| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 2 | | adverbTags | | 0 | "she asked softly [softly]" | | 1 | "He turned back [back]" |
| | dialogueSentences | 82 | | tagDensity | 0.207 | | leniency | 0.415 | | rawRatio | 0.118 | | effectiveRatio | 0.049 | |
| 85.80% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1408 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "gently" | | 1 | "lightly" | | 2 | "very" | | 3 | "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) | |
| 68.04% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1408 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "familiar" | | 1 | "silence" | | 2 | "mechanical" | | 3 | "charm" | | 4 | "tension" | | 5 | "weight" |
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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 | 81 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 81 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 144 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 58 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1400 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 1 | | matches | | 0 | "His gaze swept the cluttered room—the books piled on every surface, the scrolls unfurled across her small dining table, …" |
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| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 782 | | uniqueNames | 9 | | maxNameDensity | 1.15 | | worstName | "Aurora" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Aurora" | | discoveredNames | | Aurora | 9 | | Moreau | 1 | | French | 1 | | Evan | 1 | | Ptolemy | 4 | | Lucien | 6 | | Earl | 1 | | Grey | 1 | | Relief | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Moreau" | | 2 | "Evan" | | 3 | "Ptolemy" | | 4 | "Lucien" | | 5 | "Earl" | | 6 | "Grey" | | 7 | "Relief" |
| | places | (empty) | | globalScore | 0.925 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | 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 | 1400 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 144 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 83 | | mean | 16.87 | | std | 16.08 | | cv | 0.953 | | sampleLengths | | 0 | 51 | | 1 | 49 | | 2 | 9 | | 3 | 57 | | 4 | 13 | | 5 | 13 | | 6 | 15 | | 7 | 4 | | 8 | 4 | | 9 | 74 | | 10 | 19 | | 11 | 3 | | 12 | 35 | | 13 | 4 | | 14 | 4 | | 15 | 15 | | 16 | 6 | | 17 | 3 | | 18 | 2 | | 19 | 31 | | 20 | 21 | | 21 | 21 | | 22 | 1 | | 23 | 18 | | 24 | 4 | | 25 | 47 | | 26 | 6 | | 27 | 42 | | 28 | 5 | | 29 | 12 | | 30 | 19 | | 31 | 29 | | 32 | 7 | | 33 | 3 | | 34 | 37 | | 35 | 5 | | 36 | 1 | | 37 | 7 | | 38 | 27 | | 39 | 4 | | 40 | 42 | | 41 | 45 | | 42 | 3 | | 43 | 5 | | 44 | 3 | | 45 | 4 | | 46 | 17 | | 47 | 5 | | 48 | 1 | | 49 | 26 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 81 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 142 | | matches | | |
| 83.33% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 144 | | ratio | 0.021 | | matches | | 0 | "But there were shadows under his heterochromatic eyes—amber and black meeting hers with a weariness she hadn’t seen before." | | 1 | "His gaze swept the cluttered room—the books piled on every surface, the scrolls unfurled across her small dining table, Ptolemy observing from his tower of knowledge." | | 2 | "He sipped the tea, nodded approval at the blend—Earl Grey with lavender, her favorite." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 788 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.03299492385786802 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.011421319796954314 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 144 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 144 | | mean | 9.72 | | std | 7.8 | | cv | 0.803 | | sampleLengths | | 0 | 17 | | 1 | 22 | | 2 | 12 | | 3 | 9 | | 4 | 20 | | 5 | 20 | | 6 | 5 | | 7 | 4 | | 8 | 10 | | 9 | 20 | | 10 | 19 | | 11 | 8 | | 12 | 13 | | 13 | 9 | | 14 | 4 | | 15 | 12 | | 16 | 3 | | 17 | 4 | | 18 | 4 | | 19 | 2 | | 20 | 18 | | 21 | 21 | | 22 | 10 | | 23 | 8 | | 24 | 15 | | 25 | 19 | | 26 | 3 | | 27 | 9 | | 28 | 26 | | 29 | 4 | | 30 | 4 | | 31 | 11 | | 32 | 4 | | 33 | 6 | | 34 | 3 | | 35 | 2 | | 36 | 4 | | 37 | 23 | | 38 | 4 | | 39 | 14 | | 40 | 7 | | 41 | 11 | | 42 | 10 | | 43 | 1 | | 44 | 4 | | 45 | 14 | | 46 | 4 | | 47 | 28 | | 48 | 19 | | 49 | 3 |
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| 59.72% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.375 | | totalSentences | 144 | | uniqueOpeners | 54 | |
| 43.86% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 76 | | matches | | 0 | "Instead, his eyes tracked over" |
| | ratio | 0.013 | |
| 4.21% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 76 | | matches | | 0 | "She'd been switching off lights," | | 1 | "She moved to the door," | | 2 | "He looked exactly as she" | | 3 | "He leaned heavier on the" | | 4 | "His voice was soft, the" | | 5 | "She didn’t step back to" | | 6 | "He’d handed her a dossier" | | 7 | "She’d told him he was" | | 8 | "He’d told her she was" | | 9 | "They’d traded barbs until she’d" | | 10 | "He entered, his cane tapping" | | 11 | "His gaze swept the cluttered" | | 12 | "He turned to face her," | | 13 | "He didn’t answer immediately." | | 14 | "She crossed her arms." | | 15 | "she said, moving to clear" | | 16 | "He sat, sinking into the" | | 17 | "She laughed, a short, sharp" | | 18 | "He met her gaze" | | 19 | "she asked softly" |
| | ratio | 0.539 | |
| 6.05% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 76 | | matches | | 0 | "The knock was soft but" | | 1 | "She'd been switching off lights," | | 2 | "Ptolemy twitched his tail from" | | 3 | "She moved to the door," | | 4 | "The fish-eye lens distorted his" | | 5 | "Lucien Moreau stood in the" | | 6 | "Aurora unlatched the three deadbolts." | | 7 | "The door swung inward." | | 8 | "He looked exactly as she" | | 9 | "He leaned heavier on the" | | 10 | "His voice was soft, the" | | 11 | "She didn’t step back to" | | 12 | "A ghost of a smile" | | 13 | "The last time they’d been" | | 14 | "He’d handed her a dossier" | | 15 | "She’d told him he was" | | 16 | "He’d told her she was" | | 17 | "They’d traded barbs until she’d" | | 18 | "Aurora stepped aside." | | 19 | "He entered, his cane tapping" |
| | ratio | 0.908 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 76 | | matches | | 0 | "Now he stood on her" | | 1 | "To her surprise, Lucien reached" |
| | ratio | 0.026 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 2 | | matches | | 0 | "The knock was soft but insistent, a familiar three-tap rhythm that stopped Aurora's breath in her chest." | | 1 | "Now he stood on her threshold, smelling of rain and expensive cologne, looking like he hadn’t slept in weeks." |
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| 36.76% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 3 | | matches | | 0 | "she said, moving to clear a stack of books from the armchair" | | 1 | "He turned back, his amber eye catching the lamplight" | | 2 | "she said, the word surprising both of them" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 2 | | fancyTags | | 0 | "he admitted (admit)" | | 1 | "she repeated (repeat)" |
| | dialogueSentences | 82 | | tagDensity | 0.098 | | leniency | 0.195 | | rawRatio | 0.25 | | effectiveRatio | 0.049 | |