| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 1 | | adverbTags | | 0 | "He stepped backward [backward]" |
| | dialogueSentences | 10 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 0.333 | | effectiveRatio | 0.2 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1089 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 77.04% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1089 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "glint" | | 1 | "footsteps" | | 2 | "echoing" | | 3 | "velvet" | | 4 | "weight" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 101 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 101 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 108 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1084 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 998 | | uniqueNames | 23 | | maxNameDensity | 1.3 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 13 | | Tomás | 2 | | Herrera | 9 | | Saint | 1 | | Christopher | 1 | | Raven | 3 | | Nest | 3 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Elephant | 1 | | Castle | 1 | | Camden | 2 | | Morris | 6 | | Mornington | 1 | | Crescent | 1 | | Tube | 1 | | London | 2 | | Veil | 1 | | Market | 1 | | Three | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Nest" | | 7 | "Morris" | | 8 | "Market" |
| | places | | 0 | "Soho" | | 1 | "Charing" | | 2 | "Cross" | | 3 | "Road" | | 4 | "Elephant" | | 5 | "Mornington" | | 6 | "Crescent" | | 7 | "London" |
| | globalScore | 0.849 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1084 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 108 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 29.3 | | std | 22.85 | | cv | 0.78 | | sampleLengths | | 0 | 53 | | 1 | 4 | | 2 | 66 | | 3 | 8 | | 4 | 16 | | 5 | 34 | | 6 | 64 | | 7 | 53 | | 8 | 4 | | 9 | 44 | | 10 | 48 | | 11 | 2 | | 12 | 44 | | 13 | 7 | | 14 | 7 | | 15 | 22 | | 16 | 6 | | 17 | 29 | | 18 | 15 | | 19 | 31 | | 20 | 33 | | 21 | 5 | | 22 | 101 | | 23 | 6 | | 24 | 46 | | 25 | 30 | | 26 | 51 | | 27 | 35 | | 28 | 50 | | 29 | 32 | | 30 | 8 | | 31 | 4 | | 32 | 37 | | 33 | 25 | | 34 | 3 | | 35 | 53 | | 36 | 8 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 101 | | matches | (empty) | |
| 46.74% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 174 | | matches | | 0 | "was heading" | | 1 | "was already descending" | | 2 | "was slipping" | | 3 | "was running" |
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| 63.49% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 108 | | ratio | 0.028 | | matches | | 0 | "He glanced back once, and in the sodium glare of a streetlamp she saw his face—not panicked, not guilty." | | 1 | "He pulled something from his pocket—a small white object, bone—and pressed it to a rusted iron door that had no business opening." | | 2 | "Below her, the stairwell opened into a cavern of light and noise—a market, sprawling through the bones of an abandoned Tube station." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1004 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.0199203187250996 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.00099601593625498 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 108 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 108 | | mean | 10.04 | | std | 6.89 | | cv | 0.686 | | sampleLengths | | 0 | 12 | | 1 | 17 | | 2 | 24 | | 3 | 4 | | 4 | 17 | | 5 | 30 | | 6 | 19 | | 7 | 3 | | 8 | 2 | | 9 | 3 | | 10 | 16 | | 11 | 3 | | 12 | 19 | | 13 | 1 | | 14 | 11 | | 15 | 8 | | 16 | 18 | | 17 | 4 | | 18 | 23 | | 19 | 7 | | 20 | 4 | | 21 | 10 | | 22 | 15 | | 23 | 4 | | 24 | 8 | | 25 | 7 | | 26 | 6 | | 27 | 3 | | 28 | 4 | | 29 | 3 | | 30 | 6 | | 31 | 13 | | 32 | 11 | | 33 | 6 | | 34 | 5 | | 35 | 23 | | 36 | 3 | | 37 | 22 | | 38 | 2 | | 39 | 16 | | 40 | 10 | | 41 | 13 | | 42 | 5 | | 43 | 7 | | 44 | 7 | | 45 | 4 | | 46 | 6 | | 47 | 12 | | 48 | 6 | | 49 | 13 |
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| 50.31% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.37962962962962965 | | totalSentences | 108 | | uniqueOpeners | 41 | |
| 69.44% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 96 | | matches | | 0 | "Somewhere in that green dark," | | 1 | "Somewhere ahead, Herrera was running." |
| | ratio | 0.021 | |
| 95.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 96 | | matches | | 0 | "She'd left the unmarked car" | | 1 | "He moved like a man" | | 2 | "He cut left through a" | | 3 | "She hurdled it." | | 4 | "Her ankle screamed." | | 5 | "He didn't stop." | | 6 | "He glanced back once, and" | | 7 | "She'd seen him slip into" | | 8 | "He'd decided to run." | | 9 | "They burst out of the" | | 10 | "She didn't break stride." | | 11 | "His flat sat in Elephant" | | 12 | "She'd read the old case" | | 13 | "He didn't slow." | | 14 | "He pulled something from his" | | 15 | "His breath came in clouds." | | 16 | "He shook his head." | | 17 | "He looked down the stairs," | | 18 | "He stepped backward onto the" | | 19 | "She stood at the threshold." |
| | ratio | 0.313 | |
| 27.71% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 83 | | totalSentences | 96 | | matches | | 0 | "Detective Harlow Quinn hit the" | | 1 | "She'd left the unmarked car" | | 2 | "He moved like a man" | | 3 | "He cut left through a" | | 4 | "Quinn caught the glint of" | | 5 | "She hurdled it." | | 6 | "Her ankle screamed." | | 7 | "The words came out ragged," | | 8 | "He didn't stop." | | 9 | "He glanced back once, and" | | 10 | "The kind of scared that" | | 11 | "Quinn had been watching him" | | 12 | "Tomás Herrera, former paramedic, struck" | | 13 | "The clique's back-alley doctor." | | 14 | "She'd seen him slip into" | | 15 | "Tonight, she'd decided to bring" | | 16 | "He'd decided to run." | | 17 | "They burst out of the" | | 18 | "A taxi blared its horn," | | 19 | "She didn't break stride." |
| | ratio | 0.865 | |
| 52.08% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 96 | | matches | | | ratio | 0.01 | |
| 51.28% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 5 | | matches | | 0 | "She'd left the unmarked car two blocks back, trapped behind a delivery lorry that had materialised out of the downpour like a bad omen." | | 1 | "She'd seen him slip into the Raven's Nest at odd hours, seen him emerge with blood on his cuffs that wasn't his own." | | 2 | "He pulled something from his pocket—a small white object, bone—and pressed it to a rusted iron door that had no business opening." | | 3 | "Herrera was already descending, his footsteps echoing in the green dark." | | 4 | "The way he'd looked the last time she saw him, standing in the rain outside the Raven's Nest, telling her he'd found something." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |