| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 1 | | adverbTags | | 0 | "she asked instead [instead]" |
| | dialogueSentences | 26 | | tagDensity | 0.231 | | leniency | 0.462 | | rawRatio | 0.167 | | effectiveRatio | 0.077 | |
| 94.03% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 837 | | totalAiIsmAdverbs | 1 | | 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) | |
| 40.26% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 837 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "familiar" | | 1 | "stomach" | | 2 | "reminder" | | 3 | "pulse" | | 4 | "implication" | | 5 | "flicked" | | 6 | "jaw clenched" | | 7 | "raced" | | 8 | "navigated" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
| | 1 | | label | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | 0 | "blood ran cold" | | 1 | "jaw clenched" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 76 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 76 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 95 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 7 | | totalWords | 823 | | ratio | 0.009 | | matches | | 0 | "Eva." | | 1 | "Lucien needs to see you" | | 2 | "hers" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 46.69% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 726 | | uniqueNames | 9 | | maxNameDensity | 2.07 | | worstName | "Aurora" | | maxWindowNameDensity | 3 | | worstWindowName | "Aurora" | | discoveredNames | | Eva | 7 | | Carter | 1 | | Moreau | 2 | | Aurora | 15 | | Lucien | 13 | | Cardiff | 1 | | Evan | 2 | | Cotswolds | 1 | | London | 1 |
| | persons | | 0 | "Eva" | | 1 | "Carter" | | 2 | "Moreau" | | 3 | "Aurora" | | 4 | "Lucien" | | 5 | "Evan" |
| | places | | | globalScore | 0.467 | | windowScore | 0.667 | |
| 87.50% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 40 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 823 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 95 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 19.6 | | std | 19.4 | | cv | 0.99 | | sampleLengths | | 0 | 66 | | 1 | 9 | | 2 | 77 | | 3 | 4 | | 4 | 31 | | 5 | 7 | | 6 | 8 | | 7 | 2 | | 8 | 60 | | 9 | 16 | | 10 | 38 | | 11 | 45 | | 12 | 2 | | 13 | 35 | | 14 | 8 | | 15 | 6 | | 16 | 7 | | 17 | 19 | | 18 | 6 | | 19 | 7 | | 20 | 53 | | 21 | 15 | | 22 | 8 | | 23 | 10 | | 24 | 8 | | 25 | 4 | | 26 | 47 | | 27 | 8 | | 28 | 20 | | 29 | 7 | | 30 | 13 | | 31 | 40 | | 32 | 6 | | 33 | 5 | | 34 | 5 | | 35 | 45 | | 36 | 8 | | 37 | 6 | | 38 | 32 | | 39 | 7 | | 40 | 10 | | 41 | 13 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 76 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 156 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 16 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 95 | | ratio | 0.095 | | matches | | 0 | "She exhaled through her nose, the scent of old paper and something faintly metallic—Eva’s usual perfume—waving at her like a ghost." | | 1 | "Lucien Moreau stood there, his tailored charcoal suit immaculate, his platinum hair slicked back just enough to make his heterochromatic eyes—one amber, one black—gleam." | | 2 | "The way he’d phrased the message—*Lucien needs to see you*—had been too precise, too final." | | 3 | "The scent of his cologne—something dark and woodsy—filled the air, clashing with the musty smell of Eva’s flat." | | 4 | "But the way he moved—elegant, deliberate—made her want to watch him, to see what he’d come for." | | 5 | "But then she saw the way his fingers flexed around the cane, the way his gaze lingered on the scar on her wrist—small, crescent-shaped, a reminder of a childhood accident she’d never told him about." | | 6 | "She remembered the way Eva had looked when she’d last seen her—wide-eyed, terrified, her usual sharp wit gone." | | 7 | "That she could forget how Lucien had looked when he’d told her he was leaving—when he’d said he couldn’t risk her getting hurt again." | | 8 | "But the way he looked at her—like she was the only person in the room who mattered—made her want to believe him." |
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| 90.07% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 740 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 38 | | adverbRatio | 0.051351351351351354 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006756756756756757 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 95 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 95 | | mean | 8.66 | | std | 7.15 | | cv | 0.825 | | sampleLengths | | 0 | 16 | | 1 | 29 | | 2 | 21 | | 3 | 9 | | 4 | 24 | | 5 | 3 | | 6 | 2 | | 7 | 20 | | 8 | 28 | | 9 | 3 | | 10 | 1 | | 11 | 4 | | 12 | 15 | | 13 | 12 | | 14 | 7 | | 15 | 5 | | 16 | 3 | | 17 | 2 | | 18 | 3 | | 19 | 10 | | 20 | 18 | | 21 | 3 | | 22 | 4 | | 23 | 5 | | 24 | 17 | | 25 | 13 | | 26 | 3 | | 27 | 2 | | 28 | 9 | | 29 | 19 | | 30 | 5 | | 31 | 3 | | 32 | 35 | | 33 | 10 | | 34 | 2 | | 35 | 13 | | 36 | 12 | | 37 | 6 | | 38 | 4 | | 39 | 8 | | 40 | 3 | | 41 | 3 | | 42 | 3 | | 43 | 4 | | 44 | 5 | | 45 | 11 | | 46 | 3 | | 47 | 3 | | 48 | 3 | | 49 | 7 |
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| 42.63% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.2631578947368421 | | totalSentences | 95 | | uniqueOpeners | 25 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 71 | | matches | | 0 | "Instead, he stepped inside, the" | | 1 | "Instead, he leaned forward, his" | | 2 | "Instead, he reached into his" |
| | ratio | 0.042 | |
| 67.89% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 71 | | matches | | 0 | "She exhaled through her nose," | | 1 | "He didn’t smile." | | 2 | "His cane rested against the" | | 3 | "He wore the same expression" | | 4 | "She should have known." | | 5 | "she said, voice too sharp" | | 6 | "He didn’t answer." | | 7 | "She should have left." | | 8 | "She should have walked away." | | 9 | "he said, gesturing to the" | | 10 | "It was where she lived," | | 11 | "She didn’t want him here." | | 12 | "He didn’t offer her one" | | 13 | "He didn’t have to." | | 14 | "He didn’t answer right away." | | 15 | "She remembered the way Eva" | | 16 | "She remembered the way Lucien" | | 17 | "she said, voice low" | | 18 | "She’d been so sure, so" | | 19 | "His voice was low, dangerous" |
| | ratio | 0.38 | |
| 37.46% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 71 | | matches | | 0 | "The brass knocker rattled against" | | 1 | "Aurora Carter stepped back, her" | | 2 | "She exhaled through her nose," | | 3 | "The door swung open before" | | 4 | "Lucien Moreau stood there, his" | | 5 | "He didn’t smile." | | 6 | "His cane rested against the" | | 7 | "He wore the same expression" | | 8 | "Aurora’s breath hitched." | | 9 | "She should have known." | | 10 | "The way he’d phrased the" | | 11 | "she said, voice too sharp" | | 12 | "Lucien’s lips twitched, just once." | | 13 | "He didn’t answer." | | 14 | "The scent of his cologne—something" | | 15 | "Aurora’s stomach twisted." | | 16 | "She should have left." | | 17 | "She should have walked away." | | 18 | "he said, gesturing to the" | | 19 | "Eva’s flat was a mess," |
| | ratio | 0.845 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 1 | | matches | | 0 | "He wore the same expression Aurora had seen in the mirror too many times to count: the kind that said he was already deciding what she’d done wrong." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 3 | | matches | | 0 | "she said, voice too sharp" | | 1 | "Aurora said, finally" | | 2 | "she said, voice low" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 26 | | tagDensity | 0.231 | | leniency | 0.462 | | rawRatio | 0 | | effectiveRatio | 0 | |