| 87.64% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 29 | | adverbTagCount | 5 | | adverbTags | | 0 | "he said quietly [quietly]" | | 1 | "she said quietly [quietly]" | | 2 | "the anger leaking away [away]" | | 3 | "His expression softened slightly [slightly]" | | 4 | "she said slowly [slowly]" |
| | dialogueSentences | 89 | | tagDensity | 0.326 | | leniency | 0.652 | | rawRatio | 0.172 | | effectiveRatio | 0.112 | |
| 85.71% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1749 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "softly" | | 1 | "suddenly" | | 2 | "slightly" | | 3 | "slowly" | | 4 | "completely" |
| |
| 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) | |
| 77.13% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1749 | | totalAiIsms | 8 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | word | "down her spine" | | count | 1 |
| | 7 | |
| | highlights | | 0 | "lilt" | | 1 | "intensity" | | 2 | "shattered" | | 3 | "flickered" | | 4 | "calculated" | | 5 | "perfect" | | 6 | "down her spine" | | 7 | "stomach" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 95 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 95 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 152 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1754 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 19 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 1020 | | uniqueNames | 13 | | maxNameDensity | 0.78 | | worstName | "Lucien" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Lucien" | | discoveredNames | | Rory | 4 | | Lucien | 8 | | Moreau | 3 | | French | 1 | | Eva | 5 | | Italian | 1 | | Mayfair | 1 | | Old | 1 | | Ptolemy | 2 | | Defense | 1 | | Evan | 2 | | London | 2 | | Cardiff | 1 |
| | persons | | 0 | "Rory" | | 1 | "Lucien" | | 2 | "Moreau" | | 3 | "Eva" | | 4 | "Ptolemy" | | 5 | "Evan" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1754 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 152 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 82 | | mean | 21.39 | | std | 19 | | cv | 0.888 | | sampleLengths | | 0 | 22 | | 1 | 5 | | 2 | 27 | | 3 | 1 | | 4 | 1 | | 5 | 36 | | 6 | 3 | | 7 | 51 | | 8 | 4 | | 9 | 10 | | 10 | 1 | | 11 | 34 | | 12 | 70 | | 13 | 8 | | 14 | 3 | | 15 | 3 | | 16 | 38 | | 17 | 29 | | 18 | 6 | | 19 | 42 | | 20 | 11 | | 21 | 57 | | 22 | 19 | | 23 | 37 | | 24 | 33 | | 25 | 51 | | 26 | 19 | | 27 | 8 | | 28 | 5 | | 29 | 31 | | 30 | 2 | | 31 | 13 | | 32 | 14 | | 33 | 9 | | 34 | 1 | | 35 | 53 | | 36 | 28 | | 37 | 31 | | 38 | 8 | | 39 | 12 | | 40 | 15 | | 41 | 13 | | 42 | 4 | | 43 | 64 | | 44 | 20 | | 45 | 6 | | 46 | 30 | | 47 | 41 | | 48 | 6 | | 49 | 2 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 95 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 172 | | matches | | 0 | "was being" | | 1 | "was starting" |
| |
| 86.47% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 152 | | ratio | 0.02 | | matches | | 0 | "Behind her, she heard the click of his shoes on the threshold — expensive leather, probably Italian — and the softer snick of the door closing." | | 1 | "His mismatched eyes — amber and black, demon and human — fixed on her with an intensity that made her skin prickle." | | 2 | "He was close enough now that she could smell his cologne — something dark and expensive with notes of bergamot and smoke." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1021 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 40 | | adverbRatio | 0.039177277179236046 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.013712047012732615 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 152 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 152 | | mean | 11.54 | | std | 9.04 | | cv | 0.783 | | sampleLengths | | 0 | 22 | | 1 | 5 | | 2 | 18 | | 3 | 5 | | 4 | 4 | | 5 | 1 | | 6 | 1 | | 7 | 18 | | 8 | 6 | | 9 | 12 | | 10 | 3 | | 11 | 4 | | 12 | 17 | | 13 | 26 | | 14 | 2 | | 15 | 1 | | 16 | 1 | | 17 | 4 | | 18 | 10 | | 19 | 1 | | 20 | 29 | | 21 | 5 | | 22 | 18 | | 23 | 22 | | 24 | 2 | | 25 | 28 | | 26 | 8 | | 27 | 3 | | 28 | 3 | | 29 | 20 | | 30 | 3 | | 31 | 15 | | 32 | 5 | | 33 | 10 | | 34 | 14 | | 35 | 6 | | 36 | 5 | | 37 | 37 | | 38 | 11 | | 39 | 9 | | 40 | 28 | | 41 | 4 | | 42 | 16 | | 43 | 14 | | 44 | 5 | | 45 | 6 | | 46 | 31 | | 47 | 7 | | 48 | 22 | | 49 | 4 |
| |
| 66.23% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.4605263157894737 | | totalSentences | 152 | | uniqueOpeners | 70 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 80 | | matches | | 0 | "Of course he did." | | 1 | "Almost a smile." | | 2 | "Maybe that was enough." |
| | ratio | 0.038 | |
| 30.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 80 | | matches | | 0 | "His voice carried that insufferable" | | 1 | "She moved to shut the" | | 2 | "She'd forgotten how quick he" | | 3 | "She let go of the" | | 4 | "She spun to face him," | | 5 | "He stood framed against Eva's" | | 6 | "His mismatched eyes — amber" | | 7 | "It had been four months" | | 8 | "he said quietly" | | 9 | "Her voice rose despite herself" | | 10 | "She lowered her voice." | | 11 | "She cut herself off, jaw" | | 12 | "He took a step forward," | | 13 | "She held up a hand" | | 14 | "He was close enough now" | | 15 | "She'd learned those well with" | | 16 | "His amber eye caught the" | | 17 | "She gestured at him, at" | | 18 | "She was tired suddenly, bone-deep" | | 19 | "He was quiet for a" |
| | ratio | 0.475 | |
| 85.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 80 | | matches | | 0 | "The door cracked open just" | | 1 | "His voice carried that insufferable" | | 2 | "The door widened another inch." | | 3 | "She moved to shut the" | | 4 | "She'd forgotten how quick he" | | 5 | "She let go of the" | | 6 | "She spun to face him," | | 7 | "He stood framed against Eva's" | | 8 | "His mismatched eyes — amber" | | 9 | "It had been four months" | | 10 | "Rory crossed her arms, fighting" | | 11 | "Something flickered across his face." | | 12 | "The demon half made him" | | 13 | "he said quietly" | | 14 | "The word tasted bitter" | | 15 | "Her voice rose despite herself" | | 16 | "Ptolemy, Eva's tabby, lifted his" | | 17 | "She lowered her voice." | | 18 | "She cut herself off, jaw" | | 19 | "He took a step forward," |
| | ratio | 0.75 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 80 | | matches | (empty) | | ratio | 0 | |
| 79.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 3 | | matches | | 0 | "His mismatched eyes — amber and black, demon and human — fixed on her with an intensity that made her skin prickle." | | 1 | "He was quiet for a moment, that unnerving stillness that came from the demon side of him." | | 2 | "And beneath the anger, beneath the hurt, there was still that spark of attraction that had drawn her to him in the first place." |
| |
| 73.28% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 29 | | uselessAdditionCount | 3 | | matches | | 0 | "She cut, jaw tight" | | 1 | "He took, cane clicking softly against the worn floorboards" | | 2 | "The words came out, a wall going up" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 89 | | tagDensity | 0.112 | | leniency | 0.225 | | rawRatio | 0.1 | | effectiveRatio | 0.022 | |