| 18.18% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 3 | | adverbTags | | 0 | "she said softly [softly]" | | 1 | "she asked suddenly [suddenly]" | | 2 | "she said quietly [quietly]" |
| | dialogueSentences | 33 | | tagDensity | 0.303 | | leniency | 0.606 | | rawRatio | 0.3 | | effectiveRatio | 0.182 | |
| 75.63% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1026 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "suddenly" | | 1 | "softly" | | 2 | "slightly" |
| |
| 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) | |
| 7.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1026 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "testament" | | 1 | "echoing" | | 2 | "tinged" | | 3 | "familiar" | | 4 | "chaotic" | | 5 | "stark" | | 6 | "reminder" | | 7 | "intensity" | | 8 | "silence" | | 9 | "tension" | | 10 | "unspoken" | | 11 | "raced" | | 12 | "weight" | | 13 | "navigate" | | 14 | "eyebrow" | | 15 | "sense of" |
| |
| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 3 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "air was thick with" | | count | 1 |
| | 2 | | label | "flicker of emotion" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "The air was thick with" | | 2 | "a glimmer of hope" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 60 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 60 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 83 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1028 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 92.16% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 778 | | uniqueNames | 8 | | maxNameDensity | 1.16 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Lucien" | | discoveredNames | | Carter | 1 | | Eva | 5 | | Ptolemy | 2 | | Lucien | 8 | | Moreau | 1 | | Frenchman | 1 | | Aurora | 9 | | Evan | 1 |
| | persons | | 0 | "Carter" | | 1 | "Eva" | | 2 | "Ptolemy" | | 3 | "Lucien" | | 4 | "Moreau" | | 5 | "Frenchman" | | 6 | "Aurora" | | 7 | "Evan" |
| | places | (empty) | | globalScore | 0.922 | | windowScore | 1 | |
| 53.85% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 2 | | matches | | 0 | "quite shaken" | | 1 | "felt like they were heading in the righ" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1028 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 30.24 | | std | 18.96 | | cv | 0.627 | | sampleLengths | | 0 | 87 | | 1 | 77 | | 2 | 17 | | 3 | 15 | | 4 | 23 | | 5 | 48 | | 6 | 38 | | 7 | 9 | | 8 | 7 | | 9 | 28 | | 10 | 8 | | 11 | 43 | | 12 | 17 | | 13 | 16 | | 14 | 16 | | 15 | 19 | | 16 | 25 | | 17 | 25 | | 18 | 21 | | 19 | 30 | | 20 | 12 | | 21 | 73 | | 22 | 21 | | 23 | 26 | | 24 | 23 | | 25 | 30 | | 26 | 39 | | 27 | 33 | | 28 | 34 | | 29 | 46 | | 30 | 30 | | 31 | 28 | | 32 | 12 | | 33 | 52 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 60 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 134 | | matches | | 0 | "was like reopening" | | 1 | "were heading" |
| |
| 74.01% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 83 | | ratio | 0.024 | | matches | | 0 | "The door creaked open, and there he stood — Lucien Moreau, the Frenchman, looking as impeccably dressed as ever in a tailored charcoal suit." | | 1 | "Aurora's mind raced, memories of their time together flashing before her eyes — the stolen glances, the heated arguments, the nights spent talking until dawn." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 779 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 29 | | adverbRatio | 0.037227214377406934 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.012836970474967908 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 83 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 83 | | mean | 12.39 | | std | 6.61 | | cv | 0.533 | | sampleLengths | | 0 | 28 | | 1 | 26 | | 2 | 25 | | 3 | 8 | | 4 | 13 | | 5 | 18 | | 6 | 24 | | 7 | 22 | | 8 | 13 | | 9 | 4 | | 10 | 6 | | 11 | 9 | | 12 | 7 | | 13 | 16 | | 14 | 10 | | 15 | 14 | | 16 | 9 | | 17 | 15 | | 18 | 22 | | 19 | 16 | | 20 | 9 | | 21 | 5 | | 22 | 2 | | 23 | 17 | | 24 | 11 | | 25 | 5 | | 26 | 3 | | 27 | 8 | | 28 | 22 | | 29 | 13 | | 30 | 17 | | 31 | 8 | | 32 | 8 | | 33 | 6 | | 34 | 10 | | 35 | 14 | | 36 | 5 | | 37 | 8 | | 38 | 17 | | 39 | 11 | | 40 | 14 | | 41 | 8 | | 42 | 13 | | 43 | 16 | | 44 | 5 | | 45 | 9 | | 46 | 6 | | 47 | 6 | | 48 | 22 | | 49 | 25 |
| |
| 67.47% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.43373493975903615 | | totalSentences | 83 | | uniqueOpeners | 36 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 58 | | matches | | 0 | "Maybe things could be different" | | 1 | "Maybe they could find a" | | 2 | "Just then, the door creaked" | | 3 | "Maybe things were still complicated," |
| | ratio | 0.069 | |
| 20.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 58 | | matches | | 0 | "She glanced at the three" | | 1 | "She raised her hand and" | | 2 | "His heterochromatic eyes, one amber" | | 3 | "he said, his voice smooth" | | 4 | "She hesitated, then nodded, stepping" | | 5 | "She bent down to scratch" | | 6 | "He leaned on his ivory-handled" | | 7 | "he said, breaking the silence" | | 8 | "she replied, straightening up" | | 9 | "He moved to the small" | | 10 | "Their fingers brushed, and a" | | 11 | "She took a sip, the" | | 12 | "he said, leaning against the" | | 13 | "She took a deep breath," | | 14 | "His eyes narrowed slightly, curiosity" | | 15 | "She shook her head, a" | | 16 | "He sighed, taking a sip" | | 17 | "Her heart clenched at his" | | 18 | "she said softly" | | 19 | "He chuckled, a low, rueful" |
| | ratio | 0.5 | |
| 28.97% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 50 | | totalSentences | 58 | | matches | | 0 | "Aurora Carter stood in the" | | 1 | "The air was thick with" | | 2 | "She glanced at the three" | | 3 | "She raised her hand and" | | 4 | "A moment later, she heard" | | 5 | "The door creaked open, and" | | 6 | "His heterochromatic eyes, one amber" | | 7 | "he said, his voice smooth" | | 8 | "Aurora swallowed, her mouth suddenly" | | 9 | "Lucien stepped aside, allowing her" | | 10 | "She hesitated, then nodded, stepping" | | 11 | "The familiar clutter of books," | | 12 | "Ptolemy wound his way around" | | 13 | "She bent down to scratch" | | 14 | "Lucien closed the door behind" | | 15 | "He leaned on his ivory-handled" | | 16 | "he said, breaking the silence" | | 17 | "she replied, straightening up" | | 18 | "He moved to the small" | | 19 | "Aurora considered it, then nodded." |
| | ratio | 0.862 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 99.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 2 | | matches | | 0 | "He leaned on his ivory-handled cane, watching her with an intensity that made her skin prickle." | | 1 | "A small smile tugged at the corners of his mouth, and he finally let his hand rest on hers, a simple gesture that spoke volumes." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 3 | | matches | | 0 | "he said, his voice smooth and tinged with a hint of surprise" | | 1 | "she asked suddenly, the words slipping out before she could stop them" | | 2 | "Aurora said, a genuine smile spreading across her face" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 33 | | tagDensity | 0.303 | | leniency | 0.606 | | rawRatio | 0.1 | | effectiveRatio | 0.061 | |