| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 3 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.17% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1385 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "precisely" | | 1 | "sharply" | | 2 | "quickly" |
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| 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) | |
| 56.68% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1385 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "measured" | | 1 | "rhythmic" | | 2 | "glint" | | 3 | "pristine" | | 4 | "weight" | | 5 | "pulse" | | 6 | "crystal" | | 7 | "velvet" | | 8 | "shimmered" | | 9 | "gleaming" | | 10 | "predator" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 88 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 88 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 90 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 12 | | totalWords | 1378 | | ratio | 0.009 | | matches | | 0 | "Call it in. Secure the perimeter. Wait for dogs, wait for backup." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 91.43% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 51 | | wordCount | 1366 | | uniqueNames | 20 | | maxNameDensity | 1.17 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | Harlow | 2 | | Quinn | 16 | | Herrera | 9 | | Saint | 1 | | Christopher | 1 | | London | 3 | | Silas | 1 | | Soho | 2 | | Raven | 1 | | Nest | 1 | | Morris | 4 | | East | 1 | | End | 1 | | Victorian | 1 | | Overground | 1 | | Glock | 1 | | Tube | 1 | | Blitz | 1 | | Northern | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Raven" | | 6 | "Nest" | | 7 | "Morris" |
| | places | | 0 | "London" | | 1 | "Silas" | | 2 | "Soho" | | 3 | "East" | | 4 | "End" |
| | globalScore | 0.914 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 74 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 54.86% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.451 | | wordCount | 1378 | | matches | | 0 | "Not in a hospital bed, not on the cold zinc of the morgue, but down here, in the dark beneath Camden" | | 1 | "not on the cold zinc of the morgue, but down here, in the dark beneath Camden" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 90 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 47.52 | | std | 26.93 | | cv | 0.567 | | sampleLengths | | 0 | 82 | | 1 | 94 | | 2 | 21 | | 3 | 86 | | 4 | 115 | | 5 | 31 | | 6 | 58 | | 7 | 17 | | 8 | 42 | | 9 | 34 | | 10 | 66 | | 11 | 12 | | 12 | 59 | | 13 | 20 | | 14 | 54 | | 15 | 50 | | 16 | 46 | | 17 | 23 | | 18 | 12 | | 19 | 10 | | 20 | 87 | | 21 | 52 | | 22 | 78 | | 23 | 40 | | 24 | 21 | | 25 | 39 | | 26 | 54 | | 27 | 41 | | 28 | 34 |
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| 93.30% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 88 | | matches | | 0 | "was cornered" | | 1 | "been pulled" | | 2 | "been paved" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 214 | | matches | | 0 | "wasn’t running" | | 1 | "was walking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 2 | | flaggedSentences | 7 | | totalSentences | 90 | | ratio | 0.078 | | matches | | 0 | "Detective Harlow Quinn did not run like a woman panicking; she ran with the measured, rhythmic violence of someone who had logged five miles before dawn every morning of her eighteen years on the force." | | 1 | "He had youth on his side—twenty-nine against her forty-one—and the desperate, feral agility of a man who knew precisely what awaited him if he was cornered." | | 2 | "He veered sharply toward a padlocked security gate set into an abandoned brick archway—a forgotten maintenance spur leading down into the subterranean belly of the city." | | 3 | "She raised her left wrist to check the time; the cracked crystal of her worn leather watch caught the faint light from the street." | | 4 | "Beneath the tang of rust and damp London clay, there was something cloying and thick—rosemary, dried tallow, and the sharp, chemical bite of burning phosphor." | | 5 | "He produced a small, pale object—a carved token of yellowed bone, no larger than a domino—and dropped it into the watcher’s outstretched palm." | | 6 | "A sound drifted toward her through the warm, sulfurous draft—a man’s quiet laugh, followed by the clink of a glass vial." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1387 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.0136986301369863 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.010814708002883922 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 90 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 90 | | mean | 15.31 | | std | 8.98 | | cv | 0.586 | | sampleLengths | | 0 | 24 | | 1 | 35 | | 2 | 23 | | 3 | 16 | | 4 | 26 | | 5 | 25 | | 6 | 27 | | 7 | 13 | | 8 | 8 | | 9 | 4 | | 10 | 25 | | 11 | 19 | | 12 | 11 | | 13 | 27 | | 14 | 8 | | 15 | 32 | | 16 | 11 | | 17 | 32 | | 18 | 13 | | 19 | 19 | | 20 | 9 | | 21 | 22 | | 22 | 13 | | 23 | 15 | | 24 | 4 | | 25 | 26 | | 26 | 14 | | 27 | 3 | | 28 | 7 | | 29 | 3 | | 30 | 25 | | 31 | 7 | | 32 | 7 | | 33 | 27 | | 34 | 9 | | 35 | 12 | | 36 | 24 | | 37 | 1 | | 38 | 8 | | 39 | 12 | | 40 | 3 | | 41 | 3 | | 42 | 6 | | 43 | 4 | | 44 | 12 | | 45 | 17 | | 46 | 26 | | 47 | 5 | | 48 | 15 | | 49 | 5 |
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| 61.05% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.42696629213483145 | | totalSentences | 89 | | uniqueOpeners | 38 | |
| 38.31% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 87 | | matches | | 0 | "Instead, he vaulted an iron" |
| | ratio | 0.011 | |
| 72.87% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 87 | | matches | | 0 | "Her combat boots struck the" | | 1 | "He had youth on his" | | 2 | "He didn’t look back." | | 3 | "Her brown eyes narrowed, tracking" | | 4 | "She noted the roll of" | | 5 | "She had followed him across" | | 6 | "She had waited outside Silas’s" | | 7 | "He was the line she" | | 8 | "He was the grease in" | | 9 | "He veered sharply toward a" | | 10 | "It wasn't locked." | | 11 | "He slipped into the yawning" | | 12 | "She paused on the threshold," | | 13 | "Her breathing was steady, her" | | 14 | "She raised her left wrist" | | 15 | "Her radio sat heavy on" | | 16 | "She reached for the lapel" | | 17 | "She pulled a penlight from" | | 18 | "It lacked the simple mustiness" | | 19 | "He wasn’t running anymore." |
| | ratio | 0.368 | |
| 63.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 87 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn did not" | | 2 | "Her combat boots struck the" | | 3 | "Tomás Herrera was faster than" | | 4 | "He had youth on his" | | 5 | "Every few paces, the strap" | | 6 | "Quinn’s voice snapped through the" | | 7 | "He didn’t look back." | | 8 | "Quinn took the corner tight," | | 9 | "Her brown eyes narrowed, tracking" | | 10 | "She noted the roll of" | | 11 | "She had followed him across" | | 12 | "She had waited outside Silas’s" | | 13 | "He was the line she" | | 14 | "The official report called Morris’s" | | 15 | "Quinn had looked at the" | | 16 | "Herrera wasn't a killer, but" | | 17 | "He was the grease in" | | 18 | "The alley ended at the" | | 19 | "Herrera didn’t slow down." |
| | ratio | 0.793 | |
| 57.47% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 87 | | matches | | | ratio | 0.011 | |
| 56.65% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 58 | | technicalSentenceCount | 7 | | matches | | 0 | "Detective Harlow Quinn did not run like a woman panicking; she ran with the measured, rhythmic violence of someone who had logged five miles before dawn every m…" | | 1 | "He had youth on his side—twenty-nine against her forty-one—and the desperate, feral agility of a man who knew precisely what awaited him if he was cornered." | | 2 | "Instead, he vaulted an iron railing into the narrow alleyway behind an auto repair shop, his trainers splashing through an ankle-deep puddle of oily runoff." | | 3 | "Quinn took the corner tight, her shoulder clipping the brick wall, water sluicing off the brim of her coat." | | 4 | "He was walking quickly, clutching his bag, stepping into a vast, open concourse where the railway tracks had long since been paved over with rough-hewn stone." | | 5 | "Bundles of dried roots, silver ingots stamped with archaic symbols, and racks of hand-ground blades that shimmered with an oily, iridescent sheen." | | 6 | "She looked the watcher dead in the eye, moving with the cold, absolute certainty of a predator that belonged in the dark, and walked straight past the fire." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "Quinn’s voice snapped (snap)" |
| | dialogueSentences | 3 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 1 | | effectiveRatio | 0.667 | |