| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 94.03% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 838 | | 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) | |
| 22.43% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 838 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "pounding" | | 1 | "reminder" | | 2 | "scanned" | | 3 | "eyebrow" | | 4 | "traced" | | 5 | "racing" | | 6 | "scanning" | | 7 | "anticipation" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 94.14% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 54 | | matches | | 0 | "g in surprise" | | 1 | "g with anticipation" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 54 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 87 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 837 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 23.10% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 591 | | uniqueNames | 7 | | maxNameDensity | 2.54 | | worstName | "Lucien" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Rory" | | discoveredNames | | Aurora | 1 | | Carter | 1 | | Moreau | 2 | | Eva | 1 | | Ptolemy | 1 | | Lucien | 15 | | Rory | 11 |
| | persons | | 0 | "Aurora" | | 1 | "Carter" | | 2 | "Moreau" | | 3 | "Eva" | | 4 | "Ptolemy" | | 5 | "Lucien" | | 6 | "Rory" |
| | places | (empty) | | globalScore | 0.231 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | 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 | 837 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 87 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 22.62 | | std | 12.55 | | cv | 0.555 | | sampleLengths | | 0 | 20 | | 1 | 39 | | 2 | 47 | | 3 | 51 | | 4 | 38 | | 5 | 15 | | 6 | 20 | | 7 | 15 | | 8 | 15 | | 9 | 26 | | 10 | 8 | | 11 | 8 | | 12 | 13 | | 13 | 16 | | 14 | 18 | | 15 | 30 | | 16 | 11 | | 17 | 9 | | 18 | 19 | | 19 | 20 | | 20 | 18 | | 21 | 22 | | 22 | 12 | | 23 | 30 | | 24 | 33 | | 25 | 14 | | 26 | 42 | | 27 | 11 | | 28 | 16 | | 29 | 13 | | 30 | 17 | | 31 | 24 | | 32 | 15 | | 33 | 8 | | 34 | 56 | | 35 | 39 | | 36 | 29 |
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| 98.77% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 54 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 117 | | matches | (empty) | |
| 77.18% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 87 | | ratio | 0.023 | | matches | | 0 | "She had no idea what lay ahead, but she knew one thing for certain - her life was about to change, and Lucien Moreau was at the center of it all." | | 1 | "She didn't know what the future held, but she knew one thing - she was ready to face it, with Lucien by her side." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 592 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar," |
| | adverbCount | 15 | | adverbRatio | 0.02533783783783784 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.005067567567567568 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 87 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 87 | | mean | 9.62 | | std | 5.67 | | cv | 0.59 | | sampleLengths | | 0 | 14 | | 1 | 6 | | 2 | 21 | | 3 | 13 | | 4 | 5 | | 5 | 6 | | 6 | 17 | | 7 | 19 | | 8 | 5 | | 9 | 15 | | 10 | 26 | | 11 | 4 | | 12 | 6 | | 13 | 13 | | 14 | 11 | | 15 | 14 | | 16 | 9 | | 17 | 6 | | 18 | 8 | | 19 | 12 | | 20 | 10 | | 21 | 5 | | 22 | 13 | | 23 | 2 | | 24 | 11 | | 25 | 15 | | 26 | 4 | | 27 | 4 | | 28 | 2 | | 29 | 6 | | 30 | 12 | | 31 | 1 | | 32 | 15 | | 33 | 1 | | 34 | 8 | | 35 | 10 | | 36 | 11 | | 37 | 19 | | 38 | 5 | | 39 | 6 | | 40 | 6 | | 41 | 3 | | 42 | 7 | | 43 | 12 | | 44 | 9 | | 45 | 11 | | 46 | 5 | | 47 | 9 | | 48 | 4 | | 49 | 11 |
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| 48.66% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.3333333333333333 | | totalSentences | 87 | | uniqueOpeners | 29 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 8.68% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 53 | | matches | | 0 | "He leaned on his ivory-handled" | | 1 | "She hadn't seen him since" | | 2 | "She glanced at the three" | | 3 | "Her straight black hair was" | | 4 | "He cleared his throat." | | 5 | "He turned to her, his" | | 6 | "She crossed her arms, her" | | 7 | "She pushed off the door," | | 8 | "He leaned against the counter," | | 9 | "She scoffed, turning back to" | | 10 | "He reached out, his fingers" | | 11 | "She turned to face him," | | 12 | "She pulled her arm away," | | 13 | "He sighed, his hand dropping" | | 14 | "She saw only sincerity, only" | | 15 | "She sighed, turning back to" | | 16 | "She had questions, so many" | | 17 | "She handed him a cup" | | 18 | "He looked up at her," | | 19 | "She nodded, her heart pounding." |
| | ratio | 0.528 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 53 | | matches | | 0 | "The door creaked open, revealing" | | 1 | "Lucien Moreau stood in the" | | 2 | "He leaned on his ivory-handled" | | 3 | "Rory stepped back, her heart" | | 4 | "She hadn't seen him since" | | 5 | "She glanced at the three" | | 6 | "Lucien's gaze swept over her," | | 7 | "Her straight black hair was" | | 8 | "He cleared his throat." | | 9 | "Rory hesitated, then stepped aside," | | 10 | "The flat was cramped, every" | | 11 | "A tabby cat, Ptolemy, eyed" | | 12 | "Lucien's eyes scanned the room," | | 13 | "Rory closed the door, her" | | 14 | "He turned to her, his" | | 15 | "She crossed her arms, her" | | 16 | "Lucien sighed, running a hand" | | 17 | "Rory raised an eyebrow." | | 18 | "She pushed off the door," | | 19 | "Lucien watched her, his eyes" |
| | ratio | 0.981 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |