| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1458 | | 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) | |
| 96.57% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1458 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 112 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 112 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 130 | | 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 | 1458 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 56.10% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 65 | | wordCount | 1065 | | uniqueNames | 14 | | maxNameDensity | 1.88 | | worstName | "Lucien" | | maxWindowNameDensity | 3 | | worstWindowName | "Lucien" | | discoveredNames | | Lucien | 20 | | Moreau | 1 | | Eva | 5 | | Brick | 2 | | Lane | 2 | | Rory | 15 | | Marseille | 1 | | Cardiff | 1 | | London | 2 | | Golden | 1 | | Empress | 1 | | Ptolemy | 8 | | Water | 3 | | Steam | 3 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Eva" | | 3 | "Rory" | | 4 | "Ptolemy" | | 5 | "Water" | | 6 | "Steam" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "Marseille" | | 3 | "Cardiff" | | 4 | "London" |
| | globalScore | 0.561 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 91 | | 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 | 1458 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 130 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 45.56 | | std | 29.79 | | cv | 0.654 | | sampleLengths | | 0 | 115 | | 1 | 6 | | 2 | 92 | | 3 | 25 | | 4 | 76 | | 5 | 34 | | 6 | 75 | | 7 | 29 | | 8 | 53 | | 9 | 26 | | 10 | 83 | | 11 | 17 | | 12 | 74 | | 13 | 23 | | 14 | 38 | | 15 | 22 | | 16 | 68 | | 17 | 20 | | 18 | 67 | | 19 | 34 | | 20 | 66 | | 21 | 26 | | 22 | 17 | | 23 | 87 | | 24 | 27 | | 25 | 16 | | 26 | 85 | | 27 | 15 | | 28 | 21 | | 29 | 86 | | 30 | 13 | | 31 | 22 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 112 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 163 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 130 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1068 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.010299625468164793 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 130 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 130 | | mean | 11.22 | | std | 6.38 | | cv | 0.569 | | sampleLengths | | 0 | 16 | | 1 | 10 | | 2 | 12 | | 3 | 10 | | 4 | 6 | | 5 | 4 | | 6 | 10 | | 7 | 18 | | 8 | 5 | | 9 | 8 | | 10 | 16 | | 11 | 6 | | 12 | 7 | | 13 | 10 | | 14 | 18 | | 15 | 18 | | 16 | 8 | | 17 | 6 | | 18 | 14 | | 19 | 11 | | 20 | 25 | | 21 | 15 | | 22 | 8 | | 23 | 13 | | 24 | 7 | | 25 | 18 | | 26 | 6 | | 27 | 9 | | 28 | 34 | | 29 | 8 | | 30 | 18 | | 31 | 5 | | 32 | 5 | | 33 | 4 | | 34 | 19 | | 35 | 6 | | 36 | 10 | | 37 | 29 | | 38 | 4 | | 39 | 9 | | 40 | 7 | | 41 | 7 | | 42 | 7 | | 43 | 6 | | 44 | 6 | | 45 | 7 | | 46 | 26 | | 47 | 9 | | 48 | 14 | | 49 | 5 |
| |
| 44.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3153846153846154 | | totalSentences | 130 | | uniqueOpeners | 41 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 112 | | matches | (empty) | | ratio | 0 | |
| 80.71% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 112 | | matches | | 0 | "His ivory-handled cane tapped the" | | 1 | "Her straight black hair dripped" | | 2 | "Her bright blue eyes met" | | 3 | "Her left wrist rested against" | | 4 | "She had cleaned that scar" | | 5 | "It hammered the curry house" | | 6 | "It hammered the hood of" | | 7 | "His platinum hair fell loose" | | 8 | "His tailored suit clung to" | | 9 | "He held the other hand" | | 10 | "He stepped over the mat" | | 11 | "He laid his cane across" | | 12 | "He kept his hands in" | | 13 | "He kept his eyes on" | | 14 | "She cleared a patch with" | | 15 | "His coat dripped onto the" | | 16 | "His blood dripped with it." | | 17 | "Her jaw tightened." | | 18 | "She pressed her palm flat" | | 19 | "He did not wipe it." |
| | ratio | 0.348 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 108 | | totalSentences | 112 | | matches | | 0 | "The door opened on Lucien" | | 1 | "Water ran from his charcoal" | | 2 | "His ivory-handled cane tapped the" | | 3 | "Curry smoke rose through the" | | 4 | "Books leaned in towers behind" | | 5 | "Scrolls filled the shelves." | | 6 | "Ptolemy, Eva's tabby, shot past" | | 7 | "Rory gripped the door edge." | | 8 | "Her straight black hair dripped" | | 9 | "Her bright blue eyes met" | | 10 | "Rory kept her shoulder in" | | 11 | "The third deadbolt had taken" | | 12 | "Her left wrist rested against" | | 13 | "She had cleaned that scar" | | 14 | "London rain hammered the awning" | | 15 | "It hammered the curry house" | | 16 | "It hammered the hood of" | | 17 | "Lucien lifted his cane and" | | 18 | "Water dripped from his chin" | | 19 | "His platinum hair fell loose" |
| | ratio | 0.964 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 112 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 1 | | matches | | 0 | "He kept his eyes on Ptolemy, who had changed tactics and rubbed his head against Lucien's ankle." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |