| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 54 | | tagDensity | 0.315 | | leniency | 0.63 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.31% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1403 | | totalAiIsmAdverbs | 3 | | 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) | |
| 89.31% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1403 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "weight" | | 1 | "calculating" | | 2 | "streaming" |
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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 | 59 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 59 | | 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 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 70 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1417 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 20 | | unquotedAttributions | 0 | | matches | (empty) | |
| 80.56% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 720 | | uniqueNames | 10 | | maxNameDensity | 1.39 | | worstName | "Lucien" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Lucien" | | discoveredNames | | Eva | 4 | | Brick | 1 | | Lane | 1 | | Moreau | 1 | | Ptolemy | 3 | | Lucien | 10 | | Marseille | 1 | | London | 1 | | Rory | 9 | | Mrs | 2 |
| | persons | | 0 | "Eva" | | 1 | "Moreau" | | 2 | "Ptolemy" | | 3 | "Lucien" | | 4 | "Rory" | | 5 | "Mrs" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "Marseille" | | 3 | "London" |
| | globalScore | 0.806 | | windowScore | 0.833 | |
| 84.21% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 38 | | glossingSentenceCount | 1 | | matches | | 0 | "as though arranging himself for a performance" |
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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 | 1417 | | matches | (empty) | |
| 97.22% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 96 | | matches | | 0 | "hated that she" | | 1 | "found that the" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 59 | | mean | 24.02 | | std | 22.94 | | cv | 0.955 | | sampleLengths | | 0 | 38 | | 1 | 6 | | 2 | 67 | | 3 | 8 | | 4 | 19 | | 5 | 2 | | 6 | 22 | | 7 | 6 | | 8 | 2 | | 9 | 28 | | 10 | 6 | | 11 | 11 | | 12 | 42 | | 13 | 31 | | 14 | 3 | | 15 | 45 | | 16 | 57 | | 17 | 4 | | 18 | 1 | | 19 | 23 | | 20 | 1 | | 21 | 4 | | 22 | 41 | | 23 | 5 | | 24 | 8 | | 25 | 51 | | 26 | 16 | | 27 | 6 | | 28 | 5 | | 29 | 6 | | 30 | 34 | | 31 | 52 | | 32 | 2 | | 33 | 20 | | 34 | 63 | | 35 | 5 | | 36 | 9 | | 37 | 92 | | 38 | 8 | | 39 | 8 | | 40 | 31 | | 41 | 17 | | 42 | 3 | | 43 | 73 | | 44 | 15 | | 45 | 5 | | 46 | 76 | | 47 | 11 | | 48 | 26 | | 49 | 33 |
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| 93.37% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 59 | | matches | | 0 | "was gone" | | 1 | "been allowed" |
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| 95.01% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 127 | | matches | | 0 | "was standing" | | 1 | "was holding" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 96 | | ratio | 0.052 | | matches | | 0 | "Rory wrenched it free, already rehearsing her complaint — Eva had a key, she'd told her a dozen times to use it — and pulled the door open onto the Brick Lane landing." | | 1 | "His tie was gone, his collar open, and his platinum hair — always so immaculate, always slicked back as if nothing in the world could touch him — had fallen loose across his forehead." | | 2 | "He shifted his weight, and she caught it — the small tell, the half-second where his composure slipped and something raw looked out from behind it." | | 3 | "Books covered every surface of the flat — stacked on the coffee table, wedged sideways in the shelves, fanned across the kitchen counter beside a cooling mug of tea." | | 4 | "When she looked back, Lucien hadn't moved, but something in his face had — that raw thing behind the composure, closer to the surface now than she'd ever been allowed to see." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 760 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.031578947368421054 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.002631578947368421 | |
| 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 | 14.76 | | std | 13.63 | | cv | 0.923 | | sampleLengths | | 0 | 5 | | 1 | 33 | | 2 | 6 | | 3 | 17 | | 4 | 2 | | 5 | 34 | | 6 | 9 | | 7 | 5 | | 8 | 8 | | 9 | 9 | | 10 | 10 | | 11 | 2 | | 12 | 15 | | 13 | 7 | | 14 | 6 | | 15 | 2 | | 16 | 9 | | 17 | 19 | | 18 | 6 | | 19 | 11 | | 20 | 26 | | 21 | 16 | | 22 | 5 | | 23 | 26 | | 24 | 3 | | 25 | 22 | | 26 | 23 | | 27 | 9 | | 28 | 19 | | 29 | 29 | | 30 | 4 | | 31 | 1 | | 32 | 23 | | 33 | 1 | | 34 | 4 | | 35 | 27 | | 36 | 14 | | 37 | 5 | | 38 | 8 | | 39 | 26 | | 40 | 25 | | 41 | 5 | | 42 | 1 | | 43 | 10 | | 44 | 6 | | 45 | 5 | | 46 | 6 | | 47 | 1 | | 48 | 3 | | 49 | 14 |
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| 72.22% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4583333333333333 | | totalSentences | 96 | | uniqueOpeners | 44 | |
| 66.67% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 50 | | matches | | | ratio | 0.02 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 50 | | matches | | 0 | "His tie was gone, his" | | 1 | "She gripped the edge of" | | 2 | "Her knuckles whitened" | | 3 | "He shifted his weight, and" | | 4 | "Her name again, softer this" | | 5 | "She folded her arms" | | 6 | "His voice stayed level, but" | | 7 | "She laughed, and it came" | | 8 | "Her hands hung at her" | | 9 | "He set the cane against" | | 10 | "He stood three feet away," | | 11 | "She could put it down." | | 12 | "She wasn't ready to say" | | 13 | "She crossed to the door," | | 14 | "She pulled a second mug" |
| | ratio | 0.3 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 50 | | matches | | 0 | "The third deadbolt always stuck." | | 1 | "Rory wrenched it free, already" | | 2 | "Lucien Moreau stood in the" | | 3 | "Water darkened the shoulders of" | | 4 | "His tie was gone, his" | | 5 | "The black one drank it." | | 6 | "The sound of her full" | | 7 | "She gripped the edge of" | | 8 | "Her knuckles whitened" | | 9 | "He shifted his weight, and" | | 10 | "The landing seemed to tilt." | | 11 | "Rory braced a hand against" | | 12 | "Lucien's jaw tightened" | | 13 | "The rain hammered the metal" | | 14 | "Downstairs, the curry house vented" | | 15 | "Rory became aware that she" | | 16 | "Ptolemy abandoned her ankles and" | | 17 | "Lucien glanced down at him," | | 18 | "Her name again, softer this" | | 19 | "Rory looked over her shoulder." |
| | ratio | 0.92 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 49.69% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 23 | | technicalSentenceCount | 3 | | matches | | 0 | "Rory braced a hand against the doorframe, and Ptolemy wound between her ankles, purring up at the stranger as if he'd brought the cat treats himself." | | 1 | "Ptolemy abandoned her ankles and trotted straight to Lucien, headbutting his shin with the shameless enthusiasm of a cat who had never once been loyal to anyone…" | | 2 | "A sound escaped her that was half laugh, half something more dangerous." |
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| 36.76% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 3 | | matches | | 0 | "she hated, the harbour salt under the polished London vowels" | | 1 | "His voice stayed, but his thumb worked the head of the cane, circling, circling" | | 2 | "Lucien said, each word laid down like a card," |
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| 94.44% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 3 | | fancyTags | | 0 | "Lucien corrected (correct)" | | 1 | "She laughed (laugh)" | | 2 | "he agreed (agree)" |
| | dialogueSentences | 54 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0.333 | | effectiveRatio | 0.111 | |