| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 65.64% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 291 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 291 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "echoed" | | 1 | "standard" | | 2 | "complex" | | 3 | "encounter" | | 4 | "trembled" | | 5 | "could feel" |
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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 | 19 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 19 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 22 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 289 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 69.03% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 16 | | wordCount | 247 | | uniqueNames | 10 | | maxNameDensity | 1.62 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 2 | | Detective | 2 | | Harlow | 1 | | Quinn | 4 | | Marcus | 1 | | Reyes | 2 | | Morris | 1 | | Veil | 1 | | Compass | 1 | | Camden | 1 |
| | persons | | 0 | "Detective" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Marcus" | | 4 | "Reyes" | | 5 | "Morris" | | 6 | "Camden" |
| | places | (empty) | | globalScore | 0.69 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 14 | | 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 | 289 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 22 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 10 | | mean | 28.9 | | std | 16.18 | | cv | 0.56 | | sampleLengths | | 0 | 42 | | 1 | 14 | | 2 | 18 | | 3 | 56 | | 4 | 42 | | 5 | 19 | | 6 | 14 | | 7 | 46 | | 8 | 5 | | 9 | 33 |
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| 86.80% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 19 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 38 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 22 | | ratio | 0.182 | | matches | | 0 | "The body had been positioned with deliberate care - each limb angled exactly twenty-seven degrees from the central axis, hands positioned palm-up in a pattern that suggested something more than mere murder." | | 1 | "They formed a complex geometric design that reminded her of something she'd seen in her partner Morris's old case files - before he disappeared three years ago." | | 2 | "The verdigris-coated instrument was a relic from her last encounter with the supernatural - a Veil Compass that could detect supernatural energy." | | 3 | "This crime scene was a message - but to whom?" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 249 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 8 | | adverbRatio | 0.0321285140562249 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.01606425702811245 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 22 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 22 | | mean | 13.14 | | std | 7.77 | | cv | 0.592 | | sampleLengths | | 0 | 17 | | 1 | 25 | | 2 | 14 | | 3 | 5 | | 4 | 13 | | 5 | 4 | | 6 | 20 | | 7 | 32 | | 8 | 11 | | 9 | 4 | | 10 | 27 | | 11 | 10 | | 12 | 9 | | 13 | 2 | | 14 | 12 | | 15 | 12 | | 16 | 22 | | 17 | 12 | | 18 | 5 | | 19 | 8 | | 20 | 15 | | 21 | 10 |
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| 93.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5909090909090909 | | totalSentences | 22 | | uniqueOpeners | 13 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 18 | | matches | (empty) | | ratio | 0 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 6 | | totalSentences | 18 | | matches | | 0 | "Her leather-strapped watch caught the" | | 1 | "she muttered, her salt-and-pepper hair" | | 2 | "She'd seen enough supernatural crime" | | 3 | "She pulled on a latex" | | 4 | "They formed a complex geometric" | | 5 | "She ignored him, pulling a" |
| | ratio | 0.333 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 18 | | totalSentences | 18 | | matches | | 0 | "The abandoned Tube station echoed" | | 1 | "Her leather-strapped watch caught the" | | 2 | "she muttered, her salt-and-pepper hair" | | 3 | "Detective Marcus Reyes shifted uncomfortably." | | 4 | "Quinn's sharp jaw tightened." | | 5 | "She'd seen enough supernatural crime" | | 6 | "The body had been positioned" | | 7 | "She pulled on a latex" | | 8 | "The punctures weren't random." | | 9 | "They formed a complex geometric" | | 10 | "Quinn said, her brown eyes" | | 11 | "She ignored him, pulling a" | | 12 | "The verdigris-coated instrument was a" | | 13 | "The needle trembled, then spun" | | 14 | "Something was very wrong here." | | 15 | "The abandoned Tube station beneath" | | 16 | "Quinn could feel them pressing" | | 17 | "This crime scene was a" |
| | ratio | 1 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 18 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 10 | | technicalSentenceCount | 2 | | matches | | 0 | "They formed a complex geometric design that reminded her of something she'd seen in her partner Morris's old case files - before he disappeared three years ago." | | 1 | "The verdigris-coated instrument was a relic from her last encounter with the supernatural - a Veil Compass that could detect supernatural energy." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn said, her brown eyes narrowing" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 5 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.5 | | effectiveRatio | 0.4 | |