| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 25 | | adverbTagCount | 3 | | adverbTags | | 0 | "Eva said softly [softly]" | | 1 | "Silas asked flatly [flatly]" | | 2 | "Rory said softly [softly]" |
| | dialogueSentences | 61 | | tagDensity | 0.41 | | leniency | 0.82 | | rawRatio | 0.12 | | effectiveRatio | 0.098 | |
| 72.51% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1273 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "slowly" | | 1 | "slightly" | | 2 | "softly" | | 3 | "suddenly" |
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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) | |
| 48.94% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1273 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "weight" | | 1 | "silence" | | 2 | "scanned" | | 3 | "trembled" | | 4 | "shattered" | | 5 | "silk" | | 6 | "echoed" | | 7 | "gloom" | | 8 | "pulse" | | 9 | "calculating" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 71 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 71 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 106 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 5 | | totalWords | 1273 | | ratio | 0.004 | | matches | | 0 | "clack-clack" | | 1 | "Tap, tap, tap." | | 2 | "twist-twist" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 22 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 825 | | uniqueNames | 10 | | maxNameDensity | 2.06 | | worstName | "Eva" | | maxWindowNameDensity | 4 | | worstWindowName | "Eva" | | discoveredNames | | Cardiff | 2 | | Raven | 1 | | Nest | 1 | | Eva | 17 | | Silas | 9 | | Central | 1 | | Europe | 1 | | Welsh | 1 | | London | 1 | | Rory | 16 |
| | persons | | 0 | "Raven" | | 1 | "Eva" | | 2 | "Silas" | | 3 | "Rory" |
| | places | | 0 | "Cardiff" | | 1 | "Europe" | | 2 | "London" |
| | globalScore | 0.47 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like dried blood" |
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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 | 1273 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 106 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 23.15 | | std | 17.04 | | cv | 0.736 | | sampleLengths | | 0 | 7 | | 1 | 26 | | 2 | 3 | | 3 | 64 | | 4 | 3 | | 5 | 10 | | 6 | 59 | | 7 | 15 | | 8 | 17 | | 9 | 32 | | 10 | 25 | | 11 | 32 | | 12 | 5 | | 13 | 13 | | 14 | 33 | | 15 | 6 | | 16 | 40 | | 17 | 8 | | 18 | 5 | | 19 | 3 | | 20 | 5 | | 21 | 5 | | 22 | 25 | | 23 | 6 | | 24 | 54 | | 25 | 33 | | 26 | 15 | | 27 | 3 | | 28 | 3 | | 29 | 32 | | 30 | 13 | | 31 | 42 | | 32 | 1 | | 33 | 42 | | 34 | 34 | | 35 | 7 | | 36 | 19 | | 37 | 8 | | 38 | 54 | | 39 | 4 | | 40 | 38 | | 41 | 17 | | 42 | 31 | | 43 | 33 | | 44 | 35 | | 45 | 47 | | 46 | 12 | | 47 | 44 | | 48 | 18 | | 49 | 30 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 71 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 129 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 106 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 830 | | adjectiveStacks | 2 | | stackExamples | | 0 | "small crescent-shaped scar" | | 1 | "lay limp beneath her" |
| | adverbCount | 23 | | adverbRatio | 0.027710843373493974 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.01566265060240964 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 106 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 106 | | mean | 12.01 | | std | 7.68 | | cv | 0.64 | | sampleLengths | | 0 | 7 | | 1 | 10 | | 2 | 16 | | 3 | 3 | | 4 | 21 | | 5 | 14 | | 6 | 29 | | 7 | 3 | | 8 | 8 | | 9 | 2 | | 10 | 11 | | 11 | 15 | | 12 | 19 | | 13 | 14 | | 14 | 6 | | 15 | 9 | | 16 | 15 | | 17 | 2 | | 18 | 5 | | 19 | 12 | | 20 | 15 | | 21 | 5 | | 22 | 13 | | 23 | 7 | | 24 | 15 | | 25 | 9 | | 26 | 8 | | 27 | 5 | | 28 | 13 | | 29 | 17 | | 30 | 16 | | 31 | 6 | | 32 | 16 | | 33 | 24 | | 34 | 8 | | 35 | 5 | | 36 | 3 | | 37 | 5 | | 38 | 5 | | 39 | 16 | | 40 | 9 | | 41 | 6 | | 42 | 4 | | 43 | 14 | | 44 | 36 | | 45 | 17 | | 46 | 16 | | 47 | 6 | | 48 | 9 | | 49 | 3 |
