| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 2 | | adverbTags | | 0 | "Harlow said softly [softly]" | | 1 | "Harlow said softly [softly]" |
| | dialogueSentences | 53 | | tagDensity | 0.377 | | leniency | 0.755 | | rawRatio | 0.1 | | effectiveRatio | 0.075 | |
| 67.64% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1236 | | totalAiIsmAdverbs | 8 | | found | | | highlights | | 0 | "nervously" | | 1 | "carefully" | | 2 | "slowly" | | 3 | "warily" | | 4 | "softly" |
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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) | |
| 55.50% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1236 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "silence" | | 1 | "traced" | | 2 | "familiar" | | 3 | "shimmered" | | 4 | "etched" | | 5 | "scanning" | | 6 | "methodical" | | 7 | "shattered" | | 8 | "whisper" | | 9 | "echoed" |
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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 | 65 | | matches | (empty) | |
| 32.97% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 4 | | narrationSentences | 65 | | 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 | 97 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1232 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 20 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 56 | | wordCount | 742 | | uniqueNames | 12 | | maxNameDensity | 3.5 | | worstName | "Harlow" | | maxWindowNameDensity | 6 | | worstWindowName | "Harlow" | | discoveredNames | | Detective | 1 | | Harlow | 26 | | Quinn | 1 | | Veil | 2 | | Market | 2 | | Tube | 1 | | Camden | 1 | | Metropolitan | 1 | | Police | 1 | | Eva | 17 | | Kowalski | 1 | | Thorne | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Police" | | 4 | "Eva" | | 5 | "Kowalski" | | 6 | "Thorne" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 43.62% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 47 | | glossingSentenceCount | 2 | | matches | | 0 | "corners that seemed to shift when you weren't looking directly at them" | | 1 | "places that seemed to shift when you weren't looking directly at them" |
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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 | 1232 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 97 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 25.67 | | std | 14.51 | | cv | 0.566 | | sampleLengths | | 0 | 64 | | 1 | 74 | | 2 | 47 | | 3 | 39 | | 4 | 25 | | 5 | 44 | | 6 | 27 | | 7 | 21 | | 8 | 38 | | 9 | 29 | | 10 | 20 | | 11 | 25 | | 12 | 26 | | 13 | 35 | | 14 | 42 | | 15 | 30 | | 16 | 35 | | 17 | 5 | | 18 | 16 | | 19 | 33 | | 20 | 20 | | 21 | 27 | | 22 | 2 | | 23 | 18 | | 24 | 5 | | 25 | 27 | | 26 | 9 | | 27 | 24 | | 28 | 10 | | 29 | 19 | | 30 | 17 | | 31 | 10 | | 32 | 14 | | 33 | 27 | | 34 | 8 | | 35 | 19 | | 36 | 21 | | 37 | 8 | | 38 | 44 | | 39 | 38 | | 40 | 14 | | 41 | 25 | | 42 | 37 | | 43 | 13 | | 44 | 33 | | 45 | 35 | | 46 | 11 | | 47 | 22 |
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| 94.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 65 | | matches | | 0 | "was etched" | | 1 | "was clenched" |
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| 48.48% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 132 | | matches | | 0 | "weren't looking" | | 1 | "was just finishing" | | 2 | "weren't looking" |
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| 25.04% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 97 | | ratio | 0.041 | | matches | | 0 | "It defied physics in ways that made her teeth ache—a cavernous space that should have been impossible underground, with stalls stretching into shadowy corners that seemed to shift when you weren't looking directly at them." | | 1 | "At 5'9\", she towered over the 5'4\" researcher, who kept nervously tucking her hair behind her left ear—a habit Harlow had noticed in their three previous encounters involving cases with unexplainable elements." | | 2 | "Some watched with expressions that hovered between fear and fascination; others had simply shuttered their stalls and vanished into the shadows." | | 3 | "Something nagged at her—the arrangement of the overturned manuscripts, the way Thorne's body lay, the complete lack of defensive wounds." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 745 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.03355704697986577 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.01879194630872483 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 97 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 97 | | mean | 12.7 | | std | 7.55 | | cv | 0.594 | | sampleLengths | | 0 | 24 | | 1 | 18 | | 2 | 22 | | 3 | 22 | | 4 | 35 | | 5 | 17 | | 6 | 11 | | 7 | 25 | | 8 | 11 | | 9 | 7 | | 10 | 32 | | 11 | 21 | | 12 | 4 | | 13 | 26 | | 14 | 11 | | 15 | 7 | | 16 | 7 | | 17 | 20 | | 18 | 14 | | 19 | 7 | | 20 | 13 | | 21 | 15 | | 22 | 10 | | 23 | 5 | | 24 | 24 | | 25 | 12 | | 26 | 8 | | 27 | 15 | | 28 | 10 | | 29 | 9 | | 30 | 17 | | 31 | 8 | | 32 | 27 | | 33 | 21 | | 34 | 21 | | 35 | 15 | | 36 | 15 | | 37 | 11 | | 38 | 15 | | 39 | 9 | | 40 | 5 | | 41 | 8 | | 42 | 8 | | 43 | 13 | | 44 | 20 | | 45 | 20 | | 46 | 4 | | 47 | 23 | | 48 | 2 | | 49 | 2 |
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| 70.10% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.4329896907216495 | | totalSentences | 97 | | uniqueOpeners | 42 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 58 | | matches | | 0 | "She traced the familiar pattern" | | 1 | "It defied physics in ways" | | 2 | "She pointed to a small" | | 3 | "Its casing had a patina" | | 4 | "She knelt again, pulling tweezers" | | 5 | "She continued her examination, her" | | 6 | "She pointed to Thorne's right" | | 7 | "She pulled out the evidence" | | 8 | "She stood and gestured to" | | 9 | "She walked to the spot" | | 10 | "She turned to face Eva" |
| | ratio | 0.19 | |
| 11.72% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 58 | | matches | | 0 | "The bone token felt cold" | | 1 | "The abandoned Tube station beneath" | | 2 | "She traced the familiar pattern" | | 3 | "It defied physics in ways" | | 4 | "The air smelled of ozone," | | 5 | "Eva Kowalski pushed her round" | | 6 | "Harlow's sharp jaw tightened as" | | 7 | "Harlow said, her worn leather" | | 8 | "The victim lay in the" | | 9 | "A man in his late" | | 10 | "The medical examiner was just" | | 11 | "Eva stepped forward, her worn" | | 12 | "She pointed to a small" | | 13 | "Its casing had a patina" | | 14 | "Harlow crouched down, her military" | | 15 | "Harlow picked up an evidence" | | 16 | "Eva said, her green eyes" | | 17 | "Harlow stood slowly, her brown" | | 18 | "Eva said, relief crossing her" | | 19 | "Harlow walked a careful perimeter" |
| | ratio | 0.897 | |
| 86.21% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 58 | | matches | | | ratio | 0.017 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 2 | | matches | | 0 | "The bone token felt cold against Detective Harlow Quinn's palm as she approached the brick wall that concealed the entrance to the Veil Market." | | 1 | "It defied physics in ways that made her teeth ache—a cavernous space that should have been impossible underground, with stalls stretching into shadowy corners t…" |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 4 | | matches | | 0 | "Eva said, her green eyes wide behind her glasses" | | 1 | "Eva said, relief crossing her freckled face" | | 2 | "Harlow asked, her voice steady" | | 3 | "Harlow said softly, her eyes scanning the darkness" |
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| 74.53% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 17 | | fancyCount | 4 | | fancyTags | | 0 | "Eva explained (explain)" | | 1 | "Eva explained (explain)" | | 2 | "the vendor corrected (correct)" | | 3 | "Eva whispered (whisper)" |
| | dialogueSentences | 53 | | tagDensity | 0.321 | | leniency | 0.642 | | rawRatio | 0.235 | | effectiveRatio | 0.151 | |