| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 2 | | adverbTags | | 0 | "Lucien stepped back [back]" | | 1 | "Lucien turned back [back]" |
| | dialogueSentences | 70 | | tagDensity | 0.243 | | leniency | 0.486 | | rawRatio | 0.118 | | effectiveRatio | 0.057 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1535 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 67.43% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1535 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "navigated" | | 1 | "unreadable" | | 2 | "silence" | | 3 | "weight" | | 4 | "roaring" | | 5 | "could feel" | | 6 | "predator" | | 7 | "stark" | | 8 | "warmth" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 141 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 141 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 194 | | 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 | 1535 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 1075 | | uniqueNames | 6 | | maxNameDensity | 2.05 | | worstName | "Lucien" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Lucien" | | discoveredNames | | Moreau | 1 | | London | 1 | | Lucien | 22 | | Aurora | 15 | | Eva | 1 | | Avaros | 1 |
| | persons | | 0 | "Moreau" | | 1 | "Lucien" | | 2 | "Aurora" | | 3 | "Eva" |
| | places | | | globalScore | 0.477 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 78 | | 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 | 1535 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 194 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 89 | | mean | 17.25 | | std | 15.78 | | cv | 0.915 | | sampleLengths | | 0 | 54 | | 1 | 4 | | 2 | 48 | | 3 | 7 | | 4 | 20 | | 5 | 43 | | 6 | 6 | | 7 | 45 | | 8 | 21 | | 9 | 43 | | 10 | 12 | | 11 | 56 | | 12 | 4 | | 13 | 4 | | 14 | 54 | | 15 | 15 | | 16 | 5 | | 17 | 47 | | 18 | 12 | | 19 | 4 | | 20 | 2 | | 21 | 6 | | 22 | 49 | | 23 | 9 | | 24 | 4 | | 25 | 5 | | 26 | 12 | | 27 | 55 | | 28 | 10 | | 29 | 41 | | 30 | 3 | | 31 | 3 | | 32 | 36 | | 33 | 14 | | 34 | 24 | | 35 | 11 | | 36 | 6 | | 37 | 4 | | 38 | 28 | | 39 | 8 | | 40 | 56 | | 41 | 4 | | 42 | 22 | | 43 | 25 | | 44 | 4 | | 45 | 6 | | 46 | 17 | | 47 | 12 | | 48 | 17 | | 49 | 17 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 141 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 194 | | matches | | 0 | "was quieting" | | 1 | "was packing" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 194 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1078 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.01948051948051948 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0027829313543599257 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 194 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 194 | | mean | 7.91 | | std | 5.01 | | cv | 0.634 | | sampleLengths | | 0 | 5 | | 1 | 9 | | 2 | 9 | | 3 | 8 | | 4 | 5 | | 5 | 18 | | 6 | 4 | | 7 | 18 | | 8 | 20 | | 9 | 10 | | 10 | 7 | | 11 | 8 | | 12 | 12 | | 13 | 10 | | 14 | 13 | | 15 | 20 | | 16 | 6 | | 17 | 9 | | 18 | 14 | | 19 | 6 | | 20 | 11 | | 21 | 5 | | 22 | 8 | | 23 | 13 | | 24 | 6 | | 25 | 16 | | 26 | 21 | | 27 | 9 | | 28 | 3 | | 29 | 2 | | 30 | 14 | | 31 | 26 | | 32 | 14 | | 33 | 4 | | 34 | 4 | | 35 | 5 | | 36 | 13 | | 37 | 10 | | 38 | 16 | | 39 | 10 | | 40 | 9 | | 41 | 6 | | 42 | 5 | | 43 | 5 | | 44 | 15 | | 45 | 10 | | 46 | 17 | | 47 | 6 | | 48 | 6 | | 49 | 4 |
| |
| 34.02% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 31 | | diversityRatio | 0.14432989690721648 | | totalSentences | 194 | | uniqueOpeners | 28 | |
| 25.84% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 129 | | matches | | 0 | "Only then did she reach" |
| | ratio | 0.008 | |
| 12.25% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 67 | | totalSentences | 129 | | matches | | 0 | "She slid the second bolt" | | 1 | "She peered through the peephole." | | 2 | "She opened the door." | | 3 | "He held his ivory-handled cane" | | 4 | "His eyes, one amber and" | | 5 | "He pushed past her before" | | 6 | "He navigated the clutter of" | | 7 | "He reached out a gloved" | | 8 | "She leaned her back against" | | 9 | "He placed the cane against" | | 10 | "He walked to the small" | | 11 | "He picked up a pen," | | 12 | "He stopped twirling the pen." | | 13 | "She remembered the way his" | | 14 | "She remembered the blood on" | | 15 | "She walked toward the kitchenette," | | 16 | "She filled a kettle at" | | 17 | "She turned around." | | 18 | "He knew this place." | | 19 | "He knew where Eva kept" |
| | ratio | 0.519 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 127 | | totalSentences | 129 | | matches | | 0 | "Aurora turned the first deadbolt." | | 1 | "The metal latch clicked with" | | 2 | "She slid the second bolt" | | 3 | "She peered through the peephole." | | 4 | "The distorted fisheye view showed" | | 5 | "She opened the door." | | 6 | "Lucien Moreau stood on the" | | 7 | "He held his ivory-handled cane" | | 8 | "His eyes, one amber and" | | 9 | "Aurora stepped back, letting the" | | 10 | "He pushed past her before" | | 11 | "The scent of expensive cologne" | | 12 | "He navigated the clutter of" | | 13 | "Ptolemy, the tabby cat, wound" | | 14 | "The cat hissed, then rubbed" | | 15 | "Lucien looked down, his expression" | | 16 | "He reached out a gloved" | | 17 | "The movement was slow, deliberate." | | 18 | "Aurora closed the door behind" | | 19 | "She leaned her back against" |
| | ratio | 0.984 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 129 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 70 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0.1 | | effectiveRatio | 0.029 | |