| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva’s smile faded quickly [quickly]" |
| | dialogueSentences | 38 | | tagDensity | 0.447 | | leniency | 0.895 | | rawRatio | 0.059 | | effectiveRatio | 0.053 | |
| 77.52% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1112 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "cautiously" | | 1 | "quickly" | | 2 | "truly" | | 3 | "very" |
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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) | |
| 73.02% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1112 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "familiar" | | 1 | "wavered" | | 2 | "whisper" | | 3 | "silence" | | 4 | "unspoken" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 75 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 75 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 94 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1099 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 1 | | matches | | 0 | "Finally, Eva sighed and shuffled some papers into a pile." |
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| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 770 | | uniqueNames | 7 | | maxNameDensity | 2.21 | | worstName | "Rory" | | maxWindowNameDensity | 4 | | worstWindowName | "Rory" | | discoveredNames | | Ptolemy | 2 | | Aurora | 6 | | Rory | 17 | | Eva | 16 | | Laughter | 1 | | Ordered | 1 | | Adrian | 1 |
| | persons | | 0 | "Ptolemy" | | 1 | "Aurora" | | 2 | "Rory" | | 3 | "Eva" | | 4 | "Laughter" | | 5 | "Adrian" |
| | places | (empty) | | globalScore | 0.396 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | glossingSentenceCount | 1 | | matches | | 0 | "not quite meeting Eva's eyes" |
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| 18.02% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.82 | | wordCount | 1099 | | matches | | 0 | "not the past but the future" | | 1 | "Not a future she wanted or would ever choose, but one" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 94 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 30.53 | | std | 23.85 | | cv | 0.781 | | sampleLengths | | 0 | 38 | | 1 | 15 | | 2 | 27 | | 3 | 30 | | 4 | 17 | | 5 | 33 | | 6 | 31 | | 7 | 16 | | 8 | 11 | | 9 | 13 | | 10 | 99 | | 11 | 25 | | 12 | 11 | | 13 | 34 | | 14 | 21 | | 15 | 20 | | 16 | 32 | | 17 | 12 | | 18 | 17 | | 19 | 22 | | 20 | 33 | | 21 | 32 | | 22 | 111 | | 23 | 82 | | 24 | 9 | | 25 | 25 | | 26 | 26 | | 27 | 7 | | 28 | 32 | | 29 | 27 | | 30 | 34 | | 31 | 79 | | 32 | 20 | | 33 | 14 | | 34 | 27 | | 35 | 17 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 75 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 147 | | matches | | |
| 82.07% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 94 | | ratio | 0.021 | | matches | | 0 | "She understood—duty first, everything else second." | | 1 | "She wanted to say yes; she never wanted to say anything else." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 778 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.02313624678663239 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.008997429305912597 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 94 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 94 | | mean | 11.69 | | std | 6.79 | | cv | 0.581 | | sampleLengths | | 0 | 18 | | 1 | 4 | | 2 | 14 | | 3 | 2 | | 4 | 15 | | 5 | 12 | | 6 | 14 | | 7 | 1 | | 8 | 30 | | 9 | 2 | | 10 | 15 | | 11 | 9 | | 12 | 16 | | 13 | 8 | | 14 | 13 | | 15 | 18 | | 16 | 13 | | 17 | 3 | | 18 | 5 | | 19 | 6 | | 20 | 13 | | 21 | 22 | | 22 | 10 | | 23 | 3 | | 24 | 3 | | 25 | 14 | | 26 | 6 | | 27 | 4 | | 28 | 3 | | 29 | 2 | | 30 | 8 | | 31 | 15 | | 32 | 9 | | 33 | 25 | | 34 | 11 | | 35 | 30 | | 36 | 4 | | 37 | 14 | | 38 | 7 | | 39 | 5 | | 40 | 15 | | 41 | 8 | | 42 | 24 | | 43 | 12 | | 44 | 7 | | 45 | 10 | | 46 | 12 | | 47 | 10 | | 48 | 5 | | 49 | 13 |
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| 92.55% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.574468085106383 | | totalSentences | 94 | | uniqueOpeners | 54 | |
| 92.59% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 72 | | matches | | 0 | "Often not even second." | | 1 | "Finally, Eva sighed and shuffled" |
| | ratio | 0.028 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 72 | | matches | | 0 | "She weaved through the stacks" | | 1 | "Her shoulder-length red curls, once" | | 2 | "She picked up a sheaf" | | 3 | "They had been inseparable in" | | 4 | "She understood—duty first, everything else" | | 5 | "She hadn't relished saying no" | | 6 | "She let go of Eva's" | | 7 | "She leaned into the door" | | 8 | "Her words felt stilted in" | | 9 | "It was tough trying not" | | 10 | "She looked up at her" | | 11 | "She wanted to say yes;" |
| | ratio | 0.167 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 50 | | totalSentences | 72 | | matches | | 0 | "The flat's heavy locks hadn’t" | | 1 | "Rory placed her bag on" | | 2 | "A crash rang out from" | | 3 | "Rory muttered, shutting the door" | | 4 | "She weaved through the stacks" | | 5 | "A woman's exasperated voice trailed" | | 6 | "Eva's glare softened at the" | | 7 | "Her shoulder-length red curls, once" | | 8 | "She picked up a sheaf" | | 9 | "Eva's blue eyes glinted with" | | 10 | "Laughter spilled from Rory's lips." | | 11 | "A faint blush tinted her" | | 12 | "They had been inseparable in" | | 13 | "A tingle of discomfort ran" | | 14 | "She understood—duty first, everything else" | | 15 | "Aurora knew this." | | 16 | "The years hadn’t dimmed the" | | 17 | "Eva groaned, pushing her glasses" | | 18 | "Rory teased, searching the cluttered" | | 19 | "Rory held up a worn" |
| | ratio | 0.694 | |
| 69.44% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 72 | | matches | | 0 | "Even though the why behind" |
| | ratio | 0.014 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 1 | | matches | | 0 | "How she'd imagined pressing their bodies together, learning each other's shapes, kissing every inch of skin." |
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| 66.18% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 2 | | matches | | 0 | "Rory held up, its pages loose and frayed" | | 1 | "Eva stopped, her cheeks flushing" |
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| 71.05% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 3 | | fancyTags | | 0 | "Rory muttered (mutter)" | | 1 | "Eva groaned (groan)" | | 2 | "Rory teased (tease)" |
| | dialogueSentences | 38 | | tagDensity | 0.132 | | leniency | 0.263 | | rawRatio | 0.6 | | effectiveRatio | 0.158 | |