| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 1 | | adverbTags | | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0.5 | | effectiveRatio | 0.5 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1381 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 49.31% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1381 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "pulse" | | 1 | "flicker" | | 2 | "measured" | | 3 | "efficient" | | 4 | "maw" | | 5 | "gleaming" | | 6 | "echo" | | 7 | "footsteps" | | 8 | "pulsed" | | 9 | "velvet" | | 10 | "vibrated" | | 11 | "flickered" | | 12 | "scanning" | | 13 | "glint" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 89 | | matches | (empty) | |
| 94.70% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 89 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 89 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 59 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 10 | | totalWords | 1370 | | ratio | 0.007 | | matches | | 0 | "They’re not what you think, Quinn. None of it is." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 1335 | | uniqueNames | 19 | | maxNameDensity | 0.52 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Berwick | 1 | | Street | 1 | | Harlow | 1 | | Quinn | 7 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Vauxhall | 1 | | Herrera | 4 | | Camden | 1 | | Town | 1 | | Sauer | 1 | | Morris | 2 | | Veil | 2 | | Market | 2 | | Victorian | 1 | | Saint | 1 | | Christopher | 1 | | Latin | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Herrera" | | 4 | "Morris" | | 5 | "Market" | | 6 | "Saint" | | 7 | "Christopher" |
| | places | | 0 | "Berwick" | | 1 | "Street" | | 2 | "Soho" | | 3 | "Vauxhall" | | 4 | "Camden" | | 5 | "Town" | | 6 | "Victorian" |
| | globalScore | 1 | | windowScore | 1 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | glossingSentenceCount | 4 | | matches | | 0 | "quite obey the physics of light" | | 1 | "looked like human teeth" | | 2 | "walked into a seemingly routine warehouse" | | 3 | "looked like funeral silks" |
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| 54.01% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.46 | | wordCount | 1370 | | matches | | 0 | "not as a conscious thought but as a motion" | | 1 | "not drawing, but ready" |
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| 91.76% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 89 | | matches | | 0 | "Find that glint." | | 1 | "learning that the" |
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| 94.04% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 19 | | mean | 72.11 | | std | 34.55 | | cv | 0.479 | | sampleLengths | | 0 | 73 | | 1 | 102 | | 2 | 2 | | 3 | 90 | | 4 | 92 | | 5 | 87 | | 6 | 38 | | 7 | 86 | | 8 | 41 | | 9 | 121 | | 10 | 69 | | 11 | 5 | | 12 | 104 | | 13 | 133 | | 14 | 54 | | 15 | 78 | | 16 | 77 | | 17 | 84 | | 18 | 34 |
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| 89.49% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 89 | | matches | | 0 | "was panicked" | | 1 | "been peeled" | | 2 | "was drowned" | | 3 | "being struck" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 201 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 4 | | flaggedSentences | 11 | | totalSentences | 89 | | ratio | 0.124 | | matches | | 0 | "She’d clocked him through the rain-blurred windshield of the unmarked Vauxhall ten minutes earlier: short, tight curls plastered to his forehead, olive skin, a flicker of something metallic at his throat—a saint’s medallion." | | 1 | "She could read it in the way his shoulders bunched, the wild glance he threw over his shoulder—warm brown eyes wide, the scar on his left forearm catching a sliver of streetlight." | | 2 | "The barrier—chain-link fence strung with faded warning signs—had been peeled back like a sardine tin, the edges gleaming with fresh cuts." | | 3 | "The air thickened, carrying a perfume that didn’t belong underground—cardamom, black tea, the copper tang of blood." | | 4 | "Quinn’s mind, trained to catalogue detail even in chaos, registered anomalies: a vendor with scales where eyes should have been, weighing something that whispered in a velvet bag; a rack of tarnished birdcages, each holding a single, still-beating heart; a woman in a moth-wing cloak trading glass vials of liquid smoke for what looked like human teeth." | | 5 | "He could have darted anywhere—behind a stall, into one of the yawning side tunnels, up the warped iron staircase at the far end that led to another level of this subterranean cathedral." | | 6 | "None of it is.* He’d walked into a seemingly routine warehouse, and then he’d simply ceased to exist—no body, no trace, just a file that the brass eventually sealed." | | 7 | "A stallkeeper—something with too many joints in its fingers—hissed a warning she didn’t heed." | | 8 | "He’d thought he’d lost her; his shoulders had relaxed, and he was murmuring to a vendor, a tall figure swathed in what looked like funeral silks." | | 9 | "Danger was a certainty; she could smell it, thick as incense." | | 10 | "She followed the suspect toward the iron staircase, her hand finally resting on the grip of her pistol—not drawing, but ready." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 337 | | adjectiveStacks | 1 | | stackExamples | | 0 | "earlier: short, tight curls" |
| | adverbCount | 7 | | adverbRatio | 0.020771513353115726 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.002967359050445104 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 89 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 89 | | mean | 15.39 | | std | 11.59 | | cv | 0.753 | | sampleLengths | | 0 | 20 | | 1 | 16 | | 2 | 5 | | 3 | 32 | | 4 | 33 | | 5 | 2 | | 6 | 2 | | 7 | 2 | | 8 | 37 | | 9 | 4 | | 10 | 2 | | 11 | 20 | | 12 | 2 | | 13 | 12 | | 14 | 18 | | 15 | 26 | | 16 | 15 | | 17 | 19 | | 18 | 7 | | 19 | 32 | | 20 | 7 | | 21 | 46 | | 22 | 42 | | 23 | 21 | | 24 | 12 | | 25 | 2 | | 26 | 10 | | 27 | 6 | | 28 | 12 | | 29 | 2 | | 30 | 13 | | 31 | 5 | | 32 | 2 | | 33 | 13 | | 34 | 22 | | 35 | 32 | | 36 | 17 | | 37 | 12 | | 38 | 17 | | 39 | 12 | | 40 | 3 | | 41 | 32 | | 42 | 2 | | 43 | 2 | | 44 | 24 | | 45 | 31 | | 46 | 27 | | 47 | 57 | | 48 | 12 | | 49 | 5 |
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| 64.79% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4157303370786517 | | totalSentences | 89 | | uniqueOpeners | 37 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 78 | | matches | (empty) | | ratio | 0 | |
| 61.03% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 78 | | matches | | 0 | "She didn’t feel the cold." | | 1 | "Her pulse had already spiked" | | 2 | "She’d clocked him through the" | | 3 | "He’d been on her board" | | 4 | "He hadn’t seen her." | | 5 | "She wove through a knot" | | 6 | "Her quarry ducked left into" | | 7 | "She hurdled them without breaking" | | 8 | "He was fast, but he" | | 9 | "She could read it in" | | 10 | "He knew she wasn’t going" | | 11 | "He knew, too, the neighborhood" | | 12 | "She paused only long enough" | | 13 | "Her hand dropped to her" | | 14 | "Her partner, DS Morris, had" | | 15 | "Her eyes adjusted to the" | | 16 | "She’d heard whispers over the" | | 17 | "She lost sight of Herrera." | | 18 | "Her boots scraped to a" | | 19 | "He could have darted anywhere—behind" |
| | ratio | 0.397 | |
| 49.74% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 78 | | matches | | 0 | "The rain came sideways down" | | 1 | "Water sluiced along the gutter," | | 2 | "She didn’t feel the cold." | | 3 | "Her pulse had already spiked" | | 4 | "She’d clocked him through the" | | 5 | "He’d been on her board" | | 6 | "He hadn’t seen her." | | 7 | "Quinn pushed off the balls" | | 8 | "She wove through a knot" | | 9 | "Her quarry ducked left into" | | 10 | "She hurdled them without breaking" | | 11 | "He was fast, but he" | | 12 | "She could read it in" | | 13 | "He knew she wasn’t going" | | 14 | "He knew, too, the neighborhood" | | 15 | "The barrier—chain—link fence strung with" | | 16 | "She paused only long enough" | | 17 | "This was a recent breach," | | 18 | "Her hand dropped to her" | | 19 | "The SIG Sauer was snug" |
| | ratio | 0.821 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 78 | | matches | | 0 | "By the time she reached" | | 1 | "If she let him go" |
| | ratio | 0.026 | |
| 38.96% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 8 | | matches | | 0 | "He’d been on her board for six weeks, the connective tissue between two suspicious cardiac arrests, a missing antiquities dealer, and the clique of wealthy ecce…" | | 1 | "The air thickened, carrying a perfume that didn’t belong underground—cardamom, black tea, the copper tang of blood." | | 2 | "Dozens of figures moved between them, some cloaked, some unnervingly still, their faces obscured by masks or low-hanging shadows that didn’t quite obey the phys…" | | 3 | "Quinn’s mind, trained to catalogue detail even in chaos, registered anomalies: a vendor with scales where eyes should have been, weighing something that whisper…" | | 4 | "He could have darted anywhere—behind a stall, into one of the yawning side tunnels, up the warped iron staircase at the far end that led to another level of thi…" | | 5 | "Heads turned with an insectile unison that made the small hairs on her neck rise." | | 6 | "Herrera was pressed against the side of a stall selling leather-bound tomes that exhaled dust motes of their own accord." | | 7 | "She flowed between the stalls, avoiding the pools of unnatural light, her focus narrowing to a single point." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |