AI Check

AI-Check Skill

Forensic analysis of text for AI-generation signals. Grounded in the published detection literature (Wu et al. 2025, Mitchell et al. 2023, Kujur 2025, AAAI 2025 shared task).

The output is a structured report, not a vague judgment. Every fired signal cites evidence.

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The nine signal categories

Score each category 0–3:

• 0 = No signal detected (human-consistent)
• 1 = Weak signal (possible AI, could be human)
• 2 = Moderate signal (likely AI pattern)
• 3 = Strong signal (near-certain AI pattern)

Severity-to-score mapping (use for every category):

Double-counting policy: a single phrase can fire at most two distinct signals when the phrase is genuinely diagnostic for both. Example: "it is important to note that" is both Signal A (banned vocabulary) and Signal C (institutional hedge). Log it under both, but the same phrase cannot count as two separate weak instances inside the same category.

Total score cap: 9 categories × 3 = 27 maximum.

Signal A: Perplexity (word predictability)

Look for vocabulary that is maximally safe and expected — words that are technically correct but never the most precise or interesting choice a knowledgeable human would make.

Flags:

• Generic verbs where domain-specific ones belong ("address" instead of "untangle", "implement" instead of "wire up")
• Adjectives that describe without adding information ("significant improvements", "notable progress", "key challenges")
• Hedged assertions that swap specificity for safety ("can often lead to", "may result in", "tends to")
• Any of the canonical AI vocabulary list: delve, leverage (verb), utilize, robust, comprehensive, streamline, foster, facilitate, pivotal, nuanced, notable, notably, enduring, garner, it is worth noting, it is important to note, multifaceted, in the realm of, the landscape of, a myriad of, a plethora of

Cite the exact word or phrase that fired.

Signal B: Burstiness deficit (sentence uniformity)

Measure the variation in sentence length across the text.

Flags:

• Three or more consecutive sentences within 5 words of the same length
• No sentence shorter than 8 words in any 150-word block
• Metronomic rhythm — reading the passage aloud produces a steady pulse rather than natural variation
• No fragments used for emphasis

Report: list the sentence lengths in sequence (e.g. "14, 16, 13, 15, 17 — five consecutive sentences within 4 words of each other").

Signal C: Hedge density

Count the softening and epistemic hedge words.

Flags:

• "often", "generally", "typically", "in many cases", "it can be argued" appearing where direct assertion is warranted
• "it is important to note that", "it is worth mentioning", "one might consider"
• Diplomatic framing of obvious tradeoffs: "while X has benefits, it also presents challenges"
• Uncertainty expressed as institutional hedging rather than personal ("results may vary") vs human ("I'm not sure this holds when...")

Report: quote each hedge and note whether it was warranted by genuine uncertainty or reflexive softening.

Signal D: Structural tells

Look for document architecture patterns AI imposes regardless of content.

Flags:

• Bullet list where prose would serve better
• Topic sentence + evidence + restatement of topic sentence (humans skip the restatement)
• "In conclusion / To summarize / In summary" openers on closing paragraphs
• "In this [post/article/section] I will..." openers
• Numbered steps for content that isn't genuinely sequential
• Three-part structure imposed on every paragraph (intro, body, conclusion at micro-scale)
• Tricolon parallel structure: three examples or beats with identical grammatical shape e.g. "You X. Y. Does Z? You X. Y. Does Z? You X. Y. Does Z?" — perfectly symmetrical triplets in prose are AI-constructed. Real writers use two examples or vary the shape. Severity: strong.
• Perfect paragraph-per-idea arc: every paragraph does exactly one narrative job and advances the arc cleanly (setup → tension → lesson → evidence → reflection). Real personal writing has a paragraph that meanders, does two jobs, or doesn't fully resolve. A piece where every paragraph lands cleanly is architecturally perfect in a way human writing isn't. Severity: moderate in isolation, strong combined with other signals.
• Three-act Slack/update structure: for informal async messages, accomplishment → caveat → next steps maps directly to intro/body/conclusion. Real updates loop back, add a mid-message second thought, or end with something that doesn't fit the structure.
• Strawman pivot: "The case for X isn't about Y, it's about Z" / "It's not about X, it's about Y." Leading with what something is NOT before saying what it IS. Real writers lead with the actual point. Severity: moderate.

