| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 48 | | tagDensity | 0.188 | | leniency | 0.375 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.03% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1434 | | totalAiIsmAdverbs | 2 | | 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) | |
| 58.16% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1434 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "shattered" | | 1 | "aftermath" | | 2 | "scanning" | | 3 | "silence" | | 4 | "chill" | | 5 | "etched" | | 6 | "perfect" | | 7 | "structure" | | 8 | "database" | | 9 | "furrowing" | | 10 | "vibrated" |
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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 | 88 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 88 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 127 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 51 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1434 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 82.99% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 970 | | uniqueNames | 12 | | maxNameDensity | 1.34 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 13 | | Victorian | 2 | | Miller | 9 | | Tube | 1 | | Camden | 1 | | Silence | 1 | | Rotherhithe | 1 | | Morris | 1 | | Commander | 1 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Victorian" | | 3 | "Miller" | | 4 | "Silence" | | 5 | "Morris" | | 6 | "Commander" |
| | places | | | globalScore | 0.83 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 1 | | matches | | 0 | "seemed thicker clinging to the corners in defiance of the halogen floodlights" |
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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 | 1434 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 127 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 67 | | mean | 21.4 | | std | 18.51 | | cv | 0.865 | | sampleLengths | | 0 | 25 | | 1 | 4 | | 2 | 37 | | 3 | 4 | | 4 | 18 | | 5 | 2 | | 6 | 7 | | 7 | 66 | | 8 | 36 | | 9 | 4 | | 10 | 26 | | 11 | 35 | | 12 | 26 | | 13 | 32 | | 14 | 15 | | 15 | 5 | | 16 | 4 | | 17 | 5 | | 18 | 5 | | 19 | 41 | | 20 | 2 | | 21 | 25 | | 22 | 8 | | 23 | 4 | | 24 | 62 | | 25 | 3 | | 26 | 7 | | 27 | 13 | | 28 | 42 | | 29 | 69 | | 30 | 13 | | 31 | 5 | | 32 | 2 | | 33 | 39 | | 34 | 2 | | 35 | 4 | | 36 | 59 | | 37 | 7 | | 38 | 34 | | 39 | 24 | | 40 | 14 | | 41 | 5 | | 42 | 3 | | 43 | 51 | | 44 | 48 | | 45 | 17 | | 46 | 27 | | 47 | 42 | | 48 | 9 | | 49 | 2 |
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| 81.34% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 88 | | matches | | 0 | "were twisted" | | 1 | "was frozen" | | 2 | "was blackened" | | 3 | "were scored" | | 4 | "were etched" | | 5 | "been carried" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 162 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 127 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 976 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.012295081967213115 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.006147540983606557 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 127 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 127 | | mean | 11.29 | | std | 7.84 | | cv | 0.694 | | sampleLengths | | 0 | 8 | | 1 | 17 | | 2 | 4 | | 3 | 8 | | 4 | 29 | | 5 | 4 | | 6 | 18 | | 7 | 2 | | 8 | 7 | | 9 | 14 | | 10 | 17 | | 11 | 1 | | 12 | 7 | | 13 | 10 | | 14 | 10 | | 15 | 7 | | 16 | 7 | | 17 | 29 | | 18 | 4 | | 19 | 26 | | 20 | 9 | | 21 | 10 | | 22 | 16 | | 23 | 12 | | 24 | 14 | | 25 | 8 | | 26 | 3 | | 27 | 21 | | 28 | 4 | | 29 | 11 | | 30 | 5 | | 31 | 4 | | 32 | 5 | | 33 | 2 | | 34 | 3 | | 35 | 25 | | 36 | 16 | | 37 | 2 | | 38 | 25 | | 39 | 8 | | 40 | 4 | | 41 | 13 | | 42 | 6 | | 43 | 18 | | 44 | 13 | | 45 | 12 | | 46 | 3 | | 47 | 7 | | 48 | 13 | | 49 | 12 |
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| 83.99% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5275590551181102 | | totalSentences | 127 | | uniqueOpeners | 67 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 82 | | matches | (empty) | | ratio | 0 | |
| 73.66% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 82 | | matches | | 0 | "She stepped over the yellow" | | 1 | "He held a disposable coffee" | | 2 | "She adjusted the worn leather" | | 3 | "She frowned and tapped the" | | 4 | "They rounded a corner tiled" | | 5 | "His limbs were twisted at" | | 6 | "Her jaw tightened." | | 7 | "She stood with her feet" | | 8 | "His face was frozen in" | | 9 | "She rubbed it between her" | | 10 | "It didn't smudge like soot." | | 11 | "It dissolved, leaving a faint," | | 12 | "She had found DS Morris" | | 13 | "She stood up, her brown" | | 14 | "She pointed toward the tunnel" | | 15 | "She pulled a pair of" | | 16 | "It was porous, slightly curved," | | 17 | "She dropped it into an" | | 18 | "They weren't chalk." | | 19 | "She walked the perimeter of" |
| | ratio | 0.366 | |
| 51.46% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 67 | | totalSentences | 82 | | matches | | 0 | "Latex gloves snapped against Detective" | | 1 | "She stepped over the yellow" | | 2 | "DS Miller ducked beneath a" | | 3 | "He held a disposable coffee" | | 4 | "Quinn followed him deeper into" | | 5 | "The air tasted of wet" | | 6 | "She adjusted the worn leather" | | 7 | "The second hand twitched, stopped," | | 8 | "She frowned and tapped the" | | 9 | "They rounded a corner tiled" | | 10 | "A dozen white-suited technicians moved" | | 11 | "A man lay sprawled near" | | 12 | "His limbs were twisted at" | | 13 | "Quinn stopped at the edge" | | 14 | "Her jaw tightened." | | 15 | "She stood with her feet" | | 16 | "Quinn crouched, the knees of" | | 17 | "Quinn leaned closer to the" | | 18 | "The victim was male, mid-forties," | | 19 | "His face was frozen in" |
| | ratio | 0.817 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 82 | | matches | (empty) | | ratio | 0 | |
| 97.26% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 3 | | matches | | 0 | "The victim was male, mid-forties, dressed in a charcoal wool coat that cost more than her monthly mortgage." | | 1 | "The wooden structure was rotted through, the wood petrified by decades of damp, yet the brass grill at the window gleamed as if polished that morning." | | 2 | "A hairline fracture split the ceramic tiles from ceiling to floor, bleeding a thick, violet light that smelled of copper and dead stars." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 3 | | matches | | 0 | "Quinn crouched, the knees of her trousers popping in the quiet cavern" | | 1 | "Quinn said, her voice dropping an octave" | | 2 | "Miller flipped, his brow furrowing" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 1 | | fancyTags | | 0 | "Miller guessed (guess)" |
| | dialogueSentences | 48 | | tagDensity | 0.125 | | leniency | 0.25 | | rawRatio | 0.167 | | effectiveRatio | 0.042 | |