| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 53 | | tagDensity | 0.264 | | leniency | 0.528 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 87.97% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1662 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "suddenly" | | 1 | "lightly" | | 2 | "gently" | | 3 | "utterly" |
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| 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) | |
| 57.88% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1662 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "velvet" | | 1 | "chaotic" | | 2 | "weight" | | 3 | "warmth" | | 4 | "navigated" | | 5 | "flicker" | | 6 | "traced" | | 7 | "raced" | | 8 | "pulse" | | 9 | "could feel" | | 10 | "stark" | | 11 | "electric" | | 12 | "perfect" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 113 | | matches | (empty) | |
| 92.29% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 4 | | narrationSentences | 113 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 151 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 48 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1655 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 1083 | | uniqueNames | 10 | | maxNameDensity | 0.65 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Eva" | | discoveredNames | | Eva | 3 | | Celtic | 1 | | British | 1 | | Library | 1 | | Lucien | 6 | | Moreau | 1 | | Rory | 7 | | Marseille | 1 | | London | 1 | | Ptolemy | 5 |
| | persons | | 0 | "Eva" | | 1 | "Lucien" | | 2 | "Moreau" | | 3 | "Rory" | | 4 | "Ptolemy" |
| | places | | 0 | "British" | | 1 | "Library" | | 2 | "Marseille" | | 3 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 87 | | glossingSentenceCount | 1 | | matches | | 0 | "not quite reaching his eyes" |
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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 | 1655 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 151 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 66 | | mean | 25.08 | | std | 20.13 | | cv | 0.803 | | sampleLengths | | 0 | 56 | | 1 | 8 | | 2 | 33 | | 3 | 7 | | 4 | 55 | | 5 | 89 | | 6 | 19 | | 7 | 14 | | 8 | 22 | | 9 | 53 | | 10 | 2 | | 11 | 20 | | 12 | 18 | | 13 | 5 | | 14 | 7 | | 15 | 7 | | 16 | 1 | | 17 | 6 | | 18 | 33 | | 19 | 65 | | 20 | 6 | | 21 | 5 | | 22 | 16 | | 23 | 11 | | 24 | 14 | | 25 | 4 | | 26 | 34 | | 27 | 10 | | 28 | 34 | | 29 | 25 | | 30 | 50 | | 31 | 20 | | 32 | 34 | | 33 | 54 | | 34 | 10 | | 35 | 59 | | 36 | 17 | | 37 | 34 | | 38 | 44 | | 39 | 6 | | 40 | 24 | | 41 | 12 | | 42 | 13 | | 43 | 37 | | 44 | 24 | | 45 | 8 | | 46 | 40 | | 47 | 5 | | 48 | 54 | | 49 | 25 |
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| 99.05% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 113 | | matches | | 0 | "was tangled" | | 1 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 180 | | matches | (empty) | |
| 29.33% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 151 | | ratio | 0.04 | | matches | | 0 | "One hand rested on the ivory handle of his cane, the other held a flat, dark object—a file, perhaps." | | 1 | "His voice was exactly as she remembered—low, smooth, with that faint Marseille accent that softened the edges of his consonants." | | 2 | "He took in the cramped space with a single glance—the towers of books, the scrolls spilling from the dining table, the herb bundles drying above the radiator." | | 3 | "A flicker of something—amusement, or irritation—crossed his face." | | 4 | "The scent of him—sandalwood and something metallic, like ozone after a storm—wrapped around her." | | 5 | "He tasted of black coffee and something dark, sweet—like pomegranate." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1093 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, crescent-shaped scar" |
