| 91.89% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 2 | | adverbTags | | 0 | "His accent wrapped around [around]" | | 1 | "The endearment stung like [like]" |
| | dialogueSentences | 37 | | tagDensity | 0.324 | | leniency | 0.649 | | rawRatio | 0.167 | | effectiveRatio | 0.108 | |
| 75.03% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 801 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "really" | | 1 | "slightly" | | 2 | "truly" | | 3 | "very" |
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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) | |
| 62.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 801 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "pulse" | | 1 | "quickened" | | 2 | "otherworldly" | | 3 | "silk" | | 4 | "raced" | | 5 | "intensity" |
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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 | 50 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 50 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 71 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 805 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 94.44% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 14 | | wordCount | 540 | | uniqueNames | 5 | | maxNameDensity | 1.11 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Aurora" | | discoveredNames | | Eva | 2 | | Lucien | 3 | | French | 1 | | Aurora | 6 | | Ptolemy | 2 |
| | persons | | 0 | "Eva" | | 1 | "Lucien" | | 2 | "Aurora" | | 3 | "Ptolemy" |
| | places | (empty) | | globalScore | 0.944 | | windowScore | 1 | |
| 85.90% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 805 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 71 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 24.39 | | std | 13 | | cv | 0.533 | | sampleLengths | | 0 | 42 | | 1 | 3 | | 2 | 14 | | 3 | 48 | | 4 | 14 | | 5 | 40 | | 6 | 25 | | 7 | 17 | | 8 | 9 | | 9 | 19 | | 10 | 21 | | 11 | 27 | | 12 | 44 | | 13 | 13 | | 14 | 6 | | 15 | 30 | | 16 | 20 | | 17 | 38 | | 18 | 26 | | 19 | 41 | | 20 | 20 | | 21 | 48 | | 22 | 15 | | 23 | 12 | | 24 | 25 | | 25 | 27 | | 26 | 22 | | 27 | 53 | | 28 | 20 | | 29 | 23 | | 30 | 22 | | 31 | 7 | | 32 | 14 |
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| 98.25% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 50 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 90 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 71 | | ratio | 0.056 | | matches | | 0 | "The distinctive outline of that cane, the tall lean frame — her pulse quickened as memories flooded back." | | 1 | "\"So you pushed me away instead.\" The hurt was still there, but something else was rising alongside it – understanding, and with it, a dangerous hope." | | 2 | "This close, she could see the demon blood in him more clearly – the slight glow in his amber eye, the too-sharp edge to his teeth." | | 3 | "It wasn't just an invitation for tonight – it was a question about tomorrow, about all the tomorrows after that." |
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| 76.16% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 538 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.06505576208178439 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.022304832713754646 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 71 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 71 | | mean | 11.34 | | std | 6.64 | | cv | 0.586 | | sampleLengths | | 0 | 16 | | 1 | 18 | | 2 | 5 | | 3 | 3 | | 4 | 3 | | 5 | 9 | | 6 | 5 | | 7 | 25 | | 8 | 23 | | 9 | 10 | | 10 | 4 | | 11 | 4 | | 12 | 16 | | 13 | 12 | | 14 | 8 | | 15 | 19 | | 16 | 6 | | 17 | 12 | | 18 | 5 | | 19 | 9 | | 20 | 10 | | 21 | 9 | | 22 | 10 | | 23 | 11 | | 24 | 3 | | 25 | 14 | | 26 | 10 | | 27 | 11 | | 28 | 33 | | 29 | 10 | | 30 | 3 | | 31 | 6 | | 32 | 5 | | 33 | 11 | | 34 | 14 | | 35 | 6 | | 36 | 14 | | 37 | 4 | | 38 | 20 | | 39 | 14 | | 40 | 26 | | 41 | 19 | | 42 | 13 | | 43 | 9 | | 44 | 6 | | 45 | 14 | | 46 | 8 | | 47 | 26 | | 48 | 14 | | 49 | 15 |
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| 82.16% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5352112676056338 | | totalSentences | 71 | | uniqueOpeners | 38 | |
| 70.92% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 47 | | matches | | 0 | "Instead, she found herself stepping" |
| | ratio | 0.021 | |
| 24.26% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 47 | | matches | | 0 | "She shouldn't open the door." | | 1 | "She really shouldn't." | | 2 | "His name came out steadier" | | 3 | "He stood in the narrow" | | 4 | "His accent wrapped around her" | | 5 | "She should say no." | | 6 | "He turned to face her," | | 7 | "His smile faded." | | 8 | "She broke off, unconsciously rubbing" | | 9 | "He took a step closer." | | 10 | "She held her ground, though" | | 11 | "She could smell his cologne" | | 12 | "His free hand came up," | | 13 | "His laugh was low, rich." | | 14 | "She shouldn't forgive him this" | | 15 | "She shouldn't let him back" | | 16 | "she said, reaching up to" | | 17 | "He turned his face into" | | 18 | "His cane clattered to the" | | 19 | "They broke apart only when" |
| | ratio | 0.489 | |
| 23.83% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 41 | | totalSentences | 47 | | matches | | 0 | "Aurora's hand froze on the" | | 1 | "The distinctive outline of that" | | 2 | "She shouldn't open the door." | | 3 | "She really shouldn't." | | 4 | "His name came out steadier" | | 5 | "He stood in the narrow" | | 6 | "The amber eye caught the" | | 7 | "His accent wrapped around her" | | 8 | "She should say no." | | 9 | "The smart thing would be" | | 10 | "Ptolemy immediately twined between Lucien's" | | 11 | "Aurora said, watching him take" | | 12 | "He turned to face her," | | 13 | "A slight smile played at" | | 14 | "The endearment stung like salt" | | 15 | "His smile faded." | | 16 | "Aurora crossed her arms, hating" | | 17 | "She broke off, unconsciously rubbing" | | 18 | "Lucien's voice was soft, dangerous" | | 19 | "He took a step closer." |
| | ratio | 0.872 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 47 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 1 | | matches | | 0 | "He turned his face into her palm, pressing a kiss there that made her shiver." |
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| 83.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 1 | | matches | | 0 | "free hand came up, fingers hovering near her cheek without quite touching" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "she whispered (whisper)" |
| | dialogueSentences | 37 | | tagDensity | 0.081 | | leniency | 0.162 | | rawRatio | 0.333 | | effectiveRatio | 0.054 | |