| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 1 | | adverbTags | | | dialogueSentences | 52 | | tagDensity | 0.365 | | leniency | 0.731 | | rawRatio | 0.053 | | effectiveRatio | 0.038 | |
| 95.76% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1179 | | 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) | |
| 74.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1179 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "tinged" | | 1 | "throbbed" | | 2 | "could feel" | | 3 | "traced" | | 4 | "flicked" | | 5 | "shattered" |
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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 | 77 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 77 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 109 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1179 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 81.51% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 19 | | wordCount | 730 | | uniqueNames | 7 | | maxNameDensity | 1.37 | | worstName | "Lucien" | | maxWindowNameDensity | 2 | | worstWindowName | "Lucien" | | discoveredNames | | Moreau | 2 | | French-tinged | 1 | | London | 1 | | Lucien | 10 | | Avaros | 1 | | Evan | 1 | | Ptolemy | 3 |
| | persons | | 0 | "Moreau" | | 1 | "Lucien" | | 2 | "Evan" | | 3 | "Ptolemy" |
| | places | | | globalScore | 0.815 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | 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 | 1179 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 109 | | matches | | 0 | "having that power" | | 1 | "remembered that night" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 54 | | mean | 21.83 | | std | 16.67 | | cv | 0.764 | | sampleLengths | | 0 | 21 | | 1 | 15 | | 2 | 44 | | 3 | 11 | | 4 | 9 | | 5 | 23 | | 6 | 4 | | 7 | 8 | | 8 | 45 | | 9 | 9 | | 10 | 31 | | 11 | 15 | | 12 | 4 | | 13 | 3 | | 14 | 32 | | 15 | 3 | | 16 | 41 | | 17 | 15 | | 18 | 35 | | 19 | 34 | | 20 | 29 | | 21 | 62 | | 22 | 13 | | 23 | 16 | | 24 | 43 | | 25 | 5 | | 26 | 36 | | 27 | 25 | | 28 | 18 | | 29 | 5 | | 30 | 15 | | 31 | 4 | | 32 | 38 | | 33 | 35 | | 34 | 20 | | 35 | 55 | | 36 | 4 | | 37 | 29 | | 38 | 4 | | 39 | 53 | | 40 | 2 | | 41 | 57 | | 42 | 20 | | 43 | 2 | | 44 | 10 | | 45 | 56 | | 46 | 15 | | 47 | 20 | | 48 | 13 | | 49 | 21 |
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| 96.15% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 77 | | matches | | 0 | "was torn" | | 1 | "been abandoned" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 131 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 109 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 735 | | adjectiveStacks | 1 | | stackExamples | | 0 | "smooth French-tinged baritone," |
| | adverbCount | 23 | | adverbRatio | 0.031292517006802724 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.005442176870748299 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 109 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 109 | | mean | 10.82 | | std | 7.92 | | cv | 0.732 | | sampleLengths | | 0 | 5 | | 1 | 9 | | 2 | 7 | | 3 | 15 | | 4 | 16 | | 5 | 26 | | 6 | 1 | | 7 | 1 | | 8 | 11 | | 9 | 9 | | 10 | 5 | | 11 | 3 | | 12 | 15 | | 13 | 4 | | 14 | 8 | | 15 | 6 | | 16 | 3 | | 17 | 29 | | 18 | 7 | | 19 | 4 | | 20 | 5 | | 21 | 8 | | 22 | 5 | | 23 | 17 | | 24 | 1 | | 25 | 15 | | 26 | 4 | | 27 | 3 | | 28 | 20 | | 29 | 12 | | 30 | 3 | | 31 | 11 | | 32 | 30 | | 33 | 3 | | 34 | 7 | | 35 | 5 | | 36 | 24 | | 37 | 11 | | 38 | 11 | | 39 | 8 | | 40 | 15 | | 41 | 7 | | 42 | 22 | | 43 | 8 | | 44 | 26 | | 45 | 2 | | 46 | 2 | | 47 | 8 | | 48 | 5 | | 49 | 11 |
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| 59.02% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.4036697247706422 | | totalSentences | 109 | | uniqueOpeners | 44 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 69 | | matches | | 0 | "Just a cold look and" | | 1 | "Then I'd stuffed it down," | | 2 | "Instead, I just stood there," |
| | ratio | 0.043 | |
| 28.70% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 69 | | matches | | 0 | "I opened the door a" | | 1 | "He wore a charcoal suit" | | 2 | "His voice, that smooth French-tinged" | | 3 | "I should have slammed the" | | 4 | "I should have." | | 5 | "I stepped back" | | 6 | "He crossed the threshold, and" | | 7 | "It always changed around him." | | 8 | "He turned, his heterochromatic eyes," | | 9 | "My heart stuttered." | | 10 | "I pressed my back against" | | 11 | "He stepped closer, closing the" | | 12 | "I'd never met Lucien's father," | | 13 | "I remembered that night two" | | 14 | "I'd cried for a week." | | 15 | "I thought I'd been abandoned" | | 16 | "I thought I wasn't worth" | | 17 | "I said, and my voice" | | 18 | "I could feel his heat," | | 19 | "I whispered, my voice breaking" |
| | ratio | 0.478 | |
| 10.72% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 69 | | matches | | 0 | "The fourth deadbolt scraped open." | | 1 | "Ptolemy's hiss came from the" | | 2 | "The tabby always knew before" | | 3 | "I opened the door a" | | 4 | "Lucien Moreau stood on my" | | 5 | "He wore a charcoal suit" | | 6 | "His voice, that smooth French-tinged" | | 7 | "A genuine wobble." | | 8 | "Lucien Moreau, who moved through" | | 9 | "I should have slammed the" | | 10 | "I should have." | | 11 | "I stepped back" | | 12 | "He crossed the threshold, and" | | 13 | "It always changed around him." | | 14 | "A current, a charge, the" | | 15 | "Ptolemy flattened his ears and" | | 16 | "He turned, his heterochromatic eyes," | | 17 | "A thin smile curved his" | | 18 | "My heart stuttered." | | 19 | "I pressed my back against" |
| | ratio | 0.899 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 22 | | technicalSentenceCount | 2 | | matches | | 0 | "He wore a charcoal suit that cost more than my monthly rent, but the collar was torn, and a smear of rust-red trailed down his cheek." | | 1 | "A creature who collected souls like poker chips, who had no use for human women." |
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| 98.68% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 1 | | matches | | 0 | "I whispered, my voice breaking" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 2 | | fancyTags | | 0 | "I whispered (whisper)" | | 1 | "the voice drawled (drawl)" |
| | dialogueSentences | 52 | | tagDensity | 0.077 | | leniency | 0.154 | | rawRatio | 0.5 | | effectiveRatio | 0.077 | |