| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 4 | | adverbTags | | 0 | "Harlow said sharply [sharply]" | | 1 | "Eva asked quietly [quietly]" | | 2 | "she said slowly [slowly]" | | 3 | "Harlow said quietly [quietly]" |
| | dialogueSentences | 40 | | tagDensity | 0.45 | | leniency | 0.9 | | rawRatio | 0.222 | | effectiveRatio | 0.2 | |
| 67.86% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1400 | | totalAiIsmAdverbs | 9 | | found | | | highlights | | 0 | "carefully" | | 1 | "softly" | | 2 | "nervously" | | 3 | "really" | | 4 | "sharply" | | 5 | "precisely" | | 6 | "gently" | | 7 | "slowly" |
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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) | |
| 39.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1400 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "measured" | | 1 | "scanning" | | 2 | "oppressive" | | 3 | "velvet" | | 4 | "flicked" | | 5 | "resonance" | | 6 | "disrupt" | | 7 | "structure" | | 8 | "raced" | | 9 | "silence" | | 10 | "chill" | | 11 | "etched" | | 12 | "intricate" | | 13 | "pristine" |
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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 | 1 | | narrationSentences | 149 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 149 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 171 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 6 | | markdownWords | 15 | | totalWords | 1376 | | ratio | 0.011 | | matches | | 0 | "marketplaces in the dark" | | 1 | "things that shouldn’t breathe" | | 2 | "The Veil Market." | | 3 | "down" | | 4 | "Too clean." | | 5 | "after" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 58 | | wordCount | 1018 | | uniqueNames | 15 | | maxNameDensity | 2.26 | | worstName | "Harlow" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Harlow" | | discoveredNames | | Tube | 1 | | Harlow | 23 | | Quinn | 1 | | Camden | 1 | | Station | 1 | | Morris | 2 | | Veil | 2 | | Kowalski | 1 | | Aurora | 1 | | Eva | 17 | | Bryson | 4 | | Whitechapel | 1 | | Compass | 1 | | Silas | 1 | | Vane | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Kowalski" | | 4 | "Aurora" | | 5 | "Eva" | | 6 | "Bryson" | | 7 | "Silas" | | 8 | "Vane" |
| | places | | | globalScore | 0.37 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | glossingSentenceCount | 1 | | matches | | 0 | "as if protecting something inside" |
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| 54.65% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.453 | | wordCount | 1376 | | matches | | 0 | "No footprints but" | | 1 | "not just with her eyes, but with everything she’d learned in eighteen years" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 171 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 80 | | mean | 17.2 | | std | 19.6 | | cv | 1.139 | | sampleLengths | | 0 | 102 | | 1 | 6 | | 2 | 56 | | 3 | 103 | | 4 | 10 | | 5 | 6 | | 6 | 7 | | 7 | 5 | | 8 | 84 | | 9 | 10 | | 10 | 34 | | 11 | 43 | | 12 | 16 | | 13 | 3 | | 14 | 11 | | 15 | 43 | | 16 | 26 | | 17 | 26 | | 18 | 16 | | 19 | 3 | | 20 | 28 | | 21 | 8 | | 22 | 13 | | 23 | 4 | | 24 | 1 | | 25 | 16 | | 26 | 28 | | 27 | 26 | | 28 | 3 | | 29 | 5 | | 30 | 27 | | 31 | 19 | | 32 | 5 | | 33 | 6 | | 34 | 21 | | 35 | 6 | | 36 | 5 | | 37 | 39 | | 38 | 12 | | 39 | 11 | | 40 | 39 | | 41 | 7 | | 42 | 6 | | 43 | 26 | | 44 | 7 | | 45 | 18 | | 46 | 3 | | 47 | 13 | | 48 | 3 | | 49 | 3 |
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| 95.84% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 149 | | matches | | 0 | "been found" | | 1 | "been pried" | | 2 | "been transformed" | | 3 | "were curled" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 181 | | matches | | 0 | "was still running" | | 1 | "was reporting" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 20 | | semicolonCount | 0 | | flaggedSentences | 16 | | totalSentences | 171 | | ratio | 0.094 | | matches | | 0 | "The air in the abandoned Tube station was thick with the scent of damp earth, old wiring, and something else—something sharp and acrid, like burnt sugar and wet copper." | | 1 | "Three hours ago, a call had come in—an anonymous tip, garbled through distortion, claiming a body had been found beneath Camden Station." | | 2 | "Beyond it, the station—long decommissioned in the 1960s—had been transformed." | | 3 | "Not here, not exactly—but in the frayed edges of her memory, in the last hours of her partner DS Morris, when he’d whispered about *marketplaces in the dark* and *things that shouldn’t breathe*." | | 4 | "She’d never believed in it—until tonight." | | 5 | "She fiddled with a lock of hair, tucking it behind her left ear—the nervous habit Harlow had noticed the first time they’d met, after Aurora’s brother had vanished and Eva had shown up with a folder full of censored museum records." | | 6 | "Harlow studied her—really studied her." | | 7 | "But his face—twisted in terror, lips pulled back, eyes wide and frozen mid-scream." | | 8 | "But her own wristwatch—the worn leather band snug against her skin—read 1:12 a.m." | | 9 | "Clenched in his fist—a small, yellowed thing the size of a fingertip." | | 10 | "Her partner Morris—three years ago, in that alley in Whitechapel—had vanished mid-radio call." | | 11 | "The candles—arranged in a loose pentagram." | | 12 | "The sigils—worn but visible." | | 13 | "The face etched with tiny, intricate markings—sigils, Eva would call them." | | 14 | "Harlow looked around—not just with her eyes, but with everything she’d learned in eighteen years." | | 15 | "And the real evidence—whatever had torn Silas Vane apart—was still out there." |
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| 98.09% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1043 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 44 | | adverbRatio | 0.04218600191754554 | | lyAdverbCount | 18 | | lyAdverbRatio | 0.01725790987535954 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 171 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 171 | | mean | 8.05 | | std | 7.69 | | cv | 0.955 | | sampleLengths | | 0 | 29 | | 1 | 18 | | 2 | 16 | | 3 | 39 | | 4 | 6 | | 5 | 22 | | 6 | 2 | | 7 | 2 | | 8 | 24 | | 9 | 6 | | 10 | 12 | | 11 | 10 | | 12 | 16 | | 13 | 13 | | 14 | 15 | | 15 | 4 | | 16 | 33 | | 17 | 10 | | 18 | 6 | | 19 | 7 | | 20 | 5 | | 21 | 21 | | 22 | 22 | | 23 | 41 | | 24 | 4 | | 25 | 6 | | 26 | 21 | | 27 | 13 | | 28 | 5 | | 29 | 14 | | 30 | 6 | | 31 | 6 | | 32 | 12 | | 33 | 2 | | 34 | 14 | | 35 | 3 | | 36 | 11 | | 37 | 12 | | 38 | 2 | | 39 | 3 | | 40 | 13 | | 41 | 13 | | 42 | 7 | | 43 | 8 | | 44 | 6 | | 45 | 2 | | 46 | 2 | | 47 | 1 | | 48 | 12 | | 49 | 14 |
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| 55.69% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.3941176470588235 | | totalSentences | 170 | | uniqueOpeners | 67 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 110 | | matches | | 0 | "Precisely 11:47 p.m." | | 1 | "Then she saw it." | | 2 | "Maybe beneath their feet." | | 3 | "Maybe waiting to open again." |
| | ratio | 0.036 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 110 | | matches | | 0 | "Her breath came slow and" | | 1 | "She’d seen this before." | | 2 | "She’d never believed in it—until" | | 3 | "She turned, lowering her flashlight." | | 4 | "Her curly red hair was" | | 5 | "She fiddled with a lock" | | 6 | "They approached the center of" | | 7 | "His fingers were curled into" | | 8 | "She pulled on gloves from" | | 9 | "She checked his pockets." | | 10 | "His watch was still running." | | 11 | "she said without looking up" | | 12 | "She gently pried it open." | | 13 | "Her partner Morris—three years ago," | | 14 | "They’d found his service weapon." | | 15 | "She stepped back, scanning the" | | 16 | "She moved toward it, flashlight" | | 17 | "It pointed *down*." | | 18 | "She crouched, pressing her hand" | | 19 | "she said slowly" |
| | ratio | 0.209 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 110 | | matches | | 0 | "The air in the abandoned" | | 1 | "Detective Harlow Quinn stepped carefully" | | 2 | "Her breath came slow and" | | 3 | "The beam of her flashlight" | | 4 | "This wasn’t just any crime" | | 5 | "An access tunnel had been" | | 6 | "Candles lined the curved walls," | | 7 | "Stalls made of blackened wood" | | 8 | "The smell of incense, of" | | 9 | "She’d seen this before." | | 10 | "*The Veil Market.* Even the" | | 11 | "She’d never believed in it—until" | | 12 | "a voice said from behind" | | 13 | "She turned, lowering her flashlight." | | 14 | "Eva Kowalski stood a few" | | 15 | "Her curly red hair was" | | 16 | "She fiddled with a lock" | | 17 | "Eva said, her green eyes" | | 18 | "Harlow studied her—really studied her." | | 19 | "The way her fingers twitched" |
| | ratio | 0.691 | |
| 45.45% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 110 | | matches | | 0 | "Even though he’d supposedly been" |
| | ratio | 0.009 | |
| 81.63% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 3 | | matches | | 0 | "The beam of her flashlight cut through the oppressive dim, catching glints on broken glass, the rusted tracks still buried beneath decades of grime, and the pal…" | | 1 | "Not here, not exactly—but in the frayed edges of her memory, in the last hours of her partner DS Morris, when he’d whispered about *marketplaces in the dark* an…" | | 2 | "She glanced at Eva, whose fingers were now pressed to her satchel, as if protecting something inside." |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 3 | | matches | | 0 | "Eva said, voice low," | | 1 | "Eva said, voice urgent" | | 2 | "Harlow said, voice rising," |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 17 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 40 | | tagDensity | 0.425 | | leniency | 0.85 | | rawRatio | 0.059 | | effectiveRatio | 0.05 | |