| 75.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 1 | | adverbTags | | 0 | "she called again [again]" |
| | dialogueSentences | 16 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0.167 | | effectiveRatio | 0.125 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1625 | | 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) | |
| 81.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1625 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "gloom" | | 1 | "measured" | | 2 | "glint" | | 3 | "echoes" | | 4 | "silence" | | 5 | "velvet" |
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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 | 174 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 174 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 184 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1624 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.06% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 56 | | wordCount | 1551 | | uniqueNames | 20 | | maxNameDensity | 2 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Shaftesbury | 1 | | Avenue | 1 | | Quinn | 31 | | Morris | 3 | | Deptford | 1 | | Charing | 1 | | Cross | 1 | | Road | 4 | | Tottenham | 1 | | Court | 1 | | Warren | 1 | | Street | 1 | | Euston | 1 | | Camden | 2 | | London | 1 | | Underground | 1 | | Metropolitan | 1 | | Police | 1 |
| | persons | | 0 | "Raven" | | 1 | "Quinn" | | 2 | "Morris" |
| | places | | 0 | "Shaftesbury" | | 1 | "Avenue" | | 2 | "Deptford" | | 3 | "Charing" | | 4 | "Cross" | | 5 | "Road" | | 6 | "Tottenham" | | 7 | "Court" | | 8 | "Warren" | | 9 | "Street" | | 10 | "Euston" | | 11 | "Camden" | | 12 | "London" |
| | globalScore | 0.501 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 113 | | 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 | 1624 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 184 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 68 | | mean | 23.88 | | std | 19.87 | | cv | 0.832 | | sampleLengths | | 0 | 12 | | 1 | 41 | | 2 | 3 | | 3 | 11 | | 4 | 65 | | 5 | 3 | | 6 | 7 | | 7 | 27 | | 8 | 14 | | 9 | 39 | | 10 | 42 | | 11 | 27 | | 12 | 17 | | 13 | 2 | | 14 | 60 | | 15 | 38 | | 16 | 4 | | 17 | 56 | | 18 | 9 | | 19 | 4 | | 20 | 37 | | 21 | 48 | | 22 | 44 | | 23 | 61 | | 24 | 6 | | 25 | 10 | | 26 | 18 | | 27 | 4 | | 28 | 42 | | 29 | 6 | | 30 | 1 | | 31 | 33 | | 32 | 60 | | 33 | 6 | | 34 | 4 | | 35 | 15 | | 36 | 4 | | 37 | 46 | | 38 | 24 | | 39 | 44 | | 40 | 29 | | 41 | 5 | | 42 | 10 | | 43 | 21 | | 44 | 14 | | 45 | 73 | | 46 | 12 | | 47 | 32 | | 48 | 10 | | 49 | 15 |
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| 99.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 174 | | matches | | 0 | "were layered" | | 1 | "been scraped" | | 2 | "was hidden" |
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| 55.07% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 276 | | matches | | 0 | "was coming" | | 1 | "was turning" | | 2 | "was going" | | 3 | "was helping" | | 4 | "was already disappearing" | | 5 | "was speaking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 184 | | ratio | 0.011 | | matches | | 0 | "Behind him, the bar’s green neon bled across the pavement; inside, the old maps and black-and-white photographs sat in their smoky gloom." | | 1 | "Quinn had seen the same thing once before—in a photograph tucked into an evidence file tied to the clique she’d been watching." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1560 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 46 | | adverbRatio | 0.029487179487179487 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.003205128205128205 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 184 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 184 | | mean | 8.83 | | std | 5.62 | | cv | 0.636 | | sampleLengths | | 0 | 12 | | 1 | 8 | | 2 | 22 | | 3 | 11 | | 4 | 3 | | 5 | 11 | | 6 | 8 | | 7 | 3 | | 8 | 17 | | 9 | 10 | | 10 | 27 | | 11 | 3 | | 12 | 2 | | 13 | 5 | | 14 | 10 | | 15 | 17 | | 16 | 5 | | 17 | 9 | | 18 | 6 | | 19 | 9 | | 20 | 24 | | 21 | 4 | | 22 | 22 | | 23 | 6 | | 24 | 2 | | 25 | 2 | | 26 | 6 | | 27 | 16 | | 28 | 11 | | 29 | 5 | | 30 | 12 | | 31 | 2 | | 32 | 9 | | 33 | 9 | | 34 | 5 | | 35 | 17 | | 36 | 11 | | 37 | 1 | | 38 | 1 | | 39 | 4 | | 40 | 3 | | 41 | 13 | | 42 | 17 | | 43 | 4 | | 44 | 4 | | 45 | 4 | | 46 | 23 | | 47 | 4 | | 48 | 6 | | 49 | 23 |
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| 44.57% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.29347826086956524 | | totalSentences | 184 | | uniqueOpeners | 54 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 8 | | totalSentences | 157 | | matches | | 0 | "Then he ran." | | 1 | "Usually the fear had a" | | 2 | "Then the city narrowed into" | | 3 | "Somewhere below, voices murmured in" | | 4 | "Instead, she stepped through the" | | 5 | "Then the market closed around" | | 6 | "Somewhere, someone laughed too loudly." | | 7 | "Somewhere else, a child cried." |
| | ratio | 0.051 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 45 | | totalSentences | 157 | | matches | | 0 | "She shoved away from the" | | 1 | "He cut across the street" | | 2 | "He had a narrow lead" | | 3 | "She touched the radio at" | | 4 | "She’d get an answer when" | | 5 | "His face was pale, his" | | 6 | "He looked less like a" | | 7 | "She’d seen it in interview" | | 8 | "He’d said, and gone through" | | 9 | "He clipped a stack of" | | 10 | "They crashed across the paving." | | 11 | "Her stride stayed measured even" | | 12 | "He burst onto Charing Cross" | | 13 | "She barely heard him." | | 14 | "He knew where he was" | | 15 | "He’d picked a route, and" | | 16 | "She didn’t let her pace" | | 17 | "she called again" | | 18 | "He flung one hand out" | | 19 | "His shoulders hitched with each" |
| | ratio | 0.287 | |
| 55.54% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 127 | | totalSentences | 157 | | matches | | 0 | "The man saw her in" | | 1 | "Quinn watched his eyes shift" | | 2 | "She shoved away from the" | | 3 | "He cut across the street" | | 4 | "A horn blared." | | 5 | "Quinn slipped on the slick" | | 6 | "The man’s dark coat flashed" | | 7 | "He had a narrow lead" | | 8 | "The rain swallowed her voice." | | 9 | "She touched the radio at" | | 10 | "Static rasped back at her." | | 11 | "She’d get an answer when" | | 12 | "The man glanced over his" | | 13 | "His face was pale, his" | | 14 | "He looked less like a" | | 15 | "Quinn knew that look." | | 16 | "She’d seen it in interview" | | 17 | "A friend who’d decided to" | | 18 | "He’d said, and gone through" | | 19 | "Quinn pushed the memory down." |
| | ratio | 0.809 | |
| 63.69% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 157 | | matches | | 0 | "Because Morris had once laughed" | | 1 | "By the time anyone arrived," |
| | ratio | 0.013 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 70 | | technicalSentenceCount | 1 | | matches | | 0 | "At Tottenham Court Road he cut west, then north again, threading the side streets with a certainty that tightened something in her chest." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 25.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 2 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "He’d (would)" |
| | dialogueSentences | 16 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0.333 | | effectiveRatio | 0.25 | |