| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 0 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 84.83% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 989 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slightly" | | 1 | "gently" | | 2 | "completely" |
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| 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) | |
| 29.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 989 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "echoed" | | 1 | "rhythmic" | | 2 | "silence" | | 3 | "gleaming" | | 4 | "gloom" | | 5 | "chill" | | 6 | "velvet" | | 7 | "whisper" | | 8 | "etched" | | 9 | "complex" | | 10 | "scanning" | | 11 | "chaotic" | | 12 | "pristine" | | 13 | "aftermath" |
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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 | 104 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 104 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 133 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 983 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 790 | | uniqueNames | 8 | | maxNameDensity | 0.76 | | worstName | "Harlow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Harlow" | | discoveredNames | | Tube | 1 | | Harlow | 6 | | Quinn | 1 | | Kowalski | 1 | | Shade | 1 | | Silas | 3 | | Compass | 1 | | Eva | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Silas" | | 4 | "Eva" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a robbery gone wrong" |
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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 | 983 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 133 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 36.41 | | std | 24.86 | | cv | 0.683 | | sampleLengths | | 0 | 84 | | 1 | 58 | | 2 | 2 | | 3 | 20 | | 4 | 22 | | 5 | 56 | | 6 | 5 | | 7 | 65 | | 8 | 30 | | 9 | 68 | | 10 | 34 | | 11 | 34 | | 12 | 64 | | 13 | 28 | | 14 | 2 | | 15 | 34 | | 16 | 55 | | 17 | 43 | | 18 | 5 | | 19 | 74 | | 20 | 34 | | 21 | 15 | | 22 | 45 | | 23 | 15 | | 24 | 76 | | 25 | 6 | | 26 | 9 |
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| 81.65% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 104 | | matches | | 0 | "was slumped" | | 1 | "was dressed" | | 2 | "was clenched" | | 3 | "been pulled" | | 4 | "was etched" | | 5 | "supposed" | | 6 | "been drained" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 125 | | matches | | 0 | "were resting" | | 1 | "wasn't pointing" | | 2 | "was spinning" | | 3 | "wasn’t measuring" | | 4 | "was creating" |
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| 99.89% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 133 | | ratio | 0.015 | | matches | | 0 | "He was dressed in fine, if slightly eccentric, clothes—a velvet waistcoat over a linen shirt." | | 1 | "Shelves along the wall had been pulled down, their contents—jars of strange herbs, bundles of dried flowers, intricately carved bones—scattered across the floor." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 143 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 2 | | adverbRatio | 0.013986013986013986 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.006993006993006993 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 133 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 133 | | mean | 7.39 | | std | 5.55 | | cv | 0.751 | | sampleLengths | | 0 | 17 | | 1 | 20 | | 2 | 18 | | 3 | 14 | | 4 | 15 | | 5 | 16 | | 6 | 20 | | 7 | 22 | | 8 | 2 | | 9 | 17 | | 10 | 3 | | 11 | 16 | | 12 | 2 | | 13 | 1 | | 14 | 3 | | 15 | 19 | | 16 | 20 | | 17 | 7 | | 18 | 1 | | 19 | 7 | | 20 | 2 | | 21 | 5 | | 22 | 8 | | 23 | 11 | | 24 | 15 | | 25 | 14 | | 26 | 17 | | 27 | 8 | | 28 | 2 | | 29 | 9 | | 30 | 6 | | 31 | 4 | | 32 | 1 | | 33 | 2 | | 34 | 6 | | 35 | 2 | | 36 | 8 | | 37 | 9 | | 38 | 7 | | 39 | 23 | | 40 | 7 | | 41 | 4 | | 42 | 12 | | 43 | 3 | | 44 | 5 | | 45 | 2 | | 46 | 5 | | 47 | 7 | | 48 | 19 | | 49 | 10 |
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| 55.64% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.38345864661654133 | | totalSentences | 133 | | uniqueOpeners | 51 | |
| 38.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 86 | | matches | | 0 | "Further away, a shelf had" |
| | ratio | 0.012 | |
| 52.56% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 86 | | matches | | 0 | "Her round glasses reflected the" | | 1 | "She’d tucked a strand of" | | 2 | "She stepped through the archway." | | 3 | "He was dressed in fine," | | 4 | "His skin, however, was the" | | 5 | "His eyes were open, staring" | | 6 | "It’s like he just…" | | 7 | "She didn’t touch the body" | | 8 | "His right hand was clenched" | | 9 | "It looked like a robbery" | | 10 | "He was a Shade artisan." | | 11 | "He dealt in rare items." | | 12 | "It’s too loud." | | 13 | "She finally reached out, her" | | 14 | "It was stiff, resistant." | | 15 | "Its casing had a greenish" | | 16 | "It was spinning, a frantic," | | 17 | "They’re supposed to point toward" | | 18 | "They’re incredibly rare." | | 19 | "It’s not broken." |
| | ratio | 0.419 | |
| 47.21% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 86 | | matches | | 0 | "The air in the abandoned" | | 1 | "Detective Harlow Quinn descended the" | | 2 | "The sound of her hard-soled" | | 3 | "Tiles cracked and flaked away" | | 4 | "A disused platform stretched into" | | 5 | "Eva Kowalski stood near the" | | 6 | "Her round glasses reflected the" | | 7 | "She’d tucked a strand of" | | 8 | "Eva pointed a trembling finger" | | 9 | "Harlow moved towards the archway," | | 10 | "The air grew colder as" | | 11 | "A new smell cut through" | | 12 | "She stepped through the archway." | | 13 | "The service tunnel was a" | | 14 | "He was dressed in fine," | | 15 | "His skin, however, was the" | | 16 | "His eyes were open, staring" | | 17 | "Coroner’s preliminary on the phone" | | 18 | "It’s like he just…" | | 19 | "She didn’t touch the body" |
| | ratio | 0.826 | |
| 58.14% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 86 | | matches | | 0 | "If there was a struggle," |
| | ratio | 0.012 | |
| 96.77% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 2 | | matches | | 0 | "Harlow moved towards the archway, her posture straight, her movements carrying the military precision that had become second nature." | | 1 | "The air grew colder as she approached, a deep bone-chill that had nothing to do with the station’s subterranean location." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |