| 85.71% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 2 | | adverbTags | | 0 | "she said sharply [sharply]" | | 1 | "she said quietly [quietly]" |
| | dialogueSentences | 35 | | tagDensity | 0.343 | | leniency | 0.686 | | rawRatio | 0.167 | | effectiveRatio | 0.114 | |
| 90.51% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1054 | | 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) | |
| 38.33% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1054 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "reminder" | | 1 | "could feel" | | 2 | "pulse" | | 3 | "quickened" | | 4 | "etched" | | 5 | "gloom" | | 6 | "glint" | | 7 | "navigate" | | 8 | "eyebrow" | | 9 | "flicked" | | 10 | "stomach" |
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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 | 78 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 78 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 100 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 3 | | totalWords | 1054 | | ratio | 0.003 | | matches | | 0 | "recognition" | | 1 | "occult" | | 2 | "running" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 38.45% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 762 | | uniqueNames | 12 | | maxNameDensity | 2.23 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Harlow | 1 | | Quinn | 17 | | Priya | 1 | | Kapoor | 5 | | Morris | 4 | | British | 1 | | Museum | 1 | | Eva | 9 | | Kowalski | 1 | | Like | 3 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Priya" | | 4 | "Kapoor" | | 5 | "Morris" | | 6 | "Museum" | | 7 | "Eva" | | 8 | "Kowalski" |
| | places | (empty) | | globalScore | 0.385 | | windowScore | 0.5 | |
| 94.44% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | glossingSentenceCount | 1 | | matches | | 0 | "not quite touching the plastic" |
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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 | 1054 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 100 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 27.03 | | std | 20.46 | | cv | 0.757 | | sampleLengths | | 0 | 89 | | 1 | 55 | | 2 | 16 | | 3 | 71 | | 4 | 37 | | 5 | 21 | | 6 | 51 | | 7 | 20 | | 8 | 22 | | 9 | 35 | | 10 | 6 | | 11 | 11 | | 12 | 34 | | 13 | 24 | | 14 | 66 | | 15 | 6 | | 16 | 9 | | 17 | 8 | | 18 | 1 | | 19 | 55 | | 20 | 33 | | 21 | 19 | | 22 | 16 | | 23 | 14 | | 24 | 39 | | 25 | 4 | | 26 | 16 | | 27 | 38 | | 28 | 10 | | 29 | 55 | | 30 | 22 | | 31 | 23 | | 32 | 22 | | 33 | 5 | | 34 | 17 | | 35 | 29 | | 36 | 39 | | 37 | 4 | | 38 | 12 |
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| 96.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 78 | | matches | | 0 | "was frozen" | | 1 | "been found" |
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| 51.85% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 135 | | matches | | 0 | "wasn’t pointing" | | 1 | "wasn’t pointing" | | 2 | "was waiting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 100 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 767 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.018252933507170794 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.00651890482398957 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 100 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 100 | | mean | 10.53 | | std | 7.92 | | cv | 0.752 | | sampleLengths | | 0 | 29 | | 1 | 18 | | 2 | 17 | | 3 | 7 | | 4 | 18 | | 5 | 8 | | 6 | 17 | | 7 | 17 | | 8 | 13 | | 9 | 5 | | 10 | 11 | | 11 | 23 | | 12 | 36 | | 13 | 12 | | 14 | 18 | | 15 | 11 | | 16 | 4 | | 17 | 2 | | 18 | 2 | | 19 | 17 | | 20 | 4 | | 21 | 2 | | 22 | 20 | | 23 | 7 | | 24 | 4 | | 25 | 3 | | 26 | 15 | | 27 | 7 | | 28 | 13 | | 29 | 9 | | 30 | 13 | | 31 | 3 | | 32 | 12 | | 33 | 5 | | 34 | 5 | | 35 | 10 | | 36 | 6 | | 37 | 11 | | 38 | 3 | | 39 | 18 | | 40 | 2 | | 41 | 7 | | 42 | 4 | | 43 | 24 | | 44 | 20 | | 45 | 2 | | 46 | 3 | | 47 | 24 | | 48 | 17 | | 49 | 4 |
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| 57.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.38 | | totalSentences | 100 | | uniqueOpeners | 38 | |
| 95.24% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 70 | | matches | | 0 | "Instead, she turned her attention" | | 1 | "Instead, she asked," |
| | ratio | 0.029 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 70 | | matches | | 0 | "She could feel it in" | | 1 | "She held a evidence bag" | | 2 | "She didn’t touch it." | | 3 | "It was something else." | | 4 | "She stood, brushing dust from" | | 5 | "It wasn’t pointing at anything." | | 6 | "It spun lazily, as if" | | 7 | "She’d seen enough strange things" | | 8 | "She thought of the last" | | 9 | "she said sharply" | | 10 | "She’d called ahead, and Eva" | | 11 | "she said, tucking a curl" | | 12 | "She placed the evidence bag" | | 13 | "She reached for the bag," | | 14 | "she said quietly" | | 15 | "She didn’t answer." | | 16 | "She gestured to the shadows" | | 17 | "She thought of the way" | | 18 | "She stood abruptly, her chair" | | 19 | "She thought of Morris again," |
| | ratio | 0.286 | |
| 60.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 56 | | totalSentences | 70 | | matches | | 0 | "The abandoned Tube station beneath" | | 1 | "Detective Harlow Quinn adjusted the" | | 2 | "The air hummed with the" | | 3 | "She could feel it in" | | 4 | "a voice called from the" | | 5 | "DS Priya Kapoor stepped into" | | 6 | "She held a evidence bag" | | 7 | "Quinn exhaled through her nose." | | 8 | "Kapoor gestured to a huddled" | | 9 | "The body was half-hidden in" | | 10 | "Quinn crouched beside the body," | | 11 | "A bone token, carved with" | | 12 | "She didn’t touch it." | | 13 | "Kapoor’s voice was low, urgent" | | 14 | "The man’s mouth was frozen" | | 15 | "It was something else." | | 16 | "Something like *recognition*." | | 17 | "She stood, brushing dust from" | | 18 | "Quinn didn’t answer." | | 19 | "The needle wasn’t pointing north." |
| | ratio | 0.8 | |
| 71.43% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 70 | | matches | | 0 | "To the token she’d left" |
| | ratio | 0.014 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 2 | | matches | | 0 | "The abandoned Tube station beneath Camden reeked of damp concrete and old urine, the kind of stink that clung to the back of the throat and wouldn’t let go." | | 1 | "The air smelled of old paper and lemon oil, the kind of scent that made Quinn’s nose itch." |
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| 83.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 1 | | matches | | 0 | "She gestured, as if the market might be lurking in the stacks" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 35 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |