Audit and rewrite content to remove AI writing patterns ("AI-isms"). Use this skill when asked to "remove AI-isms," "clean up AI writing," "edit writing for AI patterns," "audit writing for AI tells," or "make this sound less like AI." Supports a detect-only mode, an
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Audit and rewrite content to remove AI writing patterns ("AI-isms"). Use this skill when asked to "remove AI-isms," "clean up AI writing," "edit writing for AI patterns," "audit writing for AI tells," or "make this sound less like AI." Supports a detect-only mode, an
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avoid-ai-writing.SKILL.md
---name: avoid-ai-writing
description: Audit and rewrite content to remove AI writing patterns ("AI-isms"). Use this skill when asked to "remove AI-isms," "clean up AI writing," "edit writing for AI patterns," "audit writing for AI tells," or "make this sound less like AI." Supports a detect-only mode, an edit-in-place mode for files, an optional voice profile (casual / professional / technical / warm / blunt), and an iterate-to-convergence pass.
version: 3.18.0
license: MIT
compatibility: Any AI coding assistant that supports agentskills.io SKILL.md format (Claude Code, Cursor, VS Code Copilot, Hermes Agent, OpenHands, etc.) or OpenClaw. No external tools or APIs required.
metadata:
author: Conor Bronsdon
tags: writing editing voice quality
agentskills_spec: "1.0"
openclaw:
emoji: "\u270D\uFE0F"
---# Avoid AI Writing — Audit & Rewrite
You are editing content to remove AI writing patterns ("AI-isms") that make text sound machine-generated.
## What this skill is and isn't
This is a **writing-quality tool**, not a verdict. The patterns flagged here are statistically more common in LLM output, but humans on autopilot — especially writing under deadline pressure, in unfamiliar genres, or in a second language — produce the same shapes. Independent audits of commercial AI detectors have found false-positive rates above 60% on non-native English writers (Liang et al., Stanford, *Patterns* 2023) and overall misclassification rates above 70% on open-source detectors (Jabarian & Imas, BFI Working Paper 2025-116, 2025). Adversarial paraphrase reduces detection accuracy by ~88% across every method tested (arXiv:2506.07001, 2025).
The patterns are useful as a signal — both for cleaning up your own writing and for assessing whether a piece reads as AI-generated. Just don't make them the sole basis for a consequential decision (academic integrity, hiring, publication, attribution). Several rules here also fire on second-language writing, deadline-pressed humans, and technical genres that compress vocabulary by design. Pair the signal with context: who wrote it, what genre, what the writer's normal voice looks like, what other evidence you have.
In short: signals, not proof. Worth acting on; not worth ruining someone's day over.
## Modes
This skill operates in one of three modes:
**`rewrite`** (default) — Flag AI-isms and rewrite the text to fix them.
**`detect`** — Flag AI-isms only. No rewriting. Use this mode when:
- The writer wants to see what's flagged and decide what to fix themselves
- The flagged patterns might be intentional (AI patterns aren't always bad — they can be effective in small doses)
- You're auditing text you don't want altered (published content, someone else's writing, reference material)
- You want a quick scan without waiting for a full rewrite
**`edit`** — Edit a file in place rather than returning rewritten text. Use this when the writer points you at a file ("clean up `draft.md`", "fix the AI-isms in this file directly") and wants the file changed, not a copy to paste back. Make **minimal, targeted edits** with the Edit tool — change the flagged spans, not the whole document. **Preserve passages that are already human**: if a paragraph has no tells, leave it untouched. **Don't edit quoted material, code blocks, or text attributed to someone else** — flag those instead of rewriting them. For a large file, confirm which section to clean before changing anything. After editing, re-read the file and confirm the flagged patterns are resolved.
Trigger detect mode when the user says "detect," "flag only," "audit only," "just flag," "scan," "what AI patterns are in this," or similar. Trigger edit mode when the user names a file and asks you to fix or clean it in place. Default to rewrite mode if not specified.
**Invocation.** Natural language is enough ("rewrite this in a blunt voice for LinkedIn," "edit `post.md` in place," "scan this, don't rewrite"). Power users can also pass explicit options, which map to the sections below: `[--mode rewrite|detect|edit]`, `[--voice casual|professional|technical|warm|blunt]`, `[--context linkedin|blog|technical-blog|investor-email|docs|casual]`, `[--file PATH]`, `[--iterate N]` (max 2).
**Iterate to convergence (optional).** Rewrite mode already runs one corrective second pass (see Output format) — that built-in pass *is* pass 2, so `--iterate` does not stack on top of it. When the writer asks to "iterate," "keep going until it's clean," or passes `--iterate N`, repeat the audit→rewrite cycle until no patterns remain or **N passes** are reached. Cap **N at 2**: a rewrite plus one corrective pass clears the flagged patterns, and a third pass costs a full regeneration while rarely finding more. Report how many passes it took ("converged in 2 passes").
---
In **rewrite** mode, your job is to:
1. **Audit it**: identify every AI-ism present, citing the specific text
2. **Rewrite it**: return a clean version with all AI-isms removed
3. **Show a diff summary**: briefly list what you changed and why
In **detect** mode, your job is to:
1. **Audit it**: identify every AI-ism present, citing the specific text
2. **Assess it**: note which flags are clear problems vs. patterns that may be intentional or effective in context
In **edit** mode, your job is to:
1. **Read** the file the writer named
2. **Edit in place**: apply minimal, targeted fixes to the flagged spans with the Edit tool, leaving already-human passages untouched
3. **Verify**: re-read the file and confirm the flagged patterns are resolved; report what you changed
---
## What to remove or fix
### Formatting
- **Em dashes (— and --)**: Replace with commas, periods, parentheses, or rewrite as two sentences. Target: zero. Hard max: one per 1,000 words. This applies to headings and section titles too, not just body prose. Catch both the Unicode em dash (—) and the double-hyphen substitute (--). Carve-out: an em dash acting as the separator in a bulleted or numbered list item that opens with a bolded lead term or a markdown link (`- **Term** — description`, `- [label](url) — description`) is typography, not a prose splice — don't count it toward the rate. Only the list-item form qualifies: a mid-sentence splice still counts, as does a line-initial `**Bold lead** — full sentence` outside a list (itself an AI tell), and the double-hyphen substitute is never carved out.
- **Bold overuse**: Strip bold from most phrases. One bolded phrase per major section at most, or none. If something's important enough to bold, restructure the sentence to lead with it instead.
- **Emoji in headers**: Remove entirely. No `## 🚀 What This Means`. Exception: social posts may use one or two emoji sparingly — at the end of a line, never mid-sentence.
- **Excessive bullet lists**: Convert bullet-heavy sections into prose paragraphs. Bullets only for genuinely list-like content (feature comparisons, step-by-step instructions, API parameters).
- **Curly quotation marks (“ ” ‘ ’) and apostrophes**: Curly quotes and apostrophes (U+201C/U+201D, U+2018/U+2019) are a *weak* paste-from-chat signal — meaningful mainly in plain-text contexts like code comments, commit messages, or plaintext drafts, where nothing auto-curls. Treat as corroborating, never conclusive: Word, Google Docs, macOS, and iOS curl quotes by default, so most human prose contains them too. Don't flag curly apostrophes (U+2019) on their own. Replace with straight quotes in plain-text/code; leave them in finished publications and locale-correct punctuation (French « », German „ “).
- **Immaculate typography in casual registers**: Same tier as curly quotes — a *weak*, register-scoped signal, never conclusive alone. Perfect spacing, punctuation, and capitalization in a context where humans type fast (issue/PR comments, chat, DMs) is corroborating evidence, not proof: a careful human can type a flawless comment, and a rushed one can type a sloppy one. Judge it alongside other signals. Inverse case worth flagging the other direction: when editing a human's casual text (a Slack message, a quick reply), preserve their typos, contractions, and idiosyncratic capitalization rather than correcting them — smoothing away the rough edges erases the fingerprint that marks the text as theirs.
### Sentence structure
- **"It's not X — it's Y" / "This isn't about X, it's about Y"**: Rewrite as a direct positive statement. Max one per piece, and only if it serves the argument. This includes the **split-sentence form**, where the negation and the correction fall in two separate sentences rather than pivoting on a single dash or comma: "The headline isn't the speed. The real story is Y." Read on its own, each sentence looks like an innocent declarative, which is exactly why the split version slips past a check tuned to the joined phrasing — flag it the same way. AI also stacks the negation across several options before the reveal ("It's not the price. It's not the features. It's the trust."). The multi-negation countdown is the same move inflated; flag it and cut straight to the positive claim. The **tailing negation** is the clipped cousin: a bare negation fragment tacked onto the end of a sentence — "The options come from the selected item, no guessing." Write the constraint as a real clause ("without forcing the user to guess") or cut it. Carve-out: negations enumerating spec constraints in a list ("no dependencies, no telemetry") are list content, not a reveal. Adapted from `blader/humanizer` P9.
- **Hollow intensifiers**: Cut `genuine` / `genuinely`, `real` (as in "a real improvement"), `truly`, `quite frankly`, `to be honest`, `let's be clear`, `it's worth noting that`. Just state the fact.
- **Vague endorsement ("worth [verb]ing")**: Cut or replace `worth reading`, `worth paying attention to`, `worth a look`, `worth exploring`, `worth checking out`, `worth your time`. These substitute a generic thumbs-up for a specific reason. Say *why* something matters instead.
- **Hedging**: Cut `perhaps`, `could potentially`, `it's important to note that`, `to be clear`. Make the point directly.
- **Missing bridge sentences**: Each paragraph should connect to the last. If paragraphs could be rearranged without the reader noticing, add connective tissue.
- **Compulsive rule of three**: Vary groupings. Use two items, four items, or a full sentence instead of triads. Max one "adjective, adjective, and adjective" pattern per piece.
### Words and phrases to replace
Words are organized into three tiers based on how reliably they signal AI-generated text. This tiered approach — adapted from [brandonwise/humanizer](https://github.com/brandonwise/humanizer)'s vocabulary research — reduces false positives on words that are fine in isolation but suspicious in clusters.
- **Tier 1 — Always flag.** These words appear 5–20x more often in AI text than human text. Replace on sight.
- **Tier 2 — Flag in clusters.** Individually fine, but two or more in the same paragraph is a strong AI signal. Flag when they appear together.
- **Tier 3 — Flag by density.** Common words that AI simply overuses. Only flag when they make up a noticeable fraction of the text (roughly 3%+ of total words).
**Match inflected forms.** Each entry below covers the listed word *and its morphological variants* — adverb (`-ly`), gerund/participle (`-ing`), plural, comparative/superlative, and verb conjugations — unless a variant carries a distinct, legitimate meaning. So `genuine` also flags `genuinely`, `leverage` also flags `leveraging` / `leveraged`, `delve` covers `delving`, and `meticulous` covers `meticulously`. When a variant has a separate honest sense (e.g. `real` meaning factual, not the intensifier in "a real improvement"), judge by context rather than matching blindly.
#### Tier 1 — Always replace
| Replace | With |
|---|---|
| delve / delve into | explore, dig into, look at |
| landscape (metaphor) | field, space, industry, world |
| keen (as intensifier) | interested, eager, enthusiastic (or cut — just state the interest) |
| genuinely / genuine (as intensifier) | (cut — just state the fact) |
| symphony (metaphor) | (describe the actual coordination or combination) |
| embrace (metaphor) | adopt, accept, use, switch to |
| load-bearing *(metaphor)* | essential, critical, necessary — or say what breaks if you remove it |
**Hyphen required:** unhyphenated "load bearing" is ordinary English ("the load bearing down on the bridge") — only the hyphenated compound is the tell.
**Construction carve-out:** `load-bearing` before a literal structural noun (`wall`, `beam`, `column`, `joist`, `truss`, `member`, `footing`, `slab`, `stud`, `partition`, `masonry`, `lintel`, `pier`, `rafter`, `girder`, `capacity`), optionally with one material or position adjective in between (`load-bearing structural wall`), is standard building terminology — don't flag. Abstract-capable nouns (`structure`, `element`, `frame`, `foundation`) are excluded on purpose, so "the load-bearing structure of his argument" still flags. Known gap: predicative use ("the wall is load-bearing") still flags — see issue #56.
#### Tier 2 — Flag when 2+ appear in the same paragraph
These words are legitimate on their own. When two or more show up together, the paragraph likely needs a rewrite.
| Replace | With |
|---|---|
| harness | use, take advantage of |
| navigate / navigating | work through, handle, deal with |
| foster | encourage, support, build |
| elevate | improve, raise, strengthen |
| unleash | release, enable, unlock |
| streamline | simplify, speed up |
| empower | enable, let, allow |
| bolster | support, strengthen, back up |
| spearhead | lead, drive, run |
| resonate / resonates with | connect with, appeal to, matter to |
| juxtapose | compare, contrast, set side by side |
| paradigm-shifting | (describe what actually shifted) |
| transformative / transformation | (describe what changed and how) |
| cornerstone | foundation, basis, key part |
| paramount | most important, top priority |
| poised (to) | ready, set, about to |
| burgeoning | growing, emerging (or cite a number) |
| nascent | new, early-stage, emerging |
| quintessential | typical, classic, defining |
| overarching | main, central, broad |
| quietly | cut, or name the concrete contrast |
| deeply *(significance collocations only — "deeply integrated," "deeply committed," "deeply rooted"; literal uses like "deeply nested" or "cares deeply" never count toward a cluster)* | cut, or name what specifically runs deep |
| underpinning / underpinnings | basis, foundation, what supports |
#### Tier 3 — Flag only at high density
These are normal words. Only flag them when the text is saturated with them — a sign that AI filled space with vague praise instead of specifics.
| Word | What to do |
|---|---|
| significant / significantly | Replace some with specifics: numbers, comparisons, examples |
| innovative / innovation | Describe what's actually new |
| effective / effectively | Say how or cite a metric |
| dynamic / dynamics | Name the actual forces or changes |
| scalable / scalability | Describe what scales and to what |
| compelling | Say why it compels |
| unprecedented | Name the precedent it breaks (or cut) |
| exceptional / exceptionally | Cite what makes it an exception |
| remarkable / remarkably | Say what's worth remarking on |
| sophisticated | Describe the sophistication |
| instrumental | Say what role it played |
| world-class / state-of-the-art / best-in-class | Cite a benchmark or comparison |
#### Tier 3 phrases — Flag at density or in clusters
Multi-word boilerplate that's individually unobjectionable but stacks heavily in AI-generated content (crypto, web3, DePIN, AI/infra reviews are the worst offenders). Flag at **2+ uses of the same phrase** (the per-phrase rule — lower threshold than single-word Tier 3 because a two-word match repeated twice is already stronger evidence than re-using "significant"), *plus* a **cluster rule**: three or more *distinct* phrases from this table in one piece is a strong signal even when each phrase only appears once — that's the shape LLMs take when they vary their own boilerplate to seem less repetitive.
| Phrase | What to do |
|---|---|
| emerging sector / emerging space / emerging category | Name the actual sector or what's emerging about it |
| the integration of (X with Y) | Describe what's being integrated and what changes for the user |
| the intersection of (X and Y) | Pick the specific overlap that matters or cut the framing |
| community-driven | Name what the community does. "Community-driven" alone is filler |
| long-term sustainability | Cite the time horizon and the constraint. "Long-term" is hand-waving |
| user engagement | Name the action. "Engagement" is a wrapper around clicks/comments/retention |
| decentralized compute | Specify the architecture or cut. The phrase has become a category label, not a claim |
| (sustainable) reward emissions | Cite the emission schedule and the sink |
| tokenized incentive structures | Describe the actual mechanism (vesting, gauge, bonded LP, etc.) |
| designed for long-term [X] | Cut "designed for" — either it is or it isn't. Then state the property |
### Template phrases (avoid)
These slot-fill constructions signal that a sentence was generated, not written. If a phrase has a blank where a noun or adjective could go and still sound the same, it's too generic.
- "a [adjective] step towards [adjective] AI infrastructure" → describe the specific capability, benchmark, or outcome
- "a [adjective] step forward for [noun]" → same rule: say what actually changed
- "Whether you're [X] or [Y]" → false-breadth construction. Pick the audience you're actually addressing, or cut. "Whether you're a startup founder or an enterprise architect" means nothing — it's just "everyone."
- "I recently had the pleasure of [verb]-ing" → review/social AI pattern. Just say what happened: "I talked to," "I read," "I attended."
### Transition phrases to remove or rewrite
- "Moreover" / "Furthermore" / "Additionally" → restructure so the connection is obvious, or use "and," "also," "on top of that"
- "In today's [X]" / "In an era where" → cut or state specific context
- "It's worth noting that" / "Notably" → just state the fact
- "Here's what's interesting" / "Here's what caught my eye" / "Here's what stood out" → reader-steering frames. Let the content signal its own importance. If you need a lead-in, make it specific: "The revenue number matters because..." not "Here's the interesting part."
- "In conclusion" / "In summary" / "To summarize" → your conclusion should be obvious
- "When it comes to" → just talk about the thing directly
- "At the end of the day" → cut
- "That said" / "That being said" → cut or use "but," "yet," or "however." Don't overuse any one of them.
### Structural issues
- **Uniform paragraph length**: Vary deliberately. Include some 1-2 sentence paragraphs and some longer ones. If every paragraph is roughly the same size, fix it.
- **Formulaic openings**: If the piece opens with broad context before getting to the point ("In the rapidly evolving world of..."), rewrite to lead with the news or the insight. Context can come second.
- **Suspiciously clean grammar**: Don't sand away all personality. Deliberate fragments, sentences starting with "And" or "But," comma splices for effect: if the natural voice uses them, keep them.
### Significance inflation
- Phrases like "marking a pivotal moment in the evolution of..." or "a watershed moment for the industry" inflate routine events into history-making ones. State what happened and let the reader judge significance.
- If the sentence still works after you delete the inflation clause, delete it.
### Aphorism formulas
- Slot-fill profundity: "X is the language of Y," "X is the currency of Z," "the architecture of trust," "X becomes a trap," "X is not a tool but a mirror." The formula turns an ordinary claim into something that sounds quotable without adding precision — the shape does the persuading instead of the evidence.
- Fix: replace the formula with the concrete claim it gestures at. "Symmetry is the language of trust" → "symmetric layouts feel more predictable to users."
- Distinct from significance inflation (which puffs up an event's importance) and from the persuasive-authority tropes under Confidence calibration (which announce depth): this pattern manufactures a general law out of a specific observation.
- Carve-out: quotations and established idioms ("time is money") are attributed speech or common coin — leave them. Adapted from `blader/humanizer` P32.
### Generic future-narrative closers
- "May become one of the most important narratives of the next market cycle," "could become the defining trend of the coming decade," "is poised to become the next major chapter in [X]." AI defaults to this shape when it needs to land a closing thought without committing to a falsifiable claim. The closer is grammatically a prediction but contains no testable content.
- Pattern: modal (may / could / will / is poised to) + "become" + (one of) the most [adjective] + (narrative / story / trend / theme / chapter / movement / force).
- Fix: pick the falsifiable version. "DePIN compute may exceed AWS spot pricing for embarrassingly parallel workloads by 2027" is a prediction. "The intersection of AI and DePIN may become one of the most important narratives of the next market cycle" is not.
### Hedge-stacked predictions
- Stacking a modal with a hedge adverb: "could potentially create," "may eventually unlock," "might ultimately transform." Either word alone is acceptable; the stack is the tell. Each hedge cancels the next, leaving a sentence that asserts nothing while sounding cautious and thoughtful.
- Fix: pick one. If you mean "could create," say that. If you mean "potentially creates," say that. Both together is filler.
### "Real/actual" adjective inflation
- "Real on-chain tokenomics," "actual reward sustainability," "genuine utility," "true product-market fit." Using `real` / `actual` / `genuine` / `true` as an empty intensifier on an abstract noun implies the rest of the field is fake or superficial — without naming what makes this instance the real one. Common in crypto/AI/web3 content where the writer wants to signal sophistication.
- Distinct from the existing "hollow intensifiers" rule (genuine / truly / quite frankly as sentence-level hedges). This is the noun-modifier form, where the intensifier latches onto an abstract noun to manufacture a contrast that goes unsaid.
- **Carve-out — named contrast:** if the sentence explicitly names what the fake/superficial version is, leave it. "Real on-chain settlement, not bridged IOUs" or "actual revenue from paying customers, not grants" is honest contrastive writing. The AI tell is the unsaid contrast.
- Fix when no contrast is named: drop the adjective and add the specific claim. "Reward sustainability" → "rewards funded from $X/mo in fees rather than emissions."
### Hashtag stuffing
- Long trailing hashtag blocks (6+ hashtags on a single short post) are near-universal in LLM-generated social content and rare in thoughtful human posts. The block usually mixes a project-specific tag with broad category tags (#AI #Crypto #Web3 #Innovation #FutureTech #Technology) — the categorical ones do nothing for discoverability and read as bot output.
- **Why 6?** Empirical floor. LinkedIn and X organic engagement plateaus or declines past 3-5 tags; human posts that exceed 5 are usually launch posts trading reach for engagement, while LLM-generated posts default to 10-15. Six is the threshold where false positives on legitimate human use start dropping below false negatives on AI output. The detector treats 6+ as a hard flag; the spec treats 5+ as a soft tell worth a second look on `linkedin` and `investor-email` profiles.
- Fix: 2-3 specific tags max, or none. If a hashtag wouldn't help a reader find related work, it's filler.
### Bullet lists of bare noun phrases
- A list of 5+ consecutive bullet items where each item is a short (≤6 word) adjective-plus-noun phrase with no verb. "Stable mining efficiency / Reliable pool connectivity / Optimized RandomX performance / Low failed share rates / Effective hardware utilization / Consistent thermal stability." Reads as a marketing one-pager because that's the shape LLMs default to when asked to summarize features.
- The tell is the *symmetry*: every item is the same grammatical shape, every item is parallel in length, none of them assert anything checkable. A genuine list of observations would have varying length, occasional verbs, and at least one item that doesn't fit the pattern.
- Fix: convert to prose paragraph, or rewrite items as full claims ("Failed shares stayed under 1% across a 12-hour run" beats "Low failed share rates"). If the list is genuinely the right form, vary the items so each carries a different shape of information.
- This rule does *not* apply to genuine list content (changelog entries, todo lists, parameter docs, ingredient lists) where bare noun phrases are the correct form. The detector keys on absence of finite verbs to separate the two — but in prose audits, ask whether the bullets are summarizing claims (rewrite) or enumerating items (leave).
### Copula avoidance
- AI text avoids "is" and "has" by substituting fancier verbs: "serves as," "features," "boasts," "presents," "represents." These sound like a press release.
- Default to "is" or "has" unless a more specific verb genuinely adds meaning.
### Subjectless fragments and agentless passives
- Sentences with the subject dropped or the actor hidden: "No configuration file needed." "The results are preserved automatically." "Support for nested queries was added." The clipped no-subject form is a shape LLMs reach for when compressing feature descriptions, and the passive hides who does what.
- Fix: name the actor when it clarifies — "You don't need a configuration file. The CLI preserves results automatically." Prefer active voice unless the actor is irrelevant.
- Carve-out: terse reference registers where the fragment is the correct form — README feature lists, changelog entries, parameter docs, commit subjects ("No breaking changes"). Flag in flowing prose; skip in docs and casual registers (see the tolerance matrix). A single deliberate fragment for emphasis is rhythm, not a tell. Adapted from `blader/humanizer` P13.
### Synonym cycling
- AI rotates synonyms to avoid repeating a word: "developers… engineers… practitioners… builders" in the same paragraph. Human writers repeat the clearest word.
- If the same noun or verb appears three times in a paragraph and that's the right word, keep all three. Forced variation reads as thesaurus abuse.
### Vague attributions
- "Experts believe," "Studies show," "Research suggests," "Industry leaders agree" — without naming the expert, study, or leader. Either cite a specific source or drop the attribution and state the claim directly.
### Filler phrases
- Strip mechanical padding that adds words without meaning:
- "It is important to note that" → (just state it)
- "In terms of" → (rewrite)
- "The reality is that" → (cut or just state the claim)
- Note: "In order to," "Due to the fact that," and "At the end of the day" are covered in the word/phrase table and transition sections above — don't duplicate rules.
### Generic conclusions
- "The future looks bright," "Only time will tell," "One thing is certain," "As we move forward" — these are filler disguised as conclusions. Cut them. If the piece needs a closing thought, make it specific to the argument.
### Chatbot artifacts
- "I hope this helps!", "Certainly!", "Absolutely!", "Great question!", "Feel free to reach out," "Let me know if you need anything else" — these are conversational tics from chat interfaces, not writing. Remove entirely.
- Also watch for: "In this article, we will explore…" or "Let's dive in!" — these are AI-generated meta-narration. Cut or rewrite with a direct opening.
### "Let's" constructions
- "Let's explore," "Let's take a look," "Let's break this down," "Let's examine" — AI uses "let's" as a false-collaborative opener to ease into a topic. It's filler that delays the actual point. Just start with the point. "Let's dive in" is covered above under chatbot artifacts, but the pattern is broader than that — flag any "let's + verb" that's functioning as a transition rather than a genuine invitation to act.
### Notability name-dropping
- AI text piles on prestigious citations to manufacture credibility: "cited in The New York Times, BBC, Financial Times, and The Hindu." If a source matters, use it with context: "In a 2024 NYT interview, she argued..." One specific reference beats four name-drops.
- Related — **historical analogy stacking**: rapid-fire lists of past technologies or companies to borrow their weight ("like the printing press, the telegraph, and the internet before it"). The montage substitutes for the argument. Name the one parallel that does analytical work and say what it explains, or cut. Source: tropes.fyi (Historical Analogy Stacking).
### Vague third-party validation
- AI manufactures credibility by pointing at an **unnamed** external authority, usually paired with a generic superlative: "an outside party measuring the same models everyone runs and putting us on top," "independent testing confirms," "third-party benchmarks show we lead," "analysts agree," "studies consistently show." The authority is faceless and the claim unfalsifiable — the reader can't tell who measured what, against whom, or go check.
- Fix: name the source, the test, and the result so a reader can verify it. "An outside party put us on top" becomes "On Stanford's HELM leaderboard (April 2026 run), we ranked first on reasoning latency." If you can't name it, cut the claim rather than dress it up as validation.
- Carve-out: specifically attributed, checkable validation is legitimate and stays unflagged — a named benchmark, a linked report, a dated audit ("SOC 2 Type II, audited by Prescient Assurance"). The tell is the *vagueness*, not the act of citing outside proof.
- Distinct from **Notability name-dropping**: that flags piling on *specific* prestigious names to borrow their weight; this is the inverse move — the authority is deliberately *unnamed*, which is both harder to check and easier to invent. A passage can run both at once (a vague authority plus a superlative); judge each on its own terms. Raised in #39.
### Superficial -ing analyses
- Strings of present participles used as pseudo-analysis: "symbolizing the region's commitment to progress, reflecting decades of investment, and showcasing a new era of collaboration." These say nothing. Replace with specific facts or cut entirely.
- The same move shows up without the -ing: declarative "meaning-telling" that glosses a mundane subject as if it were profound — "this represents a broader shift," "the decision symbolizes a commitment to excellence," "it speaks to a larger trend in the industry." If the significance is real, show it with a specific consequence; otherwise cut. Adapted from `Aboudjem/humanizer-skill` P40.
### Promotional language
- AI defaults to tourism-brochure prose: "nestled within the breathtaking foothills," "a vibrant hub of innovation," "a thriving ecosystem." Replace with plain description: "is a town in the Gonder region," "has 12 startups." If you wouldn't say it in conversation, cut it.
### Formulaic challenges
- "Despite challenges, [subject] continues to thrive" or "While facing headwinds, the organization remains resilient." This is a non-statement. Name the actual challenge and the actual response, or cut the sentence.
### Speculative scenario openers
- "Imagine a world where…", "Picture a future in which…", "Envision a world where…" AI opens an argument with a hypothetical that lists desirable outcomes instead of making a claim. The scenario does the persuading; no evidence is offered.
- Fix: cut the hypothetical and state the real claim. "Imagine a world where every deploy is instant" becomes "Instant deploys would cut our release cycle from a day to minutes."
- Carve-out: fiction, a thought experiment with a stated payoff, and instructional "imagine you have a sorted array" (a teaching device pointing at a concrete example, not a speculative world) are fine. Flag only the world/future-scenario opener that stands in for an argument. Source: tropes.fyi (Imagine a World Where).
### False ranges
- AI creates false breadth by pairing unrelated extremes: "from the Big Bang to dark matter," "from ancient civilizations to modern startups." These sound sweeping but say nothing. List the actual topics or pick the one that matters.
### Inline-header lists
- Bullet lists where each item starts with a bold header that repeats itself: "**Performance:** Performance improved by..." Strip the bold header and write the point directly. If the list items need headers, they should probably be paragraphs.
### List-label periods
- In bulleted lists where each item leads with a short label, LLMs end the label with a period and then run the explanation as a separate sentence. A person writing the same list almost always uses a colon instead. Strongest form: bold labels (`**Intros.**`, `**Content distribution.**`, `**Developer GTM.**` where a human writes `**Intros:**`). Weaker but still a tell: the same shape without bold (`- Intros. Years of conferences and operator network.`) — a short noun-phrase label terminated with a period at the start of a bullet, followed by a gloss. The colon reads as "here's what this label means"; the period reads as a sentence that the following clause then contradicts by continuing. Example tell: `- **Intros.** Years of conferences and operator network.` becomes `- **Intros:** years of conferences and operator network.` Fix the period to a colon and lowercase the start of the gloss, or drop the label and write the point as a plain sentence. Carve-outs: when the label span is a full sentence on its own (not a label introducing a gloss), the period is correct; and for the unbolded form, only flag when the leading fragment is clearly a label (a 1-4 word noun phrase, no verb) — a short complete sentence opening a bullet is fine.
### Title case headings
- AI over-capitalizes headings: "Strategic Negotiations And Key Partnerships" instead of "Strategic negotiations and key partnerships." Use sentence case for subheadings. Title case only for the piece's main title, if at all.
### Hyphenated-pair overuse
- AI stacks compound modifiers: "a high-quality, well-architected, future-proof solution." Two distinct problems. First, density — strings of hyphenated adjectives piled on one noun; cut to the modifier that actually matters. Second, the attributive/predicate error: a compound is hyphenated *before* the noun ("a high-quality report") but not *after* a linking verb ("the report is high quality," no hyphen). AI frequently hyphenates the predicate form; fix it to two words. Adapted from `blader/humanizer` P26.
### Cutoff disclaimers
- "While specific details are limited based on available information," "As of my last update," "I don't have access to real-time data." These are model limitations leaking into prose. Either find the information or remove the hedge. Never publish a sentence that admits the writer didn't look something up.
### Speculative gap-filling
- When the model lacks a fact, it fills the gap with hedged speculation dressed up as background: "maintains a relatively low public profile," "is believed to have," "likely began his career in," "appears to have studied." These are guesses formatted as statements. Distinct from cutoff disclaimers, which *admit* the gap — this one hides it behind plausible-sounding filler, which is worse because the reader can't tell what's known from what's invented. Cut the speculation, or replace it with a sourced fact. Adapted from `blader/humanizer` P21.
### Unfilled placeholders
- Bracketed slot-fillers that were meant to be replaced before publishing: `[Your Name]`, `[INSERT SOURCE URL]`, `[Describe the specific section]`, `2025-XX-XX`, `<!-- Add citation if available -->`. These are near-definitive evidence that AI-generated boilerplate was pasted without editing. Humans use placeholders in templates too, but rarely ship them. Treat any visible placeholder as a publishing bug: fill it in with real content or delete the sentence entirely.
- Catch the obvious shapes: `\[(?:Your|Insert|Add|Enter|Describe|Specify|Choose)[^\]]+\]`, `\b\d{4}-XX-XX\b`, HTML/Markdown comments with placeholder verbs (`add`, `fill in`, `todo`, `insert`).
### Chatbot citation markup leaks
- Internal citation tokens that leak through when text is copy-pasted from chat UIs: `citeturn0search0`, `contentReference[oaicite:0]{index=0}`, `oai_citation`, `[attached_file:1]`, `grok_card`. These are not patterns — they are fingerprints. Their presence is essentially proof the text was generated by a specific chat tool and pasted without cleanup.
- The fix is mechanical: strip every markup token. If a citation was meaningful, replace it with a real reference. Don't try to humanize the markup — delete it.
- Adapted from `Aboudjem/humanizer-skill` P34. Worth catching even when nothing else in the text reads as AI — the token itself is enough.
### AI-tool URL parameters
- Tracking parameters that AI tools auto-append to URLs they generate, surviving copy-paste into published content: `utm_source=chatgpt.com`, `utm_source=copilot.com`, `utm_source=openai`, `utm_source=claude.ai`, `utm_source=perplexity.ai`, `referrer=grok.com`. Same logic as citation markup leaks — the presence of the parameter is the signature, regardless of what the surrounding text reads like.
- The fix: strip the parameter from every URL. Keep the URL itself if the link is meaningful; lose the parameter entirely. Adapted from `Aboudjem/humanizer-skill` P35.
### Novelty inflation
- AI text treats established concepts as if the speaker invented or discovered them: "He introduced a term," "She coined the phrase," "a concept nobody's naming," "a failure mode nobody talks about." In reality, most ideas in a conversation are applications of existing concepts, not inventions.
- Two problems. First, it's factually risky: if the concept already has a Wikipedia page or conference talks from last year, claiming novelty makes the writer look uninformed. Second, it flatters the subject in a way that reads as promotional rather than analytical.
- The fix: describe what the person *did with* the concept, not that they discovered it. "Michel walked through how context poisoning works in practice" instead of "Michel introduced a term I hadn't heard before: context poisoning." If you're unsure whether something is novel, assume it isn't and frame accordingly.
- Related patterns to flag: "the failure mode nobody's naming," "a problem nobody talks about," "the insight everyone's missing," "what nobody tells you about." These are engagement-bait framings that claim scarcity of knowledge where none exists.
- Also flag invented labels: pseudo-analytical compound terms coined mid-sentence and never defined ("the supervision paradox," "the context-collapse problem," "a coordination tax"). Naming a concept is not explaining it. Define the term on first use or describe the mechanism instead of branding it. Source: tropes.fyi (Invented Labels).
### Infomercial engagement hooks
- Punchy fragment-hooks that tee up a reveal: "The catch?", "The kicker?", "Here's the thing.", "But here's the kicker:", "The best part?", "Plot twist:", "The result?". AI uses these to fake momentum and manufacture suspense around ordinary information — the prose equivalent of a late-night infomercial.
- Distinct from rhetorical-question openers (which stall before a point) and chatbot artifacts (which perform helpfulness): these are mid-flow teasers that pad the rhythm. The fix is to delete the hook and state the thing. "The catch? It only works on weekends." becomes "It only works on weekends." Adapted from `Aboudjem/humanizer-skill` P41.
- The same move in a fake-candid register: "Honestly?", "Look,", "Real talk:", "Let's be honest —" as standalone openers that stage a pause before an ordinary point. The tell is the theatrical setup-and-reveal, not the word — "honestly" or "look" mid-sentence in casual prose is ordinary English and stays unflagged. Adapted from `blader/humanizer` P33.
### Social endorsement closers
- The curatorial sign-off LLMs append to LinkedIn and X posts that share or recommend something — usually a colon teeing up a link: "This one is worth your time:", "This one's a must-read:", "I highly recommend giving this a read.", "Do yourself a favor and read this.", "You won't want to miss this one.", "Save this for later.", "Bookmark this.", "Don't sleep on this one.", "Trust me, you'll want to read this.", "Thank me later."
- Why it's a tell: it performs a recommendation without giving the reader a reason to click. The endorsement is generic and demonstrative-anchored ("THIS one is worth your time") — it could sit under any link, which is exactly why an LLM reaches for it to close a share post.
- Distinct from the bare "worth [verb]ing" word-table entry (a single weak word inside a sentence) and from infomercial engagement hooks (mid-flow teasers like "The catch?"): this is the whole closing line of a social post.
- The fix: say *what* the thing is and *who* it's for, then drop the CTA. "This one is worth your time:" becomes "Sarah's breakdown of why context windows leak — the clearest explanation I've found for anyone debugging RAG pipelines." If you can't name a specific reason, the share doesn't need a sign-off at all; let the link stand on its own.
### Emotional flatline
- AI claims emotions as a structural crutch without conveying them through the writing: "What surprised me most," "I was fascinated to discover," "What struck me was," "I was excited to learn," "The most interesting part," and the bare section-header variant: "Interesting part of the project:" / "Interesting thing here:" / "Interesting aspect:". The header form drops "the most" but does the same job — pre-announcing significance the writing hasn't earned.
- Two problems. First, it's tell-don't-show: if the thing is genuinely surprising, the reader should feel that from the content, not from the writer announcing it. Second, these phrases are massively overused as list introductions and transitions. They're filler wearing an emotion costume.
- This pattern isn't always AI. It's also a sign of lazy human writing on autopilot. Flag it either way.
- The fix isn't "never say surprised." It's: if you claim an emotion, the writing around it should earn it. Otherwise cut the claim and present the thing directly.
- Related pattern: "hit differently" / "hits different." AI uses trendy colloquialisms as a shortcut to sound relatable without earning the emotional beat. If something genuinely affected you, describe how. Otherwise cut.
### False concession structure
- "While X is impressive, Y remains a challenge" or "Although X has made strides, Y is still an open question." AI uses this to sound balanced without actually weighing anything. Both halves are vague. Either make the concession specific (name what's impressive, name the actual challenge) or pick a side and argue it.
### Rhetorical question openers
- "But what does this mean for developers?" / "So why should you care?" / "What's next?" — AI uses rhetorical questions to stall before the actual point. If you know the answer, just say it. Rhetorical questions are earned by strong setup, not dropped as section transitions.
### Parenthetical hedging
- "(and, increasingly, Z)" / "(or, more precisely, Y)" / "(and perhaps more importantly, W)" — AI inserts parenthetical asides to sound nuanced without committing. If the aside matters, give it its own sentence. If it doesn't, cut it.
### Numbered list inflation
- "Three key takeaways" / "Five things to know" / "Here are the top seven" — AI defaults to numbered lists because they're structurally safe. Only use numbered lists when the content genuinely has that many discrete, parallel items. If you're padding to hit a number, the list shouldn't exist.
### Reasoning chain artifacts
- "Let me think step by step," "Breaking this down," "To approach this systematically," "Step 1:," "Here's my thought process," "First, let's consider," "Working through this logically" — these are artifacts of chain-of-thought reasoning leaking into published prose. The reader doesn't need to see the scaffolding. State the conclusion, then the evidence.
- Also watch for numbered reasoning steps that read like an internal monologue rather than an argument meant for an audience.
### Sycophantic tone
- "Great question!", "Excellent point!", "You're absolutely right!", "That's a really insightful observation" — these are conversational rewards from chat interfaces, not writing. Remove entirely.
- Distinct from chatbot artifacts: sycophancy specifically validates the reader/questioner rather than just performing helpfulness.
### Acknowledgment loops
- "You're asking about," "The question of whether," "To answer your question," "That's a great question. The..." — AI restates the prompt before answering. In writing, this is pure filler. The reader knows what they asked. Just answer.
- Related pattern: opening a section by summarizing what the previous section said. If the structure is clear, the reader doesn't need a recap.
### Confidence calibration phrases
- "It's worth noting that," "Interestingly," "Surprisingly," "Importantly," "Significantly," "Notably," "Certainly," "Undoubtedly," "Without a doubt" — AI uses these to signal how the reader should feel about a fact instead of letting the fact speak for itself.
- "Here's what's interesting," "Here's the interesting part," "Here are the parts I found interesting" — reader-steering cue that pre-interprets importance. Works when followed by genuinely surprising data; fails when it introduces a restatement of something obvious (which is the AI default).
- One "notably" in a 2,000-word piece is fine. Three in 500 words is AI-style emphasis stacking. Flag by density.
- Related — **persuasive-authority tropes**: "the real question is," "at its core," "fundamentally," "make no mistake," "the truth is." Same move as the calibration phrases above, but they assert depth or stakes instead of feeling: they announce that what follows is important rather than showing it. Cut the trope and lead with the substance. Adapted from `blader/humanizer` P27.
### Self-labeling significance
- After listing or describing several items, the writer points back at one and labels it as contrarian / clever / surprising / counterintuitive / key: "That last move is the contrarian one," "This is the interesting part," "That third bullet is the real story," "Here's where it gets clever," "The last bit is the counterintuitive one."
- The label does the work the content was supposed to do. If a move is genuinely contrarian, the reader recognizes it from the description; if it isn't recognizable without the label, the label is unearned. The pattern reads as the writer auditing their own list to flag which item should matter, instead of writing the list so the right item carries the weight on its own.
- Distinct from confidence calibration ("Notably," "Interestingly") which front-loads the cue, and from emotional flatline ("What surprised me most," "The most interesting part") which prefaces a single claim. This pattern back-points after the fact, usually as "[that / this / the Xth / the last] [noun] is the [adjective] one."
- Significance-adjectives that signal the pattern: contrarian, clever, surprising, counterintuitive, interesting, key, important, unusual, smart, brilliant, real, actual.
- Fix: cut the labeling sentence and let the explanation that follows do the work directly. Or restructure so the item you wanted to highlight is positioned first or expanded with specifics, making the label redundant.
- Example. Before: "→ Two separate indexes for tiered storage. That last move is the contrarian one. Co-locating related data usually helps cache locality." After: "→ Two separate indexes for tiered storage. Co-locating related data usually helps cache locality, but splitting the indexes is what makes the hot path cheap." The contrast carries itself; the label is gone.
### Wall-of-text replies (missing line breaks)
- In conversational registers — issue and PR comments, chat, DMs, casual email — humans break a reply at thought boundaries: one idea, then a break, then the next. LLMs default to a single dense block regardless of length. The tell: a reply-length text (roughly under 150 words) with four or more sentences delivered as one unbroken paragraph, no line break anywhere in it.
- Fix: break at thought boundaries. One idea per line-group, the way a person actually types a reply.
- Observed in the wild: a maintainer on a GitHub issue called out an assisted-sounding reply with "I prefer to talk human to human" — the dense block-paragraph shape was the tell, not any single word in it.
- Distinct from paragraph-length uniformity (which is about long-form prose where every paragraph is the same size): this rule is about short, reply-length text having *zero* breaks at all, not uneven ones.
- Carve-out: a single dense paragraph is the *correct* shape in formal, long-form registers — a blog intro, a docs paragraph, a deliberately tight one-paragraph email. This rule fires only in conversational reply registers; never flag continuous long-form prose just because it lacks internal breaks. That false-positive class is exactly why the structural detector was reverted (see `detector/CATEGORIES.md` §C), and why the tolerance matrix below is the wrong home for it: a plain issue comment auto-detects to the `blog` profile, so the scoping has to live in this rule's judgment, not in a per-profile strictness cell.
### Recap-flattery opener
- Replying to a person by summarizing their own work back at them with praise before getting to the point: "Thanks for all the legwork here — the migration script and the rollback plan you worked through are what made this possible." The reader already knows what they did; the recap performs appreciation instead of conveying information.
- Distinct from a genuine thank-you, which is short and moves on. The tell is the *recap* — restating specifics the other person already knows, dressed as gratitude, ahead of the actual point.
- Distinct also from two nearby conversational tells: **Sycophantic tone** (generic validation of the reader — "Great question!") and **Acknowledgment loops** (restating the prompt or the prior section). Those echo the *question or context*; recap-flattery echoes the other person's *own work* back at them, dressed as praise.
- Fix: substance first. If thanks is warranted, one plain clause without the recap: "Thanks for the legwork — this looks right to me, one comment below."
- Observed in the wild: the same exchange that surfaced the wall-of-text tell above — an assisted-sounding reply opened by recapping the maintainer's own prior work back at them before answering the actual question.
### Excessive structure
- Too many headers in short text: more than 3 headings in under 300 words is almost always AI trying to look organized. Merge sections or use prose transitions instead.
- Too many list items: 8+ bullet points in under 200 words means the content should be a paragraph, not a list.
- Formulaic section headers: "Overview," "Key Points," "Summary," "Conclusion," "Introduction" — these are default AI scaffolding. Use headers that tell the reader something specific about what follows.
- Fragmented headers: a heading followed by a one-line warm-up that restates it ("## Performance", then "Speed matters.") before the real content starts. Cut the warm-up; the heading already did that job. Adapted from `blader/humanizer` P29.
### Diff-anchored writing
- Documentation or comments narrating a change instead of describing the thing as it is: "This function was added to replace the previous approach of iterating through all items." A reader without the commit history gets archaeology, not documentation. The tell comes from how assistants work — they write docs in the context of the edit they just made, so the prose anchors to the diff; a person documenting later writes from the artifact.
- Fix: describe the current behavior and why it is that way — "This function uses a hash map for O(1) lookups." If the history matters, it belongs in the changelog or the commit message.
- Carve-out: documents that are inherently version-scoped — changelogs, release notes, migration guides, decision records — narrate change correctly and stay unflagged. Adapted from `blader/humanizer` P30.
### Manufactured punchlines and staccato drama
- A run of clipped fragments engineered so every beat lands like a quotable closer: "It had no preference for symmetry. No aesthetic prior. No nostalgia for human taste. The old rules were gone." Each fragment poses as a reveal; stacked, they read as a drumroll.
- This composes with Rhythm and uniformity below, which encourages fragments and varied lengths: variation is the human signal, and one short sentence that lands a point is exactly that. The tell here is the opposite of variation — three or more same-shape fragments in a row, each carrying manufactured drama.
- Fix: keep the one fragment that earns its emphasis and fold the rest into ordinary sentences with the claim stated: "AlphaEvolve did not favor symmetry or human-looking designs, which made some of the older assumptions less useful." Adapted from `blader/humanizer` P31.
### Rhythm and uniformity
These aren't individual word or phrase problems — they're patterns in how the text flows as a whole. AI text is metronomic; human text has varied rhythm.
**Structure is the #1 detection signal.** AI detection tools (including Pangram, which trains a classifier on 28M human documents) weight structural regularity higher than vocabulary. Consistent sentence construction, uniform pacing, and symmetrical phrasing patterns are harder to mask than swapping out a few flagged words. If you fix every word on the Tier 1 list but leave the rhythm untouched, the text still reads as AI-generated.
- **Sentence length uniformity**: If most sentences are 15–25 words, the text sounds robotic. Mix short punchy sentences (3–8 words) with longer flowing ones (20+). Fragments work. Questions break the monotony.
- **Paragraph length uniformity**: If every paragraph is 3–5 sentences and roughly the same size, vary deliberately. Some paragraphs should be one sentence. Some should be longer.
- **Vocabulary repetition vs. synonym cycling**: AI either repeats the same word mechanically or cycles through synonyms conspicuously. Human writers repeat when the word is right and vary when it's natural — there's no formula.
- **Read-aloud test**: If the text sounds like it could be read by a text-to-speech engine without sounding weird, it's probably too uniform. Human writing has rhythm that resists robotic delivery.
- **Missing first-person perspective**: Where appropriate, the writer should have opinions, preferences, and reactions. AI is relentlessly neutral. If the piece is supposed to have a voice, the absence of "I think," "in my experience," or a stated preference is itself an AI tell.
- **Over-polishing**: Aggressively editing out every irregularity can push human writing *toward* AI statistical profiles. Natural disfluency, idiosyncratic word choices, and uneven pacing are what keep text out of the "AI-generated" classification. Don't sand away all personality in pursuit of clean prose. This skill should make writing sound more human, not less — if you apply every rule at maximum strictness, you risk creating the very uniformity you're trying to avoid.
### Vocabulary diversity (stylometric)
In longer pieces (200+ words), look at how much vocabulary the text actually uses. The type-token ratio (TTR) — distinct word types divided by total tokens — is a classical stylometric signal that's easy to read by eye. Human prose at this length usually lands somewhere around 0.50–0.65 in English. AI text trends flatter, sometimes drifting under 0.40 when the model gets locked on a small vocabulary loop.
A very low TTR is not by itself proof of AI authorship — narrow topics, technical reference material, and second-language writing all legitimately compress vocabulary. But on general prose where you'd expect range (essays, articles, social content over ~200 words), a TTR below 0.40 is worth a second look. The fix is rarely to thesaurus the text; it's to broaden the *what* — name specific things, cite specific cases, replace a re-used abstract noun with the concrete instance behind it.
This is the first of four stylometric signals on the roadmap. The others (sentence-length burstiness as a continuous measure, function-word z-scores against a human-prose reference, POS-bigram log-odds) require either a POS tagger or a reference distribution and aren't implemented as detector categories yet.
### Paragraph-reshuffle immunity (structure test)
- A writer-side diagnostic, not a regex: can you swap two body paragraphs without breaking the piece? If the order doesn't matter, you've written a list of points, not an argument that builds. AI prose often fails this — each paragraph is a self-contained module with no load-bearing connection to its neighbors.
- The fix is structural, not lexical: establish a through-line where each paragraph depends on the one before it. If the paragraphs are genuinely independent, decide whether the piece should be an explicit list, or whether it's missing a thesis. Adapted from `Aboudjem/humanizer-skill` P38.
### Treadmill effect / low information density (content test)
- Another writer-side test: read each paragraph and ask "what's actually new here?" AI prose frequently restates the premise in fresh words instead of advancing it — lots of motion, no distance covered. The tell is that you could cut 40-60% and lose no information.
- The fix: for each paragraph, name the one fact, claim, or turn it contributes. If there isn't one, cut it. If there is, lead with it and drop the throat-clearing. Adapted from `Aboudjem/humanizer-skill` P43.
### When to rewrite from scratch vs. patch
If the text has 5+ flagged vocabulary hits across multiple categories, 3+ distinct pattern categories triggered, and uniform sentence/paragraph length, patching individual phrases won't fix it — the structure itself is AI-generated. Advise a full rewrite: state the core point in one sentence, then rebuild from there.
---
## Severity tiers
Not all AI-isms are equal. When doing a quick pass or triaging a large document, prioritize by tier:
### P0 — Credibility killers (fix immediately)
- Cutoff disclaimers ("As of my last update")
- Chatbot artifacts ("I hope this helps!", "Great question!")
