| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 3 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 366 | | 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) | |
| 59.02% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 366 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "fractured" | | 1 | "pulse" | | 2 | "whisper" |
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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 | 38 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 38 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 39 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 21 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 4 | | totalWords | 359 | | ratio | 0.011 | | matches | | 0 | "Camden Tube Station (Closed)" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 63.29% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 13 | | wordCount | 346 | | uniqueNames | 7 | | maxNameDensity | 1.73 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Quinn | 6 | | Tube | 1 | | Station | 1 | | Veil | 1 | | Market | 1 | | Morris | 2 |
| | persons | | | places | | | globalScore | 0.633 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 31 | | 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 | 359 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 39 | | matches | (empty) | |
| 67.85% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 10 | | mean | 35.9 | | std | 13.91 | | cv | 0.387 | | sampleLengths | | 0 | 56 | | 1 | 48 | | 2 | 33 | | 3 | 46 | | 4 | 53 | | 5 | 10 | | 6 | 24 | | 7 | 34 | | 8 | 31 | | 9 | 24 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 38 | | matches | (empty) | |
| 87.01% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 59 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 39 | | ratio | 0.103 | | matches | | 0 | "The suspect—lean, fast, a shadow darting ahead—ducked into an alley." | | 1 | "The suspect glanced back, eyes wide—then vanished down a set of stairs leading underground." | | 2 | "The air below was thick with the scent of damp and something else—copper, maybe, or old magic." | | 3 | "Her radio crackled—dispatch, asking for her location." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 353 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.0339943342776204 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0056657223796034 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 39 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 39 | | mean | 9.21 | | std | 4.71 | | cv | 0.511 | | sampleLengths | | 0 | 21 | | 1 | 13 | | 2 | 10 | | 3 | 12 | | 4 | 16 | | 5 | 13 | | 6 | 6 | | 7 | 13 | | 8 | 12 | | 9 | 9 | | 10 | 6 | | 11 | 4 | | 12 | 2 | | 13 | 7 | | 14 | 14 | | 15 | 8 | | 16 | 3 | | 17 | 14 | | 18 | 17 | | 19 | 15 | | 20 | 12 | | 21 | 9 | | 22 | 6 | | 23 | 4 | | 24 | 4 | | 25 | 20 | | 26 | 6 | | 27 | 7 | | 28 | 7 | | 29 | 3 | | 30 | 11 | | 31 | 8 | | 32 | 4 | | 33 | 9 | | 34 | 4 | | 35 | 6 | | 36 | 7 | | 37 | 11 | | 38 | 6 |
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| 52.14% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.358974358974359 | | totalSentences | 39 | | uniqueOpeners | 14 | |
| 90.09% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 37 | | matches | | 0 | "Instead, he vaulted over a" |
| | ratio | 0.027 | |
| 79.46% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 37 | | matches | | 0 | "She followed, her leather jacket" | | 1 | "Her voice cut through the" | | 2 | "Her wrist stung where the" | | 3 | "She wasn’t losing him." | | 4 | "She sprinted, her boots slapping" | | 5 | "She took the steps two" | | 6 | "He turned, smirking, and pressed" | | 7 | "He stepped through the gap," | | 8 | "She stared at the blank" | | 9 | "Her radio crackled—dispatch, asking for" | | 10 | "She ignored it." | | 11 | "She knew the stories." | | 12 | "She pressed her palm against" |
| | ratio | 0.351 | |
| 0.54% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 34 | | totalSentences | 37 | | matches | | 0 | "The rain came down in" | | 1 | "Harlow Quinn’s boots splashed through" | | 2 | "The suspect—lean, fast, a shadow" | | 3 | "She followed, her leather jacket" | | 4 | "Her voice cut through the" | | 5 | "Quinn cursed, hauling herself after" | | 6 | "Her wrist stung where the" | | 7 | "The alley spilled into a" | | 8 | "A car horn blared as" | | 9 | "Quinn’s pulse hammered in her" | | 10 | "She wasn’t losing him." | | 11 | "She sprinted, her boots slapping" | | 12 | "The suspect glanced back, eyes" | | 13 | "The sign above read *Camden" | | 14 | "Quinn didn’t hesitate." | | 15 | "She took the steps two" | | 16 | "The air below was thick" | | 17 | "The suspect stood at the" | | 18 | "He turned, smirking, and pressed" | | 19 | "A seam split open, revealing" |
| | ratio | 0.919 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 37 | | matches | (empty) | | ratio | 0 | |
| 91.84% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 14 | | technicalSentenceCount | 1 | | matches | | 0 | "The rain came down in sheets, turning the pavement into a slick mirror that fractured the neon glow of Soho’s nightlife." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 1 | | matches | | 0 | "He stepped, the wall sealing behind him with a whisper of stone" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |