| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 50 | | tagDensity | 0.38 | | leniency | 0.76 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.83% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1621 | | 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) | |
| 47.56% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1621 | | totalAiIsms | 17 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | word | "down her spine" | | count | 1 |
| | 12 | | | 13 | | | 14 | |
| | highlights | | 0 | "traced" | | 1 | "measured" | | 2 | "pulse" | | 3 | "glinting" | | 4 | "flicked" | | 5 | "flickered" | | 6 | "echo" | | 7 | "weight" | | 8 | "eyebrow" | | 9 | "stomach" | | 10 | "tension" | | 11 | "down her spine" | | 12 | "disrupting" | | 13 | "rhythmic" | | 14 | "charged" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "sent a shiver through" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 120 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 120 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 150 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 15 | | markdownWords | 29 | | totalWords | 1598 | | ratio | 0.018 | | matches | | 0 | "rat-tat-tat" | | 1 | "measured" | | 2 | "He wouldn’t." | | 3 | "malphora" | | 4 | "Malphora" | | 5 | "Carter. Malphora. Bad luck." | | 6 | "A mistake" | | 7 | "her" | | 8 | "Used" | | 9 | "details" | | 10 | "You don’t tell me the truth either." | | 11 | "trust me" | | 12 | "pay attention" | | 13 | "Aurora Carter" | | 14 | "malphora" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1298 | | uniqueNames | 12 | | maxNameDensity | 0.69 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Eva" | | discoveredNames | | Eva | 6 | | Ptolemy | 5 | | Yu | 1 | | Fei | 1 | | Camden | 1 | | Moreau | 1 | | Friends | 2 | | Luc | 5 | | London | 1 | | Obsidian | 1 | | Circle | 1 | | Rory | 9 |
| | persons | | 0 | "Eva" | | 1 | "Ptolemy" | | 2 | "Fei" | | 3 | "Moreau" | | 4 | "Friends" | | 5 | "Luc" | | 6 | "Rory" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 78 | | 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 | 1598 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 150 | | matches | | 0 | "knew that knock" | | 1 | "chose that moment" | | 2 | "chose that moment" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 57 | | mean | 28.04 | | std | 24.3 | | cv | 0.867 | | sampleLengths | | 0 | 120 | | 1 | 9 | | 2 | 36 | | 3 | 4 | | 4 | 4 | | 5 | 75 | | 6 | 9 | | 7 | 10 | | 8 | 78 | | 9 | 43 | | 10 | 12 | | 11 | 15 | | 12 | 59 | | 13 | 52 | | 14 | 32 | | 15 | 21 | | 16 | 51 | | 17 | 36 | | 18 | 20 | | 19 | 45 | | 20 | 9 | | 21 | 10 | | 22 | 12 | | 23 | 35 | | 24 | 5 | | 25 | 14 | | 26 | 5 | | 27 | 53 | | 28 | 47 | | 29 | 9 | | 30 | 8 | | 31 | 53 | | 32 | 54 | | 33 | 14 | | 34 | 12 | | 35 | 8 | | 36 | 27 | | 37 | 65 | | 38 | 3 | | 39 | 13 | | 40 | 12 | | 41 | 7 | | 42 | 14 | | 43 | 48 | | 44 | 55 | | 45 | 7 | | 46 | 42 | | 47 | 8 | | 48 | 7 | | 49 | 66 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 120 | | matches | | |
| 86.52% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 235 | | matches | | 0 | "was trying" | | 1 | "were memorizing" | | 2 | "was remembering" | | 3 | "was looking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 17 | | semicolonCount | 0 | | flaggedSentences | 12 | | totalSentences | 150 | | ratio | 0.08 | | matches | | 0 | "She should’ve been working—Yu-Fei had texted about a last-minute delivery to Camden—but the thought of pedaling through the downpour made her shoulders ache before she even stood." | | 1 | "Not Eva’s knock—too precise, too *measured*." | | 2 | "Not after the way they’d left things—him with that infuriating half-smile, her with her pride burning like a brand in her chest." | | 3 | "His cane—ivory handle glinting—rested against his thigh, the other hand tucked into his pocket like he hadn’t just barged into her life unannounced." | | 4 | "His smile didn’t waver, but something flickered in his gaze—amusement, maybe, or the echo of an old argument." | | 5 | "He shed his damp overcoat with a flick of his wrists, draping it over the back of Eva’s armchair—the one piled with scrolls." | | 6 | "She’d heard whispers—Eva’s paranoid ramblings about a cabal of warlocks trafficking in cursed artifacts, dabbling in blood magic." | | 7 | "His expression darkened, and she knew he was remembering the same thing she was—the warehouse, the way his hands had gripped her hips, the way he’d whispered *trust me* against her ear before vanishing into the night." | | 8 | "Below it, details she’d never given anyone—her route, her habits, the scar on her wrist." | | 9 | "But the memory of his mouth on hers was a brand she couldn’t ignore, and the way he was looking at her now—like she was something precious, something worth fighting for—made her knees weak." | | 10 | "Could’ve closed the gap and pressed her mouth to his, just to see if he’d kiss her back the way he had before—like she was the only thing keeping him grounded." | | 11 | "Rory looked at him—the tailored suit, the cane, the way his eyes never quite hid the demon blood running through his veins—and knew, with a certainty that terrified her, that she trusted him." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1320 | | adjectiveStacks | 1 | | stackExamples | | 0 | "old leather-bound grimoire." |
