| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 80.96% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1313 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "suddenly" | | 1 | "slightly" | | 2 | "quickly" | | 3 | "softly" | | 4 | "completely" |
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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) | |
| 58.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1313 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "pulsed" | | 1 | "silence" | | 2 | "vibrated" | | 3 | "trembled" | | 4 | "structure" | | 5 | "wavered" | | 6 | "pulse" | | 7 | "flickered" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 243 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 243 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 243 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 11 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1313 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 3 | | matches | | 0 | "No, she told the voice." | | 1 | "I am the only thing, she said." | | 2 | "Yes, she told the voice." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 1311 | | uniqueNames | 5 | | maxNameDensity | 0.99 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Aurora" | | discoveredNames | | Heartstone | 1 | | Pendant | 1 | | Yu-Fei | 1 | | Cheung | 1 | | Aurora | 13 |
| | persons | | 0 | "Pendant" | | 1 | "Yu-Fei" | | 2 | "Cheung" | | 3 | "Aurora" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 79.91% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 107 | | glossingSentenceCount | 3 | | matches | | 0 | "tasted like copper and old rain" | | 1 | "looked like the liar who ran from her hou" | | 2 | "smelled like the outside world" |
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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 | 1313 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 243 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 10 | | mean | 131.3 | | std | 127.21 | | cv | 0.969 | | sampleLengths | | 0 | 74 | | 1 | 112 | | 2 | 46 | | 3 | 88 | | 4 | 103 | | 5 | 57 | | 6 | 79 | | 7 | 135 | | 8 | 114 | | 9 | 505 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 243 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 271 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 243 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 600 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.02 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0033333333333333335 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 243 | | echoCount | 0 | | echoWords | (empty) | |
| 83.92% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 243 | | mean | 5.4 | | std | 1.94 | | cv | 0.36 | | sampleLengths | | 0 | 8 | | 1 | 8 | | 2 | 7 | | 3 | 8 | | 4 | 6 | | 5 | 8 | | 6 | 6 | | 7 | 8 | | 8 | 3 | | 9 | 4 | | 10 | 8 | | 11 | 7 | | 12 | 7 | | 13 | 9 | | 14 | 8 | | 15 | 3 | | 16 | 6 | | 17 | 6 | | 18 | 7 | | 19 | 7 | | 20 | 9 | | 21 | 10 | | 22 | 8 | | 23 | 9 | | 24 | 10 | | 25 | 6 | | 26 | 10 | | 27 | 4 | | 28 | 7 | | 29 | 5 | | 30 | 4 | | 31 | 7 | | 32 | 3 | | 33 | 6 | | 34 | 4 | | 35 | 7 | | 36 | 10 | | 37 | 4 | | 38 | 4 | | 39 | 5 | | 40 | 10 | | 41 | 7 | | 42 | 11 | | 43 | 2 | | 44 | 6 | | 45 | 7 | | 46 | 4 | | 47 | 3 | | 48 | 4 | | 49 | 10 |
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| 31.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 46 | | diversityRatio | 0.16049382716049382 | | totalSentences | 243 | | uniqueOpeners | 39 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 235 | | matches | (empty) | | ratio | 0 | |
| 15.74% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 120 | | totalSentences | 235 | | matches | | 0 | "She knew the coordinates by" | | 1 | "Her phone map showed nothing" | | 2 | "She checked the phone screen" | | 3 | "They looked wide." | | 4 | "She pocketed the device." | | 5 | "She pressed her thumb to" | | 6 | "It grew hot." | | 7 | "She walked deeper into the" | | 8 | "She closed her eyes to" | | 9 | "It came from inside the" | | 10 | "She did not turn around." | | 11 | "She looked straight ahead." | | 12 | "Her boots squeaked on the" | | 13 | "She stepped forward." | | 14 | "They did not sway in" | | 15 | "She could not find the" | | 16 | "It vibrated harder every step" | | 17 | "Her breath caught in her" | | 18 | "She spoke aloud." | | 19 | "I didn’t come here to" |
| | ratio | 0.511 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 217 | | totalSentences | 235 | | matches | | 0 | "Aurora stepped through the ancient" | | 1 | "Night sank around her shoulders" | | 2 | "The moonlight did not fall," | | 3 | "She knew the coordinates by" | | 4 | "Her phone map showed nothing" | | 5 | "She checked the phone screen" | | 6 | "They looked wide." | | 7 | "She pocketed the device." | | 8 | "The air tasted like copper" | | 9 | "The Heartstone Pendant dangled against" | | 10 | "The stone hummed against her" | | 11 | "She pressed her thumb to" | | 12 | "It grew hot." | | 13 | "Aurora pulled her shirt collar" | | 14 | "She walked deeper into the" | | 15 | "The ground beneath her boots" | | 16 | "Moss grew in shapes that" | | 17 | "Some looked she had worn" | | 18 | "Others looked like the liar" | | 19 | "She closed her eyes to" |
| | ratio | 0.923 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 235 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 9 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |