| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 91.42% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1166 | | totalAiIsmAdverbs | 2 | | 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) | |
| 57.12% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1166 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "fractured" | | 1 | "streaming" | | 2 | "rhythmic" | | 3 | "pounding" | | 4 | "perfect" | | 5 | "electric" | | 6 | "weight" | | 7 | "predator" | | 8 | "stomach" | | 9 | "scanned" |
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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 | 119 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 119 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 119 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1165 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 98.50% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 1165 | | uniqueNames | 15 | | maxNameDensity | 1.03 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Quinn | 12 | | Met | 1 | | Morris | 4 | | Camden | 2 | | High | 1 | | Street | 1 | | Tube | 1 | | Glock | 1 | | Veil | 1 | | Market | 2 | | Soho | 1 | | Victorian | 1 | | Herrera | 1 | | Harlow | 2 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Market" | | 3 | "Herrera" | | 4 | "Harlow" |
| | places | | 0 | "London" | | 1 | "Camden" | | 2 | "High" | | 3 | "Street" | | 4 | "Soho" |
| | globalScore | 0.985 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 88 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like the same frequency" |
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| 28.33% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.717 | | wordCount | 1165 | | matches | | 0 | "not twenty, but she'd kept herself sharp" | | 1 | "not electric, but something warmer, flickering like torchlight" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 119 | | matches | (empty) | |
| 86.82% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 52.95 | | std | 24.03 | | cv | 0.454 | | sampleLengths | | 0 | 87 | | 1 | 77 | | 2 | 63 | | 3 | 66 | | 4 | 60 | | 5 | 52 | | 6 | 89 | | 7 | 55 | | 8 | 11 | | 9 | 55 | | 10 | 49 | | 11 | 55 | | 12 | 38 | | 13 | 79 | | 14 | 31 | | 15 | 32 | | 16 | 2 | | 17 | 66 | | 18 | 3 | | 19 | 69 | | 20 | 66 | | 21 | 60 |
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| 93.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 119 | | matches | | 0 | "get arrested" | | 1 | "got consumed" | | 2 | "was distracted" | | 3 | "was forgotten" |
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| 75.97% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 215 | | matches | | 0 | "was crawling" | | 1 | "wasn't entering" | | 2 | "was entering" | | 3 | "was handing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 119 | | ratio | 0.008 | | matches | | 0 | "The staircase descended into darkness, but light bloomed from below—not electric, but something warmer, flickering like torchlight." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1171 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.02134927412467976 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.007685738684884714 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 119 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 119 | | mean | 9.79 | | std | 5.6 | | cv | 0.572 | | sampleLengths | | 0 | 18 | | 1 | 19 | | 2 | 17 | | 3 | 14 | | 4 | 2 | | 5 | 11 | | 6 | 6 | | 7 | 25 | | 8 | 10 | | 9 | 7 | | 10 | 12 | | 11 | 13 | | 12 | 10 | | 13 | 22 | | 14 | 14 | | 15 | 10 | | 16 | 17 | | 17 | 12 | | 18 | 15 | | 19 | 16 | | 20 | 9 | | 21 | 14 | | 22 | 5 | | 23 | 15 | | 24 | 7 | | 25 | 2 | | 26 | 6 | | 27 | 19 | | 28 | 6 | | 29 | 5 | | 30 | 3 | | 31 | 9 | | 32 | 17 | | 33 | 18 | | 34 | 10 | | 35 | 3 | | 36 | 23 | | 37 | 22 | | 38 | 8 | | 39 | 9 | | 40 | 6 | | 41 | 8 | | 42 | 14 | | 43 | 14 | | 44 | 11 | | 45 | 10 | | 46 | 3 | | 47 | 3 | | 48 | 7 | | 49 | 4 |
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| 49.58% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.35294117647058826 | | totalSentences | 119 | | uniqueOpeners | 42 | |
| 89.29% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 112 | | matches | | 0 | "Too late for honest citizens," | | 1 | "Just her watch and her" | | 2 | "Maybe ten seconds." |
| | ratio | 0.027 | |
| 62.86% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 44 | | totalSentences | 112 | | matches | | 0 | "Her boots slapped against the" | | 1 | "She checked her wrist, the" | | 2 | "She didn't draw her service" | | 3 | "They turned sharply off Camden" | | 4 | "She was forty-one, not twenty," | | 5 | "She pushed a wet strand" | | 6 | "They produced a key from" | | 7 | "She watched through the gap" | | 8 | "She remembered the files on" | | 9 | "She had spent three years" | | 10 | "It was ajar." | | 11 | "She pushed it open, the" | | 12 | "She took the steps two" | | 13 | "She had heard the rumors" | | 14 | "She looked down at her" | | 15 | "She had a badge, a" | | 16 | "She checked her pockets." | | 17 | "She smiled, revealing teeth that" | | 18 | "She held the woman's gaze" | | 19 | "They got consumed." |
| | ratio | 0.393 | |
| 49.29% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 92 | | totalSentences | 112 | | matches | | 0 | "Harlow Quinn ignored the water" | | 1 | "Her boots slapped against the" | | 2 | "She checked her wrist, the" | | 3 | "The suspect wore a dark" | | 4 | "Quinn tightened her grip on" | | 5 | "She didn't draw her service" | | 6 | "They turned sharply off Camden" | | 7 | "Quinn followed, her military precision" | | 8 | "She was forty-one, not twenty," | | 9 | "She pushed a wet strand" | | 10 | "The alley dead-ended at a" | | 11 | "Rust coated the iron gates," | | 12 | "They produced a key from" | | 13 | "Quinn slowed, pressing her back" | | 14 | "She watched through the gap" | | 15 | "This wasn't a normal squat." | | 16 | "The air down there hummed," | | 17 | "She remembered the files on" | | 18 | "She had spent three years" | | 19 | "This felt like the same" |
| | ratio | 0.821 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 112 | | matches | | 0 | "Even from this distance, Quinn" | | 1 | "If she went back, she" | | 2 | "If tonight was the night," | | 3 | "If she lost him, she" |
| | ratio | 0.036 | |
| 80.20% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 5 | | matches | | 0 | "Rain hammered against the pavement, turning the London streets into slick mirrors that fractured the glow of streetlights." | | 1 | "Her boots slapped against the wet concrete, a rhythmic percussion that matched the pounding in her chest." | | 2 | "The ceiling was high, lost in shadow, supported by Victorian pillars wrapped in ivy that shouldn't have been able to grow underground." | | 3 | "The suspect was ahead, approaching a turnstile that blocked access to the main platform." | | 4 | "It crackled with static energy that made the hair on her arms stand up." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |