| 51.85% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 2 | | adverbTags | | 0 | "Aurora said shortly [shortly]" | | 1 | "Aurora said defiantly [defiantly]" |
| | dialogueSentences | 27 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0.167 | | effectiveRatio | 0.148 | |
| 80.84% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 783 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "perfectly" | | 1 | "suddenly" | | 2 | "gently" |
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| 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) | |
| 68.07% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 783 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "could feel" | | 1 | "trembled" | | 2 | "pulse" | | 3 | "race" |
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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 | 42 | | matches | (empty) | |
| 40.82% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 42 | | filterMatches | (empty) | | hedgeMatches | | 0 | "tried to" | | 1 | "seem to" | | 2 | "began to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 57 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 788 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 9.61% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 463 | | uniqueNames | 3 | | maxNameDensity | 2.81 | | worstName | "Aurora" | | maxWindowNameDensity | 4 | | worstWindowName | "Aurora" | | discoveredNames | | | persons | | | places | (empty) | | globalScore | 0.096 | | windowScore | 0.333 | |
| 69.35% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 31 | | glossingSentenceCount | 1 | | matches | | 0 | "it was as if all the air had been sucked out of the cramped flat" |
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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 | 788 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 57 | | matches | | |
| 70.78% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 34.26 | | std | 13.63 | | cv | 0.398 | | sampleLengths | | 0 | 41 | | 1 | 9 | | 2 | 41 | | 3 | 49 | | 4 | 16 | | 5 | 30 | | 6 | 34 | | 7 | 27 | | 8 | 65 | | 9 | 34 | | 10 | 46 | | 11 | 34 | | 12 | 31 | | 13 | 24 | | 14 | 31 | | 15 | 22 | | 16 | 24 | | 17 | 19 | | 18 | 35 | | 19 | 43 | | 20 | 68 | | 21 | 33 | | 22 | 32 |
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| 96.91% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 42 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 85 | | matches | (empty) | |
| 42.61% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 57 | | ratio | 0.035 | | matches | | 0 | "Aurora's heart skipped as she swung open the door, and there stood Lucien, taller and broader than she remembered - also too damn good-looking, with his perfectly coiffed platinum hair and tailored charcoal suit." | | 1 | "Lucien's full lips curled upward, his heterochromatic gaze - one amber, one black - appraising her from head to toe." |
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| 72.17% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 423 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.06382978723404255 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.028368794326241134 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 57 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 57 | | mean | 13.82 | | std | 6.97 | | cv | 0.504 | | sampleLengths | | 0 | 34 | | 1 | 7 | | 2 | 9 | | 3 | 20 | | 4 | 21 | | 5 | 10 | | 6 | 16 | | 7 | 23 | | 8 | 16 | | 9 | 12 | | 10 | 18 | | 11 | 7 | | 12 | 7 | | 13 | 15 | | 14 | 5 | | 15 | 14 | | 16 | 13 | | 17 | 6 | | 18 | 20 | | 19 | 12 | | 20 | 11 | | 21 | 16 | | 22 | 14 | | 23 | 20 | | 24 | 11 | | 25 | 7 | | 26 | 28 | | 27 | 5 | | 28 | 11 | | 29 | 18 | | 30 | 21 | | 31 | 10 | | 32 | 10 | | 33 | 14 | | 34 | 10 | | 35 | 14 | | 36 | 7 | | 37 | 10 | | 38 | 12 | | 39 | 11 | | 40 | 13 | | 41 | 10 | | 42 | 5 | | 43 | 4 | | 44 | 10 | | 45 | 25 | | 46 | 10 | | 47 | 5 | | 48 | 28 | | 49 | 8 |
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| 80.70% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5263157894736842 | | totalSentences | 57 | | uniqueOpeners | 30 | |
| 83.33% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 40 | | matches | | 0 | "Suddenly, it was as if" |
| | ratio | 0.025 | |
| 50.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 40 | | matches | | 0 | "She gripped the door frame," | | 1 | "He stepped forward, and Aurora" | | 2 | "She could feel the heat" | | 3 | "Her voice trembled" | | 4 | "she called out" | | 5 | "Her tabby sauntered in from" | | 6 | "he murmured, nudging the cat" | | 7 | "He wasn't, exactly, but Aurora" | | 8 | "His gaze softened" | | 9 | "It was the only sign" | | 10 | "She couldn't seem to still" | | 11 | "They were toe to toe" | | 12 | "He brought a hand up" | | 13 | "Her words came out high" | | 14 | "They spilled down her cheeks" | | 15 | "She kissed him back with" | | 16 | "she whispered between kisses" |
| | ratio | 0.425 | |
| 10.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 36 | | totalSentences | 40 | | matches | | 0 | "Aurora's heart skipped as she" | | 1 | "She gripped the door frame," | | 2 | "Lucien's full lips curled upward," | | 3 | "He stepped forward, and Aurora" | | 4 | "She could feel the heat" | | 5 | "Her voice trembled" | | 6 | "Lucien's eyes flashed, a hint" | | 7 | "Aurora put a hand on" | | 8 | "she called out" | | 9 | "Her tabby sauntered in from" | | 10 | "Traitors, the both of them." | | 11 | "he murmured, nudging the cat" | | 12 | "Aurora said shortly" | | 13 | "He wasn't, exactly, but Aurora" | | 14 | "A spark ignited within her" | | 15 | "His gaze softened" | | 16 | "It was the only sign" | | 17 | "Aurora crossed her arms, then" | | 18 | "She couldn't seem to still" | | 19 | "Lucien took another step forward." |
| | ratio | 0.9 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 40 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 1 | | matches | | 0 | "She could feel the heat of him, smell the leather of his coat, and the dark, intoxicating cedar scent that was uniquely his." |
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| 83.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 1 | | matches | | 0 | "Aurora said defiantly, though her voice sounded small" |
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| 1.85% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 4 | | fancyTags | | 0 | "he murmured (murmur)" | | 1 | "Lucien breathed (breathe)" | | 2 | "he whispered (whisper)" | | 3 | "she whispered (whisper)" |
| | dialogueSentences | 27 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0.667 | | effectiveRatio | 0.296 | |