| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.72% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1525 | | 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) | |
| 83.61% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1525 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "gloom" | | 1 | "silence" | | 2 | "flickered" | | 3 | "pulse" |
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| 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 | 83 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 83 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 127 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 54 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1525 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 935 | | uniqueNames | 11 | | maxNameDensity | 1.39 | | worstName | "Lucien" | | maxWindowNameDensity | 3 | | worstWindowName | "Lucien" | | discoveredNames | | Rory | 9 | | Brick | 1 | | Lane | 1 | | Ptolemy | 3 | | Lucien | 13 | | Eva | 4 | | Evan | 3 | | French | 1 | | Cardiff | 1 | | Bengali | 1 | | Moreau | 1 |
| | persons | | 0 | "Rory" | | 1 | "Ptolemy" | | 2 | "Lucien" | | 3 | "Eva" | | 4 | "Evan" | | 5 | "Moreau" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "Cardiff" | | 3 | "Bengali" |
| | globalScore | 0.805 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | 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 | 1525 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 127 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 87 | | mean | 17.53 | | std | 15.5 | | cv | 0.884 | | sampleLengths | | 0 | 13 | | 1 | 65 | | 2 | 9 | | 3 | 18 | | 4 | 1 | | 5 | 17 | | 6 | 10 | | 7 | 1 | | 8 | 1 | | 9 | 22 | | 10 | 17 | | 11 | 14 | | 12 | 46 | | 13 | 21 | | 14 | 19 | | 15 | 10 | | 16 | 9 | | 17 | 48 | | 18 | 12 | | 19 | 28 | | 20 | 5 | | 21 | 31 | | 22 | 2 | | 23 | 3 | | 24 | 25 | | 25 | 19 | | 26 | 8 | | 27 | 32 | | 28 | 47 | | 29 | 28 | | 30 | 2 | | 31 | 20 | | 32 | 17 | | 33 | 22 | | 34 | 4 | | 35 | 2 | | 36 | 2 | | 37 | 13 | | 38 | 20 | | 39 | 24 | | 40 | 5 | | 41 | 11 | | 42 | 22 | | 43 | 27 | | 44 | 6 | | 45 | 13 | | 46 | 28 | | 47 | 18 | | 48 | 81 | | 49 | 8 |
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| 96.81% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 83 | | matches | | 0 | "was, arched" | | 1 | "were thrown" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 153 | | matches | | |
| 52.87% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 4 | | flaggedSentences | 4 | | totalSentences | 127 | | ratio | 0.031 | | matches | | 0 | "One amber eye caught the weak bulb; the other, black as a drowned star, drank the light." | | 1 | "The bulb overhead flickered; she knew the difference between bad wiring and a lie." | | 2 | "The door swung shut; she threw the top bolt, then the middle, then the bottom, each clunk a small defiance." | | 3 | "The amber eye glittered; the black one stayed flat and endless." |
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| 93.96% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 938 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 44 | | adverbRatio | 0.046908315565031986 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0021321961620469083 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 127 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 127 | | mean | 12.01 | | std | 10.37 | | cv | 0.864 | | sampleLengths | | 0 | 13 | | 1 | 20 | | 2 | 24 | | 3 | 4 | | 4 | 17 | | 5 | 3 | | 6 | 6 | | 7 | 18 | | 8 | 1 | | 9 | 17 | | 10 | 10 | | 11 | 1 | | 12 | 1 | | 13 | 6 | | 14 | 16 | | 15 | 17 | | 16 | 14 | | 17 | 6 | | 18 | 40 | | 19 | 11 | | 20 | 10 | | 21 | 19 | | 22 | 10 | | 23 | 9 | | 24 | 13 | | 25 | 19 | | 26 | 16 | | 27 | 12 | | 28 | 4 | | 29 | 6 | | 30 | 18 | | 31 | 5 | | 32 | 31 | | 33 | 2 | | 34 | 3 | | 35 | 25 | | 36 | 5 | | 37 | 14 | | 38 | 8 | | 39 | 19 | | 40 | 3 | | 41 | 10 | | 42 | 47 | | 43 | 16 | | 44 | 12 | | 45 | 2 | | 46 | 7 | | 47 | 13 | | 48 | 17 | | 49 | 22 |
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| 49.61% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.33070866141732286 | | totalSentences | 127 | | uniqueOpeners | 42 | |
| 83.33% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 80 | | matches | | 0 | "Of course he saw." | | 1 | "Somewhere below, a wok clanged" |
| | ratio | 0.025 | |
| 75.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 80 | | matches | | 0 | "He hadn't changed." | | 1 | "She made the syllable a" | | 2 | "His gaze dropped to her" | | 3 | "His knuckles flexed on the" | | 4 | "She didn't invite him in," | | 5 | "Her spine stayed straight, shoulders" | | 6 | "She hated that." | | 7 | "She hated that he knew" | | 8 | "He lifted the cane and" | | 9 | "She didn't miss the way" | | 10 | "Her mouth went dry." | | 11 | "He reached into his jacket." | | 12 | "Her gaze tracked every inch" | | 13 | "He held out a folded" | | 14 | "She didn't take it." | | 15 | "Her hand had closed around" | | 16 | "He'd never once lied to" | | 17 | "He'd simply decided which pieces" | | 18 | "She should have slammed the" | | 19 | "Her fingers itched to." |
| | ratio | 0.363 | |
| 22.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 80 | | matches | | 0 | "The third deadbolt gave a" | | 1 | "Lucien stood in the grimy" | | 2 | "Platinum hair slicked back." | | 3 | "He hadn't changed." | | 4 | "That was the hell of" | | 5 | "Rory tightened her grip on" | | 6 | "The word left her before" | | 7 | "Lucien's mouth curved, but the" | | 8 | "She made the syllable a" | | 9 | "His gaze dropped to her" | | 10 | "His knuckles flexed on the" | | 11 | "The flat behind her held" | | 12 | "A tabby shadow slid around" | | 13 | "Ptolemy, traitor that he was," | | 14 | "Lucien bent enough to scratch" | | 15 | "Books stacked in leaning towers," | | 16 | "The place smelled of old" | | 17 | "Rory didn't step back." | | 18 | "She didn't invite him in," | | 19 | "Her spine stayed straight, shoulders" |
| | ratio | 0.875 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 80 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 2 | | matches | | 0 | "His gaze dropped to her mouth, then rose with a drag that scraped her nerve endings." | | 1 | "The deadbolts were thrown, the cat was hiding, and Lucien Moreau stood in Eva's cluttered hallway with his cane and his secrets and the same mouth that had ruin…" |
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| 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 | |