| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 64 | | tagDensity | 0.266 | | leniency | 0.531 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.12% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1518 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "perfectly" | | 1 | "very" | | 2 | "slowly" |
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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) | |
| 100.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1518 | | totalAiIsms | 0 | | found | (empty) | | highlights | (empty) | |
| 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 | 1 | | narrationSentences | 85 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 85 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 132 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 65 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 7 | | totalWords | 1518 | | ratio | 0.005 | | matches | | 0 | "Don't look for me." | | 1 | "Greetings from Tenby" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 1007 | | uniqueNames | 8 | | maxNameDensity | 0.7 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 7 | | Rain | 2 | | Marseille | 1 | | Lucien | 5 | | Eva | 4 | | Ptolemy | 2 | | London | 1 | | Close | 1 |
| | persons | | 0 | "Rory" | | 1 | "Rain" | | 2 | "Lucien" | | 3 | "Eva" | | 4 | "Ptolemy" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 63.79% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like a man who'd walked from Marse" | | 1 | "quite a smile, but it was the closest thing she'd seen on anyone's face in a month and a half that made her chest do something stupid" |
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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 | 1518 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 132 | | matches | | 0 | "hated that she" | | 1 | "Hated that some" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 74 | | mean | 20.51 | | std | 21.82 | | cv | 1.064 | | sampleLengths | | 0 | 68 | | 1 | 4 | | 2 | 15 | | 3 | 31 | | 4 | 34 | | 5 | 4 | | 6 | 2 | | 7 | 22 | | 8 | 26 | | 9 | 25 | | 10 | 3 | | 11 | 14 | | 12 | 1 | | 13 | 71 | | 14 | 20 | | 15 | 7 | | 16 | 11 | | 17 | 79 | | 18 | 17 | | 19 | 44 | | 20 | 11 | | 21 | 2 | | 22 | 3 | | 23 | 35 | | 24 | 21 | | 25 | 2 | | 26 | 2 | | 27 | 7 | | 28 | 74 | | 29 | 5 | | 30 | 6 | | 31 | 29 | | 32 | 2 | | 33 | 13 | | 34 | 80 | | 35 | 12 | | 36 | 3 | | 37 | 47 | | 38 | 3 | | 39 | 36 | | 40 | 30 | | 41 | 7 | | 42 | 9 | | 43 | 3 | | 44 | 4 | | 45 | 38 | | 46 | 11 | | 47 | 9 | | 48 | 3 | | 49 | 3 |
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| 97.01% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 85 | | matches | | 0 | "was wrapped" | | 1 | "was supposed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 175 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 132 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1008 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.026785714285714284 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003968253968253968 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 132 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 132 | | mean | 11.5 | | std | 11 | | cv | 0.957 | | sampleLengths | | 0 | 22 | | 1 | 13 | | 2 | 21 | | 3 | 3 | | 4 | 6 | | 5 | 3 | | 6 | 4 | | 7 | 7 | | 8 | 8 | | 9 | 11 | | 10 | 20 | | 11 | 16 | | 12 | 9 | | 13 | 9 | | 14 | 4 | | 15 | 2 | | 16 | 22 | | 17 | 6 | | 18 | 10 | | 19 | 10 | | 20 | 19 | | 21 | 6 | | 22 | 3 | | 23 | 14 | | 24 | 1 | | 25 | 5 | | 26 | 2 | | 27 | 57 | | 28 | 3 | | 29 | 4 | | 30 | 10 | | 31 | 6 | | 32 | 4 | | 33 | 7 | | 34 | 11 | | 35 | 6 | | 36 | 15 | | 37 | 23 | | 38 | 15 | | 39 | 20 | | 40 | 4 | | 41 | 3 | | 42 | 10 | | 43 | 20 | | 44 | 14 | | 45 | 10 | | 46 | 9 | | 47 | 2 | | 48 | 2 | | 49 | 3 |
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| 58.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.38636363636363635 | | totalSentences | 132 | | uniqueOpeners | 51 | |
| 44.44% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 75 | | matches | | 0 | "Instead she stepped back, and" |
| | ratio | 0.013 | |
| 6.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 75 | | matches | | 0 | "His charcoal jacket had gone" | | 1 | "He looked like a man" | | 2 | "He didn't step forward, and" | | 3 | "He never called her Rory." | | 4 | "She'd told him." | | 5 | "She never told anyone." | | 6 | "She should have shut the" | | 7 | "She had three deadbolts on" | | 8 | "He had to turn sideways" | | 9 | "His left hand was wrapped" | | 10 | "She crossed to him before" | | 11 | "It wasn't quite a smile," | | 12 | "He looked at the book" | | 13 | "She knelt on the rug" | | 14 | "He didn't give her his." | | 15 | "She kept her hand out" | | 16 | "He unwrapped the handkerchief and" | | 17 | "She'd forgotten that." | | 18 | "She'd made herself forget it." | | 19 | "She dipped the towel and" |
| | ratio | 0.533 | |
| 20.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 66 | | totalSentences | 75 | | matches | | 0 | "The third deadbolt stuck, as" | | 1 | "The only glow came from" | | 2 | "A polished shoe." | | 3 | "The ivory knob of a" | | 4 | "Rory kept her hand on" | | 5 | "Lucien tipped his head towards" | | 6 | "Rain had flattened the careful" | | 7 | "His charcoal jacket had gone" | | 8 | "He looked like a man" | | 9 | "A muscle jumped in his" | | 10 | "He didn't step forward, and" | | 11 | "Ptolemy slid between her ankles," | | 12 | "Lucien glanced down at the" | | 13 | "He never called her Rory." | | 14 | "Everyone else wore the nickname" | | 15 | "She'd told him." | | 16 | "She never told anyone." | | 17 | "The next morning there'd been" | | 18 | "She should have shut the" | | 19 | "She had three deadbolts on" |
| | ratio | 0.88 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 1 | | matches | | 0 | "Everyone else wore the nickname down to nothing, but Lucien said her full name as if he'd had to pay for it, and the sound of it in that narrow stairwell dragge…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 64 | | tagDensity | 0.141 | | leniency | 0.281 | | rawRatio | 0.111 | | effectiveRatio | 0.031 | |