| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 75 | | tagDensity | 0.173 | | leniency | 0.347 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1799 | | 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) | |
| 80.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1799 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "weight" | | 2 | "stomach" | | 3 | "velvet" | | 4 | "flicked" |
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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 | 192 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 192 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 254 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1799 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 70 | | wordCount | 1432 | | uniqueNames | 16 | | maxNameDensity | 2.44 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 35 | | Brewer | 1 | | Street | 1 | | Shaftesbury | 1 | | Avenue | 1 | | Underground | 1 | | Transport | 1 | | London | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 10 | | Eli | 8 | | Rain | 3 | | Water | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Tomás" | | 6 | "Eli" | | 7 | "Rain" | | 8 | "Water" |
| | places | | 0 | "Brewer" | | 1 | "Street" | | 2 | "Shaftesbury" | | 3 | "Avenue" | | 4 | "Transport" | | 5 | "London" |
| | globalScore | 0.278 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 113 | | 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 | 1799 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 254 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 171 | | mean | 10.52 | | std | 11.16 | | cv | 1.061 | | sampleLengths | | 0 | 13 | | 1 | 21 | | 2 | 16 | | 3 | 4 | | 4 | 4 | | 5 | 26 | | 6 | 2 | | 7 | 45 | | 8 | 1 | | 9 | 3 | | 10 | 40 | | 11 | 3 | | 12 | 5 | | 13 | 3 | | 14 | 40 | | 15 | 1 | | 16 | 7 | | 17 | 4 | | 18 | 24 | | 19 | 7 | | 20 | 2 | | 21 | 16 | | 22 | 13 | | 23 | 8 | | 24 | 43 | | 25 | 3 | | 26 | 9 | | 27 | 24 | | 28 | 11 | | 29 | 4 | | 30 | 7 | | 31 | 20 | | 32 | 5 | | 33 | 26 | | 34 | 41 | | 35 | 7 | | 36 | 11 | | 37 | 4 | | 38 | 9 | | 39 | 6 | | 40 | 5 | | 41 | 7 | | 42 | 51 | | 43 | 13 | | 44 | 3 | | 45 | 11 | | 46 | 3 | | 47 | 17 | | 48 | 6 | | 49 | 8 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 192 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 252 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 254 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1435 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.020209059233449476 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0020905923344947735 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 254 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 254 | | mean | 7.08 | | std | 4.94 | | cv | 0.697 | | sampleLengths | | 0 | 13 | | 1 | 21 | | 2 | 16 | | 3 | 4 | | 4 | 4 | | 5 | 13 | | 6 | 13 | | 7 | 2 | | 8 | 4 | | 9 | 15 | | 10 | 19 | | 11 | 7 | | 12 | 1 | | 13 | 3 | | 14 | 2 | | 15 | 1 | | 16 | 12 | | 17 | 25 | | 18 | 3 | | 19 | 5 | | 20 | 3 | | 21 | 6 | | 22 | 6 | | 23 | 16 | | 24 | 12 | | 25 | 1 | | 26 | 7 | | 27 | 4 | | 28 | 8 | | 29 | 16 | | 30 | 7 | | 31 | 2 | | 32 | 7 | | 33 | 9 | | 34 | 13 | | 35 | 8 | | 36 | 6 | | 37 | 23 | | 38 | 14 | | 39 | 3 | | 40 | 9 | | 41 | 6 | | 42 | 9 | | 43 | 2 | | 44 | 2 | | 45 | 5 | | 46 | 11 | | 47 | 4 | | 48 | 7 | | 49 | 20 |
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| 53.54% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3425196850393701 | | totalSentences | 254 | | uniqueOpeners | 87 | |
| 20.70% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 161 | | matches | | | ratio | 0.006 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 161 | | matches | | 0 | "He landed badly, recovered, and" | | 1 | "Her shoulder scraped brick." | | 2 | "He glanced back." | | 3 | "His eyes caught the glow" | | 4 | "He vanished around the corner." | | 5 | "She ran harder." | | 6 | "He shoved through a knot" | | 7 | "She hit the steps two" | | 8 | "Her radio crackled." | | 9 | "It had been cut free" | | 10 | "He wore a long waxed" | | 11 | "His skin carried the colour" | | 12 | "he went on" | | 13 | "His teeth had been filed" | | 14 | "Their flames burned blue, green," | | 15 | "He caught her wrist." | | 16 | "His fingers felt dry and" | | 17 | "He folded against the turnstile" | | 18 | "she told him" | | 19 | "Their hands spun backwards." |
| | ratio | 0.211 | |
| 15.90% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 143 | | totalSentences | 161 | | matches | | 0 | "Rain lashed the windscreen hard" | | 1 | "Detective Harlow Quinn cut across" | | 2 | "Static chewed her words." | | 3 | "The runner vaulted a stack" | | 4 | "He landed badly, recovered, and" | | 5 | "Her shoulder scraped brick." | | 6 | "Rain poured from a broken" | | 7 | "The alley smelled of sour" | | 8 | "He glanced back." | | 9 | "Blood ran from a cut" | | 10 | "His eyes caught the glow" | | 11 | "Quinn’s breath snagged." | | 12 | "He vanished around the corner." | | 13 | "She ran harder." | | 14 | "The alley spilled onto Shaftesbury" | | 15 | "A taxi fishtailed past, horn" | | 16 | "Theatre signs burned through the" | | 17 | "Quinn crossed between cars, forcing" | | 18 | "The driver leaned out of" | | 19 | "The suspect had already reached" |
| | ratio | 0.888 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 161 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 1 | | matches | | 0 | "His head sat too low on his chest, as though his neck had sunk into his body." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 56.67% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 7 | | fancyTags | | 0 | "Quinn shouted (shout)" | | 1 | "Quinn shouted (shout)" | | 2 | "he whispered (whisper)" | | 3 | "Quinn snapped (snap)" | | 4 | "he shouted (shout)" | | 5 | "Tomás warned (warn)" | | 6 | "he whispered (whisper)" |
| | dialogueSentences | 75 | | tagDensity | 0.147 | | leniency | 0.293 | | rawRatio | 0.636 | | effectiveRatio | 0.187 | |