| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 2 | | adverbTags | | 0 | "Lucien corrected smoothly [smoothly]" | | 1 | "His voice cracked like [like]" |
| | dialogueSentences | 41 | | tagDensity | 0.415 | | leniency | 0.829 | | rawRatio | 0.118 | | effectiveRatio | 0.098 | |
| 91.66% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1199 | | totalAiIsmAdverbs | 2 | | 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) | |
| 41.62% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1199 | | totalAiIsms | 14 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | word | "down my spine" | | count | 1 |
| | 11 | | | 12 | | | 13 | |
| | highlights | | 0 | "pristine" | | 1 | "velvet" | | 2 | "predator" | | 3 | "flicked" | | 4 | "flickered" | | 5 | "pulse" | | 6 | "dancing" | | 7 | "rhythmic" | | 8 | "silk" | | 9 | "chill" | | 10 | "down my spine" | | 11 | "traced" | | 12 | "echoed" | | 13 | "gleaming" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 65 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 65 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 89 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1199 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 18 | | wordCount | 814 | | uniqueNames | 9 | | maxNameDensity | 0.98 | | worstName | "Lucien" | | maxWindowNameDensity | 2 | | worstWindowName | "Lucien" | | discoveredNames | | Eva | 2 | | Brick | 1 | | Lane | 1 | | Old | 1 | | Bailey | 1 | | Ptolemy | 2 | | Lucien | 8 | | Avaros | 1 | | London | 1 |
| | persons | | 0 | "Eva" | | 1 | "Old" | | 2 | "Bailey" | | 3 | "Ptolemy" | | 4 | "Lucien" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | 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 | 1199 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 89 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 47 | | mean | 25.51 | | std | 20.63 | | cv | 0.809 | | sampleLengths | | 0 | 27 | | 1 | 9 | | 2 | 26 | | 3 | 58 | | 4 | 3 | | 5 | 2 | | 6 | 6 | | 7 | 79 | | 8 | 29 | | 9 | 21 | | 10 | 31 | | 11 | 10 | | 12 | 36 | | 13 | 1 | | 14 | 51 | | 15 | 8 | | 16 | 4 | | 17 | 10 | | 18 | 44 | | 19 | 6 | | 20 | 3 | | 21 | 42 | | 22 | 53 | | 23 | 9 | | 24 | 2 | | 25 | 61 | | 26 | 21 | | 27 | 5 | | 28 | 11 | | 29 | 73 | | 30 | 18 | | 31 | 8 | | 32 | 11 | | 33 | 52 | | 34 | 9 | | 35 | 20 | | 36 | 34 | | 37 | 28 | | 38 | 64 | | 39 | 45 | | 40 | 14 | | 41 | 42 | | 42 | 16 | | 43 | 13 | | 44 | 40 | | 45 | 27 | | 46 | 17 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 65 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 122 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 89 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 821 | | adjectiveStacks | 2 | | stackExamples | | 0 | "small crescent-shaped scar" | | 1 | "narrow, gleaming silver steel" |
| | adverbCount | 29 | | adverbRatio | 0.03532277710109622 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.010962241169305725 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 89 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 89 | | mean | 13.47 | | std | 8.06 | | cv | 0.599 | | sampleLengths | | 0 | 27 | | 1 | 9 | | 2 | 26 | | 3 | 8 | | 4 | 30 | | 5 | 20 | | 6 | 3 | | 7 | 2 | | 8 | 6 | | 9 | 12 | | 10 | 19 | | 11 | 16 | | 12 | 32 | | 13 | 10 | | 14 | 19 | | 15 | 7 | | 16 | 14 | | 17 | 11 | | 18 | 20 | | 19 | 10 | | 20 | 12 | | 21 | 22 | | 22 | 2 | | 23 | 1 | | 24 | 19 | | 25 | 32 | | 26 | 8 | | 27 | 4 | | 28 | 10 | | 29 | 5 | | 30 | 20 | | 31 | 6 | | 32 | 13 | | 33 | 6 | | 34 | 3 | | 35 | 15 | | 36 | 27 | | 37 | 12 | | 38 | 11 | | 39 | 13 | | 40 | 17 | | 41 | 4 | | 42 | 5 | | 43 | 2 | | 44 | 13 | | 45 | 18 | | 46 | 7 | | 47 | 14 | | 48 | 1 | | 49 | 8 |
| |
| 52.81% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3707865168539326 | | totalSentences | 89 | | uniqueOpeners | 33 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 56 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 56 | | matches | | 0 | "His polished leather oxfords clicked" | | 1 | "He swept a critical glance" | | 2 | "He leaned his cane against" | | 3 | "He looked entirely too pristine" | | 4 | "My fingers tightened around the" | | 5 | "My arm tensed, ready to" | | 6 | "He flicked the latch open" | | 7 | "His voice cracked like a" | | 8 | "He closed the distance between" | | 9 | "His hand, cold and immaculately" | | 10 | "His thumb pressed directly over" | | 11 | "My voice did not shake." | | 12 | "I jerked my hand up," | | 13 | "He pinned my hands against" | | 14 | "I felt the solid, rhythmic" | | 15 | "His breath carried the sharp" | | 16 | "He leaned down, his mouth" | | 17 | "I pulled against his grip," | | 18 | "I breathed, forcing my gaze" | | 19 | "He tilted his head, his" |
| | ratio | 0.554 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 56 | | matches | | 0 | "The third deadbolt rattled in" | | 1 | "Lucien stepped across the threshold" | | 2 | "His polished leather oxfords clicked" | | 3 | "He swept a critical glance" | | 4 | "He leaned his cane against" | | 5 | "The amber eye caught the" | | 6 | "Platinum blond hair, slicked back" | | 7 | "He looked entirely too pristine" | | 8 | "My fingers tightened around the" | | 9 | "Lucien corrected smoothly" | | 10 | "Ptolemy padded out from beneath" | | 11 | "The ginger tabby sniffed the" | | 12 | "Lucien reached into his breast" | | 13 | "My arm tensed, ready to" | | 14 | "He flicked the latch open" | | 15 | "The room went dead silent." | | 16 | "The faint rumble of double-decker" | | 17 | "Lucien took one slow step" | | 18 | "The ivory handle of his" | | 19 | "His voice cracked like a" |
| | ratio | 0.946 | |
| 89.29% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 56 | | matches | | 0 | "Before I could speak, a" |
| | ratio | 0.018 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 2 | | matches | | 0 | "He looked entirely too pristine for a cramped flat on Brick Lane, entirely too alive for a man who had vanished after leaving me in the ruins of the Old Bailey …" | | 1 | "He stepped close again, his dark eye swallowing the light around us, his hand reaching out to touch the curve of my cheek." |
| |
| 95.59% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 1 | | matches | | 0 | "I whispered, my heart slamming against my ribs" |
| |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 7 | | fancyTags | | 0 | "he murmured (murmur)" | | 1 | "Lucien corrected smoothly (correct)" | | 2 | "he whispered (whisper)" | | 3 | "he muttered (mutter)" | | 4 | "I breathed (breathe)" | | 5 | "I whispered (whisper)" | | 6 | "he snarled (snarl)" |
| | dialogueSentences | 41 | | tagDensity | 0.293 | | leniency | 0.585 | | rawRatio | 0.583 | | effectiveRatio | 0.341 | |