| 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 | 1614 | | 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) | |
| 84.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1614 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "weight" | | 1 | "flicked" | | 2 | "footsteps" |
| |
| 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 | 84 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 84 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 175 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 21 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1614 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 57.61% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 920 | | uniqueNames | 4 | | maxNameDensity | 1.85 | | worstName | "Lucien" | | maxWindowNameDensity | 3 | | worstWindowName | "Lucien" | | discoveredNames | | Lucien | 17 | | Moreau | 1 | | Rory | 16 | | Ptolemy | 10 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Rory" | | 3 | "Ptolemy" |
| | places | (empty) | | globalScore | 0.576 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | 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 | 1614 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 175 | | matches | (empty) | |
| 84.48% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 169 | | mean | 9.55 | | std | 4.26 | | cv | 0.446 | | sampleLengths | | 0 | 20 | | 1 | 16 | | 2 | 28 | | 3 | 14 | | 4 | 3 | | 5 | 16 | | 6 | 28 | | 7 | 6 | | 8 | 21 | | 9 | 9 | | 10 | 10 | | 11 | 7 | | 12 | 9 | | 13 | 6 | | 14 | 7 | | 15 | 13 | | 16 | 7 | | 17 | 8 | | 18 | 8 | | 19 | 4 | | 20 | 12 | | 21 | 16 | | 22 | 11 | | 23 | 1 | | 24 | 14 | | 25 | 10 | | 26 | 8 | | 27 | 4 | | 28 | 7 | | 29 | 6 | | 30 | 12 | | 31 | 12 | | 32 | 13 | | 33 | 21 | | 34 | 7 | | 35 | 15 | | 36 | 5 | | 37 | 8 | | 38 | 16 | | 39 | 11 | | 40 | 4 | | 41 | 10 | | 42 | 9 | | 43 | 6 | | 44 | 9 | | 45 | 12 | | 46 | 5 | | 47 | 7 | | 48 | 9 | | 49 | 1 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 84 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 160 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 175 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 920 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 4 | | adverbRatio | 0.004347826086956522 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 175 | | echoCount | 0 | | echoWords | (empty) | |
| 98.54% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 175 | | mean | 9.22 | | std | 3.66 | | cv | 0.396 | | sampleLengths | | 0 | 20 | | 1 | 16 | | 2 | 12 | | 3 | 7 | | 4 | 4 | | 5 | 5 | | 6 | 14 | | 7 | 3 | | 8 | 16 | | 9 | 5 | | 10 | 9 | | 11 | 14 | | 12 | 6 | | 13 | 11 | | 14 | 10 | | 15 | 9 | | 16 | 10 | | 17 | 7 | | 18 | 9 | | 19 | 6 | | 20 | 7 | | 21 | 13 | | 22 | 7 | | 23 | 8 | | 24 | 8 | | 25 | 4 | | 26 | 12 | | 27 | 16 | | 28 | 11 | | 29 | 1 | | 30 | 14 | | 31 | 10 | | 32 | 8 | | 33 | 4 | | 34 | 7 | | 35 | 6 | | 36 | 12 | | 37 | 12 | | 38 | 13 | | 39 | 21 | | 40 | 7 | | 41 | 15 | | 42 | 5 | | 43 | 8 | | 44 | 16 | | 45 | 11 | | 46 | 4 | | 47 | 10 | | 48 | 9 | | 49 | 6 |
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| 44.86% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.26285714285714284 | | totalSentences | 175 | | uniqueOpeners | 46 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 84 | | matches | (empty) | | ratio | 0 | |
| 53.33% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 84 | | matches | | 0 | "She pulled the door open" | | 1 | "His cane rested across his" | | 2 | "He shifted his weight and" | | 3 | "Her shoulder blocked the gap" | | 4 | "She kept her blue eyes" | | 5 | "He placed his cane tip" | | 6 | "She shook her head and" | | 7 | "His amber eye narrowed." | | 8 | "She glanced at the pinned" | | 9 | "He lifted his cane and" | | 10 | "She tugged her sleeve over" | | 11 | "He gave a short nod" | | 12 | "She folded her arms across" | | 13 | "His black eye flicked to" | | 14 | "She remained on the flat" | | 15 | "He leaned close enough for" | | 16 | "Her back hit the doorframe" | | 17 | "She scooped Ptolemy into her" | | 18 | "She shifted the cat to" | | 19 | "She looked over her shoulder" |
| | ratio | 0.417 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 82 | | totalSentences | 84 | | matches | | 0 | "She pulled the door open" | | 1 | "Rain dripped from the hem" | | 2 | "His cane rested across his" | | 3 | "Platinum hair stuck wet to" | | 4 | "The other eye stayed black." | | 5 | "Ptolemy wedged his tabby head" | | 6 | "Lucien tapped the ivory handle" | | 7 | "Eva's books filled every surface." | | 8 | "Scrolls tied with red thread" | | 9 | "Rory gripped the edge of" | | 10 | "The crescent scar on her" | | 11 | "He shifted his weight and" | | 12 | "Her shoulder blocked the gap" | | 13 | "She kept her blue eyes" | | 14 | "Ptolemy gave Lucien's ankle a" | | 15 | "He placed his cane tip" | | 16 | "Rory planted her feet on" | | 17 | "She shook her head and" | | 18 | "His amber eye narrowed." | | 19 | "Ptolemy jumped onto a stack" |
| | ratio | 0.976 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 84 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 63 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |