| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 81.75% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 822 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "very" | | 1 | "quickly" | | 2 | "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) | |
| 8.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 822 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "echoing" | | 1 | "loomed" | | 2 | "maw" | | 3 | "glinting" | | 4 | "scanned" | | 5 | "could feel" | | 6 | "pulse" | | 7 | "scanning" | | 8 | "gleaming" | | 9 | "tapestry" | | 10 | "pounding" |
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| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 3 | | found | | 0 | | label | "heart pounded in chest" | | count | 2 |
| | 1 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "heart pounded in her chest" | | 1 | "The air was thick with" |
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| 96.26% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 58 | | matches | | 0 | "d with determination" | | 1 | "was nervous" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 58 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 58 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 818 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 88.88% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 818 | | uniqueNames | 8 | | maxNameDensity | 1.22 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 10 | | Tube | 1 | | Camden | 1 | | Veil | 1 | | Market | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" |
| | places | | | globalScore | 0.889 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 818 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 58 | | matches | (empty) | |
| 26.66% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 14 | | mean | 58.43 | | std | 14.22 | | cv | 0.243 | | sampleLengths | | 0 | 62 | | 1 | 69 | | 2 | 48 | | 3 | 53 | | 4 | 65 | | 5 | 64 | | 6 | 51 | | 7 | 61 | | 8 | 57 | | 9 | 72 | | 10 | 52 | | 11 | 49 | | 12 | 90 | | 13 | 25 |
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| 93.16% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 58 | | matches | | 0 | "was soaked" | | 1 | "was, buried" |
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| 92.47% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 124 | | matches | | 0 | "was heading" | | 1 | "was pressing" |
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| 93.60% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 58 | | ratio | 0.017 | | matches | | 0 | "She'd lost sight of her quarry, but the trail was fresh; she could taste it, like the metallic tang of blood in the back of her throat." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 822 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.02068126520681265 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006082725060827251 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 58 | | echoCount | 0 | | echoWords | (empty) | |
| 70.89% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 58 | | mean | 14.1 | | std | 4.62 | | cv | 0.327 | | sampleLengths | | 0 | 19 | | 1 | 16 | | 2 | 27 | | 3 | 14 | | 4 | 16 | | 5 | 17 | | 6 | 12 | | 7 | 10 | | 8 | 13 | | 9 | 12 | | 10 | 14 | | 11 | 9 | | 12 | 11 | | 13 | 20 | | 14 | 22 | | 15 | 11 | | 16 | 17 | | 17 | 9 | | 18 | 14 | | 19 | 14 | | 20 | 14 | | 21 | 12 | | 22 | 21 | | 23 | 15 | | 24 | 2 | | 25 | 17 | | 26 | 15 | | 27 | 16 | | 28 | 3 | | 29 | 19 | | 30 | 16 | | 31 | 10 | | 32 | 16 | | 33 | 12 | | 34 | 15 | | 35 | 11 | | 36 | 19 | | 37 | 12 | | 38 | 10 | | 39 | 17 | | 40 | 17 | | 41 | 16 | | 42 | 16 | | 43 | 20 | | 44 | 16 | | 45 | 4 | | 46 | 10 | | 47 | 15 | | 48 | 14 | | 49 | 6 |
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| 25.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.1724137931034483 | | totalSentences | 58 | | uniqueOpeners | 10 | |
| 58.48% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 57 | | matches | | 0 | "Suddenly, the man stopped." |
| | ratio | 0.018 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 57 | | matches | | 0 | "She'd lost sight of her" | | 1 | "She rounded a corner, her" | | 2 | "He was heading for the" | | 3 | "She couldn't let him slip" | | 4 | "She caught sight of her" | | 5 | "She followed, her boots echoing" | | 6 | "She dashed forward, but the" | | 7 | "She was soaked, her hair" | | 8 | "She wouldn't let him get" | | 9 | "She turned and pushed her" | | 10 | "She'd follow him, even if" | | 11 | "She could feel it, the" | | 12 | "She'd heard the rumors, the" | | 13 | "She followed the man down" | | 14 | "She could hear the distant" | | 15 | "She was close." | | 16 | "She spotted her quarry again," | | 17 | "He was moving quickly, weaving" | | 18 | "He was nervous, she could" | | 19 | "She followed, her hand still" |
| | ratio | 0.579 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 57 | | matches | | 0 | "The rain pounded the cobblestones" | | 1 | "Detective Harlow Quinn's boots splashed" | | 2 | "She'd lost sight of her" | | 3 | "She rounded a corner, her" | | 4 | "The man darted across the" | | 5 | "He was heading for the" | | 6 | "Quinn's heart pounded in her" | | 7 | "She couldn't let him slip" | | 8 | "The station loomed ahead, its" | | 9 | "Quinn pushed through the crowd," | | 10 | "She caught sight of her" | | 11 | "She followed, her boots echoing" | | 12 | "The platform was a blur" | | 13 | "Quinn scanned the crowd, her" | | 14 | "She dashed forward, but the" | | 15 | "Quinn cursed under her breath," | | 16 | "She was soaked, her hair" | | 17 | "She wouldn't let him get" | | 18 | "She turned and pushed her" | | 19 | "She'd follow him, even if" |
| | ratio | 0.93 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 57 | | matches | (empty) | | ratio | 0 | |
| 58.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 6 | | matches | | 0 | "She dashed forward, but the doors slid shut in her face, leaving her staring at her own reflection in the rain-streaked glass." | | 1 | "The air grew colder, heavier, as if the very atmosphere was pressing down on her." | | 2 | "He was moving quickly, weaving through the crowd, his eyes darting from side to side." | | 3 | "She followed, her hand still resting on her gun, her eyes scanning the crowd for any sign of danger." | | 4 | "She saw creatures she couldn't even describe, their forms shifting and changing in the flickering light." | | 5 | "She thought of the case that had led her here, the case that had cost him his life." |
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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 | |