| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1974 | | 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) | |
| 89.87% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1974 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "scanned" | | 1 | "flicker" | | 2 | "fluttered" | | 3 | "pumping" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 176 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 176 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 264 | | 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 | 1974 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 31.89% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 94 | | wordCount | 1524 | | uniqueNames | 22 | | maxNameDensity | 2.36 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 36 | | Raven | 1 | | Nest | 1 | | Tomás | 1 | | Herrera | 28 | | Wardour | 1 | | Street | 2 | | Christopher | 2 | | Brewer | 1 | | Shaftesbury | 1 | | Avenue | 1 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Camden | 1 | | Underground | 2 | | Saint | 2 | | Morris | 2 | | Rain | 4 | | Warm | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Christopher" | | 7 | "Saint" | | 8 | "Morris" | | 9 | "Rain" |
| | places | | 0 | "Soho" | | 1 | "Wardour" | | 2 | "Street" | | 3 | "Brewer" | | 4 | "Shaftesbury" | | 5 | "Avenue" | | 6 | "Charing" | | 7 | "Cross" | | 8 | "Road" | | 9 | "Camden" |
| | globalScore | 0.319 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 116 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1974 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 264 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 197 | | mean | 10.02 | | std | 10.67 | | cv | 1.065 | | sampleLengths | | 0 | 22 | | 1 | 39 | | 2 | 2 | | 3 | 38 | | 4 | 5 | | 5 | 42 | | 6 | 6 | | 7 | 24 | | 8 | 3 | | 9 | 22 | | 10 | 6 | | 11 | 2 | | 12 | 7 | | 13 | 19 | | 14 | 36 | | 15 | 8 | | 16 | 2 | | 17 | 6 | | 18 | 2 | | 19 | 5 | | 20 | 42 | | 21 | 5 | | 22 | 24 | | 23 | 6 | | 24 | 5 | | 25 | 18 | | 26 | 30 | | 27 | 5 | | 28 | 42 | | 29 | 3 | | 30 | 3 | | 31 | 27 | | 32 | 22 | | 33 | 12 | | 34 | 2 | | 35 | 1 | | 36 | 11 | | 37 | 5 | | 38 | 5 | | 39 | 3 | | 40 | 6 | | 41 | 10 | | 42 | 18 | | 43 | 4 | | 44 | 8 | | 45 | 24 | | 46 | 13 | | 47 | 4 | | 48 | 32 | | 49 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 176 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 257 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 264 | | ratio | 0.004 | | matches | | 0 | "Her forearm pinned his throat; the baton pressed beneath his jaw." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1537 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.011711125569290826 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0006506180871828237 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 264 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 264 | | mean | 7.48 | | std | 5.3 | | cv | 0.709 | | sampleLengths | | 0 | 22 | | 1 | 15 | | 2 | 13 | | 3 | 11 | | 4 | 2 | | 5 | 34 | | 6 | 4 | | 7 | 5 | | 8 | 9 | | 9 | 6 | | 10 | 17 | | 11 | 10 | | 12 | 6 | | 13 | 8 | | 14 | 2 | | 15 | 14 | | 16 | 3 | | 17 | 9 | | 18 | 13 | | 19 | 6 | | 20 | 2 | | 21 | 7 | | 22 | 8 | | 23 | 4 | | 24 | 7 | | 25 | 20 | | 26 | 16 | | 27 | 8 | | 28 | 2 | | 29 | 6 | | 30 | 2 | | 31 | 5 | | 32 | 5 | | 33 | 23 | | 34 | 14 | | 35 | 5 | | 36 | 3 | | 37 | 6 | | 38 | 15 | | 39 | 6 | | 40 | 5 | | 41 | 18 | | 42 | 7 | | 43 | 2 | | 44 | 21 | | 45 | 5 | | 46 | 13 | | 47 | 11 | | 48 | 18 | | 49 | 3 |
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| 52.53% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.32196969696969696 | | totalSentences | 264 | | uniqueOpeners | 85 | |
| 19.96% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 167 | | matches | | 0 | "Then Tomás Herrera stepped out." |
| | ratio | 0.006 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 167 | | matches | | 0 | "He wore a waxed jacket" | | 1 | "He paused under the green" | | 2 | "He headed north." | | 3 | "She slapped the bonnet without" | | 4 | "His shoulders pitched forward." | | 5 | "His shoes struck wet brick" | | 6 | "She cleared the gate and" | | 7 | "He glanced back." | | 8 | "He snatched a stack of" | | 9 | "She planted a palm on" | | 10 | "He reached the far end" | | 11 | "She turned once, mapping exits," | | 12 | "Its rear door remained open" | | 13 | "He looked over the roof." | | 14 | "She caught his wrist." | | 15 | "His skin felt cold." | | 16 | "He tore free, leaving a" | | 17 | "She climbed out." | | 18 | "He launched into traffic." | | 19 | "It darted through amber lights," |
| | ratio | 0.192 | |
| 13.89% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 149 | | totalSentences | 167 | | matches | | 0 | "Rain glazed the green neon" | | 1 | "Detective Harlow Quinn watched from" | | 2 | "Water gathered on her closely" | | 3 | "The worn leather strap of" | | 4 | "The Raven’s Nest had disgorged" | | 5 | "None had interested her." | | 6 | "He wore a waxed jacket" | | 7 | "A canvas medical bag hung" | | 8 | "He paused under the green" | | 9 | "Quinn shifted behind the bus" | | 10 | "Herrera touched the silver medallion" | | 11 | "Patron saint of travellers, paramedics" | | 12 | "He headed north." | | 13 | "Quinn let him reach the" | | 14 | "A taxi shouldered through a" | | 15 | "The driver leaned on his" | | 16 | "She slapped the bonnet without" | | 17 | "Herrera didn’t look back, but" | | 18 | "His shoulders pitched forward." | | 19 | "The medical bag bounced against" |
| | ratio | 0.892 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 167 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 63 | | technicalSentenceCount | 1 | | matches | | 0 | "Patron saint of travellers, paramedics and men who had lied through three separate interviews." |
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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 | |