| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said softly [softly]" |
| | dialogueSentences | 40 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.063 | | effectiveRatio | 0.05 | |
| 80.04% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1002 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "very" | | 1 | "really" | | 2 | "softly" |
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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) | |
| 55.09% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1002 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "thundered" | | 1 | "echoing" | | 2 | "tension" | | 3 | "familiar" | | 4 | "charm" | | 5 | "flickered" | | 6 | "could feel" | | 7 | "pulse" | | 8 | "whisper" |
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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 | 1 | | narrationSentences | 58 | | matches | | |
| 68.97% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 58 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 82 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1006 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 629 | | uniqueNames | 8 | | maxNameDensity | 1.27 | | worstName | "Lucien" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Lucien" | | discoveredNames | | Lucien | 8 | | Moreau | 1 | | Eva | 3 | | London | 1 | | Aurora | 8 | | French | 1 | | Right | 1 | | Ptolemy | 3 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Eva" | | 3 | "Aurora" | | 4 | "Ptolemy" |
| | places | | | globalScore | 0.864 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like a physical ache" |
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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 | 1006 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 82 | | matches | (empty) | |
| 99.84% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 26.47 | | std | 13.22 | | cv | 0.499 | | sampleLengths | | 0 | 50 | | 1 | 43 | | 2 | 33 | | 3 | 17 | | 4 | 32 | | 5 | 25 | | 6 | 32 | | 7 | 56 | | 8 | 11 | | 9 | 22 | | 10 | 36 | | 11 | 29 | | 12 | 11 | | 13 | 22 | | 14 | 31 | | 15 | 14 | | 16 | 37 | | 17 | 12 | | 18 | 3 | | 19 | 30 | | 20 | 6 | | 21 | 10 | | 22 | 29 | | 23 | 35 | | 24 | 29 | | 25 | 23 | | 26 | 33 | | 27 | 52 | | 28 | 36 | | 29 | 25 | | 30 | 23 | | 31 | 22 | | 32 | 6 | | 33 | 30 | | 34 | 51 | | 35 | 23 | | 36 | 20 | | 37 | 7 |
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| 99.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 58 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 120 | | matches | (empty) | |
| 38.33% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 82 | | ratio | 0.037 | | matches | | 0 | "He leaned casually on his ivory-handled cane, but Aurora knew better – nothing about Lucien was ever casual." | | 1 | "Something flickered in his heterochromatic eyes – pain, maybe, or anger." | | 2 | "She looked at him – really looked at him." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 627 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.03668261562998405 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.014354066985645933 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 82 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 82 | | mean | 12.27 | | std | 6.7 | | cv | 0.546 | | sampleLengths | | 0 | 7 | | 1 | 18 | | 2 | 8 | | 3 | 17 | | 4 | 18 | | 5 | 10 | | 6 | 15 | | 7 | 15 | | 8 | 18 | | 9 | 12 | | 10 | 5 | | 11 | 26 | | 12 | 6 | | 13 | 4 | | 14 | 21 | | 15 | 18 | | 16 | 14 | | 17 | 12 | | 18 | 13 | | 19 | 12 | | 20 | 19 | | 21 | 11 | | 22 | 22 | | 23 | 20 | | 24 | 11 | | 25 | 5 | | 26 | 6 | | 27 | 20 | | 28 | 3 | | 29 | 11 | | 30 | 17 | | 31 | 5 | | 32 | 10 | | 33 | 16 | | 34 | 5 | | 35 | 11 | | 36 | 3 | | 37 | 9 | | 38 | 18 | | 39 | 4 | | 40 | 6 | | 41 | 8 | | 42 | 4 | | 43 | 3 | | 44 | 6 | | 45 | 24 | | 46 | 6 | | 47 | 10 | | 48 | 10 | | 49 | 13 |
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| 82.93% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.5121951219512195 | | totalSentences | 82 | | uniqueOpeners | 42 | |
| 59.52% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 56 | | matches | | 0 | "Of course he'd track her" |
| | ratio | 0.018 | |
| 27.14% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 56 | | matches | | 0 | "She should have known better" | | 1 | "Her heart thundered against her" | | 2 | "He leaned casually on his" | | 3 | "she said, proud of how" | | 4 | "His French accent had thickened," | | 5 | "He straightened, and even without" | | 6 | "She stepped aside, letting him" | | 7 | "His tailored charcoal suit looked" | | 8 | "She wanted him to know" | | 9 | "He straightened, turning to face" | | 10 | "He took a step toward" | | 11 | "Her left wrist, where the" | | 12 | "It usually comforted her." | | 13 | "She spun back to face" | | 14 | "he said, his voice low" | | 15 | "His laugh was bitter." | | 16 | "She tilted her chin up," | | 17 | "His free hand came up" | | 18 | "She could feel the heat" | | 19 | "His voice had dropped to" |
| | ratio | 0.482 | |
| 31.43% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 48 | | totalSentences | 56 | | matches | | 0 | "Aurora's hand froze on the" | | 1 | "She should have known better" | | 2 | "Her heart thundered against her" | | 3 | "Ptolemy wound between her ankles," | | 4 | "Lucien's mismatched eyes studied her" | | 5 | "He leaned casually on his" | | 6 | "she said, proud of how" | | 7 | "His French accent had thickened," | | 8 | "Aurora crossed her arms." | | 9 | "He straightened, and even without" | | 10 | "The smart thing would be" | | 11 | "She stepped aside, letting him" | | 12 | "His tailored charcoal suit looked" | | 13 | "Ptolemy immediately abandoned Aurora to" | | 14 | "Lucien said, reaching down to" | | 15 | "Aurora shut the door but" | | 16 | "She wanted him to know" | | 17 | "He straightened, turning to face" | | 18 | "He took a step toward" | | 19 | "The name hit her like" |
| | ratio | 0.857 | |
| 89.29% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 56 | | matches | | 0 | "Before she could respond, he" |
| | ratio | 0.018 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 2 | | matches | | 0 | "he said, his voice low and intense," | | 1 | "he said, his voice returning to its usual controlled timbre" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 2 | | fancyTags | | 0 | "Aurora snapped (snap)" | | 1 | "he agreed (agree)" |
| | dialogueSentences | 40 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0.25 | | effectiveRatio | 0.1 | |