| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 2 | | adverbTags | | 0 | "Rory stepped further [further]" | | 1 | "She hesitated then [then]" |
| | dialogueSentences | 48 | | tagDensity | 0.188 | | leniency | 0.375 | | rawRatio | 0.222 | | effectiveRatio | 0.083 | |
| 96.35% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1368 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 48.83% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1368 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "aftermath" | | 1 | "weight" | | 2 | "flickered" | | 3 | "throbbed" | | 4 | "echo" | | 5 | "unreadable" | | 6 | "familiar" | | 7 | "trembled" | | 8 | "stomach" | | 9 | "raced" | | 10 | "silence" | | 11 | "traced" | | 12 | "racing" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "stomach dropped/sank" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 101 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 101 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 140 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 5 | | markdownWords | 11 | | totalWords | 1363 | | ratio | 0.008 | | matches | | 0 | "something" | | 1 | "I can’t do this anymore" | | 2 | "Not like this." | | 3 | "supernatural" | | 4 | "furious" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 79 | | wordCount | 949 | | uniqueNames | 9 | | maxNameDensity | 3.58 | | worstName | "Eva" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 31 | | Eva | 34 | | Lucien | 5 | | Soho | 1 | | Cardiff | 2 | | London | 1 | | Silas | 1 | | Yu-Fei-and | 1 | | Evan | 3 |
| | persons | | 0 | "Rory" | | 1 | "Eva" | | 2 | "Lucien" | | 3 | "Silas" | | 4 | "Evan" |
| | places | | 0 | "Soho" | | 1 | "Cardiff" | | 2 | "London" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1363 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 140 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 27.26 | | std | 19.8 | | cv | 0.726 | | sampleLengths | | 0 | 78 | | 1 | 53 | | 2 | 31 | | 3 | 6 | | 4 | 7 | | 5 | 28 | | 6 | 39 | | 7 | 20 | | 8 | 59 | | 9 | 5 | | 10 | 67 | | 11 | 29 | | 12 | 47 | | 13 | 4 | | 14 | 31 | | 15 | 61 | | 16 | 6 | | 17 | 34 | | 18 | 54 | | 19 | 10 | | 20 | 4 | | 21 | 26 | | 22 | 24 | | 23 | 18 | | 24 | 42 | | 25 | 5 | | 26 | 4 | | 27 | 36 | | 28 | 57 | | 29 | 14 | | 30 | 8 | | 31 | 41 | | 32 | 49 | | 33 | 8 | | 34 | 29 | | 35 | 47 | | 36 | 20 | | 37 | 55 | | 38 | 30 | | 39 | 12 | | 40 | 19 | | 41 | 23 | | 42 | 14 | | 43 | 12 | | 44 | 1 | | 45 | 3 | | 46 | 10 | | 47 | 44 | | 48 | 8 | | 49 | 31 |
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| 98.32% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 101 | | matches | | 0 | "been thrown" | | 1 | "was gone" |
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| 93.62% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 188 | | matches | | 0 | "was lying" | | 1 | "was already racing" | | 2 | "was looking" |
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| 81.63% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 140 | | ratio | 0.021 | | matches | | 0 | "The flat was exactly as Rory remembered—books stacked in precarious towers, scrolls unfurled across the coffee table like the aftermath of some arcane explosion." | | 1 | "The flat was too small for this—too small for the history pressing between them, for the way Eva’s gaze flickered away when Rory looked at her too long." | | 2 | "Eva was a lot of things—brilliant, stubborn, infuriating—but she wasn’t the type to crumble over nothing." |
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| 91.07% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 956 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 48 | | adverbRatio | 0.0502092050209205 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.011506276150627616 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 140 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 140 | | mean | 9.74 | | std | 8.21 | | cv | 0.843 | | sampleLengths | | 0 | 19 | | 1 | 24 | | 2 | 22 | | 3 | 13 | | 4 | 10 | | 5 | 24 | | 6 | 16 | | 7 | 3 | | 8 | 19 | | 9 | 12 | | 10 | 3 | | 11 | 3 | | 12 | 7 | | 13 | 2 | | 14 | 15 | | 15 | 4 | | 16 | 7 | | 17 | 4 | | 18 | 21 | | 19 | 2 | | 20 | 12 | | 21 | 3 | | 22 | 17 | | 23 | 12 | | 24 | 28 | | 25 | 19 | | 26 | 2 | | 27 | 3 | | 28 | 42 | | 29 | 12 | | 30 | 13 | | 31 | 7 | | 32 | 15 | | 33 | 7 | | 34 | 23 | | 35 | 3 | | 36 | 16 | | 37 | 5 | | 38 | 2 | | 39 | 2 | | 40 | 5 | | 41 | 17 | | 42 | 9 | | 43 | 8 | | 44 | 1 | | 45 | 5 | | 46 | 30 | | 47 | 7 | | 48 | 7 | | 49 | 3 |
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| 45.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.2642857142857143 | | totalSentences | 140 | | uniqueOpeners | 37 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 87 | | matches | | 0 | "Of course it was Lucien." | | 1 | "Of course he’d be the" | | 2 | "Of course he’d be the" | | 3 | "Instead, she just nodded." |
| | ratio | 0.046 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 87 | | matches | | 0 | "It was the one Eva" | | 1 | "She stirred sugar into the" | | 2 | "*I can’t do this anymore*," | | 3 | "She’d had to." | | 4 | "She set the mug down" | | 5 | "She’d been careful." | | 6 | "Her breath hitched." | | 7 | "She wanted to yell, to" | | 8 | "She hesitated, then reached out," | | 9 | "She stepped closer, close enough" | | 10 | "Her hand found Rory’s, fingers" | | 11 | "She looked down at their" | | 12 | "She pulled away, suddenly too" | | 13 | "She looked back." | | 14 | "She wanted to say it" | | 15 | "She wanted to cross the" |
| | ratio | 0.184 | |
| 46.21% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 72 | | totalSentences | 87 | | matches | | 0 | "The door swung open before" | | 1 | "The scent of turmeric and" | | 2 | "Eva stood there in an" | | 3 | "Ptolemy wound between her ankles," | | 4 | "Eva said, stepping back to" | | 5 | "The flat was exactly as" | | 6 | "The three deadbolts on the" | | 7 | "Rory exhaled, rolling her shoulders" | | 8 | "Eva didn’t smile." | | 9 | "The cat leapt onto the" | | 10 | "Eva crossed her arms." | | 11 | "Rory knew that tone." | | 12 | "It was the one Eva" | | 13 | "Eva’s jaw tightened." | | 14 | "Rory stepped further inside, the" | | 15 | "The flat was too small" | | 16 | "Rory’s voice was steady, but" | | 17 | "The scar on her wrist" | | 18 | "Eva turned away, moving to" | | 19 | "The kettle whistled as she" |
| | ratio | 0.828 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 87 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 2 | | matches | | 0 | "The only people who knew she was in London were Eva, Silas, Yu-Fei—and Lucien." | | 1 | "And then she was gone, the door clicking shut behind her, the deadbolts sliding into place like the final word in a conversation that wasn’t over." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 3 | | matches | | 0 | "Rory stepped further, the door clicking shut behind her" | | 1 | "Her hand found, fingers intertwining" | | 2 | "Rory turned, but Eva’s voice stopped her" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "Rory admitted (admit)" | | 1 | "Eva whispered (whisper)" |
| | dialogueSentences | 48 | | tagDensity | 0.063 | | leniency | 0.125 | | rawRatio | 0.667 | | effectiveRatio | 0.083 | |