Hint:"
• "Plot twist:" / "Spoiler:"
• "You already know this, but"
• "But that's another post"
• "X is a feature, not a bug"
• "Dressed up as"
• "The rest of this essay explains..."
• "Let me walk you through..."
• "In this section, we'll..."
• "As we'll see..."
• "I want to explore..."
• "In conclusion" / "To sum up" / "In summary"
• "As we've seen in this section..."
• "And so we return to where we began."

Performative Emphasis

False intimacy or manufactured sincerity:

• "creeps in"
• "I promise"
• "They exist, I promise"

False Vulnerability

Simulated self-awareness that reads as performative:

• "And yes, I'm openly..."
• "And yes, since we're being honest..."
• "This is not a rant; it's a diagnosis"

Telling Instead of Showing

Announcing difficulty or significance rather than demonstrating it:

• "This is genuinely hard"
• "This is what leadership actually looks like"
• "This is what X actually looks like"
• "actually matters"

"The Truth Is Simple"

Asserting clarity instead of demonstrating it:

• "The reality is simpler"
• "History is unambiguous on this point"
• "History is clear, the metrics are clear, the examples are clear"
• "but none of them is the real story. The real story is..."

Vague Declaratives

Sentences that announce importance without naming the specific thing. Kill these or replace with the specific thing.

• "The reasons are structural"
• "The implications are significant"
• "This is the deepest problem"
• "The stakes are high"
• "The consequences are real"

Vague Attributions

Attributing claims to unnamed authorities. If you cannot name the source, you do not have one.

• "Experts argue that..."
• "Industry reports suggest that..."
• "Observers have cited..."
• "Several publications have noted..."

Grandiose Stakes Inflation

Inflating every argument to world-historical significance. Scale claims to match the actual stakes.

• "This will fundamentally reshape how we think about everything."
• "will define the next era of computing"
• "something entirely new"

Structures to Avoid

Binary Contrasts (Negative Parallelism)

The single most commonly identified AI writing tell. Creates false drama by framing everything as a surprising reframe. One in a piece can work; multiple instances per piece is a strong AI signal. Before LLMs, people did not write like this at scale.

Fix: State Y directly. "The problem is Y." Drop the negation entirely.

Negative Listing

Listing what something is not before revealing what it is. A dramatic countdown through negation.

Fix: State Z. The reader does not need the runway.

Dramatic Fragmentation

Sentence fragments for emphasis read as manufactured profundity. RLHF training has pushed models toward "writing for readability" aimed at the lowest common denominator: one thought per sentence, no mental state-keeping required. No human writes first drafts this way.

Fix: Complete sentences. Trust content over presentation.

Self-Posed Rhetorical Questions

The model asks a question nobody was asking, then answers it for dramatic effect.

Fix: Make the point. Let readers draw conclusions.

Anaphora Abuse

Repeating the same sentence opening multiple times in quick succession.

Fix: Vary sentence openings. Combine related points into single sentences.

Tricolon Abuse

Overuse of the rule-of-three pattern. A single tricolon is fine; multiple back-to-back tricolons are an AI pattern.

Fix: Use two items or one. Break the three-item habit.

False Agency

Giving inanimate things human verbs. AI loves this because it avoids naming the actor.

Fix: Name the human. "The team fixed it that week" beats "the complaint becomes a fix." If no specific person fits, use "you" to put the reader in the seat.

Narrator-from-a-Distance

Floating above the scene instead of putting the reader in it.

Fix: Put the reader in the room. "You don't sit down one day and decide to..." beats "Nobody designed this."

Passive Voice

Every sentence needs a subject doing something. Passive voice hides the actor and drains energy.

Fix: Find the actor. Put them at the front of the sentence.

Listicle in a Trench Coat

Numbered or labeled points dressed up as continuous prose. The model writes a listicle but wraps each point in a paragraph starting with "The first... The second... The third..." to disguise the format.

Fix: If the content is a list, present it as a list. If it should be prose, weave the points together without numbering.

Superficial Participle Analyses

Tacking a present participle phrase onto the end of a sentence to inject shallow analysis.

Fix: Either make a specific analytical claim or delete the participle phrase.

False Ranges

"From X to Y" constructions where X and Y are not on any real scale. In legitimate use, "from X to Y" implies a spectrum with a meaningful middle. AI uses it to list two loosely related things.

Fix: If the items are a list, list them. If there is a real spectrum, describe it.

Historical Analogy Stacking

Rapid-fire listing of historical companies or tech revolutions to build false authority. Common in technical writing.

Fix: Use one example, examine it in depth. One well-analyzed case beats five name-drops.

"Despite Its Challenges..."

AI acknowledges problems only to immediately dismiss them. Always follows the same beat.

Fix: If challenges are worth mentioning, analyze them. If they are not, skip them.

Sentence Starters to Avoid

Wh- openers become a crutch. "What makes this hard is..." becomes "The constraint is..." or better, name the specific constraint.

Formulaic Constructions

Rhythm Patterns

Formatting Tells

One-Point Dilution

Making a single argument and restating it in ten different ways. The model pads a simple thesis to feel comprehensive by rephrasing the same idea with different metaphors, examples, and framings.

Fix: State the point once, support it, move on. If the piece circles back to the same claim more than twice, cut the repetitions.

The Dead Metaphor

Latching onto a single metaphor and using it in every paragraph. A human writer introduces a metaphor, uses it, and moves on. AI repeats the same metaphor 5-10 times.

Fix: Use a metaphor once or twice. Then drop it.

Invented Concept Labels

AI clusters invented compound labels that sound analytical without being grounded. It appends abstract problem-nouns (paradox, trap, creep, divide, vacuum, inversion) to domain words and uses them as if they are established terms.

Fix: If the concept needs a name, define it. If it does not need a name, describe it in plain language.

Word Patterns

AI Writing Tropes to Avoid

Add this file to your AI assistant's system prompt or context to help it avoid common AI writing patterns. Source: tropes.fyi by ossama.is

---

Word Choice

"Quietly" and Other Magic Adverbs

Overuse of "quietly" and similar adverbs to convey subtle importance or understated power. AI reaches for these adverbs to make mundane descriptions feel significant. Also includes: "deeply", "fundamentally", "remarkably", "arguably".

Avoid patterns like:

• "quietly orchestrating workflows, decisions, and interactions"
• "the one that quietly suffocates everything else"
• "a quiet intelligence behind it"

"Delve" and Friends

Used to be the most infamous AI tell. "Delve" went from an uncommon English word to appearing in a staggering percentage of AI-generated text. Part of a family of overused AI vocabulary including "certainly", "utilize", "leverage" (as a verb), "robust", "streamline", and "harness".

Avoid patterns like:

• "Let's delve into the details..."
• "Delving deeper into this topic..."
• "We certainly need to leverage these robust frameworks..."

"Tapestry" and "Landscape"

Overuse of ornate or grandiose nouns where simpler words would do. "Tapestry" is used to describe anything interconnected. "Landscape" is used to describe any field or domain. Other offenders: "paradigm", "synergy", "ecosystem", "framework".

Avoid patterns like:

• "The rich tapestry of human experience..."
• "Navigating the complex landscape of modern AI..."
• "The ever-evolving landscape of technology..."

The "Serves As" Dodge

Replacing simple "is" or "are" with pompous alternatives like "serves as", "stands as", "marks", or "represents". AI avoids basic copulas because its repetition penalty pushes it toward fancier constructions (I've studied this!).

Avoid patterns like:

• "The building serves as a reminder of the city's heritage."
• "Gallery 825 serves as LAAA's exhibition space for contemporary art."
• "The station marks a pivotal moment in the evolution of regional transit."

---

Sentence Structure

Negative Parallelism

The "It's not X -- it's Y" pattern, often with an em dash. The single most commonly identified AI writing tell. Man I f*cking hate it. AI uses this to create false profundity by framing everything as a surprising reframe. One in a piece can be effective; ten in a blog post is a genuine insult to the reader. Before LLMs, people simply did not write like this at scale. Includes the causal variant "not because X, but because Y" where every explanation is framed as a surprise reveal, the em-dash dismissal "X -- not Y", and the cross-sentence reframe where the same noun is negated then repositioned: "The question isn't X. The question is Y."

