| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 2 | | adverbTags | | 0 | "He turned slowly [slowly]" | | 1 | "Herrera’s voice dropped barely [barely]" |
| | dialogueSentences | 18 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.333 | | effectiveRatio | 0.222 | |
| 94.44% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 900 | | 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) | |
| 77.78% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 900 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "reminder" | | 1 | "framework" | | 2 | "maw" | | 3 | "grave" |
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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 | 61 | | matches | (empty) | |
| 96.02% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 61 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 73 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 893 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 722 | | uniqueNames | 11 | | maxNameDensity | 1.25 | | worstName | "Herrera" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Herrera" | | discoveredNames | | Soho | 2 | | Quinn | 7 | | Severn | 1 | | Bore | 1 | | Herrera | 9 | | Chamberlayne | 1 | | Road | 1 | | Tube | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 2 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Chamberlayne" | | 2 | "Road" |
| | globalScore | 0.877 | | windowScore | 0.833 | |
| 95.65% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | glossingSentenceCount | 1 | | matches | | 0 | "figure that seemed to occupy more space than the lane he was taking" |
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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 | 893 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 73 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 27.91 | | std | 20.96 | | cv | 0.751 | | sampleLengths | | 0 | 60 | | 1 | 88 | | 2 | 6 | | 3 | 40 | | 4 | 9 | | 5 | 23 | | 6 | 60 | | 7 | 49 | | 8 | 52 | | 9 | 28 | | 10 | 4 | | 11 | 24 | | 12 | 38 | | 13 | 2 | | 14 | 26 | | 15 | 6 | | 16 | 10 | | 17 | 41 | | 18 | 6 | | 19 | 51 | | 20 | 30 | | 21 | 5 | | 22 | 12 | | 23 | 19 | | 24 | 15 | | 25 | 6 | | 26 | 19 | | 27 | 20 | | 28 | 41 | | 29 | 44 | | 30 | 8 | | 31 | 51 |
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| 93.76% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 61 | | matches | | 0 | "been gone" | | 1 | "were plastered" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 109 | | matches | | 0 | "was ticking" | | 1 | "was taking" | | 2 | "was turning" | | 3 | "was risking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 73 | | ratio | 0.014 | | matches | | 0 | "The thought of it—the endless questions, the suicide notes, the creeping realization that everything she believed in was a lie—weighed heavier than the lead in her gun." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 729 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.019204389574759947 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.00823045267489712 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 73 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 73 | | mean | 12.23 | | std | 7.53 | | cv | 0.616 | | sampleLengths | | 0 | 19 | | 1 | 16 | | 2 | 25 | | 3 | 11 | | 4 | 17 | | 5 | 5 | | 6 | 14 | | 7 | 6 | | 8 | 24 | | 9 | 2 | | 10 | 2 | | 11 | 7 | | 12 | 6 | | 13 | 14 | | 14 | 21 | | 15 | 5 | | 16 | 9 | | 17 | 3 | | 18 | 20 | | 19 | 12 | | 20 | 15 | | 21 | 33 | | 22 | 23 | | 23 | 4 | | 24 | 22 | | 25 | 9 | | 26 | 10 | | 27 | 16 | | 28 | 17 | | 29 | 13 | | 30 | 15 | | 31 | 4 | | 32 | 8 | | 33 | 16 | | 34 | 15 | | 35 | 23 | | 36 | 2 | | 37 | 12 | | 38 | 14 | | 39 | 6 | | 40 | 10 | | 41 | 2 | | 42 | 6 | | 43 | 17 | | 44 | 3 | | 45 | 3 | | 46 | 10 | | 47 | 6 | | 48 | 29 | | 49 | 22 |
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| 52.97% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3835616438356164 | | totalSentences | 73 | | uniqueOpeners | 28 | |
| 58.48% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 57 | | matches | | 0 | "Just a trace of sulfur" |
| | ratio | 0.018 | |
| 37.54% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 57 | | matches | | 0 | "Her lungs burned, a harsh" | | 1 | "She slowed her stride, adjusting" | | 2 | "She checked her left wrist." | | 3 | "She caught sight of him" | | 4 | "His dark curls were plastered" | | 5 | "He didn't stop." | | 6 | "He slipped between two pedestrians," | | 7 | "She vaulted a retaining barrier" | | 8 | "He didn't run anymore." | | 9 | "He moved with a predatory" | | 10 | "It took Quinn twenty minutes" | | 11 | "She skidded to a halt" | | 12 | "It was a gaping wound" | | 13 | "He turned slowly, placing his" | | 14 | "Her finger hovered over the" | | 15 | "It was dangerous." | | 16 | "It was unauthorized." | | 17 | "It was everything she had" | | 18 | "She took a step forward." | | 19 | "she asked, nodding toward the" |
| | ratio | 0.456 | |
| 30.18% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 49 | | totalSentences | 57 | | matches | | 0 | "Harlow Quinn spat a mouthful" | | 1 | "Her lungs burned, a harsh" | | 2 | "She slowed her stride, adjusting" | | 3 | "The rain plastered her salt-and-pepper" | | 4 | "She checked her left wrist." | | 5 | "The worn leather strap of" | | 6 | "Morris had been gone three" | | 7 | "She caught sight of him" | | 8 | "Tomás Herrera moved through the" | | 9 | "His dark curls were plastered" | | 10 | "That made her blood boil." | | 11 | "He didn't stop." | | 12 | "He slipped between two pedestrians," | | 13 | "Quinn broke into a sprint," | | 14 | "She vaulted a retaining barrier" | | 15 | "The target was turning the" | | 16 | "Herrera glanced over his shoulder," | | 17 | "He didn't run anymore." | | 18 | "He moved with a predatory" | | 19 | "It took Quinn twenty minutes" |
| | ratio | 0.86 | |
| 87.72% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 57 | | matches | | 0 | "If she went down there," |
| | ratio | 0.018 | |
| 73.73% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 3 | | matches | | 0 | "Rain hammered the pavement of Soho, turning the cobblestones into slick, black ribbons that reflected the city’s dying neon." | | 1 | "He slipped between two pedestrians, a broad-shouldered figure that seemed to occupy more space than the lane he was taking." | | 2 | "He moved with a predatory precision, sliding through the maze of warehouses and back alleys that led out of the city proper." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0 | | effectiveRatio | 0 | |