| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 22 | | tagDensity | 0.409 | | leniency | 0.818 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.93% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1628 | | 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) | |
| 93.86% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1628 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 163 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 163 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 176 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1627 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 70 | | wordCount | 1538 | | uniqueNames | 19 | | maxNameDensity | 2.02 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Herrera | 21 | | Raven | 1 | | Nest | 1 | | Harlow | 1 | | Quinn | 31 | | Soho | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Bloomsbury | 1 | | Northern | 1 | | Camden | 2 | | Town | 1 | | Static | 1 | | Underground | 1 | | Tube | 1 | | Heads | 1 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Herrera" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Harlow" | | 4 | "Quinn" | | 5 | "Static" | | 6 | "Heads" | | 7 | "Saint" | | 8 | "Christopher" |
| | places | | 0 | "Soho" | | 1 | "Tottenham" | | 2 | "Court" | | 3 | "Road" | | 4 | "Bloomsbury" | | 5 | "Northern" | | 6 | "Camden" | | 7 | "Town" |
| | globalScore | 0.492 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 125 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared under a bare bulb" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.615 | | wordCount | 1627 | | matches | | 0 | "not surprise, as she’d expected, but dismay" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 176 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 72 | | mean | 22.6 | | std | 18.27 | | cv | 0.808 | | sampleLengths | | 0 | 22 | | 1 | 52 | | 2 | 17 | | 3 | 46 | | 4 | 65 | | 5 | 9 | | 6 | 47 | | 7 | 6 | | 8 | 41 | | 9 | 2 | | 10 | 15 | | 11 | 70 | | 12 | 10 | | 13 | 32 | | 14 | 24 | | 15 | 38 | | 16 | 38 | | 17 | 14 | | 18 | 25 | | 19 | 4 | | 20 | 1 | | 21 | 6 | | 22 | 46 | | 23 | 34 | | 24 | 38 | | 25 | 6 | | 26 | 18 | | 27 | 12 | | 28 | 23 | | 29 | 4 | | 30 | 6 | | 31 | 42 | | 32 | 17 | | 33 | 47 | | 34 | 3 | | 35 | 13 | | 36 | 20 | | 37 | 53 | | 38 | 23 | | 39 | 58 | | 40 | 11 | | 41 | 6 | | 42 | 6 | | 43 | 33 | | 44 | 6 | | 45 | 7 | | 46 | 12 | | 47 | 38 | | 48 | 15 | | 49 | 36 |
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| 96.65% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 163 | | matches | | 0 | "been seen" | | 1 | "been found" | | 2 | "were gone" | | 3 | "was made" | | 4 | "was battered" |
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| 78.79% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 275 | | matches | | 0 | "was taking" | | 1 | "was running" | | 2 | "was aiming" | | 3 | "was pulling" | | 4 | "was dripping" |
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| 94.16% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 2 | | flaggedSentences | 3 | | totalSentences | 176 | | ratio | 0.017 | | matches | | 0 | "Herrera’s expression changed—not surprise, as she’d expected, but dismay." | | 1 | "She thumbed the power button; nothing happened." | | 2 | "A gate rattled above; someone was pulling it across the stairhead." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1545 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.020711974110032363 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.003236245954692557 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 176 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 176 | | mean | 9.24 | | std | 5.61 | | cv | 0.607 | | sampleLengths | | 0 | 22 | | 1 | 8 | | 2 | 13 | | 3 | 20 | | 4 | 6 | | 5 | 5 | | 6 | 6 | | 7 | 11 | | 8 | 9 | | 9 | 8 | | 10 | 16 | | 11 | 13 | | 12 | 11 | | 13 | 17 | | 14 | 28 | | 15 | 5 | | 16 | 4 | | 17 | 9 | | 18 | 7 | | 19 | 24 | | 20 | 16 | | 21 | 6 | | 22 | 7 | | 23 | 13 | | 24 | 6 | | 25 | 9 | | 26 | 6 | | 27 | 2 | | 28 | 15 | | 29 | 4 | | 30 | 4 | | 31 | 16 | | 32 | 11 | | 33 | 35 | | 34 | 10 | | 35 | 11 | | 36 | 21 | | 37 | 2 | | 38 | 8 | | 39 | 14 | | 40 | 9 | | 41 | 7 | | 42 | 22 | | 43 | 12 | | 44 | 10 | | 45 | 4 | | 46 | 12 | | 47 | 3 | | 48 | 11 | | 49 | 6 |
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| 51.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3352272727272727 | | totalSentences | 176 | | uniqueOpeners | 59 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 153 | | matches | (empty) | | ratio | 0 | |
| 84.05% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 52 | | totalSentences | 153 | | matches | | 0 | "He had gone in empty-handed." | | 1 | "He turned north and walked" | | 2 | "He fished something from his" | | 3 | "He rubbed it between his" | | 4 | "She had two uniformed officers" | | 5 | "His ledgers were gone." | | 6 | "She wanted to know where" | | 7 | "He went down to the" | | 8 | "Its windows, bright with passengers," | | 9 | "He glanced back once and" | | 10 | "Her shoes struck water pooled" | | 11 | "She had spent eighteen years" | | 12 | "He was aiming east, away" | | 13 | "She keyed her radio as" | | 14 | "He took a narrow lane" | | 15 | "He turned sharply before the" | | 16 | "She drew her torch." | | 17 | "She started down." | | 18 | "It scraped across concrete, loud" | | 19 | "She would not let a" |
| | ratio | 0.34 | |
| 35.16% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 130 | | totalSentences | 153 | | matches | | 0 | "Tomás Herrera came out of" | | 1 | "Detective Harlow Quinn watched from" | | 2 | "Rain slid off the bar’s" | | 3 | "Herrera had spent forty minutes" | | 4 | "He had gone in empty-handed." | | 5 | "He turned north and walked" | | 6 | "Quinn waited until a delivery" | | 7 | "He fished something from his" | | 8 | "He rubbed it between his" | | 9 | "Quinn followed him through Soho’s" | | 10 | "She had two uniformed officers" | | 11 | "A man matching Herrera’s description" | | 12 | "The dealer’s stock was untouched." | | 13 | "His ledgers were gone." | | 14 | "She wanted to know where" | | 15 | "He went down to the" | | 16 | "Quinn bought a ticket at" | | 17 | "Quinn let two passengers step" | | 18 | "A bus forced her to" | | 19 | "Its windows, bright with passengers," |
| | ratio | 0.85 | |
| 65.36% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 153 | | matches | | 0 | "By the time she reached" | | 1 | "Either way, she could not" |
| | ratio | 0.013 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 65 | | technicalSentenceCount | 2 | | matches | | 0 | "Tomás Herrera came out of the side door of The Raven’s Nest carrying a black medical bag and looking over his shoulder." | | 1 | "She had spent eighteen years learning what people did when they ran: the instinctive turn toward home, the bad choice at a blind corner, the glance that told he…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 59.09% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 2 | | fancyTags | | 0 | "Quinn shouted (shout)" | | 1 | "Quinn followed (follow)" |
| | dialogueSentences | 22 | | tagDensity | 0.318 | | leniency | 0.636 | | rawRatio | 0.286 | | effectiveRatio | 0.182 | |