| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 32 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.8 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.88% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1605 | | 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) | |
| 81.31% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1605 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "pulse" | | 1 | "could feel" | | 2 | "familiar" | | 3 | "weight" | | 4 | "stomach" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "stomach dropped/sank" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 84 | | matches | (empty) | |
| 91.84% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 84 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 88 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 122 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1609 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 28 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 49 | | wordCount | 1300 | | uniqueNames | 17 | | maxNameDensity | 0.92 | | worstName | "Lucien" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Lucien" | | discoveredNames | | Eva | 8 | | Brick | 2 | | Lane | 2 | | Yu-Fei | 1 | | Cheung | 1 | | Golden | 1 | | Empress | 1 | | Lucien | 12 | | Moreau | 1 | | French | 1 | | Ptolemy | 4 | | Marseille | 1 | | Aurora | 8 | | Cardiff | 2 | | Avaros | 1 | | Evan | 1 | | You | 2 |
| | persons | | 0 | "Eva" | | 1 | "Yu-Fei" | | 2 | "Cheung" | | 3 | "Lucien" | | 4 | "Moreau" | | 5 | "Ptolemy" | | 6 | "Aurora" | | 7 | "Evan" | | 8 | "You" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "Marseille" | | 3 | "Cardiff" | | 4 | "Avaros" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | glossingSentenceCount | 4 | | matches | | 0 | "smelled like old paper and cumin from down" | | 1 | "as if acknowledging a breach of etiquette" | | 2 | "quite" | | 3 | "felt like she wasn’t alone in the room" |
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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 | 1609 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 88 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 27.74 | | std | 26.1 | | cv | 0.941 | | sampleLengths | | 0 | 8 | | 1 | 99 | | 2 | 38 | | 3 | 29 | | 4 | 71 | | 5 | 53 | | 6 | 69 | | 7 | 11 | | 8 | 10 | | 9 | 24 | | 10 | 16 | | 11 | 16 | | 12 | 15 | | 13 | 122 | | 14 | 36 | | 15 | 26 | | 16 | 7 | | 17 | 37 | | 18 | 5 | | 19 | 8 | | 20 | 16 | | 21 | 88 | | 22 | 7 | | 23 | 4 | | 24 | 14 | | 25 | 5 | | 26 | 49 | | 27 | 28 | | 28 | 7 | | 29 | 5 | | 30 | 68 | | 31 | 4 | | 32 | 44 | | 33 | 5 | | 34 | 21 | | 35 | 3 | | 36 | 14 | | 37 | 46 | | 38 | 39 | | 39 | 26 | | 40 | 13 | | 41 | 45 | | 42 | 24 | | 43 | 14 | | 44 | 6 | | 45 | 42 | | 46 | 79 | | 47 | 11 | | 48 | 6 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 84 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 259 | | matches | | 0 | "was translating" | | 1 | "was translating" | | 2 | "was already parsing" |
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| 12.99% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 88 | | ratio | 0.045 | | matches | | 0 | "Cool-headed, intelligent, quick out-of-the-box thinking — that was what the files said about her, what she told herself when her pulse started doing stupid things." | | 1 | "They had left things unsaid. She’d wanted to ask him why he’d kept watching her after that night in Marseille he’d never talked about. He’d wanted — she thought — to ask her why she ran." | | 2 | "She could feel the heat rising off him despite the rain, the familiar pull she’d told herself was just relief at having someone competent near her when things got messy. Attraction had been there from the start — the way he moved like a blade in a sheath, the precise way he spoke, the casual danger of a half-demon who chose suits over claws. Hurt had come later, because he’d been useful and never gentle, and because she’d let herself need him and then punished him for it." | | 3 | "Ptolemy yowled in protest as Lucien stepped in, brushing past her shoulder, close enough that she could smell rain and that cologne. The flat seemed to exhale. He didn’t look around like a visitor; he moved like someone who knew where the floorboards creaked." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 515 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 9 | | adverbRatio | 0.017475728155339806 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 88 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 88 | | mean | 18.28 | | std | 20.01 | | cv | 1.094 | | sampleLengths | | 0 | 8 | | 1 | 32 | | 2 | 9 | | 3 | 45 | | 4 | 13 | | 5 | 32 | | 6 | 4 | | 7 | 2 | | 8 | 7 | | 9 | 9 | | 10 | 13 | | 11 | 7 | | 12 | 24 | | 13 | 8 | | 14 | 25 | | 15 | 7 | | 16 | 8 | | 17 | 14 | | 18 | 26 | | 19 | 5 | | 20 | 21 | | 21 | 48 | | 22 | 11 | | 23 | 10 | | 24 | 16 | | 25 | 2 | | 26 | 6 | | 27 | 16 | | 28 | 16 | | 29 | 15 | | 30 | 122 | | 31 | 36 | | 32 | 26 | | 33 | 7 | | 34 | 37 | | 35 | 5 | | 36 | 8 | | 37 | 16 | | 38 | 88 | | 39 | 7 | | 40 | 4 | | 41 | 9 | | 42 | 5 | | 43 | 5 | | 44 | 20 | | 45 | 16 | | 46 | 13 | | 47 | 28 | | 48 | 7 | | 49 | 5 |
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| 41.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3181818181818182 | | totalSentences | 88 | | uniqueOpeners | 28 | |
| 46.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 72 | | matches | | 0 | "Instead she grabbed the jacket" |
| | ratio | 0.014 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 72 | | matches | | 0 | "She’d been home half an" | | 1 | "She’d meant to change." | | 2 | "It was the rhythm Lucien" | | 3 | "She could see the street" | | 4 | "She didn’t move for a" | | 5 | "It didn’t stop her throat" | | 6 | "She slid the bolts back" | | 7 | "She kept her palm on" | | 8 | "It was a grounding point." | | 9 | "he said, French accent softening" | | 10 | "She didn’t step back. She" | | 11 | "He inclined his head, as" | | 12 | "I was told you were" | | 13 | "he said, and his smile" | | 14 | "She wanted to be annoyed." | | 15 | "They had left things unsaid." | | 16 | "It was the kind of" | | 17 | "She could feel the heat" | | 18 | "she asked, quiet" | | 19 | "He’s not your problem" |
| | ratio | 0.556 | |
| 29.44% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 72 | | matches | | 0 | "The deadbolts were the first" | | 1 | "Aurora had learned to check" | | 2 | "Habit was a better lock" | | 3 | "The flat above the curry" | | 4 | "Ptolemy was asleep on the" | | 5 | "She’d been home half an" | | 6 | "She’d meant to change." | | 7 | "The knock was not a" | | 8 | "It was the rhythm Lucien" | | 9 | "Aurora’s hand stilled on the" | | 10 | "She could see the street" | | 11 | "She didn’t move for a" | | 12 | "It didn’t stop her throat" | | 13 | "She slid the bolts back" | | 14 | "The chain first, then the" | | 15 | "She kept her palm on" | | 16 | "It was a grounding point." | | 17 | "he said, French accent softening" | | 18 | "She didn’t step back. She" | | 19 | "He inclined his head, as" |
| | ratio | 0.861 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 72 | | matches | | 0 | "Now he was on her" | | 1 | "If you’re here because you" |
| | ratio | 0.028 | |
| 32.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 4 | | matches | | 0 | "She could feel the heat rising off him despite the rain, the familiar pull she’d told herself was just relief at having someone competent near her when things g…" | | 1 | "She looked at him, at the amber eye that held hers and the black one that always seemed to look past her. She thought about the three deadbolts, about the scar …" | | 2 | "Ptolemy yowled in protest as Lucien stepped in, brushing past her shoulder, close enough that she could smell rain and that cologne. The flat seemed to exhale. …" | | 3 | "The rain picked up against the window. Ptolemy jumped onto the chair and stared at Lucien with absolute disdain. Aurora watched the way Lucien’s eyes tracked th…" |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 3 | | matches | | 0 | "He inclined, as if acknowledging a breach of etiquette" | | 1 | "she asked, quiet" | | 2 | "he said, low" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 2 | | fancyTags | | 0 | "he corrected (correct)" | | 1 | "she murmured (murmur)" |
| | dialogueSentences | 40 | | tagDensity | 0.325 | | leniency | 0.65 | | rawRatio | 0.154 | | effectiveRatio | 0.1 | |