| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said quietly [quietly]" |
| | dialogueSentences | 3 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0.5 | | effectiveRatio | 0.5 | |
| 68.23% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 787 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "carefully" | | 1 | "softly" | | 2 | "sharply" | | 3 | "very" |
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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) | |
| 100.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 787 | | totalAiIsms | 0 | | found | (empty) | | highlights | (empty) | |
| 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 | 56 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 56 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 57 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 787 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 778 | | uniqueNames | 18 | | maxNameDensity | 0.64 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Quinn | 5 | | Greek | 1 | | Street | 2 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Herrera | 3 | | Old | 1 | | Compton | 1 | | Saint | 1 | | Christopher | 1 | | Camden | 1 | | Morris | 2 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Met | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Camden" | | 7 | "Morris" |
| | places | | 0 | "London" | | 1 | "Greek" | | 2 | "Street" | | 3 | "Soho" | | 4 | "Old" | | 5 | "Compton" | | 6 | "Tottenham" | | 7 | "Court" | | 8 | "Road" |
| | globalScore | 1 | | windowScore | 1 | |
| 91.86% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 787 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 57 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 18 | | mean | 43.72 | | std | 27.03 | | cv | 0.618 | | sampleLengths | | 0 | 67 | | 1 | 68 | | 2 | 10 | | 3 | 33 | | 4 | 51 | | 5 | 76 | | 6 | 46 | | 7 | 8 | | 8 | 66 | | 9 | 31 | | 10 | 25 | | 11 | 114 | | 12 | 27 | | 13 | 67 | | 14 | 29 | | 15 | 27 | | 16 | 28 | | 17 | 14 |
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| 80.20% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 56 | | matches | | 0 | "was gone" | | 1 | "been painted" | | 2 | "been gone" | | 3 | "been found" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 129 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 57 | | ratio | 0 | | matches | (empty) | |
| 95.84% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 782 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.04475703324808184 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.01278772378516624 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 57 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 57 | | mean | 13.81 | | std | 8.68 | | cv | 0.629 | | sampleLengths | | 0 | 18 | | 1 | 28 | | 2 | 21 | | 3 | 6 | | 4 | 28 | | 5 | 13 | | 6 | 21 | | 7 | 3 | | 8 | 7 | | 9 | 15 | | 10 | 18 | | 11 | 2 | | 12 | 13 | | 13 | 3 | | 14 | 22 | | 15 | 6 | | 16 | 5 | | 17 | 20 | | 18 | 7 | | 19 | 15 | | 20 | 13 | | 21 | 21 | | 22 | 7 | | 23 | 19 | | 24 | 20 | | 25 | 8 | | 26 | 10 | | 27 | 27 | | 28 | 8 | | 29 | 21 | | 30 | 5 | | 31 | 14 | | 32 | 12 | | 33 | 5 | | 34 | 2 | | 35 | 2 | | 36 | 5 | | 37 | 11 | | 38 | 24 | | 39 | 19 | | 40 | 31 | | 41 | 5 | | 42 | 21 | | 43 | 5 | | 44 | 9 | | 45 | 27 | | 46 | 5 | | 47 | 30 | | 48 | 32 | | 49 | 29 |
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| 78.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5263157894736842 | | totalSentences | 57 | | uniqueOpeners | 30 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 52 | | matches | | 0 | "Then he was gone again," | | 1 | "Brightly lit barges bobbed beneath" | | 2 | "Then at the darkness beneath" |
| | ratio | 0.058 | |
| 66.15% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 52 | | matches | | 0 | "He took the corner onto" | | 1 | "She had seen it in" | | 2 | "He glanced over his shoulder," | | 3 | "Her left wrist ached where" | | 4 | "She ignored it." | | 5 | "Her mind wanted to turn" | | 6 | "Her body had already committed." | | 7 | "He vaulted a low wall" | | 8 | "She reached it thirty seconds" | | 9 | "It carried the smell of" | | 10 | "She checked the time: 23:47." | | 11 | "She keyed the radio on" | | 12 | "She had gone down after" | | 13 | "She had come back up" | | 14 | "His car had been found" | | 15 | "She could call it in." | | 16 | "They would send a car," | | 17 | "Her jaw tightened, and she" | | 18 | "She descended the first step." | | 19 | "she said quietly, to nobody" |
| | ratio | 0.385 | |
| 85.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 39 | | totalSentences | 52 | | matches | | 0 | "The rain had settled into" | | 1 | "Harlow Quinn kept her pace" | | 2 | "Tomás Herrera did not look" | | 3 | "He took the corner onto" | | 4 | "The Saint Christopher medallion would" | | 5 | "She had seen it in" | | 6 | "He glanced over his shoulder," | | 7 | "Her left wrist ached where" | | 8 | "She ignored it." | | 9 | "Her mind wanted to turn" | | 10 | "Her body had already committed." | | 11 | "The alley opened onto a" | | 12 | "Herrera was faster than his" | | 13 | "He vaulted a low wall" | | 14 | "Quinn took the wall more" | | 15 | "Herrera ran toward the crowd" | | 16 | "She reached it thirty seconds" | | 17 | "The arch sheltered a stairwell" | | 18 | "Iron railings curled down into" | | 19 | "The sign above it had" |
| | ratio | 0.75 | |
| 96.15% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 52 | | matches | | | ratio | 0.019 | |
| 53.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 4 | | matches | | 0 | "Harlow Quinn kept her pace even along Greek Street, her shoes slapping the wet paving, the green neon of the Raven's Nest bleeding across the puddles behind her…" | | 1 | "He took the corner onto Old Compton Street with his collar up and one hand pressed flat against his chest, as though he were holding something in place." | | 2 | "Describe a stairwell that wasn't on any map, a fugitive who might be running toward something she could not name, a symbol she had never seen in any case file." | | 3 | "The second step rang softly, and somewhere far below, a voice stopped mid-sentence, as if listening." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 3 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |