| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 1 | | adverbTags | | 0 | "She advanced slowly [slowly]" |
| | dialogueSentences | 33 | | tagDensity | 0.455 | | leniency | 0.909 | | rawRatio | 0.067 | | effectiveRatio | 0.061 | |
| 82.48% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1712 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "slowly" | | 1 | "slightly" | | 2 | "really" | | 3 | "quickly" | | 4 | "completely" |
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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) | |
| 50.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1712 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "jaw clenched" | | 1 | "measured" | | 2 | "pulse" | | 3 | "echoed" | | 4 | "silence" | | 5 | "familiar" | | 6 | "lilt" | | 7 | "scanning" | | 8 | "flickered" | | 9 | "could feel" | | 10 | "tracing" | | 11 | "loomed" | | 12 | "pounding" |
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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 | 1 |
| | 1 | | label | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "jaw clenched" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 172 | | matches | | 0 | "t in determination" | | 1 | "t with fear" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 172 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 189 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1712 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 77.79% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 67 | | wordCount | 1454 | | uniqueNames | 21 | | maxNameDensity | 1.44 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Tomás" | | discoveredNames | | Harlow | 1 | | Quinn | 21 | | Herrera | 1 | | Raven | 1 | | Nest | 2 | | Dean | 1 | | Street | 1 | | Morris | 6 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Camden | 1 | | Tube | 1 | | Tomás | 21 | | Laughter | 1 | | Veil | 1 | | Market | 1 | | Saint | 1 | | Christopher | 1 | | Seville | 1 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Raven" | | 4 | "Morris" | | 5 | "Tomás" | | 6 | "Laughter" | | 7 | "Market" | | 8 | "Saint" | | 9 | "Christopher" |
| | places | | 0 | "Nest" | | 1 | "Dean" | | 2 | "Street" | | 3 | "Charing" | | 4 | "Cross" | | 5 | "Road" | | 6 | "Camden" | | 7 | "Seville" | | 8 | "London" |
| | globalScore | 0.778 | | windowScore | 0.833 | |
| 38.39% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 112 | | glossingSentenceCount | 5 | | matches | | 0 | "looked like runes" | | 1 | "looked like a man trying to protect somet" | | 2 | "looked like a human finger bone strung on" | | 3 | "something like grief crossing his features b" | | 4 | "looked like bottled nightmares" |
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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 | 1712 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 189 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 31.13 | | std | 18.3 | | cv | 0.588 | | sampleLengths | | 0 | 51 | | 1 | 76 | | 2 | 76 | | 3 | 49 | | 4 | 71 | | 5 | 47 | | 6 | 39 | | 7 | 46 | | 8 | 41 | | 9 | 45 | | 10 | 5 | | 11 | 18 | | 12 | 27 | | 13 | 73 | | 14 | 32 | | 15 | 51 | | 16 | 20 | | 17 | 6 | | 18 | 44 | | 19 | 19 | | 20 | 28 | | 21 | 23 | | 22 | 23 | | 23 | 21 | | 24 | 41 | | 25 | 31 | | 26 | 35 | | 27 | 6 | | 28 | 29 | | 29 | 42 | | 30 | 17 | | 31 | 32 | | 32 | 25 | | 33 | 46 | | 34 | 34 | | 35 | 51 | | 36 | 6 | | 37 | 14 | | 38 | 27 | | 39 | 48 | | 40 | 14 | | 41 | 16 | | 42 | 26 | | 43 | 4 | | 44 | 42 | | 45 | 28 | | 46 | 4 | | 47 | 27 | | 48 | 9 | | 49 | 35 |
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| 99.14% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 172 | | matches | | 0 | "was plastered" | | 1 | "been found" | | 2 | "being readied" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 276 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 189 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1460 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 38 | | adverbRatio | 0.026027397260273973 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.007534246575342466 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 189 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 189 | | mean | 9.06 | | std | 5.28 | | cv | 0.583 | | sampleLengths | | 0 | 17 | | 1 | 15 | | 2 | 19 | | 3 | 4 | | 4 | 6 | | 5 | 13 | | 6 | 2 | | 7 | 22 | | 8 | 14 | | 9 | 15 | | 10 | 11 | | 11 | 6 | | 12 | 18 | | 13 | 11 | | 14 | 5 | | 15 | 3 | | 16 | 22 | | 17 | 6 | | 18 | 6 | | 19 | 7 | | 20 | 13 | | 21 | 5 | | 22 | 12 | | 23 | 12 | | 24 | 19 | | 25 | 4 | | 26 | 11 | | 27 | 8 | | 28 | 17 | | 29 | 11 | | 30 | 4 | | 31 | 8 | | 32 | 14 | | 33 | 10 | | 34 | 9 | | 35 | 3 | | 36 | 4 | | 37 | 15 | | 38 | 8 | | 39 | 5 | | 40 | 6 | | 41 | 10 | | 42 | 5 | | 43 | 3 | | 44 | 17 | | 45 | 5 | | 46 | 7 | | 47 | 5 | | 48 | 12 | | 49 | 12 |
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| 64.37% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4021164021164021 | | totalSentences | 189 | | uniqueOpeners | 76 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 154 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 44 | | totalSentences | 154 | | matches | | 0 | "He kept running, shoulders hunched," | | 1 | "She had clocked him leaving" | | 2 | "She rounded the corner into" | | 3 | "She ignored it." | | 4 | "He burst onto Charing Cross" | | 5 | "Her sharp jaw clenched tight." | | 6 | "she muttered under her breath" | | 7 | "She remembered the file." | | 8 | "Their eyes met across twenty" | | 9 | "His warm brown gaze held" | | 10 | "He looked like a man" | | 11 | "He veered left into a" | | 12 | "She knew this area." | | 13 | "Her hand hovered near the" | | 14 | "She glanced at her watch." | | 15 | "Her torch beam swept across" | | 16 | "She kept her tread light," | | 17 | "Her voice bounced back distorted" | | 18 | "She advanced, shoulders squared in" | | 19 | "She'd heard whispers in interrogation" |
| | ratio | 0.286 | |
| 15.19% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 137 | | totalSentences | 154 | | matches | | 0 | "The suspect's coat whipped round" | | 1 | "Detective Harlow Quinn dug her" | | 2 | "The words vanished into the" | | 3 | "He kept running, shoulders hunched," | | 4 | "She had clocked him leaving" | | 5 | "The barman had sworn the" | | 6 | "She rounded the corner into" | | 7 | "A skip blocked half the" | | 8 | "Tomás vaulted it without breaking" | | 9 | "Quinn followed, her worn leather" | | 10 | "Pain flared in her wrist." | | 11 | "She ignored it." | | 12 | "He burst onto Charing Cross" | | 13 | "A black cab swerved, horn" | | 14 | "Tomás dodged between two vans," | | 15 | "Quinn's lungs protested but she" | | 16 | "Her sharp jaw clenched tight." | | 17 | "Military precision kept her steps" | | 18 | "she muttered under her breath" | | 19 | "The scar on his forearm" |
| | ratio | 0.89 | |
| 64.94% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 154 | | matches | | 0 | "Now the stench of strange" | | 1 | "Before Tomás could respond, the" |
| | ratio | 0.013 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 64 | | technicalSentenceCount | 4 | | matches | | 0 | "Rain hammered the pavement, turning every step into a slap against black tarmac that reflected the city's sickly glow." | | 1 | "A narrow passage revealed itself, lit by flickering candles that smelled of myrrh and something sharper." | | 2 | "Red seeped from the paper continents, dripping onto the stone floor with wet sounds that echoed like distant screams." | | 3 | "Quinn's torch flickered once and died completely, plunging them into a darkness that felt alive." |
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| 91.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 1 | | matches | | 0 | "he whispered, voice tight with fear," |
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| 28.79% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 4 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "she muttered (mutter)" | | 2 | "she demanded (demand)" | | 3 | "he whispered (whisper)" |
| | dialogueSentences | 33 | | tagDensity | 0.152 | | leniency | 0.303 | | rawRatio | 0.8 | | effectiveRatio | 0.242 | |