| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.19% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 861 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 861 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "gloom" | | 1 | "flickered" | | 2 | "glint" | | 3 | "footsteps" | | 4 | "echoing" | | 5 | "pulse" | | 6 | "velvet" | | 7 | "unreadable" | | 8 | "flicked" | | 9 | "stomach" | | 10 | "lurching" | | 11 | "familiar" | | 12 | "oppressive" | | 13 | "weight" |
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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 | 1 | | narrationSentences | 94 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 94 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 98 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 21 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 857 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 71.21% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 825 | | uniqueNames | 9 | | maxNameDensity | 1.58 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 13 | | Raven | 1 | | Nest | 1 | | London | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 |
| | persons | | | places | | | globalScore | 0.712 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like they didn’t belong in this wo" |
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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 | 857 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 98 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 30.61 | | std | 17.7 | | cv | 0.578 | | sampleLengths | | 0 | 78 | | 1 | 51 | | 2 | 40 | | 3 | 47 | | 4 | 54 | | 5 | 48 | | 6 | 52 | | 7 | 18 | | 8 | 25 | | 9 | 26 | | 10 | 19 | | 11 | 23 | | 12 | 5 | | 13 | 13 | | 14 | 34 | | 15 | 25 | | 16 | 24 | | 17 | 4 | | 18 | 51 | | 19 | 39 | | 20 | 5 | | 21 | 45 | | 22 | 29 | | 23 | 30 | | 24 | 6 | | 25 | 30 | | 26 | 20 | | 27 | 16 |
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| 94.06% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 94 | | matches | | 0 | "was gone" | | 1 | "were crossed" | | 2 | "was gone" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 138 | | matches | | 0 | "was leading" | | 1 | "was already moving" | | 2 | "was going" | | 3 | "was ticking" | | 4 | "was watching" | | 5 | "was falling" |
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| 26.24% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 98 | | ratio | 0.041 | | matches | | 0 | "She kept her eyes locked on the figure ahead—tall, lean, moving with the kind of urgency that screamed guilt." | | 1 | "At the bottom, a dim light flickered—an abandoned Tube station, its arches blackened with time." | | 2 | "Then she saw it—the glint of a bone token in his hand." | | 3 | "Not a drop, not a stumble—an actual collapse." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 831 | | adjectiveStacks | 1 | | stackExamples | | 0 | "ahead—tall, lean, moving" |
| | adverbCount | 18 | | adverbRatio | 0.021660649819494584 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0036101083032490976 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 98 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 98 | | mean | 8.74 | | std | 5.12 | | cv | 0.585 | | sampleLengths | | 0 | 13 | | 1 | 14 | | 2 | 19 | | 3 | 20 | | 4 | 12 | | 5 | 12 | | 6 | 15 | | 7 | 16 | | 8 | 6 | | 9 | 2 | | 10 | 3 | | 11 | 2 | | 12 | 15 | | 13 | 9 | | 14 | 4 | | 15 | 7 | | 16 | 14 | | 17 | 17 | | 18 | 16 | | 19 | 3 | | 20 | 14 | | 21 | 9 | | 22 | 13 | | 23 | 15 | | 24 | 11 | | 25 | 8 | | 26 | 8 | | 27 | 5 | | 28 | 11 | | 29 | 2 | | 30 | 3 | | 31 | 14 | | 32 | 18 | | 33 | 11 | | 34 | 9 | | 35 | 5 | | 36 | 6 | | 37 | 7 | | 38 | 14 | | 39 | 11 | | 40 | 5 | | 41 | 21 | | 42 | 12 | | 43 | 3 | | 44 | 4 | | 45 | 11 | | 46 | 7 | | 47 | 5 | | 48 | 4 | | 49 | 1 |
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| 37.76% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.29591836734693877 | | totalSentences | 98 | | uniqueOpeners | 29 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 84 | | matches | | 0 | "Then he was gone, vanishing" | | 1 | "Then she saw it—the glint" | | 2 | "Then the ground gave way." | | 3 | "Then he was gone, his" | | 4 | "Just the oppressive weight of" |
| | ratio | 0.06 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 84 | | matches | | 0 | "She kept her eyes locked" | | 1 | "She rounded a corner, her" | | 2 | "She wasn’t about to lose" | | 3 | "His face was half-hidden under" | | 4 | "She took the stairs two" | | 5 | "She’d chased worse than this" | | 6 | "she said, her voice like" | | 7 | "He was going deeper." | | 8 | "His arms were crossed, his" | | 9 | "She sidestepped, but he mirrored" | | 10 | "She exhaled through her nose." | | 11 | "She reached into her coat," | | 12 | "He was watching her, waiting." | | 13 | "She made her decision." | | 14 | "He grunted, stumbling back, and" | | 15 | "She hit the ground hard," | | 16 | "She could turn back." | | 17 | "She wiped the dirt from" | | 18 | "She’d come this far." | | 19 | "She wasn’t about to stop" |
| | ratio | 0.25 | |
| 43.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 84 | | matches | | 0 | "The rain hammered the pavement," | | 1 | "Detective Harlow Quinn’s boots splashed" | | 2 | "She kept her eyes locked" | | 3 | "The suspect had bolted from" | | 4 | "She rounded a corner, her" | | 5 | "The green neon glow of" | | 6 | "The suspect’s silhouette flickered under" | | 7 | "A left turn." | | 8 | "The streets narrowed, the buildings" | | 9 | "The rain eased, but the" | | 10 | "Quinn’s sharp jaw tightened." | | 11 | "She wasn’t about to lose" | | 12 | "The suspect skidded to a" | | 13 | "His face was half-hidden under" | | 14 | "Quinn didn’t hesitate." | | 15 | "She took the stairs two" | | 16 | "The descent was steep, the" | | 17 | "The scent of wet stone" | | 18 | "The suspect was already moving" | | 19 | "Quinn followed, her pulse steady" |
| | ratio | 0.833 | |
| 59.52% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 84 | | matches | | 0 | "Now, he was leading her" |
| | ratio | 0.012 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 2 | | matches | | 0 | "The suspect was already moving through the shadows, his footsteps echoing." | | 1 | "The floor beneath her splintered, and Quinn was falling, her stomach lurching." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 1 | | matches | | 0 | "the man said, his voice dropping" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |