| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 125 | | tagDensity | 0.088 | | leniency | 0.176 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.80% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2268 | | 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) | |
| 73.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2268 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "weight" | | 1 | "pulsed" | | 2 | "stomach" | | 3 | "silence" | | 4 | "whisper" | | 5 | "lilt" | | 6 | "charm" | | 7 | "flickered" | | 8 | "traced" |
| |
| 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 | 167 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 167 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 281 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 53 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2267 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 52.49% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 71 | | wordCount | 1487 | | uniqueNames | 12 | | maxNameDensity | 1.95 | | worstName | "Aurora" | | maxWindowNameDensity | 3 | | worstWindowName | "Aurora" | | discoveredNames | | Lucien | 25 | | Moreau | 1 | | Aurora | 29 | | Eva | 3 | | Mayfair | 1 | | Brick | 1 | | Lane | 1 | | Malphora | 1 | | Ptolemy | 4 | | French | 1 | | London | 1 | | Rain | 3 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Aurora" | | 3 | "Eva" | | 4 | "Mayfair" | | 5 | "Ptolemy" | | 6 | "Rain" |
| | places | | | globalScore | 0.525 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 110 | | 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 | 2267 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 281 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 207 | | mean | 10.95 | | std | 12.09 | | cv | 1.104 | | sampleLengths | | 0 | 11 | | 1 | 25 | | 2 | 39 | | 3 | 11 | | 4 | 2 | | 5 | 6 | | 6 | 1 | | 7 | 36 | | 8 | 5 | | 9 | 9 | | 10 | 21 | | 11 | 12 | | 12 | 13 | | 13 | 5 | | 14 | 4 | | 15 | 22 | | 16 | 1 | | 17 | 15 | | 18 | 24 | | 19 | 7 | | 20 | 2 | | 21 | 4 | | 22 | 1 | | 23 | 18 | | 24 | 1 | | 25 | 2 | | 26 | 2 | | 27 | 14 | | 28 | 3 | | 29 | 13 | | 30 | 2 | | 31 | 16 | | 32 | 59 | | 33 | 18 | | 34 | 6 | | 35 | 3 | | 36 | 2 | | 37 | 6 | | 38 | 12 | | 39 | 4 | | 40 | 11 | | 41 | 16 | | 42 | 5 | | 43 | 24 | | 44 | 19 | | 45 | 15 | | 46 | 3 | | 47 | 6 | | 48 | 4 | | 49 | 37 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 167 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 241 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 281 | | ratio | 0.004 | | matches | | 0 | "Restaurants, back alleys, illegal auction houses beneath Mayfair—Lucien entered, and people noticed their collars sat crooked." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1495 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 45 | | adverbRatio | 0.030100334448160536 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.002006688963210702 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 281 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 281 | | mean | 8.07 | | std | 7.1 | | cv | 0.879 | | sampleLengths | | 0 | 5 | | 1 | 3 | | 2 | 3 | | 3 | 25 | | 4 | 8 | | 5 | 16 | | 6 | 8 | | 7 | 7 | | 8 | 11 | | 9 | 2 | | 10 | 6 | | 11 | 1 | | 12 | 23 | | 13 | 13 | | 14 | 5 | | 15 | 9 | | 16 | 6 | | 17 | 15 | | 18 | 12 | | 19 | 9 | | 20 | 4 | | 21 | 5 | | 22 | 4 | | 23 | 22 | | 24 | 1 | | 25 | 7 | | 26 | 8 | | 27 | 4 | | 28 | 4 | | 29 | 16 | | 30 | 3 | | 31 | 4 | | 32 | 2 | | 33 | 4 | | 34 | 1 | | 35 | 6 | | 36 | 3 | | 37 | 9 | | 38 | 1 | | 39 | 2 | | 40 | 2 | | 41 | 14 | | 42 | 3 | | 43 | 13 | | 44 | 2 | | 45 | 11 | | 46 | 5 | | 47 | 10 | | 48 | 18 | | 49 | 18 |
| |
| 43.59% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.2597864768683274 | | totalSentences | 281 | | uniqueOpeners | 73 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 154 | | matches | | 0 | "Then the second." | | 1 | "Then the third." | | 2 | "Then the line pulsed." | | 3 | "Then pale gold scales swam" | | 4 | "Somewhere below, a child screamed" | | 5 | "Always gentle with her, even" | | 6 | "Then the black lines withdrew." |
| | ratio | 0.045 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 154 | | matches | | 0 | "He rested both hands over" | | 1 | "Her fingers tightened around the" | | 2 | "He lifted one hand from" | | 3 | "It did not fall." | | 4 | "His mouth moved at one" | | 5 | "His black eye fixed on" | | 6 | "She unhooked the chain, opened" | | 7 | "He walked past her, his" | | 8 | "He had always made rooms" | | 9 | "He placed his cane against" | | 10 | "His knuckles had gone pale" | | 11 | "He slid out of the" | | 12 | "His jaw locked." | | 13 | "She saw the man from" | | 14 | "She saw the moment he" | | 15 | "She had kissed him instead." | | 16 | "Her hand froze over the" | | 17 | "He did not flinch at" | | 18 | "His fingers closed around the" | | 19 | "Her black hair had slipped" |
| | ratio | 0.253 | |
| 47.66% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 127 | | totalSentences | 154 | | matches | | 0 | "The first deadbolt scraped back." | | 1 | "Aurora opened the door with" | | 2 | "Rain darkened the shoulders of" | | 3 | "Water beaded on his slicked-back" | | 4 | "The other held no light" | | 5 | "He rested both hands over" | | 6 | "Her fingers tightened around the" | | 7 | "Ptolemy sprang from the heap" | | 8 | "The cat stared through the" | | 9 | "Lucien looked down at him." | | 10 | "The words landed with no" | | 11 | "That made them worse." | | 12 | "Aurora kept the chain hooked." | | 13 | "He lifted one hand from" | | 14 | "Blood coated the cuff of" | | 15 | "A dark, soaking stain that" | | 16 | "Aurora’s anger stumbled." | | 17 | "It did not fall." | | 18 | "His mouth moved at one" | | 19 | "Something that had once known" |
| | ratio | 0.825 | |
| 32.47% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 154 | | matches | | 0 | "Now his left side dipped" |
| | ratio | 0.006 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 59 | | technicalSentenceCount | 2 | | matches | | 0 | "A dark, soaking stain that had crept from beneath his sleeve and turned the fabric wine-black." | | 1 | "Most people only knew it as a whisper traded in the supernatural markets: Malphora, the woman who walked away from a dead god’s bargain." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 5 | | fancyTags | | 0 | "Aurora snapped (snap)" | | 1 | "Aurora ordered (order)" | | 2 | "he murmured (murmur)" | | 3 | "he continued (continue)" | | 4 | "he agreed (agree)" |
| | dialogueSentences | 125 | | tagDensity | 0.072 | | leniency | 0.144 | | rawRatio | 0.556 | | effectiveRatio | 0.08 | |