| 46.15% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 26 | | adverbTagCount | 4 | | adverbTags | | 0 | "Jennifer repeated slowly [slowly]" | | 1 | "Jennifer said quietly [quietly]" | | 2 | "she said finally [finally]" | | 3 | "she said softly [softly]" |
| | dialogueSentences | 52 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.154 | | effectiveRatio | 0.154 | |
| 42.03% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1035 | | totalAiIsmAdverbs | 12 | | found | | | highlights | | 0 | "perfectly" | | 1 | "very" | | 2 | "softly" | | 3 | "slightly" | | 4 | "really" | | 5 | "slowly" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 51.69% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1035 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "stark" | | 1 | "charm" | | 2 | "perfect" | | 3 | "familiar" | | 4 | "trembled" | | 5 | "weight" | | 6 | "tension" | | 7 | "silence" |
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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 | 0 | | narrationSentences | 52 | | matches | (empty) | |
| 60.44% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 52 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 78 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1037 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 20 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 662 | | uniqueNames | 8 | | maxNameDensity | 2.57 | | worstName | "Aurora" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Jennifer" | | discoveredNames | | Aurora | 17 | | Raven | 2 | | Nest | 2 | | Jennifer | 17 | | Walsh | 1 | | Chen | 1 | | Silas | 5 | | London | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Jennifer" | | 4 | "Walsh" | | 5 | "Chen" | | 6 | "Silas" |
| | places | | | globalScore | 0.216 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 42 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.964 | | wordCount | 1037 | | matches | | 0 | "not the path anyone expected, but it's mine" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 78 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 28.03 | | std | 14.91 | | cv | 0.532 | | sampleLengths | | 0 | 69 | | 1 | 28 | | 2 | 16 | | 3 | 56 | | 4 | 15 | | 5 | 39 | | 6 | 40 | | 7 | 44 | | 8 | 15 | | 9 | 23 | | 10 | 26 | | 11 | 19 | | 12 | 28 | | 13 | 20 | | 14 | 29 | | 15 | 18 | | 16 | 37 | | 17 | 15 | | 18 | 11 | | 19 | 29 | | 20 | 19 | | 21 | 17 | | 22 | 42 | | 23 | 41 | | 24 | 43 | | 25 | 17 | | 26 | 54 | | 27 | 11 | | 28 | 15 | | 29 | 25 | | 30 | 15 | | 31 | 25 | | 32 | 27 | | 33 | 5 | | 34 | 39 | | 35 | 10 | | 36 | 55 |
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| 98.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 52 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 112 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 78 | | ratio | 0.013 | | matches | | 0 | "Jennifer Walsh - formerly Jennifer Chen - stood in the doorway, her designer coat and perfectly styled hair a stark contrast to the pub's worn wooden floors and shabby charm." |
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| 99.10% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 664 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.03313253012048193 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.02108433734939759 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 78 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 78 | | mean | 13.29 | | std | 7.32 | | cv | 0.55 | | sampleLengths | | 0 | 23 | | 1 | 21 | | 2 | 25 | | 3 | 20 | | 4 | 8 | | 5 | 16 | | 6 | 30 | | 7 | 26 | | 8 | 8 | | 9 | 7 | | 10 | 21 | | 11 | 10 | | 12 | 8 | | 13 | 17 | | 14 | 16 | | 15 | 7 | | 16 | 22 | | 17 | 22 | | 18 | 15 | | 19 | 8 | | 20 | 15 | | 21 | 22 | | 22 | 4 | | 23 | 8 | | 24 | 11 | | 25 | 10 | | 26 | 13 | | 27 | 5 | | 28 | 9 | | 29 | 11 | | 30 | 9 | | 31 | 20 | | 32 | 8 | | 33 | 10 | | 34 | 19 | | 35 | 15 | | 36 | 3 | | 37 | 8 | | 38 | 7 | | 39 | 5 | | 40 | 6 | | 41 | 15 | | 42 | 14 | | 43 | 5 | | 44 | 14 | | 45 | 9 | | 46 | 8 | | 47 | 20 | | 48 | 22 | | 49 | 23 |
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| 86.75% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5512820512820513 | | totalSentences | 78 | | uniqueOpeners | 43 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 10 | | totalSentences | 50 | | matches | | 0 | "It was that dead hour" | | 1 | "She nearly dropped the rag" | | 2 | "She'd lost the pink streaks" | | 3 | "she said, forcing a smile" | | 4 | "She settled onto a barstool," | | 5 | "His silver signet ring caught" | | 6 | "She took a sip of" | | 7 | "he said, his voice carrying" | | 8 | "she said finally" | | 9 | "she said softly" |
| | ratio | 0.2 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 50 | | matches | | 0 | "The green neon sign cast" | | 1 | "The old maps on the" | | 2 | "It was that dead hour" | | 3 | "The door creaked open, letting" | | 4 | "She nearly dropped the rag" | | 5 | "The name felt strange on" | | 6 | "Jennifer Walsh - formerly Jennifer" | | 7 | "She'd lost the pink streaks" | | 8 | "Jennifer's manicured hand flew to" | | 9 | "Aurora ran a self-conscious hand" | | 10 | "she said, forcing a smile" | | 11 | "Jennifer moved to the bar" | | 12 | "She settled onto a barstool," | | 13 | "His silver signet ring caught" | | 14 | "Aurora asked, falling back on" | | 15 | "Jennifer said, then laughed softly" | | 16 | "Aurora reached for a wine" | | 17 | "Jennifer watched her pour the" | | 18 | "Aurora's hand trembled slightly as" | | 19 | "The crescent-shaped scar on her" |
| | ratio | 0.94 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 2 | | matches | | 0 | "She'd lost the pink streaks and nose ring that had been her signature at university, but her eyes were the same deep brown that Aurora remembered." | | 1 | "The door closed behind her with a gentle click, leaving Aurora alone with Silas and the weight of everything that had and hadn't been said." |
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| 67.31% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 26 | | uselessAdditionCount | 3 | | matches | | 0 | "She settled, her posture perfect" | | 1 | "Jennifer said, her voice gentler now" | | 2 | "Aurora said, her voice steady despite the tremor in her hands" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 16 | | fancyCount | 1 | | fancyTags | | 0 | "Jennifer repeated slowly (repeat)" |
| | dialogueSentences | 52 | | tagDensity | 0.308 | | leniency | 0.615 | | rawRatio | 0.063 | | effectiveRatio | 0.038 | |