| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 89.78% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1468 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "perfectly" | | 1 | "slowly" | | 2 | "slightly" |
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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) | |
| 42.10% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1468 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "sanctuary" | | 1 | "weight" | | 2 | "silence" | | 3 | "warmth" | | 4 | "throbbed" | | 5 | "could feel" | | 6 | "traced" | | 7 | "perfect" | | 8 | "footsteps" |
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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 | 232 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 232 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 232 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 18 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1468 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1468 | | uniqueNames | 5 | | maxNameDensity | 0.95 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Lucien" | | discoveredNames | | London | 1 | | Moreau | 1 | | Lucien | 9 | | Rory | 14 | | You | 9 |
| | persons | | 0 | "Moreau" | | 1 | "Lucien" | | 2 | "Rory" | | 3 | "You" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 114 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1468 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 232 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 80 | | mean | 18.35 | | std | 16.91 | | cv | 0.921 | | sampleLengths | | 0 | 62 | | 1 | 55 | | 2 | 22 | | 3 | 25 | | 4 | 4 | | 5 | 3 | | 6 | 55 | | 7 | 7 | | 8 | 6 | | 9 | 46 | | 10 | 6 | | 11 | 26 | | 12 | 2 | | 13 | 8 | | 14 | 59 | | 15 | 4 | | 16 | 36 | | 17 | 7 | | 18 | 24 | | 19 | 5 | | 20 | 26 | | 21 | 3 | | 22 | 2 | | 23 | 11 | | 24 | 40 | | 25 | 5 | | 26 | 5 | | 27 | 48 | | 28 | 4 | | 29 | 46 | | 30 | 25 | | 31 | 2 | | 32 | 22 | | 33 | 2 | | 34 | 44 | | 35 | 5 | | 36 | 10 | | 37 | 1 | | 38 | 43 | | 39 | 7 | | 40 | 35 | | 41 | 3 | | 42 | 6 | | 43 | 7 | | 44 | 37 | | 45 | 4 | | 46 | 24 | | 47 | 9 | | 48 | 25 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 232 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 286 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 232 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1469 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 38 | | adverbRatio | 0.025867937372362152 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.006126616746085773 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 232 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 232 | | mean | 6.33 | | std | 3.48 | | cv | 0.55 | | sampleLengths | | 0 | 8 | | 1 | 2 | | 2 | 11 | | 3 | 10 | | 4 | 17 | | 5 | 14 | | 6 | 6 | | 7 | 9 | | 8 | 11 | | 9 | 15 | | 10 | 10 | | 11 | 2 | | 12 | 2 | | 13 | 3 | | 14 | 11 | | 15 | 8 | | 16 | 10 | | 17 | 7 | | 18 | 8 | | 19 | 4 | | 20 | 3 | | 21 | 5 | | 22 | 16 | | 23 | 14 | | 24 | 13 | | 25 | 7 | | 26 | 7 | | 27 | 6 | | 28 | 14 | | 29 | 9 | | 30 | 5 | | 31 | 8 | | 32 | 10 | | 33 | 6 | | 34 | 9 | | 35 | 9 | | 36 | 4 | | 37 | 4 | | 38 | 2 | | 39 | 8 | | 40 | 5 | | 41 | 7 | | 42 | 18 | | 43 | 15 | | 44 | 5 | | 45 | 5 | | 46 | 4 | | 47 | 4 | | 48 | 12 | | 49 | 12 |
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| 29.31% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 48 | | diversityRatio | 0.1896551724137931 | | totalSentences | 232 | | uniqueOpeners | 44 | |
| 15.95% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 209 | | matches | | 0 | "Then why are you still" |
| | ratio | 0.005 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 126 | | totalSentences | 209 | | matches | | 0 | "She stood in the frame," | | 1 | "He leaned his weight on" | | 2 | "She remembered the last time" | | 3 | "She remembered the silence that" | | 4 | "She stepped back, letting the" | | 5 | "She didn't turn to face" | | 6 | "She engaged the deadbolts, metal" | | 7 | "It is past midnight." | | 8 | "He hadn’t knocked." | | 9 | "Her blue eyes were flat," | | 10 | "She wiped flour from her" | | 11 | "She watched his gaze drop" | | 12 | "You have the nerve to" | | 13 | "It isn't your place to" | | 14 | "He shifted his weight, the" | | 15 | "You said you wouldn't come" | | 16 | "She picked up a glass" | | 17 | "She took a sip." | | 18 | "Your plans were written in" | | 19 | "She leaned against the counter," |
| | ratio | 0.603 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 195 | | totalSentences | 209 | | matches | | 0 | "The lock clicked open on" | | 1 | "A security habit born from" | | 2 | "Rory slid the bolt back" | | 3 | "She stood in the frame," | | 4 | "Lucien Moreau stood in the" | | 5 | "Charcoal fabric swallowed the dim" | | 6 | "Platinum hair slicked back, perfectly" | | 7 | "He leaned his weight on" | | 8 | "Rory’s throat tightened." | | 9 | "She remembered the last time" | | 10 | "She remembered the silence that" | | 11 | "She stepped back, letting the" | | 12 | "She didn't turn to face" | | 13 | "She engaged the deadbolts, metal" | | 14 | "It is past midnight." | | 15 | "He hadn’t knocked." | | 16 | "Rory turned to face him." | | 17 | "Her blue eyes were flat," | | 18 | "She wiped flour from her" | | 19 | "A smudge of dough remained" |
| | ratio | 0.933 | |
| 47.85% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 209 | | matches | | 0 | "Because you left." | | 1 | "If you come back again..." |
| | ratio | 0.01 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |