| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 90 | | tagDensity | 0.244 | | leniency | 0.489 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 80.96% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1576 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "slightly" | | 1 | "precisely" | | 2 | "slowly" | | 3 | "really" | | 4 | "suddenly" |
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| 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) | |
| 39.72% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1576 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "silence" | | 1 | "familiar" | | 2 | "pulse" | | 3 | "crystal" | | 4 | "gleaming" | | 5 | "unreadable" | | 6 | "weight" | | 7 | "resolve" | | 8 | "wavered" | | 9 | "chaotic" | | 10 | "calculated" | | 11 | "pristine" | | 12 | "efficient" | | 13 | "firmly" | | 14 | "absolutely" | | 15 | "intensity" | | 16 | "whisper" |
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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 | 88 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 88 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 152 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1566 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 9 | | wordCount | 947 | | uniqueNames | 5 | | maxNameDensity | 0.42 | | worstName | "Eva" | | maxWindowNameDensity | 1 | | worstWindowName | "Eva" | | discoveredNames | | Eva | 4 | | Whitechapel | 1 | | Ptolemy | 2 | | Professional | 1 | | Evan | 1 |
| | persons | | | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 1 | | matches | | 0 | "tasted like blood and bad decisions" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1566 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 152 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 92 | | mean | 17.02 | | std | 14.19 | | cv | 0.834 | | sampleLengths | | 0 | 43 | | 1 | 42 | | 2 | 1 | | 3 | 35 | | 4 | 11 | | 5 | 21 | | 6 | 30 | | 7 | 6 | | 8 | 14 | | 9 | 20 | | 10 | 25 | | 11 | 10 | | 12 | 27 | | 13 | 21 | | 14 | 27 | | 15 | 13 | | 16 | 15 | | 17 | 38 | | 18 | 14 | | 19 | 37 | | 20 | 2 | | 21 | 44 | | 22 | 10 | | 23 | 2 | | 24 | 48 | | 25 | 3 | | 26 | 24 | | 27 | 45 | | 28 | 1 | | 29 | 3 | | 30 | 27 | | 31 | 2 | | 32 | 8 | | 33 | 5 | | 34 | 22 | | 35 | 2 | | 36 | 8 | | 37 | 37 | | 38 | 25 | | 39 | 2 | | 40 | 5 | | 41 | 16 | | 42 | 23 | | 43 | 7 | | 44 | 2 | | 45 | 33 | | 46 | 8 | | 47 | 2 | | 48 | 21 | | 49 | 31 |
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| 97.29% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 88 | | matches | | 0 | "was torn" | | 1 | "were focused" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 177 | | matches | (empty) | |
| 11.28% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 152 | | ratio | 0.046 | | matches | | 0 | "Through the peephole, a familiar figure waited in the dim hallway—platinum hair catching the fluorescent light, shoulders set in that particular way that meant trouble." | | 1 | "His heterochromatic eyes found hers immediately—the amber one gleaming, the black one unreadable as always." | | 2 | "\"Rory.\" Just her name, but the way he said it—low and rough around the edges—made something twist in her chest." | | 3 | "He complied without argument—another warning sign." | | 4 | "The bruise was just the beginning—split lip, dried blood at his temple, knuckles scraped raw." | | 5 | "Absolutely not noticing the way muscle moved under skin, or remembering—" | | 6 | "\"I know he hurt you. Know you ran to London with bruises that weren't from any accident.\" His eyes—both of them—were focused on her with an intensity that made her chest tight." |
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| 91.02% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 955 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 48 | | adverbRatio | 0.050261780104712044 | | lyAdverbCount | 16 | | lyAdverbRatio | 0.016753926701570682 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 152 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 152 | | mean | 10.3 | | std | 7.71 | | cv | 0.749 | | sampleLengths | | 0 | 20 | | 1 | 13 | | 2 | 5 | | 3 | 5 | | 4 | 17 | | 5 | 25 | | 6 | 1 | | 7 | 6 | | 8 | 19 | | 9 | 6 | | 10 | 4 | | 11 | 11 | | 12 | 21 | | 13 | 15 | | 14 | 15 | | 15 | 6 | | 16 | 8 | | 17 | 6 | | 18 | 18 | | 19 | 2 | | 20 | 25 | | 21 | 3 | | 22 | 7 | | 23 | 8 | | 24 | 19 | | 25 | 20 | | 26 | 1 | | 27 | 7 | | 28 | 17 | | 29 | 3 | | 30 | 13 | | 31 | 15 | | 32 | 19 | | 33 | 19 | | 34 | 14 | | 35 | 6 | | 36 | 9 | | 37 | 22 | | 38 | 2 | | 39 | 19 | | 40 | 10 | | 41 | 15 | | 42 | 6 | | 43 | 4 | | 44 | 2 | | 45 | 31 | | 46 | 17 | | 47 | 3 | | 48 | 20 | | 49 | 4 |
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| 65.79% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.4342105263157895 | | totalSentences | 152 | | uniqueOpeners | 66 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 82 | | matches | | 0 | "Just her name, but the" | | 1 | "Absolutely not noticing the way" | | 2 | "Instead, she fisted her hands" |
| | ratio | 0.037 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 50 | | totalSentences | 82 | | matches | | 0 | "She set the food aside" | | 1 | "Her pulse kicked up despite" | | 2 | "She'd told him to stay" | | 3 | "She kept the door chained," | | 4 | "His heterochromatic eyes found hers" | | 5 | "She tightened her grip on" | | 6 | "He shifted his weight, and" | | 7 | "His jaw tightened." | | 8 | "She laughed, short and bitter" | | 9 | "She'd never heard him say" | | 10 | "Her resolve wavered." | | 11 | "He stepped inside, favoring his" | | 12 | "He complied without argument—another warning" | | 13 | "She watched him ease out" | | 14 | "He followed her through the" | | 15 | "She pointed at a chair" | | 16 | "She pulled the first aid" | | 17 | "She set the kit on" | | 18 | "He shrugged out of it" | | 19 | "She sucked in a breath" |
| | ratio | 0.61 | |
| 57.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 66 | | totalSentences | 82 | | matches | | 0 | "The knock came at half" | | 1 | "Rory froze, chopsticks halfway to" | | 2 | "Nobody visited Eva this late." | | 3 | "Nobody who meant well, anyway." | | 4 | "She set the food aside" | | 5 | "Her pulse kicked up despite" | | 6 | "She'd told him to stay" | | 7 | "The deadbolts clicked open one" | | 8 | "She kept the door chained," | | 9 | "His heterochromatic eyes found hers" | | 10 | "A bruise darkened his left" | | 11 | "She tightened her grip on" | | 12 | "He shifted his weight, and" | | 13 | "His jaw tightened." | | 14 | "She laughed, short and bitter" | | 15 | "She'd never heard him say" | | 16 | "Her resolve wavered." | | 17 | "He stepped inside, favoring his" | | 18 | "The charcoal suit that usually" | | 19 | "He complied without argument—another warning" |
| | ratio | 0.805 | |
| 60.98% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 82 | | matches | | 0 | "If anything, he pressed closer," |
| | ratio | 0.012 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 2 | | matches | | 0 | "Through the peephole, a familiar figure waited in the dim hallway—platinum hair catching the fluorescent light, shoulders set in that particular way that meant …" | | 1 | "She kept her eyes firmly on the medical supplies, organizing things that didn't need organizing." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 1 | | matches | | 0 | "The black eye caught, seeming to swallow it whole" |
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| 94.44% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 5 | | fancyTags | | 0 | "She laughed (laugh)" | | 1 | "She snapped on (snap on)" | | 2 | "She pressed (press)" | | 3 | "she repeated (repeat)" | | 4 | "He laughed (laugh)" |
| | dialogueSentences | 90 | | tagDensity | 0.067 | | leniency | 0.133 | | rawRatio | 0.833 | | effectiveRatio | 0.111 | |