| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 37 | | tagDensity | 0.297 | | leniency | 0.595 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 79.47% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 974 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "tightly" | | 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) | |
| 79.47% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 974 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "measured" | | 1 | "traced" | | 2 | "pulse" | | 3 | "silence" |
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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 | 56 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 56 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 83 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 974 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 5 | | wordCount | 605 | | uniqueNames | 3 | | maxNameDensity | 0.5 | | worstName | "Lucien" | | maxWindowNameDensity | 1 | | worstWindowName | "Lucien" | | discoveredNames | | | persons | | | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 37 | | 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 | 974 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 22.14 | | std | 19.82 | | cv | 0.895 | | sampleLengths | | 0 | 66 | | 1 | 57 | | 2 | 17 | | 3 | 9 | | 4 | 34 | | 5 | 3 | | 6 | 29 | | 7 | 44 | | 8 | 3 | | 9 | 14 | | 10 | 5 | | 11 | 9 | | 12 | 79 | | 13 | 5 | | 14 | 5 | | 15 | 5 | | 16 | 6 | | 17 | 47 | | 18 | 11 | | 19 | 7 | | 20 | 63 | | 21 | 4 | | 22 | 10 | | 23 | 9 | | 24 | 41 | | 25 | 6 | | 26 | 31 | | 27 | 10 | | 28 | 10 | | 29 | 13 | | 30 | 40 | | 31 | 9 | | 32 | 4 | | 33 | 42 | | 34 | 35 | | 35 | 41 | | 36 | 34 | | 37 | 14 | | 38 | 37 | | 39 | 14 | | 40 | 4 | | 41 | 5 | | 42 | 36 | | 43 | 7 |
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| 99.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 56 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 95 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 83 | | ratio | 0 | | matches | (empty) | |
| 92.52% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 309 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.04854368932038835 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.006472491909385114 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 83 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 83 | | mean | 11.73 | | std | 9.04 | | cv | 0.771 | | sampleLengths | | 0 | 25 | | 1 | 4 | | 2 | 37 | | 3 | 17 | | 4 | 16 | | 5 | 2 | | 6 | 7 | | 7 | 15 | | 8 | 5 | | 9 | 12 | | 10 | 9 | | 11 | 20 | | 12 | 14 | | 13 | 3 | | 14 | 29 | | 15 | 6 | | 16 | 10 | | 17 | 14 | | 18 | 14 | | 19 | 3 | | 20 | 14 | | 21 | 5 | | 22 | 9 | | 23 | 6 | | 24 | 26 | | 25 | 15 | | 26 | 2 | | 27 | 14 | | 28 | 16 | | 29 | 5 | | 30 | 5 | | 31 | 5 | | 32 | 6 | | 33 | 29 | | 34 | 2 | | 35 | 4 | | 36 | 12 | | 37 | 5 | | 38 | 6 | | 39 | 7 | | 40 | 31 | | 41 | 32 | | 42 | 4 | | 43 | 10 | | 44 | 4 | | 45 | 5 | | 46 | 26 | | 47 | 15 | | 48 | 6 | | 49 | 16 |
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| 57.03% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.42168674698795183 | | totalSentences | 83 | | uniqueOpeners | 35 | |
| 72.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 46 | | matches | | 0 | "Instead she eased the chain" |
| | ratio | 0.022 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 46 | | matches | | 0 | "She had been staring at" | | 1 | "She slid off the bed," | | 2 | "She didn't open the door." | | 3 | "She slid the chain into" | | 4 | "she said through the gap" | | 5 | "She looked at him properly" | | 6 | "His right hand held the" | | 7 | "She should have shut the" | | 8 | "He ducked under the low" | | 9 | "He leaned the cane against" | | 10 | "She noticed too much." | | 11 | "She always had, and that" | | 12 | "He pressed two fingers to" | | 13 | "She folded her arms." | | 14 | "He looked up, and for" | | 15 | "He tipped his head toward" | | 16 | "Her voice came out flat," | | 17 | "He didn't look away" | | 18 | "She gave him that much." | | 19 | "She wanted to say something" |
| | ratio | 0.652 | |
| 3.48% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 42 | | totalSentences | 46 | | matches | | 0 | "The knock came at ten" | | 1 | "Rory was already awake." | | 2 | "She had been staring at" | | 3 | "She slid off the bed," | | 4 | "An ivory-handled cane tucked under" | | 5 | "Platinum hair, slicked back as" | | 6 | "She didn't open the door." | | 7 | "She slid the chain into" | | 8 | "she said through the gap" | | 9 | "Lucien tilted his head, and" | | 10 | "She looked at him properly" | | 11 | "A split at his lower" | | 12 | "A dark line of dried" | | 13 | "His right hand held the" | | 14 | "She should have shut the" | | 15 | "He ducked under the low" | | 16 | "The stack of Golden Empress" | | 17 | "The bottle of cheap red" | | 18 | "He leaned the cane against" | | 19 | "She noticed too much." |
| | ratio | 0.913 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 46 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 1 | | matches | | 0 | "His thumb found the small crescent scar near the bone, the one she'd got falling off a garden wall at nine years old, and traced its curve with a care that made…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 37 | | tagDensity | 0.189 | | leniency | 0.378 | | rawRatio | 0.143 | | effectiveRatio | 0.054 | |