| 90.41% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 27 | | adverbTagCount | 4 | | adverbTags | | 0 | "Eva's hand moved instinctively [instinctively]" | | 1 | "Quinn said slowly [slowly]" | | 2 | "Eva glanced around [around]" | | 3 | "The forensic technician called again [again]" |
| | dialogueSentences | 73 | | tagDensity | 0.37 | | leniency | 0.74 | | rawRatio | 0.148 | | effectiveRatio | 0.11 | |
| 82.98% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1469 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "quickly" | | 1 | "slowly" | | 2 | "slightly" |
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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) | |
| 65.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1469 | | totalAiIsms | 10 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | word | "practiced ease" | | count | 1 |
| | 5 | | | 6 | | | 7 | | | 8 | | | 9 | |
| | highlights | | 0 | "pulse" | | 1 | "echoing" | | 2 | "eyebrow" | | 3 | "flickered" | | 4 | "practiced ease" | | 5 | "etched" | | 6 | "intricate" | | 7 | "intensity" | | 8 | "standard" | | 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 | 87 | | matches | (empty) | |
| 93.60% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 87 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 129 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1455 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 70 | | wordCount | 839 | | uniqueNames | 12 | | maxNameDensity | 3.34 | | worstName | "Quinn" | | maxWindowNameDensity | 7 | | worstWindowName | "Eva" | | discoveredNames | | Camden | 2 | | Town | 1 | | Metropolitan | 1 | | Police | 1 | | Londoners | 1 | | Quinn | 28 | | Eva | 26 | | Kowalski | 1 | | Dr | 3 | | Amari | 3 | | Tube | 1 | | Morris | 2 |
| | persons | | 0 | "Police" | | 1 | "Londoners" | | 2 | "Quinn" | | 3 | "Eva" | | 4 | "Kowalski" | | 5 | "Dr" | | 6 | "Amari" | | 7 | "Morris" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | glossingSentenceCount | 4 | | matches | | 0 | "graffiti that seemed to shimmer and shift when not directly illuminated" | | 1 | "looked like carved bone tokens, and somet" | | 2 | "looked like he'd laid down for a nap on a" | | 3 | "seemed newer than the rest" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1455 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 129 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 64 | | mean | 22.73 | | std | 12.76 | | cv | 0.561 | | sampleLengths | | 0 | 54 | | 1 | 29 | | 2 | 16 | | 3 | 12 | | 4 | 29 | | 5 | 49 | | 6 | 13 | | 7 | 13 | | 8 | 8 | | 9 | 13 | | 10 | 50 | | 11 | 11 | | 12 | 23 | | 13 | 34 | | 14 | 28 | | 15 | 24 | | 16 | 1 | | 17 | 33 | | 18 | 19 | | 19 | 60 | | 20 | 3 | | 21 | 42 | | 22 | 33 | | 23 | 18 | | 24 | 12 | | 25 | 6 | | 26 | 31 | | 27 | 25 | | 28 | 7 | | 29 | 21 | | 30 | 41 | | 31 | 17 | | 32 | 24 | | 33 | 2 | | 34 | 16 | | 35 | 11 | | 36 | 30 | | 37 | 31 | | 38 | 24 | | 39 | 19 | | 40 | 32 | | 41 | 36 | | 42 | 11 | | 43 | 24 | | 44 | 22 | | 45 | 30 | | 46 | 7 | | 47 | 34 | | 48 | 24 | | 49 | 21 |
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| 93.16% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 87 | | matches | | 0 | "was pulled" | | 1 | "was etched" | | 2 | "was aimed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 157 | | matches | | 0 | "wasn't pointing" | | 1 | "wasn't adding" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 129 | | ratio | 0.078 | | matches | | 0 | "She'd investigated murders in plenty of derelict places—abandoned warehouses, condemned flats, forgotten service alleys—but something about this station felt different." | | 1 | "The platform was alive with activity—crime scene technicians in white suits, photographers documenting everything, officers securing the perimeter." | | 2 | "Recognition flickered across her freckled face, followed quickly by something else—alarm?" | | 3 | "Eva gestured to several evidence markers surrounding small objects near the body—a brass item that resembled a compass, what looked like carved bone tokens, and something that might have been a small vial." | | 4 | "No obvious cause of death—no blood, no visible trauma." | | 5 | "The needle wasn't pointing north—it was aimed directly at the dark tunnel leading away from the platform." | | 6 | "She'd investigated a case in an abandoned Tube station before, and Eva was right—the proportions were off." | | 7 | "Quinn checked her watch—the worn leather band a gift from her former partner, DS Morris, before his disappearance." | | 8 | "\"Things that people like me study, and people like him—\" she nodded toward the body, \"—trade in.\"" | | 9 | "\"Between worlds.\" Eva held up the compass from her bag—a twin to the one found with the corpse." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 842 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.030878859857482184 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.015439429928741092 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 129 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 129 | | mean | 11.28 | | std | 6.67 | | cv | 0.591 | | sampleLengths | | 0 | 7 | | 1 | 28 | | 2 | 19 | | 3 | 11 | | 4 | 13 | | 5 | 5 | | 6 | 11 | | 7 | 5 | | 8 | 12 | | 9 | 7 | | 10 | 22 | | 11 | 9 | | 12 | 20 | | 13 | 20 | | 14 | 13 | | 15 | 13 | | 16 | 4 | | 17 | 4 | | 18 | 7 | | 19 | 6 | | 20 | 7 | | 21 | 18 | | 22 | 5 | | 23 | 20 | | 24 | 11 | | 25 | 12 | | 26 | 11 | | 27 | 8 | | 28 | 19 | | 29 | 7 | | 30 | 10 | | 31 | 18 | | 32 | 21 | | 33 | 3 | | 34 | 1 | | 35 | 33 | | 36 | 4 | | 37 | 6 | | 38 | 9 | | 39 | 13 | | 40 | 15 | | 41 | 9 | | 42 | 23 | | 43 | 3 | | 44 | 7 | | 45 | 1 | | 46 | 21 | | 47 | 13 | | 48 | 10 | | 49 | 23 |
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| 68.73% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.4263565891472868 | | totalSentences | 129 | | uniqueOpeners | 55 | |
| 42.74% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 78 | | matches | | 0 | "Just a man in expensive" |
| | ratio | 0.013 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 78 | | matches | | 0 | "His face was too fresh" | | 1 | "She followed him through the" | | 2 | "She'd investigated murders in plenty" | | 3 | "she asked, her footfalls echoing" | | 4 | "They reached the bottom of" | | 5 | "Her attention fixed immediately on" | | 6 | "Her curly red hair was" | | 7 | "She pointed to the man's" | | 8 | "Its face was etched with" | | 9 | "she asked, directing the question" | | 10 | "Her gaze returned to Eva," | | 11 | "She'd investigated a case in" | | 12 | "She pointed to a section" | | 13 | "she nodded toward the body," | | 14 | "She made a decision." | | 15 | "She gripped Quinn's arm with" | | 16 | "She checked her watch one" |
| | ratio | 0.218 | |
| 11.28% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 78 | | matches | | 0 | "Quinn got the call at" | | 1 | "A small army of uniformed" | | 2 | "His face was too fresh" | | 3 | "Quinn nodded, tucking her warrant" | | 4 | "She followed him through the" | | 5 | "The Metropolitan Police had sealed" | | 6 | "The deeper they descended, the" | | 7 | "She'd investigated murders in plenty" | | 8 | "The constable's torch beam bounced" | | 9 | "she asked, her footfalls echoing" | | 10 | "Quinn raised an eyebrow." | | 11 | "The constable shrugged" | | 12 | "They reached the bottom of" | | 13 | "The platform was alive with" | | 14 | "Her attention fixed immediately on" | | 15 | "The name escaped her lips" | | 16 | "Eva Kowalski looked up, her" | | 17 | "Recognition flickered across her freckled" | | 18 | "Eva said, rising to her" | | 19 | "Her curly red hair was" |
| | ratio | 0.897 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 78 | | matches | (empty) | | ratio | 0 | |
| 96.77% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 2 | | matches | | 0 | "Eva gestured to several evidence markers surrounding small objects near the body—a brass item that resembled a compass, what looked like carved bone tokens, and…" | | 1 | "Her gaze returned to Eva, who was now examining the wall of the platform with unusual intensity." |
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| 87.96% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 27 | | uselessAdditionCount | 2 | | matches | | 0 | "she asked, her footfalls echoing on the concrete steps" | | 1 | "Quinn said slowly, pieces clicking into place" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 2 | | fancyTags | | 0 | "them called out (call out)" | | 1 | "she ordered (order)" |
| | dialogueSentences | 73 | | tagDensity | 0.137 | | leniency | 0.274 | | rawRatio | 0.2 | | effectiveRatio | 0.055 | |