| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 17 | | tagDensity | 0.235 | | leniency | 0.471 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 82.76% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 870 | | totalAiIsmAdverbs | 3 | | 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) | |
| 54.02% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 870 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "scanning" | | 1 | "etched" | | 2 | "silence" | | 3 | "echoed" | | 4 | "measured" | | 5 | "racing" | | 6 | "whisper" |
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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 | 64 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 64 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 76 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 5 | | markdownWords | 5 | | totalWords | 862 | | ratio | 0.006 | | matches | | 0 | "wrong" | | 1 | "supposed" | | 2 | "wrong" | | 3 | "placed" | | 4 | "collecting" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 74.76% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 731 | | uniqueNames | 9 | | maxNameDensity | 1.5 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Tube | 1 | | Camden | 2 | | Detective | 1 | | Harlow | 11 | | Quinn | 1 | | Kowalski | 1 | | Veil | 1 | | Market | 1 | | Eva | 7 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Kowalski" | | 4 | "Eva" |
| | places | (empty) | | globalScore | 0.748 | | windowScore | 1 | |
| 89.02% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared beneath Camden during the full moon—had been rumored to be active again" |
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| 83.99% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.16 | | wordCount | 862 | | matches | | 0 | "not from lack of oxygen, but from something else" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 76 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 24 | | mean | 35.92 | | std | 25.12 | | cv | 0.699 | | sampleLengths | | 0 | 75 | | 1 | 75 | | 2 | 77 | | 3 | 16 | | 4 | 66 | | 5 | 24 | | 6 | 50 | | 7 | 9 | | 8 | 61 | | 9 | 16 | | 10 | 15 | | 11 | 49 | | 12 | 13 | | 13 | 18 | | 14 | 42 | | 15 | 7 | | 16 | 67 | | 17 | 47 | | 18 | 8 | | 19 | 11 | | 20 | 56 | | 21 | 10 | | 22 | 46 | | 23 | 4 |
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| 66.89% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 64 | | matches | | 0 | "been sealed" | | 1 | "been caught" | | 2 | "been stripped" | | 3 | "was tarnished" | | 4 | "been submerged" | | 5 | "been arranged" | | 6 | "been *placed" | | 7 | "been rumored" | | 8 | "been chipped" |
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| 16.51% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 109 | | matches | | 0 | "was already moving" | | 1 | "was already stepping" | | 2 | "was walking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 76 | | ratio | 0.092 | | matches | | 0 | "The air smelled of damp concrete and something older—something *wrong*." | | 1 | "\"I wasn’t *supposed* to be anywhere near this case, but here I am.\" She nodded toward the body—a man in a tailored suit, his face frozen in an expression of frozen terror." | | 2 | "But beneath the dust, she saw something else—scratches, like claws or talons, etched into the surface." | | 3 | "And then there was the watch—a sleek, modern piece with a digital display, but the band was tarnished, as if it had been submerged in water for weeks." | | 4 | "But there was nothing there—just the usual creaks of an old building settling, the distant drip of water." | | 5 | "And then there was the fact that the Veil Market—an underground supernatural black market that only appeared beneath Camden during the full moon—had been rumored to be active again." | | 6 | "She reached into her coat pocket and pulled out a small, folded piece of paper—a sketch she’d made earlier that day, of the man’s face, the compass, the scratches on the wall." |
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| 87.67% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 741 | | adjectiveStacks | 1 | | stackExamples | | 0 | "underground supernatural black market" |
| | adverbCount | 35 | | adverbRatio | 0.04723346828609987 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.01349527665317139 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 76 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 76 | | mean | 11.34 | | std | 8.82 | | cv | 0.778 | | sampleLengths | | 0 | 26 | | 1 | 12 | | 2 | 27 | | 3 | 10 | | 4 | 28 | | 5 | 25 | | 6 | 11 | | 7 | 11 | | 8 | 5 | | 9 | 32 | | 10 | 14 | | 11 | 17 | | 12 | 7 | | 13 | 1 | | 14 | 1 | | 15 | 3 | | 16 | 13 | | 17 | 15 | | 18 | 22 | | 19 | 19 | | 20 | 10 | | 21 | 8 | | 22 | 16 | | 23 | 9 | | 24 | 15 | | 25 | 16 | | 26 | 5 | | 27 | 5 | | 28 | 6 | | 29 | 3 | | 30 | 3 | | 31 | 17 | | 32 | 11 | | 33 | 28 | | 34 | 2 | | 35 | 12 | | 36 | 4 | | 37 | 11 | | 38 | 4 | | 39 | 6 | | 40 | 41 | | 41 | 2 | | 42 | 9 | | 43 | 4 | | 44 | 5 | | 45 | 13 | | 46 | 4 | | 47 | 12 | | 48 | 8 | | 49 | 18 |
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| 50.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.34210526315789475 | | totalSentences | 76 | | uniqueOpeners | 26 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 58 | | matches | | 0 | "Too precise to be accidental." | | 1 | "Too deliberate to be random." | | 2 | "Instead, she turned her attention" | | 3 | "Then, from the shadows near" |
| | ratio | 0.069 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 58 | | matches | | 0 | "She stepped over the yellow" | | 1 | "She tucked a strand of" | | 2 | "she murmured, her voice low" | | 3 | "She nodded toward the body—a" | | 4 | "His eyes were wide, his" | | 5 | "she said, her voice flat" | | 6 | "She straightened, her military bearing" | | 7 | "She gestured to the man’s" | | 8 | "She was already moving, her" | | 9 | "She reached the far corner" | | 10 | "She reached into her coat" | | 11 | "She unfolded it, holding it" | | 12 | "She was already stepping through" | | 13 | "She knew what she was" |
| | ratio | 0.241 | |
| 63.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 58 | | matches | | 0 | "The abandoned Tube station beneath" | | 1 | "She stepped over the yellow" | | 2 | "The air smelled of damp" | | 3 | "Eva Kowalski stood near the" | | 4 | "She tucked a strand of" | | 5 | "she murmured, her voice low" | | 6 | "Harlow didn’t slow her stride." | | 7 | "She nodded toward the body—a" | | 8 | "His eyes were wide, his" | | 9 | "The coroner’s team had already" | | 10 | "Eva exhaled sharply." | | 11 | "Harlow crouched beside the body," | | 12 | "The man’s fingers were still" | | 13 | "The needle was pointed straight" | | 14 | "she said, her voice flat" | | 15 | "A uniformed officer stepped forward," | | 16 | "Harlow ignored him, her gaze" | | 17 | "The station had been stripped" | | 18 | "Eva followed her line of" | | 19 | "Harlow didn’t answer." |
| | ratio | 0.793 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 47.62% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 4 | | matches | | 0 | "The needle was pointed straight at the ceiling, as if he’d been holding it there in his final moments." | | 1 | "And then there was the watch—a sleek, modern piece with a digital display, but the band was tarnished, as if it had been submerged in water for weeks." | | 2 | "She was already moving, her steps measured, her mind racing." | | 3 | "She was already stepping through the passage, her boots sinking into the damp earth." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 2 | | matches | | 0 | "she murmured, her voice low" | | 1 | "she said, her voice flat" |
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| 91.18% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 17 | | tagDensity | 0.118 | | leniency | 0.235 | | rawRatio | 0.5 | | effectiveRatio | 0.118 | |