| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 1 | | adverbTags | | 0 | "His French accent curved around [around]" |
| | dialogueSentences | 44 | | tagDensity | 0.341 | | leniency | 0.682 | | rawRatio | 0.067 | | effectiveRatio | 0.045 | |
| 85.75% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 702 | | 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) | |
| 64.39% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 702 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "familiar" | | 1 | "traced" | | 2 | "flickered" | | 3 | "unreadable" |
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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 | 36 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 36 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 65 | | 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 | 704 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 59.09% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 14 | | wordCount | 330 | | uniqueNames | 7 | | maxNameDensity | 1.82 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Aurora" | | discoveredNames | | French | 1 | | Six | 1 | | Marseille | 1 | | Lucien | 3 | | Aurora | 6 | | Heat | 1 | | Eva | 1 |
| | persons | | 0 | "Lucien" | | 1 | "Aurora" | | 2 | "Heat" | | 3 | "Eva" |
| | places | | | globalScore | 0.591 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 24 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 704 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 65 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 21.33 | | std | 11.41 | | cv | 0.535 | | sampleLengths | | 0 | 36 | | 1 | 35 | | 2 | 25 | | 3 | 10 | | 4 | 36 | | 5 | 26 | | 6 | 26 | | 7 | 8 | | 8 | 27 | | 9 | 12 | | 10 | 35 | | 11 | 4 | | 12 | 11 | | 13 | 12 | | 14 | 40 | | 15 | 22 | | 16 | 29 | | 17 | 15 | | 18 | 21 | | 19 | 15 | | 20 | 25 | | 21 | 6 | | 22 | 16 | | 23 | 25 | | 24 | 18 | | 25 | 5 | | 26 | 35 | | 27 | 8 | | 28 | 37 | | 29 | 22 | | 30 | 13 | | 31 | 44 | | 32 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 36 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 53 | | matches | (empty) | |
| 98.90% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 65 | | ratio | 0.015 | | matches | | 0 | "Something flickered in his amber eye – pain, maybe, or anger." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 329 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.0364741641337386 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.00303951367781155 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 65 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 65 | | mean | 10.83 | | std | 6.34 | | cv | 0.586 | | sampleLengths | | 0 | 7 | | 1 | 20 | | 2 | 9 | | 3 | 11 | | 4 | 12 | | 5 | 12 | | 6 | 5 | | 7 | 15 | | 8 | 5 | | 9 | 10 | | 10 | 8 | | 11 | 15 | | 12 | 13 | | 13 | 12 | | 14 | 14 | | 15 | 22 | | 16 | 4 | | 17 | 8 | | 18 | 9 | | 19 | 18 | | 20 | 4 | | 21 | 8 | | 22 | 19 | | 23 | 16 | | 24 | 4 | | 25 | 11 | | 26 | 8 | | 27 | 4 | | 28 | 18 | | 29 | 22 | | 30 | 17 | | 31 | 5 | | 32 | 16 | | 33 | 13 | | 34 | 15 | | 35 | 11 | | 36 | 5 | | 37 | 5 | | 38 | 7 | | 39 | 8 | | 40 | 17 | | 41 | 8 | | 42 | 3 | | 43 | 3 | | 44 | 11 | | 45 | 5 | | 46 | 9 | | 47 | 16 | | 48 | 3 | | 49 | 15 |
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| 84.10% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.5230769230769231 | | totalSentences | 65 | | uniqueOpeners | 34 | |
| 92.59% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 36 | | matches | | 0 | "Instead, she stepped aside, watching" |
| | ratio | 0.028 | |
| 20.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 36 | | matches | | 0 | "His French accent curved around" | | 1 | "He leaned against his cane," | | 2 | "She should shut the door" | | 3 | "His charcoal suit was immaculate" | | 4 | "He turned, resting both hands" | | 5 | "His lips curved" | | 6 | "He closed the distance between" | | 7 | "She backed up until her" | | 8 | "His cane clicked against the" | | 9 | "Her voice cracked" | | 10 | "He reached out, his fingers" | | 11 | "His voice dropped lower, rougher" | | 12 | "He went still." | | 13 | "His fingers tightened on the" | | 14 | "He stepped closer, and this" | | 15 | "She lifted her chin" | | 16 | "His eyes searched her face." | | 17 | "He broke off, jaw tight" |
| | ratio | 0.5 | |
| 15.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 32 | | totalSentences | 36 | | matches | | 0 | "Aurora's fingers froze on the" | | 1 | "Lucien stood in the dim" | | 2 | "The spices from downstairs mingled" | | 3 | "His French accent curved around" | | 4 | "He leaned against his cane," | | 5 | "Aurora's throat tightened" | | 6 | "She should shut the door" | | 7 | "His charcoal suit was immaculate" | | 8 | "Ptolemy darted under the coffee" | | 9 | "Aurora closed the door, counting" | | 10 | "Lucien traced a finger along" | | 11 | "He turned, resting both hands" | | 12 | "Aurora crossed her arms." | | 13 | "His lips curved" | | 14 | "Heat crept up her neck" | | 15 | "He closed the distance between" | | 16 | "She backed up until her" | | 17 | "His cane clicked against the" | | 18 | "Something flickered in his amber" | | 19 | "The black one remained unreadable." |
| | ratio | 0.889 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 36 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 11 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 3 | | matches | | 0 | "He reached out, his fingers brushing the crescent scar on her wrist" | | 1 | "His voice dropped, rougher" | | 2 | "He broke, jaw tight" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |