| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 2 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1061 | | 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) | |
| 95.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1061 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 85 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 85 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 86 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1072 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 1052 | | uniqueNames | 22 | | maxNameDensity | 0.57 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Camden | 2 | | High | 1 | | Street | 2 | | Detective | 2 | | Harlow | 2 | | Quinn | 6 | | Raven | 1 | | Nest | 1 | | Herrera | 6 | | Met | 1 | | Kentish | 1 | | Town | 1 | | Road | 1 | | Christopher | 2 | | Inverness | 1 | | Tube | 1 | | Morris | 3 | | Ovaltine | 1 | | London | 1 | | Saint | 2 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Met" | | 4 | "Christopher" | | 5 | "Morris" | | 6 | "Ovaltine" | | 7 | "Saint" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Raven" | | 4 | "Kentish" | | 5 | "Town" | | 6 | "Road" | | 7 | "Inverness" | | 8 | "London" | | 9 | "Veil" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like black beads — except as the t" |
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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 | 1072 | | matches | (empty) | |
| 89.15% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 86 | | matches | | 0 | "tired, that the keep, that he" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 36.97 | | std | 25.46 | | cv | 0.689 | | sampleLengths | | 0 | 39 | | 1 | 6 | | 2 | 87 | | 3 | 18 | | 4 | 6 | | 5 | 78 | | 6 | 81 | | 7 | 13 | | 8 | 42 | | 9 | 8 | | 10 | 12 | | 11 | 59 | | 12 | 38 | | 13 | 51 | | 14 | 48 | | 15 | 3 | | 16 | 86 | | 17 | 48 | | 18 | 27 | | 19 | 6 | | 20 | 47 | | 21 | 40 | | 22 | 61 | | 23 | 11 | | 24 | 51 | | 25 | 22 | | 26 | 47 | | 27 | 8 | | 28 | 29 |
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| 80.50% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 85 | | matches | | 0 | "been chased" | | 1 | "was clipped" | | 2 | "was tired" | | 3 | "been closed" | | 4 | "was snapped" | | 5 | "was hushed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 176 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 86 | | ratio | 0.081 | | matches | | 0 | "He moved like someone who had been chased before — head down, elbows tight, no wasted glances behind." | | 1 | "Her eyes were on the back of Herrera's head — short curly hair plastered flat, the gleam of something on a chain around his neck swinging free of his shirt as he ran." | | 2 | "Beyond it she found a narrow service road, a skip piled high with sodden cardboard, and — there — a wrought-iron railing hanging loose from its post, peeled back like a page." | | 3 | "At the top of the steps she paused, just long enough to hear — beneath the hiss of the rain — the slap of his shoes on stone, dropping away and away." | | 4 | "Inside the arch, a curtain of what looked like black beads — except as the torchlight brushed them they shifted and clicked, and she realized they were small bones, polished, strung on wire." | | 5 | "Something — she could not tell what — barked once, and was hushed." | | 6 | "She clicked her torch off — whatever light was on the other side of the curtain would be enough, and she did not want to announce herself — and she let her eyes adjust to the thin red glow seeping through the bones." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 143 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 2 | | adverbRatio | 0.013986013986013986 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 86 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 86 | | mean | 12.47 | | std | 10.12 | | cv | 0.812 | | sampleLengths | | 0 | 9 | | 1 | 30 | | 2 | 6 | | 3 | 18 | | 4 | 3 | | 5 | 33 | | 6 | 7 | | 7 | 26 | | 8 | 18 | | 9 | 6 | | 10 | 29 | | 11 | 10 | | 12 | 5 | | 13 | 2 | | 14 | 32 | | 15 | 12 | | 16 | 4 | | 17 | 14 | | 18 | 4 | | 19 | 33 | | 20 | 2 | | 21 | 2 | | 22 | 10 | | 23 | 10 | | 24 | 3 | | 25 | 5 | | 26 | 23 | | 27 | 14 | | 28 | 3 | | 29 | 5 | | 30 | 4 | | 31 | 8 | | 32 | 4 | | 33 | 10 | | 34 | 32 | | 35 | 13 | | 36 | 17 | | 37 | 6 | | 38 | 7 | | 39 | 8 | | 40 | 4 | | 41 | 15 | | 42 | 32 | | 43 | 24 | | 44 | 24 | | 45 | 3 | | 46 | 15 | | 47 | 20 | | 48 | 4 | | 49 | 8 |
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| 49.61% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.3953488372093023 | | totalSentences | 86 | | uniqueOpeners | 34 | |
| 85.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 78 | | matches | | 0 | "Then he had bolted, and" | | 1 | "Then Detective Harlow Quinn pushed" |
| | ratio | 0.026 | |
| 71.28% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 78 | | matches | | 0 | "It rattled on the iron" | | 1 | "He moved like someone who" | | 2 | "She'd clocked the scar on" | | 3 | "He had looked up, and" | | 4 | "Her radio was clipped to" | | 5 | "She didn't call it in." | | 6 | "He cut left down Kentish" | | 7 | "She barely felt it." | | 8 | "Her eyes were on the" | | 9 | "He made a mistake at" | | 10 | "He glanced back." | | 11 | "She closed three meters in" | | 12 | "He didn't go down." | | 13 | "He ducked between two parked" | | 14 | "She knew this stretch of" | | 15 | "She drew her torch." | | 16 | "She drew her sidearm, a" | | 17 | "Her watch ticked against her" | | 18 | "She went down." | | 19 | "You went underground in London," |
| | ratio | 0.372 | |
| 49.74% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 78 | | matches | | 0 | "The rain came down like" | | 1 | "It rattled on the iron" | | 2 | "Herrera had twenty meters on" | | 3 | "He moved like someone who" | | 4 | "A runner's economy." | | 5 | "She'd clocked the scar on" | | 6 | "The envelope had gone into" | | 7 | "He had looked up, and" | | 8 | "That had been eight blocks" | | 9 | "Quinn kept her breathing metered," | | 10 | "Her radio was clipped to" | | 11 | "She didn't call it in." | | 12 | "The moment she put Herrera's" | | 13 | "He cut left down Kentish" | | 14 | "A cyclist swerved, shouted." | | 15 | "A bus heaved past, sending" | | 16 | "She barely felt it." | | 17 | "Her eyes were on the" | | 18 | "Patron saint of travelers, of" | | 19 | "He made a mistake at" |
| | ratio | 0.821 | |
| 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 | 41 | | technicalSentenceCount | 1 | | matches | | 0 | "On the floor at her feet, a Saint Christopher medallion lay in a puddle of rain that had run down from her coat." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 2 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 1 | | effectiveRatio | 1 | |