| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva said quietly [quietly]" |
| | dialogueSentences | 37 | | tagDensity | 0.297 | | leniency | 0.595 | | rawRatio | 0.091 | | effectiveRatio | 0.054 | |
| 85.04% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1003 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slowly" | | 1 | "sharply" | | 2 | "slightly" |
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| 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) | |
| 90.03% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1003 | | totalAiIsms | 2 | | 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 | 50 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 50 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 74 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 52 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1003 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 47.48% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 634 | | uniqueNames | 9 | | maxNameDensity | 2.05 | | worstName | "Rory" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Aurora | 1 | | Oxford | 1 | | Street | 1 | | Rory | 13 | | Cardiff | 1 | | Eva | 13 | | Silas | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Aurora" | | 3 | "Rory" | | 4 | "Eva" | | 5 | "Silas" |
| | places | | 0 | "Oxford" | | 1 | "Street" | | 2 | "Cardiff" |
| | globalScore | 0.475 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 40 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.997 | | wordCount | 1003 | | matches | | 0 | "not sure I'm capable of being kind back, but I'm trying" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 74 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 25.08 | | std | 22.16 | | cv | 0.884 | | sampleLengths | | 0 | 69 | | 1 | 64 | | 2 | 52 | | 3 | 15 | | 4 | 24 | | 5 | 3 | | 6 | 18 | | 7 | 22 | | 8 | 77 | | 9 | 1 | | 10 | 37 | | 11 | 7 | | 12 | 5 | | 13 | 49 | | 14 | 4 | | 15 | 46 | | 16 | 7 | | 17 | 19 | | 18 | 43 | | 19 | 7 | | 20 | 13 | | 21 | 12 | | 22 | 2 | | 23 | 65 | | 24 | 46 | | 25 | 5 | | 26 | 12 | | 27 | 1 | | 28 | 52 | | 29 | 2 | | 30 | 10 | | 31 | 34 | | 32 | 9 | | 33 | 64 | | 34 | 6 | | 35 | 37 | | 36 | 17 | | 37 | 17 | | 38 | 15 | | 39 | 15 |
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| 98.25% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 50 | | matches | | |
| 11.32% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 106 | | matches | | 0 | "was already sitting" | | 1 | "was pretending" | | 2 | "was listening" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 74 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 635 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.03779527559055118 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.012598425196850394 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 74 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 74 | | mean | 13.55 | | std | 9.1 | | cv | 0.672 | | sampleLengths | | 0 | 31 | | 1 | 14 | | 2 | 24 | | 3 | 10 | | 4 | 21 | | 5 | 21 | | 6 | 4 | | 7 | 8 | | 8 | 16 | | 9 | 15 | | 10 | 21 | | 11 | 15 | | 12 | 24 | | 13 | 3 | | 14 | 18 | | 15 | 6 | | 16 | 9 | | 17 | 7 | | 18 | 24 | | 19 | 30 | | 20 | 8 | | 21 | 15 | | 22 | 1 | | 23 | 9 | | 24 | 11 | | 25 | 17 | | 26 | 7 | | 27 | 5 | | 28 | 7 | | 29 | 14 | | 30 | 28 | | 31 | 4 | | 32 | 30 | | 33 | 16 | | 34 | 7 | | 35 | 10 | | 36 | 9 | | 37 | 23 | | 38 | 10 | | 39 | 10 | | 40 | 7 | | 41 | 4 | | 42 | 9 | | 43 | 12 | | 44 | 2 | | 45 | 52 | | 46 | 13 | | 47 | 9 | | 48 | 10 | | 49 | 27 |
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| 68.92% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.44594594594594594 | | totalSentences | 74 | | uniqueOpeners | 33 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 47 | | matches | | 0 | "Then she saw who was" | | 1 | "Then he limped back toward" |
| | ratio | 0.043 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 10 | | totalSentences | 47 | | matches | | 0 | "Her hair was shorter than" | | 1 | "He glanced up, his hazel" | | 2 | "She nodded her thanks." | | 3 | "Her hair was cropped to" | | 4 | "It was a habit she" | | 5 | "she said instead" | | 6 | "It came out flat, like" | | 7 | "He set one down in" | | 8 | "His voice was low and" | | 9 | "He looked at Rory for" |
| | ratio | 0.213 | |
| 34.47% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 40 | | totalSentences | 47 | | matches | | 0 | "The green neon above the" | | 1 | "Her hair was shorter than" | | 2 | "Silas stood behind the counter," | | 3 | "He glanced up, his hazel" | | 4 | "She nodded her thanks." | | 5 | "Eva had her back to" | | 6 | "Her hair was cropped to" | | 7 | "A charcoal coat hung over" | | 8 | "Rory stood at the end" | | 9 | "Eva said, without turning around" | | 10 | "Rory slid into the seat" | | 11 | "The vinyl was cracked and" | | 12 | "Eva finally looked at her," | | 13 | "The eyes were steady and" | | 14 | "Eva turned the glass slowly" | | 15 | "A thin gold band caught" | | 16 | "Rory opened her mouth, then" | | 17 | "Silas had moved to the" | | 18 | "Eva's mouth twisted" | | 19 | "Rory ran her thumb along" |
| | ratio | 0.851 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 47 | | matches | (empty) | | ratio | 0 | |
| 85.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 2 | | matches | | 0 | "Silas stood behind the counter, polishing a tumbler with the slow care of a man who had nowhere else to be." | | 1 | "Rory wanted to reach across the table, to say something that would make the last two years smaller, but her hands stayed flat on the cold wood." |
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| 79.55% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 1 | | matches | | 0 | "Eva said, without turning around," |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 37 | | tagDensity | 0.135 | | leniency | 0.27 | | rawRatio | 0.2 | | effectiveRatio | 0.054 | |