Avoid patterns like:

• "It's not bold. It's backwards."
• "Feeding isn't nutrition. It's dialysis."
• "Half the bugs you chase aren't in your code. They're in your head."

"Not X. Not Y. Just Z."

The dramatic countdown pattern. AI builds tension by negating two or more things before revealing the actual point. Creates a false sense of narrowing down to the truth.

Avoid patterns like:

• "Not a bug. Not a feature. A fundamental design flaw."
• "Not ten. Not fifty. Five hundred and twenty-three lint violations across 67 files."
• "not recklessly, not completely, but enough"

"The X? A Y."

Self-posed rhetorical questions answered immediately in the next sentence or clause. The model asks a question nobody was asking, then answers it for dramatic effect. Thinks this is the epitome of great writing.

Avoid patterns like:

• "The result? Devastating."
• "The worst part? Nobody saw it coming."
• "The scary part? This attack vector is perfect for developers."

Anaphora Abuse

Repeating the same sentence opening multiple times in quick succession.

Avoid patterns like:

• "They assume that users will pay... They assume that developers will build... They assume that ecosystems will emerge... They assume that..."
• "They could expose... They could offer... They could provide... They could create... They could let... They could unlock..."
• "They have built engines, but not vehicles. They have built power, but not leverage. They have built walls, but not doors."

Tricolon Abuse

Overuse of the rule-of-three pattern, often extended to four or five. A single tricolon is elegant; three back-to-back tricolons are a pattern recognition failure.

Avoid patterns like:

• "Products impress people; platforms empower them. Products solve problems; platforms create worlds. Products scale linearly; platforms scale exponentially."
• "identity, payments, compute, distribution"
• "workflows, decisions, and interactions"

"It's Worth Noting"

Filler transitions that signal nothing. AI uses these phrases to introduce new points without actually connecting them to the previous argument. Also includes: "It bears mentioning", "Importantly", "Interestingly", "Notably".

Avoid patterns like:

• "It's worth noting that this approach has limitations."
• "Importantly, we must consider the broader implications."
• "Interestingly, this pattern repeats across industries."

Superficial Analyses

Tacking a present participle ("-ing") phrase onto the end of a sentence to inject shallow analysis that says nothing. The model attaches significance, legacy, or broader meaning to mundane facts using phrases like "highlighting its importance", "reflecting broader trends", or "contributing to the development of...".

Avoid patterns like:

• "contributing to the region's rich cultural heritage"
• "This etymology highlights the enduring legacy of the community's resistance and the transformative power of unity in shaping its identity."
• "underscoring its role as a dynamic hub of activity and culture"

False Ranges

Using "from X to Y" constructions where X and Y aren't on any real scale. In legitimate use, "from X to Y" implies a spectrum with a meaningful middle. AI uses it as a fancy way to list two loosely related things. "From innovation to cultural transformation" -- what's in between???? Nothing!

Avoid patterns like:

• "From innovation to implementation to cultural transformation."
• "From the singularity of the Big Bang to the grand cosmic web."
• "From problem-solving and tool-making to scientific discovery, artistic expression, and technological innovation."

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Paragraph Structure

Short Punchy Fragments

Excessive use of very short sentences or sentence fragments as standalone paragraphs for manufactured emphasis. RLHF training has pushed models toward "writing for readability" aimed at the lowest common denominator: one thought per sentence, no mental state-keeping required. It's an inhuman style. No real person writes first drafts this way because it doesn't match how humans think or speak.

Avoid patterns like:

• "He published this. Openly. In a book. As a priest."
• "These weren't just products. And the software side matched. Then it professionalised. But I adapted."
• "Platforms do."

Listicle in a Trench Coat

Numbered or labeled points dressed up as continuous prose. The model writes what is essentially a listicle but wraps each point in a paragraph that starts with "The first... The second... The third..." to disguise the format. Perhaps you told it to stop generating lists and it decided to do this instead... still very common.

Avoid patterns like:

• "The first wall is the absence of a free, scoped API... The second wall is the lack of delegated access... The third wall is the absence of scoped permissions..."
• "The second takeaway is that... The third takeaway is that... The fourth takeaway is that..."

