| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 33 | | tagDensity | 0.273 | | leniency | 0.545 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1371 | | 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) | |
| 78.12% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1371 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "weight" | | 1 | "footsteps" | | 2 | "silence" | | 3 | "pulse" |
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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 | 85 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 85 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 108 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1370 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 1173 | | uniqueNames | 22 | | maxNameDensity | 0.51 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Street" | | discoveredNames | | Harlow | 1 | | Quinn | 6 | | Raven | 1 | | Nest | 1 | | Herrera | 5 | | Seville-soft | 1 | | Dean | 1 | | Street | 4 | | Oxford | 1 | | Tottenham | 1 | | Court | 1 | | Road | 2 | | Euston | 1 | | Warren | 1 | | High | 1 | | Blitz | 1 | | Saint | 1 | | Christopher | 1 | | Hackney | 1 | | Silence | 1 | | Procedure | 2 | | Whitechapel | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Nest" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Silence" | | 7 | "Procedure" |
| | places | | 0 | "Raven" | | 1 | "Dean" | | 2 | "Street" | | 3 | "Oxford" | | 4 | "Tottenham" | | 5 | "Court" | | 6 | "Road" | | 7 | "Euston" | | 8 | "Warren" | | 9 | "High" | | 10 | "Whitechapel" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | 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 | 1370 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 108 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 53 | | mean | 25.85 | | std | 25.34 | | cv | 0.98 | | sampleLengths | | 0 | 56 | | 1 | 42 | | 2 | 54 | | 3 | 16 | | 4 | 4 | | 5 | 29 | | 6 | 11 | | 7 | 10 | | 8 | 1 | | 9 | 2 | | 10 | 6 | | 11 | 32 | | 12 | 4 | | 13 | 14 | | 14 | 3 | | 15 | 2 | | 16 | 10 | | 17 | 79 | | 18 | 46 | | 19 | 69 | | 20 | 105 | | 21 | 14 | | 22 | 6 | | 23 | 18 | | 24 | 91 | | 25 | 14 | | 26 | 17 | | 27 | 27 | | 28 | 11 | | 29 | 13 | | 30 | 32 | | 31 | 75 | | 32 | 4 | | 33 | 10 | | 34 | 1 | | 35 | 80 | | 36 | 8 | | 37 | 18 | | 38 | 18 | | 39 | 21 | | 40 | 20 | | 41 | 13 | | 42 | 11 | | 43 | 27 | | 44 | 10 | | 45 | 22 | | 46 | 45 | | 47 | 19 | | 48 | 64 | | 49 | 7 |
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| 97.01% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 85 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 182 | | matches | | 0 | "was swinging" | | 1 | "was searching" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 108 | | ratio | 0.009 | | matches | | 0 | "\"—moves with the moon! Come tomorrow and it's under Hackney! You'll dig for a month and find gravel!\"" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 957 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.01671891327063741 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0010449320794148381 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 108 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 108 | | mean | 12.69 | | std | 8.45 | | cv | 0.666 | | sampleLengths | | 0 | 31 | | 1 | 25 | | 2 | 4 | | 3 | 38 | | 4 | 6 | | 5 | 28 | | 6 | 4 | | 7 | 16 | | 8 | 16 | | 9 | 4 | | 10 | 2 | | 11 | 5 | | 12 | 22 | | 13 | 7 | | 14 | 4 | | 15 | 10 | | 16 | 1 | | 17 | 2 | | 18 | 6 | | 19 | 4 | | 20 | 28 | | 21 | 4 | | 22 | 9 | | 23 | 5 | | 24 | 3 | | 25 | 2 | | 26 | 10 | | 27 | 30 | | 28 | 10 | | 29 | 18 | | 30 | 21 | | 31 | 22 | | 32 | 7 | | 33 | 4 | | 34 | 13 | | 35 | 12 | | 36 | 18 | | 37 | 13 | | 38 | 26 | | 39 | 31 | | 40 | 16 | | 41 | 26 | | 42 | 15 | | 43 | 17 | | 44 | 9 | | 45 | 5 | | 46 | 6 | | 47 | 5 | | 48 | 2 | | 49 | 1 |
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| 72.22% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4722222222222222 | | totalSentences | 108 | | uniqueOpeners | 51 | |
| 40.65% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 82 | | matches | | 0 | "Somewhere behind the bricks, a" |
| | ratio | 0.012 | |
| 59.02% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 82 | | matches | | 0 | "He checked the street both" | | 1 | "His eyes went from the" | | 2 | "He shifted his grip." | | 3 | "She read it in his" | | 4 | "She went after him." | | 5 | "Her voice bounced off shutters" | | 6 | "He cut through a gap" | | 7 | "He threaded the gap between" | | 8 | "He knew the gaps in" | | 9 | "He crossed Euston Road against" | | 10 | "She counted her breathing in" | | 11 | "He dragged a plywood panel" | | 12 | "She keyed her radio twice." | | 13 | "She clipped the radio back" | | 14 | "He pressed it into a" | | 15 | "He kissed his thumb and" | | 16 | "She came down the last" | | 17 | "He turned, medallion swinging, sleeve" | | 18 | "He backed toward the gap" | | 19 | "She took the last three" |
| | ratio | 0.402 | |
| 33.17% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 82 | | matches | | 0 | "Rain had come off the" | | 1 | "Detective Harlow Quinn stood in" | | 2 | "Tomás Herrera stepped out into" | | 3 | "Rain flattened his curls." | | 4 | "He checked the street both" | | 5 | "Quinn crossed the road and" | | 6 | "Water crawled down his face." | | 7 | "His eyes went from the" | | 8 | "The word came out round," | | 9 | "He shifted his grip." | | 10 | "She read it in his" | | 11 | "She went after him." | | 12 | "Her voice bounced off shutters" | | 13 | "He cut through a gap" | | 14 | "A night bus hissed past" | | 15 | "He threaded the gap between" | | 16 | "Quinn gave the cab three" | | 17 | "The box slowed him." | | 18 | "He knew the gaps in" | | 19 | "He crossed Euston Road against" |
| | ratio | 0.854 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 82 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 3 | | matches | | 0 | "Through the glass she could see the walls inside, papered with old maps and photographs of men who'd died before her grandmother was born, and above the entranc…" | | 1 | "Quinn stood in the dark with a dead radio, a stolen jacket, and a box that breathed cold against her hip." | | 2 | "Morris's torch spinning on the concrete, its beam wheeling round and round like it was searching for something." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |