| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 46 | | tagDensity | 0.261 | | leniency | 0.522 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1030 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 51.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1030 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "gleaming" | | 1 | "throbbed" | | 2 | "traced" | | 3 | "glint" | | 4 | "flicked" | | 5 | "raced" | | 6 | "reminder" | | 7 | "silence" | | 8 | "sanctuary" | | 9 | "predator" |
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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 | 60 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 60 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 94 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1030 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 754 | | uniqueNames | 14 | | maxNameDensity | 1.19 | | worstName | "Rory" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Rory" | | discoveredNames | | Eva | 5 | | Moreau | 1 | | Brick | 1 | | Lane | 1 | | English | 1 | | Marseille | 1 | | Rory | 9 | | Ptolemy | 3 | | Carter | 1 | | Lucien | 5 | | Cardiff | 1 | | London | 3 | | Whitechapel | 1 | | Silas | 1 |
| | persons | | 0 | "Eva" | | 1 | "Moreau" | | 2 | "English" | | 3 | "Rory" | | 4 | "Ptolemy" | | 5 | "Carter" | | 6 | "Lucien" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "Marseille" | | 3 | "Cardiff" | | 4 | "London" | | 5 | "Silas" |
| | globalScore | 0.903 | | windowScore | 0.833 | |
| 45.83% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 48 | | glossingSentenceCount | 2 | | matches | | 0 | "seemed smaller the books pressing closer" | | 1 | "smelled like burnt sugar and distant storm" |
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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 | 1030 | | matches | (empty) | |
| 60.28% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 94 | | matches | | 0 | "knew that page" | | 1 | "hated that she" | | 2 | "Hated that her" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 19.81 | | std | 14.86 | | cv | 0.75 | | sampleLengths | | 0 | 46 | | 1 | 42 | | 2 | 4 | | 3 | 13 | | 4 | 38 | | 5 | 41 | | 6 | 16 | | 7 | 5 | | 8 | 2 | | 9 | 37 | | 10 | 5 | | 11 | 28 | | 12 | 17 | | 13 | 5 | | 14 | 7 | | 15 | 25 | | 16 | 12 | | 17 | 38 | | 18 | 6 | | 19 | 10 | | 20 | 26 | | 21 | 2 | | 22 | 37 | | 23 | 9 | | 24 | 9 | | 25 | 9 | | 26 | 45 | | 27 | 6 | | 28 | 37 | | 29 | 17 | | 30 | 19 | | 31 | 36 | | 32 | 3 | | 33 | 1 | | 34 | 2 | | 35 | 12 | | 36 | 13 | | 37 | 20 | | 38 | 6 | | 39 | 12 | | 40 | 17 | | 41 | 29 | | 42 | 14 | | 43 | 4 | | 44 | 6 | | 45 | 24 | | 46 | 23 | | 47 | 36 | | 48 | 16 | | 49 | 52 |
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| 99.42% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 60 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 137 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 94 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 759 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.03293807641633729 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007905138339920948 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 94 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 94 | | mean | 10.96 | | std | 8.26 | | cv | 0.754 | | sampleLengths | | 0 | 27 | | 1 | 19 | | 2 | 14 | | 3 | 28 | | 4 | 4 | | 5 | 11 | | 6 | 2 | | 7 | 3 | | 8 | 20 | | 9 | 6 | | 10 | 9 | | 11 | 14 | | 12 | 27 | | 13 | 13 | | 14 | 3 | | 15 | 5 | | 16 | 2 | | 17 | 11 | | 18 | 26 | | 19 | 5 | | 20 | 19 | | 21 | 9 | | 22 | 3 | | 23 | 10 | | 24 | 4 | | 25 | 5 | | 26 | 4 | | 27 | 3 | | 28 | 6 | | 29 | 8 | | 30 | 11 | | 31 | 8 | | 32 | 4 | | 33 | 33 | | 34 | 5 | | 35 | 3 | | 36 | 3 | | 37 | 10 | | 38 | 8 | | 39 | 18 | | 40 | 2 | | 41 | 31 | | 42 | 6 | | 43 | 4 | | 44 | 5 | | 45 | 7 | | 46 | 2 | | 47 | 9 | | 48 | 9 | | 49 | 10 |
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| 74.47% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4787234042553192 | | totalSentences | 94 | | uniqueOpeners | 45 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 59 | | matches | | 0 | "Instead she stepped back, leaving" | | 1 | "Only people who knew her" | | 2 | "Only people who had hurt" | | 3 | "Instead she heard herself ask," | | 4 | "Instead she pulled up her" | | 5 | "Then she pushed into the" |
| | ratio | 0.102 | |
| 43.73% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 59 | | matches | | 0 | "She expected Eva's usual post-grad-school" | | 1 | "His ivory cane rested in" | | 2 | "His English curled with Marseille," | | 3 | "She should have slammed the" | | 4 | "He moved past the curry-house" | | 5 | "He gestured to the three" | | 6 | "He turned, and she saw" | | 7 | "She hadn't noticed." | | 8 | "She flicked it off." | | 9 | "He smiled, sharp" | | 10 | "She folded her arms, bright" | | 11 | "He set his cane against" | | 12 | "He moved closer, close enough" | | 13 | "She had burned her copy." | | 14 | "She stepped back, hitting the" | | 15 | "His black eye seemed to" | | 16 | "She hated that she believed" | | 17 | "He studied her, head tilting." | | 18 | "He retrieved his cane" | | 19 | "She should have said no." |
| | ratio | 0.441 | |
| 44.75% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 49 | | totalSentences | 59 | | matches | | 0 | "Rory yanked the flat's second" | | 1 | "She expected Eva's usual post-grad-school" | | 2 | "Lucien Moreau leaned against the" | | 3 | "His ivory cane rested in" | | 4 | "His English curled with Marseille," | | 5 | "Rory's spine locked." | | 6 | "The flat behind her smelled" | | 7 | "She should have slammed the" | | 8 | "Lucien entered without invitation, his" | | 9 | "He moved past the curry-house" | | 10 | "He gestured to the three" | | 11 | "Rory dropped her bag onto" | | 12 | "The crescent scar on her" | | 13 | "He turned, and she saw" | | 14 | "She hadn't noticed." | | 15 | "She flicked it off." | | 16 | "He smiled, sharp" | | 17 | "The nickname hit like a" | | 18 | "She folded her arms, bright" | | 19 | "He set his cane against" |
| | ratio | 0.831 | |
| 84.75% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 59 | | matches | | 0 | "Now she did, her fingers" |
| | ratio | 0.017 | |
| 5.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 5 | | matches | | 0 | "She expected Eva's usual post-grad-school chaos, not the precise, expensive silhouette that filled the doorway like a tailored warning." | | 1 | "His ivory cane rested in his right hand, and his heterochromatic eyes, one amber, one black, fixed on her with the patience of something that hunted by waiting." | | 2 | "He moved past the curry-house smell that seeped through the floorboards, past towers of occult texts, and stopped near the cramped kitchen where a kettle hissed…" | | 3 | "She pulled her phone from her pocket, her fingers flying, but stopped before dialling Eva's number." | | 4 | "Then she pushed into the street, the London air biting her cheeks, and walked toward the restaurant that was her prison and her only income, knowing that tomorr…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 3 | | matches | | 0 | "He smiled, sharp" | | 1 | "she said, her voice steady despite the tremor in her hands" | | 2 | "He moved, cane tapping its slow, inevitable rhythm" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 46 | | tagDensity | 0.022 | | leniency | 0.043 | | rawRatio | 0 | | effectiveRatio | 0 | |