| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 112 | | tagDensity | 0.125 | | leniency | 0.25 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1923 | | 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) | |
| 68.80% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1923 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "warmth" | | 1 | "raced" | | 2 | "silence" | | 3 | "charm" | | 4 | "familiar" | | 5 | "affection" |
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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 | 131 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 131 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 229 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1923 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 27 | | unquotedAttributions | 0 | | matches | (empty) | |
| 73.66% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 1179 | | uniqueNames | 8 | | maxNameDensity | 1.53 | | worstName | "Lucien" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Lucien" | | discoveredNames | | Lucien | 18 | | Moreau | 1 | | Eva | 6 | | Mediterranean | 1 | | Silas | 1 | | Avarosi | 1 | | Rory | 17 | | Ptolemy | 3 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Eva" | | 3 | "Silas" | | 4 | "Rory" | | 5 | "Ptolemy" |
| | places | (empty) | | globalScore | 0.737 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 87 | | 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 | 1923 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 229 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 153 | | mean | 12.57 | | std | 12.44 | | cv | 0.99 | | sampleLengths | | 0 | 36 | | 1 | 32 | | 2 | 14 | | 3 | 8 | | 4 | 6 | | 5 | 8 | | 6 | 26 | | 7 | 16 | | 8 | 20 | | 9 | 18 | | 10 | 7 | | 11 | 70 | | 12 | 19 | | 13 | 3 | | 14 | 4 | | 15 | 11 | | 16 | 26 | | 17 | 10 | | 18 | 19 | | 19 | 14 | | 20 | 28 | | 21 | 4 | | 22 | 15 | | 23 | 3 | | 24 | 8 | | 25 | 1 | | 26 | 6 | | 27 | 11 | | 28 | 19 | | 29 | 11 | | 30 | 21 | | 31 | 4 | | 32 | 18 | | 33 | 3 | | 34 | 6 | | 35 | 47 | | 36 | 7 | | 37 | 2 | | 38 | 1 | | 39 | 8 | | 40 | 10 | | 41 | 10 | | 42 | 20 | | 43 | 37 | | 44 | 13 | | 45 | 5 | | 46 | 5 | | 47 | 1 | | 48 | 4 | | 49 | 40 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 131 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 209 | | matches | | |
| 92.95% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 4 | | flaggedSentences | 4 | | totalSentences | 229 | | ratio | 0.017 | | matches | | 0 | "A lock hung over the amber eye; the black one fixed on her through the narrow gap." | | 1 | "The task demanded more attention than it should have; his fingers missed the same button twice." | | 2 | "The charm at his ribs pulled; he ignored it." | | 3 | "She took his lower lip between her teeth; he gripped the chair again and laughed when she pulled back." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1185 | | adjectiveStacks | 1 | | stackExamples | | 0 | "other pressed against his" |
| | adverbCount | 36 | | adverbRatio | 0.030379746835443037 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0016877637130801688 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 229 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 229 | | mean | 8.4 | | std | 6.19 | | cv | 0.737 | | sampleLengths | | 0 | 5 | | 1 | 31 | | 2 | 10 | | 3 | 17 | | 4 | 5 | | 5 | 7 | | 6 | 7 | | 7 | 8 | | 8 | 6 | | 9 | 8 | | 10 | 7 | | 11 | 15 | | 12 | 2 | | 13 | 1 | | 14 | 1 | | 15 | 16 | | 16 | 20 | | 17 | 10 | | 18 | 8 | | 19 | 7 | | 20 | 11 | | 21 | 20 | | 22 | 4 | | 23 | 5 | | 24 | 30 | | 25 | 19 | | 26 | 3 | | 27 | 4 | | 28 | 11 | | 29 | 18 | | 30 | 4 | | 31 | 4 | | 32 | 10 | | 33 | 11 | | 34 | 8 | | 35 | 6 | | 36 | 8 | | 37 | 8 | | 38 | 9 | | 39 | 11 | | 40 | 3 | | 41 | 1 | | 42 | 15 | | 43 | 3 | | 44 | 8 | | 45 | 1 | | 46 | 3 | | 47 | 3 | | 48 | 11 | | 49 | 14 |
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| 48.91% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.314410480349345 | | totalSentences | 229 | | uniqueOpeners | 72 | |
| 29.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 113 | | matches | | | ratio | 0.009 | |
| 57.17% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 113 | | matches | | 0 | "She started to close the" | | 1 | "She caught his arm, pulled" | | 2 | "He lowered himself onto the" | | 3 | "She took the first-aid box" | | 4 | "His gloves were wet." | | 5 | "He held it out" | | 6 | "Her full name crossed its" | | 7 | "She didn’t sit." | | 8 | "His jaw tightened." | | 9 | "She gave him a long" | | 10 | "She carried the paper to" | | 11 | "He sank back into the" | | 12 | "She dropped the last fragment" | | 13 | "She helped him out of" | | 14 | "She folded a clean tea" | | 15 | "He caught a breath through" | | 16 | "She cleaned the cut with" | | 17 | "She straightened before the contact" | | 18 | "She opened the first-aid box" | | 19 | "He had the grace to" |
| | ratio | 0.407 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 106 | | totalSentences | 113 | | matches | | 0 | "The third deadbolt snapped back." | | 1 | "Aurora opened the door with" | | 2 | "Water had loosened his platinum" | | 3 | "A lock hung over the" | | 4 | "Blood darkened his charcoal waistcoat." | | 5 | "Rory gripped the edge of" | | 6 | "She started to close the" | | 7 | "The chain rattled as she" | | 8 | "She caught his arm, pulled" | | 9 | "The tabby looked up from" | | 10 | "Lucien set the cane beside" | | 11 | "He lowered himself onto the" | | 12 | "The cramped flat smelled of" | | 13 | "Scrolls crowded the windowsill." | | 14 | "Eva had annotated the lampshade." | | 15 | "Rory had been sleeping on" | | 16 | "Lucien’s gaze touched the open" | | 17 | "She took the first-aid box" | | 18 | "His gloves were wet." | | 19 | "Blood slicked his fingertips." |
| | ratio | 0.938 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 113 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | 10 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 112 | | tagDensity | 0.089 | | leniency | 0.179 | | rawRatio | 0 | | effectiveRatio | 0 | |