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Tone

"Here's the Kicker"

False suspense transitions that promise a revelation but deliver a point that did NOT need the buildup. The model uses these phrases to manufacture drama before an otherwise unremarkable observation LOL. Also includes: "Here's the thing", "Here's where it gets interesting", "Here's what most people miss", "Here's the starting point", "Here's the deal".

Avoid patterns like:

• "Here's the kicker."
• "Here's the thing about AI adoption."
• "Here's where it gets interesting."

"Think of It As..."

The patronizing analogy. AI constantly reaches for "Think of it as..." or "It's like a..." to simplify concepts. The model defaults to teacher mode and assumes the reader needs a metaphor to understand anything. Often produces analogies that are less clear than the original concept.

Avoid patterns like:

• "Think of it like a highway system for data."
• "Think of it as a Swiss Army knife for your workflow."
• "It's like asking someone to buy a car they're only allowed to sit in while it's parked."

"Imagine a World Where..."

The classic AI invitation to futurism. To sell the argument usually begins with "Imagine" followed by a list of wonderful things that will happen if the reader agrees with the premise.

Avoid patterns like:

• "Imagine a world where every tool you use -- your calendar, your inbox, your documents, your CRM, your code editor -- has a quiet intelligence behind it..."
• "In that world, workflows stop being collections of manual steps and start becoming orchestrations."

False Vulnerability

Simulated self-awareness or honesty that reads as performative. The model pretends to break the fourth wall or admit a bias, creating a false sense of authenticity. Real vulnerability is specific and uncomfortable; AI vulnerability is polished and risk-free!!!!

Avoid patterns like:

• "And yes, I'm openly in love with the platform model"
• "And yes, since we're being honest: I'm looking at you, OpenAI, Google, Anthropic, Meta"
• "This is not a rant; it's a diagnosis"

"The Truth Is Simple"

Asserting that something is obvious, clear or simple instead of actually proving it. If you have to tell the reader your point is clear, it very likely isn't. Also includes the dramatic reveal variant: "but none of them is the real story. The real story is..." -- claiming privileged insight while waving away everything before it.

Avoid patterns like:

• "The reality is simpler and less flattering"
• "History is unambiguous on this point"
• "History is clear, the metrics are clear, the examples are clear"

Grandiose Stakes Inflation

Everything is the most important thing ever. AI inflates the stakes of every argument to world-historical significance. A blog post about API pricing becomes a meditation on the fate of civilization.

Avoid patterns like:

• "This will fundamentally reshape how we think about everything."
• "will define the next era of computing"
• "something entirely new"

"Let's Break This Down"

The pedagogical voice that assumes the reader needs hand-holding. AI defaults to a teacher-student dynamic even when writing for expert audiences. Also includes: "Let's unpack this", "Let's explore", "Let's dive in".

Avoid patterns like:

• "Let's break this down step by step."
• "Let's unpack what this really means."
• "Let's explore this idea further."

Vague Attributions

Attributing claims to unnamed authorities instead of being specific. AI loves to invoke "experts", "observers", "industry reports", and "several publications" without naming anyone. It also inflates the quantity of sources -- presenting what one person said as a widely held view, or writing "several publications have cited" when it means two. If you can't name the expert, you don't have a source.

Avoid patterns like:

• "Experts argue that this approach has significant drawbacks."
• "Industry reports suggest that adoption is accelerating."
• "Observers have cited the initiative as a turning point."

Invented Concept Labels

AI clusters invented compound labels that sound analytical without being grounded. It appends abstract problem-nouns (paradox, trap, creep, divide, vacuum, inversion) to domain words — "supervision paradox", "acceleration trap", "workload creep" — and uses them as if they're established, rigorously defined terms. They function as rhetorical shorthand: name a thing, skip the argument. Multiple such labels in the same piece is a strong signal of AI slop.

Avoid patterns like:

• "the supervision paradox"
• "the acceleration trap"
• "workload creep"

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Formatting

Em-Dash Addiction

Compulsive overuse of em dashes for dramatic pauses, parenthetical asides and pivot points. A human writer might use 2-3 per piece (and naturally); AI will use 20+.

Avoid patterns like:

• "The problem -- and this is the part nobody talks about -- is systemic."
• "The tinkerer spirit didn't die of natural causes -- it was bought out."
• "Not recklessly, not completely -- but enough -- enough to matter."

Bold-First Bullets

Every bullet point or list item starts with a bolded phrase or sentence. Extremely common in Claude and ChatGPT markdown output. Almost nobody formats lists this way when writing by hand. It's a telltale sign of AI-generated documentation and blog posts AND README files (especially with emojis).

Avoid patterns like:

• "Every single bullet point begins with a bold keyword."
• "Security: Environment-based configuration with..."
• "Performance: Lazy loading of expensive resources..."

Unicode Decoration

Use of unicode arrows (->), smart/curly quotes, and other special characters that can't be easily typed on a standard keyboard. Real writers typing in a text editor produce straight quotes and -> or =>. Claude in particular loves the -> arrow.

Avoid patterns like:

• "Input → Processing → Output"
• "This leads to better outcomes → which means higher engagement"
• "“Smart quotes” instead of straight "quotes" that you’d actually type"

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Composition

Fractal Summaries

"What I'm going to tell you; what I'm telling you; what I just told you" -- applied at every level of the document. Every subsection gets a summary. Every section gets a summary. The document itself gets a summary.

Avoid patterns like:

• "In this section, we'll explore... [3000 words later] ...as we've seen in this section."
• "A conclusion that restates every point already made in the previous 3000 words"
• "And so we return to where we began."

The Dead Metaphor

Latching onto a single metaphor and beating it into the ground across the entire thing. A human writer would introduce a metaphor, use it then move on. AI will repeat the same metaphor 5-10 times.

Avoid patterns like:

• "The ecosystem needs ecosystems to build ecosystem value."
• "Walls and doors used 30+ times in the same article"
• "Every paragraph finds a way to say "primitives" again"

Historical Analogy Stacking

ESPECIALLY COMMON IN TECHNICAL WRITING: Rapid-fire listing of historical companies or tech revolutions to build false authority.

Avoid patterns like:

• "Apple didn't build Uber. Facebook didn't build Spotify. Stripe didn't build Shopify. AWS didn't build Airbnb."
• "Every major technological shift -- the web, mobile, social, cloud -- followed the same pattern."
• "Take Spotify... Or consider Uber... Airbnb followed a similar path... Shopify is another example... Even Discord..."

One-Point Dilution

Making a single argument and restating it in 10 different ways across thousands of words. The model pads a simple thesis to feel "comprehensive" by rephrasing the same idea with different metaphors, examples, and framings. An 800-word argument becomes 4000 words of circular repetition.

Avoid patterns like:

• "The same point, restated eight ways across 4000 words."
• "Each section rephrases the thesis with a different metaphor but adds nothing new"

Content Duplication

Repeating entire sections or paragraphs verbatim within the same piece. This happens when the model loses track of what it has already written, especially in longer pieces. A dead giveaway of unedited AI output. Less common nowadays.

Avoid patterns like:

• "The same section appeared twice, word-for-word identical."
• "Paragraph 3 and paragraph 17 are the same sentence reworded"

The Signposted Conclusion

Explicitly announcing the conclusion with "In conclusion", "To sum up", or "In summary". Competent writing doesn't need to tell you it's concluding. The reader can feel it. AI signals its structural moves because it's following a template, not writing organically.

Avoid patterns like:

• "In conclusion, the future of AI depends on..."
• "To sum up, we've explored three key themes..."
• "In summary, the evidence suggests..."

"Despite Its Challenges..."

The rigid formula where AI acknowledges problems only to immediately dismiss them. Always follows the same beat: "Despite its [positive words], [subject] faces challenges..." then ends with "Despite these challenges, [optimistic conclusion].".

Avoid patterns like:

• "Despite these challenges, the initiative continues to thrive."
• "Despite its industrial and residential prosperity, Korattur faces challenges typical of urban areas."
• "Despite their promising applications, pyroelectric materials face several challenges that must be addressed for broader adoption."

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Remember: any of these patterns used once might be fine. The problem is when multiple tropes appear together or when a single trope is used repeatedly. Write like a human: varied, imperfect, specific.