| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva finally laughed [finally]" |
| | dialogueSentences | 36 | | tagDensity | 0.389 | | leniency | 0.778 | | rawRatio | 0.071 | | effectiveRatio | 0.056 | |
| 82.36% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1134 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "very" | | 1 | "slowly" | | 2 | "lightly" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 95.59% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1134 | | 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 | 68 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 68 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 88 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 32 | | totalWords | 1134 | | ratio | 0.028 | | matches | | 0 | "she's got her mother's chin." | | 1 | "She knew I'd come here. Silas told her." | | 2 | "We'll go together when we're grown" | | 3 | "Get on a train. Don't tell him where. Get on the train, Ror." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 55 | | wordCount | 825 | | uniqueNames | 13 | | maxNameDensity | 2.55 | | worstName | "Eva" | | maxWindowNameDensity | 5 | | worstWindowName | "Eva" | | discoveredNames | | Aurora | 2 | | Carter | 2 | | Empress | 1 | | Raven | 1 | | Nest | 1 | | Thursday | 1 | | Morgan | 1 | | Eva | 21 | | Rory | 15 | | London | 1 | | Cardiff | 4 | | Paddington | 1 | | Silas | 4 |
| | persons | | 0 | "Aurora" | | 1 | "Carter" | | 2 | "Empress" | | 3 | "Morgan" | | 4 | "Eva" | | 5 | "Rory" | | 6 | "Silas" |
| | places | | 0 | "Raven" | | 1 | "London" | | 2 | "Cardiff" | | 3 | "Paddington" |
| | globalScore | 0.227 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 48 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1134 | | matches | (empty) | |
| 90.91% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 88 | | matches | | 0 | "sit that way" | | 1 | "let that sit" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 29.84 | | std | 27.36 | | cv | 0.917 | | sampleLengths | | 0 | 58 | | 1 | 87 | | 2 | 8 | | 3 | 95 | | 4 | 29 | | 5 | 4 | | 6 | 30 | | 7 | 21 | | 8 | 22 | | 9 | 17 | | 10 | 50 | | 11 | 5 | | 12 | 7 | | 13 | 71 | | 14 | 5 | | 15 | 43 | | 16 | 3 | | 17 | 81 | | 18 | 6 | | 19 | 4 | | 20 | 11 | | 21 | 2 | | 22 | 4 | | 23 | 1 | | 24 | 87 | | 25 | 34 | | 26 | 19 | | 27 | 45 | | 28 | 2 | | 29 | 50 | | 30 | 5 | | 31 | 35 | | 32 | 30 | | 33 | 64 | | 34 | 22 | | 35 | 34 | | 36 | 4 | | 37 | 39 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 68 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 139 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 88 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 829 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.03498190591073583 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007237635705669481 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 88 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 88 | | mean | 12.89 | | std | 9.62 | | cv | 0.746 | | sampleLengths | | 0 | 38 | | 1 | 7 | | 2 | 8 | | 3 | 5 | | 4 | 10 | | 5 | 21 | | 6 | 22 | | 7 | 11 | | 8 | 16 | | 9 | 7 | | 10 | 8 | | 11 | 35 | | 12 | 28 | | 13 | 17 | | 14 | 15 | | 15 | 7 | | 16 | 14 | | 17 | 5 | | 18 | 3 | | 19 | 4 | | 20 | 8 | | 21 | 12 | | 22 | 10 | | 23 | 18 | | 24 | 3 | | 25 | 11 | | 26 | 11 | | 27 | 12 | | 28 | 5 | | 29 | 12 | | 30 | 38 | | 31 | 5 | | 32 | 7 | | 33 | 6 | | 34 | 3 | | 35 | 12 | | 36 | 30 | | 37 | 20 | | 38 | 5 | | 39 | 27 | | 40 | 16 | | 41 | 3 | | 42 | 17 | | 43 | 9 | | 44 | 27 | | 45 | 28 | | 46 | 6 | | 47 | 4 | | 48 | 3 | | 49 | 8 |
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| 63.26% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.42045454545454547 | | totalSentences | 88 | | uniqueOpeners | 37 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 61 | | matches | | 0 | "Then she saw who was" | | 1 | "Then Eva had stayed in" | | 2 | "Somewhere in the back room," |
| | ratio | 0.049 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 61 | | matches | | 0 | "Her shirt smelled of ginger" | | 1 | "She pushed the door open." | | 2 | "He saw her, nodded once," | | 3 | "She had a glass of" | | 4 | "Her left hand rested on" | | 5 | "*She knew I'd come here." | | 6 | "She slid into the booth" | | 7 | "She thought of the night" | | 8 | "She had got on the" | | 9 | "She had not once, in" | | 10 | "She let that sit" | | 11 | "She reached across the table" | | 12 | "She had never once seen" | | 13 | "It was, Rory noticed, exactly" |
| | ratio | 0.23 | |
| 58.36% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 49 | | totalSentences | 61 | | matches | | 0 | "The green neon above the" | | 1 | "Her shirt smelled of ginger" | | 2 | "She pushed the door open." | | 3 | "Silas stood behind the bar" | | 4 | "The silver ring caught the" | | 5 | "He saw her, nodded once," | | 6 | "Silas had never needed to" | | 7 | "Eva Morgan sat with her" | | 8 | "This woman wore a charcoal" | | 9 | "She had a glass of" | | 10 | "Her left hand rested on" | | 11 | "Rory's first thought was absurd:" | | 12 | "*She knew I'd come here." | | 13 | "Silas told her.*" | | 14 | "Nobody had called her Rory" | | 15 | "The name landed in her" | | 16 | "She slid into the booth" | | 17 | "Eva almost smiled" | | 18 | "Rory tried to laugh and" | | 19 | "Eva glanced past her toward" |
| | ratio | 0.803 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 61 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 1 | | matches | | 0 | "Old maps curled on the walls beside black-and-white photographs of men in suits who had long since stopped mattering to anyone." |
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| 89.29% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 1 | | matches | | 0 | "She slid, and the vinyl stuck to the backs of her legs" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | 0 | "Eva finally laughed (laugh)" |
| | dialogueSentences | 36 | | tagDensity | 0.194 | | leniency | 0.389 | | rawRatio | 0.143 | | effectiveRatio | 0.056 | |