| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 62 | | tagDensity | 0.29 | | leniency | 0.581 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.86% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1209 | | 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) | |
| 95.86% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1209 | | 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 | 53 | | matches | (empty) | |
| 88.95% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 53 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 52 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1219 | | ratio | 0 | | matches | (empty) | |
| 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 | 581 | | uniqueNames | 8 | | maxNameDensity | 0.52 | | worstName | "Eva" | | maxWindowNameDensity | 1 | | worstWindowName | "Eva" | | discoveredNames | | Ptolemy | 2 | | Eva | 3 | | Rain | 1 | | Brick | 1 | | Lane | 1 | | Aurora | 1 | | Lucien | 3 | | Moreau | 2 |
| | persons | | 0 | "Ptolemy" | | 1 | "Eva" | | 2 | "Lucien" | | 3 | "Moreau" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 37 | | 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 | 1219 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 96 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 64 | | mean | 19.05 | | std | 21.01 | | cv | 1.103 | | sampleLengths | | 0 | 36 | | 1 | 1 | | 2 | 1 | | 3 | 5 | | 4 | 59 | | 5 | 38 | | 6 | 4 | | 7 | 41 | | 8 | 6 | | 9 | 8 | | 10 | 2 | | 11 | 6 | | 12 | 4 | | 13 | 11 | | 14 | 11 | | 15 | 38 | | 16 | 74 | | 17 | 19 | | 18 | 24 | | 19 | 2 | | 20 | 1 | | 21 | 65 | | 22 | 11 | | 23 | 31 | | 24 | 56 | | 25 | 3 | | 26 | 1 | | 27 | 46 | | 28 | 3 | | 29 | 31 | | 30 | 3 | | 31 | 2 | | 32 | 3 | | 33 | 41 | | 34 | 2 | | 35 | 49 | | 36 | 20 | | 37 | 2 | | 38 | 25 | | 39 | 5 | | 40 | 51 | | 41 | 21 | | 42 | 3 | | 43 | 68 | | 44 | 4 | | 45 | 10 | | 46 | 13 | | 47 | 26 | | 48 | 30 | | 49 | 2 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 53 | | matches | (empty) | |
| 61.11% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 96 | | matches | | 0 | "was, filling" | | 1 | "was like arguing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 96 | | ratio | 0.052 | | matches | | 0 | "The chain caught at six inches and there he was, filling the gap with rain and expensive wool — charcoal shoulders darkened to ink, platinum hair still swept back, a manila folder tucked under one arm." | | 1 | "He set the ivory cane against Eva's tower of scrolls — out of arm's reach, she noted." | | 2 | "\"Silas asked, and lying to Silas is a full-time occupation I already hold.\" His eyes didn't leave her face — one brass, one jet." | | 3 | "Then the typed description block — height five-six, hair black, eyes blue, bright blue, someone had underlined it twice — and across the top of page three, in his neat slanted hand, beneath a photograph of her laughing outside a curry house with two bags of food she'd delivered to the wrong door:" | | 4 | "Her fist found his lapel and stayed, and his hands landed at last — one at her jaw, careful of nothing now, the other sliding into her black hair — and somewhere the cane clattered against Eva's scrolls and neither of them cared." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 577 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.029462738301559793 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0017331022530329288 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 12.7 | | std | 11.83 | | cv | 0.932 | | sampleLengths | | 0 | 36 | | 1 | 1 | | 2 | 1 | | 3 | 5 | | 4 | 25 | | 5 | 34 | | 6 | 6 | | 7 | 32 | | 8 | 4 | | 9 | 18 | | 10 | 17 | | 11 | 6 | | 12 | 6 | | 13 | 8 | | 14 | 2 | | 15 | 6 | | 16 | 3 | | 17 | 1 | | 18 | 11 | | 19 | 11 | | 20 | 9 | | 21 | 7 | | 22 | 22 | | 23 | 11 | | 24 | 28 | | 25 | 9 | | 26 | 26 | | 27 | 19 | | 28 | 9 | | 29 | 15 | | 30 | 2 | | 31 | 1 | | 32 | 20 | | 33 | 45 | | 34 | 11 | | 35 | 11 | | 36 | 20 | | 37 | 24 | | 38 | 32 | | 39 | 3 | | 40 | 1 | | 41 | 11 | | 42 | 35 | | 43 | 3 | | 44 | 31 | | 45 | 3 | | 46 | 2 | | 47 | 3 | | 48 | 13 | | 49 | 28 |
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| 70.14% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.4583333333333333 | | totalSentences | 96 | | uniqueOpeners | 44 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 52 | | matches | | 0 | "Only the shoulders had taken" | | 1 | "Then the typed description block" |
| | ratio | 0.038 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 52 | | matches | | 0 | "Her knuckles whitened on the" | | 1 | "He set the ivory cane" | | 2 | "She glared at the cat" | | 3 | "He straightened, dripping onto Eva's" | | 4 | "Her jaw tightened." | | 5 | "He unbuttoned his coat" | | 6 | "Her voice came out flat," | | 7 | "She shoved her sleeve up" | | 8 | "He looked at her wrist" | | 9 | "Her hands curled into fists" | | 10 | "He crossed onto the rug," | | 11 | "She jabbed a finger at" | | 12 | "His eyes didn't leave her" | | 13 | "He lifted the manila thing" | | 14 | "He closed the last two" | | 15 | "She should step back." | | 16 | "She had the whole rug" | | 17 | "His voice dropped" | | 18 | "He held the folder out." | | 19 | "She took it." |
| | ratio | 0.635 | |
| 17.69% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 52 | | matches | | 0 | "The chain caught at six" | | 1 | "Water ran off the end" | | 2 | "Her knuckles whitened on the" | | 3 | "Cardamom and fried onion drifted" | | 4 | "The chain rattled loose." | | 5 | "Lucien stepped over the threshold" | | 6 | "He set the ivory cane" | | 7 | "The blade sleeps by the" | | 8 | "She glared at the cat" | | 9 | "He straightened, dripping onto Eva's" | | 10 | "Her jaw tightened." | | 11 | "He unbuttoned his coat" | | 12 | "Her voice came out flat," | | 13 | "She shoved her sleeve up" | | 14 | "The crescent scar sat pale" | | 15 | "He looked at her wrist" | | 16 | "Her hands curled into fists" | | 17 | "He crossed onto the rug," | | 18 | "The laugh climbed halfway up" | | 19 | "She jabbed a finger at" |
| | ratio | 0.885 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 52 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 16 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 97.22% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 1 | | matches | | 0 | "He crossed, water marking it" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |