ai-pattern-detector

name	ai-pattern-detector
description	Use when the user wants writing de-AI'd or checked for AI patterns — whether they paste the text, point to a file or URL, or ask you to review a draft — to make it sound more human and less like ChatGPT-default prose. Flags telltale vocabulary, constructions, punctuation, hedges, closers, formatting, and voice tells, and proposes specific rewrites.

AI Pattern Detector

Read the text the user provides and find phrases, words, and structures that read as AI-generated. For each finding, quote the exact span, name the pattern, and propose a concrete rewrite.

The pattern catalog lives in references/patterns.md — eleven categories drawn from Wikipedia's "Signs of AI writing" reference page, Pangram Labs' detection guide, GPTZero's AI vocabulary index, Grammarly's "Common AI Words" research, Originality.ai's analyses, the Kobak et al. (2024) academic study on excess vocabulary in post-ChatGPT scientific abstracts, the Terčon & Dobrovoljc (2025) survey of AI linguistic characteristics, and a 2025 Computer Science Review survey of detection methods. Sources are listed at the bottom of this file.

Input

Scan whatever the user wants checked, from whatever source they give you:

• Pasted text — the common case. Scan it directly.
• A file ("scan draft.md", "check this doc") — read it and scan the prose. For code or config files, scan only the human-facing prose (comments, docstrings, README/markdown body), never the syntax.
• A URL ("check this post: …") — fetch it and scan the main body text (article, post, page copy); ignore navigation, boilerplate, and markup.
• "Review / check / de-AI this" aimed at something already in context — a draft you just wrote, a PR description, the text above — scan that.

Scan prose written for humans. When the source mixes prose with code, data, or markup, scan the prose and leave the rest alone (see "What not to flag"). If it's genuinely unclear which text to scan — the message points at several things, or a file is almost all code with a line or two of prose — ask before scanning rather than guessing.

Language: this skill is built on English phrases and patterns, so it's designed for scanning English text.

Step 0 — identify register

Before measuring anything, infer the text's register — the kind of writing it is. Register decides what counts as a tell: a feature-adjective stack is native to a product description but a tell in an essay; short punchy lines are native to a social post but a metronome tell in a report. Register never changes the AI-feel score formula — only which findings you raise and how heavily each weighs.

Infer exactly one register from these signals:

Register	Inferring signals
Social post (LinkedIn / X / Instagram)	short lines, one-thought-per-line breaks, hook opener, hashtags, emoji, @-mentions or handles
Marketing / landing page	headlines, benefit framing, CTAs, title-case section heads, feature blocks
Product description / e-commerce	feature/spec listing, "whether you're…", short benefit blurbs, bullet specs
Blog / editorial / newsletter	long-form prose, personal anecdote, section structure
Scientific / academic	citations, methods/passive constructions, hedged claims, domain terminology, "we" for own work, IMRaD structure, figures/tables
Business / professional	reports, memos, formal emails, corporate register
General	none of the above dominates (default)

Rules:

• Auto-infer; ask only when genuinely ambiguous. Social, marketing, and product copy overlap — when a text sits between two, pick the closest and say which in the Register line. Ask the user only when you can't even pick a closest register.
• A short paragraph with no clear signals is general — don't ask.
• Report the inferred register in the output (see Output format) and apply its calibration from "What not to flag." Register never changes the score.

Step 1 — quantified pre-scan

Before pattern-hunting, compute these numbers from the text — they anchor the scan in evidence and let the user compare drafts across edits. Report them in the output's Metrics block.

Metric	How to compute	Tell threshold
Word count / sentence count	count them	— (context for the rest)
Sentence-length spread	words in shortest vs. longest sentence; rough variation (stdev ÷ mean)	shortest ≥ 1/3 of longest, or variation below ~0.4, = metronome tell. Judge variation relative to the mean — uniformly short lines (punchy social style) are human; uniform 15–25-word sentences are the AI shape
Contraction count	count only verb contractions (don't / it's / we've / can't) vs. uncontracted forms (do not / it is / we have); do not count possessives (today's) or let's	uncontracted forms present but zero contractions in 40+ words = Strong tell; if the text has no contraction opportunities at all, no tell
Em-dash density	em-dashes ÷ words	> 1 per ~150 words = Medium; > 1 per paragraph = Strong (full rules in patterns.md §8)
Telltale-vocab density	occurrences of patterns.md §1 vocabulary ÷ total words (exclude the faux-sincerity adverb sublist)	≥ 2 per 100 words = Strong; ~1 per 100 = Medium
Paragraph uniformity	sentence count per paragraph	every paragraph 3–5 sentences of similar length = Medium tell
Lexical diversity / repetition	unique content words ÷ total content words over a ~150–250-word window (moving-average TTR, to control for length); also note any content word repeated 4+ times that isn't the topic term	TTR < ~0.40 over the window = Medium (corroborating only — never conclusive alone). Under-100-words cap applies (→ Weak).

