| 85.71% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 2 | | adverbTags | | 0 | "The word tasted like [like]" | | 1 | "I asked again [again]" |
| | dialogueSentences | 35 | | tagDensity | 0.314 | | leniency | 0.629 | | rawRatio | 0.182 | | effectiveRatio | 0.114 | |
| 95.84% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1201 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 70.86% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1201 | | totalAiIsms | 7 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | word | "down my spine" | | count | 1 |
| | 5 | |
| | highlights | | 0 | "throbbed" | | 1 | "flicked" | | 2 | "reminder" | | 3 | "pulse" | | 4 | "down my spine" | | 5 | "velvet" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "sent a shiver through" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 88 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 88 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 111 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 3 | | totalWords | 1192 | | ratio | 0.003 | | matches | | 0 | "chérie" | | 1 | "Aurora" | | 2 | "chérie" |
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| 75.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 1 | | matches | | 0 | "Three of them, Eva insisted, like the flat was Fort Knox instead of a shoebox above a curry house." |
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| 74.59% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 862 | | uniqueNames | 14 | | maxNameDensity | 1.51 | | worstName | "Lucien" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Lucien" | | discoveredNames | | Eva | 4 | | Fort | 1 | | Knox | 1 | | Moreau | 3 | | London | 3 | | French | 1 | | Lucien | 13 | | Cardiff | 1 | | Silas | 2 | | Rory | 1 | | Laila | 1 | | Velvet | 1 | | Hour | 1 | | Three | 3 |
| | persons | | 0 | "Eva" | | 1 | "Moreau" | | 2 | "Lucien" | | 3 | "Silas" | | 4 | "Rory" | | 5 | "Laila" |
| | places | | 0 | "Fort" | | 1 | "Knox" | | 2 | "London" | | 3 | "Cardiff" | | 4 | "Velvet" |
| | globalScore | 0.746 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1192 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 111 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 29.8 | | std | 19.86 | | cv | 0.666 | | sampleLengths | | 0 | 52 | | 1 | 76 | | 2 | 24 | | 3 | 43 | | 4 | 16 | | 5 | 12 | | 6 | 47 | | 7 | 27 | | 8 | 18 | | 9 | 17 | | 10 | 53 | | 11 | 63 | | 12 | 19 | | 13 | 16 | | 14 | 88 | | 15 | 39 | | 16 | 6 | | 17 | 31 | | 18 | 57 | | 19 | 39 | | 20 | 8 | | 21 | 28 | | 22 | 23 | | 23 | 46 | | 24 | 50 | | 25 | 25 | | 26 | 25 | | 27 | 44 | | 28 | 24 | | 29 | 17 | | 30 | 15 | | 31 | 15 | | 32 | 7 | | 33 | 19 | | 34 | 2 | | 35 | 29 | | 36 | 43 | | 37 | 4 | | 38 | 19 | | 39 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 88 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 159 | | matches | | 0 | "was savouring" | | 1 | "was memorising" |
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| 39.90% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 111 | | ratio | 0.036 | | matches | | 0 | "Lucien’s cologne—something dark and expensive, like oud and leather—cut through it, unwelcome." | | 1 | "But the way he said Silas’s name—like it was a curse, like it was a secret—made my skin prickle." | | 2 | "The door swung wider, revealing the chaos of Eva’s flat—the books stacked in precarious towers, the scrolls unrolled across the coffee table, the half-empty mug of tea gone cold." | | 3 | "But the way he looked at me—like he was memorising my face, like he thought this might be the last time—made my chest tighten." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 871 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.03673938002296211 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.002296211251435132 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 111 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 111 | | mean | 10.74 | | std | 6.8 | | cv | 0.633 | | sampleLengths | | 0 | 11 | | 1 | 19 | | 2 | 4 | | 3 | 18 | | 4 | 19 | | 5 | 12 | | 6 | 6 | | 7 | 22 | | 8 | 17 | | 9 | 11 | | 10 | 13 | | 11 | 6 | | 12 | 2 | | 13 | 16 | | 14 | 19 | | 15 | 11 | | 16 | 5 | | 17 | 7 | | 18 | 5 | | 19 | 10 | | 20 | 18 | | 21 | 19 | | 22 | 15 | | 23 | 6 | | 24 | 6 | | 25 | 14 | | 26 | 4 | | 27 | 2 | | 28 | 5 | | 29 | 10 | | 30 | 11 | | 31 | 19 | | 32 | 23 | | 33 | 19 | | 34 | 12 | | 35 | 13 | | 36 | 19 | | 37 | 11 | | 38 | 8 | | 39 | 6 | | 40 | 10 | | 41 | 5 | | 42 | 13 | | 43 | 23 | | 44 | 2 | | 45 | 2 | | 46 | 13 | | 47 | 10 | | 48 | 20 | | 49 | 9 |
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| 54.35% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.36936936936936937 | | totalSentences | 111 | | uniqueOpeners | 41 | |
| 45.66% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 73 | | matches | | 0 | "Instead, he pulled out a" |
| | ratio | 0.014 | |
| 77.53% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 73 | | matches | | 0 | "My fingers froze mid-air." | | 1 | "His platinum hair was slicked" | | 2 | "My name rolled off his" | | 3 | "I should have slammed the" | | 4 | "My voice came out steadier" | | 5 | "He stepped forward, and I" | | 6 | "It came out sharp, bitter." | | 7 | "His cane tapped against the" | | 8 | "I crossed my arms, the" | | 9 | "His gaze flicked to the" | | 10 | "He reached into his jacket," | | 11 | "He held it between two" | | 12 | "I recognised the handwriting before" | | 13 | "They always did." | | 14 | "I didn’t take it." | | 15 | "He tucked it back into" | | 16 | "I should have laughed." | | 17 | "I asked again, quieter this" | | 18 | "I stepped back, putting more" | | 19 | "He just stood there, watching" |
| | ratio | 0.356 | |
| 35.34% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 73 | | matches | | 0 | "The lock clicked before I" | | 1 | "My fingers froze mid-air." | | 2 | "The door swung open on" | | 3 | "Lucien Moreau stood in the" | | 4 | "The amber eye caught the" | | 5 | "The black one swallowed it" | | 6 | "His platinum hair was slicked" | | 7 | "A charcoal suit clung to" | | 8 | "My name rolled off his" | | 9 | "The French accent curled around" | | 10 | "I should have slammed the" | | 11 | "The small crescent scar on" | | 12 | "My voice came out steadier" | | 13 | "Lucien’s lips quirked, not quite" | | 14 | "The hallway suddenly felt too" | | 15 | "Ptolemy, Eva’s tabby cat, slinked" | | 16 | "The cat paused, sniffed the" | | 17 | "The police wouldn’t touch Lucien" | | 18 | "He stepped forward, and I" | | 19 | "It came out sharp, bitter." |
| | ratio | 0.849 | |
| 68.49% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 73 | | matches | | 0 | "Because I knew, even as" |
| | ratio | 0.014 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 4 | | matches | | 0 | "A charcoal suit clung to his frame, tailored to perfection, the kind that whispered money and secrets." | | 1 | "The man who’d given me a job and a roof over my head when I fled Cardiff with nothing but a duffel bag and a broken wrist." | | 2 | "The man who, according to Lucien, was about to set London’s supernatural underworld on fire." | | 3 | "I stood there, heart hammering, the scent of his cologne lingering like a ghost." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 35 | | tagDensity | 0.029 | | leniency | 0.057 | | rawRatio | 0 | | effectiveRatio | 0 | |