| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "Harlow repeated softly [softly]" |
| | dialogueSentences | 31 | | tagDensity | 0.355 | | leniency | 0.71 | | rawRatio | 0.091 | | effectiveRatio | 0.065 | |
| 76.53% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1278 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "sweetly" | | 1 | "really" | | 2 | "softly" | | 3 | "gently" |
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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) | |
| 33.49% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1278 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "chaotic" | | 1 | "velvet" | | 2 | "familiar" | | 3 | "weight" | | 4 | "shattered" | | 5 | "gloom" | | 6 | "gleaming" | | 7 | "etched" | | 8 | "intricate" | | 9 | "constructed" | | 10 | "fascinating" | | 11 | "tension" | | 12 | "imposing" | | 13 | "silence" | | 14 | "chill" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 87 | | matches | (empty) | |
| 77.18% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 87 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 103 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 56 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 2 | | totalWords | 1271 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 871 | | uniqueNames | 12 | | maxNameDensity | 1.95 | | worstName | "Harlow" | | maxWindowNameDensity | 4 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Detective | 1 | | Harlow | 17 | | Quinn | 1 | | Veil | 1 | | Market | 1 | | Shade | 2 | | Kowalski | 1 | | British | 1 | | Museum | 1 | | Eva | 15 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Museum" | | 4 | "Eva" |
| | places | (empty) | | globalScore | 0.524 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.787 | | wordCount | 1271 | | matches | | 0 | "not at the body, but at the floor a few feet away" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 103 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 37.38 | | std | 23.9 | | cv | 0.639 | | sampleLengths | | 0 | 65 | | 1 | 64 | | 2 | 67 | | 3 | 17 | | 4 | 1 | | 5 | 65 | | 6 | 29 | | 7 | 49 | | 8 | 16 | | 9 | 43 | | 10 | 73 | | 11 | 49 | | 12 | 12 | | 13 | 72 | | 14 | 12 | | 15 | 80 | | 16 | 18 | | 17 | 15 | | 18 | 23 | | 19 | 10 | | 20 | 21 | | 21 | 3 | | 22 | 44 | | 23 | 52 | | 24 | 22 | | 25 | 60 | | 26 | 19 | | 27 | 19 | | 28 | 10 | | 29 | 52 | | 30 | 15 | | 31 | 75 | | 32 | 46 | | 33 | 53 |
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| 97.20% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 87 | | matches | | 0 | "was concentrated" | | 1 | "been swept" | | 2 | "been interrupted" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 140 | | matches | | 0 | "wasn’t pointing" | | 1 | "was spinning" | | 2 | "was choosing" | | 3 | "was morphing" | | 4 | "was feeling" | | 5 | "was holding" |
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| 59.64% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 103 | | ratio | 0.029 | | matches | | 0 | "The shattered glass, the overturned table, the spilled wares—it all pointed to a simple, brutal narrative." | | 1 | "The overturned table was on the wrong side of the body—if he’d been standing behind the counter when attacked, it should have fallen toward the aisle, not back onto himself." | | 2 | "“Places that aren’t on any map.” Eva’s nervousness was morphing into something else—a defensive tension." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 876 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, disc-shaped object" |
| | adverbCount | 29 | | adverbRatio | 0.033105022831050226 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.014840182648401826 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 103 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 103 | | mean | 12.34 | | std | 9.72 | | cv | 0.788 | | sampleLengths | | 0 | 32 | | 1 | 13 | | 2 | 20 | | 3 | 21 | | 4 | 10 | | 5 | 20 | | 6 | 13 | | 7 | 19 | | 8 | 13 | | 9 | 9 | | 10 | 10 | | 11 | 16 | | 12 | 17 | | 13 | 1 | | 14 | 16 | | 15 | 14 | | 16 | 18 | | 17 | 17 | | 18 | 10 | | 19 | 19 | | 20 | 18 | | 21 | 9 | | 22 | 22 | | 23 | 14 | | 24 | 2 | | 25 | 4 | | 26 | 39 | | 27 | 7 | | 28 | 9 | | 29 | 2 | | 30 | 5 | | 31 | 50 | | 32 | 17 | | 33 | 19 | | 34 | 13 | | 35 | 5 | | 36 | 7 | | 37 | 9 | | 38 | 13 | | 39 | 22 | | 40 | 5 | | 41 | 8 | | 42 | 15 | | 43 | 9 | | 44 | 3 | | 45 | 10 | | 46 | 7 | | 47 | 4 | | 48 | 30 | | 49 | 29 |
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| 42.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3106796116504854 | | totalSentences | 103 | | uniqueOpeners | 32 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 82 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 82 | | matches | | 0 | "Her sharp jaw was set," | | 1 | "They felt the wrongness of" | | 2 | "She pushed her round glasses" | | 3 | "She clutched the worn leather" | | 4 | "She tucked a stray curl" | | 5 | "It was no larger than" | | 6 | "She used the tip of" | | 7 | "It was spinning in a" | | 8 | "Her eyes kept returning to" | | 9 | "It wasn’t a question." | | 10 | "She watched Eva closely." | | 11 | "She was feeling guilt." | | 12 | "It was a message." | | 13 | "They’d been after something specific." | | 14 | "They’d been looking for the" | | 15 | "They’d missed it." | | 16 | "they’d been interrupted." | | 17 | "Her eyes snapped to Eva’s" | | 18 | "she whispered, her voice barely" | | 19 | "It was a frantic, violent" |
| | ratio | 0.268 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 77 | | totalSentences | 82 | | matches | | 0 | "The air in the disused" | | 1 | "The Veil Market, usually a" | | 2 | "Harlow moved with the tight," | | 3 | "Her sharp jaw was set," | | 4 | "The local uniforms had done" | | 5 | "They felt the wrongness of" | | 6 | "The body lay sprawled behind" | | 7 | "A Shade artisan, his grey" | | 8 | "The official report would call" | | 9 | "A struggle, a fall, a" | | 10 | "The shattered glass, the overturned" | | 11 | "The voice came from the" | | 12 | "Eva Kowalski emerged, her curly" | | 13 | "She pushed her round glasses" | | 14 | "She clutched the worn leather" | | 15 | "Harlow’s tone was flat, professional" | | 16 | "The British Museum’s pet occult" | | 17 | "Eva’s voice was a little" | | 18 | "She tucked a stray curl" | | 19 | "Harlow asked, not taking her" |
| | ratio | 0.939 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 82 | | matches | (empty) | | ratio | 0 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 2 | | matches | | 0 | "The British Museum’s pet occult researcher was a necessary, if irritating, consultant on cases that bled into the preternatural." | | 1 | "The broken glass was concentrated in one area, as if vials had been swept off the counter in a single, deliberate motion, not knocked over in a frantic fight." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 6 | | matches | | 0 | "Harlow asked, not taking her eyes off the body" | | 1 | "Eva offered, her voice tightening" | | 2 | "Harlow stood up, her gaze sweeping the scene again" | | 3 | "Harlow said, her voice low and hard as iron" | | 4 | "Harlow took, her height imposing" | | 5 | "she whispered, her voice barely audible" |
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| 53.23% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 3 | | fancyTags | | 0 | "Harlow repeated softly (repeat)" | | 1 | "Harlow stated (state)" | | 2 | "she whispered (whisper)" |
| | dialogueSentences | 31 | | tagDensity | 0.194 | | leniency | 0.387 | | rawRatio | 0.5 | | effectiveRatio | 0.194 | |