| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1065 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 24.88% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1065 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "weight" | | 1 | "crystal" | | 2 | "scanned" | | 3 | "traced" | | 4 | "silence" | | 5 | "flickered" | | 6 | "sentinel" | | 7 | "rhythmic" | | 8 | "pulse" | | 9 | "flicker" | | 10 | "etched" | | 11 | "tension" | | 12 | "familiar" | | 13 | "sanctuary" |
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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 | 87 | | matches | (empty) | |
| 60.76% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 1 | | narrationSentences | 87 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 87 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1065 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 1065 | | uniqueNames | 11 | | maxNameDensity | 0.94 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 2 | | Europe | 1 | | Rory | 10 | | Cardiff | 1 | | Chinese | 1 | | London | 1 | | Evan | 6 | | Silas | 6 | | You | 7 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Rory" | | 3 | "Evan" | | 4 | "Silas" | | 5 | "You" |
| | places | | 0 | "Soho" | | 1 | "Europe" | | 2 | "Cardiff" | | 3 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 78.57% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | glossingSentenceCount | 2 | | matches | | 0 | "as if expecting a blow" | | 1 | "looked like a man who spent his nights re" |
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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 | 1065 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 87 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 39.44 | | std | 21.45 | | cv | 0.544 | | sampleLengths | | 0 | 91 | | 1 | 14 | | 2 | 62 | | 3 | 26 | | 4 | 14 | | 5 | 39 | | 6 | 12 | | 7 | 65 | | 8 | 33 | | 9 | 49 | | 10 | 40 | | 11 | 40 | | 12 | 52 | | 13 | 26 | | 14 | 62 | | 15 | 61 | | 16 | 52 | | 17 | 37 | | 18 | 16 | | 19 | 37 | | 20 | 11 | | 21 | 46 | | 22 | 34 | | 23 | 7 | | 24 | 39 | | 25 | 18 | | 26 | 82 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 87 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 191 | | matches | | 0 | "was already dissolving" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 87 | | ratio | 0.011 | | matches | | 0 | "He didn't look like the man who once whispered poetry to keep her awake; he looked like a man who spent his nights recalculating the cost of ruined lives." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1068 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.028089887640449437 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.00749063670411985 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 87 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 87 | | mean | 12.24 | | std | 6.73 | | cv | 0.549 | | sampleLengths | | 0 | 18 | | 1 | 11 | | 2 | 17 | | 3 | 18 | | 4 | 17 | | 5 | 10 | | 6 | 11 | | 7 | 3 | | 8 | 13 | | 9 | 22 | | 10 | 15 | | 11 | 12 | | 12 | 4 | | 13 | 22 | | 14 | 14 | | 15 | 11 | | 16 | 14 | | 17 | 14 | | 18 | 1 | | 19 | 11 | | 20 | 10 | | 21 | 13 | | 22 | 20 | | 23 | 22 | | 24 | 5 | | 25 | 18 | | 26 | 4 | | 27 | 6 | | 28 | 14 | | 29 | 24 | | 30 | 11 | | 31 | 13 | | 32 | 10 | | 33 | 17 | | 34 | 12 | | 35 | 15 | | 36 | 13 | | 37 | 2 | | 38 | 11 | | 39 | 4 | | 40 | 7 | | 41 | 3 | | 42 | 25 | | 43 | 13 | | 44 | 13 | | 45 | 6 | | 46 | 20 | | 47 | 5 | | 48 | 3 | | 49 | 2 |
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| 31.61% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.25287356321839083 | | totalSentences | 87 | | uniqueOpeners | 22 | |
| 79.37% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 84 | | matches | | 0 | "Then, she stopped." | | 1 | "Much higher stakes." |
| | ratio | 0.024 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 51 | | totalSentences | 84 | | matches | | 0 | "He caught her eye, his" | | 1 | "He nodded once, a silent" | | 2 | "He wore a charcoal suit" | | 3 | "He traced the rim of" | | 4 | "His face, once soft and" | | 5 | "She kept her distance, her" | | 6 | "She slid into the opposite" | | 7 | "You always did have a" | | 8 | "He leaned forward, eyes searching" | | 9 | "He found only the woman" | | 10 | "I didn't come to lurk." | | 11 | "I came to see if" | | 12 | "You look tired, Rory." | | 13 | "She traced the crescent mark" | | 14 | "He stayed silent, a sentinel" | | 15 | "You haven't changed the brand" | | 16 | "He let out a short," | | 17 | "He tapped a cigarette against" | | 18 | "I sold it all." | | 19 | "I trade in different debts" |
| | ratio | 0.607 | |
| 1.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 77 | | totalSentences | 84 | | matches | | 0 | "The green neon sign of" | | 1 | "Rain smeared the city lights" | | 2 | "Rory pushed inside, the scent" | | 3 | "Silas stood behind the mahogany" | | 4 | "He caught her eye, his" | | 5 | "He nodded once, a silent" | | 6 | "Rory scanned the shadows, seeking" | | 7 | "A man sat at the" | | 8 | "He wore a charcoal suit" | | 9 | "Sunlight wouldn't have touched him," | | 10 | "He traced the rim of" | | 11 | "The man looked up." | | 12 | "His face, once soft and" | | 13 | "Rory, you look exactly like" | | 14 | "She kept her distance, her" | | 15 | "The air between them sharpened," | | 16 | "She slid into the opposite" | | 17 | "You always did have a" | | 18 | "The amber liquid sloshed as" | | 19 | "The click was final, a" |
| | ratio | 0.917 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 84 | | matches | (empty) | | ratio | 0 | |
| 89.95% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 4 | | matches | | 0 | "Silas stood behind the mahogany bar, polishing a crystal glass with a cloth that had seen better decades." | | 1 | "He wore a charcoal suit that cost more than Rory’s annual rent, his posture rigid, shoulders squared as if expecting a blow." | | 2 | "He didn't look like the man who once whispered poetry to keep her awake; he looked like a man who spent his nights recalculating the cost of ruined lives." | | 3 | "She thought of the hidden room behind the bookshelf, the files Silas kept on men like Evan, the network of secrets that made the world turn." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |