| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1880 | | 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) | |
| 54.79% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1880 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "measured" | | 1 | "methodical" | | 2 | "perfect" | | 3 | "stark" | | 4 | "chill" | | 5 | "crystalline" | | 6 | "shattered" | | 7 | "traced" | | 8 | "etched" | | 9 | "magnetic" | | 10 | "wavered" | | 11 | "weight" | | 12 | "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 | 194 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 194 | | filterMatches | | | hedgeMatches | | |
| 99.89% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 194 | | gibberishSentences | 1 | | adjustedGibberishSentences | 1 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0.005 | | matches | | 0 | "The draft is coming from the通风 shaft, Quinn said, her voice flat." |
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| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1880 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 11 | | matches | | 0 | "The draft is coming from the通风 shaft, Quinn said, her voice flat." | | 1 | "The Home Office liaison badge gets me past the tape, Eva said, stepping closer." | | 2 | "Alchemical byproduct, she said." | | 3 | "Crafted by a Shade artisan, Eva whispered, her academic composure fracturing." | | 4 | "Under the victim, Quinn said." | | 5 | "A Veil Compass, Eva said, her voice tightening." | | 6 | "Your pressure wave theory is wrong, Quinn said quietly." | | 7 | "The physics never align, Quinn said." | | 8 | "This station is not just a location, Quinn said, her voice cutting through the hum of the generators." | | 9 | "Pack your notes, Kowalski, Quinn said, her voice leaving no room for argument." | | 10 | "I know it is unstable, Quinn said." |
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| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 77 | | wordCount | 1880 | | uniqueNames | 24 | | maxNameDensity | 1.33 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Harlow | 1 | | Quinn | 25 | | Metropolitan | 1 | | Police | 1 | | Victorian | 1 | | Arthur | 1 | | Penhaligon | 8 | | Detective | 4 | | Kowalski | 3 | | Home | 1 | | Office | 1 | | Eva | 9 | | British | 1 | | Museum | 1 | | Morris | 4 | | Met | 1 | | Shade | 2 | | Veil | 2 | | Compass | 1 | | Market | 3 | | London | 1 | | You | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Arthur" | | 4 | "Penhaligon" | | 5 | "Kowalski" | | 6 | "Eva" | | 7 | "Museum" | | 8 | "Morris" | | 9 | "Met" | | 10 | "Compass" | | 11 | "Market" | | 12 | "You" |
| | places | | | globalScore | 0.835 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 140 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.532 | | wordCount | 1880 | | matches | | 0 | "not toward the tunnel exit, but directly at the brick wall behind the old ticket booth" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 194 | | matches | (empty) | |
| 87.30% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 60.65 | | std | 27.63 | | cv | 0.456 | | sampleLengths | | 0 | 118 | | 1 | 70 | | 2 | 100 | | 3 | 7 | | 4 | 79 | | 5 | 22 | | 6 | 43 | | 7 | 85 | | 8 | 7 | | 9 | 83 | | 10 | 56 | | 11 | 46 | | 12 | 65 | | 13 | 97 | | 14 | 22 | | 15 | 34 | | 16 | 57 | | 17 | 58 | | 18 | 42 | | 19 | 58 | | 20 | 78 | | 21 | 87 | | 22 | 107 | | 23 | 24 | | 24 | 77 | | 25 | 62 | | 26 | 55 | | 27 | 42 | | 28 | 63 | | 29 | 55 | | 30 | 81 |
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| 96.22% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 194 | | matches | | 0 | "was scorched" | | 1 | "are supposed" | | 2 | "was etched" | | 3 | "was carved" | | 4 | "is dragged" |
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| 38.38% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 8 | | totalVerbs | 330 | | matches | | 0 | "was transporting" | | 1 | "was not carrying" | | 2 | "was carrying" | | 3 | "was fraying" | | 4 | "was already closing" | | 5 | "was not just smuggling" | | 6 | "were engineering" | | 7 | "was bleeding" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 194 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1884 | | adjectiveStacks | 1 | | stackExamples | | 0 | "slow, counter-clockwise drift," |
| | adverbCount | 35 | | adverbRatio | 0.018577494692144373 | | lyAdverbCount | 16 | | lyAdverbRatio | 0.008492569002123142 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 194 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 194 | | mean | 9.69 | | std | 5.8 | | cv | 0.599 | | sampleLengths | | 0 | 20 | | 1 | 23 | | 2 | 14 | | 3 | 9 | | 4 | 8 | | 5 | 3 | | 6 | 17 | | 7 | 5 | | 8 | 6 | | 9 | 13 | | 10 | 16 | | 11 | 23 | | 12 | 9 | | 13 | 17 | | 14 | 5 | | 15 | 9 | | 16 | 10 | | 17 | 2 | | 18 | 5 | | 19 | 16 | | 20 | 22 | | 21 | 11 | | 22 | 11 | | 23 | 14 | | 24 | 7 | | 25 | 4 | | 26 | 4 | | 27 | 19 | | 28 | 26 | | 29 | 26 | | 30 | 12 | | 31 | 10 | | 32 | 14 | | 33 | 9 | | 34 | 9 | | 35 | 8 | | 36 | 3 | | 37 | 5 | | 38 | 13 | | 39 | 26 | | 40 | 23 | | 41 | 7 | | 42 | 3 | | 43 | 8 | | 44 | 5 | | 45 | 2 | | 46 | 11 | | 47 | 20 | | 48 | 4 | | 49 | 8 |
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| 42.27% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.27319587628865977 | | totalSentences | 194 | | uniqueOpeners | 53 | |
| 36.04% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 185 | | matches | | 0 | "Instead, they hummed with a" | | 1 | "Instead, it was etched with" |
| | ratio | 0.011 | |
| 66.49% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 71 | | totalSentences | 185 | | matches | | 0 | "Her boots struck the cracked" | | 1 | "She adjusted the cuffs of" | | 2 | "She moved down the escalator" | | 3 | "Her brown eyes tracked the" | | 4 | "His clothes were intact, but" | | 5 | "They spiraled inward, defying gravity" | | 6 | "You are standing in a" | | 7 | "She knew the voice." | | 8 | "She adjusted her round glasses" | | 9 | "Her green eyes reflected the" | | 10 | "You asked for a specialist" | | 11 | "I am here." | | 12 | "She kept her posture rigid," | | 13 | "She just lacked the vocabulary" | | 14 | "She pulled a small glass" | | 15 | "My initial thought is a" | | 16 | "It is tragic, but physically" | | 17 | "It fit neatly into reports" | | 18 | "She stepped past Eva, ignoring" | | 19 | "She leaned in until the" |
| | ratio | 0.384 | |
| 65.41% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 146 | | totalSentences | 185 | | matches | | 0 | "The air in the disused" | | 1 | "Detective Harlow Quinn stepped over" | | 2 | "Her boots struck the cracked" | | 3 | "Military precision, her first training" | | 4 | "A defensive wall, her last" | | 5 | "Quinn ignored both." | | 6 | "She adjusted the cuffs of" | | 7 | "The tunnels should have been" | | 8 | "She moved down the escalator" | | 9 | "The Metropolitan Police had cordoned" | | 10 | "Floodlights cast harsh white shadows" | | 11 | "Quinn stopped at the edge" | | 12 | "Her brown eyes tracked the" | | 13 | "That was the first impossibility." | | 14 | "The victim, a known fence" | | 15 | "His clothes were intact, but" | | 16 | "The scorch marks did not" | | 17 | "They spiraled inward, defying gravity" | | 18 | "You are standing in a" | | 19 | "Quinn did not turn." |
| | ratio | 0.789 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 6 | | totalSentences | 185 | | matches | | 0 | "If he was transporting unstable" | | 1 | "If that is genuine, Penhaligon" | | 2 | "Even if a dealer stumbled" | | 3 | "Because he stepped into a" | | 4 | "What we are looking at" | | 5 | "If you are right, Detective," |
| | ratio | 0.032 | |
| 97.07% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 78 | | technicalSentenceCount | 5 | | matches | | 0 | "Her boots struck the cracked tile floor in a rhythm that allowed no hesitation." | | 1 | "His clothes were intact, but his skin held a waxen, translucent quality, as if the moisture had been flash-boiled out of him." | | 2 | "DS Morris had died in a warehouse fire that left no ash, no accelerant trace, and a corpse that looked exactly like Penhaligon." | | 3 | "But standing in the abandoned dark, holding a compass that defied geometry, the doubt shattered." | | 4 | "You are describing a metaphysical event as if it were forensic fact." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |