| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 84.92% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1326 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "loosely" | | 1 | "slowly" | | 2 | "softly" | | 3 | "carefully" |
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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) | |
| 47.21% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1326 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "velvet" | | 1 | "whisper" | | 2 | "familiar" | | 3 | "weight" | | 4 | "unreadable" | | 5 | "could feel" | | 6 | "warmth" | | 7 | "traced" | | 8 | "chaotic" | | 9 | "flicker" | | 10 | "gleaming" |
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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 | 90 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 90 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 133 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1316 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 99.66% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 894 | | uniqueNames | 11 | | maxNameDensity | 1.01 | | worstName | "You" | | maxWindowNameDensity | 2 | | worstWindowName | "You" | | discoveredNames | | Yu-Fei | 1 | | Lucien | 3 | | Moreau | 1 | | Ptolemy | 1 | | Rain | 1 | | London | 1 | | Cardiff | 1 | | You | 9 | | Avarosian | 1 | | Ice | 1 | | Rory | 3 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Ptolemy" | | 3 | "You" | | 4 | "Ice" | | 5 | "Rory" |
| | places | | | globalScore | 0.997 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | 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 | 1316 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 133 | | matches | | 0 | "liked that girl" | | 1 | "Hated that he" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 39.88 | | std | 23.2 | | cv | 0.582 | | sampleLengths | | 0 | 80 | | 1 | 67 | | 2 | 29 | | 3 | 25 | | 4 | 11 | | 5 | 32 | | 6 | 49 | | 7 | 30 | | 8 | 29 | | 9 | 59 | | 10 | 16 | | 11 | 49 | | 12 | 48 | | 13 | 75 | | 14 | 66 | | 15 | 25 | | 16 | 11 | | 17 | 62 | | 18 | 23 | | 19 | 54 | | 20 | 3 | | 21 | 22 | | 22 | 42 | | 23 | 22 | | 24 | 5 | | 25 | 57 | | 26 | 83 | | 27 | 3 | | 28 | 73 | | 29 | 41 | | 30 | 47 | | 31 | 17 | | 32 | 61 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 90 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 149 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 133 | | ratio | 0 | | matches | (empty) | |
| 91.01% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 179 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.05027932960893855 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0111731843575419 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 133 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 133 | | mean | 9.89 | | std | 6.89 | | cv | 0.697 | | sampleLengths | | 0 | 18 | | 1 | 22 | | 2 | 18 | | 3 | 22 | | 4 | 27 | | 5 | 11 | | 6 | 8 | | 7 | 21 | | 8 | 9 | | 9 | 20 | | 10 | 16 | | 11 | 9 | | 12 | 6 | | 13 | 5 | | 14 | 19 | | 15 | 13 | | 16 | 10 | | 17 | 7 | | 18 | 6 | | 19 | 4 | | 20 | 11 | | 21 | 11 | | 22 | 25 | | 23 | 5 | | 24 | 11 | | 25 | 7 | | 26 | 3 | | 27 | 8 | | 28 | 11 | | 29 | 9 | | 30 | 18 | | 31 | 21 | | 32 | 9 | | 33 | 7 | | 34 | 4 | | 35 | 27 | | 36 | 4 | | 37 | 6 | | 38 | 4 | | 39 | 4 | | 40 | 16 | | 41 | 17 | | 42 | 10 | | 43 | 3 | | 44 | 2 | | 45 | 6 | | 46 | 17 | | 47 | 14 | | 48 | 16 | | 49 | 22 |
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| 63.41% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.44360902255639095 | | totalSentences | 133 | | uniqueOpeners | 59 | |
| 86.58% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 77 | | matches | | 0 | "Instead, she found Lucien Moreau" | | 1 | "Only when it was convenient." |
| | ratio | 0.026 | |
| 12.21% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 77 | | matches | | 0 | "She expected it to be" | | 1 | "He held his ivory-handled cane" | | 2 | "His mismatched eyes, one amber," | | 3 | "She gripped the edge of" | | 4 | "It slid over her, a" | | 5 | "He took a step closer," | | 6 | "She refused to give him" | | 7 | "You send a message." | | 8 | "You have one of your" | | 9 | "You don’t climb three flights" | | 10 | "He followed her inside, his" | | 11 | "I don’t have anything for" | | 12 | "He ran a finger over" | | 13 | "He had offered her a" | | 14 | "You run when things become…" | | 15 | "You don’t get to call" | | 16 | "I liked that girl." | | 17 | "His amber eye flared with" | | 18 | "His black eye remained a" | | 19 | "He lifted a hand, his" |
| | ratio | 0.519 | |
| 63.90% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 77 | | matches | | 0 | "The sound of the three" | | 1 | "Rory pulled the heavy door" | | 2 | "She expected it to be" | | 3 | "He held his ivory-handled cane" | | 4 | "His mismatched eyes, one amber," | | 5 | "The only sound was the" | | 6 | "Rory’s heart gave a painful" | | 7 | "She gripped the edge of" | | 8 | "It slid over her, a" | | 9 | "He took a step closer," | | 10 | "She refused to give him" | | 11 | "You send a message." | | 12 | "You have one of your" | | 13 | "You don’t climb three flights" | | 14 | "An invitation she hadn’t intended" | | 15 | "The one you’re not a" | | 16 | "He followed her inside, his" | | 17 | "Books teetered in precarious stacks" | | 18 | "Scrolls were unfurled across the" | | 19 | "A fat tabby cat, Ptolemy," |
| | ratio | 0.792 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 61.22% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 4 | | matches | | 0 | "He held his ivory-handled cane loosely in one hand, the other tucked into the pocket of a tailored charcoal suit that looked sharp enough to cut glass." | | 1 | "He lifted a hand, his fingers hovering just beside her cheek, so close she could feel the warmth radiating from his skin." | | 2 | "His fingers brushed her jawline, a feather-light touch that sent a jolt straight through her." | | 3 | "She felt the callus on his skin, a roughness that contradicted his polished exterior." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |