| 78.79% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 2 | | adverbTags | | 0 | "she said aloud [aloud]" | | 1 | "Quinn crouched again [again]" |
| | dialogueSentences | 33 | | tagDensity | 0.485 | | leniency | 0.97 | | rawRatio | 0.125 | | effectiveRatio | 0.121 | |
| 72.31% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 903 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "slightly" | | 1 | "precisely" | | 2 | "carefully" |
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| 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) | |
| 77.85% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 903 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "trembled" | | 1 | "traced" | | 2 | "processed" | | 3 | "silence" |
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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 | 49 | | matches | (empty) | |
| 84.55% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 49 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 66 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 898 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 8.10% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 599 | | uniqueNames | 14 | | maxNameDensity | 2.84 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 17 | | Tube | 1 | | Kowalski | 1 | | Marcus | 1 | | Rivera | 1 | | Eva | 9 | | Veil | 2 | | Compass | 1 | | Market | 1 | | Metropolitan | 1 | | Police | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Marcus" | | 4 | "Rivera" | | 5 | "Eva" | | 6 | "Police" | | 7 | "Morris" |
| | places | | | globalScore | 0.081 | | windowScore | 0.167 | |
| 84.21% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 38 | | glossingSentenceCount | 1 | | matches | | 0 | "sigils that seemed to shift when she wasn't looking directly at them" |
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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 | 898 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 66 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 28.06 | | std | 20.02 | | cv | 0.713 | | sampleLengths | | 0 | 55 | | 1 | 1 | | 2 | 52 | | 3 | 22 | | 4 | 54 | | 5 | 14 | | 6 | 25 | | 7 | 55 | | 8 | 7 | | 9 | 53 | | 10 | 1 | | 11 | 17 | | 12 | 57 | | 13 | 7 | | 14 | 39 | | 15 | 5 | | 16 | 29 | | 17 | 4 | | 18 | 59 | | 19 | 10 | | 20 | 55 | | 21 | 23 | | 22 | 41 | | 23 | 2 | | 24 | 41 | | 25 | 41 | | 26 | 14 | | 27 | 12 | | 28 | 45 | | 29 | 32 | | 30 | 1 | | 31 | 25 |
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| 90.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 49 | | matches | | 0 | "were smudged" | | 1 | "been assigned" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 113 | | matches | | 0 | "was operating" | | 1 | "wasn't looking" | | 2 | "was happening" | | 3 | "was directing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 66 | | ratio | 0.061 | | matches | | 0 | "No signs of forced entry—only one way in, through the service tunnels, and no evidence of struggle." | | 1 | "Not just any compass—the Veil Compass, its casing darkened with verdigris, the face marked with sigils that seemed to shift when she wasn't looking directly at them." | | 2 | "The compass needle didn't just point toward supernatural rifts—it pointed toward the nearest threat." | | 3 | "The ventilation system wheezed again, and this time Quinn caught the faintest sound beneath it—a rhythm, almost like tapping." |
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| 93.06% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 605 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.047933884297520664 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.019834710743801654 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 66 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 66 | | mean | 13.61 | | std | 9.28 | | cv | 0.682 | | sampleLengths | | 0 | 28 | | 1 | 27 | | 2 | 1 | | 3 | 4 | | 4 | 4 | | 5 | 17 | | 6 | 27 | | 7 | 17 | | 8 | 5 | | 9 | 23 | | 10 | 18 | | 11 | 13 | | 12 | 14 | | 13 | 19 | | 14 | 6 | | 15 | 8 | | 16 | 17 | | 17 | 13 | | 18 | 17 | | 19 | 4 | | 20 | 3 | | 21 | 11 | | 22 | 27 | | 23 | 15 | | 24 | 1 | | 25 | 12 | | 26 | 5 | | 27 | 17 | | 28 | 26 | | 29 | 14 | | 30 | 7 | | 31 | 14 | | 32 | 15 | | 33 | 10 | | 34 | 5 | | 35 | 25 | | 36 | 4 | | 37 | 4 | | 38 | 26 | | 39 | 21 | | 40 | 12 | | 41 | 7 | | 42 | 3 | | 43 | 19 | | 44 | 36 | | 45 | 4 | | 46 | 19 | | 47 | 16 | | 48 | 25 | | 49 | 2 |
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| 79.80% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5151515151515151 | | totalSentences | 66 | | uniqueOpeners | 34 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 45 | | matches | | 0 | "Too young to have graying" | | 1 | "Too fresh for something that" |
| | ratio | 0.044 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 8 | | totalSentences | 45 | | matches | | 0 | "She didn't turn around." | | 1 | "She knew that voice." | | 2 | "Her round glasses were smudged" | | 3 | "She'd seen one before, stolen" | | 4 | "She pointed to a smear" | | 5 | "She walked back toward the" | | 6 | "she said aloud" | | 7 | "She pressed her ear to" |
| | ratio | 0.178 | |
| 37.78% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 38 | | totalSentences | 45 | | matches | | 0 | "The rain drummed against the" | | 1 | "The abandoned Tube station stretched" | | 2 | "She didn't turn around." | | 3 | "She knew that voice." | | 4 | "Eva Kowalski stood near the" | | 5 | "Her round glasses were smudged" | | 6 | "Eva said, but her voice" | | 7 | "Quinn followed her gaze to" | | 8 | "The victim was DS Marcus" | | 9 | "Quinn asked, though she already" | | 10 | "Eva paused, her green eyes" | | 11 | "Quinn's trained eye took in" | | 12 | "The body positioned precisely three" | | 13 | "A ritual placement, maybe, but" | | 14 | "Quinn's gaze dropped to the" | | 15 | "She'd seen one before, stolen" | | 16 | "Eva's voice dropped lower" | | 17 | "Quinn felt her hand move" | | 18 | "Military precision in her bearing" | | 19 | "The compass needle didn't just" |
| | ratio | 0.844 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 45 | | matches | | 0 | "If anything, it trembled slightly." |
| | ratio | 0.022 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 22 | | technicalSentenceCount | 7 | | matches | | 0 | "The rain drummed against the yellow caution tape as Detective Harlow Quinn stepped through the perimeter, her boots splashing in shallow puddles that reflected …" | | 1 | "Eva Kowalski stood near the platform end, clutching a worn leather satchel that had seen better days." | | 2 | "Quinn followed her gaze to where a figure lay sprawled near the turnstile, purple uniform stained with something that might have been blood." | | 3 | "The victim was DS Marcus Rivera, a young officer who'd been assigned to Quinn's case six months ago." | | 4 | "Something that reminded her of the night DS Morris died." | | 5 | "Too fresh for something that had supposedly been there since the last moon cycle." | | 6 | "But the compass on the ground had begun to spin, its needle whipping around in circles until it settled, finally, on pointing directly at her." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 4 | | matches | | 0 | "Eva said, but her voice carried none of the professional detachment Quinn expected" | | 1 | "Eva paused, her green eyes wide behind her glasses" | | 2 | "Quinn stood, her military bearing shifting slightly as she processed information" | | 3 | "Quinn crouched again, this time examining the bone token more carefully" |
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| 89.39% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 2 | | fancyTags | | 0 | "Eva breathed (breathe)" | | 1 | "Eva whispered (whisper)" |
| | dialogueSentences | 33 | | tagDensity | 0.182 | | leniency | 0.364 | | rawRatio | 0.333 | | effectiveRatio | 0.121 | |