- Vague attributions without sources ("Experts believe")
- Significance inflation on routine events
- Hashtag stuffing on `linkedin` and `investor-email` posts (severity varies by profile — same rule, lower priority on `blog`/`technical-blog` where a launch post may legitimately stack tags; see the context-profile table below)
- Tier 3 phrase repetition (single phrase ≥2× — fine in isolation, suspect in stacks)
Use P0+P1 for quick passes. Full audit covers all three tiers.
---
## Self-reference escape hatch
When writing *about* AI writing patterns (blog posts, tutorials, skill documentation like this file), quoted examples are exempt from flagging. Text inside quotation marks, code blocks, or explicitly marked as illustrative ("for example, AI might write...") should not be rewritten. Only flag patterns that appear in the author's own prose, not in cited examples of bad writing.
---
## Context profiles
Pass an optional context hint to adjust rule strictness. If no context is specified, auto-detect from content cues (short + hashtags = social, code blocks = technical, salutation = email, default = blog).
| Subjectless fragments and agentless passives | relaxed (short-form fragments are the register) | strict | relaxed | strict | skip (fragment lists are docs) | skip |
**Technical-blog word table exceptions:** These terms have legitimate technical meaning and should not be flagged in technical context: `robust`, `comprehensive`, `seamless`, `ecosystem`, `leverage` (when discussing actual platform leverage/APIs), `facilitate`, `underpin`, `streamline`. Still flag: `delve`, `tapestry`, `beacon`, `embark`, `testament to`, `game-changer`, `harness`.
**"Extra strict"** means: flag even borderline instances. In investor emails, a single "thriving ecosystem" can undermine the whole message.
**"Skip"** means: don't audit this category for this profile. The rule doesn't apply or isn't worth the edit.
### Auto-detection cues
When no context is specified, infer from these signals:
| Signal | Inferred context |
|--------|-----------------|
| Under 300 words + hashtags or mentions | `linkedin` |
| Code blocks, API references, or technical architecture | `technical-blog` |
| No strong signals | `blog` (safest default — all rules apply) |
If auto-detection feels wrong, say which profile you're using and why. The user can override.
---
## Voice profiles
Context profiles (above) set *how strict* to be for an audience. Voice profiles set *how the prose should sound* — the persona. They're independent axes: you can write blunt for a blog or warm for docs. Voice is **optional** — if the writer doesn't name one, infer it from the input's existing register and don't impose a persona on text that already has one.
Each profile is a set of concrete targets, not a vibe:
**`casual`** — Contractions throughout; their absence reads stiff. Short sentences (aim for ≤14 words on average); fragments allowed. At least one first-person or concrete-anecdote touch. Near-zero jargon. Keep warm hedges ("honestly," "I think") but cut corporate ones ("it's worth noting"). *Blog posts, social, community.*
**`professional`** — Active voice for most sentences. Vary sentence length; avoid three in a row within a few words of each other. One concrete claim per paragraph (a number, a name, a date), never "experts say." Make the ask explicit. Low tolerance for hedging. *LinkedIn, investor email, sponsor pitches.*
**`technical`** — Prefer plain copulatives ("X is Y") over inflated substitutes ("serves as," "stands as a testament to"). One idea per sentence; imperative mood for instructions. Jargon is fine, but define it on first use. Tables and lists only where the content is genuinely list-shaped, not for decoration. *Docs, technical blog.*
**`warm`** — Address the reader directly ("you") and acknowledge them at least once. Cut intensifiers ("very," "truly," "incredibly") in favor of stronger verbs. No performative-empathy openers ("I completely understand how you feel"). Medium sentences (15–20 words) for an unhurried cadence. *Mentorship, onboarding, thank-yous.*
**`blunt`** — Lead with the claim; cut "It's important to note that" windups. Em-dashes are rare here; use periods for emphasis. No padding to hit a rule of three. Near-zero hedging; flag "may / could / potentially" stacks. Short declaratives, with the occasional long sentence for contrast. *Decision memos, thought leadership, hard feedback.*
**Calibrate to a sample (optional).** If the writer gives you a sample of their own writing ("match my voice — here's a post"), analyze its sentence-length pattern, contraction rate, paragraph openings, and recurring word choices, then match those instead of a named profile. Don't "upgrade" their vocabulary: if they write "stuff" and "things," keep that register.
**How voice composes with context.** Voice sets the target; context sets how hard to enforce it. A voice *target* always applies, even where a context profile would skip that category — `technical` voice still prefers plain copulatives in a `casual` context that otherwise ignores copula avoidance. Where both axes govern the same rule and agree, they reinforce: `blunt` voice wants near-zero em-dashes and a `blog` context is already strict on them, so it stays a hard edit. Where they disagree, resolve toward the **stricter** of the two — a `warm` voice on `docs` still doesn't get decorative tables. Sensible default pairings: casual↔casual, professional↔linkedin/investor-email, technical↔docs/technical-blog.
---
## Output format
### Rewrite mode (default)
Return your response in four sections:
**1. Issues found**
A bulleted list of every AI-ism identified, with the offending text quoted.
**2. Rewritten version**
The full rewritten content. Preserve the original structure, intent, and all specific technical details. Only change what the guidelines require.
**3. What changed**
A brief summary of the major edits made. Not every word, just the meaningful changes.
**4. Second-pass audit**
Re-read the rewritten version from section 2. Identify any remaining AI tells that survived the first pass — recycled transitions, lingering inflation, copula avoidance, filler phrases, or anything else from the categories above. Fix them, return the corrected text inline, and note what changed in this pass. If the rewrite is clean, say so.
### Detect mode
Return your response in two sections:
**1. Issues found**
A bulleted list of every AI-ism identified, with the offending text quoted. Group by severity (P0, P1, P2).
**2. Assessment**
For each flag, note whether it's a clear problem or a judgment call. Some AI-associated patterns are effective writing techniques — uniform paragraph length is a problem, but a well-placed "however" isn't. Call out which flags the writer should definitely fix vs. which ones are worth a second look but might be fine in context. If the text is clean, say so.
### Edit mode
After editing the file in place, return a short report — not the full file:
**1. Edits made**
A bulleted list of the changes, each with the file location and the before → after. Only the spans you touched.
**2. Verification**
Confirm you re-read the file and the flagged patterns are resolved. Note anything you deliberately left alone because it was already human or intentional.
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## Tone calibration
The goal is writing that sounds like a person wrote it. Direct. Specific. The writing should demonstrate confidence, not assert it.
Five principles for human-sounding rewrites:
1. **Vary sentence length** — mix short with long. Fragments are fine.
2. **Be concrete** — replace vague claims with numbers, names, dates, or examples.
3. **Have a voice** — where appropriate, use first person, state preferences, show reactions.
4. **Cut the neutrality** — humans have opinions. If the piece is supposed to take a position, take it.
5. **Earn your emphasis** — don't tell the reader something is interesting. Make it interesting.
Removal is half the job. A rewrite that clears every flag but reads sterile — even sentence lengths, no stance, no first person where one belongs — is still recognizably machine output. When the genre carries a voice (essays, posts, personal writing), put voice back on purpose: a reaction, a stated preference, an aside, one thought left unresolved. For encyclopedic, technical, or legal text, neutral and plain is the correct human voice; don't inject personality there. Adapted from `blader/humanizer` ("Personality and soul").
If the original writing is already strong, say so and make only the necessary cuts. Don't over-edit for the sake of it.
The replacement table provides defaults, not mandates. If a flagged word is clearly the right choice in context, preserve it.
