| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 1 | | adverbTags | | 0 | "He stepped back [back]" |
| | dialogueSentences | 65 | | tagDensity | 0.185 | | leniency | 0.369 | | rawRatio | 0.083 | | effectiveRatio | 0.031 | |
| 72.31% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1264 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "precisely" | | 1 | "very" | | 2 | "slightly" | | 3 | "quickly" | | 4 | "suddenly" | | 5 | "completely" | | 6 | "sharply" |
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| 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) | |
| 56.49% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1264 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "familiar" | | 1 | "intensity" | | 2 | "stomach" | | 3 | "calculating" | | 4 | "pulse" | | 5 | "charm" | | 6 | "chilled" | | 7 | "potential" | | 8 | "throbbed" | | 9 | "unreadable" | | 10 | "whisper" |
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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 | 0 | | narrationSentences | 74 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 74 | | filterMatches | (empty) | | hedgeMatches | | 0 | "seemed to" | | 1 | "started to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 125 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1258 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 797 | | uniqueNames | 15 | | maxNameDensity | 1.38 | | worstName | "Lucien" | | maxWindowNameDensity | 3 | | worstWindowName | "Eva" | | discoveredNames | | Eva | 9 | | Lucien | 11 | | Moreau | 1 | | Southwark | 2 | | Aurora | 11 | | Brick | 1 | | Lane | 1 | | Damn | 1 | | English | 1 | | Yu-Fei | 1 | | Courts | 1 | | British | 1 | | Library | 1 | | Rory | 1 | | Three | 4 |
| | persons | | 0 | "Eva" | | 1 | "Lucien" | | 2 | "Moreau" | | 3 | "Aurora" | | 4 | "Yu-Fei" | | 5 | "Rory" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "British" | | 3 | "Library" |
| | globalScore | 0.81 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | glossingSentenceCount | 1 | | matches | | 0 | "Apparently she'd underestimate" |
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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 | 1258 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 125 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 67 | | mean | 18.78 | | std | 16.29 | | cv | 0.868 | | sampleLengths | | 0 | 8 | | 1 | 54 | | 2 | 5 | | 3 | 3 | | 4 | 29 | | 5 | 55 | | 6 | 4 | | 7 | 6 | | 8 | 67 | | 9 | 13 | | 10 | 3 | | 11 | 11 | | 12 | 18 | | 13 | 47 | | 14 | 17 | | 15 | 11 | | 16 | 34 | | 17 | 30 | | 18 | 3 | | 19 | 30 | | 20 | 6 | | 21 | 30 | | 22 | 59 | | 23 | 12 | | 24 | 4 | | 25 | 24 | | 26 | 3 | | 27 | 31 | | 28 | 43 | | 29 | 8 | | 30 | 5 | | 31 | 35 | | 32 | 5 | | 33 | 51 | | 34 | 7 | | 35 | 4 | | 36 | 24 | | 37 | 4 | | 38 | 42 | | 39 | 26 | | 40 | 5 | | 41 | 2 | | 42 | 26 | | 43 | 12 | | 44 | 20 | | 45 | 39 | | 46 | 5 | | 47 | 40 | | 48 | 24 | | 49 | 2 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 74 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 149 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 125 | | ratio | 0 | | matches | (empty) | |
| 90.10% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 799 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 41 | | adverbRatio | 0.05131414267834793 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.017521902377972465 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 125 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 125 | | mean | 10.06 | | std | 6.93 | | cv | 0.689 | | sampleLengths | | 0 | 5 | | 1 | 1 | | 2 | 1 | | 3 | 1 | | 4 | 13 | | 5 | 19 | | 6 | 22 | | 7 | 5 | | 8 | 3 | | 9 | 15 | | 10 | 12 | | 11 | 2 | | 12 | 7 | | 13 | 2 | | 14 | 22 | | 15 | 24 | | 16 | 4 | | 17 | 6 | | 18 | 6 | | 19 | 7 | | 20 | 6 | | 21 | 17 | | 22 | 15 | | 23 | 16 | | 24 | 11 | | 25 | 2 | | 26 | 3 | | 27 | 11 | | 28 | 8 | | 29 | 3 | | 30 | 7 | | 31 | 12 | | 32 | 3 | | 33 | 17 | | 34 | 15 | | 35 | 17 | | 36 | 4 | | 37 | 7 | | 38 | 20 | | 39 | 14 | | 40 | 1 | | 41 | 17 | | 42 | 12 | | 43 | 3 | | 44 | 11 | | 45 | 19 | | 46 | 6 | | 47 | 24 | | 48 | 6 | | 49 | 5 |
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| 77.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.496 | | totalSentences | 125 | | uniqueOpeners | 62 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 67 | | matches | | 0 | "Instead, Lucien Moreau stood in" | | 1 | "Just in case." | | 2 | "Apparently she'd underestimated both Yu-Fei's" |
| | ratio | 0.045 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 67 | | matches | | 0 | "His mismatched eyes caught the" | | 1 | "He stepped closer to the" | | 2 | "Her chest tightened, but she" | | 3 | "He turned to face her" | | 4 | "He moved to the window" | | 5 | "She'd thought the restaurant owner's" | | 6 | "He faced her again, amber" | | 7 | "Her name on his lips." | | 8 | "He moved closer, close enough" | | 9 | "His voice dropped, accent thickening" | | 10 | "He moved with predatory grace" | | 11 | "His free hand rose as" | | 12 | "He stepped back, the careful" | | 13 | "He flipped through the papers," | | 14 | "He looked up sharply" | | 15 | "She never knocked, just announced" |
| | ratio | 0.239 | |
| 71.94% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 67 | | matches | | 0 | "The deadbolts clicked in sequence." | | 1 | "Aurora opened the door expecting" | | 2 | "His mismatched eyes caught the" | | 3 | "He stepped closer to the" | | 4 | "The ivory handle of his" | | 5 | "Aurora's grip tightened on the" | | 6 | "The endearment hit like a" | | 7 | "Her chest tightened, but she" | | 8 | "Lucien moved past her into" | | 9 | "Books and research notes covered" | | 10 | "Ptolemy, Eva's tabby, emerged from" | | 11 | "Lucien's cane tapped against a" | | 12 | "Aurora closed the door but" | | 13 | "He turned to face her" | | 14 | "The scar on her left" | | 15 | "Ice flooded her veins." | | 16 | "He moved to the window" | | 17 | "She'd thought the restaurant owner's" | | 18 | "Lucien's reflection in the window" | | 19 | "He faced her again, amber" |
| | ratio | 0.776 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 67 | | matches | | 0 | "Now he stood here like" | | 1 | "If they thought she posed" |
| | ratio | 0.03 | |
| 91.84% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 2 | | matches | | 0 | "His mismatched eyes caught the hallway's flickering light, amber and black studying her with that familiar intensity that made her stomach twist." | | 1 | "He moved closer, close enough that she caught the scent of his cologne mixing with something else, something that reminded her of warm skin and tangled sheets." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 3 | | matches | | 0 | "He stepped back, the careful mask sliding into place again" | | 1 | "He flipped, expression growing more serious with each page" | | 2 | "Lucien closed, expression unreadable" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |