| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 1 | | adverbTags | | 0 | "She turned back [back]" |
| | dialogueSentences | 25 | | tagDensity | 0.16 | | leniency | 0.32 | | rawRatio | 0.25 | | effectiveRatio | 0.08 | |
| 93.68% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 791 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 36.79% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 791 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "gloom" | | 1 | "stomach" | | 2 | "tinged" | | 3 | "etched" | | 4 | "dancing" | | 5 | "pulse" | | 6 | "shimmered" | | 7 | "flickered" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "stomach dropped/sank" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 63 | | matches | (empty) | |
| 74.83% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 63 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 84 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 782 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 45.24% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 525 | | uniqueNames | 3 | | maxNameDensity | 2.1 | | worstName | "Patel" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | | persons | | | places | (empty) | | globalScore | 0.452 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 40 | | 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 | 782 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 84 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 24.44 | | std | 16.03 | | cv | 0.656 | | sampleLengths | | 0 | 47 | | 1 | 34 | | 2 | 11 | | 3 | 56 | | 4 | 39 | | 5 | 19 | | 6 | 31 | | 7 | 12 | | 8 | 22 | | 9 | 9 | | 10 | 68 | | 11 | 5 | | 12 | 25 | | 13 | 12 | | 14 | 29 | | 15 | 2 | | 16 | 52 | | 17 | 7 | | 18 | 29 | | 19 | 20 | | 20 | 26 | | 21 | 34 | | 22 | 24 | | 23 | 32 | | 24 | 6 | | 25 | 43 | | 26 | 6 | | 27 | 20 | | 28 | 18 | | 29 | 5 | | 30 | 24 | | 31 | 15 |
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| 94.12% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 63 | | matches | | 0 | "been sealed" | | 1 | "were arranged" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 100 | | matches | | |
| 6.80% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 84 | | ratio | 0.048 | | matches | | 0 | "The beam of her torch cut through the gloom, illuminating the graffiti-scrawled walls—tags that looked fresh, though the station had been sealed for decades." | | 1 | "The man’s clothes were expensive—tailored wool coat, polished leather shoes—but his pockets were empty." | | 2 | "She nudged it with the toe of her boot—a small, circular object, brass by the look of it." | | 3 | "Not just any door—an arched wooden thing, its surface carved with the same symbols as the compass." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 533 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.039399624765478425 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.009380863039399626 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 84 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 84 | | mean | 9.31 | | std | 6.99 | | cv | 0.751 | | sampleLengths | | 0 | 10 | | 1 | 13 | | 2 | 24 | | 3 | 22 | | 4 | 12 | | 5 | 8 | | 6 | 3 | | 7 | 23 | | 8 | 12 | | 9 | 9 | | 10 | 12 | | 11 | 5 | | 12 | 7 | | 13 | 14 | | 14 | 8 | | 15 | 5 | | 16 | 5 | | 17 | 14 | | 18 | 7 | | 19 | 10 | | 20 | 7 | | 21 | 4 | | 22 | 3 | | 23 | 12 | | 24 | 7 | | 25 | 15 | | 26 | 4 | | 27 | 5 | | 28 | 13 | | 29 | 18 | | 30 | 12 | | 31 | 9 | | 32 | 16 | | 33 | 2 | | 34 | 3 | | 35 | 5 | | 36 | 11 | | 37 | 9 | | 38 | 3 | | 39 | 9 | | 40 | 15 | | 41 | 11 | | 42 | 3 | | 43 | 2 | | 44 | 17 | | 45 | 23 | | 46 | 12 | | 47 | 2 | | 48 | 5 | | 49 | 9 |
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| 57.54% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.38095238095238093 | | totalSentences | 84 | | uniqueOpeners | 32 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 56 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 56 | | matches | | 0 | "He waved her over, the" | | 1 | "She adjusted the strap of" | | 2 | "His skin had a waxy" | | 3 | "She shone her torch over" | | 4 | "She stood, brushing dust from" | | 5 | "She nudged it with the" | | 6 | "She crouched again, slipping on" | | 7 | "They followed the voice down" | | 8 | "It stood in the middle" | | 9 | "She reached out, then hesitated." | | 10 | "She was staring at the" | | 11 | "She pocketed it." | | 12 | "She turned back toward the" | | 13 | "She ignored him, crouching beside" |
| | ratio | 0.25 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 56 | | matches | | 0 | "The abandoned Tube station smelled" | | 1 | "Quinn stepped over the police" | | 2 | "The beam of her torch" | | 3 | "Sergeant Patel stood near the" | | 4 | "He waved her over, the" | | 5 | "She adjusted the strap of" | | 6 | "Patel gestured to the body" | | 7 | "The man’s limbs were arranged" | | 8 | "His skin had a waxy" | | 9 | "Quinn crouched, her knees popping." | | 10 | "She shone her torch over" | | 11 | "The man’s clothes were expensive—tailored" | | 12 | "Patel exhaled through his nose." | | 13 | "Quinn’s fingers hovered over the" | | 14 | "The leather of her watch" | | 15 | "The skin was cold, but" | | 16 | "Rigor hadn’t set in." | | 17 | "She stood, brushing dust from" | | 18 | "Patel crossed his arms." | | 19 | "Quinn’s torch beam snagged on" |
| | ratio | 0.929 | |
| 89.29% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 56 | | matches | | 0 | "Now it pointed directly at" |
| | ratio | 0.018 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |