| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 85 | | tagDensity | 0.071 | | leniency | 0.141 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1859 | | 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) | |
| 89.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1859 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "stomach" | | 1 | "velvet" | | 2 | "scanned" | | 3 | "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 | 135 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 135 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 214 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1858 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 61 | | wordCount | 1169 | | uniqueNames | 11 | | maxNameDensity | 2.05 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Harlow | 1 | | Quinn | 24 | | Peter | 1 | | Vale | 10 | | Saira | 1 | | Bell | 5 | | Kowalski | 1 | | Vane | 5 | | Eva | 11 |
| | persons | | 0 | "Camden" | | 1 | "Tube" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Peter" | | 5 | "Vale" | | 6 | "Saira" | | 7 | "Bell" | | 8 | "Kowalski" | | 9 | "Vane" | | 10 | "Eva" |
| | places | (empty) | | globalScore | 0.473 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 89 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a crack in glass" |
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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 | 1858 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 214 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 154 | | mean | 12.06 | | std | 13.06 | | cv | 1.082 | | sampleLengths | | 0 | 61 | | 1 | 20 | | 2 | 17 | | 3 | 72 | | 4 | 16 | | 5 | 4 | | 6 | 31 | | 7 | 23 | | 8 | 4 | | 9 | 7 | | 10 | 11 | | 11 | 5 | | 12 | 1 | | 13 | 2 | | 14 | 5 | | 15 | 33 | | 16 | 46 | | 17 | 9 | | 18 | 1 | | 19 | 35 | | 20 | 41 | | 21 | 22 | | 22 | 2 | | 23 | 1 | | 24 | 6 | | 25 | 2 | | 26 | 7 | | 27 | 32 | | 28 | 4 | | 29 | 3 | | 30 | 34 | | 31 | 14 | | 32 | 5 | | 33 | 9 | | 34 | 1 | | 35 | 15 | | 36 | 5 | | 37 | 7 | | 38 | 5 | | 39 | 12 | | 40 | 8 | | 41 | 43 | | 42 | 2 | | 43 | 8 | | 44 | 17 | | 45 | 10 | | 46 | 7 | | 47 | 10 | | 48 | 8 | | 49 | 49 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 135 | | matches | | 0 | "been combed" | | 1 | "been placed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 193 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 214 | | ratio | 0.005 | | matches | | 0 | "The tape read METROPOLITAN POLICE—DO NOT CROSS in three languages." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1173 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.023870417732310314 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004262574595055414 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 214 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 214 | | mean | 8.68 | | std | 6.61 | | cv | 0.761 | | sampleLengths | | 0 | 10 | | 1 | 7 | | 2 | 8 | | 3 | 36 | | 4 | 20 | | 5 | 10 | | 6 | 7 | | 7 | 21 | | 8 | 9 | | 9 | 23 | | 10 | 19 | | 11 | 8 | | 12 | 8 | | 13 | 4 | | 14 | 14 | | 15 | 10 | | 16 | 7 | | 17 | 23 | | 18 | 4 | | 19 | 7 | | 20 | 11 | | 21 | 5 | | 22 | 1 | | 23 | 2 | | 24 | 5 | | 25 | 13 | | 26 | 20 | | 27 | 16 | | 28 | 5 | | 29 | 8 | | 30 | 17 | | 31 | 9 | | 32 | 1 | | 33 | 11 | | 34 | 8 | | 35 | 16 | | 36 | 3 | | 37 | 6 | | 38 | 12 | | 39 | 6 | | 40 | 5 | | 41 | 9 | | 42 | 16 | | 43 | 6 | | 44 | 2 | | 45 | 1 | | 46 | 6 | | 47 | 2 | | 48 | 7 | | 49 | 32 |
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| 49.14% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.3286384976525822 | | totalSentences | 213 | | uniqueOpeners | 70 | |
| 26.67% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 125 | | matches | | 0 | "Then the platform lamps flickered," |
| | ratio | 0.008 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 125 | | matches | | 0 | "It looked absurd among the" | | 1 | "He looked as if he" | | 2 | "He pointed to the tracks." | | 3 | "His bare foot lay grey" | | 4 | "His hands rested neatly over" | | 5 | "Her knees protested when she" | | 6 | "She ignored them, crouched two" | | 7 | "His shirt collar sat straight." | | 8 | "She lifted her chin in" | | 9 | "It held a scatter of" | | 10 | "His trouser knees were clean." | | 11 | "He gestured at the body" | | 12 | "Her curly red hair had" | | 13 | "She drew out a clear" | | 14 | "It spun in small, frantic" | | 15 | "She knelt, round glasses slipping" | | 16 | "She saw it now: Vane’s" | | 17 | "His thumbs pressed together at" | | 18 | "His wrists crossed just enough" | | 19 | "She pointed at the rails." |
| | ratio | 0.208 | |
| 36.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 106 | | totalSentences | 125 | | matches | | 0 | "The abandoned platform beneath Camden" | | 1 | "Victorian tiles cracked beneath sheets" | | 2 | "Rust bled down the walls" | | 3 | "Someone had strung amber bulbs" | | 4 | "Detective Harlow Quinn stepped through" | | 5 | "The tape read METROPOLITAN POLICE—DO" | | 6 | "It looked absurd among the" | | 7 | "A woman with moth wings" | | 8 | "Something pale and finger-shaped floated" | | 9 | "A boy no older than" | | 10 | "Quinn checked the face of" | | 11 | "The second hand ticked across" | | 12 | "DC Peter Vale ducked beneath" | | 13 | "He looked as if he" | | 14 | "Coffee stained the knot of" | | 15 | "Vale glanced past her, towards" | | 16 | "Quinn pulled on the overshoes." | | 17 | "He pointed to the tracks." | | 18 | "The platform edge had crumbled" | | 19 | "The tracks held no train," |
| | ratio | 0.848 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 125 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 1 | | matches | | 0 | "His hands rested neatly over his stomach, fingers interlaced as though someone had arranged him for burial." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 85 | | tagDensity | 0.059 | | leniency | 0.118 | | rawRatio | 0 | | effectiveRatio | 0 | |