| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn turned back [back]" |
| | dialogueSentences | 62 | | tagDensity | 0.29 | | leniency | 0.581 | | rawRatio | 0.056 | | effectiveRatio | 0.032 | |
| 96.58% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1463 | | totalAiIsmAdverbs | 1 | | 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) | |
| 82.91% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1463 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "gloom" | | 1 | "flickered" | | 2 | "traced" | | 3 | "pumping" | | 4 | "velvet" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 105 | | matches | (empty) | |
| 88.44% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 105 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 146 | | 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 | 1463 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 889 | | uniqueNames | 6 | | maxNameDensity | 2.59 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 23 | | Veil | 1 | | Market | 1 | | Kowalski | 1 | | Morris | 2 | | Eva | 16 |
| | persons | | 0 | "Quinn" | | 1 | "Market" | | 2 | "Kowalski" | | 3 | "Morris" | | 4 | "Eva" |
| | places | (empty) | | globalScore | 0.206 | | windowScore | 0.167 | |
| 65.25% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 2 | | matches | | 0 | "as if waiting for a customer" | | 1 | "not quite looking at Quinn" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1463 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 146 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 25.22 | | std | 18.33 | | cv | 0.727 | | sampleLengths | | 0 | 25 | | 1 | 27 | | 2 | 1 | | 3 | 17 | | 4 | 4 | | 5 | 7 | | 6 | 22 | | 7 | 67 | | 8 | 38 | | 9 | 6 | | 10 | 26 | | 11 | 12 | | 12 | 10 | | 13 | 60 | | 14 | 31 | | 15 | 4 | | 16 | 20 | | 17 | 1 | | 18 | 6 | | 19 | 38 | | 20 | 13 | | 21 | 51 | | 22 | 41 | | 23 | 6 | | 24 | 5 | | 25 | 41 | | 26 | 6 | | 27 | 45 | | 28 | 58 | | 29 | 48 | | 30 | 6 | | 31 | 34 | | 32 | 47 | | 33 | 5 | | 34 | 54 | | 35 | 18 | | 36 | 30 | | 37 | 40 | | 38 | 17 | | 39 | 8 | | 40 | 35 | | 41 | 13 | | 42 | 44 | | 43 | 29 | | 44 | 43 | | 45 | 4 | | 46 | 59 | | 47 | 33 | | 48 | 33 | | 49 | 9 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 105 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 143 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 146 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 889 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.02249718785151856 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.003374578177727784 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 146 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 146 | | mean | 10.02 | | std | 7.29 | | cv | 0.727 | | sampleLengths | | 0 | 7 | | 1 | 18 | | 2 | 16 | | 3 | 11 | | 4 | 1 | | 5 | 9 | | 6 | 8 | | 7 | 4 | | 8 | 7 | | 9 | 4 | | 10 | 18 | | 11 | 10 | | 12 | 20 | | 13 | 4 | | 14 | 6 | | 15 | 27 | | 16 | 18 | | 17 | 20 | | 18 | 6 | | 19 | 8 | | 20 | 18 | | 21 | 12 | | 22 | 8 | | 23 | 2 | | 24 | 21 | | 25 | 2 | | 26 | 2 | | 27 | 14 | | 28 | 5 | | 29 | 11 | | 30 | 5 | | 31 | 3 | | 32 | 2 | | 33 | 3 | | 34 | 23 | | 35 | 4 | | 36 | 13 | | 37 | 7 | | 38 | 1 | | 39 | 6 | | 40 | 23 | | 41 | 8 | | 42 | 7 | | 43 | 5 | | 44 | 8 | | 45 | 16 | | 46 | 35 | | 47 | 5 | | 48 | 14 | | 49 | 5 |
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| 62.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.410958904109589 | | totalSentences | 146 | | uniqueOpeners | 60 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 93 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 93 | | matches | | 0 | "He looked at the token," | | 1 | "Her round glasses caught the" | | 2 | "Her smile was thin" | | 3 | "His eyes stared at the" | | 4 | "She kept her face still." | | 5 | "She looked at the desk." | | 6 | "She flipped it open, not" | | 7 | "She photographed it from three" | | 8 | "Her sister wore the same" | | 9 | "She pressed two fingers against" | | 10 | "She stepped closer, peered at" | | 11 | "Her blood went cold." | | 12 | "She had seen this pattern" | | 13 | "she said, and her voice" | | 14 | "She tucked her hair behind" | | 15 | "She turned, and that was" |
| | ratio | 0.172 | |
| 24.52% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 81 | | totalSentences | 93 | | matches | | 0 | "The bone token clinked onto" | | 1 | "Harlow Quinn slid her badge" | | 2 | "The market guard had the" | | 3 | "He looked at the token," | | 4 | "Quinn picked up the token" | | 5 | "The guard stepped aside." | | 6 | "The black rope lifted, and" | | 7 | "The Veil Market had a" | | 8 | "Stalls crammed into the old" | | 9 | "Tonight the rhythm stuttered." | | 10 | "Traders lingered in doorways, watching" | | 11 | "Candles guttered in glass jars," | | 12 | "Eva Kowalski straightened from beside" | | 13 | "Her round glasses caught the" | | 14 | "Eva nodded at the body" | | 15 | "Her smile was thin" | | 16 | "The victim sat in a" | | 17 | "The stall itself was immaculate." | | 18 | "Every jar on the shelves" | | 19 | "A man who liked order." |
| | ratio | 0.871 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 93 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 2 | | matches | | 0 | "Her round glasses caught the light, and her satchel hung open, stuffed with books that looked older than Quinn's grandmother." | | 1 | "The final entry was in a hand that didn't match the older, steadier script above it." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 62 | | tagDensity | 0.048 | | leniency | 0.097 | | rawRatio | 0.333 | | effectiveRatio | 0.032 | |