| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1333 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 32.48% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1333 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "electric" | | 1 | "vibrated" | | 2 | "chaotic" | | 3 | "silence" | | 4 | "scanning" | | 5 | "sanctuary" | | 6 | "warmth" | | 7 | "lilt" | | 8 | "pristine" | | 9 | "weight" | | 10 | "glinting" | | 11 | "throbbed" | | 12 | "reminder" | | 13 | "flicker" | | 14 | "traced" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "weight of words/silence" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
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| | highlights | | 0 | "the weight of the moment" | | 1 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 139 | | matches | | 0 | "was afraid" | | 1 | "felt the sting" |
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| 81.19% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 1 | | narrationSentences | 139 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 139 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1333 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 7 | | matches | | 0 | "I live above it, Aurora said." | | 1 | "Come sit, Eva said." | | 2 | "You look well, Aurora said." | | 3 | "I am well, Eva replied." | | 4 | "I heard you got a job at the firm, Aurora said." | | 5 | "I had to change, Aurora said." | | 6 | "She was always like that, Silas said." |
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| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 77 | | wordCount | 1333 | | uniqueNames | 13 | | maxNameDensity | 1.73 | | worstName | "Aurora" | | maxWindowNameDensity | 4 | | worstWindowName | "Eva" | | discoveredNames | | Aurora | 23 | | Soho | 1 | | Raven | 1 | | Nest | 2 | | London | 3 | | Evan | 1 | | Cardiff | 3 | | Silas | 8 | | Eva | 23 | | Prague | 1 | | Associate | 1 | | Rory | 2 | | You | 8 |
| | persons | | 0 | "Aurora" | | 1 | "Raven" | | 2 | "Evan" | | 3 | "Silas" | | 4 | "Eva" | | 5 | "Rory" | | 6 | "You" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Cardiff" | | 3 | "Prague" | | 4 | "Associate" |
| | globalScore | 0.637 | | windowScore | 0.333 | |
| 95.05% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 91 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like a ghost from a different life" | | 1 | "looked like she was afraid of nothing, or" |
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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 | 1333 | | matches | (empty) | |
| 94.72% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 139 | | matches | | 0 | "knew that voice" | | 1 | "like that, Silas" | | 2 | "forgets that people" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 26.14 | | std | 25.37 | | cv | 0.971 | | sampleLengths | | 0 | 88 | | 1 | 78 | | 2 | 61 | | 3 | 78 | | 4 | 14 | | 5 | 1 | | 6 | 24 | | 7 | 64 | | 8 | 15 | | 9 | 23 | | 10 | 14 | | 11 | 20 | | 12 | 62 | | 13 | 11 | | 14 | 25 | | 15 | 85 | | 16 | 5 | | 17 | 46 | | 18 | 7 | | 19 | 7 | | 20 | 49 | | 21 | 11 | | 22 | 2 | | 23 | 4 | | 24 | 27 | | 25 | 4 | | 26 | 9 | | 27 | 35 | | 28 | 6 | | 29 | 7 | | 30 | 3 | | 31 | 35 | | 32 | 63 | | 33 | 18 | | 34 | 32 | | 35 | 6 | | 36 | 4 | | 37 | 31 | | 38 | 8 | | 39 | 2 | | 40 | 15 | | 41 | 50 | | 42 | 54 | | 43 | 22 | | 44 | 7 | | 45 | 1 | | 46 | 17 | | 47 | 9 | | 48 | 2 | | 49 | 2 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 139 | | matches | (empty) | |
| 94.18% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 252 | | matches | | 0 | "was wearing" | | 1 | "was, sitting" | | 2 | "was watching" | | 3 | "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 | 139 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1335 | | adjectiveStacks | 2 | | stackExamples | | 0 | "usual grey-streaked auburn" | | 1 | "small crescent-shaped scar" |
