| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 25 | | tagDensity | 0.28 | | leniency | 0.56 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.87% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1211 | | totalAiIsmAdverbs | 1 | | 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) | |
| 79.36% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1211 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "streaming" | | 1 | "echoing" | | 2 | "flickered" | | 3 | "could feel" | | 4 | "weight" |
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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 | 87 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 87 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 105 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1205 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 93.30% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 970 | | uniqueNames | 13 | | maxNameDensity | 1.13 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Greek | 1 | | Street | 3 | | Soho | 1 | | London | 4 | | Bateman | 1 | | Herrera | 6 | | Dean | 1 | | Victorian | 1 | | Morris | 1 | | Whitechapel | 1 | | Saint | 1 | | Christopher | 1 | | Quinn | 11 |
| | persons | | 0 | "Herrera" | | 1 | "Morris" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Quinn" |
| | places | | 0 | "Greek" | | 1 | "Street" | | 2 | "Soho" | | 3 | "London" | | 4 | "Bateman" | | 5 | "Dean" | | 6 | "Whitechapel" |
| | globalScore | 0.933 | | windowScore | 1 | |
| 69.35% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 2 | | matches | | 0 | "groan that seemed to come from somewhere deep underground" | | 1 | "quite people, were they" |
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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 | 1205 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 105 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 54 | | mean | 22.31 | | std | 18.9 | | cv | 0.847 | | sampleLengths | | 0 | 36 | | 1 | 3 | | 2 | 6 | | 3 | 56 | | 4 | 47 | | 5 | 53 | | 6 | 1 | | 7 | 27 | | 8 | 38 | | 9 | 1 | | 10 | 1 | | 11 | 47 | | 12 | 26 | | 13 | 12 | | 14 | 9 | | 15 | 44 | | 16 | 5 | | 17 | 2 | | 18 | 46 | | 19 | 31 | | 20 | 9 | | 21 | 25 | | 22 | 6 | | 23 | 27 | | 24 | 77 | | 25 | 2 | | 26 | 40 | | 27 | 9 | | 28 | 10 | | 29 | 51 | | 30 | 59 | | 31 | 2 | | 32 | 7 | | 33 | 30 | | 34 | 9 | | 35 | 3 | | 36 | 11 | | 37 | 39 | | 38 | 6 | | 39 | 21 | | 40 | 8 | | 41 | 47 | | 42 | 2 | | 43 | 21 | | 44 | 8 | | 45 | 6 | | 46 | 14 | | 47 | 36 | | 48 | 23 | | 49 | 23 |
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| 97.20% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 87 | | matches | | 0 | "was plastered" | | 1 | "was connected" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 176 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 105 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 976 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.015368852459016393 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0030737704918032786 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 105 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 105 | | mean | 11.48 | | std | 8.42 | | cv | 0.734 | | sampleLengths | | 0 | 10 | | 1 | 26 | | 2 | 3 | | 3 | 3 | | 4 | 3 | | 5 | 23 | | 6 | 18 | | 7 | 15 | | 8 | 21 | | 9 | 6 | | 10 | 20 | | 11 | 7 | | 12 | 10 | | 13 | 2 | | 14 | 10 | | 15 | 24 | | 16 | 1 | | 17 | 9 | | 18 | 18 | | 19 | 9 | | 20 | 29 | | 21 | 1 | | 22 | 1 | | 23 | 19 | | 24 | 13 | | 25 | 15 | | 26 | 9 | | 27 | 2 | | 28 | 15 | | 29 | 7 | | 30 | 1 | | 31 | 3 | | 32 | 1 | | 33 | 6 | | 34 | 3 | | 35 | 17 | | 36 | 8 | | 37 | 19 | | 38 | 5 | | 39 | 2 | | 40 | 15 | | 41 | 21 | | 42 | 10 | | 43 | 5 | | 44 | 9 | | 45 | 17 | | 46 | 5 | | 47 | 4 | | 48 | 12 | | 49 | 13 |
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| 93.97% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.580952380952381 | | totalSentences | 105 | | uniqueOpeners | 61 | |
| 42.74% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 78 | | matches | | | ratio | 0.013 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 78 | | matches | | 0 | "He didn't stop." | | 1 | "They never stopped." | | 2 | "Her sharp jaw was set," | | 3 | "She'd been watching him for" | | 4 | "His head turned at the" | | 5 | "She stepped into the darkness," | | 6 | "She'd seen him." | | 7 | "Her torch beam swept across" | | 8 | "She crouched, careful not to" | | 9 | "She pressed against the wall." | | 10 | "She could hear voices, dozens" | | 11 | "Her radio crackled with static." | | 12 | "She tried it anyway." | | 13 | "She checked her position" | | 14 | "She felt it in her" | | 15 | "They'd found his body two" | | 16 | "She emerged onto a platform" | | 17 | "He laughed, but there was" | | 18 | "He gestured at the market" | | 19 | "He paused, watching her face" |
| | ratio | 0.295 | |
| 75.38% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 78 | | matches | | 0 | "The suspect's trainers slapped against" | | 1 | "Quinn's lungs burned as she" | | 2 | "He didn't stop." | | 3 | "They never stopped." | | 4 | "Quinn's salt-and-pepper hair was plastered" | | 5 | "The suspect, a wiry man" | | 6 | "Quinn hurdled it without breaking" | | 7 | "Her sharp jaw was set," | | 8 | "She'd been watching him for" | | 9 | "Tomás Herrera, the name kept" | | 10 | "Tonight she'd finally spotted him" | | 11 | "His head turned at the" | | 12 | "Quinn skidded to a halt" | | 13 | "The space between two Victorian" | | 14 | "She stepped into the darkness," | | 15 | "The rain fell lighter here," | | 16 | "Water dripped from fire escapes" | | 17 | "The brick wall at the" | | 18 | "Nothing but decades of weathering" | | 19 | "Quinn ran her fingers along" |
| | ratio | 0.769 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 78 | | matches | (empty) | | ratio | 0 | |
| 27.03% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 6 | | matches | | 0 | "Rain hammered Soho like a punishment, turning the narrow streets into rivers that swallowed the neon reflections of sex shops and late-night cafes." | | 1 | "Lost his licence under circumstances that made her instincts itch." | | 2 | "Warm air rushed out, carrying smells that didn't belong in central London: cinnamon, copper, something that might have been burning sage." | | 3 | "She emerged onto a platform overlooking something that shouldn't exist." | | 4 | "Quinn's trained eye catalogued the details her conscious mind wanted to reject: a woman whose shadow didn't match her movements, a man whose teeth had far too m…" | | 5 | "Or step forward into territory that made no sense, following a suspect who was offering answers to questions she'd been afraid to ask." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |