| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 23 | | tagDensity | 0.435 | | leniency | 0.87 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1481 | | 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) | |
| 86.50% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1481 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "flickered" | | 1 | "glinting" | | 2 | "almost alive" | | 3 | "weight" |
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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 | 0 | | narrationSentences | 119 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 119 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 132 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 3 | | totalWords | 1466 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 1243 | | uniqueNames | 14 | | maxNameDensity | 0.56 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 7 | | Ford | 1 | | Morris | 4 | | Hendon | 1 | | Raven | 1 | | Glock | 1 | | Lined | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 3 | | London | 1 | | Veil | 1 | | Market | 1 | | Tube | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Ford" | | 2 | "Morris" | | 3 | "Raven" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Tomás" | | 7 | "Market" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 77 | | glossingSentenceCount | 1 | | matches | | 0 | "symbols that seemed to shift when she wasn't looking at them directly" |
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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 | 1466 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 132 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 29.32 | | std | 20.83 | | cv | 0.71 | | sampleLengths | | 0 | 59 | | 1 | 60 | | 2 | 40 | | 3 | 48 | | 4 | 3 | | 5 | 45 | | 6 | 56 | | 7 | 47 | | 8 | 7 | | 9 | 55 | | 10 | 41 | | 11 | 31 | | 12 | 66 | | 13 | 4 | | 14 | 12 | | 15 | 4 | | 16 | 24 | | 17 | 23 | | 18 | 5 | | 19 | 5 | | 20 | 58 | | 21 | 5 | | 22 | 39 | | 23 | 32 | | 24 | 49 | | 25 | 5 | | 26 | 68 | | 27 | 17 | | 28 | 8 | | 29 | 49 | | 30 | 7 | | 31 | 9 | | 32 | 46 | | 33 | 5 | | 34 | 35 | | 35 | 11 | | 36 | 7 | | 37 | 4 | | 38 | 53 | | 39 | 42 | | 40 | 6 | | 41 | 42 | | 42 | 49 | | 43 | 20 | | 44 | 13 | | 45 | 61 | | 46 | 26 | | 47 | 36 | | 48 | 18 | | 49 | 11 |
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| 90.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 119 | | matches | | 0 | "been locked" | | 1 | "was wrapped" | | 2 | "were covered" | | 3 | "were covered" | | 4 | "was gone" |
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| 23.79% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 227 | | matches | | 0 | "was heading" | | 1 | "was standing" | | 2 | "was thinking" | | 3 | "was waiting" | | 4 | "wasn't looking" | | 5 | "was limping" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 15 | | semicolonCount | 0 | | flaggedSentences | 12 | | totalSentences | 132 | | ratio | 0.091 | | matches | | 0 | "The suspect had been careful—too careful—for three weeks." | | 1 | "A figure moved at the far end—tall, fast, wearing a dark hoodie that swallowed his silhouette." | | 2 | "Not by reputation—she'd never been inside—but by the file." | | 3 | "He fumbled at his pocket, pulled something out—small, pale, glinting in the rain." | | 4 | "Lined with iron, the old kind, the kind that stopped—she shook the thought away." | | 5 | "She holstered the gun—slowly, showing him she wasn't spooked—and pointed at the black door." | | 6 | "\"I'm going to show you.\" He reached into his pocket and pulled out a small object—a knuckle of bone, carved with symbols she couldn't read." | | 7 | "She was thinking about Morris—about the night he died, about the things he'd whispered in the weeks before, about shadows that moved wrong and doctors who wrote impossible charts." | | 8 | "The air changed—warmer, denser, carrying the smell of incense and ozone and something metallic she couldn't place." | | 9 | "Light glowed at the far end, amber and green, and she could hear noise—a murmur, like a crowd, but layered with sounds she couldn't identify." | | 10 | "It sprawled through what had once been an abandoned Tube station—she could see the curved ceiling, the tiled walls, the tracks long since ripped out and replaced with stalls and cages and makeshift structures." | | 11 | "The market pressed in around her—smells, sounds, the weight of eyes that followed her with too much intelligence." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1260 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.023015873015873017 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.003968253968253968 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 132 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 132 | | mean | 11.11 | | std | 8.57 | | cv | 0.772 | | sampleLengths | | 0 | 22 | | 1 | 31 | | 2 | 3 | | 3 | 3 | | 4 | 8 | | 5 | 18 | | 6 | 6 | | 7 | 18 | | 8 | 10 | | 9 | 18 | | 10 | 13 | | 11 | 4 | | 12 | 5 | | 13 | 13 | | 14 | 16 | | 15 | 6 | | 16 | 13 | | 17 | 3 | | 18 | 18 | | 19 | 19 | | 20 | 4 | | 21 | 4 | | 22 | 22 | | 23 | 6 | | 24 | 14 | | 25 | 14 | | 26 | 4 | | 27 | 9 | | 28 | 18 | | 29 | 13 | | 30 | 3 | | 31 | 7 | | 32 | 4 | | 33 | 2 | | 34 | 1 | | 35 | 13 | | 36 | 3 | | 37 | 2 | | 38 | 21 | | 39 | 9 | | 40 | 12 | | 41 | 4 | | 42 | 18 | | 43 | 4 | | 44 | 3 | | 45 | 9 | | 46 | 14 | | 47 | 8 | | 48 | 7 | | 49 | 20 |
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| 46.21% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.3409090909090909 | | totalSentences | 132 | | uniqueOpeners | 45 | |
| 93.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 107 | | matches | | 0 | "Then he ran." | | 1 | "Then she thought of Morris's" | | 2 | "Already chasing a truth she'd" |
| | ratio | 0.028 | |
| 29.35% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 51 | | totalSentences | 107 | | matches | | 0 | "She had him." | | 1 | "She knew it." | | 2 | "He'd used burner phones, changed" | | 3 | "He'd called his sister from" | | 4 | "She was out of the" | | 5 | "She didn't feel it." | | 6 | "He glanced back and saw" | | 7 | "She'd been a sprinter at" | | 8 | "She'd counted on that." | | 9 | "He cut left, past a" | | 10 | "It swung open on rusted" | | 11 | "She followed, scraping her shoulder" | | 12 | "She knew the place." | | 13 | "She'd had it under surveillance" | | 14 | "He fumbled at his pocket," | | 15 | "He slapped it against a" | | 16 | "It swung inward, and he" | | 17 | "She hit the door two" | | 18 | "She shoved the door." | | 19 | "It didn't budge." |
| | ratio | 0.477 | |
| 62.80% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 85 | | totalSentences | 107 | | matches | | 0 | "Rain slashed across the windshield" | | 1 | "The street was a narrow" | | 2 | "She had him." | | 3 | "She knew it." | | 4 | "The suspect had been careful—too" | | 5 | "He'd used burner phones, changed" | | 6 | "He'd called his sister from" | | 7 | "She was out of the" | | 8 | "The rain hit her like" | | 9 | "She didn't feel it." | | 10 | "Adrenaline had its own weather." | | 11 | "The mews opened into a" | | 12 | "A figure moved at the" | | 13 | "He glanced back and saw" | | 14 | "Quinn ran after him, her" | | 15 | "She'd been a sprinter at" | | 16 | "She'd counted on that." | | 17 | "He cut left, past a" | | 18 | "It swung open on rusted" | | 19 | "She followed, scraping her shoulder" |
| | ratio | 0.794 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 107 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 3 | | matches | | 0 | "A figure moved at the far end—tall, fast, wearing a dark hoodie that swallowed his silhouette." | | 1 | "He cut left, past a pub with boarded windows, then through a gap in a wrought-iron gate that should have been locked." | | 2 | "She was thinking about Morris—about the night he died, about the things he'd whispered in the weeks before, about shadows that moved wrong and doctors who wrote…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 23 | | tagDensity | 0.348 | | leniency | 0.696 | | rawRatio | 0.125 | | effectiveRatio | 0.087 | |