| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 23 | | tagDensity | 0.348 | | leniency | 0.696 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.98% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1660 | | totalAiIsmAdverbs | 2 | | 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) | |
| 39.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1660 | | totalAiIsms | 20 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "navigate" | | 2 | "flickered" | | 3 | "maw" | | 4 | "footsteps" | | 5 | "echoed" | | 6 | "etched" | | 7 | "vibrated" | | 8 | "silk" | | 9 | "velvet" | | 10 | "scanned" | | 11 | "familiar" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 148 | | matches | | |
| 94.59% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 4 | | narrationSentences | 148 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 161 | | 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 | 0 | | markdownWords | 0 | | totalWords | 1660 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 56 | | wordCount | 1534 | | uniqueNames | 15 | | maxNameDensity | 1.24 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 19 | | Herrera | 15 | | Saint | 2 | | Christopher | 2 | | Camden | 2 | | Spaniard | 1 | | Northern | 1 | | Line | 1 | | Morris | 3 | | London | 1 | | Tube | 1 | | Veil | 2 | | Market | 3 | | Thames | 1 | | Harlow | 2 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Spaniard" | | 5 | "Morris" | | 6 | "Harlow" |
| | places | | | globalScore | 0.881 | | windowScore | 0.833 | |
| 12.39% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 109 | | glossingSentenceCount | 6 | | matches | | 0 | "looked like a maintenance entrance for th" | | 1 | "looked like the mouth of a trap" | | 2 | "looked like a Tube station, but the track" | | 3 | "looked like a captive thunderstorm" | | 4 | "sounded like two stones grinding together" | | 5 | "looked like her partner pointed a single" |
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| 79.52% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.205 | | wordCount | 1660 | | matches | | 0 | "No law here but" | | 1 | "not walking, but sliding across the concrete" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 161 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 66 | | mean | 25.15 | | std | 16.22 | | cv | 0.645 | | sampleLengths | | 0 | 43 | | 1 | 2 | | 2 | 55 | | 3 | 50 | | 4 | 7 | | 5 | 44 | | 6 | 45 | | 7 | 12 | | 8 | 10 | | 9 | 46 | | 10 | 48 | | 11 | 29 | | 12 | 24 | | 13 | 62 | | 14 | 31 | | 15 | 19 | | 16 | 54 | | 17 | 10 | | 18 | 30 | | 19 | 50 | | 20 | 5 | | 21 | 49 | | 22 | 19 | | 23 | 8 | | 24 | 27 | | 25 | 3 | | 26 | 31 | | 27 | 42 | | 28 | 12 | | 29 | 3 | | 30 | 32 | | 31 | 25 | | 32 | 5 | | 33 | 21 | | 34 | 9 | | 35 | 9 | | 36 | 31 | | 37 | 18 | | 38 | 35 | | 39 | 35 | | 40 | 16 | | 41 | 21 | | 42 | 21 | | 43 | 2 | | 44 | 31 | | 45 | 38 | | 46 | 5 | | 47 | 15 | | 48 | 6 | | 49 | 37 |
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| 93.41% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 148 | | matches | | 0 | "was etched" | | 1 | "were gone" | | 2 | "were stained" | | 3 | "was gone" | | 4 | "was sewn" |
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| 70.30% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 257 | | matches | | 0 | "wasn't heading" | | 1 | "wasn't just running" | | 2 | "was searching" | | 3 | "was speaking" | | 4 | "was handing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 161 | | ratio | 0.012 | | matches | | 0 | "It didn't swing; it retracted into the wall with a hiss of escaping air." | | 1 | "He didn't look angry; he looked terrified." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1545 | | adjectiveStacks | 1 | | stackExamples | | 0 | "heavy grey woollen rags." |
| | adverbCount | 33 | | adverbRatio | 0.021359223300970873 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.009708737864077669 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 161 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 161 | | mean | 10.31 | | std | 5.5 | | cv | 0.533 | | sampleLengths | | 0 | 5 | | 1 | 20 | | 2 | 18 | | 3 | 2 | | 4 | 4 | | 5 | 22 | | 6 | 15 | | 7 | 14 | | 8 | 18 | | 9 | 11 | | 10 | 21 | | 11 | 7 | | 12 | 8 | | 13 | 11 | | 14 | 7 | | 15 | 18 | | 16 | 9 | | 17 | 21 | | 18 | 7 | | 19 | 8 | | 20 | 12 | | 21 | 10 | | 22 | 4 | | 23 | 18 | | 24 | 15 | | 25 | 4 | | 26 | 5 | | 27 | 15 | | 28 | 10 | | 29 | 13 | | 30 | 10 | | 31 | 6 | | 32 | 12 | | 33 | 11 | | 34 | 4 | | 35 | 14 | | 36 | 6 | | 37 | 11 | | 38 | 9 | | 39 | 5 | | 40 | 10 | | 41 | 4 | | 42 | 23 | | 43 | 5 | | 44 | 7 | | 45 | 19 | | 46 | 4 | | 47 | 15 | | 48 | 7 | | 49 | 22 |
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| 36.34% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 22 | | diversityRatio | 0.2670807453416149 | | totalSentences | 161 | | uniqueOpeners | 43 | |
| 23.31% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 143 | | matches | | 0 | "Then, the sewing on its" |
| | ratio | 0.007 | |
| 46.57% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 62 | | totalSentences | 143 | | matches | | 0 | "He vaulted a low brick" | | 1 | "He looked back once, his" | | 2 | "She reached the wall and" | | 3 | "Her closely cropped salt-and-pepper hair" | | 4 | "He wasn't heading for the" | | 5 | "He was zig-zagging toward the" | | 6 | "He dived through a gap" | | 7 | "They were behind the old" | | 8 | "She gained on him." | | 9 | "He wasn't just running." | | 10 | "He was searching for something." | | 11 | "He reached into his pocket" | | 12 | "He pressed it into a" | | 13 | "It didn't swing; it retracted" | | 14 | "Her hand went to the" | | 15 | "She knew every inch of" | | 16 | "She remembered the night Morris" | | 17 | "She descended the steps slowly." | | 18 | "It was warmer, scented with" | | 19 | "She pulled out a small" |
| | ratio | 0.434 | |
| 19.44% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 126 | | totalSentences | 143 | | matches | | 0 | "Boots hammered the slick pavement." | | 1 | "Harlow Quinn didn't feel the" | | 2 | "Tomás Herrera didn't stop." | | 3 | "He vaulted a low brick" | | 4 | "He looked back once, his" | | 5 | "A jagged white scar on" | | 6 | "Quinn adjusted her pace, her" | | 7 | "She reached the wall and" | | 8 | "Her closely cropped salt-and-pepper hair" | | 9 | "The Spaniard ducked behind a" | | 10 | "The smell of rotting citrus" | | 11 | "He wasn't heading for the" | | 12 | "He was zig-zagging toward the" | | 13 | "He dived through a gap" | | 14 | "Quinn followed, her leather watch" | | 15 | "They were behind the old" | | 16 | "The ground turned from asphalt" | | 17 | "Herrera shouted over his shoulder" | | 18 | "She gained on him." | | 19 | "Herrera stumbled on a loose" |
| | ratio | 0.881 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 143 | | matches | | 0 | "To anyone else, it looked" | | 1 | "To Quinn, it looked like" | | 2 | "Before she could grab his" |
| | ratio | 0.021 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 73 | | technicalSentenceCount | 3 | | matches | | 0 | "Her closely cropped salt-and-pepper hair stayed perfectly in place, despite the mist that coated everything in a fine sheen of moisture." | | 1 | "She shoved past a group of hooded figures who smelled of formaldehyde." | | 2 | "The thing that looked like her partner pointed a single gaunt finger at her chest." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 2 | | matches | | 0 | "Herrera whispered, his voice shaking" | | 1 | "Quinn said, her voice steady despite the hammer of her heart" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 5 | | fancyTags | | 0 | "Herrera shouted (shout)" | | 1 | "Herrera whispered (whisper)" | | 2 | "she whispered (whisper)" | | 3 | "it croaked (croak)" | | 4 | "it whispered (whisper)" |
| | dialogueSentences | 23 | | tagDensity | 0.304 | | leniency | 0.609 | | rawRatio | 0.714 | | effectiveRatio | 0.435 | |