| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 0 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.43% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1094 | | 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) | |
| 72.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1094 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "gloom" | | 1 | "glint" | | 2 | "silence" | | 3 | "rhythmic" | | 4 | "electric" | | 5 | "pounding" |
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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 | 134 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 134 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 139 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1086 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 1065 | | uniqueNames | 13 | | maxNameDensity | 0.75 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Herrera" | | discoveredNames | | Harlow | 1 | | Quinn | 8 | | Tomás | 1 | | Herrera | 8 | | Saint | 1 | | Christopher | 1 | | Camden | 2 | | Town | 2 | | Static | 1 | | Veil | 1 | | Market | 1 | | God | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "God" | | 7 | "Morris" |
| | places | | | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | 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 | 1086 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 139 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 32.91 | | std | 24.43 | | cv | 0.742 | | sampleLengths | | 0 | 85 | | 1 | 60 | | 2 | 16 | | 3 | 29 | | 4 | 48 | | 5 | 47 | | 6 | 25 | | 7 | 31 | | 8 | 51 | | 9 | 21 | | 10 | 1 | | 11 | 2 | | 12 | 1 | | 13 | 47 | | 14 | 35 | | 15 | 24 | | 16 | 71 | | 17 | 14 | | 18 | 45 | | 19 | 38 | | 20 | 32 | | 21 | 23 | | 22 | 13 | | 23 | 112 | | 24 | 32 | | 25 | 48 | | 26 | 20 | | 27 | 19 | | 28 | 52 | | 29 | 14 | | 30 | 7 | | 31 | 18 | | 32 | 5 |
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| 86.93% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 134 | | matches | | 0 | "were fixed" | | 1 | "been sealed" | | 2 | "got stuck" | | 3 | "been expanded" | | 4 | "been adapted" | | 5 | "was lit" | | 6 | "were draped" | | 7 | "was thronged" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 176 | | matches | | 0 | "was flagging" | | 1 | "was heading" | | 2 | "was leading" | | 3 | "was bleeding" | | 4 | "were screaming" | | 5 | "was still bleeding" | | 6 | "was holding" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 139 | | ratio | 0.05 | | matches | | 0 | "Her eyes were fixed on the figure ahead—a man in a dark jacket, his gait uneven, a limp that hadn't been there before." | | 1 | "The suspect—Tomás Herrera, twenty-nine, a former paramedic with a scar on his left forearm and a Saint Christopher medallion around his neck—was fast, but he was flagging." | | 2 | "She'd seen the reports—vagrants, junkies, the occasional group of urban explorers." | | 3 | "He reached the station entrance—a rusted iron gate, padlocked at the top and bottom." | | 4 | "Graffiti—but not the usual tags." | | 5 | "The tunnel opened into a vast chamber—an old platform, maybe, or a storage space." | | 6 | "Stalls lined the edges, wooden affairs with canvas awnings, selling—what?" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 149 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 1 | | adverbRatio | 0.006711409395973154 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 139 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 139 | | mean | 7.81 | | std | 6.34 | | cv | 0.811 | | sampleLengths | | 0 | 24 | | 1 | 22 | | 2 | 12 | | 3 | 4 | | 4 | 23 | | 5 | 15 | | 6 | 3 | | 7 | 3 | | 8 | 27 | | 9 | 12 | | 10 | 10 | | 11 | 2 | | 12 | 4 | | 13 | 3 | | 14 | 12 | | 15 | 9 | | 16 | 5 | | 17 | 9 | | 18 | 9 | | 19 | 11 | | 20 | 6 | | 21 | 13 | | 22 | 14 | | 23 | 3 | | 24 | 19 | | 25 | 4 | | 26 | 7 | | 27 | 7 | | 28 | 3 | | 29 | 14 | | 30 | 1 | | 31 | 13 | | 32 | 18 | | 33 | 14 | | 34 | 5 | | 35 | 4 | | 36 | 18 | | 37 | 6 | | 38 | 4 | | 39 | 4 | | 40 | 4 | | 41 | 11 | | 42 | 2 | | 43 | 1 | | 44 | 2 | | 45 | 1 | | 46 | 5 | | 47 | 14 | | 48 | 16 | | 49 | 3 |
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| 40.53% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.31654676258992803 | | totalSentences | 139 | | uniqueOpeners | 44 | |
| 28.74% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 116 | | matches | | | ratio | 0.009 | |
| 30.34% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 55 | | totalSentences | 116 | | matches | | 0 | "She didn't need it." | | 1 | "Her eyes were fixed on" | | 2 | "He careened around the corner" | | 3 | "Her lungs burned." | | 4 | "She'd seen the way his" | | 5 | "I can see" | | 6 | "He lunged forward, shoving a" | | 7 | "Her fingers came away red." | | 8 | "He was heading for the" | | 9 | "She'd seen the reports—vagrants, junkies," | | 10 | "He reached the station entrance—a" | | 11 | "He ducked low, his body" | | 12 | "She gripped the bars, her" | | 13 | "She could see him in" | | 14 | "He glanced back once, his" | | 15 | "She unclipped her radio." | | 16 | "I'm at the old Camden" | | 17 | "She looked at the gap." | | 18 | "She was five foot nine," | | 19 | "She'd have to go in" |
| | ratio | 0.474 | |
| 7.41% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 105 | | totalSentences | 116 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn moved through" | | 2 | "The worn leather watch on" | | 3 | "She didn't need it." | | 4 | "Her eyes were fixed on" | | 5 | "He careened around the corner" | | 6 | "Quinn didn't slow." | | 7 | "Her lungs burned." | | 8 | "The suspect—Tomás Herrera, twenty-nine, a" | | 9 | "She'd seen the way his" | | 10 | "I can see" | | 11 | "Herrera didn't answer." | | 12 | "He lunged forward, shoving a" | | 13 | "Quinn vaulted over them, her" | | 14 | "Her fingers came away red." | | 15 | "He was heading for the" | | 16 | "The one that had been" | | 17 | "She'd seen the reports—vagrants, junkies," | | 18 | "Herrera was a ghost with" | | 19 | "He reached the station entrance—a" |
| | ratio | 0.905 | |
| 43.10% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 116 | | matches | | | ratio | 0.009 | |
| 89.29% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 3 | | matches | | 0 | "Detective Harlow Quinn moved through it at a dead run, her boots slamming through puddles that soaked the cuffs of her trousers." | | 1 | "She twisted, pulling herself through, her feet finding the first step down." | | 2 | "A few had faces that looked wrong, their features too sharp, their eyes reflecting the candlelight like an animal's." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |