| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 1 | | adverbTags | | 0 | "But she stepped back [back]" |
| | dialogueSentences | 36 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0.063 | | effectiveRatio | 0.056 | |
| 92.57% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 673 | | 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) | |
| 18.28% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 673 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "gleaming" | | 1 | "familiar" | | 2 | "constructed" | | 3 | "facade" | | 4 | "clenching" | | 5 | "silence" | | 6 | "unspoken" | | 7 | "tension" | | 8 | "resolved" | | 9 | "weight" | | 10 | "traced" |
| |
| 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 | 39 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 39 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 58 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 670 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 86.55% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 18 | | wordCount | 394 | | uniqueNames | 10 | | maxNameDensity | 1.27 | | worstName | "Aurora" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Aurora" | | discoveredNames | | Aurora | 5 | | Moreau | 1 | | Old | 1 | | Golden | 1 | | Empress | 1 | | Friday | 1 | | Brick | 1 | | Lane | 1 | | Lucien | 5 | | Evan | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Moreau" | | 2 | "Lucien" | | 3 | "Evan" |
| | places | | | globalScore | 0.865 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 31 | | 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 | 670 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 58 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 23.93 | | std | 14.25 | | cv | 0.596 | | sampleLengths | | 0 | 56 | | 1 | 13 | | 2 | 23 | | 3 | 23 | | 4 | 31 | | 5 | 12 | | 6 | 49 | | 7 | 4 | | 8 | 12 | | 9 | 18 | | 10 | 24 | | 11 | 10 | | 12 | 38 | | 13 | 5 | | 14 | 26 | | 15 | 26 | | 16 | 16 | | 17 | 37 | | 18 | 19 | | 19 | 40 | | 20 | 33 | | 21 | 6 | | 22 | 7 | | 23 | 54 | | 24 | 14 | | 25 | 17 | | 26 | 22 | | 27 | 35 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 39 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 74 | | matches | (empty) | |
| 93.60% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 58 | | ratio | 0.017 | | matches | | 0 | "\"Can't it be both?\" He leaned against the doorframe, close enough that she caught the familiar notes of his cologne – sandalwood and something darker she'd never been able to place." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 397 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.0327455919395466 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.005037783375314861 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 58 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 58 | | mean | 11.55 | | std | 6.89 | | cv | 0.597 | | sampleLengths | | 0 | 18 | | 1 | 6 | | 2 | 15 | | 3 | 17 | | 4 | 13 | | 5 | 16 | | 6 | 7 | | 7 | 6 | | 8 | 17 | | 9 | 31 | | 10 | 8 | | 11 | 4 | | 12 | 10 | | 13 | 30 | | 14 | 9 | | 15 | 4 | | 16 | 6 | | 17 | 6 | | 18 | 12 | | 19 | 6 | | 20 | 7 | | 21 | 17 | | 22 | 4 | | 23 | 6 | | 24 | 16 | | 25 | 22 | | 26 | 5 | | 27 | 6 | | 28 | 20 | | 29 | 6 | | 30 | 8 | | 31 | 12 | | 32 | 9 | | 33 | 7 | | 34 | 9 | | 35 | 7 | | 36 | 21 | | 37 | 19 | | 38 | 11 | | 39 | 29 | | 40 | 7 | | 41 | 26 | | 42 | 6 | | 43 | 5 | | 44 | 2 | | 45 | 11 | | 46 | 17 | | 47 | 19 | | 48 | 7 | | 49 | 4 |
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| 83.91% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5344827586206896 | | totalSentences | 58 | | uniqueOpeners | 31 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 38 | | matches | (empty) | | ratio | 0 | |
| 20.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 38 | | matches | | 0 | "Her fingers froze on the" | | 1 | "His mismatched eyes fixed on" | | 2 | "His lips curved into that" | | 3 | "He leaned against the doorframe," | | 4 | "He paused in the center" | | 5 | "His cane clicked against the" | | 6 | "She crossed her arms" | | 7 | "He settled onto her worn" | | 8 | "His mismatched eyes darkened." | | 9 | "She paced to the window," | | 10 | "She spun to face him" | | 11 | "He cut himself off, jaw" | | 12 | "She stepped closer, heart hammering" | | 13 | "His voice was soft, almost" | | 14 | "His eyes met hers" | | 15 | "She remembered late nights pouring" | | 16 | "She laughed, but there was" | | 17 | "He stood, crossing the space" | | 18 | "His hand reached for hers," |
| | ratio | 0.5 | |
| 12.63% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 34 | | totalSentences | 38 | | matches | | 0 | "The scent of cardamom and" | | 1 | "Her fingers froze on the" | | 2 | "Lucien Moreau stood in her" | | 3 | "His mismatched eyes fixed on" | | 4 | "The words came out sharper" | | 5 | "His lips curved into that" | | 6 | "Aurora's grip tightened on the" | | 7 | "He leaned against the doorframe," | | 8 | "Lucien swept past her, his" | | 9 | "He paused in the center" | | 10 | "His cane clicked against the" | | 11 | "She crossed her arms" | | 12 | "He settled onto her worn" | | 13 | "Aurora barked out a laugh" | | 14 | "His mismatched eyes darkened." | | 15 | "She paced to the window," | | 16 | "She spun to face him" | | 17 | "Lucien's fingers tightened on his" | | 18 | "He cut himself off, jaw" | | 19 | "She stepped closer, heart hammering" |
| | ratio | 0.895 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 38 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 9 | | technicalSentenceCount | 1 | | matches | | 0 | "She remembered late nights pouring over ancient texts, his shoulder brushing hers as they traced demon sigils." |
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| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 2 | | matches | | 0 | "He cut, jaw clenching" | | 1 | "She stepped, heart hammering" |
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| 66.67% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 3 | | fancyTags | | 0 | "Aurora barked (bark)" | | 1 | "she whispered (whisper)" | | 2 | "She laughed (laugh)" |
| | dialogueSentences | 36 | | tagDensity | 0.083 | | leniency | 0.167 | | rawRatio | 1 | | effectiveRatio | 0.167 | |