| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said softly [softly]" |
| | dialogueSentences | 28 | | tagDensity | 0.214 | | leniency | 0.429 | | rawRatio | 0.167 | | effectiveRatio | 0.071 | |
| 89.13% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 920 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 13.04% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 920 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "whisper" | | 1 | "flicker" | | 2 | "silence" | | 3 | "footsteps" | | 4 | "echoed" | | 5 | "pulse" | | 6 | "flicked" | | 7 | "roaring" | | 8 | "shattered" | | 9 | "depths" | | 10 | "velvet" | | 11 | "pulsed" | | 12 | "silk" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 113 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 113 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 135 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 912 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 48.51% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 739 | | uniqueNames | 8 | | maxNameDensity | 2.03 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 15 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Footsteps | 1 | | Tomás | 10 | | Camden | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Footsteps" | | 6 | "Tomás" |
| | places | (empty) | | globalScore | 0.485 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 90.35% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.096 | | wordCount | 912 | | matches | | 0 | "not supposed to be here,” he said softly, accent warm but strained" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 135 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 68 | | mean | 13.41 | | std | 12.54 | | cv | 0.935 | | sampleLengths | | 0 | 65 | | 1 | 55 | | 2 | 34 | | 3 | 22 | | 4 | 6 | | 5 | 7 | | 6 | 24 | | 7 | 13 | | 8 | 12 | | 9 | 21 | | 10 | 13 | | 11 | 16 | | 12 | 5 | | 13 | 5 | | 14 | 14 | | 15 | 17 | | 16 | 26 | | 17 | 6 | | 18 | 4 | | 19 | 9 | | 20 | 12 | | 21 | 24 | | 22 | 5 | | 23 | 2 | | 24 | 11 | | 25 | 18 | | 26 | 11 | | 27 | 5 | | 28 | 11 | | 29 | 18 | | 30 | 4 | | 31 | 3 | | 32 | 14 | | 33 | 11 | | 34 | 3 | | 35 | 12 | | 36 | 10 | | 37 | 8 | | 38 | 7 | | 39 | 24 | | 40 | 12 | | 41 | 4 | | 42 | 30 | | 43 | 14 | | 44 | 24 | | 45 | 12 | | 46 | 6 | | 47 | 10 | | 48 | 10 | | 49 | 1 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 113 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 141 | | matches | | 0 | "wasn’t panting" | | 1 | "wasn’t luring" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 135 | | ratio | 0.052 | | matches | | 0 | "Stench of stale grease and something older hung in the air—damp concrete and rust." | | 1 | "Footsteps echoed in an alley behind them—too deliberate, too close." | | 2 | "The suspect—no, the man in beige—was gone." | | 3 | "A staircase spiraled downward into blackness roaring with noise—voices overlapping, laughter sharp as shattered glass, footsteps like rain on stone." | | 4 | "Hundreds of figures moved in a sprawl, their forms flickering between human and something else—limbs too long, faces stretched like wax." | | 5 | "The suspect stood twenty feet ahead, beside a stall draped in red velvet embroidered with gold thread—not from this world." | | 6 | "Behind him, the alley sloped downward—forcing itself into the underground, into something older." |
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| 98.79% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 749 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.04138851802403204 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.009345794392523364 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 135 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 135 | | mean | 6.76 | | std | 5.03 | | cv | 0.744 | | sampleLengths | | 0 | 15 | | 1 | 27 | | 2 | 21 | | 3 | 2 | | 4 | 10 | | 5 | 13 | | 6 | 14 | | 7 | 14 | | 8 | 2 | | 9 | 2 | | 10 | 2 | | 11 | 15 | | 12 | 3 | | 13 | 6 | | 14 | 8 | | 15 | 8 | | 16 | 4 | | 17 | 2 | | 18 | 8 | | 19 | 6 | | 20 | 7 | | 21 | 12 | | 22 | 12 | | 23 | 13 | | 24 | 5 | | 25 | 7 | | 26 | 7 | | 27 | 14 | | 28 | 4 | | 29 | 9 | | 30 | 6 | | 31 | 2 | | 32 | 2 | | 33 | 6 | | 34 | 5 | | 35 | 5 | | 36 | 14 | | 37 | 10 | | 38 | 3 | | 39 | 4 | | 40 | 11 | | 41 | 3 | | 42 | 4 | | 43 | 8 | | 44 | 6 | | 45 | 4 | | 46 | 5 | | 47 | 4 | | 48 | 5 | | 49 | 7 |
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| 55.56% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.37777777777777777 | | totalSentences | 135 | | uniqueOpeners | 51 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 85 | | matches | | 0 | "Just smooth, dry metal where" | | 1 | "Just darkness thick enough to" | | 2 | "Then the world snapped back." | | 3 | "Only the faintest whisper of" | | 4 | "Then he reached into his" |
| | ratio | 0.059 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 85 | | matches | | 0 | "She’d lost sight of the" | | 1 | "She stepped off the curb" | | 2 | "Her leather shoes splashed through" | | 3 | "She crouched, brushing her fingers" | | 4 | "She twisted, hand reaching for" | | 5 | "His Saint Christopher medallion gleamed" | | 6 | "he said softly, accent warm" | | 7 | "She spun, pistol up." | | 8 | "He wasn’t panting." | | 9 | "His shoes weren’t wet." | | 10 | "His skin looked waxy under" | | 11 | "He smiled like he’d been" | | 12 | "He wasn’t luring her." | | 13 | "He was inviting." | | 14 | "She stepped up." | | 15 | "It smelled of dust and" | | 16 | "She stepped forward." | | 17 | "He gestured toward a stall" | | 18 | "She didn’t answer." | | 19 | "He tilted his head." |
| | ratio | 0.235 | |
| 30.59% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 73 | | totalSentences | 85 | | matches | | 0 | "The rain slashed sideways under" | | 1 | "Detective Harlow Quinn’s coat clung" | | 2 | "She’d lost sight of the" | | 3 | "She stepped off the curb" | | 4 | "Cigarette butts glistened under the" | | 5 | "Stench of stale grease and" | | 6 | "Her leather shoes splashed through" | | 7 | "The footprints stopped six feet" | | 8 | "She crouched, brushing her fingers" | | 9 | "A whisper of breath behind" | | 10 | "She twisted, hand reaching for" | | 11 | "Tomás Herrera stepped from the" | | 12 | "His Saint Christopher medallion gleamed" | | 13 | "he said softly, accent warm" | | 14 | "Quinn didn’t lower the gun." | | 15 | "Tomás tilted his head toward" | | 16 | "The rain drummed harder." | | 17 | "A car alarm wailed in" | | 18 | "Quinn stared down into the" | | 19 | "Footsteps echoed in an alley" |
| | ratio | 0.859 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 85 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 1 | | matches | | 0 | "Vendors with too many eyes hawked bottles of swirling liquid, jars of floating eyes, wrapped parcels that breathed." |
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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 | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 28 | | tagDensity | 0.214 | | leniency | 0.429 | | rawRatio | 0 | | effectiveRatio | 0 | |