| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 43 | | tagDensity | 0.14 | | leniency | 0.279 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1347 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 66.59% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1347 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "blown wide" | | 1 | "weight" | | 2 | "perfect" | | 3 | "etched" | | 4 | "familiar" | | 5 | "traced" | | 6 | "flickered" | | 7 | "trembled" |
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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 | 133 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 133 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 170 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1347 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 78.64% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 51 | | wordCount | 981 | | uniqueNames | 12 | | maxNameDensity | 1.43 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | Quinn | 14 | | Veil | 2 | | Market | 1 | | Kowalski | 1 | | Patel | 13 | | Eva | 11 | | Compass | 1 | | Morris | 2 | | White | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Market" | | 2 | "Kowalski" | | 3 | "Patel" | | 4 | "Eva" | | 5 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "White" |
| | globalScore | 0.786 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 73 | | 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 | 1347 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 170 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 76 | | mean | 17.72 | | std | 16.43 | | cv | 0.927 | | sampleLengths | | 0 | 18 | | 1 | 27 | | 2 | 67 | | 3 | 40 | | 4 | 3 | | 5 | 18 | | 6 | 16 | | 7 | 23 | | 8 | 3 | | 9 | 13 | | 10 | 15 | | 11 | 2 | | 12 | 59 | | 13 | 23 | | 14 | 19 | | 15 | 29 | | 16 | 4 | | 17 | 4 | | 18 | 12 | | 19 | 29 | | 20 | 9 | | 21 | 1 | | 22 | 28 | | 23 | 4 | | 24 | 19 | | 25 | 14 | | 26 | 10 | | 27 | 35 | | 28 | 23 | | 29 | 2 | | 30 | 4 | | 31 | 21 | | 32 | 25 | | 33 | 6 | | 34 | 2 | | 35 | 15 | | 36 | 71 | | 37 | 17 | | 38 | 5 | | 39 | 1 | | 40 | 1 | | 41 | 53 | | 42 | 5 | | 43 | 5 | | 44 | 6 | | 45 | 7 | | 46 | 69 | | 47 | 25 | | 48 | 40 | | 49 | 11 |
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| 99.99% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 133 | | matches | | 0 | "was clenched" | | 1 | "was blanched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 176 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 170 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 984 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.02540650406504065 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.00508130081300813 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 170 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 170 | | mean | 7.92 | | std | 5.78 | | cv | 0.73 | | sampleLengths | | 0 | 18 | | 1 | 10 | | 2 | 7 | | 3 | 10 | | 4 | 13 | | 5 | 3 | | 6 | 19 | | 7 | 5 | | 8 | 3 | | 9 | 10 | | 10 | 3 | | 11 | 11 | | 12 | 7 | | 13 | 18 | | 14 | 7 | | 15 | 4 | | 16 | 4 | | 17 | 3 | | 18 | 12 | | 19 | 6 | | 20 | 16 | | 21 | 3 | | 22 | 13 | | 23 | 1 | | 24 | 6 | | 25 | 3 | | 26 | 4 | | 27 | 9 | | 28 | 15 | | 29 | 2 | | 30 | 5 | | 31 | 21 | | 32 | 5 | | 33 | 6 | | 34 | 12 | | 35 | 2 | | 36 | 2 | | 37 | 2 | | 38 | 4 | | 39 | 4 | | 40 | 19 | | 41 | 2 | | 42 | 3 | | 43 | 14 | | 44 | 7 | | 45 | 4 | | 46 | 6 | | 47 | 4 | | 48 | 8 | | 49 | 4 |
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| 50.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.35502958579881655 | | totalSentences | 169 | | uniqueOpeners | 60 | |
| 56.98% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 117 | | matches | | 0 | "Only two sets." | | 1 | "Too far apart." |
| | ratio | 0.017 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 117 | | matches | | 0 | "It was not supposed to" | | 1 | "Her curly red hair caught" | | 2 | "Her round glasses slipped." | | 3 | "She pushed them up." | | 4 | "He did not meet Quinn's" | | 5 | "She tucked hair behind her" | | 6 | "His hands were empty, palms" | | 7 | "His eyes were open, pupils" | | 8 | "Her knees popped." | | 9 | "They were white, bloodless." | | 10 | "His skin held a waxy" | | 11 | "She hugged the satchel tighter." | | 12 | "She leaned in closer to" | | 13 | "She reached for her gloves," | | 14 | "It was warm." | | 15 | "She stood and swung her" | | 16 | "She tugged at the satchel," | | 17 | "She had seen them three" | | 18 | "She had never believed them." | | 19 | "It pointed straight down at" |
| | ratio | 0.214 | |
| 54.02% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 95 | | totalSentences | 117 | | matches | | 0 | "The service door behind Camden" | | 1 | "Harlow Quinn ducked under the" | | 2 | "Diesel and damp and rot" | | 3 | "A different cold bled out" | | 4 | "The abandoned platform stretched out" | | 5 | "A single string of work" | | 6 | "Jars without labels." | | 7 | "A table of bones wired" | | 8 | "The Veil Market." | | 9 | "It was not supposed to" | | 10 | "A uniform held a torch" | | 11 | "Eva Kowalski stood beyond him," | | 12 | "Her curly red hair caught" | | 13 | "Her round glasses slipped." | | 14 | "She pushed them up." | | 15 | "DC Patel kept his notebook" | | 16 | "He did not meet Quinn's" | | 17 | "Quinn's jaw set." | | 18 | "The leather of her watch" | | 19 | "The second hand pinned at" |
| | ratio | 0.812 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 117 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 1 | | matches | | 0 | "The other, larger, the victim's trainers, toe-first, as if he ran in." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 1 | | fancyTags | | 0 | "Eva breathed (breathe)" |
| | dialogueSentences | 43 | | tagDensity | 0.14 | | leniency | 0.279 | | rawRatio | 0.167 | | effectiveRatio | 0.047 | |