| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 2 | | adverbTags | | 0 | "Eva’s fingers tightened around [around]" | | 1 | "Eva said quietly [quietly]" |
| | dialogueSentences | 55 | | tagDensity | 0.145 | | leniency | 0.291 | | rawRatio | 0.25 | | effectiveRatio | 0.073 | |
| 91.30% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1724 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slightly" | | 1 | "carefully" | | 2 | "quickly" |
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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) | |
| 39.10% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1724 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | word | "carried the weight" | | count | 1 |
| | 15 | | | 16 | |
| | highlights | | 0 | "gloom" | | 1 | "familiar" | | 2 | "weight" | | 3 | "etched" | | 4 | "glinting" | | 5 | "sense of" | | 6 | "pulse" | | 7 | "wavered" | | 8 | "racing" | | 9 | "stomach" | | 10 | "echoing" | | 11 | "flicked" | | 12 | "raced" | | 13 | "whisper" | | 14 | "carried the weight" | | 15 | "wavering" | | 16 | "trembled" |
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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 | 128 | | matches | (empty) | |
| 75.89% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 4 | | narrationSentences | 128 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 172 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 11 | | markdownWords | 13 | | totalWords | 1716 | | ratio | 0.008 | | matches | | 0 | "magic market" | | 1 | "was" | | 2 | "scattered" | | 3 | "was" | | 4 | "it" | | 5 | "during" | | 6 | "people" | | 7 | "left behind" | | 8 | "taken" | | 9 | "warning" | | 10 | "anything" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 69 | | wordCount | 1249 | | uniqueNames | 12 | | maxNameDensity | 2.24 | | worstName | "Eva" | | maxWindowNameDensity | 5 | | worstWindowName | "Eva" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Harlow | 27 | | Quinn | 1 | | God | 1 | | Kowalski | 1 | | Eva | 28 | | British | 1 | | Museum | 1 | | Morris | 5 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Museum" | | 6 | "Morris" |
| | places | (empty) | | globalScore | 0.379 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 77 | | glossingSentenceCount | 1 | | matches | | 0 | "as if beckoning her forward" |
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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 | 1716 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 172 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 61 | | mean | 28.13 | | std | 24.77 | | cv | 0.881 | | sampleLengths | | 0 | 78 | | 1 | 96 | | 2 | 2 | | 3 | 108 | | 4 | 31 | | 5 | 23 | | 6 | 13 | | 7 | 19 | | 8 | 70 | | 9 | 71 | | 10 | 23 | | 11 | 5 | | 12 | 34 | | 13 | 19 | | 14 | 22 | | 15 | 32 | | 16 | 68 | | 17 | 8 | | 18 | 19 | | 19 | 13 | | 20 | 74 | | 21 | 11 | | 22 | 50 | | 23 | 3 | | 24 | 36 | | 25 | 7 | | 26 | 28 | | 27 | 5 | | 28 | 57 | | 29 | 20 | | 30 | 8 | | 31 | 17 | | 32 | 22 | | 33 | 11 | | 34 | 36 | | 35 | 76 | | 36 | 18 | | 37 | 4 | | 38 | 3 | | 39 | 27 | | 40 | 4 | | 41 | 18 | | 42 | 52 | | 43 | 16 | | 44 | 7 | | 45 | 32 | | 46 | 12 | | 47 | 46 | | 48 | 4 | | 49 | 19 |
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| 91.56% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 128 | | matches | | 0 | "been pulled" | | 1 | "been locked" | | 2 | "was patinated" | | 3 | "been sealed" | | 4 | "been garbled" | | 5 | "been ordered" |
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| 42.77% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 212 | | matches | | 0 | "were doing" | | 1 | "were discussing" | | 2 | "wasn’t asking" | | 3 | "wasn’t even asking" | | 4 | "was stating" |
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| 9.97% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 172 | | ratio | 0.047 | | matches | | 0 | "The beam of her torch cut through the gloom, illuminating peeling posters from decades past—faded advertisements for long-forgotten plays and tinned goods." | | 1 | "And the walls—God, the walls." | | 2 | "She knew that tone—dry, amused, with an edge of something darker." | | 3 | "She’d spent months after that trying to rationalize it—bad intel, a setup, a trick of the light." | | 4 | "The symbols etched into its surface weren’t any language she recognized, but they reminded her of the markings on Morris’s last case file—the one that had been locked away in the evidence room, the one she wasn’t supposed to have seen." | | 5 | "But the tokens, the sigils, the compass—it all pointed to something." | | 6 | "The beam of her torch caught on something half-hidden beneath a pile of old newspapers—a small, dark stain." | | 7 | "She could almost see it—the shimmer in the air, like heat rising off pavement." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1261 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 47 | | adverbRatio | 0.03727200634417129 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.008723235527359239 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 172 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 172 | | mean | 9.98 | | std | 7.46 | | cv | 0.748 | | sampleLengths | | 0 | 24 | | 1 | 16 | | 2 | 22 | | 3 | 16 | | 4 | 16 | | 5 | 14 | | 6 | 2 | | 7 | 2 | | 8 | 1 | | 9 | 1 | | 10 | 26 | | 11 | 5 | | 12 | 8 | | 13 | 21 | | 14 | 2 | | 15 | 11 | | 16 | 3 | | 17 | 11 | | 18 | 20 | | 19 | 27 | | 20 | 15 | | 21 | 6 | | 22 | 15 | | 23 | 3 | | 24 | 5 | | 25 | 23 | | 26 | 5 | | 27 | 18 | | 28 | 5 | | 29 | 8 | | 30 | 8 | | 31 | 11 | | 32 | 2 | | 33 | 15 | | 34 | 12 | | 35 | 13 | | 36 | 17 | | 37 | 11 | | 38 | 11 | | 39 | 14 | | 40 | 41 | | 41 | 5 | | 42 | 17 | | 43 | 6 | | 44 | 3 | | 45 | 2 | | 46 | 28 | | 47 | 6 | | 48 | 7 | | 49 | 12 |
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| 43.02% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.28488372093023256 | | totalSentences | 172 | | uniqueOpeners | 49 | |
| 59.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 113 | | matches | | 0 | "Of course Eva would be" | | 1 | "Somewhere in that black, there" |
| | ratio | 0.018 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 113 | | matches | | 0 | "She adjusted the worn leather" | | 1 | "She knew that tone—dry, amused," | | 2 | "Her curly red hair was" | | 3 | "She carried her worn leather" | | 4 | "She didn’t like being late." | | 5 | "She’d felt it three years" | | 6 | "She’d spent months after that" | | 7 | "She crouched, gloved fingers hovering" | | 8 | "It was cold, unnaturally so," | | 9 | "Her skin prickled." | | 10 | "Its casing was patinated with" | | 11 | "It wavered, then settled on" | | 12 | "She turned to Eva." | | 13 | "She’d checked the lunar calendar" | | 14 | "She knelt, brushing the papers" | | 15 | "She pulled a swab from" | | 16 | "She thought of Morris, of" | | 17 | "She thought of the way" | | 18 | "She turned to Eva." | | 19 | "It all fit." |
| | ratio | 0.265 | |
| 26.37% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 98 | | totalSentences | 113 | | matches | | 0 | "The abandoned Tube station beneath" | | 1 | "Detective Harlow Quinn stepped over" | | 2 | "The beam of her torch" | | 3 | "The air was thick, stale," | | 4 | "She adjusted the worn leather" | | 5 | "A scattering of bone tokens" | | 6 | "The graffiti here wasn’t the" | | 7 | "These were sigils, sharp and" | | 8 | "The voice came from the" | | 9 | "Harlow didn’t flinch." | | 10 | "She knew that tone—dry, amused," | | 11 | "Eva Kowalski stepped into the" | | 12 | "Her curly red hair was" | | 13 | "She carried her worn leather" | | 14 | "The British Museum’s restricted archives" | | 15 | "She didn’t like being late." | | 16 | "Eva gestured to the platform." | | 17 | "Harlow exhaled through her nose." | | 18 | "Eva’s voice dropped, the levity" | | 19 | "A prickle at the base" |
| | ratio | 0.867 | |
| 88.50% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 113 | | matches | | 0 | "Even if it meant stepping" | | 1 | "Even if it meant facing" |
| | ratio | 0.018 | |
| 90.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 4 | | matches | | 0 | "The abandoned Tube station beneath Camden reeked of damp concrete and something older, something that clung to the back of the throat like rust." | | 1 | "A scattering of bone tokens near the edge of the platform, their surfaces etched with symbols that made her eyes ache if she stared too long." | | 2 | "The detective’s brown eyes were sharp, her bearing rigid with the kind of precision that came from years of discipline." | | 3 | "The compass needle trembled, then pointed straight ahead, as if beckoning her forward." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 2 | | matches | | 0 | "Eva’s voice dropped, the levity fading" | | 1 | "She trailed, her throat tight" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 55 | | tagDensity | 0.036 | | leniency | 0.073 | | rawRatio | 0.5 | | effectiveRatio | 0.036 | |