| 82.35% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 2 | | adverbTags | | 0 | "He gestured vaguely [vaguely]" | | 1 | "Morris shifted uncomfortably [uncomfortably]" |
| | dialogueSentences | 27 | | tagDensity | 0.63 | | leniency | 1 | | rawRatio | 0.118 | | effectiveRatio | 0.118 | |
| 92.59% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 675 | | totalAiIsmAdverbs | 1 | | found | | 0 | | adverb | "deliberately" | | count | 1 |
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| | 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) | |
| 25.93% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 675 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "echo" | | 1 | "standard" | | 2 | "oppressive" | | 3 | "etched" | | 4 | "scanning" | | 5 | "trembled" | | 6 | "velvet" | | 7 | "pawn" | | 8 | "racing" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 39 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 39 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 49 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 671 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 25.62% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 402 | | uniqueNames | 8 | | maxNameDensity | 2.49 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 10 | | Tube | 1 | | Morris | 7 | | Eva | 1 | | Kowalski | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Eva" | | 4 | "Kowalski" |
| | places | (empty) | | globalScore | 0.256 | | windowScore | 0.5 | |
| 60.71% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 28 | | glossingSentenceCount | 1 | | matches | | 0 | "seemed unnaturally still" |
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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 | 671 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 49 | | matches | (empty) | |
| 87.05% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 15 | | mean | 44.73 | | std | 20.34 | | cv | 0.455 | | sampleLengths | | 0 | 80 | | 1 | 48 | | 2 | 46 | | 3 | 42 | | 4 | 37 | | 5 | 75 | | 6 | 16 | | 7 | 37 | | 8 | 19 | | 9 | 30 | | 10 | 36 | | 11 | 47 | | 12 | 62 | | 13 | 18 | | 14 | 78 |
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| 87.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 39 | | matches | | 0 | "was positioned" | | 1 | "was matted" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 81 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 3 | | totalSentences | 49 | | ratio | 0.061 | | matches | | 0 | "And the chair—it was positioned as if someone had deliberately pushed it over, not knocked it down in a struggle." | | 1 | "She noticed the faint, almost invisible sigil etched into the wood—a symbol she’d seen in the restricted archives Eva Kowalski accessed." | | 2 | "The needle didn’t spin; it trembled violently, then settled, pointing towards a loose floorboard near the bed." |
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| 90.21% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 406 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, velvet-lined box." |
| | adverbCount | 18 | | adverbRatio | 0.04433497536945813 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.017241379310344827 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 49 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 49 | | mean | 13.69 | | std | 8.6 | | cv | 0.628 | | sampleLengths | | 0 | 26 | | 1 | 25 | | 2 | 11 | | 3 | 18 | | 4 | 12 | | 5 | 28 | | 6 | 8 | | 7 | 3 | | 8 | 5 | | 9 | 10 | | 10 | 20 | | 11 | 8 | | 12 | 12 | | 13 | 30 | | 14 | 14 | | 15 | 12 | | 16 | 11 | | 17 | 12 | | 18 | 15 | | 19 | 8 | | 20 | 11 | | 21 | 21 | | 22 | 5 | | 23 | 3 | | 24 | 4 | | 25 | 12 | | 26 | 11 | | 27 | 16 | | 28 | 10 | | 29 | 10 | | 30 | 9 | | 31 | 6 | | 32 | 22 | | 33 | 2 | | 34 | 29 | | 35 | 7 | | 36 | 33 | | 37 | 14 | | 38 | 4 | | 39 | 17 | | 40 | 9 | | 41 | 7 | | 42 | 10 | | 43 | 15 | | 44 | 12 | | 45 | 6 | | 46 | 9 | | 47 | 31 | | 48 | 38 |
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| 84.35% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.5306122448979592 | | totalSentences | 49 | | uniqueOpeners | 26 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 38 | | matches | (empty) | | ratio | 0 | |
| 62.11% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 38 | | matches | | 0 | "Her gaze swept the scene:" | | 1 | "Her partner, DS Morris, stood" | | 2 | "He gestured vaguely at the" | | 3 | "It clung to the floor" | | 4 | "she asked, her voice low" | | 5 | "She’d seen too many cases" | | 6 | "she said, her voice devoid" | | 7 | "She noticed the faint, almost" | | 8 | "She pointed at the congealed" | | 9 | "She pulled her satchel from" | | 10 | "she said, holding it up" | | 11 | "She activated the compass." | | 12 | "she explained, her voice tight" | | 13 | "She tucked the compass away," | | 14 | "She turned, her military bearing" |
| | ratio | 0.395 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 36 | | totalSentences | 38 | | matches | | 0 | "Detective Harlow Quinn’s boots scuffed" | | 1 | "Her gaze swept the scene:" | | 2 | "The air hummed with a" | | 3 | "Her partner, DS Morris, stood" | | 4 | "Morris muttered, his voice tight" | | 5 | "He gestured vaguely at the" | | 6 | "Quinn’s eyes narrowed." | | 7 | "The blood wasn’t pooling correctly." | | 8 | "It clung to the floor" | | 9 | "she asked, her voice low" | | 10 | "Morris shifted uncomfortably" | | 11 | "Quinn’s hand drifted to her" | | 12 | "She’d seen too many cases" | | 13 | "she said, her voice devoid" | | 14 | "The bedroom was smaller, the" | | 15 | "The body lay on the" | | 16 | "Quinn knelt, her fingers brushing" | | 17 | "The victim’s hair was matted," | | 18 | "She noticed the faint, almost" | | 19 | "Morris frowned, stepping closer." |
| | ratio | 0.947 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 38 | | matches | (empty) | | ratio | 0 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 15 | | technicalSentenceCount | 1 | | matches | | 0 | "Her gaze swept the scene: a single overturned chair, a discarded notebook half-buried in grime, and a pool of congealed blood that seemed unnaturally still." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 7 | | matches | | 0 | "Morris muttered, his voice tight" | | 1 | "she asked, her voice low" | | 2 | "she said, her voice devoid of emotion" | | 3 | "Quinn countered, her eyes scanning the room" | | 4 | "Morris demanded, his voice rising" | | 5 | "she explained, her voice tight" | | 6 | "She tucked, her mind racing" |
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| 1.85% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 4 | | fancyTags | | 0 | "Morris muttered (mutter)" | | 1 | "she murmured (murmur)" | | 2 | "Morris demanded (demand)" | | 3 | "she explained (explain)" |
| | dialogueSentences | 27 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.444 | | effectiveRatio | 0.296 | |