| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.53% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1443 | | 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) | |
| 27.23% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1443 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | |
| | highlights | | 0 | "rhythmic" | | 1 | "tension" | | 2 | "intensity" | | 3 | "tracing" | | 4 | "familiar" | | 5 | "weight" | | 6 | "sanctuary" | | 7 | "jaw clenched" | | 8 | "grave" | | 9 | "efficient" | | 10 | "scanning" | | 11 | "structure" | | 12 | "magnetic" | | 13 | "dancing" | | 14 | "constructed" | | 15 | "gloom" | | 16 | "fragmented" | | 17 | "loomed" | | 18 | "porcelain" | | 19 | "variable" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 135 | | matches | (empty) | |
| 89.95% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 2 | | narrationSentences | 135 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 135 | | 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 | 1442 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1442 | | uniqueNames | 15 | | maxNameDensity | 0.83 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Harlow | 2 | | Quinn | 12 | | Met | 2 | | Herrera | 9 | | Saint | 1 | | Christopher | 1 | | Indeterminate | 1 | | Morris | 5 | | Raven | 1 | | Nest | 1 | | Camden | 1 | | Tube | 1 | | Veil | 3 | | Market | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Met" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" | | 7 | "Raven" | | 8 | "Veil" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 97 | | glossingSentenceCount | 1 | | matches | | 0 | "hood that seemed to drink the light" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1442 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 135 | | matches | (empty) | |
| 77.86% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 24 | | mean | 60.08 | | std | 25.39 | | cv | 0.423 | | sampleLengths | | 0 | 78 | | 1 | 58 | | 2 | 77 | | 3 | 71 | | 4 | 1 | | 5 | 96 | | 6 | 67 | | 7 | 94 | | 8 | 69 | | 9 | 48 | | 10 | 98 | | 11 | 61 | | 12 | 3 | | 13 | 62 | | 14 | 29 | | 15 | 27 | | 16 | 80 | | 17 | 87 | | 18 | 51 | | 19 | 72 | | 20 | 40 | | 21 | 59 | | 22 | 57 | | 23 | 57 |
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| 92.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 135 | | matches | | 0 | "were protected" | | 1 | "being hunted" | | 2 | "was lost" | | 3 | "were gone" | | 4 | "was swallowed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 251 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 135 | | ratio | 0.015 | | matches | | 0 | "They weren't just thugs; they were protected by something else." | | 1 | "She knew because she'd seen the look in Morris's eyes in the final hours—a terror that wasn't human." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1449 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.01932367149758454 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.006211180124223602 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 135 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 135 | | mean | 10.68 | | std | 7.03 | | cv | 0.658 | | sampleLengths | | 0 | 16 | | 1 | 8 | | 2 | 14 | | 3 | 19 | | 4 | 21 | | 5 | 8 | | 6 | 2 | | 7 | 31 | | 8 | 2 | | 9 | 5 | | 10 | 10 | | 11 | 7 | | 12 | 11 | | 13 | 31 | | 14 | 6 | | 15 | 22 | | 16 | 9 | | 17 | 12 | | 18 | 5 | | 19 | 11 | | 20 | 7 | | 21 | 4 | | 22 | 23 | | 23 | 1 | | 24 | 3 | | 25 | 3 | | 26 | 12 | | 27 | 4 | | 28 | 11 | | 29 | 8 | | 30 | 9 | | 31 | 3 | | 32 | 18 | | 33 | 25 | | 34 | 11 | | 35 | 7 | | 36 | 30 | | 37 | 9 | | 38 | 5 | | 39 | 5 | | 40 | 11 | | 41 | 14 | | 42 | 8 | | 43 | 11 | | 44 | 18 | | 45 | 10 | | 46 | 22 | | 47 | 3 | | 48 | 13 | | 49 | 22 |
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| 29.26% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 28 | | diversityRatio | 0.28888888888888886 | | totalSentences | 135 | | uniqueOpeners | 39 | |
| 26.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 126 | | matches | | | ratio | 0.008 | |
| 73.97% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 126 | | matches | | 0 | "Her boots struck the wet" | | 1 | "They weren't just thugs; they" | | 2 | "His dark, curly hair plastered" | | 3 | "She landed in a crouch," | | 4 | "Her left wrist twisted instinctively." | | 5 | "She didn't need to check" | | 6 | "She needed the anchor." | | 7 | "Her former partner." | | 8 | "His file was sealed, marked" | | 9 | "They'd found his service weapon," | | 10 | "She knew because she'd seen" | | 11 | "She knew because the rain" | | 12 | "He didn't head for the" | | 13 | "He cut north, away from" | | 14 | "His pace was frantic, but" | | 15 | "He was leading her somewhere." | | 16 | "Her sharp jaw clenched until" | | 17 | "She ducked under a rusted" | | 18 | "He moved with the fluid" | | 19 | "She drew her service pistol," |
| | ratio | 0.365 | |
| 27.46% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 109 | | totalSentences | 126 | | matches | | 0 | "Rain fell in sheets, turning" | | 1 | "Detective Harlow Quinn didn't slow" | | 2 | "Her boots struck the wet" | | 3 | "The name had been blinking" | | 4 | "Quinn was certain of it." | | 5 | "They weren't just thugs; they" | | 6 | "Quinn watched Herrera's form clear" | | 7 | "His dark, curly hair plastered" | | 8 | "A knife wound, deep and" | | 9 | "Quinn hit the fence harder," | | 10 | "She landed in a crouch," | | 11 | "Her left wrist twisted instinctively." | | 12 | "The worn leather of her" | | 13 | "She didn't need to check" | | 14 | "She needed the anchor." | | 15 | "The familiar weight grounded her" | | 16 | "Her former partner." | | 17 | "His file was sealed, marked" | | 18 | "Cause of death: Indeterminate." | | 19 | "They'd found his service weapon," |
| | ratio | 0.865 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 126 | | matches | | 0 | "To follow was to step" | | 1 | "To her left, a dealer" | | 2 | "To her right, a figure" | | 3 | "If she called backup, the" |
| | ratio | 0.032 | |
| 79.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 68 | | technicalSentenceCount | 6 | | matches | | 0 | "Forty-one years old, but her body held the lean, coiled tension of a recruit who'd never forgotten basic training." | | 1 | "She knew because she'd seen the look in Morris's eyes in the final hours—a terror that wasn't human." | | 2 | "He cut north, away from the green neon hum of the Raven's Nest that buzzed like a dying insect behind her, toward the crumbling brickwork of the old transport a…" | | 3 | "The architecture felt wrong, the shadows stretching at angles that defied the structure above." | | 4 | "Every rational instinct screamed at her to call it in, to seal the entrance, to bring the tactical teams who could drown this nest in floodlights and high-calib…" | | 5 | "To her left, a dealer with skin like cracked porcelain sold vials of liquid that swirled with their own bioluminescence, clearly banned alchemical substances th…" |
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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 | |