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| 52.52% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.37735849056603776 | | totalSentences | 106 | | uniqueOpeners | 40 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 62 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 62 | | matches | | 0 | "She turned slowly." | | 1 | "His hazel eyes narrowed beneath" | | 2 | "His limp dragged his left" | | 3 | "His silver signet ring clicked" | | 4 | "His voice grated like gravel" | | 5 | "She took a swift gulp" | | 6 | "She snapped the lid open" | | 7 | "She turned her glass in" | | 8 | "Her dark eyes scanned the" | | 9 | "She set the empty tumbler" | | 10 | "Her fingers trembled, a nervous" | | 11 | "His presence carried the cold" | | 12 | "She reached deeper into her" | | 13 | "She slammed it onto the" | | 14 | "He didn't touch it." | | 15 | "Its high beams cut through" | | 16 | "Her red lips parted, the" | | 17 | "He reached into his coat" |
| | ratio | 0.29 | |
| 0.32% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 57 | | totalSentences | 62 | | matches | | 0 | "Rory froze, the gin bottle" | | 1 | "The rasp in that voice" | | 2 | "She turned slowly." | | 3 | "Eva sat on the corner" | | 4 | "The oversized, paint-splattered jumpers and" | | 5 | "Eva raised two manicured fingers" | | 6 | "His hazel eyes narrowed beneath" | | 7 | "His limp dragged his left" | | 8 | "His silver signet ring clicked" | | 9 | "His voice grated like gravel" | | 10 | "Rory said, pushing the tumbler" | | 11 | "She took a swift gulp" | | 12 | "Rory flexed her left hand." | | 13 | "The small crescent-shaped scar on" | | 14 | "Eva pulled a sleek silver" | | 15 | "She snapped the lid open" | | 16 | "Rory picked up a white" | | 17 | "Eva tapped a manicured nail" | | 18 | "*Tap, tap, tap.*" | | 19 | "Eva's jaw tightened, the smooth" |
| | ratio | 0.919 | |
| 80.65% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 62 | | matches | | 0 | "Before Rory could speak, a" |
| | ratio | 0.016 | |
| 75.89% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 3 | | matches | | 0 | "In their place stood a woman wrapped in a sharp charcoal trench coat, platinum hair cropped tight to her jawline, and dark red lipstick that looked like dried b…" | | 1 | "Her fingers trembled, a nervous tic that shattered her polished mask." | | 2 | "His presence carried the cold weight of a man who had seen too many old debts settled in alleyways." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 25 | | uselessAdditionCount | 7 | | matches | | 0 | "Eva's jaw tightened, the smooth skin beneath her left eye twitching slightly" | | 1 | "Eva said softly, her Welsh accent slipping through her sharp London veneer" | | 2 | "Eva said, her voice rising slightly" | | 3 | "Rory shouted, her cool composure snapping like brittle glass" | | 4 | "Eva stood up, her bar stool scraping violently against the floorboards" | | 5 | "Rory said softly, her pulse spiking in her throat" | | 6 | "the man said, his voice flat as wet slate" |
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| 84.43% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 17 | | fancyCount | 4 | | fancyTags | | 0 | "Eva corrected (correct)" | | 1 | "Eva whispered (whisper)" | | 2 | "Eva hissed (hiss)" | | 3 | "Rory shouted (shout)" |
| | dialogueSentences | 61 | | tagDensity | 0.279 | | leniency | 0.557 | | rawRatio | 0.235 | | effectiveRatio | 0.131 | |