| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 26 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.79% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1086 | | totalAiIsmAdverbs | 2 | | 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) | |
| 49.36% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1086 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "echoed" | | 1 | "chill" | | 2 | "chaotic" | | 3 | "gloom" | | 4 | "perfect" | | 5 | "scanned" | | 6 | "intensity" | | 7 | "racing" | | 8 | "tracing" | | 9 | "echo" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
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| | highlights | | 0 | "eyes narrowed" | | 1 | "eyes widened" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 77 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 77 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 91 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1079 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 840 | | uniqueNames | 10 | | maxNameDensity | 1.67 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Harlow | 1 | | Quinn | 14 | | Kowalski | 1 | | British | 1 | | Museum | 1 | | Eva | 10 | | Veil | 1 | | Market | 1 | | Morris | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Museum" | | 4 | "Eva" | | 5 | "Morris" |
| | places | (empty) | | globalScore | 0.667 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like chalk on the pillars" |
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| 14.64% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.854 | | wordCount | 1079 | | matches | | 0 | "not in a defensive posture, but as if he had been holding something" | | 1 | "not just a crime scene, but the edge of a much deeper, darker world" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 91 | | matches | | |
| 86.09% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 41.5 | | std | 18.73 | | cv | 0.451 | | sampleLengths | | 0 | 65 | | 1 | 63 | | 2 | 21 | | 3 | 60 | | 4 | 65 | | 5 | 29 | | 6 | 42 | | 7 | 63 | | 8 | 38 | | 9 | 31 | | 10 | 63 | | 11 | 43 | | 12 | 30 | | 13 | 22 | | 14 | 36 | | 15 | 60 | | 16 | 24 | | 17 | 12 | | 18 | 28 | | 19 | 68 | | 20 | 27 | | 21 | 34 | | 22 | 40 | | 23 | 26 | | 24 | 12 | | 25 | 77 |
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| 91.59% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 77 | | matches | | 0 | "been opened" | | 1 | "was curled" | | 2 | "been placed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 135 | | matches | | 0 | "were milling" | | 1 | "was watching" |
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| 48.67% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 3 | | totalSentences | 91 | | ratio | 0.033 | | matches | | 0 | "The forensics team had already laid out numbered evidence markers, but they denoted nothing of interest—a discarded cigarette butt, a scrap of cloth." | | 1 | "The girl wasn’t just a researcher; she was a roadmap." | | 2 | "“Good.” Quinn’s gaze swept over the bizarre, hidden market one last time, seeing not just a crime scene, but the edge of a much deeper, darker world—the same world that had taken Morris." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 865 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.03468208092485549 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.009248554913294798 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 91 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 91 | | mean | 11.86 | | std | 7.45 | | cv | 0.628 | | sampleLengths | | 0 | 30 | | 1 | 9 | | 2 | 26 | | 3 | 16 | | 4 | 13 | | 5 | 34 | | 6 | 19 | | 7 | 2 | | 8 | 11 | | 9 | 14 | | 10 | 13 | | 11 | 4 | | 12 | 13 | | 13 | 2 | | 14 | 1 | | 15 | 2 | | 16 | 20 | | 17 | 23 | | 18 | 22 | | 19 | 10 | | 20 | 19 | | 21 | 5 | | 22 | 21 | | 23 | 16 | | 24 | 28 | | 25 | 6 | | 26 | 12 | | 27 | 11 | | 28 | 6 | | 29 | 15 | | 30 | 23 | | 31 | 7 | | 32 | 11 | | 33 | 7 | | 34 | 4 | | 35 | 2 | | 36 | 6 | | 37 | 19 | | 38 | 17 | | 39 | 21 | | 40 | 9 | | 41 | 15 | | 42 | 5 | | 43 | 11 | | 44 | 3 | | 45 | 14 | | 46 | 16 | | 47 | 8 | | 48 | 14 | | 49 | 3 |
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| 60.81% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.38461538461538464 | | totalSentences | 91 | | uniqueOpeners | 35 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 68 | | matches | (empty) | | ratio | 0 | |
| 72.94% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 68 | | matches | | 0 | "It was the smell of" | | 1 | "Her boots echoed with a" | | 2 | "she said, her voice a" | | 3 | "Her sharp jaw was set," | | 4 | "She adjusted the worn leather" | | 5 | "Her curly red hair was" | | 6 | "She tucked a stray coil" | | 7 | "His hands were clean, his" | | 8 | "She knelt, ignoring the protesting" | | 9 | "It was curled loosely, not" | | 10 | "She shifted her attention to" | | 11 | "It had seeped into the" | | 12 | "It didn’t radiate out from" | | 13 | "It was disturbed." | | 14 | "she said, more to herself" | | 15 | "She scanned the immediate vicinity." | | 16 | "Her gaze fell on a" | | 17 | "It was a perfect circle," | | 18 | "She’d seen marks like that" | | 19 | "she murmured, almost inaudibly" |
| | ratio | 0.368 | |
| 4.12% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 68 | | matches | | 0 | "The air in the abandoned" | | 1 | "It was the smell of" | | 2 | "The flickering blue-white glow of" | | 3 | "Her boots echoed with a" | | 4 | "A dozen uniforms were milling" | | 5 | "A young sergeant, his face" | | 6 | "she said, her voice a" | | 7 | "Her sharp jaw was set," | | 8 | "She adjusted the worn leather" | | 9 | "The stillness was wrong." | | 10 | "A murder this brutal should" | | 11 | "Eva Kowalski stood a few" | | 12 | "Her curly red hair was" | | 13 | "She tucked a stray coil" | | 14 | "Eva said, her voice softer" | | 15 | "Quinn gave a curt nod." | | 16 | "The British Museum’s" | | 17 | "Eva’s green eyes darted around" | | 18 | "This was no ordinary crime" | | 19 | "This was the Veil Market," |
| | ratio | 0.912 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 68 | | matches | (empty) | | ratio | 0 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 2 | | matches | | 0 | "It was curled loosely, not in a defensive posture, but as if he had been holding something." | | 1 | "They took something specific, something small enough to hold in one hand, something that had been placed on the ground, leaving that tell-tale ring in the dust." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 7 | | matches | | 0 | "she said, her voice a low rasp" | | 1 | "Eva said, her voice softer than the surroundings warranted" | | 2 | "Eva ventured, her freckled complexion looking almost ethereal in the bleached light" | | 3 | "she said, more to herself than to Eva" | | 4 | "Quinn called out, not taking her eyes off the mark" | | 5 | "Quinn stated, her voice cutting through the hum of the generator" | | 6 | "Quinn said, her tone leaving no room for argument" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 5 | | fancyTags | | 0 | "Quinn repeated (repeat)" | | 1 | "Quinn called out (call out)" | | 2 | "she murmured (murmur)" | | 3 | "Quinn stated (state)" | | 4 | "she whispered (whisper)" |
| | dialogueSentences | 26 | | tagDensity | 0.346 | | leniency | 0.692 | | rawRatio | 0.556 | | effectiveRatio | 0.385 | |