| | adverbCount | 44 | | adverbRatio | 0.03333333333333333 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0030303030303030303 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 150 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 150 | | mean | 10.65 | | std | 8.72 | | cv | 0.818 | | sampleLengths | | 0 | 30 | | 1 | 31 | | 2 | 32 | | 3 | 27 | | 4 | 9 | | 5 | 3 | | 6 | 3 | | 7 | 6 | | 8 | 10 | | 9 | 4 | | 10 | 8 | | 11 | 2 | | 12 | 4 | | 13 | 4 | | 14 | 18 | | 15 | 14 | | 16 | 8 | | 17 | 6 | | 18 | 22 | | 19 | 7 | | 20 | 5 | | 21 | 2 | | 22 | 2 | | 23 | 10 | | 24 | 20 | | 25 | 14 | | 26 | 23 | | 27 | 21 | | 28 | 7 | | 29 | 10 | | 30 | 1 | | 31 | 3 | | 32 | 18 | | 33 | 1 | | 34 | 1 | | 35 | 2 | | 36 | 4 | | 37 | 8 | | 38 | 8 | | 39 | 7 | | 40 | 18 | | 41 | 19 | | 42 | 22 | | 43 | 18 | | 44 | 30 | | 45 | 4 | | 46 | 5 | | 47 | 26 | | 48 | 1 | | 49 | 11 |
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| 57.78% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.36666666666666664 | | totalSentences | 150 | | uniqueOpeners | 55 | |
| 61.73% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 108 | | matches | | 0 | "Instead, he pulled a slim" | | 1 | "Then he let his hand" |
| | ratio | 0.019 | |
| 34.81% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 50 | | totalSentences | 108 | | matches | | 0 | "She should’ve been working—Yu—Fei had" | | 1 | "Her pulse kicked up." | | 2 | "She knew that knock." | | 3 | "She hesitated, fingers hovering over" | | 4 | "*He wouldn’t.* Not after six" | | 5 | "She undid the first bolt." | | 6 | "His cane—ivory handle glinting—rested against" | | 7 | "His eyes, one amber, one" | | 8 | "he said, voice low, rough-edged" | | 9 | "His little joke." | | 10 | "She didn’t step aside." | | 11 | "His smile didn’t waver, but" | | 12 | "He leaned forward just enough" | | 13 | "He shed his damp overcoat" | | 14 | "he murmured, eyes roving over" | | 15 | "She folded her arms" | | 16 | "He turned to face her," | | 17 | "She didn’t take one" | | 18 | "He snapped the case shut" | | 19 | "He held it out to" |
| | ratio | 0.463 | |
| 38.70% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 91 | | totalSentences | 108 | | matches | | 0 | "The rain had been falling" | | 1 | "Rory sat cross-legged on the" | | 2 | "The scent of cumin and" | | 3 | "She should’ve been working—Yu—Fei had" | | 4 | "A sharp *rat-tat-tat* at the" | | 5 | "Ptolemy’s ears twitched." | | 6 | "Rory froze, listening." | | 7 | "Eva hammered like she was" | | 8 | "This was something else." | | 9 | "Her pulse kicked up." | | 10 | "She knew that knock." | | 11 | "The deadbolts gleamed dully in" | | 12 | "She hesitated, fingers hovering over" | | 13 | "*He wouldn’t.* Not after six" | | 14 | "She undid the first bolt." | | 15 | "The door swung open before" | | 16 | "Lucien Moreau filled the frame," | | 17 | "Water beaded on the sharp" | | 18 | "His cane—ivory handle glinting—rested against" | | 19 | "His eyes, one amber, one" |
| | ratio | 0.843 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 108 | | matches | (empty) | | ratio | 0 | |
| 93.02% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 3 | | matches | | 0 | "The rain had been falling for hours, a relentless drumming against the grimy window of Eva’s flat, the kind of sound that made the walls feel smaller, the air t…" | | 1 | "His eyes, one amber, one black, flicked over her face, lingering on the scar at her wrist before meeting her gaze." | | 2 | "Rory looked at him—the tailored suit, the cane, the way his eyes never quite hid the demon blood running through his veins—and knew, with a certainty that terri…" |
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| 72.37% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 2 | | matches | | 0 | "he said, voice low, rough-edged" | | 1 | "she said, but her voice lacked conviction" |
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| 90.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 3 | | fancyTags | | 0 | "he murmured (murmur)" | | 1 | "she breathed (breathe)" | | 2 | "she whispered (whisper)" |
| | dialogueSentences | 50 | | tagDensity | 0.12 | | leniency | 0.24 | | rawRatio | 0.5 | | effectiveRatio | 0.12 | |