| | adverbCount | 32 | | adverbRatio | 0.02397003745318352 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.00599250936329588 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 139 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 139 | | mean | 9.59 | | std | 7.23 | | cv | 0.754 | | sampleLengths | | 0 | 24 | | 1 | 32 | | 2 | 12 | | 3 | 20 | | 4 | 16 | | 5 | 3 | | 6 | 24 | | 7 | 10 | | 8 | 25 | | 9 | 13 | | 10 | 10 | | 11 | 15 | | 12 | 12 | | 13 | 11 | | 14 | 9 | | 15 | 13 | | 16 | 11 | | 17 | 14 | | 18 | 16 | | 19 | 15 | | 20 | 14 | | 21 | 1 | | 22 | 2 | | 23 | 4 | | 24 | 15 | | 25 | 3 | | 26 | 26 | | 27 | 3 | | 28 | 35 | | 29 | 3 | | 30 | 12 | | 31 | 8 | | 32 | 15 | | 33 | 6 | | 34 | 8 | | 35 | 8 | | 36 | 2 | | 37 | 7 | | 38 | 3 | | 39 | 13 | | 40 | 9 | | 41 | 16 | | 42 | 24 | | 43 | 4 | | 44 | 7 | | 45 | 2 | | 46 | 14 | | 47 | 4 | | 48 | 5 | | 49 | 12 |
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| 33.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 23 | | diversityRatio | 0.20863309352517986 | | totalSentences | 139 | | uniqueOpeners | 29 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 122 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 78 | | totalSentences | 122 | | matches | | 0 | "It was a sound she" | | 1 | "She pushed inside, the bell" | | 2 | "She was tired." | | 3 | "Her legs ached from six" | | 4 | "She moved toward the bar," | | 5 | "It was usually her sanctuary," | | 6 | "He looked up, his hazel" | | 7 | "He wore his usual grey-streaked" | | 8 | "He did not smile, but" | | 9 | "He was the only constant" | | 10 | "She needed a whiskey, something" | | 11 | "She turned to the mirror" | | 12 | "Her eyes were bright blue," | | 13 | "She caught the small crescent-shaped" | | 14 | "She rubbed her thumb over" | | 15 | "She was about to speak" | | 16 | "She knew that voice." | | 17 | "It belonged to a time" | | 18 | "She turned slowly." | | 19 | "It was Eva." |
| | ratio | 0.639 | |
| 17.38% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 108 | | totalSentences | 122 | | matches | | 0 | "The green neon sign above" | | 1 | "It was a sound she" | | 2 | "She pushed inside, the bell" | | 3 | "The air inside was thick" | | 4 | "Aurora shook the damp from" | | 5 | "She was tired." | | 6 | "Her legs ached from six" | | 7 | "She moved toward the bar," | | 8 | "It was usually her sanctuary," | | 9 | "Silas was behind the counter," | | 10 | "He looked up, his hazel" | | 11 | "He wore his usual grey-streaked" | | 12 | "He did not smile, but" | | 13 | "He was the only constant" | | 14 | "Aurora reached the bar and" | | 15 | "She needed a whiskey, something" | | 16 | "She turned to the mirror" | | 17 | "Her eyes were bright blue," | | 18 | "She caught the small crescent-shaped" | | 19 | "She rubbed her thumb over" |
| | ratio | 0.885 | |
| 81.97% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 122 | | matches | | 0 | "Now, Eva looked like she" | | 1 | "To see if you were" |
| | ratio | 0.016 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 3 | | matches | | 0 | "The green neon sign above the door buzzed with a low, electric hum that vibrated in Aurora’s teeth before she even pushed the handle." | | 1 | "But the Eva from her memory was wearing a coat that cost more than Aurora’s monthly rent, her hair styled in a sharp, severe bob that framed a face devoid of th…" | | 2 | "He glanced at Eva, his gaze lingering a fraction of a second too long, before moving away to attend to the door as a new customer entered." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |