| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 42 | | tagDensity | 0.357 | | leniency | 0.714 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.66% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 882 | | totalAiIsmAdverbs | 2 | | 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) | |
| 71.66% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 882 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "weight" | | 1 | "flickered" | | 2 | "dance" | | 3 | "quivered" | | 4 | "pulse" |
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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 | 48 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 48 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 75 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 882 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 40.51% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 411 | | uniqueNames | 9 | | maxNameDensity | 2.19 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Constable | 1 | | Ben | 1 | | Rourke | 7 | | Camden | 1 | | Quinn | 9 | | Kentish | 1 | | Town | 1 | | Gloves | 1 |
| | persons | | 0 | "Constable" | | 1 | "Ben" | | 2 | "Rourke" | | 3 | "Camden" | | 4 | "Quinn" |
| | places | | | globalScore | 0.405 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 30 | | 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 | 882 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 75 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 26.73 | | std | 18.15 | | cv | 0.679 | | sampleLengths | | 0 | 33 | | 1 | 25 | | 2 | 2 | | 3 | 55 | | 4 | 53 | | 5 | 31 | | 6 | 3 | | 7 | 31 | | 8 | 39 | | 9 | 18 | | 10 | 13 | | 11 | 24 | | 12 | 18 | | 13 | 5 | | 14 | 16 | | 15 | 55 | | 16 | 6 | | 17 | 8 | | 18 | 32 | | 19 | 4 | | 20 | 46 | | 21 | 16 | | 22 | 4 | | 23 | 58 | | 24 | 52 | | 25 | 5 | | 26 | 46 | | 27 | 13 | | 28 | 23 | | 29 | 18 | | 30 | 55 | | 31 | 29 | | 32 | 46 |
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| 97.95% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 48 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 70 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 75 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 411 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 8 | | adverbRatio | 0.019464720194647202 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0024330900243309003 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 75 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 75 | | mean | 11.76 | | std | 8.84 | | cv | 0.751 | | sampleLengths | | 0 | 17 | | 1 | 16 | | 2 | 18 | | 3 | 7 | | 4 | 2 | | 5 | 18 | | 6 | 26 | | 7 | 4 | | 8 | 7 | | 9 | 11 | | 10 | 12 | | 11 | 5 | | 12 | 25 | | 13 | 8 | | 14 | 23 | | 15 | 3 | | 16 | 25 | | 17 | 6 | | 18 | 7 | | 19 | 9 | | 20 | 6 | | 21 | 4 | | 22 | 13 | | 23 | 10 | | 24 | 8 | | 25 | 13 | | 26 | 16 | | 27 | 2 | | 28 | 3 | | 29 | 3 | | 30 | 18 | | 31 | 5 | | 32 | 15 | | 33 | 1 | | 34 | 10 | | 35 | 21 | | 36 | 10 | | 37 | 7 | | 38 | 5 | | 39 | 2 | | 40 | 6 | | 41 | 8 | | 42 | 9 | | 43 | 23 | | 44 | 4 | | 45 | 25 | | 46 | 21 | | 47 | 9 | | 48 | 7 | | 49 | 4 |
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| 94.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.6533333333333333 | | totalSentences | 75 | | uniqueOpeners | 49 | |
| 81.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 41 | | matches | | | ratio | 0.024 | |
| 83.41% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 41 | | matches | | 0 | "It was too neat for" | | 1 | "His eyes had gone milky." | | 2 | "His lips had a grey" | | 3 | "She crouched lower and found" | | 4 | "She reached for her pocket," | | 5 | "Her boots found no resistance" | | 6 | "She pointed with her chin" | | 7 | "He crossed his arms" | | 8 | "It pointed straight down the" | | 9 | "She leaned closer" | | 10 | "They were all upside down." | | 11 | "He stepped toward the body," | | 12 | "She pointed at the dead" | | 13 | "Her own watch, worn leather" |
| | ratio | 0.341 | |
| 69.76% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 32 | | totalSentences | 41 | | matches | | 0 | "Quinn crouched beside the body" | | 1 | "Detective Constable Ben Rourke shifted" | | 2 | "The torch beam swung across" | | 3 | "Quinn noted the salt." | | 4 | "It was too neat for" | | 5 | "The man lay on his" | | 6 | "His eyes had gone milky." | | 7 | "His lips had a grey" | | 8 | "Rourke said, reading from a" | | 9 | "Rourke nodded at the sigils" | | 10 | "Quinn ignored him and studied" | | 11 | "She crouched lower and found" | | 12 | "She reached for her pocket," | | 13 | "Rourke grinned, but his eyes" | | 14 | "Quinn stood and walked the" | | 15 | "Her boots found no resistance" | | 16 | "A pale smear ran along" | | 17 | "Something had been dragged here." | | 18 | "She pointed with her chin" | | 19 | "Quinn dropped to one knee" |
| | ratio | 0.78 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 41 | | matches | (empty) | | ratio | 0 | |
| 91.84% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 14 | | technicalSentenceCount | 1 | | matches | | 0 | "The brass casing had a green bloom of verdigris along the rim, and the needle inside quivered as if caught in a draught." |
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| 91.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 1 | | matches | | 0 | "Rourke grinned, but his eyes stayed on the body" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 42 | | tagDensity | 0.071 | | leniency | 0.143 | | rawRatio | 0 | | effectiveRatio | 0 | |