| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 58 | | tagDensity | 0.276 | | leniency | 0.552 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.59% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1315 | | 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) | |
| 27.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1315 | | totalAiIsms | 19 | | found | | 0 | | | 1 | | | 2 | | word | "down her spine" | | count | 1 |
| | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | |
| | highlights | | 0 | "predator" | | 1 | "tension" | | 2 | "down her spine" | | 3 | "flicked" | | 4 | "throbbed" | | 5 | "pulse" | | 6 | "weight" | | 7 | "silence" | | 8 | "charged" | | 9 | "electric" | | 10 | "searing" | | 11 | "shattered" | | 12 | "unreadable" | | 13 | "perfect" | | 14 | "trembled" |
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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 | 105 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 105 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 147 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 9 | | markdownWords | 11 | | totalWords | 1308 | | ratio | 0.008 | | matches | | 0 | "you" | | 1 | "busy" | | 2 | "vanished" | | 3 | "chérie" | | 4 | "please" | | 5 | "hell" | | 6 | "actual fuck" | | 7 | "two seconds" | | 8 | "will" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 34.65% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 56 | | wordCount | 997 | | uniqueNames | 9 | | maxNameDensity | 2.31 | | worstName | "Rory" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Lucien" | | discoveredNames | | January | 1 | | Moreau | 1 | | Eva | 7 | | Lucien | 19 | | French | 1 | | Ptolemy | 2 | | Golden | 1 | | Empress | 1 | | Rory | 23 |
| | persons | | 0 | "January" | | 1 | "Moreau" | | 2 | "Eva" | | 3 | "Lucien" | | 4 | "Ptolemy" | | 5 | "Rory" |
| | places | | | globalScore | 0.347 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 73 | | 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 | 1308 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 147 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 64 | | mean | 20.44 | | std | 18.48 | | cv | 0.904 | | sampleLengths | | 0 | 6 | | 1 | 90 | | 2 | 11 | | 3 | 15 | | 4 | 24 | | 5 | 40 | | 6 | 65 | | 7 | 31 | | 8 | 9 | | 9 | 28 | | 10 | 28 | | 11 | 43 | | 12 | 3 | | 13 | 21 | | 14 | 25 | | 15 | 6 | | 16 | 4 | | 17 | 5 | | 18 | 47 | | 19 | 20 | | 20 | 13 | | 21 | 19 | | 22 | 33 | | 23 | 23 | | 24 | 8 | | 25 | 7 | | 26 | 34 | | 27 | 40 | | 28 | 2 | | 29 | 10 | | 30 | 3 | | 31 | 1 | | 32 | 37 | | 33 | 4 | | 34 | 48 | | 35 | 5 | | 36 | 7 | | 37 | 5 | | 38 | 63 | | 39 | 62 | | 40 | 1 | | 41 | 9 | | 42 | 1 | | 43 | 12 | | 44 | 2 | | 45 | 9 | | 46 | 22 | | 47 | 10 | | 48 | 30 | | 49 | 11 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 105 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 173 | | matches | | 0 | "was considering" | | 1 | "was working" |
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| 65.11% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 147 | | ratio | 0.027 | | matches | | 0 | "Lucien Moreau leaned against the doorframe of Eva’s flat, his heterochromatic gaze—one amber, one black—fixed on her with the precision of a predator." | | 1 | "Just the barest edge of something darker—something that sent a shiver down her spine." | | 2 | "His hand came up, fingers brushing the scar on her wrist—light, almost reverent." | | 3 | "Lucien’s head snapped up, his eyes black with hunger—and something else." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1003 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 38 | | adverbRatio | 0.03788634097706879 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.005982053838484547 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 147 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 147 | | mean | 8.9 | | std | 6.53 | | cv | 0.734 | | sampleLengths | | 0 | 6 | | 1 | 17 | | 2 | 24 | | 3 | 23 | | 4 | 26 | | 5 | 11 | | 6 | 10 | | 7 | 5 | | 8 | 3 | | 9 | 4 | | 10 | 11 | | 11 | 6 | | 12 | 26 | | 13 | 14 | | 14 | 13 | | 15 | 43 | | 16 | 9 | | 17 | 3 | | 18 | 7 | | 19 | 17 | | 20 | 4 | | 21 | 6 | | 22 | 3 | | 23 | 19 | | 24 | 9 | | 25 | 7 | | 26 | 8 | | 27 | 8 | | 28 | 5 | | 29 | 20 | | 30 | 16 | | 31 | 7 | | 32 | 3 | | 33 | 8 | | 34 | 13 | | 35 | 3 | | 36 | 18 | | 37 | 4 | | 38 | 6 | | 39 | 4 | | 40 | 3 | | 41 | 2 | | 42 | 4 | | 43 | 20 | | 44 | 15 | | 45 | 8 | | 46 | 5 | | 47 | 15 | | 48 | 9 | | 49 | 4 |
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| 45.92% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.29931972789115646 | | totalSentences | 147 | | uniqueOpeners | 44 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 98 | | matches | | 0 | "Just the barest edge of" | | 1 | "Instead, he prowled the small" | | 2 | "Instead, she leaned in." | | 3 | "Then, slowly, he bent to" |
| | ratio | 0.041 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 98 | | matches | | 0 | "She didn’t move." | | 1 | "His lips quirked, but there" | | 2 | "He never did when he" | | 3 | "His gaze flicked to the" | | 4 | "He stopped in front of" | | 5 | "His voice dropped, low and" | | 6 | "She forced herself to meet" | | 7 | "His jaw tightened." | | 8 | "He straightened, the movement fluid," | | 9 | "His expression darkened." | | 10 | "Her pulse roared in her" | | 11 | "She should have stepped back." | | 12 | "His hand came up, fingers" | | 13 | "She should have pulled away." | | 14 | "His other hand found her" | | 15 | "It wasn’t gentle." | | 16 | "It wasn’t sweet." | | 17 | "It was fire and fury," | | 18 | "His tongue swept into her" | | 19 | "he growled against her skin" |
| | ratio | 0.255 | |
| 16.12% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 98 | | matches | | 0 | "The deadbolt clicked like a" | | 1 | "Rory stood in the doorway," | | 2 | "The scent of curry from" | | 3 | "Lucien Moreau leaned against the" | | 4 | "The ivory handle of his" | | 5 | "Rory said, her voice steadier" | | 6 | "Lucien replied, his French accent" | | 7 | "She didn’t move." | | 8 | "The three deadbolts on the" | | 9 | "His lips quirked, but there" | | 10 | "Rory exhaled through her nose," | | 11 | "The flat was exactly as" | | 12 | "The door clicked shut behind" | | 13 | "Lucien didn’t sit." | | 14 | "He never did when he" | | 15 | "Rory said, crossing her arms" | | 16 | "His gaze flicked to the" | | 17 | "The crescent-shaped scar on her" | | 18 | "A childhood accident, her mother" | | 19 | "He stopped in front of" |
| | ratio | 0.888 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 98 | | matches | (empty) | | ratio | 0 | |
| 89.29% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 3 | | matches | | 0 | "Just the barest edge of something darker—something that sent a shiver down her spine." | | 1 | "Rory kissed him back just as fiercely, her nails digging into his shoulders as if she could anchor herself to him." | | 2 | "Lucien’s mouth left hers, trailing scorching kisses down her throat, his teeth grazing her pulse point." |
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| 31.25% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 3 | | matches | | 0 | "Rory said, her voice steadier than she felt" | | 1 | "Lucien replied, his French accent smooth as aged whisky" | | 2 | "Lucien stepped, his cane tapping against the floor" |
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| 81.03% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 4 | | fancyTags | | 0 | "Rory snapped (snap)" | | 1 | "she whispered (whisper)" | | 2 | "she breathed (breathe)" | | 3 | "he growled (growl)" |
| | dialogueSentences | 58 | | tagDensity | 0.121 | | leniency | 0.241 | | rawRatio | 0.571 | | effectiveRatio | 0.138 | |