| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.357 | | leniency | 0.714 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.77% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 802 | | totalAiIsmAdverbs | 1 | | 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) | |
| 31.42% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 802 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "flickered" | | 1 | "echoed" | | 2 | "footsteps" | | 3 | "pulse" | | 4 | "flicker" | | 5 | "porcelain" | | 6 | "scanned" | | 7 | "pulsed" | | 8 | "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 | 74 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 74 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 83 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 791 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 94.98% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 727 | | uniqueNames | 12 | | maxNameDensity | 1.1 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 8 | | Raven | 1 | | Nest | 1 | | Veil | 1 | | Market | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 4 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Market" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Tomás" | | 9 | "Morris" |
| | places | | | globalScore | 0.95 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 791 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 22.6 | | std | 21.41 | | cv | 0.947 | | sampleLengths | | 0 | 68 | | 1 | 58 | | 2 | 15 | | 3 | 84 | | 4 | 5 | | 5 | 49 | | 6 | 7 | | 7 | 72 | | 8 | 12 | | 9 | 30 | | 10 | 43 | | 11 | 13 | | 12 | 8 | | 13 | 40 | | 14 | 5 | | 15 | 30 | | 16 | 10 | | 17 | 11 | | 18 | 13 | | 19 | 5 | | 20 | 12 | | 21 | 6 | | 22 | 33 | | 23 | 5 | | 24 | 6 | | 25 | 7 | | 26 | 11 | | 27 | 27 | | 28 | 20 | | 29 | 41 | | 30 | 9 | | 31 | 21 | | 32 | 3 | | 33 | 8 | | 34 | 4 |
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| 95.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 74 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 117 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 83 | | ratio | 0.108 | | matches | | 0 | "Her target—a lanky figure in a dark coat—dodged between a delivery lorry and a stack of crates, vanishing into the alley beside the bar." | | 1 | "The tunnel stretched ahead, the walls lined with ancient tiles, the air thick with the scent of wet stone and something older—something that prickled the hairs on her neck." | | 2 | "The beam caught movement—a flash of coat, then nothing." | | 3 | "Figures moved in the shadows—some human, some not." | | 4 | "Then she saw him—ducking behind a stall of blackened bones, his coat flapping." | | 5 | "She spun, her gun half-drawn, but it was just a man—olive skin, dark curls, a scar running down his forearm." | | 6 | "But the case—the deaths, the whispers of something unnatural—led here." | | 7 | "The stalls here were different—cages of things that skittered, jars of things that pulsed." | | 8 | "His face was wrong—too smooth, too still." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 458 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.024017467248908297 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.004366812227074236 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 83 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 83 | | mean | 9.53 | | std | 6.21 | | cv | 0.652 | | sampleLengths | | 0 | 16 | | 1 | 14 | | 2 | 14 | | 3 | 24 | | 4 | 20 | | 5 | 17 | | 6 | 21 | | 7 | 2 | | 8 | 2 | | 9 | 11 | | 10 | 9 | | 11 | 10 | | 12 | 4 | | 13 | 13 | | 14 | 19 | | 15 | 29 | | 16 | 4 | | 17 | 1 | | 18 | 10 | | 19 | 19 | | 20 | 9 | | 21 | 11 | | 22 | 7 | | 23 | 22 | | 24 | 18 | | 25 | 8 | | 26 | 14 | | 27 | 10 | | 28 | 6 | | 29 | 6 | | 30 | 8 | | 31 | 9 | | 32 | 13 | | 33 | 21 | | 34 | 9 | | 35 | 13 | | 36 | 13 | | 37 | 4 | | 38 | 4 | | 39 | 12 | | 40 | 13 | | 41 | 15 | | 42 | 5 | | 43 | 20 | | 44 | 2 | | 45 | 8 | | 46 | 6 | | 47 | 4 | | 48 | 6 | | 49 | 5 |
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| 42.57% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3132530120481928 | | totalSentences | 83 | | uniqueOpeners | 26 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 68 | | matches | | 0 | "Then the tunnel opened into" | | 1 | "Then she saw him—ducking behind" | | 2 | "Then he smiled, and his" |
| | ratio | 0.044 | |
| 96.47% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 68 | | matches | | 0 | "Her target—a lanky figure in" | | 1 | "She took the corner hard," | | 2 | "She yanked the cover up," | | 3 | "She swung down, her grip" | | 4 | "She moved, her torch cutting" | | 5 | "She broke into a jog," | | 6 | "She scanned the sea of" | | 7 | "She pushed forward, shouldering past" | | 8 | "She followed, her torch beam" | | 9 | "She spun, her gun half-drawn," | | 10 | "His Saint Christopher medallion glinted" | | 11 | "he said, low and urgent" | | 12 | "She yanked her arm free" | | 13 | "His jaw tightened" | | 14 | "She stepped toward the curtain." | | 15 | "She met his gaze." | | 16 | "He pulled a small bone" | | 17 | "She didn’t ask what he" | | 18 | "She advanced, her boots silent" | | 19 | "His face was wrong—too smooth," |
| | ratio | 0.309 | |
| 18.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 68 | | matches | | 0 | "The rain hammered the pavement," | | 1 | "Detective Harlow Quinn’s boots splashed" | | 2 | "The green glow of The" | | 3 | "Her target—a lanky figure in" | | 4 | "She took the corner hard," | | 5 | "The alley reeked of damp" | | 6 | "The city’s underbelly always had" | | 7 | "She yanked the cover up," | | 8 | "A ladder descended into blackness," | | 9 | "She swung down, her grip" | | 10 | "The ladder ended in a" | | 11 | "The tunnel stretched ahead, the" | | 12 | "A distant clatter echoed." | | 13 | "She moved, her torch cutting" | | 14 | "The tunnel split, but the" | | 15 | "The beam caught movement—a flash" | | 16 | "She broke into a jog," | | 17 | "The Veil Market sprawled before" | | 18 | "The air hummed with murmurs," | | 19 | "Figures moved in the shadows—some" |
| | ratio | 0.882 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 68 | | matches | (empty) | | ratio | 0 | |
| 75.89% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 3 | | matches | | 0 | "The tunnel stretched ahead, the walls lined with ancient tiles, the air thick with the scent of wet stone and something older—something that prickled the hairs …" | | 1 | "Then she saw him—ducking behind a stall of blackened bones, his coat flapping." | | 2 | "The stalls here were different—cages of things that skittered, jars of things that pulsed." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0 | | effectiveRatio | 0 | |