| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 536 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 44.03% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 536 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "flickered" | | 1 | "potential" | | 2 | "pulse" | | 3 | "whisper" | | 4 | "stomach" | | 5 | "weight" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "the air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 42 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 42 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 50 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 13 | | totalWords | 531 | | ratio | 0.024 | | matches | | 0 | "The Raven’s Nest" | | 1 | "he knew something about the clique’s latest shipment" | | 2 | "he" | | 3 | "wrong" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 37.76% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 19 | | wordCount | 490 | | uniqueNames | 8 | | maxNameDensity | 2.24 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Herrera | 1 | | Soho | 1 | | Quinn | 11 | | Veil | 1 | | Market | 1 | | Camden | 1 | | Tomás | 2 |
| | persons | | 0 | "Raven" | | 1 | "Herrera" | | 2 | "Quinn" | | 3 | "Tomás" |
| | places | | | globalScore | 0.378 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 32 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 11.68% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.883 | | wordCount | 531 | | matches | | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 50 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 25.29 | | std | 19.12 | | cv | 0.756 | | sampleLengths | | 0 | 66 | | 1 | 51 | | 2 | 51 | | 3 | 15 | | 4 | 10 | | 5 | 21 | | 6 | 36 | | 7 | 4 | | 8 | 51 | | 9 | 4 | | 10 | 13 | | 11 | 38 | | 12 | 12 | | 13 | 15 | | 14 | 23 | | 15 | 12 | | 16 | 29 | | 17 | 5 | | 18 | 12 | | 19 | 7 | | 20 | 56 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 42 | | matches | (empty) | |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 86 | | matches | | 0 | "was going was dealing" | | 1 | "was running" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 50 | | ratio | 0.1 | | matches | | 0 | "Tomás Herrera’s voice had been a blade in her ribs—*he knew something about the clique’s latest shipment*—and now she had to find out what." | | 1 | "Quinn hesitated—just for a second—but the second stretched into a warning." | | 2 | "She had seen the way he moved—too precise, too controlled." | | 3 | "She twisted free, her fingers digging into his forearm where the scar ran—old, jagged, like a wound that never fully healed." | | 4 | "She had to find out what Tomás knew—and why he’d been so careful to keep it hidden." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 495 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.03434343434343434 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.006060606060606061 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 50 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 50 | | mean | 10.62 | | std | 6.89 | | cv | 0.649 | | sampleLengths | | 0 | 19 | | 1 | 23 | | 2 | 24 | | 3 | 25 | | 4 | 16 | | 5 | 10 | | 6 | 16 | | 7 | 24 | | 8 | 11 | | 9 | 9 | | 10 | 6 | | 11 | 3 | | 12 | 7 | | 13 | 8 | | 14 | 6 | | 15 | 7 | | 16 | 6 | | 17 | 4 | | 18 | 10 | | 19 | 16 | | 20 | 4 | | 21 | 16 | | 22 | 21 | | 23 | 14 | | 24 | 4 | | 25 | 3 | | 26 | 4 | | 27 | 6 | | 28 | 8 | | 29 | 9 | | 30 | 21 | | 31 | 12 | | 32 | 10 | | 33 | 5 | | 34 | 3 | | 35 | 20 | | 36 | 7 | | 37 | 5 | | 38 | 3 | | 39 | 3 | | 40 | 6 | | 41 | 17 | | 42 | 5 | | 43 | 9 | | 44 | 3 | | 45 | 4 | | 46 | 3 | | 47 | 23 | | 48 | 16 | | 49 | 17 |
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| 38.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.3 | | totalSentences | 50 | | uniqueOpeners | 15 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 42 | | matches | | 0 | "Then the man spun, his" | | 1 | "Instead, he gestured toward the" |
| | ratio | 0.048 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 42 | | matches | | 0 | "She rounded a corner and" | | 1 | "She didn’t need to." | | 2 | "She had seen the way" | | 3 | "She didn’t need to." | | 4 | "She had already made her" | | 5 | "She twisted free, her fingers" | | 6 | "she said, her voice steady" | | 7 | "She didn’t care." | | 8 | "She had come for answers," | | 9 | "She had to know more." | | 10 | "She had to find out" |
| | ratio | 0.262 | |
| 31.43% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 36 | | totalSentences | 42 | | matches | | 0 | "The neon sign of *The" | | 1 | "Quinn’s boots crunched on wet" | | 2 | "Tomás Herrera’s voice had been" | | 3 | "The suspect, a man in" | | 4 | "The rain hammered against her" | | 5 | "Every step was a gamble," | | 6 | "She rounded a corner and" | | 7 | "The man’s back was to" | | 8 | "Quinn hesitated—just for a second—but" | | 9 | "Quinn didn’t flinch." | | 10 | "The man’s eyes darkened, but" | | 11 | "Quinn didn’t ask who *he*" | | 12 | "She didn’t need to." | | 13 | "She had seen the way" | | 14 | "The door creaked open." | | 15 | "Quinn’s pulse hammered in her" | | 16 | "The man stood in the" | | 17 | "Quinn didn’t answer." | | 18 | "She didn’t need to." | | 19 | "She had already made her" |
| | ratio | 0.857 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 42 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, her voice steady despite the adrenaline burning her veins" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0 | | effectiveRatio | 0 | |