| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 1 | | adverbTags | | 0 | "He looked around [around]" |
| | dialogueSentences | 31 | | tagDensity | 0.484 | | leniency | 0.968 | | rawRatio | 0.067 | | effectiveRatio | 0.065 | |
| 91.06% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1677 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slightly" | | 1 | "quickly" | | 2 | "very" |
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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) | |
| 28.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1677 | | totalAiIsms | 24 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | word | "down her spine" | | count | 1 |
| | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | |
| | highlights | | 0 | "velvet" | | 1 | "weight" | | 2 | "silence" | | 3 | "rhythmic" | | 4 | "pounding" | | 5 | "navigated" | | 6 | "echoed" | | 7 | "familiar" | | 8 | "warmth" | | 9 | "scanning" | | 10 | "flickered" | | 11 | "unreadable" | | 12 | "tracing" | | 13 | "flicker" | | 14 | "down her spine" | | 15 | "tension" | | 16 | "beacon" | | 17 | "charged" | | 18 | "restrained" | | 19 | "resolve" | | 20 | "looming" | | 21 | "racing" |
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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 | 81 | | matches | (empty) | |
| 54.67% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 4 | | narrationSentences | 81 | | filterMatches | | | hedgeMatches | | 0 | "managed to" | | 1 | "seemed to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 98 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 57 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 8 | | totalWords | 1668 | | ratio | 0.005 | | matches | | 0 | "clack-clack-clack" | | 1 | "ma chère" | | 2 | "Rituals of the Fourth Circle" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 81.68% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 1171 | | uniqueNames | 14 | | maxNameDensity | 1.37 | | worstName | "Rory" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Rory" | | discoveredNames | | Brick | 1 | | Lane | 1 | | Eva | 3 | | Carter | 1 | | Rory | 16 | | Moreau | 1 | | London | 1 | | Avaros | 1 | | Fourth | 1 | | Lucien | 7 | | Evan | 1 | | Golden | 1 | | Empress | 1 | | Ptolemy | 3 |
| | persons | | 0 | "Eva" | | 1 | "Carter" | | 2 | "Rory" | | 3 | "Moreau" | | 4 | "Lucien" | | 5 | "Evan" | | 6 | "Ptolemy" |
| | places | | | globalScore | 0.817 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | 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 | 1668 | | matches | (empty) | |
| 98.64% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 98 | | matches | | 0 | "knew that cadence" | | 1 | "enough that she" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 43.89 | | std | 25.64 | | cv | 0.584 | | sampleLengths | | 0 | 101 | | 1 | 66 | | 2 | 4 | | 3 | 34 | | 4 | 20 | | 5 | 62 | | 6 | 20 | | 7 | 83 | | 8 | 20 | | 9 | 11 | | 10 | 67 | | 11 | 39 | | 12 | 29 | | 13 | 73 | | 14 | 46 | | 15 | 16 | | 16 | 51 | | 17 | 58 | | 18 | 12 | | 19 | 70 | | 20 | 27 | | 21 | 35 | | 22 | 67 | | 23 | 95 | | 24 | 67 | | 25 | 10 | | 26 | 70 | | 27 | 21 | | 28 | 9 | | 29 | 73 | | 30 | 65 | | 31 | 21 | | 32 | 36 | | 33 | 35 | | 34 | 29 | | 35 | 54 | | 36 | 52 | | 37 | 20 |
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| 92.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 81 | | matches | | 0 | "was supposed" | | 1 | "was tailored" | | 2 | "was slicked" |
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| 93.62% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 188 | | matches | | 0 | "was blurring" | | 1 | "was looking" | | 2 | "was glowing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 98 | | ratio | 0.061 | | matches | | 0 | "Rory rubbed the small, crescent-shaped scar on her left wrist—a nervous habit she’d never quite managed to shake." | | 1 | "Eva’s security was no joke—three heavy deadbolts that Rory had to slide back one by one." | | 2 | "He moved past her, his scent—expensive sandalwood and the faint, cold metallic tang that always clung to him—overpowering the turmeric." | | 3 | "But the way he was looking at her—with a desperate, restrained hunger that had nothing to do with his demonic heritage—pinned her to the spot." | | 4 | "The world outside—Evan, the Golden Empress, the scrolls, the looming threats—faded into a dull roar." | | 5 | "Lucien’s lips curved into a faint, dangerous smile—the first real smile she had seen on him in months." |
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| 82.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1142 | | adjectiveStacks | 3 | | stackExamples | | 0 | "small, crescent-shaped scar" | | 1 | "steady, cool-headed engine," | | 2 | "cluttered, spice-scented room," |
| | adverbCount | 30 | | adverbRatio | 0.02626970227670753 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.008756567425569177 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 98 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 98 | | mean | 17.02 | | std | 10.64 | | cv | 0.625 | | sampleLengths | | 0 | 36 | | 1 | 33 | | 2 | 32 | | 3 | 18 | | 4 | 16 | | 5 | 32 | | 6 | 4 | | 7 | 16 | | 8 | 18 | | 9 | 2 | | 10 | 14 | | 11 | 4 | | 12 | 16 | | 13 | 5 | | 14 | 16 | | 15 | 15 | | 16 | 10 | | 17 | 20 | | 18 | 6 | | 19 | 20 | | 20 | 25 | | 21 | 32 | | 22 | 3 | | 23 | 17 | | 24 | 10 | | 25 | 1 | | 26 | 27 | | 27 | 30 | | 28 | 7 | | 29 | 3 | | 30 | 11 | | 31 | 20 | | 32 | 8 | | 33 | 17 | | 34 | 12 | | 35 | 46 | | 36 | 27 | | 37 | 5 | | 38 | 8 | | 39 | 13 | | 40 | 20 | | 41 | 16 | | 42 | 10 | | 43 | 19 | | 44 | 22 | | 45 | 12 | | 46 | 18 | | 47 | 28 | | 48 | 11 | | 49 | 1 |
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| 48.98% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.336734693877551 | | totalSentences | 98 | | uniqueOpeners | 33 | |
| 86.58% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 77 | | matches | | 0 | "Then came the knock." | | 1 | "Certainly not here." |
| | ratio | 0.026 | |
| 22.60% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 77 | | matches | | 0 | "She was supposed to be" | | 1 | "It wasn't the frantic pounding" | | 2 | "It was three slow, deliberate" | | 3 | "Her heart, usually a steady," | | 4 | "She knew that cadence." | | 5 | "She stood, her movements stiff" | | 6 | "She turned the final knob" | | 7 | "He was, as always, impeccably" | | 8 | "His charcoal suit was tailored" | | 9 | "His platinum blond hair was" | | 10 | "He leaned slightly on his" | | 11 | "His voice was a low," | | 12 | "They had stood in the" | | 13 | "She hadn't expected to see" | | 14 | "he remarked, his heterochromatic eyes" | | 15 | "He gestured toward the interior" | | 16 | "She hated how easily he" | | 17 | "He moved past her, his" | | 18 | "He turned to Rory, his" | | 19 | "He looked away quickly, but" |
| | ratio | 0.494 | |
| 11.95% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 77 | | matches | | 0 | "The smell of cumin and" | | 1 | "Aurora Carter, known to the" | | 2 | "Ptolemy, a tabby cat with" | | 3 | "Rory rubbed the small, crescent-shaped" | | 4 | "She was supposed to be" | | 5 | "The silence of the flat" | | 6 | "It wasn't the frantic pounding" | | 7 | "It was three slow, deliberate" | | 8 | "Her heart, usually a steady," | | 9 | "She knew that cadence." | | 10 | "She stood, her movements stiff" | | 11 | "Eva’s security was no joke—three" | | 12 | "The metallic *clack-clack-clack* echoed in" | | 13 | "She turned the final knob" | | 14 | "Lucien Moreau stood in the" | | 15 | "He was, as always, impeccably" | | 16 | "His charcoal suit was tailored" | | 17 | "His platinum blond hair was" | | 18 | "He leaned slightly on his" | | 19 | "His voice was a low," |
| | ratio | 0.896 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 5.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 10 | | matches | | 0 | "His charcoal suit was tailored to a degree that made the peeling wallpaper of the hallway look even more offensive." | | 1 | "His voice was a low, smooth baritone that curled around her name with a familiar, dangerous warmth." | | 2 | "Ptolemy stood up on the sofa, his back arching, a low growl vibrating in his throat." | | 3 | "He turned to Rory, his gaze dropping to her wrist where she was still habitually tracing the crescent scar." | | 4 | "The air in the flat felt charged, thick with the kind of static that preceded a lightning strike." | | 5 | "But the way he was looking at her—with a desperate, restrained hunger that had nothing to do with his demonic heritage—pinned her to the spot." | | 6 | "Rory leaned into his touch, a small, involuntary movement that broke the last of her resolve." | | 7 | "Rory responded with equal fervor, her fingers moving from his suit to the nape of his neck, feeling the softness of his platinum hair." | | 8 | "He reached down, his fingers finding her left wrist, encircling the small crescent scar with a gentleness that contradicted everything he was." | | 9 | "Rory nodded, the cool-headed intelligence that usually guided her life taking a backseat to the raw, undeniable electricity humming between them." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 5 | | matches | | 0 | "he remarked, his heterochromatic eyes scanning her face" | | 1 | "Rory snapped, her defensive walls slamming into place" | | 2 | "he said, his tone softening" | | 3 | "Rory said, though her voice lacked its usual bite" | | 4 | "she reminded, the hurt finally bleeding through her cool exterior" |
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| 20.97% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 4 | | fancyTags | | 0 | "Rory snapped (snap)" | | 1 | "Lucien murmured (murmur)" | | 2 | "she whispered (whisper)" | | 3 | "he murmured (murmur)" |
| | dialogueSentences | 31 | | tagDensity | 0.258 | | leniency | 0.516 | | rawRatio | 0.5 | | effectiveRatio | 0.258 | |