| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 1 | | adverbTags | | 0 | "Evan leaned back [back]" |
| | dialogueSentences | 45 | | tagDensity | 0.311 | | leniency | 0.622 | | rawRatio | 0.071 | | effectiveRatio | 0.044 | |
| 90.89% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1646 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | |
| 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) | |
| 39.25% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1646 | | totalAiIsms | 20 | | found | | | highlights | | 0 | "familiar" | | 1 | "gloom" | | 2 | "eyebrow" | | 3 | "weight" | | 4 | "methodical" | | 5 | "flickered" | | 6 | "could feel" | | 7 | "pulse" | | 8 | "intensity" | | 9 | "stomach" | | 10 | "traced" | | 11 | "racing" | | 12 | "measured" | | 13 | "silence" | | 14 | "pristine" |
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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 | 116 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 116 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 148 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 49 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1646 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 1175 | | uniqueNames | 9 | | maxNameDensity | 0.94 | | worstName | "Evan" | | maxWindowNameDensity | 3 | | worstWindowName | "Evan" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 2 | | Golden | 1 | | Empress | 1 | | Silas | 4 | | Eva | 1 | | Evan | 11 | | Aurora | 6 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Empress" | | 3 | "Silas" | | 4 | "Eva" | | 5 | "Evan" | | 6 | "Aurora" |
| | places | | | globalScore | 1 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 77 | | glossingSentenceCount | 1 | | matches | | 0 | "as if hitting an invisible wall" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.608 | | wordCount | 1646 | | matches | | 0 | "not much, but enough to soften the sharp angles she remembered" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 148 | | matches | | 0 | "was that she" | | 1 | "filed that information" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 64 | | mean | 25.72 | | std | 23.92 | | cv | 0.93 | | sampleLengths | | 0 | 34 | | 1 | 65 | | 2 | 1 | | 3 | 38 | | 4 | 62 | | 5 | 3 | | 6 | 6 | | 7 | 1 | | 8 | 19 | | 9 | 91 | | 10 | 29 | | 11 | 42 | | 12 | 20 | | 13 | 5 | | 14 | 3 | | 15 | 30 | | 16 | 2 | | 17 | 26 | | 18 | 8 | | 19 | 53 | | 20 | 8 | | 21 | 19 | | 22 | 34 | | 23 | 48 | | 24 | 31 | | 25 | 11 | | 26 | 2 | | 27 | 38 | | 28 | 50 | | 29 | 3 | | 30 | 3 | | 31 | 10 | | 32 | 32 | | 33 | 54 | | 34 | 12 | | 35 | 13 | | 36 | 103 | | 37 | 80 | | 38 | 9 | | 39 | 62 | | 40 | 46 | | 41 | 9 | | 42 | 4 | | 43 | 26 | | 44 | 32 | | 45 | 25 | | 46 | 1 | | 47 | 6 | | 48 | 4 | | 49 | 18 |
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| 99.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 116 | | matches | | 0 | "been replaced" | | 1 | "being seen" |
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| 92.47% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 186 | | matches | | 0 | "was polishing" | | 1 | "was running" | | 2 | "was watching" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 148 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 472 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.02754237288135593 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.00211864406779661 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 148 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 148 | | mean | 11.12 | | std | 9.6 | | cv | 0.863 | | sampleLengths | | 0 | 27 | | 1 | 7 | | 2 | 20 | | 3 | 13 | | 4 | 9 | | 5 | 7 | | 6 | 4 | | 7 | 12 | | 8 | 1 | | 9 | 7 | | 10 | 3 | | 11 | 28 | | 12 | 17 | | 13 | 6 | | 14 | 20 | | 15 | 19 | | 16 | 3 | | 17 | 6 | | 18 | 1 | | 19 | 11 | | 20 | 8 | | 21 | 3 | | 22 | 6 | | 23 | 33 | | 24 | 27 | | 25 | 7 | | 26 | 15 | | 27 | 8 | | 28 | 21 | | 29 | 9 | | 30 | 23 | | 31 | 4 | | 32 | 6 | | 33 | 13 | | 34 | 7 | | 35 | 5 | | 36 | 3 | | 37 | 13 | | 38 | 17 | | 39 | 2 | | 40 | 16 | | 41 | 5 | | 42 | 5 | | 43 | 8 | | 44 | 4 | | 45 | 17 | | 46 | 18 | | 47 | 14 | | 48 | 5 | | 49 | 3 |
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| 50.23% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.33783783783783783 | | totalSentences | 148 | | uniqueOpeners | 50 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 103 | | matches | | 0 | "Then he turned." | | 1 | "Instead she slid into the" | | 2 | "Then a sound that might" | | 3 | "Then she picked up the" | | 4 | "Somewhere in the streets of" |
| | ratio | 0.049 | |
| 84.08% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 103 | | matches | | 0 | "She shook droplets from her" | | 1 | "Her shoulder ached from gripping" | | 2 | "He gestured with his chin" | | 3 | "She peeled off her wet" | | 4 | "Her eyes adjusted to the" | | 5 | "It landed flat on the" | | 6 | "He looked different." | | 7 | "He’d put on weight, not" | | 8 | "His voice cracked on the" | | 9 | "She turned her head toward" | | 10 | "His expression betrayed nothing." | | 11 | "She would deal with him" | | 12 | "She didn’t return the compliment." | | 13 | "He gestured toward the booth." | | 14 | "She should say no." | | 15 | "She could feel the shape" | | 16 | "Her left wrist, the one" | | 17 | "His jaw tightened." | | 18 | "She’d said it to the" | | 19 | "She’d said it so many" |
| | ratio | 0.34 | |
| 52.23% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 84 | | totalSentences | 103 | | matches | | 0 | "The green neon of The" | | 1 | "Rain smeared the grime on" | | 2 | "Aurora pushed through the door" | | 3 | "She shook droplets from her" | | 4 | "The lunch rush at Golden" | | 5 | "Duck sauce congealing in little" | | 6 | "Her shoulder ached from gripping" | | 7 | "He gestured with his chin" | | 8 | "She peeled off her wet" | | 9 | "Her eyes adjusted to the" | | 10 | "A man sat in the" | | 11 | "Something about the set of" | | 12 | "The name left her mouth" | | 13 | "It landed flat on the" | | 14 | "He looked different." | | 15 | "That was the first coherent" | | 16 | "The second was that she" | | 17 | "A small scar bisected his" | | 18 | "He’d put on weight, not" | | 19 | "His voice cracked on the" |
| | ratio | 0.816 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 103 | | matches | (empty) | | ratio | 0 | |
| 96.27% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 3 | | matches | | 0 | "The green neon of The Raven’s Nest hummed against the wet pavement, its light bleeding into puddles that had gathered in the cracks of the Soho cobblestones." | | 1 | "Evan took a half-step forward and stopped, as if hitting an invisible wall." | | 2 | "And in a dim-lit bar, a woman with a crescent-shaped scar sat in silence, turning over the strange mercy of being seen by someone who knew exactly who she used …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "he continued (continue)" |
| | dialogueSentences | 45 | | tagDensity | 0.089 | | leniency | 0.178 | | rawRatio | 0.25 | | effectiveRatio | 0.044 | |