| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.231 | | leniency | 0.462 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.19% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1039 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 32.63% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1039 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "silence" | | 1 | "scanning" | | 2 | "rhythmic" | | 3 | "resonated" | | 4 | "constructed" | | 5 | "velvet" | | 6 | "silk" | | 7 | "predator" | | 8 | "sanctuary" | | 9 | "echoing" | | 10 | "marble" | | 11 | "comforting" | | 12 | "flickered" |
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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 | 1 | | narrationSentences | 92 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 92 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1039 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 82.36% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 961 | | uniqueNames | 8 | | maxNameDensity | 1.35 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Met | 1 | | Veil | 1 | | Market | 1 | | Morris | 3 | | Quinn | 13 | | Tube | 1 | | Suddenly | 1 |
| | persons | | | places | (empty) | | globalScore | 0.824 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.962 | | wordCount | 1039 | | matches | | 0 | "not of copper, but of something cloyingly sweet, like rotting lilies" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 101 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 25.98 | | std | 19.37 | | cv | 0.746 | | sampleLengths | | 0 | 51 | | 1 | 3 | | 2 | 27 | | 3 | 61 | | 4 | 67 | | 5 | 1 | | 6 | 27 | | 7 | 26 | | 8 | 3 | | 9 | 31 | | 10 | 24 | | 11 | 3 | | 12 | 74 | | 13 | 55 | | 14 | 13 | | 15 | 19 | | 16 | 31 | | 17 | 7 | | 18 | 21 | | 19 | 36 | | 20 | 2 | | 21 | 47 | | 22 | 8 | | 23 | 46 | | 24 | 41 | | 25 | 8 | | 26 | 38 | | 27 | 4 | | 28 | 37 | | 29 | 4 | | 30 | 26 | | 31 | 49 | | 32 | 8 | | 33 | 28 | | 34 | 6 | | 35 | 22 | | 36 | 8 | | 37 | 25 | | 38 | 15 | | 39 | 37 |
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| 93.82% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 92 | | matches | | 0 | "been boarded" | | 1 | "was replaced" | | 2 | "were constructed" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 164 | | matches | | 0 | "was slowing" | | 1 | "was bleeding" | | 2 | "was diving" | | 3 | "was talking" | | 4 | "wasn't comforting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 101 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 973 | | adjectiveStacks | 2 | | stackExamples | | 0 | "empty, rain-lashed street." | | 1 | "small, old white bone" |
| | adverbCount | 18 | | adverbRatio | 0.018499486125385406 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.003083247687564234 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 10.29 | | std | 6.4 | | cv | 0.622 | | sampleLengths | | 0 | 16 | | 1 | 20 | | 2 | 15 | | 3 | 3 | | 4 | 11 | | 5 | 3 | | 6 | 13 | | 7 | 3 | | 8 | 14 | | 9 | 11 | | 10 | 9 | | 11 | 3 | | 12 | 21 | | 13 | 14 | | 14 | 4 | | 15 | 23 | | 16 | 10 | | 17 | 16 | | 18 | 1 | | 19 | 6 | | 20 | 21 | | 21 | 5 | | 22 | 14 | | 23 | 6 | | 24 | 1 | | 25 | 3 | | 26 | 3 | | 27 | 6 | | 28 | 8 | | 29 | 14 | | 30 | 6 | | 31 | 18 | | 32 | 3 | | 33 | 2 | | 34 | 16 | | 35 | 4 | | 36 | 25 | | 37 | 27 | | 38 | 4 | | 39 | 14 | | 40 | 5 | | 41 | 5 | | 42 | 27 | | 43 | 13 | | 44 | 8 | | 45 | 11 | | 46 | 11 | | 47 | 1 | | 48 | 12 | | 49 | 7 |
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| 38.61% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.32673267326732675 | | totalSentences | 101 | | uniqueOpeners | 33 | |
| 38.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 86 | | matches | | 0 | "Suddenly, the amber lanterns flickered" |
| | ratio | 0.012 | |
| 80.47% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 86 | | matches | | 0 | "He didn't look back, but" | | 1 | "He didn't stop." | | 2 | "He vaulted over a rusted" | | 3 | "She found the gap in" | | 4 | "She glanced at the worn" | | 5 | "He had a three-minute lead," | | 6 | "He was bleeding." | | 7 | "She caught sight of him" | | 8 | "He was diving toward a" | | 9 | "She reached the storefront just" | | 10 | "She gripped the handle and" | | 11 | "She stepped back, scanning the" | | 12 | "She noticed a small, circular" | | 13 | "She had seen him reach" | | 14 | "She knew the whispers." | | 15 | "It was a place of" | | 16 | "She thought of Morris." | | 17 | "He had never come out." | | 18 | "She looked at the iron" | | 19 | "He held the names of" |
| | ratio | 0.349 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 83 | | totalSentences | 86 | | matches | | 0 | "Quinn's boots slammed into a" | | 1 | "He didn't look back, but" | | 2 | "The man scrambled up, his" | | 3 | "He didn't stop." | | 4 | "He vaulted over a rusted" | | 5 | "Quinn didn't vault." | | 6 | "She found the gap in" | | 7 | "She glanced at the worn" | | 8 | "He had a three-minute lead," | | 9 | "He was bleeding." | | 10 | "A dark, viscous trail smeared" | | 11 | "The chase spilled out of" | | 12 | "The street felt suffocating." | | 13 | "Neon signs from late-night chip" | | 14 | "Pedestrians huddled under umbrellas, their" | | 15 | "Quinn shoved through the crowd," | | 16 | "She caught sight of him" | | 17 | "He was diving toward a" | | 18 | "Quinn accelerated, her lungs burning." | | 19 | "She reached the storefront just" |
| | ratio | 0.965 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 86 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 2 | | matches | | 0 | "The official report cited a structural collapse, but Quinn had seen the way the rubble looked, the way the dust had formed geometric patterns that defied gravit…" | | 1 | "The door swung inward, revealing a concrete staircase that plunged straight down into the earth." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 2 | | matches | | 0 | "The man leaned, his voice a dry rasp" | | 1 | "the figure said, the voice sounding like two stones grinding together" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.077 | | leniency | 0.154 | | rawRatio | 0 | | effectiveRatio | 0 | |