| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.368 | | leniency | 0.737 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1108 | | 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) | |
| 77.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1108 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "silk" | | 1 | "footsteps" | | 2 | "echoed" | | 3 | "velvet" | | 4 | "lilt" |
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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 | 1 | | narrationSentences | 107 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 107 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 118 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1108 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 950 | | uniqueNames | 23 | | maxNameDensity | 0.63 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 2 | | Tomás | 1 | | Herrera | 5 | | Tuesday | 1 | | Pratt | 1 | | Kentish | 1 | | Town | 1 | | Road | 1 | | Quinn | 6 | | Tube | 1 | | London | 1 | | Palladium | 1 | | Silk | 1 | | Cut | 1 | | Marrakesh | 1 | | Whitechapel | 1 | | Tottenham | 1 | | Seville | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 1 |
| | persons | | 0 | "Tomás" | | 1 | "Herrera" | | 2 | "Quinn" | | 3 | "Cut" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Pratt" | | 4 | "Kentish" | | 5 | "Town" | | 6 | "Road" | | 7 | "London" | | 8 | "Palladium" | | 9 | "Marrakesh" | | 10 | "Whitechapel" | | 11 | "Tottenham" | | 12 | "Seville" |
| | globalScore | 1 | | windowScore | 1 | |
| 68.03% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | glossingSentenceCount | 2 | | matches | | 0 | "quite focus on weighed a jar of something that moved" | | 1 | "quite that" |
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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 | 1108 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 118 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 25.18 | | std | 21.46 | | cv | 0.852 | | sampleLengths | | 0 | 10 | | 1 | 47 | | 2 | 2 | | 3 | 5 | | 4 | 74 | | 5 | 31 | | 6 | 28 | | 7 | 13 | | 8 | 34 | | 9 | 54 | | 10 | 9 | | 11 | 73 | | 12 | 14 | | 13 | 5 | | 14 | 40 | | 15 | 4 | | 16 | 3 | | 17 | 52 | | 18 | 9 | | 19 | 65 | | 20 | 35 | | 21 | 11 | | 22 | 6 | | 23 | 30 | | 24 | 2 | | 25 | 56 | | 26 | 3 | | 27 | 71 | | 28 | 23 | | 29 | 13 | | 30 | 2 | | 31 | 39 | | 32 | 5 | | 33 | 33 | | 34 | 32 | | 35 | 10 | | 36 | 29 | | 37 | 5 | | 38 | 4 | | 39 | 38 | | 40 | 40 | | 41 | 5 | | 42 | 33 | | 43 | 11 |
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| 92.15% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 107 | | matches | | 0 | "been ten" | | 1 | "was plastered" | | 2 | "being looked" | | 3 | "was, carried" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 164 | | matches | | 0 | "was lying" | | 1 | "was running" | | 2 | "were weighing" | | 3 | "was walking" | | 4 | "was beginning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 118 | | ratio | 0.008 | | matches | | 0 | "Below, the air changed; that flat, fungal cold of deep London, the breath of dead tunnels." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 952 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.028361344537815126 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0021008403361344537 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 118 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 118 | | mean | 9.39 | | std | 7.07 | | cv | 0.752 | | sampleLengths | | 0 | 10 | | 1 | 23 | | 2 | 24 | | 3 | 2 | | 4 | 2 | | 5 | 3 | | 6 | 12 | | 7 | 5 | | 8 | 9 | | 9 | 20 | | 10 | 6 | | 11 | 22 | | 12 | 4 | | 13 | 10 | | 14 | 9 | | 15 | 8 | | 16 | 8 | | 17 | 6 | | 18 | 14 | | 19 | 5 | | 20 | 8 | | 21 | 4 | | 22 | 14 | | 23 | 6 | | 24 | 3 | | 25 | 7 | | 26 | 6 | | 27 | 18 | | 28 | 3 | | 29 | 21 | | 30 | 2 | | 31 | 4 | | 32 | 9 | | 33 | 13 | | 34 | 9 | | 35 | 9 | | 36 | 7 | | 37 | 2 | | 38 | 1 | | 39 | 32 | | 40 | 10 | | 41 | 4 | | 42 | 5 | | 43 | 4 | | 44 | 14 | | 45 | 22 | | 46 | 4 | | 47 | 3 | | 48 | 4 | | 49 | 3 |
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| 59.32% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.4322033898305085 | | totalSentences | 118 | | uniqueOpeners | 51 | |
| 35.84% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 93 | | matches | | 0 | "Somewhere above, back in the" |
| | ratio | 0.011 | |
| 82.37% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 93 | | matches | | 0 | "They never did." | | 1 | "She pushed harder, the radio" | | 2 | "Her quarry knew these streets," | | 3 | "She'd watched him for three" | | 4 | "She knew his gait, his" | | 5 | "He was lying tonight." | | 6 | "She'd seen the body in" | | 7 | "Her salt-and-pepper hair was plastered" | | 8 | "She cleared a fallen bicycle," | | 9 | "He glanced back once." | | 10 | "He was scared of something" | | 11 | "He darted across Kentish Town" | | 12 | "She kept running." | | 13 | "He vanished into an alley" | | 14 | "She skidded to the mouth" | | 15 | "She thumbed the radio." | | 16 | "She checked the corner" | | 17 | "She clicked off." | | 18 | "Her torch beam caught peeling" | | 19 | "She drew her baton." |
| | ratio | 0.344 | |
| 94.41% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 93 | | matches | | 0 | "The bastard was fast for" | | 1 | "Quinn's boots hammered the wet" | | 2 | "They never did." | | 3 | "She pushed harder, the radio" | | 4 | "Backup was ten minutes out." | | 5 | "Her quarry knew these streets," | | 6 | "She'd watched him for three" | | 7 | "She knew his gait, his" | | 8 | "He was lying tonight." | | 9 | "She'd seen the body in" | | 10 | "Herrera's handiwork, right down to" | | 11 | "Rain sluiced down the back" | | 12 | "Her salt-and-pepper hair was plastered" | | 13 | "She cleared a fallen bicycle," | | 14 | "He glanced back once." | | 15 | "He was scared of something" | | 16 | "Something ahead, maybe." | | 17 | "Something he was running towards," | | 18 | "He darted across Kentish Town" | | 19 | "A night bus blared its" |
| | ratio | 0.731 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 93 | | matches | (empty) | | ratio | 0 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 3 | | matches | | 0 | "Quinn's boots hammered the wet pavement, each strike sending up a halo of spray that caught the sodium glare of Camden High Street." | | 1 | "Warm brown eyes, scared, but not scared of her, which was interesting and unhelpful." | | 2 | "Her torch beam caught peeling posters for shows that had closed decades ago." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 44.74% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 2 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "she muttered (mutter)" |
| | dialogueSentences | 19 | | tagDensity | 0.211 | | leniency | 0.421 | | rawRatio | 0.5 | | effectiveRatio | 0.211 | |