| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1048 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 42.75% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1048 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "measured" | | 1 | "familiar" | | 2 | "scanned" | | 3 | "perfect" | | 4 | "etched" | | 5 | "vibrated" | | 6 | "standard" | | 7 | "traced" | | 8 | "processed" | | 9 | "weight" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 174 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 174 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 174 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 19 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1048 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 45.04% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1048 | | uniqueNames | 7 | | maxNameDensity | 2.1 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Harlow | 1 | | Quinn | 22 | | Morris | 1 | | Kowalski | 1 | | Eva | 14 | | You | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "You" |
| | places | (empty) | | globalScore | 0.45 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 86 | | 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 | 1048 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 174 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 33.81 | | std | 22.05 | | cv | 0.652 | | sampleLengths | | 0 | 108 | | 1 | 41 | | 2 | 22 | | 3 | 50 | | 4 | 24 | | 5 | 35 | | 6 | 36 | | 7 | 11 | | 8 | 43 | | 9 | 42 | | 10 | 19 | | 11 | 42 | | 12 | 8 | | 13 | 28 | | 14 | 39 | | 15 | 12 | | 16 | 13 | | 17 | 46 | | 18 | 59 | | 19 | 19 | | 20 | 18 | | 21 | 22 | | 22 | 39 | | 23 | 10 | | 24 | 43 | | 25 | 40 | | 26 | 23 | | 27 | 56 | | 28 | 10 | | 29 | 83 | | 30 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 174 | | matches | | 0 | "is stuck" | | 1 | "was transported" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 197 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 174 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1049 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.019065776930409915 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0038131553860819827 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 174 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 174 | | mean | 6.02 | | std | 3.31 | | cv | 0.55 | | sampleLengths | | 0 | 10 | | 1 | 15 | | 2 | 8 | | 3 | 8 | | 4 | 8 | | 5 | 9 | | 6 | 14 | | 7 | 10 | | 8 | 6 | | 9 | 12 | | 10 | 8 | | 11 | 7 | | 12 | 7 | | 13 | 9 | | 14 | 10 | | 15 | 8 | | 16 | 5 | | 17 | 2 | | 18 | 9 | | 19 | 6 | | 20 | 4 | | 21 | 8 | | 22 | 5 | | 23 | 9 | | 24 | 8 | | 25 | 5 | | 26 | 11 | | 27 | 2 | | 28 | 2 | | 29 | 5 | | 30 | 15 | | 31 | 4 | | 32 | 7 | | 33 | 2 | | 34 | 3 | | 35 | 10 | | 36 | 9 | | 37 | 3 | | 38 | 4 | | 39 | 6 | | 40 | 7 | | 41 | 5 | | 42 | 6 | | 43 | 5 | | 44 | 2 | | 45 | 2 | | 46 | 7 | | 47 | 4 | | 48 | 14 | | 49 | 3 |
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| 50.57% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.3448275862068966 | | totalSentences | 174 | | uniqueOpeners | 60 | |
| 45.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 148 | | matches | | 0 | "Only the blood bowl remained." | | 1 | "Probably went back up." |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 148 | | matches | | 0 | "Her boots struck cracked platform" | | 1 | "She adjusted the worn leather" | | 2 | "She tapped a silver pen" | | 3 | "She did not look up" | | 4 | "You are cutting it close." | | 5 | "She tucked a damp curl" | | 6 | "It curved outward, defying gravity." | | 7 | "She kept her voice level." | | 8 | "Her sharp jaw tightened as" | | 9 | "She swept her flashlight across" | | 10 | "They did not walk." | | 11 | "It vibrated against the glass," | | 12 | "They were binding sigils." | | 13 | "It pointed straight down." | | 14 | "She pulled it aside." | | 15 | "They did not come up" | | 16 | "They never came down." | | 17 | "Her green eyes narrowed behind" | | 18 | "She traced the blood pattern" | | 19 | "It radiated outward from a" |
| | ratio | 0.27 | |
| 57.97% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 119 | | totalSentences | 148 | | matches | | 0 | "The Camden platform smelled of" | | 1 | "Flashlight beams cut through the" | | 2 | "The atmosphere felt pressurized, heavy" | | 3 | "Detective Harlow Quinn stepped over" | | 4 | "Her boots struck cracked platform" | | 5 | "Salt and pepper hair clung" | | 6 | "She adjusted the worn leather" | | 7 | "The static rose in her" | | 8 | "The station felt familiar in" | | 9 | "Eva Kowalski stood near a" | | 10 | "She tapped a silver pen" | | 11 | "The worn satchel at her" | | 12 | "She did not look up" | | 13 | "You are cutting it close." | | 14 | "She tucked a damp curl" | | 15 | "The body cooled for forty" | | 16 | "Quinn scanned the platform." | | 17 | "The other face down near" | | 18 | "Blood pooled on the tiles" | | 19 | "It curved outward, defying gravity." |
| | ratio | 0.804 | |
| 33.78% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 148 | | matches | | | ratio | 0.007 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 1 | | matches | | 0 | "Flashlight beams cut through the damp dark, catching dust that hung motionless in the air." |
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| 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 | |