| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 23 | | tagDensity | 0.391 | | leniency | 0.783 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.45% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1100 | | totalAiIsmAdverbs | 1 | | 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) | |
| 81.82% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1100 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "weight" | | 1 | "scanned" | | 2 | "velvet" | | 3 | "flicked" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 86 | | matches | (empty) | |
| 93.02% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 86 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 100 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 48 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 2 | | totalWords | 1092 | | ratio | 0.002 | | matches | | 0 | "circumstances unexplained" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 94.38% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 899 | | uniqueNames | 9 | | maxNameDensity | 1.11 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Greek | 1 | | Street | 1 | | Herrera | 7 | | Morris | 1 | | Tube | 1 | | Underground | 1 | | Quinn | 10 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Herrera" | | 1 | "Morris" | | 2 | "Quinn" |
| | places | | | globalScore | 0.944 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | 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 | 1092 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 100 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 26 | | std | 22.22 | | cv | 0.854 | | sampleLengths | | 0 | 42 | | 1 | 27 | | 2 | 1 | | 3 | 8 | | 4 | 3 | | 5 | 61 | | 6 | 6 | | 7 | 46 | | 8 | 47 | | 9 | 26 | | 10 | 34 | | 11 | 3 | | 12 | 61 | | 13 | 3 | | 14 | 2 | | 15 | 91 | | 16 | 25 | | 17 | 4 | | 18 | 1 | | 19 | 36 | | 20 | 28 | | 21 | 4 | | 22 | 8 | | 23 | 29 | | 24 | 36 | | 25 | 44 | | 26 | 3 | | 27 | 29 | | 28 | 7 | | 29 | 5 | | 30 | 90 | | 31 | 25 | | 32 | 25 | | 33 | 29 | | 34 | 33 | | 35 | 42 | | 36 | 46 | | 37 | 16 | | 38 | 29 | | 39 | 4 | | 40 | 16 | | 41 | 17 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 86 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 165 | | matches | | 0 | "was still coming" | | 1 | "was gathering" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 100 | | ratio | 0.08 | | matches | | 0 | "Eighteen years on the force had taught her to read a runner's body—the lean of the hips, the choice of turns." | | 1 | "Through the doorway she saw stairs—steep, concrete, leading down." | | 2 | "The bulb gave out halfway and she pressed a hand to the wall, feeling tile under her fingers—the curved, cracked tile of an old Tube platform." | | 3 | "Light bloomed below her in colours that didn't belong underground—green and amber and a deep arterial red, swinging from cables strung across a vaulted ceiling." | | 4 | "There—a flash of olive skin, a man shouldering past a stall hung with jars that glowed faintly from within." | | 5 | "The grip was wrong—too strong, the fingers too long." | | 6 | "Something brushed her cheek—a wing, leathery and quick." | | 7 | "\"—or you stay here and find out what happens to people who arrive without a token.\"" |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 891 | | adjectiveStacks | 1 | | stackExamples | | 0 | "deep arterial red, swinging" |
| | adverbCount | 33 | | adverbRatio | 0.037037037037037035 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.002244668911335578 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 100 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 100 | | mean | 10.92 | | std | 8.26 | | cv | 0.757 | | sampleLengths | | 0 | 14 | | 1 | 28 | | 2 | 2 | | 3 | 25 | | 4 | 1 | | 5 | 3 | | 6 | 5 | | 7 | 3 | | 8 | 21 | | 9 | 27 | | 10 | 5 | | 11 | 8 | | 12 | 6 | | 13 | 3 | | 14 | 7 | | 15 | 19 | | 16 | 4 | | 17 | 13 | | 18 | 5 | | 19 | 4 | | 20 | 9 | | 21 | 14 | | 22 | 15 | | 23 | 10 | | 24 | 3 | | 25 | 13 | | 26 | 6 | | 27 | 23 | | 28 | 5 | | 29 | 3 | | 30 | 15 | | 31 | 26 | | 32 | 2 | | 33 | 4 | | 34 | 14 | | 35 | 3 | | 36 | 2 | | 37 | 25 | | 38 | 23 | | 39 | 4 | | 40 | 4 | | 41 | 11 | | 42 | 24 | | 43 | 11 | | 44 | 14 | | 45 | 4 | | 46 | 1 | | 47 | 20 | | 48 | 16 | | 49 | 15 |
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| 65.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.44 | | totalSentences | 100 | | uniqueOpeners | 44 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 78 | | matches | | 0 | "A lot of people." | | 1 | "Just dark, and the cold" | | 2 | "Somewhere a bell began to" |
| | ratio | 0.038 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 78 | | matches | | 0 | "She'd recognised the curls, the" | | 1 | "He didn't slow." | | 2 | "He broke into a run." | | 3 | "She caught the wall, shoved" | | 4 | "Her lungs burned." | | 5 | "He hit the door with" | | 6 | "She'd done everything right, and" | | 7 | "She'd stopped believing in unexplained." | | 8 | "She went down." | | 9 | "She'd read the maps." | | 10 | "Her eyes caught the green" | | 11 | "She had too many teeth." | | 12 | "She couldn't see the bulb" | | 13 | "She couldn't see the doorway," | | 14 | "She turned back to the" | | 15 | "He glanced back, found her" | | 16 | "She plunged into the crowd." | | 17 | "She knocked it aside and" | | 18 | "She put her shoulder into" | | 19 | "He held up both hands," |
| | ratio | 0.295 | |
| 56.15% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 78 | | matches | | 0 | "The rain came sideways, slapping" | | 1 | "Quinn pulled her coat tighter" | | 2 | "She'd recognised the curls, the" | | 3 | "He didn't slow." | | 4 | "He broke into a run." | | 5 | "Herrera cut left down an" | | 6 | "Quinn's shoes slid on it." | | 7 | "She caught the wall, shoved" | | 8 | "Her lungs burned." | | 9 | "The cold air carved her" | | 10 | "Herrera didn't break stride." | | 11 | "He hit the door with" | | 12 | "Quinn slowed at the threshold." | | 13 | "The smell that rose from" | | 14 | "Every year of training told" | | 15 | "She'd done everything right, and" | | 16 | "She'd stopped believing in unexplained." | | 17 | "She went down." | | 18 | "The stairs turned, then turned" | | 19 | "The bulb gave out halfway" |
| | ratio | 0.808 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 78 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 2 | | matches | | 0 | "She'd recognised the curls, the hunch of his shoulders, the way he kept glancing back like a man who knew exactly what waited behind him." | | 1 | "She knocked it aside and kept Herrera's curls in her sight, threading between stalls that sold things her mind refused to catalogue: vials of swirling silver, a…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 23 | | tagDensity | 0.13 | | leniency | 0.261 | | rawRatio | 0.333 | | effectiveRatio | 0.087 | |