| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 42 | | tagDensity | 0.405 | | leniency | 0.81 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 82.09% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1396 | | totalAiIsmAdverbs | 5 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
| | 2 | | | 3 | | | 4 | |
| | highlights | | 0 | "carefully" | | 1 | "deliberately" | | 2 | "gently" | | 3 | "slowly" | | 4 | "lazily" |
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| 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) | |
| 67.77% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1396 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "echoing" | | 1 | "scanning" | | 2 | "stark" | | 3 | "gloom" | | 4 | "intricate" | | 5 | "traced" | | 6 | "weight" | | 7 | "perfect" | | 8 | "magnetic" |
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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 | 98 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 98 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 123 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 55 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 2 | | totalWords | 1389 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 77.54% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 828 | | uniqueNames | 13 | | maxNameDensity | 1.45 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Harlow | 1 | | Quinn | 12 | | Victorian | 1 | | Inspector | 1 | | Raj | 1 | | Singh | 10 | | Eva | 8 | | Kowalski | 2 | | Met | 1 | | Morris | 1 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raj" | | 3 | "Singh" | | 4 | "Eva" | | 5 | "Kowalski" | | 6 | "Met" | | 7 | "Morris" |
| | places | | | globalScore | 0.775 | | windowScore | 0.833 | |
| 62.28% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like something heavy with a flat," | | 1 | "smelled like burnt honey and static" |
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| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 4 | | per1kWords | 2.88 | | wordCount | 1389 | | matches | | 0 | "not on the compass but on the space around it" | | 1 | "not on the bench, but on a point about six feet in front of it" | | 2 | "Not cleaner, but the patina of dust was uniformly disturbed in a perfect, thr" | | 3 | "Not with her eyes, but with the trained observation of eighteen years" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 123 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 47 | | mean | 29.55 | | std | 22.27 | | cv | 0.754 | | sampleLengths | | 0 | 66 | | 1 | 44 | | 2 | 1 | | 3 | 31 | | 4 | 33 | | 5 | 52 | | 6 | 49 | | 7 | 6 | | 8 | 71 | | 9 | 15 | | 10 | 68 | | 11 | 29 | | 12 | 6 | | 13 | 3 | | 14 | 12 | | 15 | 7 | | 16 | 69 | | 17 | 47 | | 18 | 74 | | 19 | 19 | | 20 | 3 | | 21 | 1 | | 22 | 5 | | 23 | 19 | | 24 | 15 | | 25 | 43 | | 26 | 15 | | 27 | 6 | | 28 | 31 | | 29 | 34 | | 30 | 27 | | 31 | 10 | | 32 | 37 | | 33 | 33 | | 34 | 37 | | 35 | 11 | | 36 | 2 | | 37 | 34 | | 38 | 7 | | 39 | 50 | | 40 | 41 | | 41 | 73 | | 42 | 28 | | 43 | 9 | | 44 | 13 | | 45 | 37 | | 46 | 66 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 98 | | matches | | |
| 13.52% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 143 | | matches | | 0 | "was dusting" | | 1 | "was staring" | | 2 | "was working" | | 3 | "was watching" |
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| 96.40% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 123 | | ratio | 0.016 | | matches | | 0 | "She noted the intricate etchings on its face—sigils, not navigational markings." | | 1 | "They weren’t the random patterns of foot traffic; they formed faint, concentric arcs, centered not on the bench, but on a point about six feet in front of it." |
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| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 825 | | adjectiveStacks | 2 | | stackExamples | | 0 | "cavernous, shadow-draped space." | | 1 | "solid, dingy red brick." |
| | adverbCount | 24 | | adverbRatio | 0.02909090909090909 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.013333333333333334 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 123 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 123 | | mean | 11.29 | | std | 10.47 | | cv | 0.927 | | sampleLengths | | 0 | 14 | | 1 | 19 | | 2 | 14 | | 3 | 19 | | 4 | 25 | | 5 | 19 | | 6 | 1 | | 7 | 23 | | 8 | 8 | | 9 | 9 | | 10 | 11 | | 11 | 11 | | 12 | 2 | | 13 | 36 | | 14 | 16 | | 15 | 4 | | 16 | 28 | | 17 | 7 | | 18 | 10 | | 19 | 6 | | 20 | 37 | | 21 | 34 | | 22 | 8 | | 23 | 7 | | 24 | 18 | | 25 | 50 | | 26 | 8 | | 27 | 16 | | 28 | 5 | | 29 | 6 | | 30 | 3 | | 31 | 12 | | 32 | 3 | | 33 | 3 | | 34 | 1 | | 35 | 3 | | 36 | 8 | | 37 | 8 | | 38 | 29 | | 39 | 13 | | 40 | 5 | | 41 | 1 | | 42 | 2 | | 43 | 2 | | 44 | 5 | | 45 | 16 | | 46 | 4 | | 47 | 20 | | 48 | 19 | | 49 | 55 |
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| 46.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.34959349593495936 | | totalSentences | 123 | | uniqueOpeners | 43 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 87 | | matches | (empty) | | ratio | 0 | |
| 68.28% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 87 | | matches | | 0 | "Her sharp jaw was set," | | 1 | "She didn’t look at him," | | 2 | "She noted the intricate etchings" | | 3 | "She was staring at the" | | 4 | "She approached the bench, her" | | 5 | "She ignored him." | | 6 | "Her eyes traced the grime" | | 7 | "They weren’t the random patterns" | | 8 | "It looked like something heavy" | | 9 | "She walked over to Eva." | | 10 | "She looked at the brass" | | 11 | "She pulled a pen from" | | 12 | "It spun slowly, lazily, then" | | 13 | "It pointed unerringly toward the" | | 14 | "It was absurd." | | 15 | "It was impossible." | | 16 | "It was exactly the kind" | | 17 | "She looked at the circular" | | 18 | "she said, her voice low" | | 19 | "She met his skeptical gaze" |
| | ratio | 0.379 | |
| 46.21% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 72 | | totalSentences | 87 | | matches | | 0 | "The air in the abandoned" | | 1 | "Detective Harlow Quinn’s polished oxfords" | | 2 | "Her sharp jaw was set," | | 3 | "Detective Inspector Raj Singh emerged" | | 4 | "Quinn said, her voice flat" | | 5 | "She didn’t look at him," | | 6 | "She noted the intricate etchings" | | 7 | "Singh gestured at the compass" | | 8 | "Quinn followed his nod." | | 9 | "A young woman with a" | | 10 | "She was staring at the" | | 11 | "Singh’s tone was carefully neutral," | | 12 | "Quinn finally turned to him," | | 13 | "Singh sighed, rubbing his temple" | | 14 | "Quinn walked past him, ducking" | | 15 | "She approached the bench, her" | | 16 | "The forensic tech stepped back." | | 17 | "Quinn said, not pointing" | | 18 | "The tech blinked." | | 19 | "Singh moved closer." |
| | ratio | 0.828 | |
| 57.47% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 87 | | matches | | | ratio | 0.011 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 2 | | matches | | 0 | "Beside a faded poster advertising a circus that had left town decades ago, a forensic technician in a white suit was dusting a wrought-iron bench." | | 1 | "She walked toward it, Singh following, his protestations dying in his throat." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 5 | | matches | | 0 | "Quinn said, her voice flat" | | 1 | "Quinn said, not pointing" | | 2 | "Singh said, coming to stand beside her" | | 3 | "she said, her voice low" | | 4 | "Singh asked, his professional curiosity finally overriding his disbelief" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 2 | | fancyTags | | 0 | "Singh sighed (sigh)" | | 1 | "It was evidence that couldn’t be explained (be could be explain)" |
| | dialogueSentences | 42 | | tagDensity | 0.19 | | leniency | 0.381 | | rawRatio | 0.25 | | effectiveRatio | 0.095 | |