| 33.33% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 1 | | adverbTags | | 0 | "He stomped away [away]" |
| | dialogueSentences | 12 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.167 | | effectiveRatio | 0.167 | |
| 90.48% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 525 | | 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) | |
| 71.43% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 525 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "flickered" | | 1 | "etched" | | 2 | "racing" |
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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 | 51 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 51 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 91.35% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 57 | | gibberishSentences | 1 | | adjustedGibberishSentences | 1 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0.018 | | matches | | 0 | "nurmally appearance evaporated, her true self emerging <becomes visible>." |
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| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 525 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 77.71% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 15 | | wordCount | 415 | | uniqueNames | 7 | | maxNameDensity | 1.45 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 6 | | Impossible | 1 | | Book | 1 | | Eibon | 1 | | Hicks | 4 |
| | persons | | 0 | "Detective" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Hicks" |
| | places | | | globalScore | 0.777 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 33 | | 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 | 525 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 57 | | matches | (empty) | |
| 88.51% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 17 | | mean | 30.88 | | std | 14.2 | | cv | 0.46 | | sampleLengths | | 0 | 57 | | 1 | 30 | | 2 | 24 | | 3 | 28 | | 4 | 47 | | 5 | 41 | | 6 | 9 | | 7 | 44 | | 8 | 15 | | 9 | 15 | | 10 | 25 | | 11 | 32 | | 12 | 23 | | 13 | 30 | | 14 | 46 | | 15 | 50 | | 16 | 9 |
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| 91.50% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 51 | | matches | | 0 | "been called" | | 1 | "was cracked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 87 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 57 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 418 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.023923444976076555 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.007177033492822967 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 57 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 57 | | mean | 9.21 | | std | 5.4 | | cv | 0.587 | | sampleLengths | | 0 | 17 | | 1 | 13 | | 2 | 18 | | 3 | 9 | | 4 | 9 | | 5 | 21 | | 6 | 8 | | 7 | 16 | | 8 | 12 | | 9 | 5 | | 10 | 11 | | 11 | 16 | | 12 | 16 | | 13 | 7 | | 14 | 4 | | 15 | 4 | | 16 | 3 | | 17 | 7 | | 18 | 7 | | 19 | 5 | | 20 | 7 | | 21 | 10 | | 22 | 2 | | 23 | 9 | | 24 | 9 | | 25 | 9 | | 26 | 13 | | 27 | 3 | | 28 | 5 | | 29 | 5 | | 30 | 2 | | 31 | 13 | | 32 | 6 | | 33 | 9 | | 34 | 25 | | 35 | 16 | | 36 | 4 | | 37 | 12 | | 38 | 4 | | 39 | 19 | | 40 | 9 | | 41 | 1 | | 42 | 16 | | 43 | 4 | | 44 | 6 | | 45 | 6 | | 46 | 7 | | 47 | 2 | | 48 | 19 | | 49 | 6 |
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| 98.25% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.6842105263157895 | | totalSentences | 57 | | uniqueOpeners | 39 | |
| 75.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 44 | | matches | | 0 | "nurmally appearance evaporated, her true" |
| | ratio | 0.023 | |
| 56.36% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 44 | | matches | | 0 | "She kept her voice low" | | 1 | "He flashed her a grin." | | 2 | "She tugged on latex gloves" | | 3 | "She stepped closer, squinting at" | | 4 | "It wasn't random, nor the" | | 5 | "Their flashlights flickered over runes" | | 6 | "They were crude, hastily drawn," | | 7 | "Her blood iced, mind racing." | | 8 | "She whirled on him, eyes" | | 9 | "He laughedrese under his breath" | | 10 | "She shook it off." | | 11 | "He stomped away, leaving Quinn" | | 12 | "She reached out, stroking a" | | 13 | "It couldn't be coincidence." | | 14 | "Her gaze fell on the" | | 15 | "Her gut had kept them" | | 16 | "It wasn't just this." | | 17 | "It never was." |
| | ratio | 0.409 | |
| 96.36% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 32 | | totalSentences | 44 | | matches | | 0 | "The scene was already swarming" | | 1 | "This had the hallmarks of" | | 2 | "Quinn ducked under the tape" | | 3 | "She kept her voice low" | | 4 | "A young constable stepped forward," | | 5 | "Quinn nodded to the grizzled" | | 6 | "He flashed her a grin." | | 7 | "She tugged on latex gloves" | | 8 | "The station was like a" | | 9 | "Hicks craned his neck as" | | 10 | "Quinn's jaw locked." | | 11 | "Blood daubed the walls, thick" | | 12 | "The metallic reek caught in" | | 13 | "She stepped closer, squinting at" | | 14 | "It wasn't random, nor the" | | 15 | "Their flashlights flickered over runes" | | 16 | "They were crude, hastily drawn," | | 17 | "These were from the Book" | | 18 | "Her blood iced, mind racing." | | 19 | "She whirled on him, eyes" |
| | ratio | 0.727 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 44 | | matches | | 0 | "Although not on her beat," | | 1 | "Even if it was cracked" |
| | ratio | 0.045 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 15 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "he muttered (mutter)" | | 1 | "Hicks barked (bark)" |
| | dialogueSentences | 12 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 1 | | effectiveRatio | 0.333 | |