| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 1 | | adverbTags | | 0 | "The lie tasted like [like]" |
| | dialogueSentences | 32 | | tagDensity | 0.281 | | leniency | 0.563 | | rawRatio | 0.111 | | effectiveRatio | 0.063 | |
| 86.84% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1140 | | totalAiIsmAdverbs | 3 | | 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) | |
| 16.67% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1140 | | totalAiIsms | 19 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | word | "down her spine" | | count | 1 |
| | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "traced" | | 1 | "fractured" | | 2 | "weight" | | 3 | "chill" | | 4 | "maw" | | 5 | "sturdy" | | 6 | "scanned" | | 7 | "pristine" | | 8 | "complex" | | 9 | "furrowing" | | 10 | "intricate" | | 11 | "charm" | | 12 | "down her spine" | | 13 | "racing" | | 14 | "echo" | | 15 | "flicker" | | 16 | "stark" | | 17 | "perfect" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 84 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 84 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 107 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 10 | | totalWords | 1139 | | ratio | 0.009 | | matches | | 0 | "look" | | 1 | "Stick to the facts, Quinn. The facts don't lie." |
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| 97.22% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 1 | | matches | | 0 | "For a moment, she swore she saw a flicker of movement in the blackness at the far end of the service passage, a shadow d…" |
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| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 862 | | uniqueNames | 8 | | maxNameDensity | 1.16 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 10 | | Miller | 7 | | Northern | 1 | | Line | 1 | | Morris | 4 | | Davies | 2 | | Bristol | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Miller" | | 3 | "Morris" | | 4 | "Davies" |
| | places | | | globalScore | 0.92 | | windowScore | 0.833 | |
| 65.25% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like chalk" | | 1 | "tasted like ash in her mouth" |
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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 | 1139 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 107 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 31.64 | | std | 25.54 | | cv | 0.807 | | sampleLengths | | 0 | 21 | | 1 | 27 | | 2 | 83 | | 3 | 33 | | 4 | 1 | | 5 | 2 | | 6 | 57 | | 7 | 12 | | 8 | 98 | | 9 | 9 | | 10 | 51 | | 11 | 40 | | 12 | 76 | | 13 | 42 | | 14 | 10 | | 15 | 45 | | 16 | 5 | | 17 | 19 | | 18 | 5 | | 19 | 26 | | 20 | 1 | | 21 | 40 | | 22 | 50 | | 23 | 12 | | 24 | 74 | | 25 | 11 | | 26 | 4 | | 27 | 41 | | 28 | 8 | | 29 | 22 | | 30 | 40 | | 31 | 76 | | 32 | 20 | | 33 | 15 | | 34 | 20 | | 35 | 43 |
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| 88.55% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 84 | | matches | | 0 | "were coated" | | 1 | "been scrawled" | | 2 | "was carved" | | 3 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 131 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 107 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 789 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.019011406844106463 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0038022813688212928 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 107 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 107 | | mean | 10.64 | | std | 7.69 | | cv | 0.722 | | sampleLengths | | 0 | 21 | | 1 | 27 | | 2 | 9 | | 3 | 20 | | 4 | 24 | | 5 | 30 | | 6 | 33 | | 7 | 1 | | 8 | 2 | | 9 | 8 | | 10 | 6 | | 11 | 11 | | 12 | 32 | | 13 | 7 | | 14 | 5 | | 15 | 13 | | 16 | 8 | | 17 | 13 | | 18 | 11 | | 19 | 1 | | 20 | 16 | | 21 | 7 | | 22 | 5 | | 23 | 24 | | 24 | 9 | | 25 | 11 | | 26 | 8 | | 27 | 7 | | 28 | 14 | | 29 | 3 | | 30 | 8 | | 31 | 7 | | 32 | 33 | | 33 | 14 | | 34 | 10 | | 35 | 5 | | 36 | 8 | | 37 | 5 | | 38 | 20 | | 39 | 14 | | 40 | 5 | | 41 | 24 | | 42 | 13 | | 43 | 7 | | 44 | 3 | | 45 | 8 | | 46 | 4 | | 47 | 9 | | 48 | 4 | | 49 | 12 |
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| 71.65% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.45794392523364486 | | totalSentences | 107 | | uniqueOpeners | 49 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 79 | | matches | (empty) | | ratio | 0 | |
| 68.10% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 79 | | matches | | 0 | "She remained in a low" | | 1 | "Her torch beam cut a" | | 2 | "She moved the torch beam" | | 3 | "She scanned the rungs of" | | 4 | "They were coated in a" | | 5 | "She swept the beam around" | | 6 | "She ran her light over" | | 7 | "She ignored Miller's logic because" | | 8 | "Her old partner, Morris, used" | | 9 | "She pushed the thought away," | | 10 | "Her torchlight snagged on something." | | 11 | "It was a complex, spidery" | | 12 | "It lacked the flowing, performative" | | 13 | "It felt less like a" | | 14 | "She took a photo with" | | 15 | "She knelt opposite him." | | 16 | "He paused, his brow furrowing" | | 17 | "He held up a small" | | 18 | "It was a piece of" | | 19 | "It felt ancient." |
| | ratio | 0.38 | |
| 10.63% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 79 | | matches | | 0 | "The gloved finger traced the" | | 1 | "Detective Harlow Quinn didn't look" | | 2 | "She remained in a low" | | 3 | "The air in the disused" | | 4 | "Her torch beam cut a" | | 5 | "DS Miller shifted his weight" | | 6 | "Quinn said, her voice flat," | | 7 | "She moved the torch beam" | | 8 | "The victim wore a pair" | | 9 | "Miller sighed, a weary gust" | | 10 | "Quinn stood, her movements economical," | | 11 | "The military precision she carried" | | 12 | "She scanned the rungs of" | | 13 | "They were coated in a" | | 14 | "She swept the beam around" | | 15 | "The air was dead, still." | | 16 | "Quinn’s sharp jaw tightened" | | 17 | "She ran her light over" | | 18 | "The nails were clean, the" | | 19 | "A man falling to his" |
| | ratio | 0.899 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 79 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 2 | | matches | | 0 | "Her torch beam cut a sharp cone through the darkness, illuminating the victim sprawled at the base of a rusted iron service ladder that climbed into a black maw…" | | 1 | "She stood up, her mind racing, connecting dots that had no business being on the same page." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 3 | | matches | | 0 | "Quinn said, her voice flat, emotionless" | | 1 | "Quinn stood, her movements economical, precise" | | 2 | "she said, her voice tight" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | 0 | "Miller grumbled (grumble)" |
| | dialogueSentences | 32 | | tagDensity | 0.156 | | leniency | 0.313 | | rawRatio | 0.2 | | effectiveRatio | 0.063 | |