| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 51 | | tagDensity | 0.255 | | leniency | 0.51 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.11% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1900 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "gently" | | 1 | "perfectly" | | 2 | "tightly" |
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| 60.00% | 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) | |
| 71.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1900 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "velvet" | | 1 | "unsettled" | | 2 | "standard" | | 3 | "profound" | | 4 | "etched" | | 5 | "familiar" | | 6 | "glint" | | 7 | "database" |
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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 | 108 | | matches | (empty) | |
| 89.95% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 108 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "appeared to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 145 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 48 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1871 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 64.84% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1233 | | uniqueNames | 11 | | maxNameDensity | 1.7 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Detective | 1 | | Harlow | 1 | | Quinn | 21 | | Chen | 9 | | Victorian | 1 | | Morris | 3 | | Metropolitan | 1 | | Police | 1 | | London | 1 | | Three | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Chen" | | 3 | "Morris" | | 4 | "Police" |
| | places | | | globalScore | 0.648 | | windowScore | 0.667 | |
| 56.25% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | glossingSentenceCount | 3 | | matches | | 0 | "liquids that seemed to shift color when she looked away" | | 1 | "looked like black ink" | | 2 | "seemed deeper than they should be" |
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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 | 1871 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 145 | | matches | | 0 | "knew that rumors" | | 1 | "learned that some" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 74 | | mean | 25.28 | | std | 17.38 | | cv | 0.687 | | sampleLengths | | 0 | 53 | | 1 | 40 | | 2 | 16 | | 3 | 35 | | 4 | 9 | | 5 | 16 | | 6 | 58 | | 7 | 21 | | 8 | 4 | | 9 | 43 | | 10 | 37 | | 11 | 54 | | 12 | 6 | | 13 | 23 | | 14 | 17 | | 15 | 4 | | 16 | 56 | | 17 | 14 | | 18 | 3 | | 19 | 23 | | 20 | 19 | | 21 | 41 | | 22 | 21 | | 23 | 43 | | 24 | 5 | | 25 | 7 | | 26 | 51 | | 27 | 5 | | 28 | 21 | | 29 | 46 | | 30 | 8 | | 31 | 12 | | 32 | 8 | | 33 | 15 | | 34 | 39 | | 35 | 12 | | 36 | 33 | | 37 | 9 | | 38 | 7 | | 39 | 47 | | 40 | 1 | | 41 | 45 | | 42 | 56 | | 43 | 2 | | 44 | 43 | | 45 | 17 | | 46 | 37 | | 47 | 11 | | 48 | 45 | | 49 | 27 |
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| 79.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 8 | | totalSentences | 108 | | matches | | 0 | "been erected" | | 1 | "were curled" | | 2 | "was etched" | | 3 | "were rusted" | | 4 | "been trained" | | 5 | "was evaporated" | | 6 | "was gone" | | 7 | "been told" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 221 | | matches | | 0 | "was spinning" | | 1 | "was looking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 17 | | semicolonCount | 0 | | flaggedSentences | 14 | | totalSentences | 145 | | ratio | 0.097 | | matches | | 0 | "She ducked beneath the police tape strung across the entrance—a rusted maintenance door that shouldn't have been accessible at all—and descended the iron staircase into darkness." | | 1 | "It wasn't the body that gave her pause—she'd seen plenty of those." | | 2 | "The scene wouldn't—not once the brass decided this was too strange for standard procedure and called in whoever they called in for things like this." | | 3 | "A display case full of what appeared to be teeth—human, animal, and something else entirely." | | 4 | "She lay in the center of a stall that appeared to have sold small brass instruments—compasses, astrolabes, devices Quinn couldn't name." | | 5 | "Something tugged at the back of her mind—a memory of another case, another scene that didn't add up." | | 6 | "But there were footprints in the dust—dozens of them, overlapping, leading in both directions." | | 7 | "If this was a criminal operation—drugs or stolen goods or something else—why leave all of it behind?" | | 8 | "This woman had seen something she knew—something familiar—and it had stopped her heart." | | 9 | "Quinn looked at her colleague—young, earnest, following procedure exactly as he'd been trained." | | 10 | "A glint of light caught her eye—something beneath one of the tables, half-hidden by a fallen cloth." | | 11 | "This did—thin wisps of dark vapor rising from the liquid and dissipating into the air." | | 12 | "For just a moment, she could have sworn she saw movement—a shape that was there and then wasn't." | | 13 | "She thought about the symbols etched into its face—protective sigils, if you believed in that sort of thing." