| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 2 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 743 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 39.43% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 743 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "wavering" | | 1 | "weight" | | 2 | "pulse" | | 3 | "streaming" | | 4 | "pulsed" | | 5 | "gloom" | | 6 | "scanned" | | 7 | "shimmered" |
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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 | 71 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 71 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 71 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 4 | | totalWords | 731 | | ratio | 0.005 | | matches | | 0 | "The Raven’s Nest" | | 1 | "Down." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 74.24% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 726 | | uniqueNames | 9 | | maxNameDensity | 1.52 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Herrera" | | discoveredNames | | London | 1 | | Quinn | 11 | | Soho | 1 | | Herrera | 10 | | Saint | 1 | | Christopher | 1 | | Raven | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Raven" |
| | places | | | globalScore | 0.742 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 731 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 71 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 24 | | mean | 30.46 | | std | 19.9 | | cv | 0.653 | | sampleLengths | | 0 | 51 | | 1 | 1 | | 2 | 42 | | 3 | 57 | | 4 | 12 | | 5 | 55 | | 6 | 57 | | 7 | 50 | | 8 | 3 | | 9 | 35 | | 10 | 45 | | 11 | 4 | | 12 | 27 | | 13 | 35 | | 14 | 5 | | 15 | 38 | | 16 | 34 | | 17 | 4 | | 18 | 62 | | 19 | 8 | | 20 | 33 | | 21 | 27 | | 22 | 40 | | 23 | 6 |
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| 95.38% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 71 | | matches | | 0 | "was warped" | | 1 | "was scrawled" |
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| 31.93% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 119 | | matches | | 0 | "were walking" | | 1 | "was looking" | | 2 | "wasn’t leaving" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 71 | | ratio | 0.127 | | matches | | 0 | "Tomás Herrera—his dark curls plastered to his forehead, the Saint Christopher medallion bouncing against his chest with each stride." | | 1 | "He’d been running them for years—off the books, under the radar, patching up the kind of people who didn’t exist in official reports." | | 2 | "It was a known entry point—one of many scattered across the city." | | 3 | "A gust of stale, damp air rushed out, carrying the scent of old stone and something else—something metallic, like rust and copper." | | 4 | "Backup wouldn’t understand why Herrera had run—or why he’d led her here." | | 5 | "Then she heard it—the murmur of voices, the clink of glass, the low hum of something that wasn’t quite music." | | 6 | "Figures moved in the dim light—some human, some not." | | 7 | "A child—no, not a child, something wearing the shape of one—held out a small bone token." | | 8 | "She scanned the crowd, her sharp eyes picking out details—the way the shadows pooled unnaturally in the corners, the way the air shimmered near certain stalls." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 738 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.02168021680216802 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0013550135501355014 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 71 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 71 | | mean | 10.3 | | std | 5.65 | | cv | 0.549 | | sampleLengths | | 0 | 15 | | 1 | 18 | | 2 | 18 | | 3 | 1 | | 4 | 12 | | 5 | 19 | | 6 | 11 | | 7 | 3 | | 8 | 21 | | 9 | 15 | | 10 | 18 | | 11 | 12 | | 12 | 3 | | 13 | 12 | | 14 | 8 | | 15 | 9 | | 16 | 23 | | 17 | 19 | | 18 | 6 | | 19 | 11 | | 20 | 3 | | 21 | 6 | | 22 | 12 | | 23 | 10 | | 24 | 13 | | 25 | 8 | | 26 | 19 | | 27 | 3 | | 28 | 4 | | 29 | 22 | | 30 | 9 | | 31 | 5 | | 32 | 7 | | 33 | 9 | | 34 | 7 | | 35 | 9 | | 36 | 4 | | 37 | 4 | | 38 | 4 | | 39 | 11 | | 40 | 7 | | 41 | 9 | | 42 | 6 | | 43 | 5 | | 44 | 3 | | 45 | 9 | | 46 | 12 | | 47 | 5 | | 48 | 12 | | 49 | 14 |
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| 51.64% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.352112676056338 | | totalSentences | 71 | | uniqueOpeners | 25 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 69 | | matches | | 0 | "Instead, he veered left, toward" | | 1 | "Then he smirked." | | 2 | "Then she heard it—the murmur" |
| | ratio | 0.043 | |
| 98.26% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 69 | | matches | | 0 | "He glanced back, eyes wide," | | 1 | "She broke into a sprint," | | 2 | "He skidded around another corner," | | 3 | "She knew these streets, but" | | 4 | "He’d been running them for" | | 5 | "It was a known entry" | | 6 | "She slowed, her fingers brushing" | | 7 | "He looked back at her," | | 8 | "She knew what lay beyond" | | 9 | "It wasn’t a place you" | | 10 | "You needed a token." | | 11 | "You needed an invitation." | | 12 | "She didn’t have either." | | 13 | "Her boots splashed through a" | | 14 | "She could call it in." | | 15 | "She pushed the door open." | | 16 | "She stepped forward, her boots" | | 17 | "it asked, its voice too" | | 18 | "She scanned the crowd, her" | | 19 | "She wasn’t just looking for" |
| | ratio | 0.304 | |
| 10.72% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 69 | | matches | | 0 | "The rain came down in" | | 1 | "Harlow Quinn’s boots splashed through" | | 2 | "The neon glow of Soho’s" | | 3 | "A figure darted ahead, weaving" | | 4 | "Tomás Herrera—his dark curls plastered" | | 5 | "He glanced back, eyes wide," | | 6 | "Quinn didn’t hesitate." | | 7 | "She broke into a sprint," | | 8 | "The alley reeked of stale" | | 9 | "Herrera vaulted over a stack" | | 10 | "Quinn’s voice cut through the" | | 11 | "Herrera didn’t stop." | | 12 | "He skidded around another corner," | | 13 | "Quinn followed, her pulse hammering" | | 14 | "She knew these streets, but" | | 15 | "He’d been running them for" | | 16 | "The alley spilled into a" | | 17 | "Herrera didn’t head for the" | | 18 | "Quinn’s jaw tightened." | | 19 | "That wasn’t just any abandoned" |
| | ratio | 0.899 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 1 | | matches | | 0 | "Then she heard it—the murmur of voices, the clink of glass, the low hum of something that wasn’t quite music." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 1 | | matches | | 0 | "it asked, its voice too old, too knowing" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 2 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |