| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 15 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 746 | | 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) | |
| 12.87% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 746 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "footsteps" | | 1 | "echoed" | | 2 | "weight" | | 3 | "flicker" | | 4 | "pulse" | | 5 | "loomed" | | 6 | "marble" | | 7 | "flicked" | | 8 | "flickered" |
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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 | 62 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 62 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 74 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 737 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 51.37% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 659 | | uniqueNames | 12 | | maxNameDensity | 1.97 | | worstName | "Herrera" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Harlow | 1 | | Quinn | 12 | | Greek | 1 | | Street | 1 | | Soho | 1 | | Tomás | 1 | | Herrera | 13 | | Saint | 1 | | Christopher | 1 | | Veil | 2 | | Market | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Market" |
| | places | | 0 | "London" | | 1 | "Greek" | | 2 | "Street" | | 3 | "Soho" |
| | globalScore | 0.514 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 47 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 737 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 74 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 27.3 | | std | 19.76 | | cv | 0.724 | | sampleLengths | | 0 | 72 | | 1 | 33 | | 2 | 42 | | 3 | 38 | | 4 | 41 | | 5 | 47 | | 6 | 14 | | 7 | 7 | | 8 | 80 | | 9 | 36 | | 10 | 42 | | 11 | 51 | | 12 | 5 | | 13 | 44 | | 14 | 10 | | 15 | 10 | | 16 | 15 | | 17 | 16 | | 18 | 13 | | 19 | 21 | | 20 | 19 | | 21 | 7 | | 22 | 13 | | 23 | 6 | | 24 | 24 | | 25 | 21 | | 26 | 10 |
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| 99.60% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 62 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 113 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 74 | | ratio | 0.095 | | matches | | 0 | "Ahead, a figure darted into an alley—Tomás Herrera, his dark curls plastered to his forehead, his Saint Christopher medallion bouncing against his chest." | | 1 | "He wasn’t built for running—not like her." | | 2 | "The alley spilled into a wider street, the glow of the Veil Market’s entrance flickering in the distance—a dim, pulsing light beneath a rusted metal grate." | | 3 | "The air rising from the tunnel was thick, carrying the scent of old stone and something sharper—copper, maybe, or ozone." | | 4 | "The rain, the city, the noise—all of it vanished, replaced by the hum of voices and the flicker of lanterns strung along the walls." | | 5 | "The stone around it was carved with symbols—twisting, unfamiliar shapes." | | 6 | "A figure loomed beside him—a man with a face like carved marble, his eyes too dark, too still." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 668 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.019461077844311378 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0014970059880239522 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 74 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 74 | | mean | 9.96 | | std | 6.13 | | cv | 0.616 | | sampleLengths | | 0 | 14 | | 1 | 22 | | 2 | 20 | | 3 | 16 | | 4 | 23 | | 5 | 10 | | 6 | 3 | | 7 | 12 | | 8 | 14 | | 9 | 6 | | 10 | 7 | | 11 | 12 | | 12 | 13 | | 13 | 13 | | 14 | 26 | | 15 | 4 | | 16 | 11 | | 17 | 18 | | 18 | 20 | | 19 | 9 | | 20 | 5 | | 21 | 6 | | 22 | 3 | | 23 | 7 | | 24 | 14 | | 25 | 10 | | 26 | 24 | | 27 | 32 | | 28 | 8 | | 29 | 14 | | 30 | 14 | | 31 | 15 | | 32 | 11 | | 33 | 8 | | 34 | 8 | | 35 | 14 | | 36 | 10 | | 37 | 6 | | 38 | 8 | | 39 | 13 | | 40 | 5 | | 41 | 8 | | 42 | 15 | | 43 | 3 | | 44 | 18 | | 45 | 6 | | 46 | 4 | | 47 | 6 | | 48 | 4 | | 49 | 4 |
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| 46.40% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3108108108108108 | | totalSentences | 74 | | uniqueOpeners | 23 | |
| 53.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 62 | | matches | | 0 | "Then Herrera stepped forward, his" |
| | ratio | 0.016 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 62 | | matches | | 0 | "She adjusted the grip on" | | 1 | "He glanced back, eyes wide," | | 2 | "She sprinted after him, her" | | 3 | "He wasn’t built for running—not" | | 4 | "Her voice cut through the" | | 5 | "He vaulted over a stack" | | 6 | "He yanked the grate aside" | | 7 | "She hesitated, her fingers tightening" | | 8 | "She exhaled, sharp and decisive," | | 9 | "She caught a glimpse of" | | 10 | "She knew this place wasn’t" | | 11 | "He wasn’t alone." | | 12 | "It wasn’t a kind expression." | | 13 | "She kept her voice steady," | | 14 | "She didn’t lower her gaze." |
| | ratio | 0.242 | |
| 16.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 62 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn’s boots splashed" | | 2 | "The neon glow of Soho’s" | | 3 | "She adjusted the grip on" | | 4 | "He glanced back, eyes wide," | | 5 | "Quinn didn’t hesitate." | | 6 | "She sprinted after him, her" | | 7 | "The alley reeked of damp" | | 8 | "Herrera’s footsteps echoed, quick and" | | 9 | "He wasn’t built for running—not" | | 10 | "Her voice cut through the" | | 11 | "He vaulted over a stack" | | 12 | "Quinn followed, her boots skidding" | | 13 | "The alley spilled into a" | | 14 | "Herrera didn’t break stride." | | 15 | "He yanked the grate aside" | | 16 | "Quinn slowed at the edge," | | 17 | "The air rising from the" | | 18 | "She hesitated, her fingers tightening" | | 19 | "A shout echoed from below." |
| | ratio | 0.887 | |
| 80.65% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 62 | | matches | | | ratio | 0.016 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 2 | | matches | | 0 | "The Veil Market sprawled before her, a labyrinth of stalls and shadows, the air thick with the scent of incense and something older, something that prickled at …" | | 1 | "Quinn pushed forward, her shoulder brushing against a stall selling jars of murky liquid." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 1 | | matches | | 0 | "Herrera said, his voice rough" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 15 | | tagDensity | 0.067 | | leniency | 0.133 | | rawRatio | 0 | | effectiveRatio | 0 | |