| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 85.95% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1423 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "truly" | | 1 | "slowly" | | 2 | "softly" |
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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) | |
| 40.27% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1423 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "weight" | | 1 | "scanned" | | 2 | "familiar" | | 3 | "glint" | | 4 | "silence" | | 5 | "tracing" | | 6 | "measured" | | 7 | "pulsed" | | 8 | "reminder" | | 9 | "unspoken" | | 10 | "chill" | | 11 | "could feel" |
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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 | 1 | | narrationSentences | 137 | | matches | | |
| 80.29% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 2 | | narrationSentences | 137 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 137 | | 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 | 1421 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 5 | | matches | | 0 | "I have to see him, Mark said, gesturing vaguely toward the back room where the secret meetings happened." | | 1 | "I told you to go, she said quietly." | | 2 | "I found him, Mark said, his voice dropping." | | 3 | "I know, Rory said." | | 4 | "He knows you are safe, Mark said." |
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| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 55 | | wordCount | 1421 | | uniqueNames | 14 | | maxNameDensity | 1.06 | | worstName | "Mark" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Mark" | | discoveredNames | | Soho | 1 | | Raven | 3 | | Nest | 3 | | Mark | 15 | | Cardiff | 3 | | London | 3 | | Blackwood | 1 | | Golden | 1 | | Empress | 1 | | Eva | 1 | | Rory | 10 | | Silas | 6 | | Carter | 1 | | You | 6 |
| | persons | | 0 | "Raven" | | 1 | "Mark" | | 2 | "Blackwood" | | 3 | "Eva" | | 4 | "Rory" | | 5 | "Silas" | | 6 | "Carter" | | 7 | "You" |
| | places | | 0 | "Soho" | | 1 | "Cardiff" | | 2 | "London" |
| | globalScore | 0.972 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 90 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a woman who had learned how t" |
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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 | 1421 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 137 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 46 | | mean | 30.89 | | std | 23.79 | | cv | 0.77 | | sampleLengths | | 0 | 84 | | 1 | 94 | | 2 | 15 | | 3 | 62 | | 4 | 9 | | 5 | 49 | | 6 | 58 | | 7 | 26 | | 8 | 3 | | 9 | 28 | | 10 | 28 | | 11 | 34 | | 12 | 37 | | 13 | 26 | | 14 | 35 | | 15 | 6 | | 16 | 81 | | 17 | 6 | | 18 | 52 | | 19 | 8 | | 20 | 4 | | 21 | 14 | | 22 | 47 | | 23 | 18 | | 24 | 18 | | 25 | 44 | | 26 | 4 | | 27 | 32 | | 28 | 19 | | 29 | 56 | | 30 | 4 | | 31 | 23 | | 32 | 40 | | 33 | 5 | | 34 | 11 | | 35 | 43 | | 36 | 13 | | 37 | 15 | | 38 | 32 | | 39 | 7 | | 40 | 48 | | 41 | 2 | | 42 | 61 | | 43 | 61 | | 44 | 3 | | 45 | 56 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 137 | | matches | | 0 | "were poured" | | 1 | "been saved" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 262 | | matches | | 0 | "were studying" | | 1 | "was waiting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 137 | | ratio | 0.007 | | matches | | 0 | "She stepped around the bar counter, her own delivery uniform—a faded grey t-shirt with the logo of the Golden Empress restaurant—rubbing against the rough oak of the bar." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1425 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.02456140350877193 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.010526315789473684 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 137 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 137 | | mean | 10.37 | | std | 8.48 | | cv | 0.818 | | sampleLengths | | 0 | 29 | | 1 | 22 | | 2 | 33 | | 3 | 19 | | 4 | 16 | | 5 | 27 | | 6 | 19 | | 7 | 13 | | 8 | 6 | | 9 | 9 | | 10 | 23 | | 11 | 24 | | 12 | 4 | | 13 | 1 | | 14 | 3 | | 15 | 7 | | 16 | 9 | | 17 | 12 | | 18 | 29 | | 19 | 8 | | 20 | 6 | | 21 | 3 | | 22 | 24 | | 23 | 25 | | 24 | 1 | | 25 | 8 | | 26 | 17 | | 27 | 3 | | 28 | 28 | | 29 | 18 | | 30 | 10 | | 31 | 2 | | 32 | 15 | | 33 | 13 | | 34 | 4 | | 35 | 3 | | 36 | 28 | | 37 | 3 | | 38 | 3 | | 39 | 13 | | 40 | 6 | | 41 | 2 | | 42 | 5 | | 43 | 5 | | 44 | 22 | | 45 | 3 | | 46 | 3 | | 47 | 2 | | 48 | 6 | | 49 | 24 |
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| 39.05% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.22627737226277372 | | totalSentences | 137 | | uniqueOpeners | 31 | |
| 54.20% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 123 | | matches | | 0 | "At least for now." | | 1 | "Then she turned to the" |
| | ratio | 0.016 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 77 | | totalSentences | 123 | | matches | | 0 | "She shifted her weight, catching" | | 1 | "Her hair, straight and shoulder-length" | | 2 | "Her blue eyes scanned the" | | 3 | "It was a bar where" | | 4 | "He was too thin for" | | 5 | "It was her father." | | 6 | "It was not." | | 7 | "It was Mark, her classmate" | | 8 | "He hadn't been back in" | | 9 | "She tried to hide the" | | 10 | "She had not worn sleeves" | | 11 | "He was older." | | 12 | "His hair was longer, unkempt," | | 13 | "He looked at her, his" | | 14 | "It had a different sound" | | 15 | "It was the sound of" | | 16 | "You came back." | | 17 | "I have to see him," | | 18 | "I have to ask him" | | 19 | "He owned the room." |
| | ratio | 0.626 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 114 | | totalSentences | 123 | | matches | | 0 | "The rain in Soho did" | | 1 | "The neon sign above the" | | 2 | "Aurora stood near the edge" | | 3 | "She shifted her weight, catching" | | 4 | "Her hair, straight and shoulder-length" | | 5 | "Her blue eyes scanned the" | | 6 | "It was a bar where" | | 7 | "The back room door was" | | 8 | "A man sat there, his" | | 9 | "He was too thin for" | | 10 | "The recognition did not hit" | | 11 | "It was her father." | | 12 | "It was not." | | 13 | "It was Mark, her classmate" | | 14 | "He hadn't been back in" | | 15 | "Rory turned and walked to" | | 16 | "She tried to hide the" | | 17 | "She had not worn sleeves" | | 18 | "Mark stood up as she" | | 19 | "He was older." |
| | ratio | 0.927 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 123 | | matches | (empty) | | ratio | 0 | |
| 68.45% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 5 | | matches | | 0 | "The rain in Soho did not fall so much as it pressed, a heavy, grey curtain that blurred the world outside The Raven’s Nest into watercolors of indeterminate sha…" | | 1 | "Her hair, straight and shoulder-length black, felt heavier than usual against her neck, a consequence of the humidity that had followed her out of the delivery …" | | 2 | "He was too thin for the jacket he wore, the grey fabric straining against a spine that had taken hits from this life." | | 3 | "She had seen him in the back room, the way he moved with a limp that made no sound, the way the silver signet ring on his right hand caught the light." | | 4 | "She walked past the bar, past the maps, past the back room where the heavy door remained ajar just a fraction of an inch, revealing a dark silhouette that did n…" |
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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 | |