| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 3 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 40.83% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 169 | | totalAiIsmAdverbs | 2 | | 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) | |
| 70.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 169 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 14 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 14 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 16 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 168 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 85.48% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 6 | | wordCount | 155 | | uniqueNames | 5 | | maxNameDensity | 1.29 | | worstName | "Eva" | | maxWindowNameDensity | 1 | | worstWindowName | "Eva" | | discoveredNames | | Eva | 2 | | Ptolemy | 1 | | Lucien | 1 | | Moreau | 1 | | French | 1 |
| | persons | | | places | (empty) | | globalScore | 0.855 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 10 | | 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 | 168 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 16 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 7 | | mean | 24 | | std | 16.53 | | cv | 0.689 | | sampleLengths | | |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 14 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 26 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 16 | | ratio | 0.063 | | matches | | 0 | "He looked precisely the same as the last time they'd seen each other — which meant exactly nothing had changed, and everything had changed." |
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| 95.74% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 156 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 7 | | adverbRatio | 0.04487179487179487 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.019230769230769232 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 16 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 16 | | mean | 10.5 | | std | 6.67 | | cv | 0.635 | | sampleLengths | | 0 | 17 | | 1 | 9 | | 2 | 4 | | 3 | 10 | | 4 | 22 | | 5 | 9 | | 6 | 9 | | 7 | 20 | | 8 | 24 | | 9 | 13 | | 10 | 3 | | 11 | 4 | | 12 | 3 | | 13 | 4 | | 14 | 10 | | 15 | 7 |
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| 87.50% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.75 | | totalSentences | 16 | | uniqueOpeners | 12 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 14 | | matches | (empty) | | ratio | 0 | |
| 77.14% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 5 | | totalSentences | 14 | | matches | | 0 | "She set down the mug" | | 1 | "He looked precisely the same" | | 2 | "he said, his French accent" | | 3 | "She didn't move." | | 4 | "His slight smile didn't reach" |
| | ratio | 0.357 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 10 | | totalSentences | 14 | | matches | | 0 | "The knock came sharp and" | | 1 | "Aurora's hand froze mid-tea-pour, her" | | 2 | "Those weren't Eva's knocks." | | 3 | "Those weren't anyone's knocks who" | | 4 | "She set down the mug" | | 5 | "The three deadbolts felt suddenly" | | 6 | "He looked precisely the same" | | 7 | "he said, his French accent" | | 8 | "She didn't move." | | 9 | "His slight smile didn't reach" |
| | ratio | 0.714 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 14 | | matches | (empty) | | ratio | 0 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 7 | | technicalSentenceCount | 1 | | matches | | 0 | "The knock came sharp and unexpected, three precise raps that cut through the quiet of Eva's flat." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 1 | | matches | | 0 | "he said, his French accent sliding around the words like silk" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 3 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |