| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 139 | | tagDensity | 0.086 | | leniency | 0.173 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1992 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 100.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1992 | | totalAiIsms | 0 | | found | (empty) | | highlights | (empty) | |
| 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 | 134 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 134 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 261 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1992 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 34 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 71 | | wordCount | 1201 | | uniqueNames | 8 | | maxNameDensity | 2.08 | | worstName | "Lucien" | | maxWindowNameDensity | 4 | | worstWindowName | "Aurora" | | discoveredNames | | Eva | 10 | | Moreau | 2 | | Aurora | 24 | | Ptolemy | 5 | | Lucien | 25 | | Golden | 1 | | Empress | 1 | | Silas | 3 |
| | persons | | 0 | "Eva" | | 1 | "Moreau" | | 2 | "Aurora" | | 3 | "Ptolemy" | | 4 | "Lucien" | | 5 | "Silas" |
| | places | | | globalScore | 0.459 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 96 | | 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 | 1992 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 261 | | matches | | 0 | "taken that morning" | | 1 | "hated that she" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 163 | | mean | 12.22 | | std | 13.03 | | cv | 1.066 | | sampleLengths | | 0 | 34 | | 1 | 1 | | 2 | 27 | | 3 | 5 | | 4 | 12 | | 5 | 44 | | 6 | 3 | | 7 | 1 | | 8 | 7 | | 9 | 9 | | 10 | 3 | | 11 | 4 | | 12 | 30 | | 13 | 52 | | 14 | 5 | | 15 | 5 | | 16 | 4 | | 17 | 15 | | 18 | 7 | | 19 | 11 | | 20 | 10 | | 21 | 4 | | 22 | 3 | | 23 | 51 | | 24 | 4 | | 25 | 1 | | 26 | 2 | | 27 | 1 | | 28 | 6 | | 29 | 13 | | 30 | 40 | | 31 | 5 | | 32 | 22 | | 33 | 8 | | 34 | 8 | | 35 | 23 | | 36 | 2 | | 37 | 6 | | 38 | 7 | | 39 | 28 | | 40 | 51 | | 41 | 41 | | 42 | 40 | | 43 | 8 | | 44 | 5 | | 45 | 3 | | 46 | 17 | | 47 | 13 | | 48 | 14 | | 49 | 12 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 134 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 204 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 3 | | flaggedSentences | 3 | | totalSentences | 261 | | ratio | 0.011 | | matches | | 0 | "Lucien’s amber eye tracked the sound; his black one stayed on her." | | 1 | "Notes covered the sofa arms, the windowsill and half the floor; strips of paper marked pages in languages Aurora could not read." | | 2 | "One cuff carried a plain silver link; the other held a button that did not match." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1202 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.018302828618968387 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0033277870216306157 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 261 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 261 | | mean | 7.63 | | std | 5.55 | | cv | 0.727 | | sampleLengths | | 0 | 13 | | 1 | 21 | | 2 | 1 | | 3 | 8 | | 4 | 7 | | 5 | 12 | | 6 | 5 | | 7 | 12 | | 8 | 13 | | 9 | 12 | | 10 | 7 | | 11 | 12 | | 12 | 3 | | 13 | 1 | | 14 | 7 | | 15 | 5 | | 16 | 4 | | 17 | 3 | | 18 | 4 | | 19 | 3 | | 20 | 16 | | 21 | 11 | | 22 | 10 | | 23 | 17 | | 24 | 15 | | 25 | 10 | | 26 | 5 | | 27 | 5 | | 28 | 4 | | 29 | 9 | | 30 | 6 | | 31 | 7 | | 32 | 7 | | 33 | 4 | | 34 | 10 | | 35 | 4 | | 36 | 3 | | 37 | 15 | | 38 | 6 | | 39 | 5 | | 40 | 14 | | 41 | 7 | | 42 | 4 | | 43 | 4 | | 44 | 1 | | 45 | 2 | | 46 | 1 | | 47 | 6 | | 48 | 4 | | 49 | 9 |
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| 47.32% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.27586206896551724 | | totalSentences | 261 | | uniqueOpeners | 72 | |
| 52.08% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 128 | | matches | | 0 | "Instead, she slid the first" | | 1 | "Then the quiet after she" |
| | ratio | 0.016 | |
| 60.63% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 51 | | totalSentences | 128 | | matches | | 0 | "His platinum hair, usually combed" | | 1 | "She kept her hand on" | | 2 | "His jaw tightened." | | 3 | "He caught her looking and" | | 4 | "It lit the hall behind" | | 5 | "Its lower corner had soaked" | | 6 | "Her phone sat on Eva’s" | | 7 | "She had ignored it since" | | 8 | "It would not fit past" | | 9 | "She could leave it on" | | 10 | "She could close the door" | | 11 | "She pulled the door open." | | 12 | "She checked the landing through" | | 13 | "She slid the bolts across." | | 14 | "She slipped a nail beneath" | | 15 | "He took his hand off" | | 16 | "She set the paper down" | | 17 | "His fingers closed around the" | | 18 | "She let out a short" | | 19 | "She had spent three weeks" |
| | ratio | 0.398 | |
| 2.97% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 117 | | totalSentences | 128 | | matches | | 0 | "Aurora opened Eva’s door with" | | 1 | "The door stopped against it," | | 2 | "Lucien Moreau lowered his hand" | | 3 | "Rain darkened the shoulders of" | | 4 | "His platinum hair, usually combed" | | 5 | "A folded map slid off" | | 6 | "She kept her hand on" | | 7 | "Lucien’s amber eye tracked the" | | 8 | "Aurora glanced at the cane." | | 9 | "His jaw tightened." | | 10 | "He caught her looking and" | | 11 | "A pan struck a burner" | | 12 | "Heat carried cumin up through" | | 13 | "Aurora had left Eva’s kitchen" | | 14 | "It lit the hall behind" | | 15 | "Lucien took a folded envelope" | | 16 | "Its lower corner had soaked" | | 17 | "Aurora’s fingers pressed into the" | | 18 | "Her phone sat on Eva’s" | | 19 | "She had ignored it since" |
| | ratio | 0.914 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 128 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 139 | | tagDensity | 0.05 | | leniency | 0.101 | | rawRatio | 0 | | effectiveRatio | 0 | |