| | adverbCount | 31 | | adverbRatio | 0.028362305580969808 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.007319304666056725 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 151 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 151 | | mean | 10.96 | | std | 8.23 | | cv | 0.751 | | sampleLengths | | 0 | 16 | | 1 | 20 | | 2 | 20 | | 3 | 8 | | 4 | 4 | | 5 | 4 | | 6 | 10 | | 7 | 8 | | 8 | 7 | | 9 | 7 | | 10 | 4 | | 11 | 10 | | 12 | 11 | | 13 | 14 | | 14 | 16 | | 15 | 3 | | 16 | 5 | | 17 | 8 | | 18 | 9 | | 19 | 19 | | 20 | 19 | | 21 | 14 | | 22 | 12 | | 23 | 7 | | 24 | 6 | | 25 | 6 | | 26 | 12 | | 27 | 2 | | 28 | 8 | | 29 | 10 | | 30 | 4 | | 31 | 16 | | 32 | 19 | | 33 | 18 | | 34 | 2 | | 35 | 20 | | 36 | 15 | | 37 | 3 | | 38 | 5 | | 39 | 7 | | 40 | 3 | | 41 | 4 | | 42 | 1 | | 43 | 6 | | 44 | 11 | | 45 | 12 | | 46 | 9 | | 47 | 1 | | 48 | 7 | | 49 | 14 |
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| 40.73% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.2980132450331126 | | totalSentences | 151 | | uniqueOpeners | 45 | |
| 30.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 110 | | matches | | 0 | "Then, he bent his head" |
| | ratio | 0.009 | |
| 5.45% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 59 | | totalSentences | 110 | | matches | | 0 | "She moved to the door," | | 1 | "Her breath caught." | | 2 | "He looked exactly the same." | | 3 | "He hadn’t changed a bit" | | 4 | "Her fingers hovered over the" | | 5 | "She could pretend she wasn’t" | | 6 | "He filled the doorway, a" | | 7 | "His gaze swept over her," | | 8 | "It lingered on the small," | | 9 | "His voice was exactly as" | | 10 | "Her own voice came out" | | 11 | "He didn’t move." | | 12 | "She stepped aside, a silent," | | 13 | "He took in the cramped" | | 14 | "His expression didn’t change, but" | | 15 | "He’d never liked chaos." | | 16 | "he said, his tone dry" | | 17 | "He turned to face her," | | 18 | "He placed the dark file" | | 19 | "She let out a short," |
| | ratio | 0.536 | |
| 14.55% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 98 | | totalSentences | 110 | | matches | | 0 | "The smell of old paper" | | 1 | "Rory shifted a stack of" | | 2 | "Ptolemy, a ginger tabby with" | | 3 | "This was controlled, intentional." | | 4 | "Rory’s heart did a slow," | | 5 | "Eva was at the British" | | 6 | "The raps came again, a" | | 7 | "Ptolemy flattened his ears." | | 8 | "Rory eased him off and" | | 9 | "She moved to the door," | | 10 | "Her breath caught." | | 11 | "He looked exactly the same." | | 12 | "Platinum blond hair slicked back" | | 13 | "He hadn’t changed a bit" | | 14 | "Her fingers hovered over the" | | 15 | "She could pretend she wasn’t" | | 16 | "Ptolemy meowed loudly from the" | | 17 | "Lucien’s head tilted a fraction," | | 18 | "The last one stuck, and" | | 19 | "The door swung inward." |
| | ratio | 0.891 | |
| 45.45% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 110 | | matches | | 0 | "Even through the glass, she" |
| | ratio | 0.009 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 2 | | matches | | 0 | "His voice was exactly as she remembered—low, smooth, with that faint Marseille accent that softened the edges of his consonants." | | 1 | "The way his mismatched eyes had watched her with a reverence that terrified her." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 6 | | matches | | 0 | "he said, his tone dry" | | 1 | "Rory said, the old hurt rising like bile" | | 2 | "he said, his voice barely audible" | | 3 | "she said, her voice steadier than she felt" | | 4 | "he murmured, his lips a hair’s breadth from hers," | | 5 | "he said, his voice rough" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 2 | | fancyTags | | 0 | "she whispered (whisper)" | | 1 | "he murmured (murmur)" |
| | dialogueSentences | 53 | | tagDensity | 0.132 | | leniency | 0.264 | | rawRatio | 0.286 | | effectiveRatio | 0.075 | |