| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 1 | | adverbTags | | 0 | "Hadley stepped away [away]" |
| | dialogueSentences | 50 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 0.067 | | effectiveRatio | 0.04 | |
| 91.87% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1230 | | 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) | |
| 83.74% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1230 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "etched" | | 1 | "glint" | | 2 | "silence" |
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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 | 0 | | narrationSentences | 95 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 95 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 129 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 53 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1230 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 82.60% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 816 | | uniqueNames | 11 | | maxNameDensity | 1.35 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Quinn | 11 | | Park | 1 | | Northern | 1 | | Line | 1 | | Waterman | 11 | | Old | 1 | | Italian | 1 | | Victorian | 1 | | Hadley | 3 | | Morris | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Waterman" | | 2 | "Hadley" | | 3 | "Morris" |
| | places | | | globalScore | 0.826 | | windowScore | 0.833 | |
| 62.28% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | glossingSentenceCount | 2 | | matches | | 0 | "quite name" | | 1 | "quite find" |
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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 | 1230 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 129 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 56 | | mean | 21.96 | | std | 16.93 | | cv | 0.771 | | sampleLengths | | 0 | 70 | | 1 | 29 | | 2 | 3 | | 3 | 39 | | 4 | 28 | | 5 | 46 | | 6 | 3 | | 7 | 43 | | 8 | 25 | | 9 | 8 | | 10 | 34 | | 11 | 2 | | 12 | 8 | | 13 | 3 | | 14 | 55 | | 15 | 1 | | 16 | 28 | | 17 | 32 | | 18 | 4 | | 19 | 46 | | 20 | 33 | | 21 | 2 | | 22 | 14 | | 23 | 40 | | 24 | 6 | | 25 | 32 | | 26 | 5 | | 27 | 47 | | 28 | 4 | | 29 | 18 | | 30 | 1 | | 31 | 42 | | 32 | 15 | | 33 | 13 | | 34 | 32 | | 35 | 6 | | 36 | 43 | | 37 | 6 | | 38 | 54 | | 39 | 20 | | 40 | 23 | | 41 | 11 | | 42 | 7 | | 43 | 25 | | 44 | 3 | | 45 | 21 | | 46 | 6 | | 47 | 14 | | 48 | 39 | | 49 | 30 |
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| 94.18% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 95 | | matches | | 0 | "been closed" | | 1 | "was frozen" | | 2 | "was etched" |
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| 16.09% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 145 | | matches | | 0 | "was already crouching" | | 1 | "was spinning" | | 2 | "was searching" | | 3 | "was staring" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 129 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 819 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.03663003663003663 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.008547008547008548 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 129 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 129 | | mean | 9.53 | | std | 8.13 | | cv | 0.853 | | sampleLengths | | 0 | 11 | | 1 | 2 | | 2 | 21 | | 3 | 16 | | 4 | 2 | | 5 | 18 | | 6 | 14 | | 7 | 15 | | 8 | 3 | | 9 | 8 | | 10 | 12 | | 11 | 16 | | 12 | 3 | | 13 | 28 | | 14 | 2 | | 15 | 13 | | 16 | 9 | | 17 | 3 | | 18 | 2 | | 19 | 7 | | 20 | 10 | | 21 | 3 | | 22 | 13 | | 23 | 30 | | 24 | 6 | | 25 | 5 | | 26 | 3 | | 27 | 5 | | 28 | 3 | | 29 | 3 | | 30 | 8 | | 31 | 4 | | 32 | 12 | | 33 | 2 | | 34 | 2 | | 35 | 2 | | 36 | 12 | | 37 | 2 | | 38 | 7 | | 39 | 1 | | 40 | 3 | | 41 | 23 | | 42 | 32 | | 43 | 1 | | 44 | 21 | | 45 | 7 | | 46 | 2 | | 47 | 6 | | 48 | 15 | | 49 | 5 |
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| 62.27% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.43410852713178294 | | totalSentences | 129 | | uniqueOpeners | 56 | |
| 82.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 81 | | matches | | 0 | "Then she turned back to" | | 1 | "Somewhere in the tunnel, water" |
| | ratio | 0.025 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 81 | | matches | | 0 | "His face had that pinched" | | 1 | "His eyes were open, staring" | | 2 | "His suit was expensive, navy" | | 3 | "His shoes were Italian leather," | | 4 | "His fingernails were clean, trimmed." | | 5 | "She leaned closer." | | 6 | "She didn't look up." | | 7 | "She trailed off, tucking a" | | 8 | "Her gaze swept the platform" | | 9 | "She walked toward it." | | 10 | "She handed the bag back" | | 11 | "She pulled out her torch" | | 12 | "She didn't answer." | | 13 | "She stepped off the platform," | | 14 | "She pocketed the compass and" | | 15 | "She walked back to the" | | 16 | "She pulled out the compass" | | 17 | "It pointed steadily down the" | | 18 | "Her face had changed, shifted" | | 19 | "She was staring at the" |
| | ratio | 0.272 | |
| 46.42% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 67 | | totalSentences | 81 | | matches | | 0 | "The abandoned Tube station smelled" | | 1 | "Harlow Quinn ducked beneath the" | | 2 | "The station had been closed" | | 3 | "His face had that pinched" | | 4 | "Quinn let her gaze travel" | | 5 | "The lighting rigs the forensics" | | 6 | "Waterman jerked his chin toward" | | 7 | "The man lay on his" | | 8 | "His eyes were open, staring" | | 9 | "His suit was expensive, navy" | | 10 | "His shoes were Italian leather," | | 11 | "Waterman scratched his jaw" | | 12 | "Quinn crouched beside the body" | | 13 | "The man's hands were smooth." | | 14 | "His fingernails were clean, trimmed." | | 15 | "She leaned closer." | | 16 | "She didn't look up." | | 17 | "The man's face was frozen" | | 18 | "The pathologist glanced up from" | | 19 | "Hadley peeled off one glove," |
| | ratio | 0.827 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 81 | | matches | | 0 | "Before everything went wrong." | | 1 | "Before she'd lost him to" |
| | ratio | 0.025 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 2 | | matches | | 0 | "One of the ghost stations, the kind that whizzed past in darkness when you rode the Northern Line." | | 1 | "But she was already crouching, her torch illuminating a small brass compass lying in the gravel between the rails." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |