| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 102 | | tagDensity | 0.039 | | leniency | 0.078 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.39% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1913 | | 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) | |
| 66.02% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1913 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "weight" | | 2 | "familiar" | | 3 | "flawless" | | 4 | "standard" | | 5 | "pulse" | | 6 | "comforting" | | 7 | "silence" | | 8 | "shattered" | | 9 | "remarkable" | | 10 | "sense of" | | 11 | "flicked" |
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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 | 142 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 142 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 240 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1913 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 86.42% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 1337 | | uniqueNames | 11 | | maxNameDensity | 1.27 | | worstName | "Lucien" | | maxWindowNameDensity | 2 | | worstWindowName | "Aurora" | | discoveredNames | | Carter | 1 | | Moreau | 1 | | Soho | 2 | | Golden | 1 | | Empress | 1 | | French | 2 | | Aurora | 15 | | Lucien | 17 | | Rain | 2 | | Eva | 3 | | Ptolemy | 5 |
| | persons | | 0 | "Carter" | | 1 | "Moreau" | | 2 | "Aurora" | | 3 | "Lucien" | | 4 | "Rain" | | 5 | "Eva" | | 6 | "Ptolemy" |
| | places | | 0 | "Soho" | | 1 | "Golden" | | 2 | "French" |
| | globalScore | 0.864 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 100 | | 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 | 1913 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 240 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 174 | | mean | 10.99 | | std | 12.91 | | cv | 1.174 | | sampleLengths | | 0 | 13 | | 1 | 60 | | 2 | 6 | | 3 | 46 | | 4 | 3 | | 5 | 2 | | 6 | 4 | | 7 | 2 | | 8 | 10 | | 9 | 8 | | 10 | 59 | | 11 | 7 | | 12 | 5 | | 13 | 4 | | 14 | 28 | | 15 | 6 | | 16 | 4 | | 17 | 23 | | 18 | 1 | | 19 | 43 | | 20 | 56 | | 21 | 10 | | 22 | 11 | | 23 | 19 | | 24 | 4 | | 25 | 7 | | 26 | 3 | | 27 | 12 | | 28 | 15 | | 29 | 4 | | 30 | 4 | | 31 | 4 | | 32 | 7 | | 33 | 11 | | 34 | 3 | | 35 | 1 | | 36 | 54 | | 37 | 24 | | 38 | 4 | | 39 | 4 | | 40 | 5 | | 41 | 6 | | 42 | 8 | | 43 | 4 | | 44 | 34 | | 45 | 6 | | 46 | 11 | | 47 | 4 | | 48 | 17 | | 49 | 2 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 142 | | matches | | 0 | "been drawn" | | 1 | "been crossed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 230 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 240 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1340 | | adjectiveStacks | 1 | | stackExamples | | 0 | "faint rust-coloured smear" |
| | adverbCount | 33 | | adverbRatio | 0.024626865671641792 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0029850746268656717 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 240 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 240 | | mean | 7.97 | | std | 6.1 | | cv | 0.766 | | sampleLengths | | 0 | 13 | | 1 | 30 | | 2 | 8 | | 3 | 22 | | 4 | 6 | | 5 | 8 | | 6 | 16 | | 7 | 9 | | 8 | 7 | | 9 | 6 | | 10 | 3 | | 11 | 2 | | 12 | 4 | | 13 | 2 | | 14 | 10 | | 15 | 8 | | 16 | 22 | | 17 | 18 | | 18 | 19 | | 19 | 7 | | 20 | 5 | | 21 | 4 | | 22 | 6 | | 23 | 9 | | 24 | 13 | | 25 | 6 | | 26 | 4 | | 27 | 5 | | 28 | 18 | | 29 | 1 | | 30 | 9 | | 31 | 9 | | 32 | 9 | | 33 | 16 | | 34 | 19 | | 35 | 8 | | 36 | 29 | | 37 | 10 | | 38 | 11 | | 39 | 15 | | 40 | 4 | | 41 | 4 | | 42 | 7 | | 43 | 3 | | 44 | 12 | | 45 | 15 | | 46 | 4 | | 47 | 4 | | 48 | 4 | | 49 | 3 |
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| 43.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.25416666666666665 | | totalSentences | 240 | | uniqueOpeners | 61 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 134 | | matches | | 0 | "Somewhere behind the wall, a" | | 1 | "Of course he noticed." | | 2 | "Then he had kissed her." | | 3 | "Then Lucien reached into his" | | 4 | "Slowly, he withdrew a folded" |
| | ratio | 0.037 | |
| 64.78% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 52 | | totalSentences | 134 | | matches | | 0 | "His platinum hair remained slicked" | | 1 | "He held his ivory-handled cane" | | 2 | "His mouth tightened." | | 3 | "She had watched him put" | | 4 | "She had not asked him" | | 5 | "His gaze returned to hers." | | 6 | "Her father used it when" | | 7 | "She opened the door another" | | 8 | "He had not worn one" | | 9 | "He had stood outside the" | | 10 | "He had replied," | | 11 | "She had hated him for" | | 12 | "It came out flat." | | 13 | "He had not defended himself." | | 14 | "He had not produced a" | | 15 | "He had accepted the blow" | | 16 | "Her gaze moved over him." | | 17 | "She pushed the door wider" | | 18 | "His attention lingered there for" | | 19 | "She pulled her sleeve down." |
| | ratio | 0.388 | |
| 8.51% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 121 | | totalSentences | 134 | | matches | | 0 | "The door opened on the" | | 1 | "Aurora Carter kept one hand" | | 2 | "Ptolemy hissed from somewhere near" | | 3 | "The curry house below sent" | | 4 | "Lucien Moreau stood on the" | | 5 | "Rain darkened the shoulders of" | | 6 | "His platinum hair remained slicked" | | 7 | "He held his ivory-handled cane" | | 8 | "The amber eye fixed on" | | 9 | "The black one watched the" | | 10 | "His mouth tightened." | | 11 | "Aurora kept the knife where" | | 12 | "She had watched him put" | | 13 | "She had not asked him" | | 14 | "That had been the problem." | | 15 | "Lucien glanced down the narrow" | | 16 | "The landing smelled of wet" | | 17 | "His gaze returned to hers." | | 18 | "The amber eye warmed with" | | 19 | "Nobody used her full name" |
| | ratio | 0.903 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 134 | | matches | (empty) | | ratio | 0 | |
| 55.39% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 49 | | technicalSentenceCount | 6 | | matches | | 0 | "The curry house below sent up a thick cloud of cumin, frying onions, and burnt cardamom, which clung to the cramped hallway." | | 1 | "The amber eye warmed with something that looked too much like pain before the black one swallowed it." | | 2 | "He had not produced a clever excuse in French or one of those polished half-truths that kept the supernatural underworld trading information through him." | | 3 | "Their last conversation had taken place in an alley behind the restaurant, with rain hammering the bins and Lucien’s blood running into the gutter." | | 4 | "For three years, she had carried the memory of him looking at her as if she had handed him something breakable." | | 5 | "His eyes flicked over her face, catching on her mouth before returning to her eyes." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 102 | | tagDensity | 0.039 | | leniency | 0.078 | | rawRatio | 0.25 | | effectiveRatio | 0.02 | |