| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said quietly [quietly]" |
| | dialogueSentences | 25 | | tagDensity | 0.44 | | leniency | 0.88 | | rawRatio | 0.091 | | effectiveRatio | 0.08 | |
| 78.90% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 948 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "very" | | 1 | "suddenly" | | 2 | "slowly" |
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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) | |
| 84.18% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 948 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "silence" | | 1 | "could feel" | | 2 | "structure" |
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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 | 52 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 52 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 66 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 54 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 3 | | totalWords | 948 | | ratio | 0.003 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 14 | | wordCount | 645 | | uniqueNames | 10 | | maxNameDensity | 0.62 | | worstName | "Rory" | | maxWindowNameDensity | 1 | | worstWindowName | "Rory" | | discoveredNames | | Silas | 1 | | Moreau | 1 | | Rory | 4 | | Aurora | 1 | | Jennifer | 1 | | One | 1 | | Cardiff | 2 | | Golden | 1 | | Empress | 1 | | Eva | 1 |
| | persons | | 0 | "Silas" | | 1 | "Moreau" | | 2 | "Rory" | | 3 | "Aurora" | | 4 | "Jennifer" | | 5 | "One" | | 6 | "Eva" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 2.94% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 34 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like an insult" | | 1 | "seemed deliberate as if he were buying himself a moment" |
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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 | 948 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 66 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 29.63 | | std | 21.07 | | cv | 0.711 | | sampleLengths | | 0 | 60 | | 1 | 4 | | 2 | 54 | | 3 | 3 | | 4 | 46 | | 5 | 17 | | 6 | 22 | | 7 | 20 | | 8 | 31 | | 9 | 82 | | 10 | 6 | | 11 | 70 | | 12 | 4 | | 13 | 14 | | 14 | 49 | | 15 | 27 | | 16 | 5 | | 17 | 35 | | 18 | 67 | | 19 | 33 | | 20 | 32 | | 21 | 20 | | 22 | 25 | | 23 | 5 | | 24 | 42 | | 25 | 22 | | 26 | 7 | | 27 | 43 | | 28 | 47 | | 29 | 30 | | 30 | 13 | | 31 | 13 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 52 | | matches | (empty) | |
| 89.81% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 121 | | matches | | 0 | "was shouting" | | 1 | "were buying" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 66 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 647 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.03554868624420402 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.00927357032457496 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 66 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 66 | | mean | 14.36 | | std | 10.71 | | cv | 0.746 | | sampleLengths | | 0 | 21 | | 1 | 17 | | 2 | 22 | | 3 | 4 | | 4 | 15 | | 5 | 17 | | 6 | 11 | | 7 | 11 | | 8 | 3 | | 9 | 5 | | 10 | 4 | | 11 | 15 | | 12 | 22 | | 13 | 11 | | 14 | 6 | | 15 | 12 | | 16 | 5 | | 17 | 5 | | 18 | 20 | | 19 | 8 | | 20 | 23 | | 21 | 5 | | 22 | 47 | | 23 | 30 | | 24 | 6 | | 25 | 10 | | 26 | 23 | | 27 | 10 | | 28 | 27 | | 29 | 4 | | 30 | 7 | | 31 | 7 | | 32 | 21 | | 33 | 10 | | 34 | 7 | | 35 | 11 | | 36 | 7 | | 37 | 20 | | 38 | 5 | | 39 | 9 | | 40 | 26 | | 41 | 13 | | 42 | 54 | | 43 | 7 | | 44 | 26 | | 45 | 4 | | 46 | 28 | | 47 | 20 | | 48 | 3 | | 49 | 22 |
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| 65.15% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.45454545454545453 | | totalSentences | 66 | | uniqueOpeners | 30 | |
| 72.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 46 | | matches | | 0 | "Instead she stepped back, and" |
| | ratio | 0.022 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 46 | | matches | | 0 | "She opened the door." | | 1 | "He looked exactly the same," | | 2 | "Her friends said Rory." | | 3 | "Her mother, when she was" | | 4 | "He took it without flinching." | | 5 | "She should have said no." | | 6 | "She hated that she remembered" | | 7 | "He took in the mismatched" | | 8 | "His gaze lingered on the" | | 9 | "He noticed the scar on" | | 10 | "She folded her arms" | | 11 | "He set the cane against" | | 12 | "She could see the tiredness" | | 13 | "He held her gaze" | | 14 | "Her voice came out steadier" | | 15 | "He looked down at the" | | 16 | "he said quietly" | | 17 | "It was the first thing" | | 18 | "She had spent months building" | | 19 | "He stepped toward her, slowly," |
| | ratio | 0.565 | |
| 14.35% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 41 | | totalSentences | 46 | | matches | | 0 | "The knock came at ten" | | 1 | "Rory stood with her hand" | | 2 | "Downstairs, Silas was shouting at" | | 3 | "She opened the door." | | 4 | "Lucien Moreau leaned against the" | | 5 | "The charcoal suit was immaculate," | | 6 | "The ivory-handled cane rested in" | | 7 | "He looked exactly the same," | | 8 | "Nobody called her that anymore." | | 9 | "Her friends said Rory." | | 10 | "Her mother, when she was" | | 11 | "Lucien had always used the" | | 12 | "He took it without flinching." | | 13 | "Something almost like a smile" | | 14 | "She should have said no." | | 15 | "Every sensible part of her," | | 16 | "She hated that she remembered" | | 17 | "The flat was small, and" | | 18 | "He took in the mismatched" | | 19 | "His gaze lingered on the" |
| | ratio | 0.891 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 46 | | matches | (empty) | | ratio | 0 | |
| 91.84% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 2 | | matches | | 0 | "He set the cane against the wall with a care that seemed deliberate, as if he were buying himself a moment." | | 1 | "She also thought, unbidden and unwelcome, of the way he used to fall asleep with his hand curled over her hip, as if she might disappear if he let go." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 25 | | tagDensity | 0.28 | | leniency | 0.56 | | rawRatio | 0 | | effectiveRatio | 0 | |