| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 48 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1219 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 83.59% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1219 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "flickered" | | 2 | "throbbed" |
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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 | 1 | | narrationSentences | 94 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 94 | | filterMatches | (empty) | | hedgeMatches | | |
| 97.97% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 126 | | gibberishSentences | 1 | | adjustedGibberishSentences | 1 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 72 | | ratio | 0.008 | | matches | | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 11 | | totalWords | 1205 | | ratio | 0.009 | | matches | | 0 | "I can’t protect you from what’s coming." | | 1 | "Tried" | | 2 | "mixed up" | | 3 | "definitely" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 842 | | uniqueNames | 12 | | maxNameDensity | 0.48 | | worstName | "Silas" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Lucien" | | discoveredNames | | Camden | 1 | | Yu-Fei | 1 | | Voice | 1 | | Silas | 4 | | Leaned | 1 | | Eva | 2 | | Ptolemy | 3 | | Rory | 4 | | Luc | 2 | | Evan | 4 | | Avaros | 1 | | Lucien | 3 |
| | persons | | 0 | "Yu-Fei" | | 1 | "Voice" | | 2 | "Silas" | | 3 | "Leaned" | | 4 | "Eva" | | 5 | "Ptolemy" | | 6 | "Rory" | | 7 | "Luc" | | 8 | "Evan" | | 9 | "Lucien" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | 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 | 1205 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 126 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 24.1 | | std | 20.73 | | cv | 0.86 | | sampleLengths | | 0 | 82 | | 1 | 53 | | 2 | 16 | | 3 | 9 | | 4 | 48 | | 5 | 15 | | 6 | 46 | | 7 | 4 | | 8 | 57 | | 9 | 35 | | 10 | 10 | | 11 | 19 | | 12 | 20 | | 13 | 4 | | 14 | 11 | | 15 | 10 | | 16 | 23 | | 17 | 3 | | 18 | 6 | | 19 | 23 | | 20 | 11 | | 21 | 6 | | 22 | 22 | | 23 | 32 | | 24 | 71 | | 25 | 27 | | 26 | 5 | | 27 | 3 | | 28 | 15 | | 29 | 83 | | 30 | 38 | | 31 | 48 | | 32 | 34 | | 33 | 17 | | 34 | 60 | | 35 | 22 | | 36 | 12 | | 37 | 41 | | 38 | 3 | | 39 | 35 | | 40 | 17 | | 41 | 33 | | 42 | 7 | | 43 | 13 | | 44 | 23 | | 45 | 3 | | 46 | 13 | | 47 | 7 | | 48 | 3 | | 49 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 94 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 169 | | matches | | 0 | "was tuning" | | 1 | "was standing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 126 | | ratio | 0.071 | | matches | | 0 | "Brushed a strand of hair behind her ear—shoulder-length, black, still damp from the rain she’d run through to drop off a curry to a club in Camden." | | 1 | "French, but not the soft kind—sharp, like a blade honed." | | 2 | "The bar below rumbled—Silas was tuning his guitar, the string plucking off-key." | | 3 | "His amber eye glinted in the lamp light—the left one, the black one just a shadow." | | 4 | "Turned, looked at the mess of books—Eva’s research, about fae and runes." | | 5 | "She’d found out later, from a contact—something about his demon father’s realm, a war, and he’d been pulled back." | | 6 | "She grabbed his hand, laced their fingers together—his skin cool, hers still warming from the glass." | | 7 | "His breath hit her nose—cinnamon, like he’d been eating a pipe." | | 8 | "Let him kiss her, hard, like he meant it—like he wasn’t going to leave again." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 617 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.03241491085899514 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.004862236628849271 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 126 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 126 | | mean | 9.56 | | std | 8.77 | | cv | 0.917 | | sampleLengths | | 0 | 3 | | 1 | 10 | | 2 | 1 | | 3 | 16 | | 4 | 27 | | 5 | 7 | | 6 | 18 | | 7 | 8 | | 8 | 14 | | 9 | 10 | | 10 | 6 | | 11 | 15 | | 12 | 3 | | 13 | 3 | | 14 | 10 | | 15 | 3 | | 16 | 6 | | 17 | 13 | | 18 | 8 | | 19 | 12 | | 20 | 15 | | 21 | 13 | | 22 | 2 | | 23 | 18 | | 24 | 16 | | 25 | 6 | | 26 | 6 | | 27 | 4 | | 28 | 12 | | 29 | 6 | | 30 | 16 | | 31 | 14 | | 32 | 9 | | 33 | 6 | | 34 | 12 | | 35 | 10 | | 36 | 7 | | 37 | 6 | | 38 | 4 | | 39 | 15 | | 40 | 4 | | 41 | 4 | | 42 | 16 | | 43 | 3 | | 44 | 1 | | 45 | 7 | | 46 | 2 | | 47 | 2 | | 48 | 7 | | 49 | 3 |
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| 64.27% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.4 | | totalSentences | 125 | | uniqueOpeners | 50 | |
| 39.68% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 84 | | matches | | | ratio | 0.012 | |
| 5.71% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 45 | | totalSentences | 84 | | matches | | 0 | "She’d closed up the restaurant’s" | | 1 | "She dried her hands on" | | 2 | "She unlocked the three deadbolts," | | 3 | "She didn’t move." | | 4 | "He pushed past her before" | | 5 | "he said, nodding at her" | | 6 | "She crossed her arms, the" | | 7 | "It was a crescent, from" | | 8 | "She sighed, stepped back, and" | | 9 | "His amber eye glinted in" | | 10 | "He kept his cane pressed" | | 11 | "He nodded, like he’d known" | | 12 | "He sat in the armchair" | | 13 | "She raised an eyebrow." | | 14 | "He didn’t flinch." | | 15 | "He didn’t smile" | | 16 | "She pushed off the doorframe" | | 17 | "She stopped halfway to the" | | 18 | "Her hand tightened around the" | | 19 | "He stood, cane steady" |
| | ratio | 0.536 | |
| 13.57% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 84 | | matches | | 0 | "The bell jangled." | | 1 | "Rory paused, towel in hand," | | 2 | "She’d closed up the restaurant’s" | | 3 | "The bell jangled again, a" | | 4 | "She dried her hands on" | | 5 | "The peephole showed a man" | | 6 | "Charcoal suit, tailored to within" | | 7 | "Ivory cane propped against his" | | 8 | "She unlocked the three deadbolts," | | 9 | "French, but not the soft" | | 10 | "She didn’t move." | | 11 | "He pushed past her before" | | 12 | "The door closed behind him," | | 13 | "The bar below rumbled—Silas was" | | 14 | "Ptolemy, the tabby, stretched on" | | 15 | "he said, nodding at her" | | 16 | "She crossed her arms, the" | | 17 | "It was a crescent, from" | | 18 | "Evan had called it once." | | 19 | "She sighed, stepped back, and" |
| | ratio | 0.893 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 84 | | matches | (empty) | | ratio | 0 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 2 | | matches | | 0 | "The Luc who’d fixed her flat when Evan had trashed it, who’d stayed up all night bandaging her hand when she’d cut it on a shard of glass." | | 1 | "She could taste the bourbon on his lips, the same one she’d watched him sip from his flask last winter, when she’d brought him a cold curry that had gone off, b…" |
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| 31.25% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 3 | | matches | | 0 | "“ He’s, too" | | 1 | "she said, quiet" | | 2 | "she said, smiling" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 2 | | fancyTags | | 0 | "She sighed (sigh)" | | 1 | "“ He’s (be)" |
| | dialogueSentences | 48 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0.25 | | effectiveRatio | 0.083 | |