| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 144 | | tagDensity | 0.146 | | leniency | 0.292 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 98.03% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2535 | | 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) | |
| 80.28% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2535 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "absolutely" | | 1 | "warmth" | | 2 | "familiar" | | 3 | "tracing" | | 4 | "charm" | | 5 | "silence" | | 6 | "charged" | | 7 | "eyebrow" |
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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 | 217 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 217 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 338 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2535 | | ratio | 0 | | matches | (empty) | |
| 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 | 106 | | wordCount | 1708 | | uniqueNames | 8 | | maxNameDensity | 2.4 | | worstName | "Aurora" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Aurora" | | discoveredNames | | Aurora | 41 | | Moreau | 1 | | London | 1 | | Lucien | 28 | | Eva | 30 | | Bengali | 1 | | Carter | 1 | | Blood | 3 |
| | persons | | 0 | "Aurora" | | 1 | "Moreau" | | 2 | "Lucien" | | 3 | "Eva" | | 4 | "Carter" | | 5 | "Blood" |
| | places | | | globalScore | 0.3 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 118 | | 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 | 2535 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 338 | | matches | | 0 | "touch that scar" | | 1 | "hated that the" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 183 | | mean | 13.85 | | std | 13.98 | | cv | 1.009 | | sampleLengths | | 0 | 25 | | 1 | 23 | | 2 | 12 | | 3 | 6 | | 4 | 80 | | 5 | 9 | | 6 | 13 | | 7 | 3 | | 8 | 23 | | 9 | 38 | | 10 | 21 | | 11 | 3 | | 12 | 12 | | 13 | 12 | | 14 | 12 | | 15 | 3 | | 16 | 12 | | 17 | 12 | | 18 | 17 | | 19 | 15 | | 20 | 4 | | 21 | 10 | | 22 | 34 | | 23 | 3 | | 24 | 6 | | 25 | 70 | | 26 | 12 | | 27 | 38 | | 28 | 7 | | 29 | 18 | | 30 | 6 | | 31 | 10 | | 32 | 13 | | 33 | 14 | | 34 | 3 | | 35 | 51 | | 36 | 7 | | 37 | 38 | | 38 | 7 | | 39 | 14 | | 40 | 16 | | 41 | 14 | | 42 | 8 | | 43 | 37 | | 44 | 5 | | 45 | 33 | | 46 | 20 | | 47 | 9 | | 48 | 7 | | 49 | 33 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 217 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 320 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 338 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1702 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 68 | | adverbRatio | 0.0399529964747356 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.004112808460634548 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 338 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 338 | | mean | 7.5 | | std | 6.24 | | cv | 0.833 | | sampleLengths | | 0 | 11 | | 1 | 14 | | 2 | 23 | | 3 | 12 | | 4 | 6 | | 5 | 21 | | 6 | 11 | | 7 | 7 | | 8 | 14 | | 9 | 14 | | 10 | 8 | | 11 | 5 | | 12 | 9 | | 13 | 13 | | 14 | 3 | | 15 | 18 | | 16 | 5 | | 17 | 24 | | 18 | 14 | | 19 | 13 | | 20 | 8 | | 21 | 3 | | 22 | 7 | | 23 | 5 | | 24 | 8 | | 25 | 4 | | 26 | 7 | | 27 | 5 | | 28 | 3 | | 29 | 9 | | 30 | 3 | | 31 | 2 | | 32 | 10 | | 33 | 3 | | 34 | 14 | | 35 | 5 | | 36 | 10 | | 37 | 4 | | 38 | 10 | | 39 | 3 | | 40 | 19 | | 41 | 4 | | 42 | 8 | | 43 | 3 | | 44 | 6 | | 45 | 16 | | 46 | 7 | | 47 | 19 | | 48 | 9 | | 49 | 10 |
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| 47.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.257396449704142 | | totalSentences | 338 | | uniqueOpeners | 87 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 9 | | totalSentences | 192 | | matches | | 0 | "Then, from the other room," | | 1 | "Somewhere downstairs a pan hit" | | 2 | "Too much air." | | 3 | "Too much room for old" | | 4 | "Then a fury so clean" | | 5 | "Instead she felt sick." | | 6 | "Just the thing itself, placed" | | 7 | "Even injured, he looked dangerous." | | 8 | "Instead she watched Lucien lift" |
| | ratio | 0.047 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 55 | | totalSentences | 192 | | matches | | 0 | "She pulled the door open" | | 1 | "He always looked as if" | | 2 | "His hair lay slicked back," | | 3 | "She shoved the door wider" | | 4 | "She looked down." | | 5 | "He stepped across the threshold" | | 6 | "She shut the door with" | | 7 | "She took in Lucien, then" | | 8 | "He used to touch that" | | 9 | "She tucked her hand under" | | 10 | "He met her stare without" | | 11 | "She had come ready for" | | 12 | "She hated that the switch" | | 13 | "She caught his sleeve and" | | 14 | "He sat with stiffness hidden" | | 15 | "His amber eye held hers." | | 16 | "He drew a breath, then" | | 17 | "She looked up so fast" | | 18 | "He sucked in a breath" | | 19 | "She pressed harder." |
| | ratio | 0.286 | |
| 27.71% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 166 | | totalSentences | 192 | | matches | | 0 | "The first deadbolt scraped back," | | 1 | "Metal clicked in a chain" | | 2 | "She pulled the door open" | | 3 | "The rest snagged in her" | | 4 | "Lucien Moreau stood in the" | | 5 | "The other held nothing, which" | | 6 | "He always looked as if" | | 7 | "His hair lay slicked back," | | 8 | "The other swallowed it whole." | | 9 | "Ptolemy darted between Aurora’s ankles" | | 10 | "Aurora tightened her grip on" | | 11 | "Lucien’s gaze slid over her" | | 12 | "She shoved the door wider" | | 13 | "A corner of his mouth" | | 14 | "A mug clinked against wood." | | 15 | "Eva’s voice carried out from" | | 16 | "Aurora didn’t take her eyes" | | 17 | "Lucien dipped his chin past" | | 18 | "Aurora almost laughed." | | 19 | "The sound cracked at the" |
| | ratio | 0.865 | |
| 26.04% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 192 | | matches | | 0 | "Even quiet, he pulled the" |
| | ratio | 0.005 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 61 | | technicalSentenceCount | 2 | | matches | | 0 | "He always looked as if he carried half of London’s secrets in his pockets." | | 1 | "Lucien sat bent over Eva’s ugly floral mug as if it contained state secrets." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 4 | | fancyTags | | 0 | "Eva repeated (repeat)" | | 1 | "Eva snapped (snap)" | | 2 | "Aurora muttered (mutter)" | | 3 | "Aurora snapped (snap)" |
| | dialogueSentences | 144 | | tagDensity | 0.097 | | leniency | 0.194 | | rawRatio | 0.286 | | effectiveRatio | 0.056 | |