| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 2 | | adverbTags | | 0 | "Lucien said quietly [quietly]" | | 1 | "His thumb moved barely [barely]" |
| | dialogueSentences | 67 | | tagDensity | 0.328 | | leniency | 0.657 | | rawRatio | 0.091 | | effectiveRatio | 0.06 | |
| 88.49% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1738 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "very" | | 1 | "carefully" | | 2 | "slowly" |
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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) | |
| 85.62% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1738 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "pulse" | | 1 | "silk" | | 2 | "stomach" | | 3 | "traced" | | 4 | "silence" |
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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 | 86 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 86 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 130 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 61 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 6 | | markdownWords | 33 | | totalWords | 1739 | | ratio | 0.019 | | matches | | 0 | "Rory" | | 1 | "DO NOT OPEN — BELLADONNA (PROBABLY)" | | 2 | "Fell off a wall when I was eight" | | 3 | "Showing off for Eva." | | 4 | "I'm only doing this because I love you" | | 5 | "Rory, Castellane's hunting, here's my plan" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 28 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 1049 | | uniqueNames | 12 | | maxNameDensity | 0.76 | | worstName | "Eva" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Eva" | | discoveredNames | | Eva | 8 | | Commercial | 1 | | Road | 1 | | Moreau | 1 | | Ptolemy | 4 | | Rory | 6 | | Lucien | 6 | | Victorian | 1 | | Golden | 1 | | Empress | 1 | | French | 1 | | Evan | 1 |
| | persons | | 0 | "Eva" | | 1 | "Moreau" | | 2 | "Ptolemy" | | 3 | "Rory" | | 4 | "Lucien" | | 5 | "Evan" |
| | places | | 0 | "Commercial" | | 1 | "Road" | | 2 | "Victorian" | | 3 | "Golden" | | 4 | "French" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | glossingSentenceCount | 1 | | matches | | 0 | "as if apologising for the third" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.575 | | wordCount | 1739 | | matches | | 0 | "not to, mostly, but it came anyway: the way he'd rearranged her life around what" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 130 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 75 | | mean | 23.19 | | std | 22.65 | | cv | 0.977 | | sampleLengths | | 0 | 8 | | 1 | 58 | | 2 | 5 | | 3 | 63 | | 4 | 31 | | 5 | 3 | | 6 | 25 | | 7 | 5 | | 8 | 2 | | 9 | 7 | | 10 | 39 | | 11 | 35 | | 12 | 11 | | 13 | 1 | | 14 | 5 | | 15 | 28 | | 16 | 13 | | 17 | 3 | | 18 | 9 | | 19 | 37 | | 20 | 77 | | 21 | 18 | | 22 | 53 | | 23 | 8 | | 24 | 4 | | 25 | 3 | | 26 | 11 | | 27 | 50 | | 28 | 5 | | 29 | 14 | | 30 | 31 | | 31 | 3 | | 32 | 39 | | 33 | 7 | | 34 | 1 | | 35 | 4 | | 36 | 14 | | 37 | 10 | | 38 | 74 | | 39 | 7 | | 40 | 55 | | 41 | 54 | | 42 | 7 | | 43 | 26 | | 44 | 18 | | 45 | 9 | | 46 | 1 | | 47 | 4 | | 48 | 35 | | 49 | 4 |
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| 97.10% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 86 | | matches | | 0 | "was wrapped" | | 1 | "being caught" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 188 | | matches | | 0 | "were trying" | | 1 | "was watching" |
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| 98.90% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 130 | | ratio | 0.015 | | matches | | 0 | "She filled the kettle, because that was what you did, because her mother had done it in every crisis of her childhood, and then she dug the first-aid kit out from under the sink where Eva kept it next to a jar of something labelled *DO NOT OPEN — BELLADONNA (PROBABLY)*." | | 1 | "She realised she'd moved closer; or he had; or the flat was simply too small for distance." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1052 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 37 | | adverbRatio | 0.03517110266159696 | | lyAdverbCount | 16 | | lyAdverbRatio | 0.015209125475285171 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 130 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 130 | | mean | 13.38 | | std | 12.03 | | cv | 0.899 | | sampleLengths | | 0 | 8 | | 1 | 27 | | 2 | 31 | | 3 | 5 | | 4 | 17 | | 5 | 22 | | 6 | 9 | | 7 | 15 | | 8 | 16 | | 9 | 15 | | 10 | 3 | | 11 | 2 | | 12 | 23 | | 13 | 5 | | 14 | 2 | | 15 | 7 | | 16 | 15 | | 17 | 9 | | 18 | 15 | | 19 | 11 | | 20 | 19 | | 21 | 5 | | 22 | 9 | | 23 | 2 | | 24 | 1 | | 25 | 5 | | 26 | 6 | | 27 | 22 | | 28 | 13 | | 29 | 3 | | 30 | 9 | | 31 | 8 | | 32 | 4 | | 33 | 25 | | 34 | 14 | | 35 | 26 | | 36 | 18 | | 37 | 19 | | 38 | 3 | | 39 | 15 | | 40 | 2 | | 41 | 51 | | 42 | 8 | | 43 | 4 | | 44 | 3 | | 45 | 11 | | 46 | 4 | | 47 | 4 | | 48 | 25 | | 49 | 11 |
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| 58.72% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.38461538461538464 | | totalSentences | 130 | | uniqueOpeners | 50 | |
| 46.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 72 | | matches | | 0 | "Somewhere behind her, Ptolemy thumped" |
| | ratio | 0.014 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 72 | | matches | | 0 | "She had undone all three" | | 1 | "His platinum hair, usually slicked" | | 2 | "He held the ivory-handled cane" | | 3 | "His right hand was wrapped" | | 4 | "He had only ever called" | | 5 | "His amber eye caught the" | | 6 | "She kept one hand on" | | 7 | "Her voice came out level," | | 8 | "He glanced down at it" | | 9 | "He stepped past her into" | | 10 | "He stood in the middle" | | 11 | "She filled the kettle, because" | | 12 | "He held it out." | | 13 | "She unwound the cloth." | | 14 | "She'd seen worse on the" | | 15 | "It still made her stomach" | | 16 | "She tipped antiseptic onto a" | | 17 | "He hissed through his teeth," | | 18 | "His voice was even, the" | | 19 | "She started winding gauze around" |
| | ratio | 0.569 | |
| 29.44% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 72 | | matches | | 0 | "The door opened, and it" | | 1 | "Rory knew it the way" | | 2 | "She had undone all three" | | 3 | "Lucien Moreau stood on the" | | 4 | "His platinum hair, usually slicked" | | 5 | "He held the ivory-handled cane" | | 6 | "His right hand was wrapped" | | 7 | "The smell of cumin and" | | 8 | "He had only ever called" | | 9 | "His amber eye caught the" | | 10 | "The black one gave nothing" | | 11 | "She kept one hand on" | | 12 | "Her voice came out level," | | 13 | "He glanced down at it" | | 14 | "That landed somewhere under her" | | 15 | "Ptolemy wound between her ankles," | | 16 | "Something moved at the corner" | | 17 | "He stepped past her into" | | 18 | "Eva's flat was not built" | | 19 | "Every surface held something: books" |
| | ratio | 0.861 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 72 | | matches | (empty) | | ratio | 0 | |
| 86.47% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 3 | | matches | | 0 | "She had undone all three deadbolts because Eva always forgot her keys, and because nobody else knocked like that, two quick raps and a pause, as if apologising …" | | 1 | "His right hand was wrapped in a white handkerchief that was not entirely white anymore." | | 2 | "She turned her hand in his, laced her fingers through his uninjured ones, and felt him go very still, as if he were afraid to breathe and break it." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, not looking up" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 67 | | tagDensity | 0.194 | | leniency | 0.388 | | rawRatio | 0 | | effectiveRatio | 0 | |