| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 38 | | adverbTagCount | 5 | | adverbTags | | 0 | "He leaned back [back]" | | 1 | "He stood again [again]" | | 2 | "he said quietly [quietly]" | | 3 | "He turned back [back]" | | 4 | "He smiled then [then]" |
| | dialogueSentences | 149 | | tagDensity | 0.255 | | leniency | 0.51 | | rawRatio | 0.132 | | effectiveRatio | 0.067 | |
| 82.73% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1737 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "truly" | | 1 | "suddenly" | | 2 | "perfectly" | | 3 | "really" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 51.07% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1737 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "stomach" | | 1 | "intensity" | | 2 | "weight" | | 3 | "calculating" | | 4 | "predator" | | 5 | "velvet" | | 6 | "pulse" | | 7 | "silence" | | 8 | "traced" | | 9 | "racing" | | 10 | "warmth" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "stomach dropped/sank" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 112 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 112 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 221 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1738 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 25 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 871 | | uniqueNames | 11 | | maxNameDensity | 0.57 | | worstName | "Lucien" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Three" | | discoveredNames | | Eva | 1 | | Three | 3 | | London | 2 | | Mrs | 1 | | Chen | 1 | | Facebook | 1 | | Expensive | 1 | | Heat | 3 | | Lucien | 5 | | Cold | 1 | | Silas | 1 |
| | persons | | 0 | "Eva" | | 1 | "Mrs" | | 2 | "Chen" | | 3 | "Heat" | | 4 | "Lucien" | | 5 | "Cold" | | 6 | "Silas" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | 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 | 1738 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 221 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 131 | | mean | 13.27 | | std | 11.36 | | cv | 0.856 | | sampleLengths | | 0 | 7 | | 1 | 32 | | 2 | 7 | | 3 | 17 | | 4 | 46 | | 5 | 2 | | 6 | 12 | | 7 | 2 | | 8 | 8 | | 9 | 19 | | 10 | 53 | | 11 | 5 | | 12 | 26 | | 13 | 1 | | 14 | 6 | | 15 | 2 | | 16 | 28 | | 17 | 4 | | 18 | 46 | | 19 | 2 | | 20 | 12 | | 21 | 11 | | 22 | 7 | | 23 | 26 | | 24 | 5 | | 25 | 15 | | 26 | 8 | | 27 | 23 | | 28 | 3 | | 29 | 12 | | 30 | 39 | | 31 | 5 | | 32 | 1 | | 33 | 1 | | 34 | 16 | | 35 | 6 | | 36 | 36 | | 37 | 2 | | 38 | 34 | | 39 | 4 | | 40 | 3 | | 41 | 8 | | 42 | 3 | | 43 | 4 | | 44 | 16 | | 45 | 7 | | 46 | 35 | | 47 | 10 | | 48 | 17 | | 49 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 112 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 157 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 221 | | ratio | 0.009 | | matches | | 0 | "He surveyed the space with the same calculating look he gave everything — equal parts assessment and judgement." | | 1 | "Expensive cologne hit her first — bergamot and something darker." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 869 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.03567318757192175 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.00805523590333717 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 221 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 221 | | mean | 7.86 | | std | 5.32 | | cv | 0.676 | | sampleLengths | | 0 | 5 | | 1 | 1 | | 2 | 1 | | 3 | 7 | | 4 | 5 | | 5 | 9 | | 6 | 11 | | 7 | 4 | | 8 | 3 | | 9 | 14 | | 10 | 3 | | 11 | 2 | | 12 | 27 | | 13 | 17 | | 14 | 2 | | 15 | 5 | | 16 | 7 | | 17 | 2 | | 18 | 8 | | 19 | 10 | | 20 | 9 | | 21 | 19 | | 22 | 9 | | 23 | 8 | | 24 | 17 | | 25 | 5 | | 26 | 22 | | 27 | 4 | | 28 | 1 | | 29 | 6 | | 30 | 2 | | 31 | 19 | | 32 | 9 | | 33 | 4 | | 34 | 15 | | 35 | 13 | | 36 | 18 | | 37 | 2 | | 38 | 12 | | 39 | 3 | | 40 | 3 | | 41 | 5 | | 42 | 7 | | 43 | 18 | | 44 | 8 | | 45 | 5 | | 46 | 7 | | 47 | 8 | | 48 | 4 | | 49 | 4 |
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| 67.42% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.4343891402714932 | | totalSentences | 221 | | uniqueOpeners | 96 | |
| 65.36% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 102 | | matches | | 0 | "Almost a smile." | | 1 | "Instead, she stood frozen as" |
| | ratio | 0.02 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 57 | | totalSentences | 102 | | matches | | 0 | "They knocked like they owned" | | 1 | "She checked the peephole." | | 2 | "Her stomach dropped." | | 3 | "Her hand turned the deadbolt" | | 4 | "His platinum hair caught the" | | 5 | "He shifted his weight, and" | | 6 | "He surveyed the space with" | | 7 | "His mouth twitched." | | 8 | "He said it like a" | | 9 | "She crossed her arms." | | 10 | "He settled into her armchair" | | 11 | "She remembered the last time" | | 12 | "His office, after midnight." | | 13 | "His hands in her hair." | | 14 | "Her back against his desk." | | 15 | "He tilted his head, studying" | | 16 | "He stood in one fluid" | | 17 | "His cane rested between them," | | 18 | "His amber eye flashed." | | 19 | "He reached past her, and" |
| | ratio | 0.559 | |
| 43.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 85 | | totalSentences | 102 | | matches | | 0 | "The knock came three times." | | 1 | "Rory's hand froze on the" | | 2 | "They knocked like they owned" | | 3 | "She checked the peephole." | | 4 | "Her stomach dropped." | | 5 | "Lucien's voice filtered through the" | | 6 | "Her hand turned the deadbolt" | | 7 | "The door opened six inches," | | 8 | "Lucien stood in the hallway" | | 9 | "Charcoal today, tailored within an" | | 10 | "His platinum hair caught the" | | 11 | "He shifted his weight, and" | | 12 | "Mrs Chen from 3B was" | | 13 | "The local Facebook group would" | | 14 | "Rory unhooked the chain." | | 15 | "Lucien stepped inside without waiting" | | 16 | "The flat suddenly felt smaller," | | 17 | "He surveyed the space with" | | 18 | "His mouth twitched." | | 19 | "Lucien set the folder on" |
| | ratio | 0.833 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 102 | | matches | (empty) | | ratio | 0 | |
| 45.45% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 22 | | technicalSentenceCount | 3 | | matches | | 0 | "Lucien stepped inside without waiting for an invitation, his cane tapping once against the threshold." | | 1 | "He tilted his head, studying her like she was a particularly interesting puzzle." | | 2 | "His fingers found a strand of her hair that had escaped her ponytail." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 38 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 149 | | tagDensity | 0.047 | | leniency | 0.094 | | rawRatio | 0.143 | | effectiveRatio | 0.013 | |