| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 27 | | adverbTagCount | 3 | | adverbTags | | 0 | "he said quietly [quietly]" | | 1 | "she said abruptly [abruptly]" | | 2 | "she said finally [finally]" |
| | dialogueSentences | 62 | | tagDensity | 0.435 | | leniency | 0.871 | | rawRatio | 0.111 | | effectiveRatio | 0.097 | |
| 85.31% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2382 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "suddenly" | | 1 | "really" | | 2 | "quickly" | | 3 | "very" |
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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) | |
| 64.32% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2382 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "flickered" | | 1 | "shattered" | | 2 | "warmth" | | 3 | "scanned" | | 4 | "weight" | | 5 | "familiar" | | 6 | "pulse" | | 7 | "traced" | | 8 | "silence" | | 9 | "stomach" | | 10 | "encounter" | | 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 | 169 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 169 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 205 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2382 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 20 | | unquotedAttributions | 1 | | matches | | 0 | "Old instincts, she guessed." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1561 | | uniqueNames | 15 | | maxNameDensity | 0.83 | | worstName | "Silas" | | maxWindowNameDensity | 2 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Carter | 1 | | Golden | 1 | | Empress | 1 | | Cardiff | 1 | | Blackwood | 2 | | Prague | 1 | | Silas | 13 | | Rory | 9 | | Evan | 2 | | Spymaster | 1 | | Men | 1 | | London | 2 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Carter" | | 3 | "Empress" | | 4 | "Blackwood" | | 5 | "Silas" | | 6 | "Rory" | | 7 | "Evan" |
| | places | | 0 | "Soho" | | 1 | "Cardiff" | | 2 | "Prague" | | 3 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 44.96% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 119 | | glossingSentenceCount | 5 | | matches | | 0 | "something like a smile" | | 1 | "seemed smaller suddenly, the maps on the walls closing in like witnesses" | | 2 | "felt like a stone dropping into still w" | | 3 | "not quite touching" | | 4 | "felt like bridging a chasm years in the" |
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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 | 2382 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 205 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 64 | | mean | 37.22 | | std | 21.1 | | cv | 0.567 | | sampleLengths | | 0 | 99 | | 1 | 93 | | 2 | 22 | | 3 | 64 | | 4 | 90 | | 5 | 18 | | 6 | 15 | | 7 | 13 | | 8 | 27 | | 9 | 22 | | 10 | 18 | | 11 | 34 | | 12 | 25 | | 13 | 18 | | 14 | 33 | | 15 | 59 | | 16 | 7 | | 17 | 54 | | 18 | 45 | | 19 | 28 | | 20 | 61 | | 21 | 37 | | 22 | 15 | | 23 | 46 | | 24 | 41 | | 25 | 61 | | 26 | 18 | | 27 | 52 | | 28 | 35 | | 29 | 62 | | 30 | 32 | | 31 | 7 | | 32 | 29 | | 33 | 28 | | 34 | 21 | | 35 | 25 | | 36 | 54 | | 37 | 27 | | 38 | 35 | | 39 | 40 | | 40 | 58 | | 41 | 4 | | 42 | 48 | | 43 | 57 | | 44 | 16 | | 45 | 21 | | 46 | 31 | | 47 | 55 | | 48 | 7 | | 49 | 30 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 169 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 261 | | matches | | 0 | "wasn’t listening" | | 1 | "was lying" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 205 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1211 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 46 | | adverbRatio | 0.037985136251032205 | | lyAdverbCount | 17 | | lyAdverbRatio | 0.014037985136251032 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 205 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 205 | | mean | 11.62 | | std | 7.87 | | cv | 0.678 | | sampleLengths | | 0 | 17 | | 1 | 9 | | 2 | 27 | | 3 | 17 | | 4 | 15 | | 5 | 14 | | 6 | 13 | | 7 | 19 | | 8 | 9 | | 9 | 11 | | 10 | 19 | | 11 | 4 | | 12 | 18 | | 13 | 16 | | 14 | 6 | | 15 | 20 | | 16 | 16 | | 17 | 12 | | 18 | 16 | | 19 | 4 | | 20 | 11 | | 21 | 28 | | 22 | 21 | | 23 | 7 | | 24 | 9 | | 25 | 10 | | 26 | 5 | | 27 | 13 | | 28 | 12 | | 29 | 3 | | 30 | 9 | | 31 | 4 | | 32 | 5 | | 33 | 18 | | 34 | 4 | | 35 | 10 | | 36 | 5 | | 37 | 7 | | 38 | 12 | | 39 | 6 | | 40 | 3 | | 41 | 29 | | 42 | 2 | | 43 | 12 | | 44 | 13 | | 45 | 14 | | 46 | 4 | | 47 | 18 | | 48 | 15 | | 49 | 15 |
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| 49.27% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.32195121951219513 | | totalSentences | 205 | | uniqueOpeners | 66 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 161 | | matches | | 0 | "Instead it delivered rain and" | | 1 | "Then one day he simply" | | 2 | "Instead he turned the glass" | | 3 | "Just for a second." | | 4 | "Then the mask of the" | | 5 | "Then at his face." |
| | ratio | 0.037 | |
| 68.45% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 61 | | totalSentences | 161 | | matches | | 0 | "She shook rain from her" | | 1 | "She had only meant to" | | 2 | "She ordered a gin and" | | 3 | "His auburn hair had gone" | | 4 | "He looked up, hazel eyes" | | 5 | "She had not seen him" | | 6 | "He had taught her how" | | 7 | "Her father muttered something about" | | 8 | "He set his glass down." | | 9 | "he said, voice low and" | | 10 | "She forced her mouth into" | | 11 | "He gestured to the stool" | | 12 | "She hesitated only a second" | | 13 | "She wrapped cold fingers around" | | 14 | "she said, because it seemed" | | 15 | "His eyebrows rose." | | 16 | "He studied her face, cataloguing" | | 17 | "She took a sip, the" | | 18 | "He had carried her inside," | | 19 | "he said quietly" |
| | ratio | 0.379 | |
| 28.32% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 139 | | totalSentences | 161 | | matches | | 0 | "The green neon glow of" | | 1 | "Rain slicked the pavement, turning" | | 2 | "Aurora Carter pushed through the" | | 3 | "The warmth hit her first," | | 4 | "She shook rain from her" | | 5 | "She had only meant to" | | 6 | "Yu-Fei’s Golden Empress closed at" | | 7 | "The weight of the day" | | 8 | "Pre-law textbooks gathered dust in" | | 9 | "Evan’s shadow had thinned over" | | 10 | "London had promised escape." | | 11 | "She ordered a gin and" | | 12 | "That was when she saw" | | 13 | "Silas Blackwood sat at the" | | 14 | "His auburn hair had gone" | | 15 | "The silver signet ring caught" | | 16 | "He looked up, hazel eyes" | | 17 | "Rory’s pulse kicked hard." | | 18 | "She had not seen him" | | 19 | "He had taught her how" |
| | ratio | 0.863 | |
| 31.06% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 161 | | matches | | 0 | "To finish her drink and" |
| | ratio | 0.006 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 2 | | matches | | 0 | "Back then he had been her father’s old university friend, the one who appeared at family dinners with stories that made her mother laugh and her father frown." | | 1 | "The lines around his eyes that spoke of too many early mornings and late nights and decisions that carved grooves into a man’s soul." |
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| 87.96% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 27 | | uselessAdditionCount | 2 | | matches | | 0 | "he said, voice low and rough as gravel under tyres" | | 1 | "She rolled, the scar pulling tight" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 2 | | fancyTags | | 0 | "he had (have)" | | 1 | "she whispered (whisper)" |
| | dialogueSentences | 62 | | tagDensity | 0.177 | | leniency | 0.355 | | rawRatio | 0.182 | | effectiveRatio | 0.065 | |