| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 2 | | adverbTags | | 0 | "Quinn knelt again [again]" | | 1 | "Quinn stepped back [back]" |
| | dialogueSentences | 20 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.25 | | effectiveRatio | 0.2 | |
| 91.14% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1693 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | |
| 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) | |
| 91.14% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1693 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "predator" | | 1 | "tinged" | | 2 | "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 | 0 | | narrationSentences | 120 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 120 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 132 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1705 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 78.57% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1400 | | uniqueNames | 15 | | maxNameDensity | 1.43 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Harlow | 2 | | Quinn | 20 | | London | 1 | | Morris | 3 | | Detective | 3 | | Sergeant | 1 | | Pranav | 1 | | Kapoor | 6 | | January | 1 | | Camden | 1 | | British | 1 | | Museum | 1 | | Eva | 1 | | Kowalski | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Sergeant" | | 4 | "Pranav" | | 5 | "Kapoor" | | 6 | "Eva" | | 7 | "Kowalski" |
| | places | | 0 | "London" | | 1 | "Detective" | | 2 | "January" | | 3 | "British" |
| | globalScore | 0.786 | | windowScore | 0.833 | |
| 55.06% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 79 | | glossingSentenceCount | 3 | | matches | | 0 | "quite a scream — it was something else, something that looked closer to the way a person looks when they're trying to speak underwater" | | 1 | "something like them" | | 2 | "looked like copper but wasn't" |
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| 82.70% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.173 | | wordCount | 1705 | | matches | | 0 | "not room-temperature cold, but cold the way a window pane is cold in January" | | 1 | "no mark on his body but" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 132 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 60.89 | | std | 41.89 | | cv | 0.688 | | sampleLengths | | 0 | 108 | | 1 | 96 | | 2 | 129 | | 3 | 128 | | 4 | 94 | | 5 | 6 | | 6 | 8 | | 7 | 10 | | 8 | 31 | | 9 | 108 | | 10 | 42 | | 11 | 9 | | 12 | 25 | | 13 | 9 | | 14 | 113 | | 15 | 134 | | 16 | 7 | | 17 | 63 | | 18 | 14 | | 19 | 62 | | 20 | 34 | | 21 | 52 | | 22 | 69 | | 23 | 94 | | 24 | 42 | | 25 | 97 | | 26 | 68 | | 27 | 53 |
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| 84.80% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 120 | | matches | | 0 | "been closed" | | 1 | "been found" | | 2 | "was frozen" | | 3 | "been assigned" | | 4 | "been excited" | | 5 | "was gone" | | 6 | "been closed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 240 | | matches | | |
| 12.99% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 132 | | ratio | 0.045 | | matches | | 0 | "The station had been closed since 1932 — one of the dozens of ghost stations that riddled London's underground like forgotten organs." | | 1 | "His mouth was frozen in a shape that wasn't quite a scream — it was something else, something that looked closer to the way a person looks when they're trying to speak underwater." | | 2 | "When Quinn touched it with her gloved finger, it was cold — not room-temperature cold, but cold the way a window pane is cold in January." | | 3 | "They were old — the glaze had weathered around them — but not as old as the station." | | 4 | "Not dead — gone." | | 5 | "She didn't enter — not yet — but she stood at its edge and listened." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1396 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 48 | | adverbRatio | 0.034383954154727794 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.007879656160458453 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 132 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 132 | | mean | 12.92 | | std | 10.27 | | cv | 0.795 | | sampleLengths | | 0 | 13 | | 1 | 31 | | 2 | 22 | | 3 | 21 | | 4 | 8 | | 5 | 13 | | 6 | 13 | | 7 | 9 | | 8 | 10 | | 9 | 11 | | 10 | 4 | | 11 | 26 | | 12 | 23 | | 13 | 22 | | 14 | 14 | | 15 | 4 | | 16 | 33 | | 17 | 17 | | 18 | 3 | | 19 | 2 | | 20 | 5 | | 21 | 29 | | 22 | 17 | | 23 | 28 | | 24 | 3 | | 25 | 1 | | 26 | 28 | | 27 | 51 | | 28 | 2 | | 29 | 4 | | 30 | 23 | | 31 | 5 | | 32 | 3 | | 33 | 5 | | 34 | 14 | | 35 | 5 | | 36 | 22 | | 37 | 5 | | 38 | 6 | | 39 | 6 | | 40 | 3 | | 41 | 5 | | 42 | 10 | | 43 | 16 | | 44 | 15 | | 45 | 13 | | 46 | 13 | | 47 | 14 | | 48 | 10 | | 49 | 6 |
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| 45.71% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3333333333333333 | | totalSentences | 132 | | uniqueOpeners | 44 | |
| 61.73% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 108 | | matches | | 0 | "Instead, she walked a slow" | | 1 | "Then, three days later, he" |
| | ratio | 0.019 | |
| 94.07% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 108 | | matches | | 0 | "She paused, crouched, touched the" | | 1 | "It was faintly iridescent, like" | | 2 | "It didn't behave right." | | 3 | "She bagged a sample anyway," | | 4 | "His eyes were open." | | 5 | "His mouth was frozen in" | | 6 | "His clothes were intact: a" | | 7 | "It had a quality Quinn" | | 8 | "He was competent." | | 9 | "He'd been assigned to her" | | 10 | "She didn't respond immediately." | | 11 | "He'd died exactly where he" | | 12 | "She produced a small flashlight" | | 13 | "It was too bright." | | 14 | "She moved away from the" | | 15 | "She swept her flashlight across" | | 16 | "They were old — the" | | 17 | "She thought of Morris." | | 18 | "He'd been excited." | | 19 | "She'd stopped looking for answers" |
| | ratio | 0.315 | |
| 75.74% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 83 | | totalSentences | 108 | | matches | | 0 | "The air in the abandoned" | | 1 | "Detective Harlow Quinn ducked beneath" | | 2 | "The station had been closed" | | 3 | "Someone had pried open a" | | 4 | "That was how the body" | | 5 | "A urban explorer, nineteen and" | | 6 | "Quinn's shoes scraped against the" | | 7 | "The steps were slick with" | | 8 | "She paused, crouched, touched the" | | 9 | "It was faintly iridescent, like" | | 10 | "It didn't behave right." | | 11 | "She bagged a sample anyway," | | 12 | "The forensic team had set" | | 13 | "A man, mid-thirties by Quinn's" | | 14 | "His eyes were open." | | 15 | "His mouth was frozen in" | | 16 | "His clothes were intact: a" | | 17 | "It had a quality Quinn" | | 18 | "The voice came from behind" | | 19 | "Detective Sergeant Pranav Kapoor crouched" |
| | ratio | 0.769 | |
| 92.59% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 108 | | matches | | 0 | "If he'd injected something, he'd" | | 1 | "Because something was down here." |
| | ratio | 0.019 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 11 | | matches | | 0 | "Detective Harlow Quinn ducked beneath the yellow crime scene tape that sagged between two crumbling tile pillars, her worn leather watch catching a sliver of li…" | | 1 | "When she lifted her finger, the substance clung in a thin strand and then snapped, retracting back toward the step as if it had a preference." | | 2 | "His mouth was frozen in a shape that wasn't quite a scream — it was something else, something that looked closer to the way a person looks when they're trying t…" | | 3 | "His clothes were intact: a wool overcoat, a pressed shirt, leather shoes that had been expensive once." | | 4 | "Detective Sergeant Pranav Kapoor crouched beside the body on the opposite side, his round face creased with the particular exhaustion of someone who'd been awak…" | | 5 | "Deaths with no cause, locations with no logic, evidence that evaporated." | | 6 | "But standing here, in the green-tinged dark of a station that should have been empty, she felt the old question pressing against her chest again." | | 7 | "She studied the man's face, the strange shape of his mouth, the way his eyes stared upward as if he'd been watching something descend from the ceiling." | | 8 | "A woman named Eva Kowalski who had answered an obscure query Quinn had posted on an academic forum about pre-Christian burial symbols found in modern crime scen…" | | 9 | "Kowalski had responded with a detailed analysis that was either the work of a meticulous scholar or someone who knew entirely too much about things that shouldn…" | | 10 | "Something had killed this man in a way that left no mark on his body but twisted his face into a shape that said he'd seen something impossible in his final mom…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | 0 | "Quinn repeated (repeat)" |
| | dialogueSentences | 20 | | tagDensity | 0.1 | | leniency | 0.2 | | rawRatio | 0.5 | | effectiveRatio | 0.1 | |