| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.06% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 842 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 52.49% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 842 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "scanned" | | 1 | "glinting" | | 2 | "flicker" | | 3 | "pulse" | | 4 | "velvet" | | 5 | "charm" | | 6 | "unwavering" | | 7 | "pulsed" |
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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 | 62 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 62 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 68 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 6 | | totalWords | 835 | | ratio | 0.007 | | matches | | 0 | "The Raven’s Nest" | | 1 | "The Veil Market." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 754 | | uniqueNames | 11 | | maxNameDensity | 1.72 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Harlow | 1 | | Quinn | 13 | | Soho | 1 | | Raven | 1 | | Tomás | 1 | | Herrera | 12 | | Saint | 1 | | Christopher | 1 | | Tube | 1 | | Veil | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" |
| | places | | | globalScore | 0.638 | | windowScore | 0.5 | |
| 98.98% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 49 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 835 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 68 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 36.3 | | std | 25.48 | | cv | 0.702 | | sampleLengths | | 0 | 80 | | 1 | 70 | | 2 | 58 | | 3 | 8 | | 4 | 72 | | 5 | 51 | | 6 | 54 | | 7 | 59 | | 8 | 3 | | 9 | 27 | | 10 | 73 | | 11 | 64 | | 12 | 18 | | 13 | 11 | | 14 | 23 | | 15 | 13 | | 16 | 5 | | 17 | 16 | | 18 | 53 | | 19 | 26 | | 20 | 30 | | 21 | 19 | | 22 | 2 |
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| 93.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 62 | | matches | | 0 | "were gone" | | 1 | "were lined" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 142 | | matches | | 0 | "was going" | | 1 | "was walking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 68 | | ratio | 0.088 | | matches | | 0 | "She’d been tailing the suspect for blocks—ever since he’d bolted from *The Raven’s Nest* like a man with something to hide." | | 1 | "The kid—Tomás Herrera, if her sources were right—was fast, but Quinn had spent years chasing men who didn’t want to be caught." | | 2 | "Then she saw it—the manhole cover, slightly ajar." | | 3 | "The ladder descended into darkness, the air rising from below thick with the scent of damp earth and something else—something metallic, like old coins." | | 4 | "The sound of distant voices, the clink of glass, the murmur of a crowd—this wasn’t just a sewer." | | 5 | "Quinn had heard whispers of it—places where the rules of the world bent, where people traded in things that shouldn’t exist." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 761 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.01971090670170828 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.005256241787122208 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 68 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 68 | | mean | 12.28 | | std | 8.05 | | cv | 0.656 | | sampleLengths | | 0 | 15 | | 1 | 23 | | 2 | 18 | | 3 | 24 | | 4 | 21 | | 5 | 22 | | 6 | 27 | | 7 | 3 | | 8 | 27 | | 9 | 10 | | 10 | 18 | | 11 | 8 | | 12 | 8 | | 13 | 9 | | 14 | 8 | | 15 | 7 | | 16 | 2 | | 17 | 5 | | 18 | 9 | | 19 | 24 | | 20 | 13 | | 21 | 24 | | 22 | 14 | | 23 | 7 | | 24 | 12 | | 25 | 13 | | 26 | 18 | | 27 | 4 | | 28 | 12 | | 29 | 28 | | 30 | 19 | | 31 | 3 | | 32 | 21 | | 33 | 4 | | 34 | 2 | | 35 | 7 | | 36 | 22 | | 37 | 22 | | 38 | 22 | | 39 | 12 | | 40 | 16 | | 41 | 16 | | 42 | 20 | | 43 | 9 | | 44 | 9 | | 45 | 11 | | 46 | 3 | | 47 | 13 | | 48 | 7 | | 49 | 6 |
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| 45.59% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.27941176470588236 | | totalSentences | 68 | | uniqueOpeners | 19 | |
| 57.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 58 | | matches | | 0 | "Then she saw it—the manhole" |
| | ratio | 0.017 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 58 | | matches | | 0 | "Her breath came in controlled" | | 1 | "She’d been tailing the suspect" | | 2 | "She knew the rhythm of" | | 3 | "He ducked into a narrow" | | 4 | "She crouched, brushing her fingers" | | 5 | "She had no backup, no" | | 6 | "She pried the cover open" | | 7 | "Her boots hit packed dirt," | | 8 | "It was something else." | | 9 | "She moved forward, her hand" | | 10 | "She’d never believed it." | | 11 | "She kept her distance, her" | | 12 | "he said, his voice tight" | | 13 | "She closed the distance between" | | 14 | "She took another step forward" | | 15 | "He turned to the leathery" |
| | ratio | 0.276 | |
| 11.72% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 58 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn moved with" | | 2 | "The neon glow of Soho’s" | | 3 | "Her breath came in controlled" | | 4 | "She’d been tailing the suspect" | | 5 | "The kid—Tomás Herrera, if her" | | 6 | "She knew the rhythm of" | | 7 | "Herrera was good." | | 8 | "He ducked into a narrow" | | 9 | "Quinn followed, her leather watch" | | 10 | "The alley opened into a" | | 11 | "Quinn slowed, her pulse steady" | | 12 | "She crouched, brushing her fingers" | | 13 | "The edges were worn, but" | | 14 | "Someone had been using this" | | 15 | "She had no backup, no" | | 16 | "She pried the cover open" | | 17 | "The ladder descended into darkness," | | 18 | "Quinn swung herself onto the" | | 19 | "The descent was longer than" |
| | ratio | 0.897 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 3 | | matches | | 0 | "Detective Harlow Quinn moved with the precision of a soldier, her boots splashing through puddles that pooled in the cracks of the pavement." | | 1 | "The market was a labyrinth of stalls selling everything from glowing vials of liquid to bundles of herbs that smelled like ozone." | | 2 | "Quinn followed, pushing through the strands, her breath catching as she stepped into a smaller chamber." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 1 | | matches | | 0 | "he said, his voice tight" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0 | | effectiveRatio | 0 | |