| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.368 | | leniency | 0.737 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 79.96% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 998 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slightly" | | 1 | "carefully" | | 2 | "very" |
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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) | |
| 79.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 998 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "flicker" | | 1 | "silence" | | 2 | "weight" | | 3 | "familiar" |
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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 | 73 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 73 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 992 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 747 | | uniqueNames | 14 | | maxNameDensity | 0.54 | | worstName | "Lucien" | | maxWindowNameDensity | 1 | | worstWindowName | "Lucien" | | discoveredNames | | Eva | 2 | | Lucien | 4 | | Moreau | 1 | | Golden | 1 | | Empress | 1 | | Michelin | 1 | | Rory | 4 | | Bethnal | 2 | | Green | 2 | | Marseille | 1 | | London | 1 | | Silence | 1 | | Businesslike | 1 | | Silas | 1 |
| | persons | | 0 | "Eva" | | 1 | "Lucien" | | 2 | "Moreau" | | 3 | "Rory" | | 4 | "Silas" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 84.21% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 38 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like he'd stepped out of somewhere" |
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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 | 992 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 96 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 19.45 | | std | 21.62 | | cv | 1.111 | | sampleLengths | | 0 | 8 | | 1 | 92 | | 2 | 3 | | 3 | 5 | | 4 | 41 | | 5 | 6 | | 6 | 31 | | 7 | 14 | | 8 | 15 | | 9 | 2 | | 10 | 57 | | 11 | 49 | | 12 | 3 | | 13 | 2 | | 14 | 2 | | 15 | 42 | | 16 | 13 | | 17 | 2 | | 18 | 4 | | 19 | 47 | | 20 | 29 | | 21 | 4 | | 22 | 52 | | 23 | 19 | | 24 | 4 | | 25 | 43 | | 26 | 11 | | 27 | 4 | | 28 | 6 | | 29 | 6 | | 30 | 4 | | 31 | 4 | | 32 | 95 | | 33 | 9 | | 34 | 6 | | 35 | 1 | | 36 | 8 | | 37 | 19 | | 38 | 11 | | 39 | 13 | | 40 | 38 | | 41 | 24 | | 42 | 5 | | 43 | 39 | | 44 | 11 | | 45 | 13 | | 46 | 39 | | 47 | 6 | | 48 | 12 | | 49 | 7 |
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| 95.65% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 73 | | matches | | 0 | "was slicked" | | 1 | "been supposed" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 8 | | totalVerbs | 137 | | matches | | 0 | "was being" | | 1 | "was wearing" | | 2 | "was watching" | | 3 | "was watching was choosing was doing" | | 4 | "was running" | | 5 | "was working" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 96 | | ratio | 0 | | matches | (empty) | |
| 86.85% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 418 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.05502392344497608 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.014354066985645933 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 10.33 | | std | 10.07 | | cv | 0.974 | | sampleLengths | | 0 | 8 | | 1 | 20 | | 2 | 21 | | 3 | 34 | | 4 | 9 | | 5 | 8 | | 6 | 3 | | 7 | 5 | | 8 | 7 | | 9 | 4 | | 10 | 3 | | 11 | 23 | | 12 | 4 | | 13 | 6 | | 14 | 27 | | 15 | 4 | | 16 | 14 | | 17 | 8 | | 18 | 4 | | 19 | 3 | | 20 | 2 | | 21 | 4 | | 22 | 4 | | 23 | 15 | | 24 | 34 | | 25 | 5 | | 26 | 29 | | 27 | 6 | | 28 | 9 | | 29 | 3 | | 30 | 2 | | 31 | 2 | | 32 | 29 | | 33 | 13 | | 34 | 13 | | 35 | 2 | | 36 | 4 | | 37 | 2 | | 38 | 14 | | 39 | 21 | | 40 | 10 | | 41 | 8 | | 42 | 21 | | 43 | 4 | | 44 | 44 | | 45 | 8 | | 46 | 5 | | 47 | 14 | | 48 | 4 | | 49 | 31 |
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| 87.15% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.5729166666666666 | | totalSentences | 96 | | uniqueOpeners | 55 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 53 | | matches | | 0 | "Instead, Lucien Moreau stood in" | | 1 | "More like the doorway was" | | 2 | "Just the one word." |
| | ratio | 0.057 | |
| 76.60% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 53 | | matches | | 0 | "His platinum hair was slicked" | | 1 | "She kept her hand on" | | 2 | "He tilted his head slightly," | | 3 | "She stepped back anyway." | | 4 | "He stepped inside with that" | | 5 | "It wasn't much to catalogue." | | 6 | "He turned to face her," | | 7 | "He, meanwhile, looked like he'd" | | 8 | "He set the cane across" | | 9 | "She stayed standing, arms crossed," | | 10 | "His accent did interesting things" | | 11 | "He leaned forward, forearms on" | | 12 | "She pushed off the wall" | | 13 | "She flipped it open to" | | 14 | "she said, without looking up" | | 15 | "She wrote the three locations" | | 16 | "She kept her voice level" | | 17 | "He was watching her, and" | | 18 | "Her phone was already in" |
| | ratio | 0.358 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 38 | | totalSentences | 53 | | matches | | 0 | "The second knock hit harder" | | 1 | "Rory pulled open the door" | | 2 | "His platinum hair was slicked" | | 3 | "The amber one caught the" | | 4 | "The black one gave nothing" | | 5 | "She kept her hand on" | | 6 | "The hallway smelled of cumin" | | 7 | "He tilted his head slightly," | | 8 | "A flicker at the corner" | | 9 | "She stepped back anyway." | | 10 | "He stepped inside with that" | | 11 | "It wasn't much to catalogue." | | 12 | "A small front room with" | | 13 | "A coat rack by the" | | 14 | "The window above" | | 15 | "He turned to face her," | | 16 | "He, meanwhile, looked like he'd" | | 17 | "He set the cane across" | | 18 | "She stayed standing, arms crossed," | | 19 | "His accent did interesting things" |
| | ratio | 0.717 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 12.99% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 22 | | technicalSentenceCount | 4 | | matches | | 0 | "His platinum hair was slicked back as ever, his charcoal suit immaculate despite the hour, but there was a tightness around his mismatched eyes that hadn't been…" | | 1 | "He stepped inside with that unhurried ease of his, cane clicking once on the threshold, and looked around her flat the way a surveyor looks at a condemned build…" | | 2 | "The one she'd told herself she'd misread three months ago on a wet street in Marseille, standing outside a brasserie neither of them had technically been suppos…" | | 3 | "Dismantling one cleanly meant knowing exactly where to cut, which meant someone had either the architecture memorised or was working from a map that shouldn't e…" |
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| 89.29% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, without looking up" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 38 | | tagDensity | 0.237 | | leniency | 0.474 | | rawRatio | 0.111 | | effectiveRatio | 0.053 | |