| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 41 | | tagDensity | 0.317 | | leniency | 0.634 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.93% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 993 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 89.93% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 993 | | totalAiIsms | 2 | | 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 | 28 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 28 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 56 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 56 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 995 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 8 | | wordCount | 612 | | uniqueNames | 5 | | maxNameDensity | 0.33 | | worstName | "Aurora" | | maxWindowNameDensity | 1 | | worstWindowName | "Lucien" | | discoveredNames | | Aurora | 2 | | Lucien | 2 | | Ptolemy | 2 | | Eva | 1 | | Silas | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Lucien" | | 2 | "Ptolemy" | | 3 | "Eva" | | 4 | "Silas" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 30.95% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 21 | | glossingSentenceCount | 1 | | matches | | 0 | "seemed warmer than it had any right to be and the black seemed less like an absence and more like a door" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 995 | | matches | (empty) | |
| 47.62% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 56 | | matches | | 0 | "chose that moment" | | 1 | "doing that thing" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 25.51 | | std | 21.3 | | cv | 0.835 | | sampleLengths | | 0 | 41 | | 1 | 34 | | 2 | 14 | | 3 | 8 | | 4 | 49 | | 5 | 34 | | 6 | 7 | | 7 | 26 | | 8 | 31 | | 9 | 55 | | 10 | 5 | | 11 | 4 | | 12 | 40 | | 13 | 4 | | 14 | 3 | | 15 | 33 | | 16 | 14 | | 17 | 7 | | 18 | 65 | | 19 | 3 | | 20 | 63 | | 21 | 4 | | 22 | 25 | | 23 | 1 | | 24 | 4 | | 25 | 10 | | 26 | 44 | | 27 | 48 | | 28 | 8 | | 29 | 35 | | 30 | 44 | | 31 | 7 | | 32 | 86 | | 33 | 4 | | 34 | 38 | | 35 | 32 | | 36 | 46 | | 37 | 4 | | 38 | 15 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 28 | | matches | (empty) | |
| 78.79% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 110 | | matches | | 0 | "was closing" | | 1 | "was standing" |
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| 91.84% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 56 | | ratio | 0.018 | | matches | | 0 | "The three deadbolts took Aurora longer to work through than she wanted to admit, mostly because her hands weren't steady after hearing that particular knock — two short, one long, a pause, then one more." |
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| 83.61% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 613 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.05872756933115824 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.011419249592169658 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 56 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 56 | | mean | 17.77 | | std | 14.83 | | cv | 0.835 | | sampleLengths | | 0 | 35 | | 1 | 6 | | 2 | 34 | | 3 | 10 | | 4 | 4 | | 5 | 8 | | 6 | 25 | | 7 | 24 | | 8 | 33 | | 9 | 1 | | 10 | 7 | | 11 | 14 | | 12 | 12 | | 13 | 31 | | 14 | 55 | | 15 | 3 | | 16 | 2 | | 17 | 4 | | 18 | 27 | | 19 | 13 | | 20 | 4 | | 21 | 3 | | 22 | 33 | | 23 | 14 | | 24 | 7 | | 25 | 51 | | 26 | 14 | | 27 | 3 | | 28 | 28 | | 29 | 35 | | 30 | 4 | | 31 | 6 | | 32 | 19 | | 33 | 1 | | 34 | 4 | | 35 | 10 | | 36 | 24 | | 37 | 20 | | 38 | 22 | | 39 | 26 | | 40 | 8 | | 41 | 3 | | 42 | 30 | | 43 | 2 | | 44 | 44 | | 45 | 7 | | 46 | 38 | | 47 | 48 | | 48 | 4 | | 49 | 38 |
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| 80.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5178571428571429 | | totalSentences | 56 | | uniqueOpeners | 29 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 22 | | matches | | 0 | "Then it faded, and something" |
| | ratio | 0.045 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 22 | | matches | | 0 | "She opened the door and" | | 1 | "he said, glancing at the" | | 2 | "His amber eye moved over" | | 3 | "He said her name like" | | 4 | "She stepped back from the" | | 5 | "He walked in like he" | | 6 | "He set his cane against" | | 7 | "His jaw tightened, just slightly," | | 8 | "She crossed her arms, aware" | | 9 | "He said it quietly, and" | | 10 | "He didn't look away" | | 11 | "He almost smiled, the corner" | | 12 | "he stopped, exhaled through his" | | 13 | "He picked his cane back" | | 14 | "He looked at her for" |
| | ratio | 0.682 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 21 | | totalSentences | 22 | | matches | | 0 | "The three deadbolts took Aurora" | | 1 | "Nobody knocked like that except" | | 2 | "She opened the door and" | | 3 | "he said, glancing at the" | | 4 | "His amber eye moved over" | | 5 | "Ptolemy chose that moment to" | | 6 | "He said her name like" | | 7 | "She stepped back from the" | | 8 | "He walked in like he" | | 9 | "He set his cane against" | | 10 | "His jaw tightened, just slightly," | | 11 | "She crossed her arms, aware" | | 12 | "He said it quietly, and" | | 13 | "The bar closed, Silas long" | | 14 | "He didn't look away" | | 15 | "He almost smiled, the corner" | | 16 | "he stopped, exhaled through his" | | 17 | "The flat was too quiet" | | 18 | "He picked his cane back" | | 19 | "He looked at her for" |
| | ratio | 0.955 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 22 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 13 | | technicalSentenceCount | 4 | | matches | | 0 | "Ptolemy chose that moment to slink between her ankles and out into the hallway, sniffing at Lucien's shoes with the mild interest of a creature who'd met him be…" | | 1 | "He walked in like he owned the place, the way he walked into everything, and she watched him take in the scrolls stacked on every surface, the notes pinned croo…" | | 2 | "He almost smiled, the corner of his mouth twitching in that infuriating way that used to make her forgive him things she shouldn't have." | | 3 | "Aurora felt the old pull of it, the same gravity that had dragged her toward him a hundred times before common sense could intervene, and hated herself a little…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 41 | | tagDensity | 0.171 | | leniency | 0.341 | | rawRatio | 0.143 | | effectiveRatio | 0.049 | |