Signal E: Specificity deficit

Measure whether claims are grounded in concrete detail.

Flags:

• Abstract claim with no number, name, time reference, or example: "Many organizations have adopted..."
• Passive constructions obscuring the actor: "it has been found that", "research suggests"
• Universalist framing: "teams often find", "developers frequently encounter" (applicable to everyone, specific to no one)
• Named examples that are suspiciously generic or perfectly illustrative (AI picks canonical examples: "Netflix", "Amazon", "Stripe" without context)

Report: quote each unanchored claim.

Signal F: Transition word fingerprint

Catalog the connective tissue between sentences and paragraphs.

Flags (strong AI signals):

• "Furthermore," as paragraph opener
• "Moreover," as paragraph opener
• "Additionally," as paragraph opener
• "It is clear that"
• "This highlights / underscores / demonstrates the importance of"
• "As previously mentioned"
• "In addition to the above"
• "It goes without saying"
• "Needless to say"

Flags (moderate signals):

• "However," used more than once per 200 words
• "Therefore," used as a mechanical logical connector rather than earned conclusion
• "Turns out" / "it turns out that" as a pivot or reveal. AI uses this to create the illusion of a discovery narrative. "Turns out the config had a lower timeout" → "The config had a lower timeout." Quote each instance. Severity: moderate.
• Tutorial-voice transitions: "The standard fix is...", "The common approach is...", "Simple enough on paper" — these frame what follows as received wisdom, not personal experience. Strong signal in technical writing.
• Announcement-colon patterns: "The rule I use:", "The key insight:", "The approach here:", "The other thing I'd say:" — announcing before revealing. Severity: moderate. Also fires without a colon: "What I didn't expect was...", "What surprised me was...", "The thing I realized was..." — these are announcement sentences even without the colon. The colon isn't the tell; the announcement structure is.
• Pattern announcement: stating that a pattern exists before describing it. "The pattern is almost always the same" followed by the pattern. Real writers just describe the pattern.

Signal G: Punctuation fingerprint

Count the three AI punctuation tells:

Em dashes: Count total em dashes. More than 1 per 300 words is a signal. Specific sub-patterns:

• Double em dash wrapping (— like this —) is a near-certain AI pattern
• Em dash as pivot ("not mid-sprint — and the on-call rotation") — list-joiner em dash connecting two items within a sentence
• Em dash as dramatic aside ("X — which is worth noting — Y") Report exact count, location, and which sub-pattern.

Semicolons: Any semicolon linking two independent clauses in non-academic prose is a flag. Report exact count. Exception: comma-containing lists ("Austin, TX; Denver, CO").

Mid-sentence colons: A colon preceded by an incomplete clause ("The problem: nobody tests this" / "The answer: start earlier") is an AI structural pattern. Report each instance.

Signal H: Voice and register

Look for absence of human traces.

Flags:

• No first-person perspective anywhere in a piece where first-person would be natural
• No second-person direct address in instructional or opinionated content
• Consistent "polished neutral tone" — no personality variance, no roughness, no informality spikes
• No rhetorical questions used as transitions
• No self-correction or mid-thought qualification ("actually, that's not quite right")
• Opening sentence is a thesis, definition, or contextual framing rather than mid-thought or scene

Register collapse (Slack / informal writing): The most commonly missed signal in casual-register text. AI writes Slack messages that read like polished status reports with informal markers sprinkled in. Look for:

• Complete, well-formed sentences throughout — real Slack has fragments
• Topic-per-paragraph structure even in a short message
• Formal vocabulary underneath casual markers (~60% and lmk but the sentences themselves are well-constructed prose)
• No self-corrections mid-message ("oh also. just realized...")
• Three-act arc (accomplishment / caveat / next steps) intact beneath the informality
• Numbers written as words ("three incidents") rather than numerals with approximations ("~3 incidents", "<10min") Severity: strong when informal markers are present but prose structure is polished.

Templated closers in email/professional writing: "Happy to jump on a call if that's easier." "Let me know if you have any questions." "Feel free to reach out." These are the written equivalent of a throat-clearing opener. Real engineers end emails after the last substantive point, or with a specific ask, or with "lmk." Severity: weak in isolation, moderate when combined with other signals.

Signal I: Rhetorical scaffolding

Sentence and paragraph-level construction patterns that AI learned from polished writing and applies too consistently. These are the hardest signals to catch — they feel like good writing. Grammarly and live detectors flag these even when punctuation and vocabulary are clean.

Local coherence over-smooth (severity: moderate-strong, corpus-dependent) A pattern related to findings in recent research (DivEye, arXiv 2509.18880, TMLR 2026): every sentence connects too perfectly to the next, zero friction, zero cognitive-load artifacts. AI text often has no sentences that slightly misfire, no thoughts that shift direction mid-clause, no paragraph that doesn't close cleanly. Evidence: read each paragraph and check whether any sentence could be removed and the paragraph would still read perfectly. In human writing, removing a sentence often creates a noticeable gap. In AI writing, the paragraph usually flows better without it. Symptoms:

• Every paragraph opens with a claim and closes with a confirmation of that claim
• No sentence has a vocabulary mismatch with surrounding sentences
• No abrupt topic shift within a paragraph
• No sentence that slightly misfires before correcting

Calibration caveat (important). SHAP-based explainability analysis (arXiv 2603.23146) found that AI-text detectors rely on dataset-specific stylistic cues, not stable machine-authorship signals. Treat over-smoothness as a corpus-conditional indicator, not a universal authorship invariant. If the text is from a register that genuinely rewards tight coherence (academic abstracts, legal briefs, polished marketing copy), down-weight this signal.

Formula personal essay opener (severity: moderate) "The failure I think about most often happened in 2019." "The moment I remember most clearly was..." "The decision I regret most is..." Pattern: "The [noun] I [remember/think about/regret] most [adverb]" — AI's deliberate- introspection construction for opening personal essays. Real writers start with the incident, not with a ranked claim about their memory of it.

Asyndeton tricolon building in complexity (severity: moderate) Three items without conjunctions, each longer and more emotionally heavy than the last: "Two hours of degraded service, six engineers figuring out what I'd done wrong, a postmortem where I had to explain my reasoning to people who had been paged at home." AI constructs these to manufacture escalating emotional weight. Report the three items and their increasing length.

Intensifier/diminisher opposition (severity: moderate) "X [action] obsessively and Y [action] barely at all" — a balanced contrast using an amplifier against a diminisher. Same family as chiasmus but at the adverb level. Other forms: "X constantly / Y once", "X carefully / Y hardly". Quote the opposition.

Mini-aphorism paragraph closer (severity: moderate) A 4–7 word fragment or short sentence used to close a paragraph with a punchy lesson: "That's the part that stuck." "That's what changed." "That's the whole thing." "That's the real cost." AI appends these to tell the reader what conclusion to draw. Distinct from a sentence- length aphoristic closer — this fires even on very short fragments.

Landing phrase: "is the actual/real work" (severity: moderate) "Getting close enough to understand a failure is the actual work." "Deploying is the easy part. Debugging production is the actual work." AI's formulaic landing phrase for delivering conclusions. Quote it.

Parallel subject mirror (severity: weak-moderate) Two consecutive sentences opening with mirrored noun phrases that reflect each other: "The failure itself is just the event. Understanding it is separate." "The code is one thing. Maintaining it is another." AI constructs these as closing pairs. Report the mirrored subjects.

Participial reframe pivot (severity: moderate) Presenting a list of facts, then using a participial opener to reframe them as something more: "Laid out in a petition, the same facts read like a deliberate strategy." "Arranged that way, it sounds more planned than it was." "Seen this way, the whole arc reads differently." AI uses this pivot to manufacture the appearance of insight. The observation should be made directly without the reframing device. Quote the participial opener.

Thesis-first opener / "X is the easy/hard part" (severity: moderate) Starting a personal piece with the frame before the experience: "Gathering evidence for an EB1A petition is the easy part." "The writing is harder than the research." "X has become increasingly important." AI leads with the thesis because it's been trained on essays. Real writers start in the middle of the experience. Quote the opener.

Within-sentence anaphoric parallel list (severity: moderate-strong) Four parallel items with the same question-word structure inside a single sentence: "what existed before, what problem it solved, why the problem mattered, what changed after" Grammarly and other detectors score this identically to consecutive-sentence anaphora. The fix is varying the noun forms: "context, the actual problem, what changed" — not four parallel "what/why" question-clause starters. Quote the full list.