Notes on computing: when splitting sentences, treat line breaks as sentence boundaries too — short-form posts often drop terminal punctuation (one thought per line), and splitting only on .!? collapses them into one fake mega-sentence. On texts under ~100 words, density thresholds are unreliable (one em-dash in a short post clears the per-150-words bar) — judge function over density there, and cap such metric findings at Weak. Each metric over threshold counts as a finding with the severity given above. Lexical diversity is a supporting signal (Terčon & Dobrovoljc 2025 survey: AI text runs lower-diversity than human writing): low diversity corroborates other findings but must never move the score on its own — never raise it above Medium, and don't raise it at all if it's the only thing you'd flag.

Step 2 — pattern scan

Read references/patterns.md in full — never scan from memory of it. It holds the eleven pattern categories:

1. Tell-tale vocabulary (verbs, adjectives, nouns, transition stacks, faux-sincerity adverbs)
2. Stock openers
3. Signature constructions (antithesis, fake-depth formulas, false agency, self-answered questions)
4. Hedges and throat-clearing
5. Meta-commentary
6. Closers and conclusions
7. GPT response artifacts
8. Punctuation and formatting tells (em-dash rules, sentence rhythm, formulaic fragmentation, list cardinality)
9. Voice and content tells (vague declaratives, narrator-from-a-distance, agentless passive)
10. Structural tells
11. Engagement bait

Scan for everything. A single text may contain dozens of tells — flag every instance you find, not one example per category.

How to score severity

For each finding, assign one of:

• Strong — obvious AI fingerprint on its own. Examples: "delve into the realm of", "It's not just X — it's Y", "In today's fast-paced world", and any of the fake-depth formulas in patterns.md §3.
• Medium — common in human writing too, but suspicious in this density, position, or combination. Examples: a single false-agency sentence, a vague declarative, a self-answered question, a lone throat-clearing hedge ("it's worth noting", "make no mistake"). A hedge rises to Strong only when stacked in the same span with any other tell (e.g., an AI adjective, a stock opener, or a closer cliché — the list is illustrative, not exhaustive), scored under the one-span rule below.
• Weak — only a tell alongside several others: a single em-dash, one "robust", an agentless passive, a faux-sincerity adverb cluster of three.

One span, multiple patterns → one finding at the highest severity. When a single quoted span stacks several tells (e.g., a stock opener that also contains two telltale verbs), raise one finding, list all the patterns in its Pattern line, and score it at the highest severity any of them earns. Don't split it into several findings — that inflates the counts and the score. The pattern deep-dive still treats each distinct pattern separately; the per-finding count does not.

Severity tie-break: if a span matches both a Strong-tier and a lower-tier pattern, it's Strong. Fake-depth formulas (patterns.md §3) win over the generic "self-answered question / vague declarative" Medium examples — score the formula.

Metric findings vs. pattern findings (short texts): the under-100-words cap (Step 1) limits density-metric findings to Weak — it does not cap a pattern finding that happens to involve the same token. A lone em-dash counted as a metric is Weak in a short text; the same em-dash as the pivot of an antithesis is a §3 construction and scores on its own merits. Raise the pattern finding, not a duplicate metric finding.

If the text has zero Strong findings and only a handful of Medium/Weak, say so honestly. A scattering of "however"s does not make a text AI.

AI-feel score (0–100)

Roll the findings into one number so drafts are comparable across runs:

score = min(100, Strong × 12 + Medium × 5 + Weak × 2)

Bands: 0–20 reads human · 21–40 mostly human (minor tells) · 41–65 AI-assisted feel · 66–100 strong AI signature.

The weights are heuristic — the score is a density measure of evidence, not a probability the text is AI. Never present it as a verdict on authorship (detectors are unreliable; GPTZero famously rated the Bible 88% AI). It exists so the user can watch the number drop as they edit.

Output format

# AI Pattern Scan

**Verdict:** <one sentence — cite the finding Total from Counts, not the deep-dive group count, so the two numbers agree — e.g., "Heavy AI fingerprint: 14 tells across vocabulary, constructions, and closers." or "Light: a few stock phrases, otherwise reads human.">

**AI-feel score:** NN/100 — <band>

**Counts:** Strong: N · Medium: N · Weak: N · Total: N

**Register:** <inferred register> — <one clause on how it calibrated the scan>

**Metrics:** NNN words · NN sentences · sentence length N–NN (shortest–longest), variation ~N.NN <even/varied> · N contractions · N em-dashes (1 per ~NNN words) · telltale vocab N per 100 words
<!-- On texts under ~100 words, state densities as "N (short text — density unreliable)" rather than forcing a per-150 figure. -->
<!-- "Total" = number of findings below. The deep-dive groups those findings by distinct pattern, so it lists fewer blocks than the Total — that is expected, not a miscount. -->
<!-- Report the sentence-length variation figure here; it carries the metronome tell, not the bare range. -->


---

## Findings

### 1. "delve into the ever-evolving landscape of content marketing"
- **Pattern:** Stock opener stacked with telltale vocabulary (`delve`, `ever-evolving`, `landscape`)
- **Severity:** Strong
- **Rewrite:** "Content marketing keeps changing. Here's what's different in 2026."
- **Why this works:** Replaces three abstract words with one concrete claim and a time anchor.

### 2. "It's not just a strategy — it's a mindset."
- **Pattern:** Antithesis construction with pivot em-dash (signature ChatGPT shape)
- **Severity:** Strong
- **Rewrite:** Cut the line, or get specific: "Treating content as a system rather than a set of one-off posts is what changes the outcome."
- **Why this works:** Drops the empty strategy/mindset binary and the decorative em-dash, and states the actual distinction.

### 3. "It's worth noting that this approach can be quite robust."
- **Pattern:** Throat-clearing hedge ("it's worth noting") + AI adjective ("robust") + qualifier stacking ("can be quite")
- **Severity:** Strong
- **Rewrite:** "This approach holds up under load." — or cut the sentence; if it's worth noting, just note it.
- **Why this works:** Cuts the hedge and the qualifier, and replaces "robust" with the concrete property it was standing in for.

Every finding carries all four lines — **Pattern, Severity, Rewrite, Why this works** — in that order.

(continue for every finding…)

---

## Pattern deep-dive — what each case is and how to de-AI it

(One block per DISTINCT pattern found, ordered by impact: highest-severity, most-instances first. This is the detailed close of every scan — never skip it.)

### <Pattern name> — N instances · <severity>

- **What it is:** 2-3 sentences describing the pattern and the shape it takes in THIS text (not a generic definition — tie it to how this writer used it).
- **Why it reads as AI:** what the generator is doing when it produces this (e.g., resolving every argument with a reframe; filling an emphasis slot per section), and what a reader subconsciously notices.
- **All instances:** every quoted span, listed (for 5+ instances, quote the clearest 5 and give locations of the rest).
- **How to fix it — 2-3 strategies, each with a before → after example taken from this text:** (See [references/examples.md](references/examples.md) for worked transformations of this pattern to model the before → after shape.)
  1. <strategy — e.g., "cut and state directly"> : "<their sentence>" → "<rewritten>"
  2. <strategy — e.g., "replace with a concrete fact/number"> : "<their sentence>" → "<rewritten>"
  3. <strategy — when there's a third genuinely different approach>
- **If you only do one thing:** the single highest-leverage edit for this pattern.

---

## Summary of rewrites

Offer to apply everything: "Want me to apply these fixes and output the clean full text?" If yes:

<paste the user's text with edits inline — clean rewrite, not a diff>

Rewriting rules

When proposing a rewrite:

1. Be specific. Replace abstract claims with concrete ones. "Robust system" → name what makes it robust, or cut the word.
2. Cut, don't substitute. Most hedges and throat-clearing add nothing. Deletion usually beats rewording.
3. Don't replace one AI tell with another. "Delve into" → not "explore" (also AI default). Try "look at," "read," "study," or remove the verb entirely. "Leverage" → "use." "Utilize" → "use." "Showcase" → "show." "Underscore" → "show" or "prove."
4. Keep the user's voice. If their text has personality, preserve it. Smooth only where AI tells override their voice.
5. Shorter is usually better. AI prose is often 30–50% padding. A good rewrite is often half the length.
6. Trade abstract for concrete. "Industry reports suggest growth" → "Gartner's 2025 report puts growth at 12%." If the writer doesn't have the concrete, that's the real problem to flag.
7. Name the actor. False-agency and agentless-passive sentences get a human subject: "the data tells us" → "I looked at the numbers and…"; "mistakes were made" → who made them. If no specific person fits, "you" puts the reader in the seat.
8. In the deep-dive, offer genuinely different strategies — typically one cut option, one concretize option, one restructure option — so the writer picks what fits their voice instead of accepting a single imposed fix. Build every before → after example from the writer's own sentences, never from invented ones.
9. Consult the examples library. For a pattern you're rewriting, see references/examples.md for worked before → after transformations across registers. Read it during the rewrite / deep-dive phase only — never load it during the pre-scan.

What not to flag

• Domain-specific jargon the writer clearly chose deliberately
• Em-dashes used genuinely parenthetically in technical writing
• Bullets in step-by-step instructions, recipes, or reference material
• "You" in second-person tutorials where it's appropriate
• Words like "robust" or "comprehensive" used in their literal technical sense ("robust error handling" in code documentation)
• Oxford commas, contractions, and Title Case in styles that genuinely require them (AP, Chicago for headings, etc.)
• Passive voice in registers where it's the convention (scientific methods sections, legal drafting, formal reports)
• Conventional attribution figures like "the report concludes" or "the study found" — false agency means abstractions doing human verbs, not standard journalistic shorthand
• A single organic fragment or one earned punchline — fragmentation is a tell only when it's formulaic or recurs in a template slot (see patterns.md §8)

Register-specific calibration (from Step 0 — apply the row matching the inferred register):

• Social post — don't flag: short punchy lines (not a metronome tell — see the sentence-rhythm note in patterns.md §8), contractions, hashtags, emoji, one-line paragraphs. Weighs heavier here: engagement bait (§11), formulaic fragmentation (§8), fake-depth formulas (§3) — the dominant AI social tells.
• Marketing / landing page — don't flag: benefit language, a CTA, title-case headlines, some superlatives. Still flag: antithesis ("not just X — it's Y"), stacked buzzwords, "In today's world" openers, em-dash overuse, tricolon abuse.
• Product description / e-commerce — don't flag: feature-adjective stacking ("durable, lightweight, waterproof"), spec bullets, second-person "you". Still flag: AI vocab (elevate, seamless, game-changer), "whether you're X or Y", vague benefit inflation, "not just X — it's Y".
• Blog / editorial / newsletter — don't flag: "you", contractions, direct address, one earned fragment/punchline. Weighs heavier here: narrator-from-a-distance, engagement bait, uncontracted forms.
• Scientific / academic — don't flag: domain terminology, passive voice in methods, "we" for own work, conventional attribution ("the study found"), hedged claims ("may suggest"), standard field nominalization. Still flag: business-buzzword & AI-vocab leakage (leverage, landscape, delve, tapestry, nuanced), fake-depth formulas, em-dash overuse.
• Business / professional — don't flag: some domain jargon, formal tone. Still flag: stacked buzzwords, antithesis, closer clichés, both-sides-itis.
• General — the default calibration already described above; no register-specific relaxation.

When uncertain, mark Weak rather than skipping — and note in the verdict that some findings may be intentional.

---

Sources

The pattern lists in references/patterns.md are synthesized from:

• Wikipedia, Signs of AI writing — community-curated reference page (re-verified 2026-07)
• Pangram Labs, Comprehensive Guide to Spotting AI Writing Patterns
• GPTZero, AI Vocabulary index (Top phrases by AI-vs-human frequency, updated May 2026)
• Grammarly, Decoding AI Language: Common Words and Phrases in AI-Generated Content
• Originality.ai, What Are The Most Obvious ChatGPT AI Sayings? and Most Commonly Used ChatGPT Words & Phrases
• Kobak, Gonzalez-Marquez, & Horvát (2024), Delving into ChatGPT usage in academic writing through excess vocabulary (arXiv:2406.07016) — large-scale analysis of vocabulary shift in 14M PubMed abstracts post-ChatGPT
• Walter Writes AI, Most Common ChatGPT Words to Avoid in 2026
• Hunting the Muse, How to spot when writing is AI: 6 elements of a robot's style
• Terčon & Dobrovoljc (2025), Linguistic Characteristics of AI-Generated Text: A Survey (arXiv:2510.05136) — cross-study synthesis: AI text runs noun/determiner/adposition-heavy, adjective/adverb-light, with lower lexical diversity and a smaller vocabulary range
• AI-generated text detection: A comprehensive review of methods, datasets, and applications, Computer Science Review (2025) (ScienceDirect S1574013725000693) — survey of detection methods, datasets, and applications; reinforces that the AI-feel score is a density-of-evidence measure, not an authorship verdict