| 51.85% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 2 | | adverbTags | | 0 | "he said simply [simply]" | | 1 | "she said finally [finally]" |
| | dialogueSentences | 27 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.222 | | effectiveRatio | 0.148 | |
| 76.47% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 850 | | totalAiIsmAdverbs | 4 | | found | | 0 | | | 1 | | | 2 | | | 3 | | adverb | "barely above a whisper" | | count | 1 |
|
| | highlights | | 0 | "slightly" | | 1 | "warily" | | 2 | "really" | | 3 | "barely above a whisper" |
| |
| 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 850 | | totalAiIsms | 30 | | found | | 0 | | word | "skipped a beat" | | count | 1 |
| | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | | | 22 | | | 23 | | | 24 | |
| | highlights | | 0 | "skipped a beat" | | 1 | "intensity" | | 2 | "pulse" | | 3 | "velvet" | | 4 | "sanctuary" | | 5 | "weight" | | 6 | "reminder" | | 7 | "eyebrow" | | 8 | "echoed" | | 9 | "silence" | | 10 | "fractured" | | 11 | "unspoken" | | 12 | "magnetic" | | 13 | "raced" | | 14 | "potential" | | 15 | "tension" | | 16 | "familiar" | | 17 | "encounter" | | 18 | "unwavering" | | 19 | "resolve" | | 20 | "electric" | | 21 | "pounding" | | 22 | "tinged" | | 23 | "whisper" | | 24 | "sense of" |
| |
| 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 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 53 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 53 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 71 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 848 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 54.69% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 682 | | uniqueNames | 7 | | maxNameDensity | 1.91 | | worstName | "Aurora" | | maxWindowNameDensity | 3 | | worstWindowName | "Lucien" | | discoveredNames | | Carter | 1 | | Eva | 2 | | Lucien | 11 | | Moreau | 1 | | London | 1 | | Aurora | 13 | | Ptolemy | 2 |
| | persons | | 0 | "Carter" | | 1 | "Eva" | | 2 | "Lucien" | | 3 | "Moreau" | | 4 | "Aurora" |
| | places | | | globalScore | 0.547 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 848 | | matches | (empty) | |
| 72.77% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 71 | | matches | | 0 | "exchanged that cut" | | 1 | "knew that whatever" |
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| 87.58% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 23.56 | | std | 10.75 | | cv | 0.457 | | sampleLengths | | 0 | 60 | | 1 | 13 | | 2 | 33 | | 3 | 25 | | 4 | 19 | | 5 | 35 | | 6 | 17 | | 7 | 19 | | 8 | 24 | | 9 | 16 | | 10 | 29 | | 11 | 25 | | 12 | 10 | | 13 | 31 | | 14 | 26 | | 15 | 27 | | 16 | 22 | | 17 | 11 | | 18 | 28 | | 19 | 13 | | 20 | 17 | | 21 | 47 | | 22 | 37 | | 23 | 13 | | 24 | 12 | | 25 | 29 | | 26 | 31 | | 27 | 14 | | 28 | 22 | | 29 | 16 | | 30 | 17 | | 31 | 32 | | 32 | 14 | | 33 | 11 | | 34 | 35 | | 35 | 18 |
| |
| 85.40% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 53 | | matches | | 0 | "been exchanged" | | 1 | "was, unannounced" | | 2 | "was cramped" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 125 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 71 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 684 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.027777777777777776 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.01023391812865497 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 71 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 71 | | mean | 11.94 | | std | 5.61 | | cv | 0.47 | | sampleLengths | | 0 | 21 | | 1 | 19 | | 2 | 20 | | 3 | 13 | | 4 | 4 | | 5 | 17 | | 6 | 12 | | 7 | 25 | | 8 | 15 | | 9 | 4 | | 10 | 6 | | 11 | 13 | | 12 | 16 | | 13 | 13 | | 14 | 4 | | 15 | 10 | | 16 | 9 | | 17 | 17 | | 18 | 7 | | 19 | 11 | | 20 | 5 | | 21 | 9 | | 22 | 20 | | 23 | 9 | | 24 | 14 | | 25 | 2 | | 26 | 10 | | 27 | 6 | | 28 | 14 | | 29 | 11 | | 30 | 20 | | 31 | 6 | | 32 | 11 | | 33 | 16 | | 34 | 13 | | 35 | 9 | | 36 | 11 | | 37 | 10 | | 38 | 18 | | 39 | 7 | | 40 | 6 | | 41 | 10 | | 42 | 7 | | 43 | 26 | | 44 | 21 | | 45 | 14 | | 46 | 23 | | 47 | 13 | | 48 | 7 | | 49 | 5 |
| |
| 63.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.38028169014084506 | | totalSentences | 71 | | uniqueOpeners | 27 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 84.15% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 53 | | matches | | 0 | "His heterochromatic eyes, one amber" | | 1 | "he said, his voice smooth" | | 2 | "She blinked, momentarily speechless." | | 3 | "Her voice was steady, but" | | 4 | "He leaned slightly on his" | | 5 | "He turned to face her," | | 6 | "She studied him, searching for" | | 7 | "he said simply" | | 8 | "She glanced at him, her" | | 9 | "he replied, his voice low" | | 10 | "she said finally, her decision" | | 11 | "They spent the next hour" | | 12 | "she asked, breaking the silence" | | 13 | "He paused, his gaze steady" | | 14 | "She met his gaze, her" | | 15 | "His expression softened, a hint" | | 16 | "he admitted, his voice tinged" | | 17 | "she suggested, her voice barely" |
| | ratio | 0.34 | |
| 16.60% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 53 | | matches | | 0 | "Aurora Carter's heart skipped a" | | 1 | "The sight of Lucien Moreau" | | 2 | "His heterochromatic eyes, one amber" | | 3 | "he said, his voice smooth" | | 4 | "She blinked, momentarily speechless." | | 5 | "The last time they'd seen" | | 6 | "Her voice was steady, but" | | 7 | "He leaned slightly on his" | | 8 | "Aurora stepped aside, allowing him" | | 9 | "The flat was cramped, every" | | 10 | "Ptolemy, Eva's tabby cat, eyed" | | 11 | "Lucien's gaze swept the room," | | 12 | "Aurora replied, closing the door" | | 13 | "The click of the deadbolts" | | 14 | "He turned to face her," | | 15 | "Aurora crossed her arms, a" | | 16 | "Lucien nodded, his expression shifting" | | 17 | "She studied him, searching for" | | 18 | "Lucien was a master of" | | 19 | "he said simply" |
| | ratio | 0.887 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 61.22% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 4 | | matches | | 0 | "His heterochromatic eyes, one amber and the other black, locked onto hers with an intensity that made her pulse quicken." | | 1 | "They spent the next hour poring over maps and notes, their conversation punctuated by the occasional hiss from Ptolemy, who seemed to disapprove of Lucien's pre…" | | 2 | "Aurora found herself slipping into the familiar rhythm of their partnership, the synergy that had once made them an unstoppable force." | | 3 | "Aurora couldn't deny the attraction that simmered beneath the surface, nor could she ignore the hurt that still lingered from their last encounter." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 4 | | matches | | 0 | "he said, his voice smooth and rich, like velvet draped over steel" | | 1 | "he replied, his voice low and earnest" | | 2 | "she said finally, her decision made" | | 3 | "she suggested, her voice barely above a whisper" |
| |
| 75.93% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 2 | | fancyTags | | 0 | "he admitted (admit)" | | 1 | "she suggested (suggest)" |
| | dialogueSentences | 27 | | tagDensity | 0.296 | | leniency | 0.593 | | rawRatio | 0.25 | | effectiveRatio | 0.148 | |