| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 47 | | tagDensity | 0.426 | | leniency | 0.851 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1268 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 68.45% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1268 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "echoed" | | 1 | "stark" | | 2 | "blown wide" | | 3 | "traced" | | 4 | "shattered" | | 5 | "weight" | | 6 | "etched" | | 7 | "magnetic" |
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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 | 83 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 110 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1259 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 836 | | uniqueNames | 9 | | maxNameDensity | 1.56 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Patel" | | discoveredNames | | Tube | 2 | | Harlow | 1 | | Quinn | 13 | | Metropolitan | 1 | | Police | 1 | | Patel | 12 | | Victorian-era | 1 | | Kirkland | 5 | | Morris | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Patel" | | 4 | "Kirkland" | | 5 | "Morris" |
| | places | (empty) | | globalScore | 0.722 | | windowScore | 0.667 | |
| 65.25% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like boots, heavy-soled, the kind" | | 1 | "patterns that seemed to shift when she wasn't focusing on them" |
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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 | 1259 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 110 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 30.71 | | std | 18.62 | | cv | 0.606 | | sampleLengths | | 0 | 56 | | 1 | 18 | | 2 | 49 | | 3 | 20 | | 4 | 38 | | 5 | 41 | | 6 | 18 | | 7 | 39 | | 8 | 13 | | 9 | 34 | | 10 | 45 | | 11 | 29 | | 12 | 61 | | 13 | 8 | | 14 | 29 | | 15 | 47 | | 16 | 20 | | 17 | 6 | | 18 | 35 | | 19 | 17 | | 20 | 43 | | 21 | 4 | | 22 | 44 | | 23 | 58 | | 24 | 29 | | 25 | 9 | | 26 | 40 | | 27 | 56 | | 28 | 10 | | 29 | 21 | | 30 | 4 | | 31 | 7 | | 32 | 30 | | 33 | 11 | | 34 | 45 | | 35 | 4 | | 36 | 69 | | 37 | 33 | | 38 | 7 | | 39 | 55 | | 40 | 57 |
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| 96.81% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 83 | | matches | | 0 | "were caked" | | 1 | "was etched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 163 | | matches | | 0 | "wasn't focusing" | | 1 | "was beginning" |
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| 64.94% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 110 | | ratio | 0.027 | | matches | | 0 | "She checked his pockets systematically—phone, keys, the wallet Patel had mentioned." | | 1 | "Her boots crunched on debris—broken glass, shattered tiles, the detritus of decades of neglect." | | 2 | "The other two were harder to identify—one looked like boots, heavy-soled, the kind that left deep impressions." |
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| 99.71% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 843 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.04033214709371293 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.017793594306049824 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 110 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 110 | | mean | 11.45 | | std | 7.18 | | cv | 0.627 | | sampleLengths | | 0 | 10 | | 1 | 18 | | 2 | 28 | | 3 | 15 | | 4 | 3 | | 5 | 17 | | 6 | 15 | | 7 | 17 | | 8 | 11 | | 9 | 9 | | 10 | 8 | | 11 | 30 | | 12 | 5 | | 13 | 10 | | 14 | 8 | | 15 | 11 | | 16 | 7 | | 17 | 10 | | 18 | 8 | | 19 | 10 | | 20 | 8 | | 21 | 11 | | 22 | 10 | | 23 | 4 | | 24 | 9 | | 25 | 14 | | 26 | 11 | | 27 | 5 | | 28 | 4 | | 29 | 17 | | 30 | 28 | | 31 | 16 | | 32 | 13 | | 33 | 9 | | 34 | 11 | | 35 | 14 | | 36 | 8 | | 37 | 10 | | 38 | 9 | | 39 | 6 | | 40 | 2 | | 41 | 10 | | 42 | 19 | | 43 | 5 | | 44 | 14 | | 45 | 5 | | 46 | 17 | | 47 | 6 | | 48 | 8 | | 49 | 12 |
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| 89.70% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5545454545454546 | | totalSentences | 110 | | uniqueOpeners | 61 | |
| 85.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 78 | | matches | | 0 | "Too many for one person" | | 1 | "Just like these." |
| | ratio | 0.026 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 78 | | matches | | 0 | "Her torch swept across Victorian-era" | | 1 | "he said without looking up" | | 2 | "His eyes stared at nothing," | | 3 | "She checked his pockets systematically—phone," | | 4 | "She traced one mark with" | | 5 | "Her boots crunched on debris—broken" | | 6 | "She straightened, following the prints" | | 7 | "They led toward the mouth" | | 8 | "She'd reached the tunnel entrance" | | 9 | "Her torch revealed another twenty" | | 10 | "She moved toward it, heard" | | 11 | "She picked it up with" | | 12 | "she said, holding it up" | | 13 | "She'd seen the scratches on" | | 14 | "She returned to the platform," | | 15 | "His face held an expression" | | 16 | "She gestured at the alcove" | | 17 | "She took one last look" | | 18 | "He frowned but nodded." |
| | ratio | 0.244 | |
| 49.74% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 78 | | matches | | 0 | "The abandoned Tube station reeked" | | 1 | "Detective Harlow Quinn descended the" | | 2 | "The Metropolitan Police had cordoned" | | 3 | "DS Patel called from somewhere" | | 4 | "Quinn skipped it, landing on" | | 5 | "Her torch swept across Victorian-era" | | 6 | "Someone had spray-painted graffiti over" | | 7 | "Patel stood near a service" | | 8 | "The flash illuminated his face" | | 9 | "he said without looking up" | | 10 | "Quinn crouched beside the body." | | 11 | "Thomas Kirkland wore an expensive" | | 12 | "His eyes stared at nothing," | | 13 | "She checked his pockets systematically—phone," | | 14 | "Everything a man should have" | | 15 | "Patel continued, checking his notes" | | 16 | "Quinn stood, playing her torch" | | 17 | "The beam caught something that" | | 18 | "Scratches in the brick, dozens" | | 19 | "Patel followed her gaze." |
| | ratio | 0.821 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 78 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 2 | | matches | | 0 | "The Metropolitan Police had cordoned off the entrance with tape that snapped in the draft pushing up from below, but they hadn't ventured far into the tunnels t…" | | 1 | "The look of someone who'd finally grasped something fundamental and terrible about the world." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 2 | | fancyTags | | 0 | "Patel continued (continue)" | | 1 | "she lied (lie)" |
| | dialogueSentences | 47 | | tagDensity | 0.17 | | leniency | 0.34 | | rawRatio | 0.25 | | effectiveRatio | 0.085 | |