| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.421 | | leniency | 0.842 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.95% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 995 | | totalAiIsmAdverbs | 2 | | 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) | |
| 34.67% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 995 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "shattered" | | 1 | "echoed" | | 2 | "depths" | | 3 | "flickered" | | 4 | "gloom" | | 5 | "rhythmic" | | 6 | "velvet" | | 7 | "maw" | | 8 | "raced" | | 9 | "silence" |
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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 | 62 | | matches | | |
| 73.73% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 62 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 72 | | 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 | 1 | | markdownWords | 6 | | totalWords | 995 | | ratio | 0.006 | | matches | | 0 | "What kind of underworld is this," |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 876 | | uniqueNames | 15 | | maxNameDensity | 1.26 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Tomás" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 11 | | Camden | 1 | | Tomás | 5 | | Herrera | 1 | | Tube | 1 | | London | 1 | | Underground | 1 | | Northern | 1 | | Line | 1 | | Savile | 1 | | Row | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Market" |
| | places | | | globalScore | 0.872 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | 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 | 995 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 72 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 28.43 | | std | 21.48 | | cv | 0.756 | | sampleLengths | | 0 | 27 | | 1 | 26 | | 2 | 9 | | 3 | 56 | | 4 | 14 | | 5 | 27 | | 6 | 3 | | 7 | 31 | | 8 | 3 | | 9 | 4 | | 10 | 2 | | 11 | 61 | | 12 | 9 | | 13 | 46 | | 14 | 51 | | 15 | 10 | | 16 | 26 | | 17 | 51 | | 18 | 51 | | 19 | 10 | | 20 | 82 | | 21 | 18 | | 22 | 71 | | 23 | 14 | | 24 | 11 | | 25 | 30 | | 26 | 16 | | 27 | 35 | | 28 | 4 | | 29 | 54 | | 30 | 62 | | 31 | 15 | | 32 | 16 | | 33 | 25 | | 34 | 25 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 62 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 128 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 72 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 878 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.0193621867881549 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.009111617312072893 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 72 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 72 | | mean | 13.82 | | std | 7.3 | | cv | 0.528 | | sampleLengths | | 0 | 21 | | 1 | 6 | | 2 | 7 | | 3 | 5 | | 4 | 14 | | 5 | 9 | | 6 | 23 | | 7 | 15 | | 8 | 18 | | 9 | 9 | | 10 | 5 | | 11 | 18 | | 12 | 9 | | 13 | 3 | | 14 | 4 | | 15 | 27 | | 16 | 3 | | 17 | 4 | | 18 | 2 | | 19 | 15 | | 20 | 9 | | 21 | 14 | | 22 | 23 | | 23 | 9 | | 24 | 10 | | 25 | 22 | | 26 | 14 | | 27 | 9 | | 28 | 20 | | 29 | 22 | | 30 | 10 | | 31 | 3 | | 32 | 23 | | 33 | 7 | | 34 | 21 | | 35 | 23 | | 36 | 14 | | 37 | 26 | | 38 | 11 | | 39 | 10 | | 40 | 18 | | 41 | 13 | | 42 | 25 | | 43 | 26 | | 44 | 18 | | 45 | 9 | | 46 | 13 | | 47 | 11 | | 48 | 8 | | 49 | 13 |
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| 69.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4583333333333333 | | totalSentences | 72 | | uniqueOpeners | 33 | |
| 53.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 62 | | matches | | 0 | "Only the steady drip of" |
| | ratio | 0.016 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 62 | | matches | | 0 | "Her sharp jaw was set." | | 1 | "He cast a frantic glance" | | 2 | "He reached into his coat" | | 3 | "She rushed the gate, her" | | 4 | "She slowed her pace, switching" | | 5 | "She reached the bottom of" | | 6 | "She eased the velvet aside" | | 7 | "She stepped through the drapes," | | 8 | "He wore a heavy wool" | | 9 | "Her mind raced back to" | | 10 | "She stepped off the platform" |
| | ratio | 0.177 | |
| 8.39% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 56 | | totalSentences | 62 | | matches | | 0 | "A worn leather watch on" | | 1 | "Rain lashed the pavement of" | | 2 | "Water soaked her closely cropped" | | 3 | "Her sharp jaw was set." | | 4 | "Quinn barked into the damp" | | 5 | "Tomás Herrera turned at the" | | 6 | "He cast a frantic glance" | | 7 | "A thin scar running along" | | 8 | "Quinn said, closing the distance" | | 9 | "Tomás said, his warm brown" | | 10 | "Tomás shook his head." | | 11 | "He reached into his coat" | | 12 | "Tomás slammed the bone token" | | 13 | "A heavy metallic groan echoed" | | 14 | "The sealed doors buckled inward," | | 15 | "A rush of warm, stagnant" | | 16 | "Quinn swore under her breath" | | 17 | "She rushed the gate, her" | | 18 | "The emergency light above flickered" | | 19 | "She slowed her pace, switching" |
| | ratio | 0.903 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 62 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 2 | | matches | | 0 | "The beam cut through the damp gloom, illuminating peeling vintage posters for products that had vanished half a century ago." | | 1 | "The air grew thicker with every step, heavy with humidity and a strange, metallic tang that coated the back of her tongue." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn shouted, her voice bouncing off the tiled walls" | | 1 | "Quinn said, not lowering her weapon" |
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| 44.74% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "Quinn barked (bark)" | | 1 | "Quinn shouted (shout)" |
| | dialogueSentences | 19 | | tagDensity | 0.263 | | leniency | 0.526 | | rawRatio | 0.4 | | effectiveRatio | 0.211 | |