| 46.15% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 1 | | adverbTags | | 0 | "The dispatcher’s voice crackled back [back]" |
| | dialogueSentences | 13 | | tagDensity | 0.308 | | leniency | 0.615 | | rawRatio | 0.25 | | effectiveRatio | 0.154 | |
| 86.75% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1132 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slightly" | | 1 | "sharply" | | 2 | "really" |
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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) | |
| 42.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1132 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "scanned" | | 1 | "loomed" | | 2 | "unreadable" | | 3 | "scanning" | | 4 | "pulse" | | 5 | "quickened" | | 6 | "dancing" | | 7 | "echoed" | | 8 | "footsteps" | | 9 | "echoing" | | 10 | "wavering" |
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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 | 2 | | narrationSentences | 97 | | matches | | 0 | "was desperate" | | 1 | "was scared" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 97 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 106 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1125 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 61.07% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 1012 | | uniqueNames | 14 | | maxNameDensity | 1.78 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | London | 2 | | Harlow | 1 | | Quinn | 18 | | Greek | 1 | | Street | 1 | | Herrera | 13 | | Raven | 1 | | Nest | 3 | | Silas | 2 | | Vey | 1 | | Saint | 1 | | Christopher | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Raven" | | 4 | "Nest" | | 5 | "Silas" | | 6 | "Vey" | | 7 | "Saint" | | 8 | "Christopher" |
| | places | | 0 | "London" | | 1 | "Greek" | | 2 | "Street" |
| | globalScore | 0.611 | | windowScore | 0.667 | |
| 83.33% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like medical supplies—gauze, vials" | | 1 | "dagger that seemed to drink in the light" |
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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 | 1125 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 106 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 37.5 | | std | 25.25 | | cv | 0.673 | | sampleLengths | | 0 | 79 | | 1 | 70 | | 2 | 81 | | 3 | 76 | | 4 | 75 | | 5 | 26 | | 6 | 11 | | 7 | 62 | | 8 | 48 | | 9 | 39 | | 10 | 75 | | 11 | 13 | | 12 | 11 | | 13 | 53 | | 14 | 10 | | 15 | 27 | | 16 | 38 | | 17 | 7 | | 18 | 34 | | 19 | 19 | | 20 | 12 | | 21 | 27 | | 22 | 51 | | 23 | 5 | | 24 | 72 | | 25 | 17 | | 26 | 18 | | 27 | 30 | | 28 | 30 | | 29 | 9 |
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| 98.03% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 97 | | matches | | 0 | "were lined" | | 1 | "was strewn" |
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| 80.24% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 167 | | matches | | 0 | "was running" | | 1 | "was running" | | 2 | "was walking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 106 | | ratio | 0.057 | | matches | | 0 | "Detective Harlow Quinn moved with the precision of a woman who had spent too many nights like this—chasing shadows through the underbelly of the city." | | 1 | "Former paramedic, current ghost—always one step ahead, always just out of reach." | | 2 | "A flash of movement near the back—a door swinging shut." | | 3 | "A table in the center was strewn with maps and what looked like medical supplies—gauze, vials of clear liquid, a syringe." | | 4 | "She’d heard whispers of it—an underground bazaar where the rules of the world didn’t apply." | | 5 | "Figures moved between the stalls—some human, some decidedly not." |
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| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1023 | | adjectiveStacks | 2 | | stackExamples | | 0 | "slick, neon-lit maze." | | 1 | "Former paramedic, current ghost—" |
| | adverbCount | 33 | | adverbRatio | 0.03225806451612903 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.008797653958944282 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 106 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 106 | | mean | 10.61 | | std | 6.14 | | cv | 0.579 | | sampleLengths | | 0 | 14 | | 1 | 25 | | 2 | 23 | | 3 | 14 | | 4 | 3 | | 5 | 12 | | 6 | 28 | | 7 | 16 | | 8 | 2 | | 9 | 12 | | 10 | 18 | | 11 | 14 | | 12 | 26 | | 13 | 6 | | 14 | 17 | | 15 | 22 | | 16 | 3 | | 17 | 10 | | 18 | 22 | | 19 | 11 | | 20 | 8 | | 21 | 1 | | 22 | 10 | | 23 | 16 | | 24 | 19 | | 25 | 11 | | 26 | 3 | | 27 | 15 | | 28 | 6 | | 29 | 6 | | 30 | 13 | | 31 | 1 | | 32 | 11 | | 33 | 14 | | 34 | 21 | | 35 | 13 | | 36 | 10 | | 37 | 4 | | 38 | 9 | | 39 | 15 | | 40 | 16 | | 41 | 1 | | 42 | 7 | | 43 | 3 | | 44 | 3 | | 45 | 13 | | 46 | 15 | | 47 | 5 | | 48 | 14 | | 49 | 9 |
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| 45.28% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3113207547169811 | | totalSentences | 106 | | uniqueOpeners | 33 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 92 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 92 | | matches | | 0 | "Her breath came in controlled" | | 1 | "Her leather watch creaked as" | | 2 | "Her focus was singular: the" | | 3 | "She broke into a run," | | 4 | "She was after Herrera, and" | | 5 | "She shoved inside, her eyes" | | 6 | "She pushed through the crowd," | | 7 | "She knew about the Nest’s" | | 8 | "She hesitated for half a" | | 9 | "She could wait." | | 10 | "She could call it in," | | 11 | "She stepped onto the first" | | 12 | "he said, his voice low" | | 13 | "She’d heard whispers of it—an" | | 14 | "She glanced down." | | 15 | "He turned and walked deeper" | | 16 | "It was something else entirely." | | 17 | "She’d lost sight of Herrera’s" | | 18 | "She stepped forward, the bone" |
| | ratio | 0.207 | |
| 41.52% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 77 | | totalSentences | 92 | | matches | | 0 | "The rain fell in sheets," | | 1 | "Detective Harlow Quinn moved with" | | 2 | "Her breath came in controlled" | | 3 | "The suspect had vanished again," | | 4 | "Her leather watch creaked as" | | 5 | "The dispatcher’s voice crackled back," | | 6 | "Her focus was singular: the" | | 7 | "She broke into a run," | | 8 | "The Raven’s Nest loomed ahead," | | 9 | "The bar was a known" | | 10 | "Tonight, though, she wasn’t after" | | 11 | "She was after Herrera, and" | | 12 | "The door to the bar" | | 13 | "Quinn didn’t hesitate." | | 14 | "She shoved inside, her eyes" | | 15 | "The walls were lined with" | | 16 | "Quinn ignored them, scanning the" | | 17 | "A flash of movement near" | | 18 | "She pushed through the crowd," | | 19 | "The door led to a" |
| | ratio | 0.837 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 92 | | matches | (empty) | | ratio | 0 | |
| 86.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 4 | | matches | | 0 | "Detective Harlow Quinn moved with the precision of a woman who had spent too many nights like this—chasing shadows through the underbelly of the city." | | 1 | "She broke into a run, her boots splashing through puddles that reflected the sickly glow of the streetlights." | | 2 | "At the bottom, a tunnel stretched out before her, its walls lined with flickering torches that cast long, dancing shadows." | | 3 | "The air smelled of earth and something older, something that didn’t belong in the modern world." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 2 | | matches | | 0 | "he said, his voice low and urgent" | | 1 | "Quinn took, her boots crunching on something brittle" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.154 | | leniency | 0.308 | | rawRatio | 0 | | effectiveRatio | 0 | |