| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.28% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1060 | | 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) | |
| 90.57% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1060 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 91 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 91 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 94 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1054 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 1017 | | uniqueNames | 17 | | maxNameDensity | 0.49 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Old | 1 | | Compton | 1 | | Street | 1 | | Quinn | 5 | | Soho | 1 | | Saint | 1 | | Christopher | 1 | | Tube | 1 | | Raven | 2 | | Nest | 2 | | London | 1 | | Camden | 2 | | Silas | 1 | | Morris | 1 | | Victorian | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Saint" | | 2 | "Christopher" | | 3 | "Raven" | | 4 | "Silas" | | 5 | "Morris" |
| | places | | 0 | "Old" | | 1 | "Compton" | | 2 | "Street" | | 3 | "Soho" | | 4 | "London" | | 5 | "Camden" | | 6 | "Victorian" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a boarded-up entrance to an o" |
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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 | 1054 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 94 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 36.34 | | std | 23.13 | | cv | 0.636 | | sampleLengths | | 0 | 60 | | 1 | 41 | | 2 | 33 | | 3 | 3 | | 4 | 10 | | 5 | 48 | | 6 | 56 | | 7 | 9 | | 8 | 46 | | 9 | 40 | | 10 | 56 | | 11 | 30 | | 12 | 2 | | 13 | 4 | | 14 | 41 | | 15 | 78 | | 16 | 56 | | 17 | 5 | | 18 | 54 | | 19 | 3 | | 20 | 59 | | 21 | 33 | | 22 | 21 | | 23 | 82 | | 24 | 42 | | 25 | 49 | | 26 | 6 | | 27 | 59 | | 28 | 28 |
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| 93.70% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 91 | | matches | | 0 | "been pried" | | 1 | "been found" | | 2 | "been told" | | 3 | "been transformed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 169 | | matches | | 0 | "was already disappearing" | | 1 | "were really doing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 94 | | ratio | 0.053 | | matches | | 0 | "That medallion, the way he'd been sitting with that group at the back of the Raven's Nest, the nervous glances toward the bookshelf—she'd been watching them for weeks." | | 1 | "The air rising from below carried something else beneath the must and damp—incense, maybe, or something sweeter." | | 2 | "People moved between the stalls—some human, some..." | | 3 | "For things that operated outside the law—outside any law she understood." | | 4 | "He pulled something from his pocket—a token, white and curved—and held it up." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1026 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.025341130604288498 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.005847953216374269 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 94 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 94 | | mean | 11.21 | | std | 7.82 | | cv | 0.697 | | sampleLengths | | 0 | 16 | | 1 | 19 | | 2 | 25 | | 3 | 3 | | 4 | 3 | | 5 | 13 | | 6 | 22 | | 7 | 4 | | 8 | 10 | | 9 | 5 | | 10 | 14 | | 11 | 3 | | 12 | 3 | | 13 | 4 | | 14 | 3 | | 15 | 10 | | 16 | 6 | | 17 | 11 | | 18 | 21 | | 19 | 13 | | 20 | 14 | | 21 | 8 | | 22 | 21 | | 23 | 4 | | 24 | 4 | | 25 | 1 | | 26 | 18 | | 27 | 7 | | 28 | 21 | | 29 | 1 | | 30 | 26 | | 31 | 9 | | 32 | 4 | | 33 | 7 | | 34 | 10 | | 35 | 21 | | 36 | 11 | | 37 | 7 | | 38 | 30 | | 39 | 2 | | 40 | 4 | | 41 | 2 | | 42 | 19 | | 43 | 12 | | 44 | 8 | | 45 | 11 | | 46 | 5 | | 47 | 18 | | 48 | 9 | | 49 | 28 |
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| 66.31% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.43617021276595747 | | totalSentences | 94 | | uniqueOpeners | 41 | |
| 79.37% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 84 | | matches | | 0 | "Of course he didn't." | | 1 | "Then a burst of garbled" |
| | ratio | 0.024 | |
| 96.19% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 84 | | matches | | 0 | "She pushed harder." | | 1 | "Her lungs burned." | | 2 | "She'd seen that face before." | | 3 | "He didn't stop." | | 4 | "They never stopped." | | 5 | "Her suspect grabbed the edge" | | 6 | "She recovered, her hand catching" | | 7 | "He was through it before" | | 8 | "She eyed the gap." | | 9 | "Her shoulder would fit." | | 10 | "She didn't stop to assess" | | 11 | "They'd come full circle from" | | 12 | "She paused at the top" | | 13 | "She touched the radio at" | | 14 | "She knew that even as" | | 15 | "She could wait for backup." | | 16 | "She hadn't let it go." | | 17 | "She started down." | | 18 | "She followed them, her torch" | | 19 | "She edged forward until she" |
| | ratio | 0.31 | |
| 55.24% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 84 | | matches | | 0 | "Harlow Quinn sprinted through it," | | 1 | "She pushed harder." | | 2 | "Her lungs burned." | | 3 | "The area was a maze" | | 4 | "The suspect glanced back." | | 5 | "She'd seen that face before." | | 6 | "The Saint Christopher medallion had" | | 7 | "He didn't stop." | | 8 | "They never stopped." | | 9 | "The alley opened onto a" | | 10 | "Dumpsters overflowed with the day's" | | 11 | "The smell of rotting food" | | 12 | "Her suspect grabbed the edge" | | 13 | "Quinn rounded the dumpster and" | | 14 | "She recovered, her hand catching" | | 15 | "He was through it before" | | 16 | "She eyed the gap." | | 17 | "Her shoulder would fit." | | 18 | "The fence rattled as she" | | 19 | "She didn't stop to assess" |
| | ratio | 0.81 | |
| 59.52% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 84 | | matches | | 0 | "By the time anyone arrived," |
| | ratio | 0.012 | |
| 68.45% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 5 | | matches | | 0 | "Harlow Quinn sprinted through it, her leather shoes splashing through puddles that had long since soaked through her trousers." | | 1 | "The fence rattled as she forced herself through, the chain links catching on her jacket, tearing the fabric." | | 2 | "She paused at the top of the slope, her chest heaving, rain streaming down her face and dripping from her jaw." | | 3 | "A figure with skin that shimmered like oil on water examined something in a glass vial." | | 4 | "She'd heard whispers about it, fragments from informants who would say no more once they realised what they were revealing." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |