| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 3 | | adverbTags | | 0 | "thing she wanted more [more]" | | 1 | "she said cautiously [cautiously]" | | 2 | "she said softly [softly]" |
| | dialogueSentences | 25 | | tagDensity | 0.44 | | leniency | 0.88 | | rawRatio | 0.273 | | effectiveRatio | 0.24 | |
| 66.59% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 898 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "very" | | 1 | "cautiously" | | 2 | "gently" | | 3 | "truly" | | 4 | "quickly" | | 5 | "softly" |
| |
| 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) | |
| 44.32% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 898 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "unreadable" | | 1 | "testament" | | 2 | "familiar" | | 3 | "eyebrow" | | 4 | "affection" | | 5 | "enigmatic" | | 6 | "whisper" | | 7 | "resolve" | | 8 | "wavering" | | 9 | "searing" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "heart pounded in chest" | | count | 1 |
|
| | highlights | | 0 | "heart pounded in her chest" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 49 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 49 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 64 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 897 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 64.82% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 587 | | uniqueNames | 4 | | maxNameDensity | 1.7 | | worstName | "Lucien" | | maxWindowNameDensity | 3 | | worstWindowName | "Rory" | | discoveredNames | | Lucien | 10 | | Moreau | 1 | | Ptolemy | 2 | | Rory | 10 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Ptolemy" | | 3 | "Rory" |
| | places | (empty) | | globalScore | 0.648 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | 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 | 897 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 64 | | matches | (empty) | |
| 70.30% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 25 | | mean | 35.88 | | std | 14.21 | | cv | 0.396 | | sampleLengths | | 0 | 59 | | 1 | 51 | | 2 | 32 | | 3 | 24 | | 4 | 39 | | 5 | 38 | | 6 | 23 | | 7 | 31 | | 8 | 44 | | 9 | 47 | | 10 | 31 | | 11 | 34 | | 12 | 40 | | 13 | 16 | | 14 | 44 | | 15 | 31 | | 16 | 10 | | 17 | 66 | | 18 | 52 | | 19 | 35 | | 20 | 31 | | 21 | 58 | | 22 | 11 | | 23 | 25 | | 24 | 25 |
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| 98.10% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 49 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 114 | | matches | | |
| 53.57% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 64 | | ratio | 0.031 | | matches | | 0 | "Anger, hurt, affection, longing - none of which she wanted to examine too closely." | | 1 | "He was the one who'd always understood her - truly seen her, scars and all." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 432 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.034722222222222224 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.013888888888888888 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 64 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 64 | | mean | 14.02 | | std | 7.69 | | cv | 0.549 | | sampleLengths | | 0 | 30 | | 1 | 29 | | 2 | 9 | | 3 | 14 | | 4 | 28 | | 5 | 19 | | 6 | 13 | | 7 | 7 | | 8 | 17 | | 9 | 20 | | 10 | 19 | | 11 | 8 | | 12 | 15 | | 13 | 15 | | 14 | 20 | | 15 | 3 | | 16 | 19 | | 17 | 12 | | 18 | 11 | | 19 | 11 | | 20 | 15 | | 21 | 7 | | 22 | 17 | | 23 | 14 | | 24 | 16 | | 25 | 11 | | 26 | 20 | | 27 | 13 | | 28 | 21 | | 29 | 2 | | 30 | 33 | | 31 | 5 | | 32 | 16 | | 33 | 8 | | 34 | 6 | | 35 | 10 | | 36 | 20 | | 37 | 31 | | 38 | 4 | | 39 | 6 | | 40 | 14 | | 41 | 15 | | 42 | 31 | | 43 | 6 | | 44 | 12 | | 45 | 7 | | 46 | 15 | | 47 | 18 | | 48 | 10 | | 49 | 25 |
| |
| 76.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.484375 | | totalSentences | 64 | | uniqueOpeners | 31 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 48 | | matches | (empty) | | ratio | 0 | |
| 70.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 48 | | matches | | 0 | "I will do my best" | | 1 | "I will aim to flesh" | | 2 | "His heterochromatic eyes sparkled with" | | 3 | "He looked around the cramped" | | 4 | "he said with a raised" | | 5 | "she said cautiously" | | 6 | "He had her full attention" | | 7 | "She curled her lip." | | 8 | "He brought his hands up" | | 9 | "His voice dropped to a" | | 10 | "He was the one who'd" | | 11 | "She closed her eyes, turning" | | 12 | "she muttered, before surging up" | | 13 | "She groaned into his mouth," | | 14 | "She knew it was reckless" | | 15 | "she said softly" | | 16 | "His eyes twinkled" | | 17 | "She couldn't wait to see" |
| | ratio | 0.375 | |
| 12.08% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 43 | | totalSentences | 48 | | matches | | 0 | "I will do my best" | | 1 | "I will aim to flesh" | | 2 | "The door creaked open, groaning" | | 3 | "Rory blinked in surprise as" | | 4 | "His heterochromatic eyes sparkled with" | | 5 | "Rory drawled, leaning against the" | | 6 | "Lucien's mouth quirked into a" | | 7 | "Rory sighed, stepping aside to" | | 8 | "Ptolemy wound himself around Lucien's" | | 9 | "Rory rolled her eyes as" | | 10 | "Books and scrolls were strewn" | | 11 | "Lucien noted, scooping up the" | | 12 | "Rory warned, grabbing a half" | | 13 | "Lucien neatly side-stepped the clutter," | | 14 | "He looked around the cramped" | | 15 | "he said with a raised" | | 16 | "A maelstrom of emotions warred" | | 17 | "Rory took a sip of" | | 18 | "Lucien was quiet for a" | | 19 | "The only thing she wanted" |
| | ratio | 0.896 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 48 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 5 | | fancyTags | | 0 | "Rory drawled (drawl)" | | 1 | "Rory sighed (sigh)" | | 2 | "Lucien noted (note)" | | 3 | "Rory warned (warn)" | | 4 | "she muttered (mutter)" |
| | dialogueSentences | 25 | | tagDensity | 0.32 | | leniency | 0.64 | | rawRatio | 0.625 | | effectiveRatio | 0.4 | |