| 86.79% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 3 | | adverbTags | | 0 | "that Marseille accent wrapped around [around]" | | 1 | "he said finally [finally]" | | 2 | "He laughed again [again]" |
| | dialogueSentences | 53 | | tagDensity | 0.283 | | leniency | 0.566 | | rawRatio | 0.2 | | effectiveRatio | 0.113 | |
| 96.35% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1369 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 78.09% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1369 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "stomach" | | 1 | "silence" | | 2 | "tenderness" | | 3 | "reminder" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 83 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 118 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1358 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 87.92% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 886 | | uniqueNames | 16 | | maxNameDensity | 1.24 | | worstName | "Lucien" | | maxWindowNameDensity | 2 | | worstWindowName | "Lucien" | | discoveredNames | | Eva | 7 | | Moreau | 1 | | Brick | 1 | | Lane | 1 | | Golden | 1 | | Empress | 1 | | Lucien | 11 | | Rory | 11 | | Marseille | 1 | | London | 3 | | East | 1 | | End | 1 | | Ran | 1 | | Westminster | 2 | | Ptolemy | 2 | | Three | 3 |
| | persons | | 0 | "Eva" | | 1 | "Moreau" | | 2 | "Empress" | | 3 | "Lucien" | | 4 | "Rory" | | 5 | "Ptolemy" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "Golden" | | 3 | "Marseille" | | 4 | "London" | | 5 | "East" | | 6 | "End" | | 7 | "Three" |
| | globalScore | 0.879 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like to be the focus of that laugh" |
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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 | 1358 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 118 | | matches | | 0 | "let that sit" | | 1 | "remembered that night" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 60 | | mean | 22.63 | | std | 17.63 | | cv | 0.779 | | sampleLengths | | 0 | 50 | | 1 | 30 | | 2 | 31 | | 3 | 50 | | 4 | 48 | | 5 | 25 | | 6 | 1 | | 7 | 42 | | 8 | 4 | | 9 | 30 | | 10 | 8 | | 11 | 6 | | 12 | 7 | | 13 | 48 | | 14 | 16 | | 15 | 7 | | 16 | 28 | | 17 | 29 | | 18 | 29 | | 19 | 3 | | 20 | 18 | | 21 | 52 | | 22 | 10 | | 23 | 71 | | 24 | 6 | | 25 | 10 | | 26 | 22 | | 27 | 62 | | 28 | 24 | | 29 | 5 | | 30 | 13 | | 31 | 10 | | 32 | 24 | | 33 | 23 | | 34 | 25 | | 35 | 15 | | 36 | 11 | | 37 | 2 | | 38 | 8 | | 39 | 16 | | 40 | 64 | | 41 | 2 | | 42 | 4 | | 43 | 37 | | 44 | 9 | | 45 | 60 | | 46 | 42 | | 47 | 19 | | 48 | 9 | | 49 | 34 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 160 | | matches | | 0 | "was looking" | | 1 | "was getting" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 118 | | ratio | 0.076 | | matches | | 0 | "It wasn't Eva's pattern—she jangled her keys, cursed the deadbolts, then used her knee like a battering ram." | | 1 | "He'd taken off his sunglasses—it was past eleven, the streetlights bleeding orange through the grime of Brick Lane—and his mismatched eyes stared directly at the peephole." | | 2 | "Rory undid the three deadbolts—Eva was paranoid, but Rory had learned to appreciate that particular paranoia—and opened the door six inches, leaving the chain on." | | 3 | "He laughed—a quiet, rueful sound that used to make her stomach flip." | | 4 | "He didn't just occupy space—he defined it." | | 5 | "\"Say what you came to say.\" Rory closed the door but didn't lock it—she wasn't committed to this conversation yet." | | 6 | "\"Every kindness had a price. Every moment of tenderness was followed by a reminder that I owed him something. I don't know how to do this—\" he gestured between them, \"—without the transaction. Without the exchange. It's all I know.\"" | | 7 | "She remembered that night—the rain slick on the cobblestones, the blood on her hands, the way Lucien had found her in the alley and wrapped his coat around her shoulders." | | 8 | "She stared at him for a long moment—at the suit, the cane, the impossible eyes, the man who'd broken her trust and then come looking for her through three months of London's silence." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 839 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.023837902264600714 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.00834326579261025 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 118 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 118 | | mean | 11.51 | | std | 8.65 | | cv | 0.752 | | sampleLengths | | 0 | 24 | | 1 | 18 | | 2 | 8 | | 3 | 19 | | 4 | 4 | | 5 | 7 | | 6 | 20 | | 7 | 11 | | 8 | 19 | | 9 | 26 | | 10 | 5 | | 11 | 6 | | 12 | 9 | | 13 | 21 | | 14 | 5 | | 15 | 7 | | 16 | 25 | | 17 | 1 | | 18 | 15 | | 19 | 8 | | 20 | 14 | | 21 | 5 | | 22 | 4 | | 23 | 15 | | 24 | 12 | | 25 | 3 | | 26 | 8 | | 27 | 6 | | 28 | 7 | | 29 | 12 | | 30 | 4 | | 31 | 32 | | 32 | 9 | | 33 | 2 | | 34 | 5 | | 35 | 7 | | 36 | 4 | | 37 | 5 | | 38 | 19 | | 39 | 8 | | 40 | 21 | | 41 | 6 | | 42 | 2 | | 43 | 9 | | 44 | 12 | | 45 | 3 | | 46 | 18 | | 47 | 4 | | 48 | 2 | | 49 | 32 |
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| 50.28% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3474576271186441 | | totalSentences | 118 | | uniqueOpeners | 41 | |
| 91.32% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 73 | | matches | | 0 | "Still did, damn it." | | 1 | "Then he'd told her about" |
| | ratio | 0.027 | |
| 33.70% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 73 | | matches | | 0 | "It wasn't Eva's pattern—she jangled" | | 1 | "She checked the peephole and" | | 2 | "He'd taken off his sunglasses—it" | | 3 | "He knew she was looking." | | 4 | "She could pretend she wasn't" | | 5 | "She hadn't made noise since" | | 6 | "His voice was smoke and" | | 7 | "He tilted his head, a" | | 8 | "He laughed—a quiet, rueful sound" | | 9 | "Her actual name this time" | | 10 | "He let that sit." | | 11 | "he said finally" | | 12 | "She stared at him." | | 13 | "She closed the door, unchained" | | 14 | "He didn't just occupy space—he" | | 15 | "His charcoal suit cost more" | | 16 | "His shoes were polished leather" | | 17 | "He stood in the middle" | | 18 | "He nodded at the cat" | | 19 | "They'd met before." |
| | ratio | 0.466 | |
| 49.04% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 73 | | matches | | 0 | "Rory heard the knock through" | | 1 | "It wasn't Eva's pattern—she jangled" | | 2 | "This was someone who expected" | | 3 | "Ptolemy lifted his tabby head" | | 4 | "Cats knew things humans pretended" | | 5 | "Rory crossed the cluttered living" | | 6 | "She checked the peephole and" | | 7 | "Lucien Moreau stood on the" | | 8 | "He'd taken off his sunglasses—it" | | 9 | "He knew she was looking." | | 10 | "She could pretend she wasn't" | | 11 | "The flat was dark except" | | 12 | "She hadn't made noise since" | | 13 | "Rory undid the three deadbolts—Eva" | | 14 | "His voice was smoke and" | | 15 | "The one she'd used when" | | 16 | "The one he'd peeled away" | | 17 | "He tilted his head, a" | | 18 | "The streetlight caught the amber" | | 19 | "He laughed—a quiet, rueful sound" |
| | ratio | 0.822 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 73 | | matches | | 0 | "If he was here, he" | | 1 | "If he could find her," |
| | ratio | 0.027 | |
| 66.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 3 | | matches | | 0 | "Rory heard the knock through Eva's old oak door, three sharp raps that cut through the rumble of the curry house exhaust fan below." | | 1 | "He stood in the middle of Eva's cluttered living room, looking at the chaos of books and scrolls and takeaway containers with the careful neutrality of a man wh…" | | 2 | "She stared at him for a long moment—at the suit, the cane, the impossible eyes, the man who'd broken her trust and then come looking for her through three month…" |
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| 91.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 1 | | matches | | 0 | "He tilted, a faint smile curving the corner of his mouth" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | 0 | "He laughed again (laugh)" |
| | dialogueSentences | 53 | | tagDensity | 0.132 | | leniency | 0.264 | | rawRatio | 0.143 | | effectiveRatio | 0.038 | |