| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 1 | | adverbTags | | 0 | "She fired again [again]" |
| | dialogueSentences | 31 | | tagDensity | 0.226 | | leniency | 0.452 | | rawRatio | 0.143 | | effectiveRatio | 0.065 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1463 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 48.74% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1463 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "flicker" | | 1 | "variables" | | 2 | "footsteps" | | 3 | "flickered" | | 4 | "gloom" | | 5 | "pulsed" | | 6 | "etched" | | 7 | "flicked" | | 8 | "scanned" | | 9 | "velvet" | | 10 | "loomed" | | 11 | "gleaming" | | 12 | "glinting" | | 13 | "echoed" |
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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 | 1 | | narrationSentences | 174 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 174 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 197 | | 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 | 11 | | markdownWords | 15 | | totalWords | 1449 | | ratio | 0.01 | | matches | | 0 | "you don’t belong here" | | 1 | "guapa" | | 2 | "anywhere" | | 3 | "you" | | 4 | "they" | | 5 | "they" | | 6 | "just" | | 7 | "Detective." | | 8 | "the things" | | 9 | "that" | | 10 | "Flowing" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 78.57% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1260 | | uniqueNames | 12 | | maxNameDensity | 1.43 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Tomás" | | discoveredNames | | God | 2 | | Soho | 1 | | Quinn | 1 | | Wardour | 1 | | Street | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 9 | | Harlow | 18 | | Missed | 1 | | Ahead | 1 |
| | persons | | 0 | "God" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Tomás" | | 6 | "Harlow" |
| | places | | 0 | "Soho" | | 1 | "Wardour" | | 2 | "Street" |
| | globalScore | 0.786 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | glossingSentenceCount | 1 | | matches | | 0 | "smelled like rot and lightning" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.69 | | wordCount | 1449 | | matches | | 0 | "not with machinery, but with voices" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 197 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 24.98 | | std | 24.88 | | cv | 0.996 | | sampleLengths | | 0 | 90 | | 1 | 53 | | 2 | 86 | | 3 | 3 | | 4 | 19 | | 5 | 39 | | 6 | 83 | | 7 | 1 | | 8 | 72 | | 9 | 6 | | 10 | 58 | | 11 | 4 | | 12 | 86 | | 13 | 3 | | 14 | 22 | | 15 | 6 | | 16 | 2 | | 17 | 65 | | 18 | 12 | | 19 | 29 | | 20 | 15 | | 21 | 9 | | 22 | 2 | | 23 | 53 | | 24 | 16 | | 25 | 5 | | 26 | 29 | | 27 | 14 | | 28 | 45 | | 29 | 35 | | 30 | 15 | | 31 | 19 | | 32 | 4 | | 33 | 71 | | 34 | 6 | | 35 | 13 | | 36 | 4 | | 37 | 7 | | 38 | 3 | | 39 | 47 | | 40 | 13 | | 41 | 23 | | 42 | 3 | | 43 | 3 | | 44 | 38 | | 45 | 7 | | 46 | 5 | | 47 | 42 | | 48 | 28 | | 49 | 35 |
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| 99.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 174 | | matches | | 0 | "been greased" | | 1 | "been dug" | | 2 | "was gone" |
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| 25.33% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 229 | | matches | | 0 | "was coming" | | 1 | "was waiting" | | 2 | "was seeing" | | 3 | "wasn’t losing" | | 4 | "was arranging" | | 5 | "were coming" |
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| 12.33% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 12 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 197 | | ratio | 0.046 | | matches | | 0 | "Three blocks back, the suspect—tall, lanky, moving like he’d been greased—had glanced over his shoulder." | | 1 | "Then—footsteps." | | 2 | "Not just any tunnel—this one sloped downward, the floor worn smooth by God knew how many boots." | | 3 | "Not a natural one—this had been dug, expanded, reinforced with rusted iron supports and wooden beams blackened by age." | | 4 | "And light—flickering, uneven, casting long shadows that twisted on the walls." | | 5 | "The rest—curly hair damp with sweat, olive skin slick with it, the scar on his forearm white against the grimy sleeve of his rolled-up shirt—spoke of a man who’d been running longer than she had." | | 6 | "Two figures loomed over her—tall, too tall, their limbs stretched like taffy." | | 7 | "The things—*the things*—hissed, their forms twisting, elongating." | | 8 | "The tunnel was a blessing and a curse—narrow enough to slow them down, but dark enough to hide in." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 888 | | adjectiveStacks | 1 | | stackExamples | | 0 | "suspect—tall, lanky, moving like" |
| | adverbCount | 27 | | adverbRatio | 0.030405405405405407 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.006756756756756757 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 197 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 197 | | mean | 7.36 | | std | 5.84 | | cv | 0.794 | | sampleLengths | | 0 | 10 | | 1 | 16 | | 2 | 6 | | 3 | 21 | | 4 | 15 | | 5 | 5 | | 6 | 12 | | 7 | 5 | | 8 | 18 | | 9 | 11 | | 10 | 2 | | 11 | 6 | | 12 | 11 | | 13 | 5 | | 14 | 8 | | 15 | 4 | | 16 | 22 | | 17 | 20 | | 18 | 17 | | 19 | 12 | | 20 | 3 | | 21 | 2 | | 22 | 1 | | 23 | 1 | | 24 | 1 | | 25 | 7 | | 26 | 1 | | 27 | 3 | | 28 | 1 | | 29 | 5 | | 30 | 5 | | 31 | 11 | | 32 | 11 | | 33 | 7 | | 34 | 5 | | 35 | 4 | | 36 | 1 | | 37 | 1 | | 38 | 13 | | 39 | 12 | | 40 | 14 | | 41 | 2 | | 42 | 17 | | 43 | 19 | | 44 | 1 | | 45 | 9 | | 46 | 6 | | 47 | 13 | | 48 | 9 | | 49 | 3 |
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| 57.70% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.37055837563451777 | | totalSentences | 197 | | uniqueOpeners | 73 | |
| 94.56% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 141 | | matches | | 0 | "Too many variables in close" | | 1 | "Just laughed, a wet, gurgling" | | 2 | "Just the storm and the" | | 3 | "Somewhere in the distance, a" |
| | ratio | 0.028 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 141 | | matches | | 0 | "It turned the neon glow" | | 1 | "Her boots splashed through puddles" | | 2 | "He knew she was coming." | | 3 | "He ducked left, into a" | | 4 | "She wanted answers, not paperwork." | | 5 | "He vaulted over a low" | | 6 | "He was waiting for her." | | 7 | "She’d faced worse than some" | | 8 | "It prickled, like static before" | | 9 | "She pushed through the door." | | 10 | "Her torch flickered to life," | | 11 | "She moved forward, her free" | | 12 | "Her torchlight jumped over something" | | 13 | "She was seeing this through." | | 14 | "She stepped forward, her shoulders" | | 15 | "He didn’t speak." | | 16 | "His eyes said *you don’t" | | 17 | "She ignored him." | | 18 | "She wasn’t losing him now." | | 19 | "His Saint Christopher medallion glinted" |
| | ratio | 0.298 | |
| 66.38% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 111 | | totalSentences | 141 | | matches | | 0 | "The rain came down like" | | 1 | "It turned the neon glow" | | 2 | "Harlow Quinn didn’t slow her" | | 3 | "Her boots splashed through puddles" | | 4 | "That was all she needed." | | 5 | "The flicker of fear in" | | 6 | "He knew she was coming." | | 7 | "He ducked left, into a" | | 8 | "Harlow followed, her fingers brushing" | | 9 | "A gunshot here would bring" | | 10 | "She wanted answers, not paperwork." | | 11 | "The alley spat them out" | | 12 | "The suspect didn’t hesitate." | | 13 | "He vaulted over a low" | | 14 | "Harlow cursed under her breath," | | 15 | "The descent was slick, her" | | 16 | "The steady plink of water" | | 17 | "He was waiting for her." | | 18 | "Harlow exhaled through her nose." | | 19 | "She’d faced worse than some" |
| | ratio | 0.787 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 141 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 2 | | matches | | 0 | "His Saint Christopher medallion glinted in the lantern light, the only thing about him that looked polished." | | 1 | "The rest—curly hair damp with sweat, olive skin slick with it, the scar on his forearm white against the grimy sleeve of his rolled-up shirt—spoke of a man who’…" |
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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 | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 31 | | tagDensity | 0.032 | | leniency | 0.065 | | rawRatio | 0 | | effectiveRatio | 0 | |