| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 37 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 83 | | tagDensity | 0.446 | | leniency | 0.892 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1264 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 84.18% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1264 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "silence" | | 2 | "weight" | | 3 | "flickered" |
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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 | 123 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 123 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 169 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1264 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 43 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 79 | | wordCount | 839 | | uniqueNames | 12 | | maxNameDensity | 4.05 | | worstName | "Eva" | | maxWindowNameDensity | 7.5 | | worstWindowName | "Eva" | | discoveredNames | | Frith | 1 | | Street | 1 | | Raven | 1 | | Nest | 1 | | Steam | 1 | | Cardiff | 2 | | Aurora | 27 | | Eva | 34 | | Taff | 1 | | Evan | 2 | | Silas | 7 | | Yu-Fei | 1 |
| | persons | | 0 | "Raven" | | 1 | "Aurora" | | 2 | "Eva" | | 3 | "Evan" | | 4 | "Silas" |
| | places | | 0 | "Frith" | | 1 | "Street" | | 2 | "Cardiff" | | 3 | "Yu-Fei" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.791 | | wordCount | 1264 | | matches | | 0 | "not in years but in the way she carried them" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 169 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 97 | | mean | 13.03 | | std | 12.38 | | cv | 0.95 | | sampleLengths | | 0 | 47 | | 1 | 59 | | 2 | 22 | | 3 | 25 | | 4 | 4 | | 5 | 15 | | 6 | 24 | | 7 | 7 | | 8 | 1 | | 9 | 2 | | 10 | 75 | | 11 | 7 | | 12 | 1 | | 13 | 10 | | 14 | 22 | | 15 | 9 | | 16 | 11 | | 17 | 5 | | 18 | 12 | | 19 | 3 | | 20 | 32 | | 21 | 9 | | 22 | 15 | | 23 | 56 | | 24 | 5 | | 25 | 9 | | 26 | 3 | | 27 | 5 | | 28 | 7 | | 29 | 14 | | 30 | 8 | | 31 | 11 | | 32 | 13 | | 33 | 11 | | 34 | 4 | | 35 | 17 | | 36 | 3 | | 37 | 6 | | 38 | 35 | | 39 | 5 | | 40 | 3 | | 41 | 7 | | 42 | 14 | | 43 | 11 | | 44 | 6 | | 45 | 12 | | 46 | 6 | | 47 | 31 | | 48 | 9 | | 49 | 11 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 123 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 175 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 169 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 844 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.011848341232227487 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 169 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 169 | | mean | 7.48 | | std | 5.08 | | cv | 0.679 | | sampleLengths | | 0 | 22 | | 1 | 18 | | 2 | 7 | | 3 | 9 | | 4 | 6 | | 5 | 6 | | 6 | 16 | | 7 | 8 | | 8 | 14 | | 9 | 6 | | 10 | 8 | | 11 | 8 | | 12 | 3 | | 13 | 22 | | 14 | 4 | | 15 | 9 | | 16 | 2 | | 17 | 4 | | 18 | 3 | | 19 | 14 | | 20 | 7 | | 21 | 7 | | 22 | 1 | | 23 | 2 | | 24 | 11 | | 25 | 12 | | 26 | 12 | | 27 | 9 | | 28 | 8 | | 29 | 23 | | 30 | 7 | | 31 | 1 | | 32 | 10 | | 33 | 14 | | 34 | 8 | | 35 | 6 | | 36 | 3 | | 37 | 7 | | 38 | 4 | | 39 | 5 | | 40 | 8 | | 41 | 4 | | 42 | 3 | | 43 | 5 | | 44 | 5 | | 45 | 11 | | 46 | 4 | | 47 | 7 | | 48 | 9 | | 49 | 6 |
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| 39.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.1834319526627219 | | totalSentences | 169 | | uniqueOpeners | 31 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 87 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 87 | | matches | | 0 | "His grey-streaked auburn hair caught" | | 1 | "She set the bag down" | | 2 | "His eyes moved from her" | | 3 | "She lifted the lid" | | 4 | "He nodded once." | | 5 | "He took the bag without" | | 6 | "She had turned, and the" | | 7 | "Her hair was cut short" | | 8 | "She wore a leather jacket" | | 9 | "She slid off the stool." | | 10 | "It made a clean sound" | | 11 | "he said to Eva" | | 12 | "She picked up the glass" | | 13 | "She was older than Aurora" | | 14 | "He did not look at" | | 15 | "She closed it." | | 16 | "She smoothed it on the" | | 17 | "She let the pieces fall" | | 18 | "She stood close enough that" | | 19 | "She didn’t ask for more." |
| | ratio | 0.276 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 86 | | totalSentences | 87 | | matches | | 0 | "The green neon sign buzzed" | | 1 | "Aurora pushed the door of" | | 2 | "The bell gave a short," | | 3 | "The bar smelled of old" | | 4 | "Maps hung crooked on the" | | 5 | "Silas stood behind the bar," | | 6 | "His grey-streaked auburn hair caught" | | 7 | "The silver signet ring on" | | 8 | "She set the bag down" | | 9 | "Silas glanced up." | | 10 | "His eyes moved from her" | | 11 | "She lifted the lid" | | 12 | "He nodded once." | | 13 | "The limp in his left" | | 14 | "He took the bag without" | | 15 | "A voice came from the" | | 16 | "Eva sat at the bar" | | 17 | "She had turned, and the" | | 18 | "Her hair was cut short" | | 19 | "The softness she had worn" |
| | ratio | 0.989 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 87 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 1 | | matches | | 0 | "Silas stood behind the bar, polishing a glass with a cloth that had seen better years." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 37 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 33 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 83 | | tagDensity | 0.398 | | leniency | 0.795 | | rawRatio | 0 | | effectiveRatio | 0 | |