| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.556 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 83.66% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 306 | | totalAiIsmAdverbs | 1 | | 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) | |
| 1.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 306 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "echoed" | | 1 | "standard" | | 2 | "traced" | | 3 | "scanned" | | 4 | "etched" | | 5 | "trembled" |
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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 | 32 | | matches | (empty) | |
| 98.21% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 32 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 36 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 18 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 302 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 17.92% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 265 | | uniqueNames | 9 | | maxNameDensity | 2.64 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Detective | 1 | | Harlow | 1 | | Quinn | 7 | | Forensic | 1 | | Marcus | 1 | | Davies | 3 | | Morris | 1 | | Italian | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Marcus" | | 3 | "Davies" | | 4 | "Morris" |
| | places | (empty) | | globalScore | 0.179 | | windowScore | 0.667 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 17 | | glossingSentenceCount | 2 | | matches | | 0 | "seemed positioned with surgical precision, forming what looked like half-completed geometric shapes around the corpse" | | 1 | "looked like half-completed geometric shap" | | 2 | "quite define" |
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| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 3.311 | | wordCount | 302 | | matches | | 0 | "not north, but toward something unseen" |
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| 74.07% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 36 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 16 | | mean | 18.88 | | std | 12.55 | | cv | 0.665 | | sampleLengths | | 0 | 46 | | 1 | 7 | | 2 | 15 | | 3 | 30 | | 4 | 7 | | 5 | 23 | | 6 | 24 | | 7 | 9 | | 8 | 9 | | 9 | 37 | | 10 | 33 | | 11 | 3 | | 12 | 8 | | 13 | 24 | | 14 | 22 | | 15 | 5 |
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| 94.30% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 32 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 47 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 36 | | ratio | 0.083 | | matches | | 0 | "She'd heard that line before—three years ago, when her partner DS Morris disappeared." | | 1 | "The victim wore expensive shoes—Italian leather, recently polished." | | 2 | "The markings suggested something more—something connected to the supernatural world she'd glimpsed three years ago." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 269 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 8 | | adverbRatio | 0.02973977695167286 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.011152416356877323 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 36 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 36 | | mean | 8.39 | | std | 4.61 | | cv | 0.549 | | sampleLengths | | 0 | 16 | | 1 | 15 | | 2 | 15 | | 3 | 7 | | 4 | 6 | | 5 | 9 | | 6 | 4 | | 7 | 13 | | 8 | 13 | | 9 | 7 | | 10 | 5 | | 11 | 1 | | 12 | 17 | | 13 | 12 | | 14 | 12 | | 15 | 5 | | 16 | 4 | | 17 | 4 | | 18 | 5 | | 19 | 3 | | 20 | 16 | | 21 | 8 | | 22 | 5 | | 23 | 5 | | 24 | 8 | | 25 | 11 | | 26 | 14 | | 27 | 3 | | 28 | 5 | | 29 | 3 | | 30 | 4 | | 31 | 5 | | 32 | 15 | | 33 | 10 | | 34 | 12 | | 35 | 5 |
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| 94.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.6944444444444444 | | totalSentences | 36 | | uniqueOpeners | 25 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 27 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 8 | | totalSentences | 27 | | matches | | 0 | "Her worn leather watch caught" | | 1 | "Her sharp jaw tightened." | | 2 | "She'd heard that line before—three" | | 3 | "she instructed Davies" | | 4 | "He rolled his eyes." | | 5 | "Her brown eyes scanned every" | | 6 | "His suit suggested wealth, connections." | | 7 | "Its face was etched with" |
| | ratio | 0.296 | |
| 34.07% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 23 | | totalSentences | 27 | | matches | | 0 | "The abandoned Tube station echoed" | | 1 | "Her worn leather watch caught" | | 2 | "Blood pooled around the victim's" | | 3 | "Her sharp jaw tightened." | | 4 | "She'd heard that line before—three" | | 5 | "Quinn's fingers traced the weird" | | 6 | "The pattern suggested something deliberate." | | 7 | "Each droplet seemed positioned with" | | 8 | "Quinn pulled out her compact" | | 9 | "Military training never left you," | | 10 | "she instructed Davies" | | 11 | "He rolled his eyes." | | 12 | "Quinn ignored him." | | 13 | "Her brown eyes scanned every" | | 14 | "The victim wore expensive shoes—Italian" | | 15 | "His suit suggested wealth, connections." | | 16 | "A small brass object glinted" | | 17 | "Quinn carefully extracted an evidence" | | 18 | "Its face was etched with" | | 19 | "Davies peered over her shoulder." |
| | ratio | 0.852 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 27 | | matches | (empty) | | ratio | 0 | |
| 91.84% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 14 | | technicalSentenceCount | 1 | | matches | | 0 | "The abandoned Tube station echoed with a hollow emptiness that made Detective Harlow Quinn's skin crawl." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "she murmured (murmur)" |
| | dialogueSentences | 9 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0.5 | | effectiveRatio | 0.444 | |