| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 43 | | adverbTagCount | 2 | | adverbTags | | 0 | "Eva glanced around [around]" | | 1 | "Eva stood quickly [quickly]" |
| | dialogueSentences | 111 | | tagDensity | 0.387 | | leniency | 0.775 | | rawRatio | 0.047 | | effectiveRatio | 0.036 | |
| 79.79% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1979 | | totalAiIsmAdverbs | 8 | | found | | | highlights | | 0 | "very" | | 1 | "carefully" | | 2 | "completely" | | 3 | "nervously" | | 4 | "quickly" | | 5 | "really" | | 6 | "suddenly" |
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| 60.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 72.21% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1979 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "fluttered" | | 1 | "etched" | | 2 | "calibrated" | | 3 | "familiar" | | 4 | "weight" | | 5 | "flickered" | | 6 | "reminder" |
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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 | 154 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 154 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 218 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1976 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 25 | | unquotedAttributions | 1 | | matches | | 0 | "Quinn was back there, rain hammering on corrugated iron, Morris shouting something about the symbols on the walls." |
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| 26.16% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 74 | | wordCount | 1292 | | uniqueNames | 9 | | maxNameDensity | 2.48 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 32 | | Tube | 1 | | Patel | 5 | | Round | 1 | | Eva | 21 | | London | 3 | | Blitz | 1 | | Morris | 9 | | Kowalski | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Patel" | | 2 | "Eva" | | 3 | "Morris" | | 4 | "Kowalski" |
| | places | | | globalScore | 0.262 | | windowScore | 0.333 | |
| 73.47% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 98 | | glossingSentenceCount | 3 | | matches | | 0 | "felt like a punch" | | 1 | "looked like bottled fog" | | 2 | "sounded like Morris, the night before he d" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1976 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 218 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 104 | | mean | 19 | | std | 12.64 | | cv | 0.665 | | sampleLengths | | 0 | 12 | | 1 | 51 | | 2 | 21 | | 3 | 16 | | 4 | 13 | | 5 | 43 | | 6 | 34 | | 7 | 11 | | 8 | 25 | | 9 | 7 | | 10 | 30 | | 11 | 2 | | 12 | 15 | | 13 | 31 | | 14 | 25 | | 15 | 5 | | 16 | 17 | | 17 | 40 | | 18 | 7 | | 19 | 17 | | 20 | 5 | | 21 | 13 | | 22 | 26 | | 23 | 33 | | 24 | 8 | | 25 | 5 | | 26 | 23 | | 27 | 22 | | 28 | 2 | | 29 | 33 | | 30 | 3 | | 31 | 31 | | 32 | 12 | | 33 | 2 | | 34 | 20 | | 35 | 1 | | 36 | 12 | | 37 | 3 | | 38 | 22 | | 39 | 5 | | 40 | 36 | | 41 | 10 | | 42 | 11 | | 43 | 1 | | 44 | 1 | | 45 | 30 | | 46 | 20 | | 47 | 14 | | 48 | 2 | | 49 | 33 |
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| 93.87% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 154 | | matches | | 0 | "were clenched" | | 1 | "was etched" | | 2 | "was rumpled" | | 3 | "were polished" | | 4 | "been carved" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 241 | | matches | | 0 | "was fading" | | 1 | "was heading" | | 2 | "was planning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 218 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1295 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 47 | | adverbRatio | 0.036293436293436294 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.009266409266409266 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 218 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 218 | | mean | 9.06 | | std | 5.18 | | cv | 0.571 | | sampleLengths | | 0 | 12 | | 1 | 18 | | 2 | 17 | | 3 | 16 | | 4 | 16 | | 5 | 5 | | 6 | 11 | | 7 | 5 | | 8 | 13 | | 9 | 4 | | 10 | 12 | | 11 | 13 | | 12 | 5 | | 13 | 9 | | 14 | 16 | | 15 | 15 | | 16 | 3 | | 17 | 11 | | 18 | 15 | | 19 | 10 | | 20 | 3 | | 21 | 4 | | 22 | 10 | | 23 | 20 | | 24 | 2 | | 25 | 7 | | 26 | 8 | | 27 | 8 | | 28 | 13 | | 29 | 10 | | 30 | 12 | | 31 | 13 | | 32 | 5 | | 33 | 16 | | 34 | 1 | | 35 | 5 | | 36 | 3 | | 37 | 3 | | 38 | 13 | | 39 | 10 | | 40 | 1 | | 41 | 5 | | 42 | 7 | | 43 | 3 | | 44 | 11 | | 45 | 3 | | 46 | 5 | | 47 | 9 | | 48 | 4 | | 49 | 19 |
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| 77.37% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.48623853211009177 | | totalSentences | 218 | | uniqueOpeners | 106 | |
| 70.42% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 142 | | matches | | 0 | "Only her voice remained, hanging" | | 1 | "Somewhere in the city, Eva" | | 2 | "Somewhere else, whoever killed the" |
| | ratio | 0.021 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 142 | | matches | | 0 | "His face held the peculiar" | | 1 | "She pulled on latex gloves." | | 2 | "His hands were clenched around" | | 3 | "Its needle spun wildly, like" | | 4 | "His shoes were polished, barely" | | 5 | "She tapped a bottle" | | 6 | "She leaned forward, breath smelling" | | 7 | "Its mouth opened, and Quinn" | | 8 | "It wasn't a voice so" | | 9 | "Its movements were wrong, joints" | | 10 | "Its voice was fading, the" | | 11 | "Her partner's brother." | | 12 | "She'd been so focused on" | | 13 | "Her voice was steady, even" | | 14 | "She met Quinn's eyes" | | 15 | "She'd dismissed it as delirium." | | 16 | "She pulled out her notebook" | | 17 | "She gestured at the lingering" | | 18 | "They turned to see the" | | 19 | "She smiled with too many" |
| | ratio | 0.19 | |
| 72.68% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 110 | | totalSentences | 142 | | matches | | 0 | "Blood never lies, but this" | | 1 | "Detective Quinn crouched beside the" | | 2 | "The corpse lay twisted between" | | 3 | "DS Patel's voice carried that" | | 4 | "Quinn stood, knees protesting after" | | 5 | "Quinn studied the victim." | | 6 | "His face held the peculiar" | | 7 | "She pulled on latex gloves." | | 8 | "His hands were clenched around" | | 9 | "The shout came from a" | | 10 | "Patel moved to intercept her" | | 11 | "The woman tucked a curl" | | 12 | "Quinn's attention sharpened." | | 13 | "Eva's hands fluttered towards the" | | 14 | "Eva edged closer" | | 15 | "Quinn carefully pried the dead" | | 16 | "The compass face was etched" | | 17 | "Its needle spun wildly, like" | | 18 | "Eva pulled a notebook from" | | 19 | "Eva flipped through pages covered" |
| | ratio | 0.775 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 142 | | matches | (empty) | | ratio | 0 | |
| 85.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 4 | | matches | | 0 | "Male, mid-thirties, wearing a suit that cost more than her monthly salary." | | 1 | "Its movements were wrong, joints bending in ways that made Quinn's eyes water." | | 2 | "Eighteen years on the force, and she'd built her career on things that made sense." | | 3 | "Behind her glasses, her green eyes held the kind of exhaustion that came from carrying secrets too long." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 43 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn stood, knees protesting after eighteen years of crime scenes" | | 1 | "She leaned, breath smelling of anise and copper" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "Patel suggested (suggest)" | | 1 | "Patel muttered (mutter)" |
| | dialogueSentences | 111 | | tagDensity | 0.018 | | leniency | 0.036 | | rawRatio | 1 | | effectiveRatio | 0.036 | |