| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 24 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.43% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1095 | | 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) | |
| 58.90% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1095 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "streaming" | | 1 | "footsteps" | | 2 | "echoed" | | 3 | "structure" | | 4 | "shimmered" | | 5 | "chill" | | 6 | "warmth" | | 7 | "calculated" |
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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 | 108 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 108 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 126 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1090 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 60.61% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 895 | | uniqueNames | 16 | | maxNameDensity | 1.79 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 16 | | Soho | 1 | | Greek | 1 | | Street | 1 | | Tomás | 1 | | Herrera | 9 | | London | 3 | | Saint | 1 | | Christopher | 1 | | Leicester | 1 | | Square | 1 | | Camden | 1 | | Passage | 1 | | Tube | 2 | | Morris | 4 | | Ice | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Tomás" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Square" | | 6 | "Passage" | | 7 | "Morris" | | 8 | "Ice" |
| | places | | 0 | "Soho" | | 1 | "Greek" | | 2 | "Street" | | 3 | "London" | | 4 | "Leicester" | | 5 | "Camden" |
| | globalScore | 0.606 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like an old service tunnel" |
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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 | 1090 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 126 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 24.22 | | std | 16.99 | | cv | 0.702 | | sampleLengths | | 0 | 46 | | 1 | 3 | | 2 | 42 | | 3 | 58 | | 4 | 5 | | 5 | 45 | | 6 | 38 | | 7 | 13 | | 8 | 27 | | 9 | 31 | | 10 | 26 | | 11 | 20 | | 12 | 17 | | 13 | 6 | | 14 | 10 | | 15 | 41 | | 16 | 23 | | 17 | 14 | | 18 | 5 | | 19 | 29 | | 20 | 50 | | 21 | 5 | | 22 | 38 | | 23 | 34 | | 24 | 36 | | 25 | 71 | | 26 | 8 | | 27 | 36 | | 28 | 26 | | 29 | 23 | | 30 | 5 | | 31 | 5 | | 32 | 25 | | 33 | 4 | | 34 | 22 | | 35 | 18 | | 36 | 9 | | 37 | 6 | | 38 | 49 | | 39 | 10 | | 40 | 10 | | 41 | 39 | | 42 | 46 | | 43 | 6 | | 44 | 10 |
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| 95.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 108 | | matches | | 0 | "was gone" | | 1 | "was hidden" | | 2 | "was outnumbered" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 176 | | matches | | 0 | "was heading" | | 1 | "were fading" |
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| 52.15% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 126 | | ratio | 0.032 | | matches | | 0 | "Quinn followed, catching a glimpse of olive skin and dark curls beneath the hood—Tomás Herrera." | | 1 | "The tunnel smelled of damp earth and something else—incense?" | | 2 | "The platform teemed with people—if they were people." | | 3 | "He pressed something into her palm—a small white object." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 900 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.024444444444444446 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0022222222222222222 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 126 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 126 | | mean | 8.65 | | std | 5.52 | | cv | 0.638 | | sampleLengths | | 0 | 15 | | 1 | 12 | | 2 | 19 | | 3 | 3 | | 4 | 4 | | 5 | 9 | | 6 | 20 | | 7 | 9 | | 8 | 6 | | 9 | 13 | | 10 | 15 | | 11 | 24 | | 12 | 5 | | 13 | 13 | | 14 | 10 | | 15 | 6 | | 16 | 16 | | 17 | 9 | | 18 | 15 | | 19 | 9 | | 20 | 5 | | 21 | 1 | | 22 | 3 | | 23 | 9 | | 24 | 11 | | 25 | 11 | | 26 | 1 | | 27 | 4 | | 28 | 13 | | 29 | 9 | | 30 | 9 | | 31 | 5 | | 32 | 15 | | 33 | 6 | | 34 | 15 | | 35 | 5 | | 36 | 8 | | 37 | 9 | | 38 | 6 | | 39 | 10 | | 40 | 9 | | 41 | 5 | | 42 | 13 | | 43 | 1 | | 44 | 3 | | 45 | 10 | | 46 | 5 | | 47 | 18 | | 48 | 8 | | 49 | 6 |
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| 75.66% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.46825396825396826 | | totalSentences | 126 | | uniqueOpeners | 59 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 99 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 99 | | matches | | 0 | "She sidestepped the scattered pallets" | | 1 | "She'd been watching the former" | | 2 | "He glanced back, Saint Christopher" | | 3 | "Her partner would have radioed" | | 4 | "She shook off the thought" | | 5 | "He was heading northeast, away" | | 6 | "They pounded through side streets," | | 7 | "She slowed to a walk," | | 8 | "He looked up, rain streaming" | | 9 | "He dropped through the opening" | | 10 | "She clicked on her torch," | | 11 | "she leaned closer." | | 12 | "She tried again." | | 13 | "You didn't follow suspects into" | | 14 | "She swung onto the ladder." | | 15 | "Her torch beam cut through" | | 16 | "She pressed against the wall" | | 17 | "It was a full platform," | | 18 | "He pressed something into her" | | 19 | "Her mobile showed no signal" |
| | ratio | 0.283 | |
| 35.76% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 84 | | totalSentences | 99 | | matches | | 0 | "Detective Quinn's boots slammed against" | | 1 | "Rain pelted her face, mixing" | | 2 | "The suspect didn't slow." | | 3 | "Quinn pushed harder, her breath" | | 4 | "She sidestepped the scattered pallets" | | 5 | "The alley opened onto Greek" | | 6 | "The suspect skidded left, nearly" | | 7 | "Quinn followed, catching a glimpse" | | 8 | "She'd been watching the former" | | 9 | "He glanced back, Saint Christopher" | | 10 | "Recognition flashed across his face" | | 11 | "A black cab screeched, horn" | | 12 | "Quinn waited for a gap," | | 13 | "Herrera cut through Leicester Square," | | 14 | "Quinn's leather watch caught on" | | 15 | "Her partner would have radioed" | | 16 | "Morris always had her six." | | 17 | "Morris was gone." | | 18 | "She shook off the thought" | | 19 | "He was heading northeast, away" |
| | ratio | 0.848 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 99 | | matches | | 0 | "By the time support arrived," | | 1 | "What they were all doing." |
| | ratio | 0.02 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 1 | | matches | | 0 | "Vendors hawked their wares in accents Quinn couldn't place, selling items that defied easy description." |
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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 | |