| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 1 | | adverbTags | | 0 | "her knuckles whitened around [around]" |
| | dialogueSentences | 58 | | tagDensity | 0.276 | | leniency | 0.552 | | rawRatio | 0.063 | | effectiveRatio | 0.034 | |
| 90.75% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1081 | | 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) | |
| 53.75% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1081 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "echoed" | | 1 | "glinting" | | 2 | "tension" | | 3 | "flickered" | | 4 | "weight" | | 5 | "throbbed" | | 6 | "processed" | | 7 | "footsteps" | | 8 | "flicker" |
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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 | 78 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 78 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 119 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 8 | | markdownWords | 9 | | totalWords | 1072 | | ratio | 0.008 | | matches | | 0 | "unexpected" | | 1 | "show up" | | 2 | "this" | | 3 | "dead" | | 4 | "you’re" | | 5 | "them" | | 6 | "waited" | | 7 | "couldn’t" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 718 | | uniqueNames | 13 | | maxNameDensity | 1.25 | | worstName | "Lucien" | | maxWindowNameDensity | 3 | | worstWindowName | "Lucien" | | discoveredNames | | Moreau | 1 | | Carter | 1 | | Golden | 2 | | Empress | 2 | | Brick | 1 | | Lane | 1 | | London | 1 | | Evan | 1 | | Avaros | 2 | | Aurora | 8 | | Eva | 2 | | Lucien | 9 | | Marseille | 1 |
| | persons | | 0 | "Moreau" | | 1 | "Carter" | | 2 | "Empress" | | 3 | "Evan" | | 4 | "Aurora" | | 5 | "Eva" | | 6 | "Lucien" |
| | places | | 0 | "Golden" | | 1 | "Brick" | | 2 | "Lane" | | 3 | "London" | | 4 | "Avaros" | | 5 | "Marseille" |
| | globalScore | 0.873 | | windowScore | 0.667 | |
| 95.65% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | glossingSentenceCount | 1 | | matches | | 0 | "as if absorbing the room’s dimness" |
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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 | 1072 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 119 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 63 | | mean | 17.02 | | std | 14.33 | | cv | 0.842 | | sampleLengths | | 0 | 72 | | 1 | 58 | | 2 | 12 | | 3 | 22 | | 4 | 3 | | 5 | 23 | | 6 | 16 | | 7 | 14 | | 8 | 10 | | 9 | 19 | | 10 | 22 | | 11 | 13 | | 12 | 25 | | 13 | 16 | | 14 | 7 | | 15 | 19 | | 16 | 28 | | 17 | 29 | | 18 | 64 | | 19 | 2 | | 20 | 16 | | 21 | 25 | | 22 | 31 | | 23 | 25 | | 24 | 1 | | 25 | 35 | | 26 | 21 | | 27 | 25 | | 28 | 7 | | 29 | 18 | | 30 | 8 | | 31 | 31 | | 32 | 3 | | 33 | 25 | | 34 | 21 | | 35 | 19 | | 36 | 1 | | 37 | 8 | | 38 | 3 | | 39 | 9 | | 40 | 21 | | 41 | 2 | | 42 | 30 | | 43 | 18 | | 44 | 11 | | 45 | 12 | | 46 | 7 | | 47 | 2 | | 48 | 37 | | 49 | 14 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 78 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 135 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 119 | | ratio | 0.067 | | matches | | 0 | "The woman who’d been their friend, their confidante, now gone—taken by something that left claw marks on the truth." | | 1 | "“A name. A location. A reason to come back to *this*” —he jerked his chin at the cluttered shelves lined with scrolls and books—“when I swore I’d never return.”" | | 2 | "She turned, defiance warring with something else—fear, maybe, or the ghost of what they’d been." | | 3 | "Through it, she glimpsed the street—empty except for a delivery van idling outside Golden Empress." | | 4 | "“They’re inside. I can feel it.” He glanced at the clock on the wall—its hands frozen at 3:07." | | 5 | "Lucien’s eyes flickered—amber and black, shifting like twin flames." | | 6 | "He looked at her—really looked—and in that moment, she saw it: the man who’d once saved her from a demon’s claws in Marseille, who’d apologized in four languages when he’d broken the rule against sleeping with humans." | | 7 | "A hand—pale, clawed—broke through the wood." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 610 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.03278688524590164 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.009836065573770493 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 119 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 119 | | mean | 9.01 | | std | 7.31 | | cv | 0.811 | | sampleLengths | | 0 | 15 | | 1 | 19 | | 2 | 22 | | 3 | 16 | | 4 | 21 | | 5 | 19 | | 6 | 18 | | 7 | 12 | | 8 | 16 | | 9 | 6 | | 10 | 3 | | 11 | 12 | | 12 | 11 | | 13 | 5 | | 14 | 11 | | 15 | 7 | | 16 | 7 | | 17 | 8 | | 18 | 2 | | 19 | 6 | | 20 | 7 | | 21 | 6 | | 22 | 3 | | 23 | 9 | | 24 | 10 | | 25 | 9 | | 26 | 4 | | 27 | 5 | | 28 | 1 | | 29 | 19 | | 30 | 6 | | 31 | 10 | | 32 | 3 | | 33 | 4 | | 34 | 8 | | 35 | 11 | | 36 | 8 | | 37 | 15 | | 38 | 5 | | 39 | 29 | | 40 | 6 | | 41 | 19 | | 42 | 39 | | 43 | 2 | | 44 | 7 | | 45 | 9 | | 46 | 15 | | 47 | 10 | | 48 | 6 | | 49 | 25 |
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| 53.78% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.3277310924369748 | | totalSentences | 119 | | uniqueOpeners | 39 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 70 | | matches | | 0 | "All clicked shut." | | 1 | "Instead, she turned to the" | | 2 | "Just pressed his back against" |
| | ratio | 0.043 | |
| 48.57% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 70 | | matches | | 0 | "Her straight black hair was" | | 1 | "Her voice was flat, but" | | 2 | "He removed his coat with" | | 3 | "He smirked, but it didn’t" | | 4 | "She set the cloth down." | | 5 | "He laughed, low and bitter" | | 6 | "Her name for him landed" | | 7 | "he said, stepping closer" | | 8 | "She glanced toward the door," | | 9 | "His voice dropped" | | 10 | "He didn’t wait for her" | | 11 | "She crossed to the window," | | 12 | "—he jerked his chin at" | | 13 | "She’d left London once before," | | 14 | "She turned, defiance warring with" | | 15 | "His voice cracked" | | 16 | "He stepped closer, until the" | | 17 | "He reached for her face," | | 18 | "Her throat worked." | | 19 | "His other hand drifted to" |
| | ratio | 0.429 | |
| 2.86% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 70 | | matches | | 0 | "The door burst open with" | | 1 | "Lucien Moreau stepped inside, his" | | 2 | "The ivory cane he’d carried" | | 3 | "Ptolemy, the tabby, arched his" | | 4 | "Aurora Carter looked up from" | | 5 | "Her straight black hair was" | | 6 | "The crescent-shaped scar on her" | | 7 | "Her voice was flat, but" | | 8 | "He removed his coat with" | | 9 | "He smirked, but it didn’t" | | 10 | "The black one seemed darker," | | 11 | "She set the cloth down." | | 12 | "He laughed, low and bitter" | | 13 | "Her name for him landed" | | 14 | "he said, stepping closer" | | 15 | "The floorboards creaked under his" | | 16 | "Aurora’s jaw tightened." | | 17 | "She glanced toward the door," | | 18 | "His voice dropped" | | 19 | "The words hung between them." |
| | ratio | 0.914 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 70 | | matches | (empty) | | ratio | 0 | |
| 18.63% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 23 | | technicalSentenceCount | 4 | | matches | | 0 | "She’d left London once before, fleeing Evan, fleeing the weight of knowing too much about things that shouldn’t exist." | | 1 | "But here she was, standing in a flat that smelled of cardamom and old paper, staring at a man who’d once held her hand in the rain and told her she was smarter …" | | 2 | "Lucien leaned against the doorframe, unarmored now, vulnerable in a way that made her chest ache." | | 3 | "He looked at her—really looked—and in that moment, she saw it: the man who’d once saved her from a demon’s claws in Marseille, who’d apologized in four language…" |
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| 93.75% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 1 | | matches | | 0 | "He hesitated, thumb brushing the cane’s handle" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "He laughed (laugh)" | | 1 | "she whispered (whisper)" |
| | dialogueSentences | 58 | | tagDensity | 0.086 | | leniency | 0.172 | | rawRatio | 0.4 | | effectiveRatio | 0.069 | |