| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn looked again [again]" |
| | dialogueSentences | 89 | | tagDensity | 0.157 | | leniency | 0.315 | | rawRatio | 0.071 | | effectiveRatio | 0.022 | |
| 90.47% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2099 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "sweetly" | | 1 | "slowly" | | 2 | "quickly" | | 3 | "cautiously" |
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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) | |
| 80.94% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2099 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "traced" | | 1 | "flickered" | | 2 | "pulse" | | 3 | "warmth" | | 4 | "fluttered" | | 5 | "trembled" | | 6 | "etched" | | 7 | "silence" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 210 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 210 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 284 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2095 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 85 | | wordCount | 1641 | | uniqueNames | 13 | | maxNameDensity | 2.32 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 38 | | Camden | 2 | | Detective | 2 | | Sergeant | 1 | | Vale | 21 | | High | 1 | | Level | 1 | | Venn | 3 | | Morris | 3 | | Whitechapel | 1 | | Kowalski | 2 | | Eva | 9 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Sergeant" | | 4 | "Vale" | | 5 | "Venn" | | 6 | "Morris" | | 7 | "Kowalski" | | 8 | "Eva" |
| | places | (empty) | | globalScore | 0.342 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 120 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 9.07% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 4 | | per1kWords | 1.909 | | wordCount | 2095 | | matches | | 0 | "not north but toward the tunnel" | | 1 | "not blood but something oily" | | 2 | "not toward the tunnel now, but at the shadow beneath Vale’s shoes" | | 3 | "not in a human voice, but in the voices of everyone Quinn had ever failed to save" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 284 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 175 | | mean | 11.97 | | std | 12.73 | | cv | 1.064 | | sampleLengths | | 0 | 15 | | 1 | 45 | | 2 | 8 | | 3 | 55 | | 4 | 9 | | 5 | 5 | | 6 | 45 | | 7 | 3 | | 8 | 2 | | 9 | 3 | | 10 | 51 | | 11 | 18 | | 12 | 4 | | 13 | 1 | | 14 | 26 | | 15 | 50 | | 16 | 5 | | 17 | 10 | | 18 | 4 | | 19 | 2 | | 20 | 2 | | 21 | 2 | | 22 | 2 | | 23 | 8 | | 24 | 42 | | 25 | 3 | | 26 | 40 | | 27 | 17 | | 28 | 50 | | 29 | 6 | | 30 | 42 | | 31 | 3 | | 32 | 1 | | 33 | 9 | | 34 | 3 | | 35 | 32 | | 36 | 4 | | 37 | 18 | | 38 | 1 | | 39 | 1 | | 40 | 1 | | 41 | 14 | | 42 | 23 | | 43 | 3 | | 44 | 2 | | 45 | 2 | | 46 | 28 | | 47 | 3 | | 48 | 6 | | 49 | 6 |
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| 85.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 12 | | totalSentences | 210 | | matches | | 0 | "been painted" | | 1 | "been closed" | | 2 | "were written" | | 3 | "was gone" | | 4 | "was greened" | | 5 | "been scratched" | | 6 | "been fixed" | | 7 | "been placed" | | 8 | "was gone" | | 9 | "was gone" | | 10 | "been layered" | | 11 | "been arranged" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 273 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 284 | | ratio | 0.007 | | matches | | 0 | "The blood, the missing coat, the false witness, the impossible shadow—all of it had been arranged to make the murder look human." | | 1 | "The creature screamed—not in a human voice, but in the voices of everyone Quinn had ever failed to save." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1650 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 49 | | adverbRatio | 0.029696969696969697 | | lyAdverbCount | 16 | | lyAdverbRatio | 0.009696969696969697 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 284 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 284 | | mean | 7.38 | | std | 5.34 | | cv | 0.723 | | sampleLengths | | 0 | 15 | | 1 | 13 | | 2 | 22 | | 3 | 10 | | 4 | 8 | | 5 | 18 | | 6 | 11 | | 7 | 13 | | 8 | 13 | | 9 | 9 | | 10 | 5 | | 11 | 21 | | 12 | 9 | | 13 | 15 | | 14 | 3 | | 15 | 2 | | 16 | 3 | | 17 | 18 | | 18 | 33 | | 19 | 4 | | 20 | 8 | | 21 | 6 | | 22 | 4 | | 23 | 1 | | 24 | 20 | | 25 | 6 | | 26 | 3 | | 27 | 11 | | 28 | 14 | | 29 | 4 | | 30 | 18 | | 31 | 5 | | 32 | 10 | | 33 | 4 | | 34 | 2 | | 35 | 2 | | 36 | 2 | | 37 | 2 | | 38 | 8 | | 39 | 6 | | 40 | 17 | | 41 | 10 | | 42 | 9 | | 43 | 3 | | 44 | 5 | | 45 | 10 | | 46 | 10 | | 47 | 4 | | 48 | 6 | | 49 | 5 |
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| 42.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 23 | | diversityRatio | 0.30633802816901406 | | totalSentences | 284 | | uniqueOpeners | 87 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 8 | | totalSentences | 189 | | matches | | 0 | "Somewhere beyond the brick arches," | | 1 | "Then it swung toward Vale." | | 2 | "Then toward the tunnel." | | 3 | "Then the lights went out." | | 4 | "More like paper dragged across" | | 5 | "Then she looked at the" | | 6 | "Pale blue light leaked through" | | 7 | "Then Eva shouted her name." |
| | ratio | 0.042 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 44 | | totalSentences | 189 | | matches | | 0 | "Her torch beam slid over" | | 1 | "Their colored glass threw stains" | | 2 | "He stood near the body" | | 3 | "His blond hair had already" | | 4 | "Her military-polished instincts made every" | | 5 | "She took in the platform," | | 6 | "She glanced toward the evidence" | | 7 | "She studied the coat." | | 8 | "It could produce arterial spray." | | 9 | "It did not usually produce" | | 10 | "She leaned closer." | | 11 | "His hand had been closed" | | 12 | "He exhaled through his nose." | | 13 | "Their flames flickered without smoke." | | 14 | "His eyes were open." | | 15 | "She took out her notebook." | | 16 | "Their signs were written in" | | 17 | "His suit was clean except" | | 18 | "His boots were damp." | | 19 | "He smiled without warmth." |
| | ratio | 0.233 | |
| 49.95% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 155 | | totalSentences | 189 | | matches | | 0 | "Detective Harlow Quinn ducked beneath" | | 1 | "The air below Camden tasted" | | 2 | "Her torch beam slid over" | | 3 | "The crime scene lay on" | | 4 | "A man in a charcoal" | | 5 | "Blood had spread beneath his" | | 6 | "Their colored glass threw stains" | | 7 | "Quinn stopped at the edge" | | 8 | "He stood near the body" | | 9 | "His blond hair had already" | | 10 | "Vale gave her a look" | | 11 | "Quinn checked her watch." | | 12 | "The leather strap was cracked" | | 13 | "Vale nodded toward the body" | | 14 | "Quinn advanced slowly." | | 15 | "Her military-polished instincts made every" | | 16 | "She took in the platform," | | 17 | "Camden High Level, perhaps." | | 18 | "The name had been painted" | | 19 | "The letters read: VEIL MARKET." |
| | ratio | 0.82 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 189 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 62 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 3 | | fancyTags | | 0 | "Vale repeated (repeat)" | | 1 | "the platform Eva Kowalski screamed (scream)" | | 2 | "Eva shouted (shout)" |
| | dialogueSentences | 89 | | tagDensity | 0.135 | | leniency | 0.27 | | rawRatio | 0.25 | | effectiveRatio | 0.067 | |