| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1158 | | 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) | |
| 52.50% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1158 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "efficient" | | 1 | "footsteps" | | 2 | "quickened" | | 3 | "echoing" | | 4 | "constructed" | | 5 | "velvet" | | 6 | "gloom" | | 7 | "gleaming" | | 8 | "scanning" | | 9 | "perfect" |
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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 | 105 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 105 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 110 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1153 | | 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 | 38 | | wordCount | 1103 | | uniqueNames | 18 | | maxNameDensity | 1 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 2 | | Quinn | 11 | | Water | 1 | | Herrera | 5 | | Saint | 1 | | Christopher | 1 | | Morris | 5 | | Dean | 1 | | Street | 1 | | Tube | 1 | | Town | 1 | | Northern | 1 | | Veil | 1 | | Market | 1 | | Tomás | 2 | | Wapping | 1 | | Thames | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Water" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" | | 7 | "Tomás" |
| | places | | 0 | "Soho" | | 1 | "Dean" | | 2 | "Street" | | 3 | "Town" | | 4 | "Wapping" | | 5 | "Thames" |
| | globalScore | 1 | | windowScore | 1 | |
| 52.60% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 77 | | glossingSentenceCount | 3 | | matches | | 0 | "coat that seemed to drink the light" | | 1 | "looked like then melted back into the thr" | | 2 | "quite identify—shapes that moved wrong" |
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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 | 1153 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 110 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 41.18 | | std | 27.65 | | cv | 0.672 | | sampleLengths | | 0 | 88 | | 1 | 78 | | 2 | 40 | | 3 | 62 | | 4 | 6 | | 5 | 18 | | 6 | 74 | | 7 | 8 | | 8 | 63 | | 9 | 56 | | 10 | 25 | | 11 | 10 | | 12 | 87 | | 13 | 42 | | 14 | 52 | | 15 | 10 | | 16 | 13 | | 17 | 8 | | 18 | 23 | | 19 | 62 | | 20 | 66 | | 21 | 36 | | 22 | 89 | | 23 | 23 | | 24 | 11 | | 25 | 56 | | 26 | 42 | | 27 | 5 |
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| 91.90% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 105 | | matches | | 0 | "were fixed" | | 1 | "was gone" | | 2 | "was hidden" | | 3 | "was exposed" | | 4 | "were written" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 164 | | matches | | 0 | "was talking" | | 1 | "was finishing" |
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| 38.96% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 110 | | ratio | 0.036 | | matches | | 0 | "She kept a half-block behind, using the evening crowd as cover—theatre-goers huddled under umbrellas, late-shift workers hurrying home." | | 1 | "The air grew colder, smelling of damp concrete and something else—ozone, and a faint, coppery tang." | | 2 | "Figures moved in the gloom—some human, some decidedly not." | | 3 | "The green lantern light glinted off things in the shadows that she couldn’t quite identify—shapes that moved wrong." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 107 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 1 | | adverbRatio | 0.009345794392523364 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 110 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 110 | | mean | 10.48 | | std | 5.97 | | cv | 0.569 | | sampleLengths | | 0 | 21 | | 1 | 24 | | 2 | 12 | | 3 | 19 | | 4 | 12 | | 5 | 4 | | 6 | 22 | | 7 | 10 | | 8 | 21 | | 9 | 2 | | 10 | 16 | | 11 | 3 | | 12 | 12 | | 13 | 7 | | 14 | 21 | | 15 | 18 | | 16 | 23 | | 17 | 9 | | 18 | 12 | | 19 | 6 | | 20 | 7 | | 21 | 11 | | 22 | 8 | | 23 | 10 | | 24 | 7 | | 25 | 19 | | 26 | 11 | | 27 | 12 | | 28 | 7 | | 29 | 8 | | 30 | 3 | | 31 | 16 | | 32 | 5 | | 33 | 7 | | 34 | 16 | | 35 | 16 | | 36 | 16 | | 37 | 7 | | 38 | 9 | | 39 | 7 | | 40 | 17 | | 41 | 9 | | 42 | 4 | | 43 | 7 | | 44 | 5 | | 45 | 10 | | 46 | 4 | | 47 | 23 | | 48 | 9 | | 49 | 14 |
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| 43.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.33636363636363636 | | totalSentences | 110 | | uniqueOpeners | 37 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 100 | | matches | (empty) | | ratio | 0 | |
| 84.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 100 | | matches | | 0 | "Her eyes, brown and unblinking," | | 1 | "He glanced left, then right," | | 2 | "She’d been waiting three hours" | | 3 | "She kept a half-block behind," | | 4 | "He looked over his shoulder" | | 5 | "Her voice cut through the" | | 6 | "He froze for a split" | | 7 | "Her shoes slipped on the" | | 8 | "He was fast, younger, fueled" | | 9 | "He burst out onto a" | | 10 | "She pushed the thought of" | | 11 | "He didn’t answer." | | 12 | "He vaulted over a low" | | 13 | "She hesitated for only a" | | 14 | "She heard running footsteps echoing" | | 15 | "She ran, the tunnel walls" | | 16 | "He was talking urgently to" | | 17 | "He hadn’t seen her yet." | | 18 | "Its face was hidden in" | | 19 | "She looked past the guardian." |
| | ratio | 0.34 | |
| 30.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 86 | | totalSentences | 100 | | matches | | 0 | "The rain fell in steady," | | 1 | "Detective Harlow Quinn leaned against" | | 2 | "The worn leather watch on" | | 3 | "Her eyes, brown and unblinking," | | 4 | "Water dripped from the closely" | | 5 | "Movement at the door." | | 6 | "The heavy oak door swung" | | 7 | "A figure emerged, pulling a" | | 8 | "He glanced left, then right," | | 9 | "A nervous tell." | | 10 | "Quinn pushed off the wall," | | 11 | "She’d been waiting three hours" | | 12 | "She kept a half-block behind," | | 13 | "Herrera moved with purpose, not" | | 14 | "The rain muffled her footsteps," | | 15 | "He looked over his shoulder" | | 16 | "Her voice cut through the" | | 17 | "He froze for a split" | | 18 | "Quinn cursed under her breath" | | 19 | "Her shoes slipped on the" |
| | ratio | 0.86 | |
| 50.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 100 | | matches | | 0 | "Even in the poor light," |
| | ratio | 0.01 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 2 | | matches | | 0 | "Dozens of stalls constructed from scrap wood and moth-eaten velvet lined the walls, lit by lanterns that burned with a weird, greenish flame." | | 1 | "Herrera was finishing his transaction, accepting a small, cloth-wrapped package from the vendor." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 7.14% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 7 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 1 | | effectiveRatio | 0.286 | |