| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 94.92% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1969 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 64.45% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1969 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "fractured" | | 1 | "pumping" | | 2 | "vibrated" | | 3 | "calculated" | | 4 | "scanned" | | 5 | "flickered" | | 6 | "porcelain" | | 7 | "mechanical" | | 8 | "pulsed" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 296 | | matches | (empty) | |
| 94.59% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 5 | | narrationSentences | 296 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 296 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 19 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1966 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 86 | | wordCount | 1966 | | uniqueNames | 22 | | maxNameDensity | 1.63 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Harlow | 1 | | Quinn | 32 | | Camden | 2 | | High | 2 | | Street | 2 | | Raven | 1 | | Nest | 1 | | Morris | 5 | | Tube | 2 | | Met | 1 | | Veil | 2 | | Market | 2 | | Detective | 3 | | Saint | 1 | | Christopher | 1 | | Herrera | 3 | | Doc | 1 | | Soho | 1 | | Tomás | 8 | | You | 10 | | Open | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Tube" | | 4 | "Met" | | 5 | "Market" | | 6 | "Detective" | | 7 | "Saint" | | 8 | "Christopher" | | 9 | "Herrera" | | 10 | "Tomás" | | 11 | "You" | | 12 | "Open" |
| | places | | 0 | "London" | | 1 | "Camden" | | 2 | "High" | | 3 | "Street" | | 4 | "Raven" | | 5 | "Veil" | | 6 | "Soho" |
| | globalScore | 0.686 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 164 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like grinding stones" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.509 | | wordCount | 1966 | | matches | | 0 | "not blocking the path but watching her intent" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 296 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 72 | | mean | 27.31 | | std | 25.91 | | cv | 0.949 | | sampleLengths | | 0 | 80 | | 1 | 55 | | 2 | 3 | | 3 | 63 | | 4 | 64 | | 5 | 54 | | 6 | 50 | | 7 | 3 | | 8 | 16 | | 9 | 5 | | 10 | 23 | | 11 | 11 | | 12 | 33 | | 13 | 9 | | 14 | 4 | | 15 | 75 | | 16 | 39 | | 17 | 4 | | 18 | 49 | | 19 | 10 | | 20 | 6 | | 21 | 77 | | 22 | 8 | | 23 | 2 | | 24 | 14 | | 25 | 8 | | 26 | 56 | | 27 | 12 | | 28 | 27 | | 29 | 13 | | 30 | 44 | | 31 | 5 | | 32 | 71 | | 33 | 57 | | 34 | 43 | | 35 | 4 | | 36 | 14 | | 37 | 6 | | 38 | 12 | | 39 | 5 | | 40 | 3 | | 41 | 13 | | 42 | 12 | | 43 | 18 | | 44 | 4 | | 45 | 22 | | 46 | 7 | | 47 | 59 | | 48 | 33 | | 49 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 296 | | matches | | 0 | "wanted" | | 1 | "was gone" | | 2 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 400 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 296 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1969 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.017267648552564754 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.0030472320975114273 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 296 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 296 | | mean | 6.64 | | std | 3.19 | | cv | 0.481 | | sampleLengths | | 0 | 19 | | 1 | 13 | | 2 | 12 | | 3 | 14 | | 4 | 11 | | 5 | 11 | | 6 | 11 | | 7 | 13 | | 8 | 12 | | 9 | 4 | | 10 | 8 | | 11 | 7 | | 12 | 3 | | 13 | 9 | | 14 | 4 | | 15 | 6 | | 16 | 10 | | 17 | 9 | | 18 | 2 | | 19 | 16 | | 20 | 7 | | 21 | 12 | | 22 | 10 | | 23 | 11 | | 24 | 5 | | 25 | 7 | | 26 | 9 | | 27 | 10 | | 28 | 6 | | 29 | 16 | | 30 | 10 | | 31 | 12 | | 32 | 10 | | 33 | 7 | | 34 | 6 | | 35 | 15 | | 36 | 10 | | 37 | 6 | | 38 | 6 | | 39 | 3 | | 40 | 7 | | 41 | 9 | | 42 | 2 | | 43 | 3 | | 44 | 11 | | 45 | 7 | | 46 | 5 | | 47 | 6 | | 48 | 5 | | 49 | 3 |
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| 38.51% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 34 | | diversityRatio | 0.20945945945945946 | | totalSentences | 296 | | uniqueOpeners | 62 | |
| 47.96% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 278 | | matches | | 0 | "Somewhere in that crowd, the" | | 1 | "Then you know I don't" | | 2 | "Then I'll shoot them." | | 3 | "Only the mask remained on" |
| | ratio | 0.014 | |
| 31.51% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 131 | | totalSentences | 278 | | matches | | 0 | "She wiped her face with" | | 1 | "Her boots slapped against the" | | 2 | "She holstered the torch and" | | 3 | "Her voice cut through the" | | 4 | "She checked her worn leather" | | 5 | "She lost DS Morris three" | | 6 | "She would not make that" | | 7 | "She holstered her weapon and" | | 8 | "It groaned on hinges that" | | 9 | "She blinked and focused on" | | 10 | "He wore a coat made" | | 11 | "He held a staff topped" | | 12 | "Her hand hovered near her" | | 13 | "You cannot pass." | | 14 | "He tapped the staff against" | | 15 | "She needed to see what" | | 16 | "She knew it." | | 17 | "I saw a man enter." | | 18 | "He's wanted for questioning." | | 19 | "He slipped it into a" |
| | ratio | 0.471 | |
| 4.96% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 253 | | totalSentences | 278 | | matches | | 0 | "Detective Harlow Quinn pushed through" | | 1 | "Water streamed off her closely" | | 2 | "She wiped her face with" | | 3 | "Her boots slapped against the" | | 4 | "Military precision kept her upright" | | 5 | "The figure moved with an" | | 6 | "Quinn drew her torch and" | | 7 | "Darkness swallowed the light." | | 8 | "She holstered the torch and" | | 9 | "The metal felt cold against" | | 10 | "Her voice cut through the" | | 11 | "The runner didn't slow." | | 12 | "Quinn surged forward, her legs" | | 13 | "She checked her worn leather" | | 14 | "She lost DS Morris three" | | 15 | "She would not make that" | | 16 | "The suspect vanished into the" | | 17 | "Quinn approached the gap, her" | | 18 | "The air here smelled different." | | 19 | "Less exhaust, more ozone and" |
| | ratio | 0.91 | |
| 17.99% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 278 | | matches | | 0 | "If she walked away, it" |
| | ratio | 0.004 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 58 | | technicalSentenceCount | 2 | | matches | | 0 | "Rain hammered the pavement, turning the London streets into slick mirrors that fractured the neon glow of shop signs." | | 1 | "A cage nearby rattled, containing something that scratched at the bars." |
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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 | |