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):**

| Evidence in category | Score |
|---|---|
| No flagged instances | 0 |
| One weak instance, or vague unease without a specific quote | 1 |
| One moderate instance, or two or more weak instances | 2 |
| One strong instance, or two or more moderate instances, or four or more weak instances | 3 |

**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 highli