| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 87.59% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 403 | | 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 | 403 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "flickered" | | 1 | "navigating" | | 2 | "reminder" | | 3 | "loomed" | | 4 | "gloom" | | 5 | "charged" | | 6 | "weight" | | 7 | "pulse" | | 8 | "quickened" | | 9 | "silence" |
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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 | 33 | | matches | (empty) | |
| 99.57% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 33 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 35 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 6 | | totalWords | 401 | | ratio | 0.015 | | matches | | 0 | "The Raven’s Nest" | | 1 | "The Raven’s Nest" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 30.32% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 376 | | uniqueNames | 9 | | maxNameDensity | 2.39 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 2 | | Harlow | 1 | | Quinn | 9 | | Tomás | 1 | | Herrera | 4 | | Tube | 1 | | Camden | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Raven" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Market" |
| | places | | | globalScore | 0.303 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 27 | | 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 | 401 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 35 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 13 | | mean | 30.85 | | std | 18.88 | | cv | 0.612 | | sampleLengths | | 0 | 57 | | 1 | 58 | | 2 | 20 | | 3 | 10 | | 4 | 41 | | 5 | 29 | | 6 | 10 | | 7 | 44 | | 8 | 27 | | 9 | 52 | | 10 | 6 | | 11 | 4 | | 12 | 43 |
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| 94.63% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 33 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 59 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 35 | | ratio | 0.057 | | matches | | 0 | "She’d been chasing this lead for weeks, ever since the case in the Tube station beneath Camden—where the Veil Market thrived." | | 1 | "The air smelled of ink and something older—something that didn’t belong." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 378 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.023809523809523808 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.007936507936507936 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 35 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 35 | | mean | 11.46 | | std | 5.88 | | cv | 0.513 | | sampleLengths | | 0 | 23 | | 1 | 19 | | 2 | 15 | | 3 | 18 | | 4 | 13 | | 5 | 11 | | 6 | 16 | | 7 | 20 | | 8 | 4 | | 9 | 6 | | 10 | 8 | | 11 | 3 | | 12 | 21 | | 13 | 9 | | 14 | 16 | | 15 | 7 | | 16 | 6 | | 17 | 10 | | 18 | 3 | | 19 | 12 | | 20 | 11 | | 21 | 18 | | 22 | 15 | | 23 | 12 | | 24 | 20 | | 25 | 11 | | 26 | 21 | | 27 | 6 | | 28 | 4 | | 29 | 8 | | 30 | 6 | | 31 | 12 | | 32 | 5 | | 33 | 4 | | 34 | 8 |
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| 59.05% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.37142857142857144 | | totalSentences | 35 | | uniqueOpeners | 13 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 33 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 6 | | totalSentences | 33 | | matches | | 0 | "His scar ran sharp along" | | 1 | "She’d been chasing this lead" | | 2 | "It was what lay beneath" | | 3 | "He just turned, his stride" | | 4 | "She stepped forward, the weight" | | 5 | "It was a gamble." |
| | ratio | 0.182 | |
| 20.61% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 29 | | totalSentences | 33 | | matches | | 0 | "The neon glow of *The" | | 1 | "Detective Harlow Quinn’s boots splashed" | | 2 | "The scent of damp earth" | | 3 | "His scar ran sharp along" | | 4 | "Quinn’s fingers tightened around the" | | 5 | "The clockwork ticked, relentless, marking" | | 6 | "Quinn said, her voice low," | | 7 | "Herrera didn’t look back." | | 8 | "The words hung between them," | | 9 | "Quinn’s jaw tightened." | | 10 | "She’d been chasing this lead" | | 11 | "The place where things didn’t" | | 12 | "It was what lay beneath" | | 13 | "Quinn said, her voice sharp" | | 14 | "Herrera didn’t answer." | | 15 | "He just turned, his stride" | | 16 | "The alley narrowed, the air" | | 17 | "Quinn followed, her boots sinking" | | 18 | "The back door of the" | | 19 | "Quinn pushed it open, the" |
| | ratio | 0.879 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 33 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 2 | | matches | | 0 | "Ahead, Tomás Herrera moved with the quiet efficiency of a man who had spent years navigating unseen paths." | | 1 | "Quinn followed, her boots sinking into the wet earth, the weight of her gun pressing into her hip." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn said, her voice low, cutting through the rain’s hiss" | | 1 | "Quinn said, her voice sharp" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |