| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 26 | | adverbTagCount | 3 | | adverbTags | | 0 | "she asked sharply [sharply]" | | 1 | "she said finally [finally]" | | 2 | "she said quietly [quietly]" |
| | dialogueSentences | 73 | | tagDensity | 0.356 | | leniency | 0.712 | | rawRatio | 0.115 | | effectiveRatio | 0.082 | |
| 72.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1607 | | totalAiIsmAdverbs | 9 | | found | | | highlights | | 0 | "slightly" | | 1 | "sharply" | | 2 | "completely" | | 3 | "perfectly" | | 4 | "carefully" |
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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) | |
| 50.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1607 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "chill" | | 1 | "mechanical" | | 2 | "etched" | | 3 | "intricate" | | 4 | "magnetic" | | 5 | "standard" | | 6 | "tension" | | 7 | "measured" | | 8 | "echo" | | 9 | "intensity" | | 10 | "vibrated" | | 11 | "raced" | | 12 | "weight" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
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| | highlights | | 0 | "eyes narrowed" | | 1 | "eyes widened" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 116 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 116 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 163 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1619 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 24 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 65 | | wordCount | 1089 | | uniqueNames | 15 | | maxNameDensity | 2.39 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 26 | | Tube | 1 | | Sergeant | 1 | | Wilson | 16 | | Kowalski | 6 | | Metropolitan | 1 | | Transit | 1 | | Eva | 2 | | British | 1 | | Museum | 1 | | Morris | 5 | | Camden | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sergeant" | | 3 | "Wilson" | | 4 | "Kowalski" | | 5 | "Eva" | | 6 | "Morris" |
| | places | (empty) | | globalScore | 0.306 | | windowScore | 0.167 | |
| 57.41% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 81 | | glossingSentenceCount | 3 | | matches | | 0 | "as if waiting for a train that would never arrive" | | 1 | "symbolism was apparently meaningless" | | 2 | "appeared beside her a moment later, handing her a high-powered flashlight" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.618 | | wordCount | 1619 | | matches | | 0 | "not north but toward the darkest section of the tunnel" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 163 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 77 | | mean | 21.03 | | std | 14.33 | | cv | 0.681 | | sampleLengths | | 0 | 48 | | 1 | 19 | | 2 | 56 | | 3 | 11 | | 4 | 14 | | 5 | 22 | | 6 | 13 | | 7 | 44 | | 8 | 8 | | 9 | 10 | | 10 | 38 | | 11 | 15 | | 12 | 14 | | 13 | 38 | | 14 | 10 | | 15 | 14 | | 16 | 16 | | 17 | 2 | | 18 | 5 | | 19 | 21 | | 20 | 30 | | 21 | 4 | | 22 | 7 | | 23 | 30 | | 24 | 9 | | 25 | 21 | | 26 | 3 | | 27 | 11 | | 28 | 52 | | 29 | 30 | | 30 | 14 | | 31 | 35 | | 32 | 8 | | 33 | 23 | | 34 | 11 | | 35 | 24 | | 36 | 11 | | 37 | 26 | | 38 | 20 | | 39 | 11 | | 40 | 17 | | 41 | 6 | | 42 | 1 | | 43 | 43 | | 44 | 3 | | 45 | 14 | | 46 | 21 | | 47 | 13 | | 48 | 12 | | 49 | 8 |
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| 99.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 116 | | matches | | 0 | "was etched" | | 1 | "was bought" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 181 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 163 | | ratio | 0.061 | | matches | | 0 | "The victim – male, early thirties, business attire – lay sprawled as if waiting for a train that would never arrive." | | 1 | "The body displayed the same ritualistic positioning as the others – arms extended, palms up, three small objects arranged in a triangle on the chest." | | 2 | "Its face was etched with intricate symbols she didn't recognize, but something about them triggered a memory – case files from three years ago." | | 3 | "The voice was female, cultured, measured – and completely untroubled by finding a dead body." | | 4 | "Twenty meters in, her torch revealed an alcove – a maintenance door with a broken lock." | | 5 | "The beam of her torch caught a splash of color on the concrete floor – a small smear of red." | | 6 | "Quinn swept her torch along the corridor walls, revealing faded markings – genuine symbols, not the fake ones on the parchment." | | 7 | "Modern, sophisticated – not Metropolitan Transit standard issue." | | 8 | "Quinn's fingers brushed the worn leather watch on her wrist – a gift from Morris before he vanished." | | 9 | "Whatever lay beyond that metal door connected to Morris's last case – the one that had cost him his life." |
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| 94.32% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1079 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 45 | | adverbRatio | 0.04170528266913809 | | lyAdverbCount | 27 | | lyAdverbRatio | 0.025023169601482854 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 163 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 163 | | mean | 9.93 | | std | 6.24 | | cv | 0.628 | | sampleLengths | | 0 | 14 | | 1 | 21 | | 2 | 13 | | 3 | 15 | | 4 | 4 | | 5 | 13 | | 6 | 14 | | 7 | 8 | | 8 | 21 | | 9 | 11 | | 10 | 5 | | 11 | 9 | | 12 | 3 | | 13 | 16 | | 14 | 3 | | 15 | 13 | | 16 | 25 | | 17 | 3 | | 18 | 7 | | 19 | 9 | | 20 | 8 | | 21 | 10 | | 22 | 12 | | 23 | 24 | | 24 | 2 | | 25 | 12 | | 26 | 3 | | 27 | 9 | | 28 | 5 | | 29 | 18 | | 30 | 11 | | 31 | 9 | | 32 | 10 | | 33 | 8 | | 34 | 6 | | 35 | 4 | | 36 | 12 | | 37 | 2 | | 38 | 3 | | 39 | 2 | | 40 | 21 | | 41 | 15 | | 42 | 15 | | 43 | 4 | | 44 | 2 | | 45 | 5 | | 46 | 30 | | 47 | 9 | | 48 | 13 | | 49 | 8 |
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| 82.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.5214723926380368 | | totalSentences | 163 | | uniqueOpeners | 85 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 100 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 100 | | matches | | 0 | "she said, snapping on latex" | | 1 | "She surveyed the platform with" | | 2 | "she asked sharply" | | 3 | "Its face was etched with" | | 4 | "she said, more to herself" | | 5 | "His wallet contained standard identification," | | 6 | "She carefully lifted the brass" | | 7 | "she murmured to the corpse" | | 8 | "She fumbled in her satchel," | | 9 | "she said finally" | | 10 | "She pointed to a specific" | | 11 | "She turned back to Kowalski." | | 12 | "It hung partially open, revealing" | | 13 | "He appeared beside her a" | | 14 | "she said quietly" | | 15 | "She followed the corridor further," | | 16 | "It ended at another door," | | 17 | "She photographed it, then turned" | | 18 | "They resembled the markings on" | | 19 | "Her phone buzzed with a" |
| | ratio | 0.22 | |
| 40.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 84 | | totalSentences | 100 | | matches | | 0 | "Detective Harlow Quinn stepped under" | | 1 | "The abandoned Tube station's concrete" | | 2 | "Quinn moved deeper into the" | | 3 | "The crime scene lay on" | | 4 | "The victim – male, early" | | 5 | "she said, snapping on latex" | | 6 | "Quinn's eyes narrowed." | | 7 | "She surveyed the platform with" | | 8 | "The body displayed the same" | | 9 | "A tarnished coin." | | 10 | "A scrap of parchment with" | | 11 | "A small brass item that" | | 12 | "she asked sharply" | | 13 | "Quinn crouched beside the body," | | 14 | "Its face was etched with" | | 15 | "she said, more to herself" | | 16 | "Wilson replied with poorly concealed" | | 17 | "Quinn's fingers hovered over the" | | 18 | "The needle twitched slightly, though" | | 19 | "Quinn stood, rolling her shoulders" |
| | ratio | 0.84 | |
| 50.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 100 | | matches | | 0 | "While Wilson made the call," |
| | ratio | 0.01 | |
| 85.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 4 | | matches | | 0 | "The abandoned Tube station's concrete walls wept with decades of accumulated moisture, creating a pervasive chill that penetrated her wool blazer." | | 1 | "The man's fingernails were perfectly manicured, shoes expensive but with mud that didn't match the station's dusty concrete." | | 2 | "They resembled the markings on the compass but were more elaborate, forming a circular pattern around a central design that looked almost like a doorway." | | 3 | "Quinn's mind raced, connecting fragments of a puzzle that had begun three years ago with Morris's disappearance." |
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| 67.31% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 26 | | uselessAdditionCount | 3 | | matches | | 0 | "She surveyed, her gaze methodically cataloging details" | | 1 | "she said, more to herself than to Wilson" | | 2 | "she said, the realization dawning" |
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| 95.21% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 18 | | fancyCount | 4 | | fancyTags | | 0 | "she murmured (murmur)" | | 1 | "Quinn muttered (mutter)" | | 2 | "she ordered (order)" | | 3 | "she whispered (whisper)" |
| | dialogueSentences | 73 | | tagDensity | 0.247 | | leniency | 0.493 | | rawRatio | 0.222 | | effectiveRatio | 0.11 | |