| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 44 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 65.72% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1021 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "slowly" | | 1 | "very" | | 2 | "lazily" |
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| 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) | |
| 85.31% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1021 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "standard" | | 1 | "echoed" | | 2 | "processed" |
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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 | 83 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 116 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1018 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 19.38% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 42 | | wordCount | 689 | | uniqueNames | 10 | | maxNameDensity | 2.61 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Town | 1 | | London | 2 | | Kowalski | 2 | | British | 1 | | Museum | 2 | | Quinn | 18 | | Eva | 13 | | Greek | 1 | | Kensington | 1 | | Mayfair | 1 |
| | persons | | 0 | "Kowalski" | | 1 | "Museum" | | 2 | "Quinn" | | 3 | "Eva" |
| | places | | 0 | "Town" | | 1 | "London" | | 2 | "Kensington" | | 3 | "Mayfair" |
| | globalScore | 0.194 | | windowScore | 0.333 | |
| 45.83% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 48 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like London's strangest suicide" | | 1 | "hy gravity had apparently decided to take the" |
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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 | 1018 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 116 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 59 | | mean | 17.25 | | std | 15.14 | | cv | 0.877 | | sampleLengths | | 0 | 20 | | 1 | 50 | | 2 | 5 | | 3 | 43 | | 4 | 15 | | 5 | 11 | | 6 | 32 | | 7 | 3 | | 8 | 17 | | 9 | 32 | | 10 | 18 | | 11 | 39 | | 12 | 4 | | 13 | 1 | | 14 | 18 | | 15 | 36 | | 16 | 37 | | 17 | 31 | | 18 | 5 | | 19 | 11 | | 20 | 2 | | 21 | 8 | | 22 | 1 | | 23 | 36 | | 24 | 30 | | 25 | 2 | | 26 | 4 | | 27 | 37 | | 28 | 41 | | 29 | 3 | | 30 | 9 | | 31 | 8 | | 32 | 2 | | 33 | 29 | | 34 | 29 | | 35 | 5 | | 36 | 36 | | 37 | 5 | | 38 | 3 | | 39 | 33 | | 40 | 4 | | 41 | 48 | | 42 | 5 | | 43 | 4 | | 44 | 31 | | 45 | 20 | | 46 | 6 | | 47 | 6 | | 48 | 2 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 83 | | matches | | |
| 86.04% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 117 | | matches | | 0 | "wasn't carrying" | | 1 | "were adjusting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 116 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 692 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.02745664739884393 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.010115606936416185 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 116 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 116 | | mean | 8.78 | | std | 6.7 | | cv | 0.764 | | sampleLengths | | 0 | 20 | | 1 | 17 | | 2 | 16 | | 3 | 17 | | 4 | 5 | | 5 | 15 | | 6 | 11 | | 7 | 15 | | 8 | 2 | | 9 | 15 | | 10 | 6 | | 11 | 3 | | 12 | 2 | | 13 | 14 | | 14 | 3 | | 15 | 2 | | 16 | 13 | | 17 | 3 | | 18 | 11 | | 19 | 6 | | 20 | 18 | | 21 | 14 | | 22 | 18 | | 23 | 8 | | 24 | 19 | | 25 | 7 | | 26 | 5 | | 27 | 4 | | 28 | 1 | | 29 | 14 | | 30 | 4 | | 31 | 9 | | 32 | 13 | | 33 | 14 | | 34 | 20 | | 35 | 17 | | 36 | 8 | | 37 | 14 | | 38 | 9 | | 39 | 5 | | 40 | 11 | | 41 | 2 | | 42 | 8 | | 43 | 1 | | 44 | 9 | | 45 | 7 | | 46 | 15 | | 47 | 5 | | 48 | 15 | | 49 | 15 |
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| 63.79% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.4396551724137931 | | totalSentences | 116 | | uniqueOpeners | 51 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 71 | | matches | | 0 | "She'd tucked her curly red" | | 1 | "She stood, brushing off her" | | 2 | "She moved closer to the" | | 3 | "He'd died afraid." | | 4 | "He'd died fighting." | | 5 | "She checked his pockets." | | 6 | "Her face went white." | | 7 | "They moved together, step by" | | 8 | "She could see shapes in" | | 9 | "Her foot hit something." | | 10 | "She'd dropped it without realising." |
| | ratio | 0.155 | |
| 44.51% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 59 | | totalSentences | 71 | | matches | | 0 | "The body hung suspended three" | | 1 | "Quinn ducked under the crime" | | 2 | "The abandoned tube station smelled" | | 3 | "Camden Town's forgotten twin, sealed" | | 4 | "Eva Kowalski stood near the" | | 5 | "The British Museum's credentials hung" | | 6 | "She'd tucked her curly red" | | 7 | "Eva gestured toward the walls" | | 8 | "Quinn ignored the symbols for" | | 9 | "Symbols could wait." | | 10 | "The victim was male, mid-thirties," | | 11 | "Eva's voice dropped" | | 12 | "Quinn circled the body, her" | | 13 | "Quinn crouched, examining the dust" | | 14 | "Nothing leading to or from" | | 15 | "Nothing beneath it at all." | | 16 | "She stood, brushing off her" | | 17 | "Eva pulled a small brass" | | 18 | "The casing had a patina" | | 19 | "The needle spun lazily, then" |
| | ratio | 0.831 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 45.45% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 22 | | technicalSentenceCount | 3 | | matches | | 0 | "The body hung suspended three feet above the platform, rotating slowly in a breeze that shouldn't exist forty metres underground." | | 1 | "The abandoned tube station smelled of rust and something older, something that made her teeth ache." | | 2 | "Undisturbed except for two sets of footprints: the transport officers who'd found him, judging by the standard-issue boot treads." |
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| 79.55% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 1 | | matches | | 0 | "She moved, her freckled face pale in the torchlight" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 44 | | tagDensity | 0.045 | | leniency | 0.091 | | rawRatio | 0 | | effectiveRatio | 0 | |