| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 30 | | tagDensity | 0.133 | | leniency | 0.267 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 81.41% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 807 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 87.61% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 807 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 49 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 49 | | filterMatches | | | hedgeMatches | (empty) | |
| 94.25% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 75 | | gibberishSentences | 1 | | adjustedGibberishSentences | 1 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0.013 | | matches | | 0 | "\"Call me soon, yeah?\" Sul選手 pushed her hair out of her face." |
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| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 807 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 505 | | uniqueNames | 7 | | maxNameDensity | 4.36 | | worstName | "Rory" | | maxWindowNameDensity | 8 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Tuesday | 1 | | Rory | 22 | | Abigail | 13 | | Silas | 2 | | Five | 3 |
| | persons | | 0 | "Raven" | | 1 | "Rory" | | 2 | "Abigail" | | 3 | "Silas" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 35 | | 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 | 807 | | matches | (empty) | |
| 77.78% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 75 | | matches | | 0 | "recognize that face" | | 1 | "smiling that Rory" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 21.81 | | std | 14.86 | | cv | 0.682 | | sampleLengths | | 0 | 63 | | 1 | 28 | | 2 | 49 | | 3 | 1 | | 4 | 47 | | 5 | 6 | | 6 | 30 | | 7 | 9 | | 8 | 15 | | 9 | 14 | | 10 | 19 | | 11 | 28 | | 12 | 15 | | 13 | 33 | | 14 | 18 | | 15 | 19 | | 16 | 9 | | 17 | 32 | | 18 | 18 | | 19 | 20 | | 20 | 35 | | 21 | 3 | | 22 | 30 | | 23 | 34 | | 24 | 53 | | 25 | 20 | | 26 | 5 | | 27 | 36 | | 28 | 21 | | 29 | 21 | | 30 | 6 | | 31 | 1 | | 32 | 8 | | 33 | 5 | | 34 | 11 | | 35 | 18 | | 36 | 27 |
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| 90.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 49 | | matches | | 0 | "was polished" | | 1 | "were bothered" |
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| 55.07% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 92 | | matches | | 0 | "was still, silencing" | | 1 | "were still coming" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 75 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 506 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.025691699604743084 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.001976284584980237 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 75 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 75 | | mean | 10.76 | | std | 7.86 | | cv | 0.73 | | sampleLengths | | 0 | 9 | | 1 | 20 | | 2 | 34 | | 3 | 14 | | 4 | 2 | | 5 | 12 | | 6 | 7 | | 7 | 11 | | 8 | 9 | | 9 | 9 | | 10 | 13 | | 11 | 1 | | 12 | 13 | | 13 | 3 | | 14 | 16 | | 15 | 15 | | 16 | 5 | | 17 | 1 | | 18 | 19 | | 19 | 11 | | 20 | 9 | | 21 | 2 | | 22 | 6 | | 23 | 7 | | 24 | 10 | | 25 | 4 | | 26 | 6 | | 27 | 13 | | 28 | 28 | | 29 | 3 | | 30 | 12 | | 31 | 7 | | 32 | 26 | | 33 | 7 | | 34 | 11 | | 35 | 11 | | 36 | 8 | | 37 | 9 | | 38 | 15 | | 39 | 3 | | 40 | 8 | | 41 | 6 | | 42 | 5 | | 43 | 13 | | 44 | 20 | | 45 | 6 | | 46 | 29 | | 47 | 3 | | 48 | 30 | | 49 | 9 |
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| 66.22% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4533333333333333 | | totalSentences | 75 | | uniqueOpeners | 34 | |
| 77.52% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 43 | | matches | | 0 | "Instead of fish and chips," |
| | ratio | 0.023 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 6 | | totalSentences | 43 | | matches | | 0 | "They grew wide and came" | | 1 | "It was just seconds after" | | 2 | "Her eyes stung." | | 3 | "She turned to Abigail." | | 4 | "They continued to talk, and" | | 5 | "She watched as Abigail walked" |
| | ratio | 0.14 | |
| 53.02% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 35 | | totalSentences | 43 | | matches | | 0 | "The Raven's Nest was busy" | | 1 | "Silas maneuvered through the crowd," | | 2 | "The chrome bun of the" | | 3 | "The bell above the door" | | 4 | "A woman in a black" | | 5 | "Aqua eyes that matched her" | | 6 | "They grew wide and came" | | 7 | "Rory's heart mounted the steps" | | 8 | "Abigail had changed." | | 9 | "The lines on her face" | | 10 | "Abigail walked right to her." | | 11 | "The last time she'd said" | | 12 | "Rory will get the divorce" | | 13 | "Rory would put everything in" | | 14 | "Rory's throat was still, silencing" | | 15 | "Rory squinted, groaning." | | 16 | "Abigail closed her eyes and" | | 17 | "A full minute passed and" | | 18 | "Abigail broke first." | | 19 | "Rory shrugged dully, and nodded." |
| | ratio | 0.814 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 43 | | matches | | 0 | "Even with the years she'd" | | 1 | "Now Abigail was here, pinning" |
| | ratio | 0.047 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 83.33% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "Abigail hissed (hiss)" | | 1 | "Rory muttered (mutter)" |
| | dialogueSentences | 30 | | tagDensity | 0.067 | | leniency | 0.133 | | rawRatio | 1 | | effectiveRatio | 0.133 | |