| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 2 | | adverbTags | | 0 | "Silas said evenly [evenly]" | | 1 | "Aurora said flatly [flatly]" |
| | dialogueSentences | 85 | | tagDensity | 0.141 | | leniency | 0.282 | | rawRatio | 0.167 | | effectiveRatio | 0.047 | |
| 79.67% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1230 | | totalAiIsmAdverbs | 5 | | found | | 0 | | | 1 | | | 2 | | adverb | "deliberately" | | count | 1 |
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| | highlights | | 0 | "slightly" | | 1 | "slowly" | | 2 | "deliberately" |
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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) | |
| 39.02% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1230 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "methodical" | | 1 | "flickered" | | 2 | "silence" | | 3 | "calibrated" | | 4 | "perfect" | | 5 | "predictable" | | 6 | "tracing" | | 7 | "flicked" | | 8 | "eyebrow" | | 9 | "traced" | | 10 | "gleaming" | | 11 | "trembled" | | 12 | "pulse" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "the air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 124 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 124 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 196 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 10 | | totalWords | 1229 | | ratio | 0.008 | | matches | | 0 | "I don’t want to cry... I don’t want to cry..." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 72 | | wordCount | 681 | | uniqueNames | 17 | | maxNameDensity | 3.23 | | worstName | "Eva" | | maxWindowNameDensity | 8 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Billie | 2 | | Holiday | 2 | | Berlin | 1 | | Tehran | 1 | | Havana | 1 | | Polished | 2 | | Aurora | 21 | | Mayfair | 1 | | City | 1 | | Like | 1 | | English | 1 | | Silas | 10 | | Eva | 22 | | Pulled | 1 | | Silence | 3 |
| | persons | | 0 | "Billie" | | 1 | "Holiday" | | 2 | "Aurora" | | 3 | "Silas" | | 4 | "Eva" | | 5 | "Pulled" | | 6 | "Silence" |
| | places | | 0 | "Raven" | | 1 | "Berlin" | | 2 | "Tehran" | | 3 | "Havana" |
| | globalScore | 0 | | windowScore | 0 | |
| 85.90% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | glossingSentenceCount | 1 | | matches | | 0 | "Smelled like cedar and old tobacco" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.814 | | wordCount | 1229 | | matches | | 0 | "not in surprise, but in the way old wounds react to weather change" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 196 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 105 | | mean | 11.7 | | std | 10.87 | | cv | 0.929 | | sampleLengths | | 0 | 10 | | 1 | 60 | | 2 | 62 | | 3 | 18 | | 4 | 10 | | 5 | 27 | | 6 | 6 | | 7 | 21 | | 8 | 29 | | 9 | 12 | | 10 | 4 | | 11 | 20 | | 12 | 4 | | 13 | 30 | | 14 | 6 | | 15 | 19 | | 16 | 24 | | 17 | 1 | | 18 | 44 | | 19 | 4 | | 20 | 3 | | 21 | 6 | | 22 | 15 | | 23 | 22 | | 24 | 8 | | 25 | 4 | | 26 | 13 | | 27 | 23 | | 28 | 13 | | 29 | 5 | | 30 | 6 | | 31 | 13 | | 32 | 10 | | 33 | 8 | | 34 | 1 | | 35 | 18 | | 36 | 4 | | 37 | 19 | | 38 | 1 | | 39 | 4 | | 40 | 5 | | 41 | 10 | | 42 | 12 | | 43 | 2 | | 44 | 3 | | 45 | 6 | | 46 | 4 | | 47 | 3 | | 48 | 11 | | 49 | 10 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 124 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 139 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 196 | | ratio | 0.005 | | matches | | 0 | "Her breath caught, not in surprise, but in the way old wounds react to weather change — a dull, predictable ache." |
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| 93.82% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 680 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.047058823529411764 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.013235294117647059 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 196 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 196 | | mean | 6.27 | | std | 5.7 | | cv | 0.909 | | sampleLengths | | 0 | 10 | | 1 | 3 | | 2 | 14 | | 3 | 11 | | 4 | 10 | | 5 | 22 | | 6 | 18 | | 7 | 16 | | 8 | 14 | | 9 | 14 | | 10 | 8 | | 11 | 9 | | 12 | 1 | | 13 | 2 | | 14 | 8 | | 15 | 4 | | 16 | 11 | | 17 | 5 | | 18 | 7 | | 19 | 6 | | 20 | 16 | | 21 | 5 | | 22 | 18 | | 23 | 4 | | 24 | 6 | | 25 | 1 | | 26 | 8 | | 27 | 1 | | 28 | 1 | | 29 | 2 | | 30 | 4 | | 31 | 2 | | 32 | 3 | | 33 | 15 | | 34 | 4 | | 35 | 10 | | 36 | 1 | | 37 | 2 | | 38 | 11 | | 39 | 6 | | 40 | 6 | | 41 | 6 | | 42 | 13 | | 43 | 3 | | 44 | 21 | | 45 | 1 | | 46 | 8 | | 47 | 3 | | 48 | 4 | | 49 | 29 |
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| 64.97% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.40816326530612246 | | totalSentences | 196 | | uniqueOpeners | 80 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 8 | | totalSentences | 93 | | matches | | 0 | "Then the door creaked." | | 1 | "Just stared, eyes tracing the" | | 2 | "Just for a second." | | 3 | "Then she slid onto a" | | 4 | "Almost a laugh." | | 5 | "Just the pulse in her" | | 6 | "Just a red seal." | | 7 | "Then slowly, deliberately, she tore" |
| | ratio | 0.086 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 93 | | matches | | 0 | "She stared at the broken" | | 1 | "She exhaled through her nose," | | 2 | "He set the glass down." | | 3 | "He reached under the counter," | | 4 | "They were too big." | | 5 | "He didn’t ask." | | 6 | "He poured her a glass" | | 7 | "She froze when she saw" | | 8 | "Her breath caught, not in" | | 9 | "She didn’t hug." | | 10 | "Her gaze flicked to Silas" | | 11 | "He wiped the counter" | | 12 | "She exhaled through her nose." | | 13 | "Their fingers didn’t touch." | | 14 | "She stared into her water." | | 15 | "I don’t want to cry...*" | | 16 | "She stared at it." | | 17 | "Her thumb brushed the crescent" | | 18 | "She slid it across the" | | 19 | "She turned to go." |
| | ratio | 0.226 | |
| 78.28% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 93 | | matches | | 0 | "The latch on the delivery" | | 1 | "Aurora didn’t flinch." | | 2 | "She stared at the broken" | | 3 | "Rain had seeped through the" | | 4 | "She exhaled through her nose," | | 5 | "The green neon sign buzzed" | | 6 | "A chessboard sat abandoned near" | | 7 | "the bartender asked without looking" | | 8 | "That got his attention." | | 9 | "Silas turned, hazel eyes narrowing" | | 10 | "He set the glass down." | | 11 | "He reached under the counter," | | 12 | "Aurora kicked off her wet" | | 13 | "They were too big." | | 14 | "Silas tapped the signet ring" | | 15 | "He didn’t ask." | | 16 | "He poured her a glass" | | 17 | "A woman stepped in, shaking" | | 18 | "The cut of her suit" | | 19 | "She froze when she saw" |
| | ratio | 0.763 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 93 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 1 | | matches | | 0 | "Just stared, eyes tracing the sharp line of Aurora’s jaw, the black hair now falling just past her shoulders, the scar on her wrist peeping from beneath her sle…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 85 | | tagDensity | 0.071 | | leniency | 0.141 | | rawRatio | 0 | | effectiveRatio | 0 | |