| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 121 | | tagDensity | 0.099 | | leniency | 0.198 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.03% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2153 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "quickly" | | 1 | "very" | | 2 | "suddenly" |
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| 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) | |
| 76.78% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2153 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "unreadable" | | 1 | "silence" | | 2 | "eyebrow" | | 3 | "comforting" | | 4 | "fractured" | | 5 | "unsettled" | | 6 | "stomach" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "stomach dropped/sank" | | count | 1 |
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| | highlights | | 0 | "eyes narrowed" | | 1 | "stomach dropped" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 151 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 151 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 260 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2153 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 26 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 59 | | wordCount | 1578 | | uniqueNames | 7 | | maxNameDensity | 1.46 | | worstName | "Tomás" | | maxWindowNameDensity | 3 | | worstWindowName | "Tomás" | | discoveredNames | | Aurora | 1 | | Carter | 1 | | Tomás | 23 | | Rory | 17 | | Silas | 15 | | Silence | 1 | | Evan | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Carter" | | 2 | "Tomás" | | 3 | "Rory" | | 4 | "Silas" | | 5 | "Silence" | | 6 | "Evan" |
| | places | (empty) | | globalScore | 0.771 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 96 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a man who stood near exits wi" |
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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 | 2153 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 260 | | matches | | 0 | "let that sit" | | 1 | "knew that coat" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 156 | | mean | 13.8 | | std | 18.1 | | cv | 1.312 | | sampleLengths | | 0 | 1 | | 1 | 37 | | 2 | 75 | | 3 | 1 | | 4 | 17 | | 5 | 31 | | 6 | 16 | | 7 | 11 | | 8 | 3 | | 9 | 5 | | 10 | 11 | | 11 | 46 | | 12 | 5 | | 13 | 7 | | 14 | 23 | | 15 | 9 | | 16 | 90 | | 17 | 13 | | 18 | 6 | | 19 | 4 | | 20 | 3 | | 21 | 59 | | 22 | 55 | | 23 | 18 | | 24 | 6 | | 25 | 6 | | 26 | 7 | | 27 | 1 | | 28 | 7 | | 29 | 3 | | 30 | 3 | | 31 | 34 | | 32 | 3 | | 33 | 5 | | 34 | 24 | | 35 | 8 | | 36 | 10 | | 37 | 8 | | 38 | 5 | | 39 | 66 | | 40 | 8 | | 41 | 2 | | 42 | 6 | | 43 | 2 | | 44 | 6 | | 45 | 57 | | 46 | 14 | | 47 | 9 | | 48 | 3 | | 49 | 1 |
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| 95.97% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 151 | | matches | | 0 | "been paid" | | 1 | "been burned" | | 2 | "got shushed" | | 3 | "was blurred" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 265 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 260 | | ratio | 0 | | matches | (empty) | |
| 97.87% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1579 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 67 | | adverbRatio | 0.04243191893603546 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.006333122229259025 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 260 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 260 | | mean | 8.28 | | std | 6.96 | | cv | 0.84 | | sampleLengths | | 0 | 1 | | 1 | 15 | | 2 | 5 | | 3 | 17 | | 4 | 20 | | 5 | 25 | | 6 | 2 | | 7 | 5 | | 8 | 17 | | 9 | 6 | | 10 | 1 | | 11 | 11 | | 12 | 6 | | 13 | 12 | | 14 | 13 | | 15 | 6 | | 16 | 16 | | 17 | 4 | | 18 | 7 | | 19 | 3 | | 20 | 5 | | 21 | 3 | | 22 | 8 | | 23 | 23 | | 24 | 10 | | 25 | 4 | | 26 | 9 | | 27 | 5 | | 28 | 7 | | 29 | 14 | | 30 | 9 | | 31 | 9 | | 32 | 25 | | 33 | 31 | | 34 | 14 | | 35 | 20 | | 36 | 7 | | 37 | 6 | | 38 | 6 | | 39 | 4 | | 40 | 3 | | 41 | 12 | | 42 | 9 | | 43 | 25 | | 44 | 13 | | 45 | 10 | | 46 | 5 | | 47 | 6 | | 48 | 10 | | 49 | 24 |
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| 46.92% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.25 | | totalSentences | 260 | | uniqueOpeners | 65 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 134 | | matches | | 0 | "Then he set the cloth" | | 1 | "Then, one winter, he had" | | 2 | "Once, years ago, he had" | | 3 | "Just enough for her to" | | 4 | "Somewhere behind her, the hidden" |
| | ratio | 0.037 | |
| 64.78% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 52 | | totalSentences | 134 | | matches | | 0 | "She caught the tumbler before" | | 1 | "He had the same long" | | 2 | "He wore age like a" | | 3 | "He gave a short laugh" | | 4 | "His hazel eyes moved from" | | 5 | "His gaze snagged there for" | | 6 | "She leaned against the counter," | | 7 | "He smiled at that, but" | | 8 | "He looked like a man" | | 9 | "She hated how quickly the" | | 10 | "His hands stained with printer" | | 11 | "He had once talked too" | | 12 | "She gave him a look." | | 13 | "His jaw tightened." | | 14 | "He glanced at the bar" | | 15 | "He had a way of" | | 16 | "She let that sit between" | | 17 | "He knew her too well" | | 18 | "She had hated him for" | | 19 | "He tapped the rim of" |
| | ratio | 0.388 | |
| 12.24% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 120 | | totalSentences | 134 | | matches | | 0 | "The glass slipped from Aurora" | | 1 | "Beer hissed over her knuckles." | | 2 | "She caught the tumbler before" | | 3 | "The man at the end" | | 4 | "He had the same long" | | 5 | "He wore age like a" | | 6 | "He gave a short laugh" | | 7 | "Silas looked up from where" | | 8 | "His hazel eyes moved from" | | 9 | "Tomás glanced at him." | | 10 | "Silas’s mouth twitched." | | 11 | "Rory bent for a napkin," | | 12 | "The crescent on her wrist" | | 13 | "Tomás saw it too." | | 14 | "His gaze snagged there for" | | 15 | "She leaned against the counter," | | 16 | "Silas moved away, giving them" | | 17 | "The room held its usual" | | 18 | "The green neon above the" | | 19 | "Tomás raised his glass to" |
| | ratio | 0.896 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 134 | | matches | (empty) | | ratio | 0 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 64 | | technicalSentenceCount | 4 | | matches | | 0 | "He had a way of inhabiting a room that made everyone else feel briefly under observation." | | 1 | "Back then they had drifted through the city as if they had borrowed it for a week, taking night buses, stealing cigarettes from each other, arguing about everyt…" | | 2 | "She had the impression of meeting a version of herself that had learned to bite." | | 3 | "Her fingers pressed against the bar, searching for a steadiness that had gone missing." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 121 | | tagDensity | 0.091 | | leniency | 0.182 | | rawRatio | 0 | | effectiveRatio | 0 | |