| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 85.11% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1343 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slightly" | | 1 | "really" | | 2 | "softly" |
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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) | |
| 47.88% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1343 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "echoed" | | 1 | "silence" | | 2 | "weight" | | 3 | "tension" | | 4 | "stomach" | | 5 | "rhythmic" | | 6 | "throbbed" | | 7 | "echoing" | | 8 | "perfect" | | 9 | "pulse" | | 10 | "footsteps" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 143 | | matches | (empty) | |
| 52.95% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 4 | | narrationSentences | 143 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 143 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1343 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 3 | | matches | | 0 | "Rory, he said." | | 1 | "Okay, she whispered." | | 2 | "Lock it behind me, he said, not looking back." |
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| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 61 | | wordCount | 1343 | | uniqueNames | 20 | | maxNameDensity | 0.97 | | worstName | "Lucien" | | maxWindowNameDensity | 3 | | worstWindowName | "You" | | discoveredNames | | Carter | 1 | | Brick | 1 | | Lane | 1 | | Moreau | 1 | | London | 3 | | French | 1 | | Marseille | 1 | | England | 1 | | Evan | 3 | | Eva | 2 | | Rory | 12 | | Lucien | 13 | | Golden | 1 | | Empress | 1 | | Yu-Fei | 1 | | Aurora | 3 | | Avaros | 1 | | East | 1 | | You | 10 | | Ptolemy | 3 |
| | persons | | 0 | "Carter" | | 1 | "Moreau" | | 2 | "Evan" | | 3 | "Eva" | | 4 | "Rory" | | 5 | "Lucien" | | 6 | "Aurora" | | 7 | "You" | | 8 | "Ptolemy" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "London" | | 3 | "Marseille" | | 4 | "England" | | 5 | "Avaros" | | 6 | "East" |
| | globalScore | 1 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | 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 | 1343 | | matches | (empty) | |
| 96.74% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 143 | | matches | | 0 | "scrolls that cluttered" | | 1 | "wanted that I" | | 2 | "know that I" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 33.58 | | std | 25.64 | | cv | 0.764 | | sampleLengths | | 0 | 93 | | 1 | 4 | | 2 | 84 | | 3 | 24 | | 4 | 38 | | 5 | 47 | | 6 | 80 | | 7 | 11 | | 8 | 52 | | 9 | 17 | | 10 | 61 | | 11 | 26 | | 12 | 8 | | 13 | 57 | | 14 | 3 | | 15 | 4 | | 16 | 49 | | 17 | 66 | | 18 | 15 | | 19 | 25 | | 20 | 60 | | 21 | 5 | | 22 | 61 | | 23 | 2 | | 24 | 6 | | 25 | 47 | | 26 | 29 | | 27 | 25 | | 28 | 42 | | 29 | 26 | | 30 | 2 | | 31 | 3 | | 32 | 3 | | 33 | 45 | | 34 | 47 | | 35 | 14 | | 36 | 37 | | 37 | 43 | | 38 | 9 | | 39 | 73 |
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| 97.90% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 143 | | matches | | 0 | "was slicked" | | 1 | "was tailored" | | 2 | "were unrolled" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 254 | | matches | | 0 | "was already overflowing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 143 | | ratio | 0.014 | | matches | | 0 | "The amber one glowed warm; the black one seemed to swallow the light entirely." | | 1 | "The amber eye seemed to hold a question; the black one held a promise." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1350 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 46 | | adverbRatio | 0.034074074074074076 | | lyAdverbCount | 17 | | lyAdverbRatio | 0.012592592592592593 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 143 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 143 | | mean | 9.39 | | std | 6.65 | | cv | 0.709 | | sampleLengths | | 0 | 16 | | 1 | 22 | | 2 | 24 | | 3 | 31 | | 4 | 4 | | 5 | 18 | | 6 | 26 | | 7 | 17 | | 8 | 23 | | 9 | 3 | | 10 | 15 | | 11 | 6 | | 12 | 1 | | 13 | 8 | | 14 | 20 | | 15 | 9 | | 16 | 5 | | 17 | 11 | | 18 | 14 | | 19 | 11 | | 20 | 4 | | 21 | 2 | | 22 | 2 | | 23 | 11 | | 24 | 15 | | 25 | 19 | | 26 | 21 | | 27 | 12 | | 28 | 8 | | 29 | 3 | | 30 | 12 | | 31 | 18 | | 32 | 5 | | 33 | 17 | | 34 | 14 | | 35 | 3 | | 36 | 4 | | 37 | 18 | | 38 | 5 | | 39 | 15 | | 40 | 14 | | 41 | 5 | | 42 | 9 | | 43 | 8 | | 44 | 1 | | 45 | 8 | | 46 | 4 | | 47 | 4 | | 48 | 15 | | 49 | 6 |
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| 39.51% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.27972027972027974 | | totalSentences | 143 | | uniqueOpeners | 40 | |
| 51.28% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 130 | | matches | | 0 | "Then stop treating me like" | | 1 | "Then she thought of the" |
| | ratio | 0.015 | |
| 16.92% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 66 | | totalSentences | 130 | | matches | | 0 | "She opened the door." | | 1 | "He looked exactly as he" | | 2 | "His platinum blond hair was" | | 3 | "His voice was low, smoothed" | | 4 | "She didn't step back to" | | 5 | "She kept her body framed" | | 6 | "You know I have three" | | 7 | "His gaze shifted, the heterochromatic" | | 8 | "He tapped the ivory handle" | | 9 | "It wasn't just the jobs" | | 10 | "It was the night in" | | 11 | "She had left without saying" | | 12 | "She couldn't risk letting someone" | | 13 | "It's Eva's flat, Rory said" | | 14 | "He leaned the cane against" | | 15 | "He didn't remove his coat." | | 16 | "She turned to face him." | | 17 | "He straightened, smoothing the front" | | 18 | "I heard you were back" | | 19 | "I work for Yu-Fei." |
| | ratio | 0.508 | |
| 56.15% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 105 | | totalSentences | 130 | | matches | | 0 | "The third deadbolt slid home" | | 1 | "Aurora Carter paused, her hand" | | 2 | "She opened the door." | | 3 | "Lucien Moreau stood on the" | | 4 | "He looked exactly as he" | | 5 | "His platinum blond hair was" | | 6 | "The charcoal suit was tailored" | | 7 | "Rory, he said." | | 8 | "His voice was low, smoothed" | | 9 | "Tonight, it made her stomach" | | 10 | "She didn't step back to" | | 11 | "She kept her body framed" | | 12 | "You know I have three" | | 13 | "His gaze shifted, the heterochromatic" | | 14 | "The amber one glowed warm;" | | 15 | "He tapped the ivory handle" | | 16 | "A rhythmic, impatient sound." | | 17 | "The history between them sat" | | 18 | "It wasn't just the jobs" | | 19 | "It was the night in" |
| | ratio | 0.808 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 130 | | matches | | 0 | "Because of him?" | | 1 | "Because of everything." | | 2 | "Because I'm not built for..." | | 3 | "Because there is a threat." | | 4 | "Now they were close enough" |
| | ratio | 0.038 | |
| 67.67% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 6 | | matches | | 0 | "Aurora Carter paused, her hand lingering on the cold brass knob, listening to the silence on the other side of the door." | | 1 | "Below, the faint, perpetual hum of the curry house on Brick Lane seeped through the floor, carrying the scent of cumin and fried oil that had long since permeat…" | | 2 | "The charcoal suit was tailored to within an inch of its life, hugging shoulders that held a tension she recognized all too well." | | 3 | "He leaned the cane against the wall near the coat rack, which was already overflowing with Eva's jackets." | | 4 | "She was five-six, solid on her feet, but Lucien carried a presence that made the room feel smaller." | | 5 | "She stood there for a long moment in the quiet flat, the scent of his cologne lingering in the air, mixing with the curry and the rain." |
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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 | |