| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.84% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 760 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 27.63% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 760 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "loomed" | | 1 | "pulsed" | | 2 | "pounding" | | 3 | "scanned" | | 4 | "whisper" | | 5 | "looming" | | 6 | "familiar" | | 7 | "potential" | | 8 | "silence" | | 9 | "amidst" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 96.26% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 58 | | matches | | 0 | "n with confusion" | | 1 | "t with joy" |
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| 93.60% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 58 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 61 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 761 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 729 | | uniqueNames | 13 | | maxNameDensity | 1.23 | | worstName | "Rory" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 9 | | Richmond | 1 | | Park | 1 | | Golden | 1 | | Empress | 1 | | London | 1 | | Sight | 1 | | Helena | 1 | | Windcutty | 1 | | Hellportal | 1 | | Danger | 1 | | Lindon | 2 | | Gasping | 1 |
| | persons | | 0 | "Rory" | | 1 | "Helena" | | 2 | "Windcutty" | | 3 | "Danger" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "London" | | 3 | "Lindon" |
| | globalScore | 0.883 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 47 | | 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 | 761 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 61 | | matches | | |
| 77.55% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 19 | | mean | 40.05 | | std | 16.88 | | cv | 0.421 | | sampleLengths | | 0 | 62 | | 1 | 60 | | 2 | 37 | | 3 | 54 | | 4 | 37 | | 5 | 47 | | 6 | 26 | | 7 | 49 | | 8 | 12 | | 9 | 61 | | 10 | 19 | | 11 | 29 | | 12 | 32 | | 13 | 18 | | 14 | 54 | | 15 | 23 | | 16 | 58 | | 17 | 21 | | 18 | 62 |
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| 87.11% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 58 | | matches | | 0 | "was thought" | | 1 | "was gone" | | 2 | "been opened" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 118 | | matches | | |
| 96.02% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 61 | | ratio | 0.016 | | matches | | 0 | "He reached out to take her hand, and Rory, frozen with confusion, felt her heart nearly burst with joy - until a flash of pain ripped through her." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 728 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.03021978021978022 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.01510989010989011 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 61 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 61 | | mean | 12.48 | | std | 6.68 | | cv | 0.536 | | sampleLengths | | 0 | 21 | | 1 | 19 | | 2 | 22 | | 3 | 14 | | 4 | 9 | | 5 | 23 | | 6 | 14 | | 7 | 13 | | 8 | 24 | | 9 | 8 | | 10 | 18 | | 11 | 19 | | 12 | 2 | | 13 | 7 | | 14 | 14 | | 15 | 2 | | 16 | 3 | | 17 | 18 | | 18 | 6 | | 19 | 11 | | 20 | 22 | | 21 | 8 | | 22 | 3 | | 23 | 8 | | 24 | 15 | | 25 | 4 | | 26 | 20 | | 27 | 16 | | 28 | 5 | | 29 | 2 | | 30 | 2 | | 31 | 12 | | 32 | 7 | | 33 | 18 | | 34 | 13 | | 35 | 12 | | 36 | 11 | | 37 | 8 | | 38 | 5 | | 39 | 6 | | 40 | 9 | | 41 | 13 | | 42 | 4 | | 43 | 3 | | 44 | 19 | | 45 | 13 | | 46 | 12 | | 47 | 6 | | 48 | 28 | | 49 | 12 |
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| 80.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.4918032786885246 | | totalSentences | 61 | | uniqueOpeners | 30 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 53 | | matches | | 0 | "Then it was gone." | | 1 | "Then another rustling from behind." |
| | ratio | 0.038 | |
| 84.15% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 53 | | matches | | 0 | "She knew from her university" | | 1 | "She let the heartstone pendant's" | | 2 | "she couldn't shake that prickle" | | 3 | "She pressed a hand to" | | 4 | "It stood at the edge" | | 5 | "She caught only a flash" | | 6 | "Her voice emerged pathetic and" | | 7 | "She blinked as a black" | | 8 | "Her parents, arms outstretched, desperate" | | 9 | "Her hands reached out, but" | | 10 | "he spoke from across" | | 11 | "He reached out to take" | | 12 | "Her arm splattered with blood" | | 13 | "She screamed as her ex-boyfriend's" | | 14 | "He shook his bitten hand" | | 15 | "Her vision clouded until she" | | 16 | "she heard his voice whisper," | | 17 | "She watched her best friend" |
| | ratio | 0.34 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 37 | | totalSentences | 53 | | matches | | 0 | "The first drops of rain" | | 1 | "Mist clung to the ground," | | 2 | "She knew from her university" | | 3 | "She let the heartstone pendant's" | | 4 | "The gemstone pulsed faintly with" | | 5 | "Rory hugged her arms tightly," | | 6 | "The only sound was the" | | 7 | "she couldn't shake that prickle" | | 8 | "A deeper hush settled over" | | 9 | "An unnatural stillness." | | 10 | "She pressed a hand to" | | 11 | "Nothing but wetness on her" | | 12 | "The temperature in the grove" | | 13 | "The skin of her neck" | | 14 | "A dark figure." | | 15 | "It stood at the edge" | | 16 | "She caught only a flash" | | 17 | "Rory swiveled on her heels," | | 18 | "Her voice emerged pathetic and" | | 19 | "Danger looming, Rory knew with" |
| | ratio | 0.698 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 2 | | matches | | 0 | "The skin of her neck buzzed, eyes bulging as she turned to look over her shoulder, squinting for shapes in the fog." | | 1 | "Gasping, she woke to Lindon standing over her as his tendrils submerged deep inside her body, tearing her apart from within." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 1 | | matches | | 0 | "she heard, before the world went black" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "she whispered (whisper)" | | 1 | "he spoke (speak)" |
| | dialogueSentences | 8 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 1 | | effectiveRatio | 0.5 | |