| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 27 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 62 | | tagDensity | 0.435 | | leniency | 0.871 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1317 | | 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) | |
| 50.65% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1317 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "echoing" | | 1 | "standard" | | 2 | "chill" | | 3 | "traced" | | 4 | "scanned" | | 5 | "shattered" | | 6 | "perfect" | | 7 | "intricate" | | 8 | "familiar" | | 9 | "stomach" | | 10 | "vibrated" | | 11 | "echoed" |
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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 | 86 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 86 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 121 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1317 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 53 | | wordCount | 745 | | uniqueNames | 7 | | maxNameDensity | 3.76 | | worstName | "Harlow" | | maxWindowNameDensity | 6 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Thorne | 20 | | Harlow | 28 | | Morris | 1 | | Thames | 1 | | Glock | 1 |
| | persons | | 0 | "Thorne" | | 1 | "Harlow" | | 2 | "Morris" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | 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 | 1317 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 121 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 25.33 | | std | 13.14 | | cv | 0.519 | | sampleLengths | | 0 | 51 | | 1 | 14 | | 2 | 29 | | 3 | 33 | | 4 | 24 | | 5 | 25 | | 6 | 17 | | 7 | 42 | | 8 | 11 | | 9 | 21 | | 10 | 8 | | 11 | 15 | | 12 | 19 | | 13 | 17 | | 14 | 46 | | 15 | 13 | | 16 | 59 | | 17 | 31 | | 18 | 30 | | 19 | 18 | | 20 | 10 | | 21 | 13 | | 22 | 25 | | 23 | 26 | | 24 | 25 | | 25 | 13 | | 26 | 42 | | 27 | 8 | | 28 | 12 | | 29 | 29 | | 30 | 24 | | 31 | 19 | | 32 | 38 | | 33 | 5 | | 34 | 24 | | 35 | 14 | | 36 | 56 | | 37 | 16 | | 38 | 37 | | 39 | 37 | | 40 | 28 | | 41 | 20 | | 42 | 49 | | 43 | 26 | | 44 | 36 | | 45 | 17 | | 46 | 8 | | 47 | 43 | | 48 | 12 | | 49 | 40 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 86 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 142 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 121 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 745 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.01476510067114094 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0026845637583892616 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 121 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 121 | | mean | 10.88 | | std | 6.09 | | cv | 0.56 | | sampleLengths | | 0 | 7 | | 1 | 14 | | 2 | 15 | | 3 | 15 | | 4 | 14 | | 5 | 19 | | 6 | 10 | | 7 | 5 | | 8 | 12 | | 9 | 16 | | 10 | 14 | | 11 | 10 | | 12 | 8 | | 13 | 17 | | 14 | 5 | | 15 | 6 | | 16 | 6 | | 17 | 26 | | 18 | 16 | | 19 | 4 | | 20 | 7 | | 21 | 13 | | 22 | 4 | | 23 | 4 | | 24 | 8 | | 25 | 5 | | 26 | 10 | | 27 | 11 | | 28 | 8 | | 29 | 12 | | 30 | 5 | | 31 | 11 | | 32 | 35 | | 33 | 6 | | 34 | 7 | | 35 | 3 | | 36 | 18 | | 37 | 7 | | 38 | 12 | | 39 | 6 | | 40 | 7 | | 41 | 6 | | 42 | 18 | | 43 | 13 | | 44 | 12 | | 45 | 18 | | 46 | 4 | | 47 | 10 | | 48 | 4 | | 49 | 10 |
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| 66.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4214876033057851 | | totalSentences | 121 | | uniqueOpeners | 51 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 85 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 85 | | matches | | 0 | "She pulled a pair of" | | 1 | "She scanned the platform." | | 2 | "She walked to the edge" | | 3 | "She stopped at a rusted" | | 4 | "She checked her worn leather" | | 5 | "She crouched, shining her beam" | | 6 | "She felt the lining." | | 7 | "Her fingers brushed a hidden" | | 8 | "She pulled a small, folding" | | 9 | "She looked back at the" | | 10 | "He pried the victim's stiff" | | 11 | "Her chest tightened, a familiar" | | 12 | "She rubbed the face of" | | 13 | "She knelt and examined the" |
| | ratio | 0.165 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 85 | | matches | | 0 | "Harlow ducked under the yellow" | | 1 | "The air in the abandoned" | | 2 | "Water dripped from a ruptured" | | 3 | "DS Thorne stood by the" | | 4 | "Thorne clicked his torch off" | | 5 | "Harlow knelt beside the body." | | 6 | "The victim wore a heavy" | | 7 | "She pulled a pair of" | | 8 | "Harlow traced the edge of" | | 9 | "Thorne gestured to the tunnel" | | 10 | "Harlow examined the victim's neck." | | 11 | "The wound was clean, almost" | | 12 | "Harlow leaned closer, her nose" | | 13 | "Thorne crossed his arms." | | 14 | "Harlow stood, her joints popping" | | 15 | "She scanned the platform." | | 16 | "Harlow examined the victim's hands." | | 17 | "The fingernails were clean, devoid" | | 18 | "Harlow turned the victim's palm" | | 19 | "Thorne pointed to the dark" |
| | ratio | 0.941 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 85 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 22 | | technicalSentenceCount | 1 | | matches | | 0 | "The face featured intricate, interlocking sigils that made her eyes water when she focused on them." |
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| 87.96% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 27 | | uselessAdditionCount | 2 | | matches | | 0 | "Harlow stood, her joints popping in the quiet station" | | 1 | "Harlow kept, her military bearing locking her spine straight" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 62 | | tagDensity | 0.016 | | leniency | 0.032 | | rawRatio | 0 | | effectiveRatio | 0 | |