| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 1 | | adverbTags | | 0 | "Herrera hesitated then [then]" |
| | dialogueSentences | 20 | | tagDensity | 0.45 | | leniency | 0.9 | | rawRatio | 0.111 | | effectiveRatio | 0.1 | |
| 95.24% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1050 | | totalAiIsmAdverbs | 1 | | found | | 0 | | adverb | "barely above a whisper" | | count | 1 |
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| | highlights | | 0 | "barely above a whisper" |
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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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1050 | | totalAiIsms | 28 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | word | "down her spine" | | count | 1 |
| | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | | | 22 | |
| | highlights | | 0 | "chill" | | 1 | "pounding" | | 2 | "fleeting" | | 3 | "looming" | | 4 | "glinting" | | 5 | "sinister" | | 6 | "echoing" | | 7 | "silence" | | 8 | "gleaming" | | 9 | "flickered" | | 10 | "scanning" | | 11 | "testament" | | 12 | "glistening" | | 13 | "whisper" | | 14 | "down her spine" | | 15 | "stomach" | | 16 | "racing" | | 17 | "could feel" | | 18 | "weight" | | 19 | "familiar" | | 20 | "resolve" | | 21 | "tracing" | | 22 | "intricate" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
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| | highlights | | |
| 59.90% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 5 | | narrationSentences | 64 | | matches | | 0 | "felt a chill" | | 1 | "filled with resolve" | | 2 | "d with resolve" | | 3 | "filled with determination" | | 4 | "d with determination" |
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| 75.89% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 64 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 75 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1044 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 48.57% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 838 | | uniqueNames | 14 | | maxNameDensity | 2.03 | | worstName | "Herrera" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 15 | | Tomás | 1 | | Herrera | 17 | | Soho | 1 | | Raven | 2 | | Nest | 2 | | Silence | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 2 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Raven" | | 5 | "Nest" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Morris" |
| | places | | | globalScore | 0.486 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like miles, the former paramedic's" |
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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 | 1044 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 75 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 36 | | std | 18.68 | | cv | 0.519 | | sampleLengths | | 0 | 66 | | 1 | 76 | | 2 | 54 | | 3 | 23 | | 4 | 36 | | 5 | 35 | | 6 | 23 | | 7 | 18 | | 8 | 38 | | 9 | 34 | | 10 | 26 | | 11 | 58 | | 12 | 37 | | 13 | 37 | | 14 | 14 | | 15 | 32 | | 16 | 54 | | 17 | 13 | | 18 | 44 | | 19 | 53 | | 20 | 5 | | 21 | 15 | | 22 | 27 | | 23 | 58 | | 24 | 19 | | 25 | 27 | | 26 | 50 | | 27 | 66 | | 28 | 6 |
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| 99.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 64 | | matches | | |
| 58.16% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 141 | | matches | | 0 | "was waiting" | | 1 | "was wearing" | | 2 | "was stepping" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 75 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 844 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.02132701421800948 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004739336492890996 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 75 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 75 | | mean | 13.92 | | std | 6.77 | | cv | 0.487 | | sampleLengths | | 0 | 17 | | 1 | 22 | | 2 | 27 | | 3 | 14 | | 4 | 23 | | 5 | 14 | | 6 | 25 | | 7 | 22 | | 8 | 8 | | 9 | 11 | | 10 | 2 | | 11 | 11 | | 12 | 14 | | 13 | 9 | | 14 | 1 | | 15 | 4 | | 16 | 24 | | 17 | 7 | | 18 | 7 | | 19 | 28 | | 20 | 11 | | 21 | 12 | | 22 | 5 | | 23 | 13 | | 24 | 12 | | 25 | 19 | | 26 | 7 | | 27 | 15 | | 28 | 19 | | 29 | 16 | | 30 | 10 | | 31 | 16 | | 32 | 19 | | 33 | 23 | | 34 | 20 | | 35 | 11 | | 36 | 6 | | 37 | 9 | | 38 | 28 | | 39 | 6 | | 40 | 8 | | 41 | 32 | | 42 | 18 | | 43 | 19 | | 44 | 17 | | 45 | 13 | | 46 | 14 | | 47 | 30 | | 48 | 8 | | 49 | 18 |
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| 43.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.29333333333333333 | | totalSentences | 75 | | uniqueOpeners | 22 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 62 | | matches | | 0 | "Then, a low chuckle." | | 1 | "Then, she followed Herrera, her" | | 2 | "Then, she made her decision." |
| | ratio | 0.048 | |
| 58.71% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 62 | | matches | | 0 | "Her breath misted in the" | | 1 | "She'd been chasing Tomás Herrera" | | 2 | "She skidded to a halt," | | 3 | "She could hear Herrera's ragged" | | 4 | "He was waiting, probably planning" | | 5 | "She needed answers, and she" | | 6 | "she called out, her voice" | | 7 | "He seemed to be weighing" | | 8 | "He turned and walked towards" | | 9 | "she said, her voice low" | | 10 | "She knew Morris's death had" | | 11 | "she asked, her voice steady" | | 12 | "She could feel the weight" | | 13 | "She thought of Morris, of" | | 14 | "She thought of the truth," | | 15 | "she said, her voice filled" | | 16 | "he said, sliding a bone" | | 17 | "She looked at Herrera, then" | | 18 | "She knew she was stepping" | | 19 | "She stood up, the bone" |
| | ratio | 0.403 | |
| 32.58% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 62 | | matches | | 0 | "The rain lashed against Detective" | | 1 | "Her breath misted in the" | | 2 | "She'd been chasing Tomás Herrera" | | 3 | "Quinn's lungs burned, but she" | | 4 | "Herrera had slipped into an" | | 5 | "She skidded to a halt," | | 6 | "The bar was a known" | | 7 | "She could hear Herrera's ragged" | | 8 | "He was waiting, probably planning" | | 9 | "Quinn's hand hovered over her" | | 10 | "She needed answers, and she" | | 11 | "she called out, her voice" | | 12 | "Herrera stepped into the light," | | 13 | "Quinn stepped closer, her sharp" | | 14 | "Herrera's gaze flickered, a hint" | | 15 | "Quinn's patience was wearing thin." | | 16 | "Herrera looked at her, then" | | 17 | "He seemed to be weighing" | | 18 | "He turned and walked towards" | | 19 | "Quinn took a deep breath," |
| | ratio | 0.855 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 62 | | matches | (empty) | | ratio | 0 | |
| 55.75% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 5 | | matches | | 0 | "Her breath misted in the chill, her heart pounding a rhythm that matched the drumroll of her boots on the slick cobblestones." | | 1 | "She could hear Herrera's ragged breaths echoing from the alley, see the faint glow of his phone lighting up the brick wall." | | 2 | "The patrons were a mix of humans and supernaturals, their eyes following Quinn as she walked in, her salt-and-pepper hair glistening with rain." | | 3 | "She knew she was stepping into the unknown, into a world that was dangerous and unpredictable." | | 4 | "She followed Herrera out of the bar, her heart pounding in her chest, her breath misting in the cold night air." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 3 | | matches | | 0 | "she called out, her voice steady despite the adrenaline coursing through her veins" | | 1 | "she said, her voice low" | | 2 | "she asked, her voice steady despite the turmoil inside her" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | 0 | "she called out (call out)" |
| | dialogueSentences | 20 | | tagDensity | 0.35 | | leniency | 0.7 | | rawRatio | 0.143 | | effectiveRatio | 0.1 | |