| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1781 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 77.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1781 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "footsteps" | | 1 | "unreadable" | | 2 | "gleaming" | | 3 | "throbbed" | | 4 | "warmth" | | 5 | "pulse" | | 6 | "traced" |
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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 | 109 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 109 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 212 | | 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 | 1781 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 76.05% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 51 | | wordCount | 1217 | | uniqueNames | 8 | | maxNameDensity | 1.48 | | worstName | "Lucien" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Aurora" | | discoveredNames | | Brick | 2 | | Lane | 2 | | Moreau | 1 | | London | 1 | | Eva | 3 | | Lucien | 18 | | Aurora | 17 | | Ptolemy | 7 |
| | persons | | 0 | "Moreau" | | 1 | "Eva" | | 2 | "Lucien" | | 3 | "Aurora" | | 4 | "Ptolemy" |
| | places | | | globalScore | 0.76 | | windowScore | 0.833 | |
| 89.02% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 82 | | glossingSentenceCount | 2 | | matches | | 0 | "sounded like a tooth breaking" | | 1 | "as if tasting metal" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.561 | | wordCount | 1781 | | matches | | 0 | "Not much, but enough to make his breath catch" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 212 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 150 | | mean | 11.87 | | std | 14.32 | | cv | 1.206 | | sampleLengths | | 0 | 33 | | 1 | 43 | | 2 | 5 | | 3 | 1 | | 4 | 38 | | 5 | 4 | | 6 | 2 | | 7 | 9 | | 8 | 69 | | 9 | 5 | | 10 | 4 | | 11 | 5 | | 12 | 5 | | 13 | 8 | | 14 | 5 | | 15 | 2 | | 16 | 25 | | 17 | 3 | | 18 | 6 | | 19 | 12 | | 20 | 4 | | 21 | 40 | | 22 | 3 | | 23 | 15 | | 24 | 8 | | 25 | 44 | | 26 | 18 | | 27 | 17 | | 28 | 4 | | 29 | 2 | | 30 | 5 | | 31 | 39 | | 32 | 3 | | 33 | 1 | | 34 | 2 | | 35 | 26 | | 36 | 1 | | 37 | 5 | | 38 | 1 | | 39 | 24 | | 40 | 5 | | 41 | 4 | | 42 | 3 | | 43 | 4 | | 44 | 52 | | 45 | 2 | | 46 | 8 | | 47 | 3 | | 48 | 6 | | 49 | 41 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 109 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 203 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 212 | | ratio | 0.005 | | matches | | 0 | "Lucien stood too close behind her; she could smell his aftershave, vetiver and smoke, and the old argument in her chest lost its footing." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1222 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 37 | | adverbRatio | 0.030278232405891982 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0016366612111292963 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 212 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 212 | | mean | 8.4 | | std | 6.7 | | cv | 0.798 | | sampleLengths | | 0 | 13 | | 1 | 20 | | 2 | 23 | | 3 | 7 | | 4 | 13 | | 5 | 5 | | 6 | 1 | | 7 | 3 | | 8 | 10 | | 9 | 25 | | 10 | 4 | | 11 | 2 | | 12 | 9 | | 13 | 34 | | 14 | 13 | | 15 | 5 | | 16 | 17 | | 17 | 5 | | 18 | 4 | | 19 | 5 | | 20 | 5 | | 21 | 5 | | 22 | 3 | | 23 | 5 | | 24 | 2 | | 25 | 7 | | 26 | 18 | | 27 | 3 | | 28 | 6 | | 29 | 12 | | 30 | 4 | | 31 | 9 | | 32 | 7 | | 33 | 24 | | 34 | 3 | | 35 | 15 | | 36 | 8 | | 37 | 19 | | 38 | 9 | | 39 | 16 | | 40 | 18 | | 41 | 3 | | 42 | 14 | | 43 | 4 | | 44 | 2 | | 45 | 5 | | 46 | 11 | | 47 | 15 | | 48 | 13 | | 49 | 3 |
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| 45.28% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.2688679245283019 | | totalSentences | 212 | | uniqueOpeners | 57 | |
| 31.75% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 105 | | matches | | 0 | "Then he pulled her behind" |
| | ratio | 0.01 | |
| 82.86% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 105 | | matches | | 0 | "His ivory-handled cane hooked over" | | 1 | "Her thumb worried the crescent" | | 2 | "His mismatched eyes caught the" | | 3 | "He leaned into the flat." | | 4 | "She unlatched the chain." | | 5 | "He came in fast enough" | | 6 | "He closed the door, dropped" | | 7 | "Her courier jacket hung on" | | 8 | "He took off his coat" | | 9 | "His knee pressed close to" | | 10 | "Her voice cracked only a" | | 11 | "She moved to the table" | | 12 | "She pressed two fingers to" | | 13 | "He caught her wrist where" | | 14 | "His thumb settled over the" | | 15 | "She yanked her hand back." | | 16 | "He moved like a man" | | 17 | "She laughed, no warmth." | | 18 | "She looked away from his" | | 19 | "He followed her eyes, then" |
| | ratio | 0.343 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 99 | | totalSentences | 105 | | matches | | 0 | "The third deadbolt gave with" | | 1 | "Aurora kept the chain tight" | | 2 | "Lucien Moreau stood on the" | | 3 | "His ivory-handled cane hooked over" | | 4 | "Ptolemy threaded between his polished" | | 5 | "Lucien raised his free hand." | | 6 | "Aurora didn’t answer." | | 7 | "Her thumb worried the crescent" | | 8 | "The small habit had survived" | | 9 | "Lucien smiled, and the left" | | 10 | "His mismatched eyes caught the" | | 11 | "The black eye stayed unreadable." | | 12 | "The amber one looked at" | | 13 | "He leaned into the flat." | | 14 | "Lucien set his cane inside" | | 15 | "The ivory handle caught the" | | 16 | "She unlatched the chain." | | 17 | "He came in fast enough" | | 18 | "The contact sent heat down" | | 19 | "He closed the door, dropped" |
| | ratio | 0.943 | |
| 47.62% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 105 | | matches | | 0 | "Now she had to listen." |
| | ratio | 0.01 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 5 | | matches | | 0 | "Lucien Moreau stood on the landing, platinum hair darkened at the temples, charcoal suit creased as if he’d slept upright in moving vehicles." | | 1 | "Lucien smiled, and the left corner of his mouth moved slower than the right, the same crooked expression that had talked her into believing a fixer could choose…" | | 2 | "His hand rose to the back of her neck, fingers brushing hair that had escaped its braid." | | 3 | "He gave the smallest laugh, a sound that used to make her forget his worst habit: disappearing when the story stopped flattering him." | | 4 | "His heterochromatic eyes didn’t flinch, but his jaw shifted as if tasting metal." |
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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 | |