| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.93% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 993 | | 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) | |
| 44.61% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 993 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "familiar" | | 1 | "chill" | | 2 | "intricate" | | 3 | "charm" | | 4 | "scanned" | | 5 | "methodical" | | 6 | "etched" | | 7 | "dance" | | 8 | "weight" | | 9 | "maw" |
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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 | 79 | | matches | (empty) | |
| 88.61% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 79 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 92 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 987 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 85.32% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 773 | | uniqueNames | 10 | | maxNameDensity | 1.29 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 10 | | London | 1 | | Underground | 1 | | Bailey | 3 | | Italian | 1 | | Morris | 1 | | Medical | 1 | | Examiner | 1 | | Dr | 1 | | Sharma | 2 |
| | persons | | 0 | "Quinn" | | 1 | "Bailey" | | 2 | "Morris" | | 3 | "Dr" | | 4 | "Sharma" |
| | places | | | globalScore | 0.853 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 98.68% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.013 | | wordCount | 987 | | matches | | 0 | "Not just the familiar damp chill of the London Underground, but something else" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 92 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 37.96 | | std | 27.42 | | cv | 0.722 | | sampleLengths | | 0 | 53 | | 1 | 28 | | 2 | 80 | | 3 | 21 | | 4 | 20 | | 5 | 88 | | 6 | 24 | | 7 | 11 | | 8 | 18 | | 9 | 24 | | 10 | 61 | | 11 | 19 | | 12 | 95 | | 13 | 39 | | 14 | 65 | | 15 | 73 | | 16 | 4 | | 17 | 21 | | 18 | 58 | | 19 | 7 | | 20 | 4 | | 21 | 70 | | 22 | 29 | | 23 | 52 | | 24 | 6 | | 25 | 17 |
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| 91.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 79 | | matches | | 0 | "was dressed" | | 1 | "was closed" | | 2 | "were etched" |
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| 26.09% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 115 | | matches | | 0 | "wasn’t pointing" | | 1 | "wasn’t pointing" | | 2 | "was trying" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 92 | | ratio | 0.011 | | matches | | 0 | "The usual detritus of forgotten spaces—dust, grime, the ghosts of old posters—was present, but the area around the body felt vacuumed, scoured of life and incident." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 776 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.029639175257731958 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007731958762886598 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 92 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 92 | | mean | 10.73 | | std | 6.58 | | cv | 0.613 | | sampleLengths | | 0 | 17 | | 1 | 5 | | 2 | 13 | | 3 | 18 | | 4 | 16 | | 5 | 3 | | 6 | 9 | | 7 | 10 | | 8 | 7 | | 9 | 26 | | 10 | 13 | | 11 | 18 | | 12 | 6 | | 13 | 21 | | 14 | 20 | | 15 | 10 | | 16 | 10 | | 17 | 9 | | 18 | 13 | | 19 | 4 | | 20 | 1 | | 21 | 3 | | 22 | 4 | | 23 | 6 | | 24 | 9 | | 25 | 8 | | 26 | 11 | | 27 | 16 | | 28 | 8 | | 29 | 6 | | 30 | 5 | | 31 | 3 | | 32 | 11 | | 33 | 4 | | 34 | 24 | | 35 | 19 | | 36 | 12 | | 37 | 15 | | 38 | 15 | | 39 | 4 | | 40 | 15 | | 41 | 11 | | 42 | 12 | | 43 | 11 | | 44 | 8 | | 45 | 7 | | 46 | 7 | | 47 | 14 | | 48 | 21 | | 49 | 4 |
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| 71.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.4673913043478261 | | totalSentences | 92 | | uniqueOpeners | 43 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 79.74% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 77 | | matches | | 0 | "It was cleaner than it" | | 1 | "He was dressed in a" | | 2 | "His shoes were Italian leather," | | 3 | "She leaned closer, her own" | | 4 | "She glanced down at his" | | 5 | "His right was open on" | | 6 | "His left was closed in" | | 7 | "She looked from the bone" | | 8 | "Her eyes scanned the surrounding" | | 9 | "Her military bearing asserted itself" | | 10 | "She ran a gloved finger" | | 11 | "She angled her head, letting" | | 12 | "She said it to herself" | | 13 | "It tasted of ashes and" | | 14 | "It tasted of the night" | | 15 | "She refocused, pushing the memory" | | 16 | "It had no place here." | | 17 | "She began to walk the" | | 18 | "It was old, the casing" | | 19 | "She picked it up." |
| | ratio | 0.351 | |
| 50.91% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 77 | | matches | | 0 | "Harlow Quinn ducked under the" | | 1 | "The air hit her first." | | 2 | "A deeper cold that settled" | | 3 | "DC Bailey stood near the" | | 4 | "A nervous habit." | | 5 | "Quinn’s gaze swept the tiled" | | 6 | "It was cleaner than it" | | 7 | "The usual detritus of forgotten" | | 8 | "The man lay on his" | | 9 | "He was dressed in a" | | 10 | "His shoes were Italian leather," | | 11 | "Quinn’s voice was flat, absorbing" | | 12 | "Quinn knelt beside the body," | | 13 | "The man’s face was placid," | | 14 | "She leaned closer, her own" | | 15 | "Copper stung her nostrils." | | 16 | "A memory of it." | | 17 | "She glanced down at his" | | 18 | "His right was open on" | | 19 | "His left was closed in" |
| | ratio | 0.818 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 1 | | matches | | 0 | "An impossible cause for a man who looked perfectly healthy." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.05 | | leniency | 0.1 | | rawRatio | 0 | | effectiveRatio | 0 | |