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1255 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 41 | | adverbRatio | 0.03266932270916335 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.006374501992031873 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 145 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 145 | | mean | 12.9 | | std | 9.46 | | cv | 0.733 | | sampleLengths | | 0 | 27 | | 1 | 26 | | 2 | 9 | | 3 | 31 | | 4 | 12 | | 5 | 4 | | 6 | 18 | | 7 | 17 | | 8 | 9 | | 9 | 12 | | 10 | 4 | | 11 | 2 | | 12 | 16 | | 13 | 19 | | 14 | 21 | | 15 | 13 | | 16 | 8 | | 17 | 4 | | 18 | 43 | | 19 | 8 | | 20 | 4 | | 21 | 25 | | 22 | 8 | | 23 | 14 | | 24 | 7 | | 25 | 15 | | 26 | 10 | | 27 | 6 | | 28 | 18 | | 29 | 5 | | 30 | 2 | | 31 | 15 | | 32 | 4 | | 33 | 15 | | 34 | 2 | | 35 | 2 | | 36 | 21 | | 37 | 16 | | 38 | 3 | | 39 | 11 | | 40 | 3 | | 41 | 20 | | 42 | 3 | | 43 | 7 | | 44 | 12 | | 45 | 10 | | 46 | 20 | | 47 | 11 | | 48 | 10 | | 49 | 11 |
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| 60.46% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.4206896551724138 | | totalSentences | 145 | | uniqueOpeners | 61 | |
| 67.34% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 99 | | matches | | 0 | "Somewhere below, the crime scene" | | 1 | "Somewhere in this city was" |
| | ratio | 0.02 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 99 | | matches | | 0 | "She ducked beneath the police" | | 1 | "Her torch cut a pale" | | 2 | "It wasn't the body that" | | 3 | "It was everything else." | | 4 | "He gestured toward the center" | | 5 | "She reached for one of" | | 6 | "She also knew that rumors," | | 7 | "She approached the tent." | | 8 | "She lay in the center" | | 9 | "Her eyes were open, fixed" | | 10 | "Its face was etched with" | | 11 | "She pushed the thought away." | | 12 | "she asked, straightening up" | | 13 | "She returned to the body," | | 14 | "It was recognition." | | 15 | "She couldn't fault him for" | | 16 | "She'd been the same way" | | 17 | "She crouched, pulled back the" | | 18 | "He came at a jog," | | 19 | "She was looking past the" |
| | ratio | 0.273 | |
| 66.06% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 99 | | matches | | 0 | "The abandoned Tube station smelled" | | 1 | "She ducked beneath the police" | | 2 | "Her torch cut a pale" | | 3 | "Quinn skipped the step without" | | 4 | "The platform opened up before" | | 5 | "It wasn't the body that" | | 6 | "It was everything else." | | 7 | "Dozens of them, arranged in" | | 8 | "The crime scene lights cast" | | 9 | "DS Chen approached, tablet in" | | 10 | "He gestured toward the center" | | 11 | "Quinn walked the perimeter first," | | 12 | "The body would keep." | | 13 | "The scene wouldn't—not once the" | | 14 | "The stalls held merchandise that" | | 15 | "Glass vials filled with liquids" | | 16 | "Books bound in materials she" | | 17 | "A display case full of" | | 18 | "She reached for one of" | | 19 | "She also knew that rumors," |
| | ratio | 0.788 | |
| 50.51% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 99 | | matches | | 0 | "If this was a criminal" |
| | ratio | 0.01 | |
| 66.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 6 | | matches | | 0 | "She ducked beneath the police tape strung across the entrance—a rusted maintenance door that shouldn't have been accessible at all—and descended the iron stairc…" | | 1 | "It wasn't the body that gave her pause—she'd seen plenty of those." | | 2 | "The victim was female, mid-thirties, dressed in clothes that looked Victorian but felt wrong somehow." | | 3 | "She lay in the center of a stall that appeared to have sold small brass instruments—compasses, astrolabes, devices Quinn couldn't name." | | 4 | "Somewhere in this city was a researcher who specialized in impossible things." | | 5 | "Someone who might be able to tell her what kind of market sold smoking boxes and spinning compasses." |
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| 86.54% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, her voice steady despite the cold settling into her bones" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 51 | | tagDensity | 0.098 | | leniency | 0.196 | | rawRatio | 0 | | effectiveRatio | 0 | |