| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 1 | | adverbTags | | 0 | "haired man said softly [softly]" |
| | dialogueSentences | 27 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0.083 | | effectiveRatio | 0.074 | |
| 94.82% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 965 | | 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) | |
| 94.82% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 965 | | 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 | 67 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 67 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 82 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 963 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 99.28% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 15 | | wordCount | 690 | | uniqueNames | 9 | | maxNameDensity | 1.01 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | Harlow | 1 | | Quinn | 7 | | Tube | 1 | | Caught | 1 | | Morris | 1 | | Rain | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Rain" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" |
| | globalScore | 0.993 | | windowScore | 1 | |
| 33.72% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like dried bat wings" | | 1 | "quite focus on" |
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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 | 963 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 82 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 26.75 | | std | 19.45 | | cv | 0.727 | | sampleLengths | | 0 | 52 | | 1 | 2 | | 2 | 30 | | 3 | 49 | | 4 | 43 | | 5 | 46 | | 6 | 9 | | 7 | 33 | | 8 | 18 | | 9 | 17 | | 10 | 2 | | 11 | 9 | | 12 | 37 | | 13 | 9 | | 14 | 68 | | 15 | 12 | | 16 | 2 | | 17 | 33 | | 18 | 10 | | 19 | 15 | | 20 | 43 | | 21 | 10 | | 22 | 41 | | 23 | 4 | | 24 | 62 | | 25 | 11 | | 26 | 36 | | 27 | 17 | | 28 | 33 | | 29 | 7 | | 30 | 44 | | 31 | 1 | | 32 | 56 | | 33 | 55 | | 34 | 10 | | 35 | 37 |
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| 84.32% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 67 | | matches | | 0 | "were gone" | | 1 | "been peeled" | | 2 | "was gone" | | 3 | "was gone" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 113 | | matches | | 0 | "were selling" | | 1 | "was haggling" | | 2 | "was handing" | | 3 | "was watching" | | 4 | "was watching" |
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| 38.33% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 82 | | ratio | 0.037 | | matches | | 0 | "The alley swallowed her whole—brick walls close enough to touch, a single caged bulb buzzing overhead." | | 1 | "The alley opened onto a set of stairs leading down—old Tube architecture, the kind of derelict entrance the council had board up a decade ago." | | 2 | "If she lost him now—" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 697 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.03156384505021521 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.005738880918220947 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 82 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 82 | | mean | 11.74 | | std | 8.76 | | cv | 0.746 | | sampleLengths | | 0 | 16 | | 1 | 17 | | 2 | 19 | | 3 | 2 | | 4 | 3 | | 5 | 1 | | 6 | 26 | | 7 | 17 | | 8 | 16 | | 9 | 16 | | 10 | 22 | | 11 | 3 | | 12 | 18 | | 13 | 25 | | 14 | 5 | | 15 | 16 | | 16 | 9 | | 17 | 13 | | 18 | 20 | | 19 | 14 | | 20 | 1 | | 21 | 2 | | 22 | 1 | | 23 | 5 | | 24 | 12 | | 25 | 2 | | 26 | 9 | | 27 | 9 | | 28 | 16 | | 29 | 9 | | 30 | 3 | | 31 | 9 | | 32 | 17 | | 33 | 25 | | 34 | 26 | | 35 | 9 | | 36 | 1 | | 37 | 2 | | 38 | 2 | | 39 | 2 | | 40 | 4 | | 41 | 22 | | 42 | 5 | | 43 | 10 | | 44 | 6 | | 45 | 9 | | 46 | 15 | | 47 | 2 | | 48 | 26 | | 49 | 6 |
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| 95.12% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.5853658536585366 | | totalSentences | 82 | | uniqueOpeners | 48 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 56 | | matches | | 0 | "Instead of answering, the kid" | | 1 | "Somewhere in here, maybe, were" |
| | ratio | 0.036 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 56 | | matches | | 0 | "Her suspect took the corner" | | 1 | "She could hear him, the" | | 2 | "She closed the gap as" | | 3 | "He vaulted it." | | 4 | "She followed, palms burning on" | | 5 | "He turned, just enough for" | | 6 | "He smiled like a knife." | | 7 | "She raised her badge" | | 8 | "He flipped the bone coin" | | 9 | "He spread his hands" | | 10 | "He pointed across the platform," | | 11 | "She thought of Morris." | | 12 | "He fished in his coat" | | 13 | "His eyes caught the sodium" | | 14 | "He rolled the bone coin" |
| | ratio | 0.268 | |
| 76.07% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 43 | | totalSentences | 56 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn kept her" | | 2 | "The hoodie flinched." | | 3 | "Her suspect took the corner" | | 4 | "Quinn gave chase now, her" | | 5 | "The alley swallowed her whole—brick" | | 6 | "She could hear him, the" | | 7 | "She closed the gap as" | | 8 | "He vaulted it." | | 9 | "She followed, palms burning on" | | 10 | "The alley opened onto a" | | 11 | "The suspect hesitated at the" | | 12 | "Quinn unclipped her cuffs with" | | 13 | "He turned, just enough for" | | 14 | "Quinn reached the landing in" | | 15 | "The passage beyond opened into" | | 16 | "Hundreds of stalls." | | 17 | "A woman with eyes like" | | 18 | "A man three stalls down" | | 19 | "Quinn's hand drifted to the" |
| | ratio | 0.768 | |
| 89.29% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 56 | | matches | | | ratio | 0.018 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 1 | | matches | | 0 | "A woman with eyes like opals was haggling over glass vials of something that glowed faintly blue." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "haired man said softly (hair say)" |
| | dialogueSentences | 27 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0.333 | | effectiveRatio | 0.074 | |