| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 23 | | tagDensity | 0.174 | | leniency | 0.348 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 781 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 29.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 781 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "echoed" | | 1 | "chaotic" | | 2 | "standard" | | 3 | "scanning" | | 4 | "etched" | | 5 | "rhythmic" | | 6 | "velvet" | | 7 | "shimmered" | | 8 | "firmly" | | 9 | "familiar" | | 10 | "shattered" |
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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 | 63 | | matches | (empty) | |
| 52.15% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 63 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 82 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 22 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 781 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 60.42% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 614 | | uniqueNames | 5 | | maxNameDensity | 1.79 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Miller | 9 | | Veil | 1 | | Market | 1 | | Quinn | 11 |
| | persons | | 0 | "Miller" | | 1 | "Market" | | 2 | "Quinn" |
| | places | (empty) | | globalScore | 0.604 | | windowScore | 0.667 | |
| 94.44% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like grinding stones" |
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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 | 781 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 82 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 17.36 | | std | 15.83 | | cv | 0.912 | | sampleLengths | | 0 | 52 | | 1 | 1 | | 2 | 36 | | 3 | 47 | | 4 | 7 | | 5 | 2 | | 6 | 35 | | 7 | 39 | | 8 | 5 | | 9 | 12 | | 10 | 28 | | 11 | 4 | | 12 | 3 | | 13 | 3 | | 14 | 6 | | 15 | 45 | | 16 | 5 | | 17 | 8 | | 18 | 3 | | 19 | 35 | | 20 | 18 | | 21 | 8 | | 22 | 4 | | 23 | 57 | | 24 | 11 | | 25 | 6 | | 26 | 3 | | 27 | 21 | | 28 | 4 | | 29 | 20 | | 30 | 2 | | 31 | 32 | | 32 | 20 | | 33 | 21 | | 34 | 4 | | 35 | 6 | | 36 | 4 | | 37 | 23 | | 38 | 35 | | 39 | 6 | | 40 | 7 | | 41 | 43 | | 42 | 31 | | 43 | 7 | | 44 | 12 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 63 | | matches | (empty) | |
| 76.54% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 108 | | matches | | 0 | "wasn't lying" | | 1 | "was sitting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 82 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 620 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.024193548387096774 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0064516129032258064 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 82 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 82 | | mean | 9.52 | | std | 5.79 | | cv | 0.607 | | sampleLengths | | 0 | 11 | | 1 | 21 | | 2 | 20 | | 3 | 1 | | 4 | 7 | | 5 | 14 | | 6 | 15 | | 7 | 17 | | 8 | 12 | | 9 | 18 | | 10 | 7 | | 11 | 2 | | 12 | 19 | | 13 | 16 | | 14 | 8 | | 15 | 13 | | 16 | 7 | | 17 | 5 | | 18 | 6 | | 19 | 5 | | 20 | 12 | | 21 | 3 | | 22 | 11 | | 23 | 14 | | 24 | 4 | | 25 | 3 | | 26 | 3 | | 27 | 6 | | 28 | 11 | | 29 | 12 | | 30 | 22 | | 31 | 5 | | 32 | 8 | | 33 | 3 | | 34 | 3 | | 35 | 8 | | 36 | 13 | | 37 | 11 | | 38 | 18 | | 39 | 8 | | 40 | 4 | | 41 | 11 | | 42 | 18 | | 43 | 8 | | 44 | 8 | | 45 | 12 | | 46 | 7 | | 47 | 2 | | 48 | 2 | | 49 | 6 |
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| 54.47% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.4146341463414634 | | totalSentences | 82 | | uniqueOpeners | 34 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 57 | | matches | (empty) | | ratio | 0 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 57 | | matches | | 0 | "He stepped aside, gesturing toward" | | 1 | "She adjusted the worn leather" | | 2 | "They reached the center of" | | 3 | "He wore a fine charcoal" | | 4 | "She stood over the body," | | 5 | "She noted the way the" | | 6 | "It didn't even point toward" | | 7 | "It spun in a slow," | | 8 | "She stopped at a nearby" | | 9 | "She looked at the reflection" | | 10 | "He was sitting up, his" | | 11 | "She looked back at the" | | 12 | "It wasn't blood." | | 13 | "It looked like liquified sapphire." | | 14 | "She looked at the brass" | | 15 | "It locked firmly in one" | | 16 | "She didn't reach for her" | | 17 | "She drew her service weapon" | | 18 | "They didn't walk, they glided," |
| | ratio | 0.333 | |
| 12.63% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 51 | | totalSentences | 57 | | matches | | 0 | "Quinn slid the yellowed bone" | | 1 | "The vendor, a creature with" | | 2 | "He stepped aside, gesturing toward" | | 3 | "Quinn's boots echoed against the" | | 4 | "She adjusted the worn leather" | | 5 | "The Veil Market sprawled beneath" | | 6 | "The scent of ozone and" | | 7 | "Vendors hawked vials of shimmering" | | 8 | "They reached the center of" | | 9 | "A man lay sprawled on" | | 10 | "He wore a fine charcoal" | | 11 | "A single, precise hole pierced" | | 12 | "Miller knelt, clicking his tongue." | | 13 | "Quinn didn't kneel." | | 14 | "She stood over the body," | | 15 | "She noted the way the" | | 16 | "Miller glanced down." | | 17 | "Quinn reached into the victim's" | | 18 | "A small brass compass fell" | | 19 | "The casing had a thick" |
| | ratio | 0.895 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 57 | | matches | (empty) | | ratio | 0 | |
| 75.89% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 3 | | matches | | 0 | "He stepped aside, gesturing toward the heavy iron gate that led deeper into the belly of the abandoned Camden station." | | 1 | "He wore a fine charcoal wool suit that cost more than Miller's car." | | 2 | "The compass in Quinn's other hand began to scream, a high-pitched frequency that shattered the obsidian mirrors on the nearby table." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 63.04% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 2 | | fancyTags | | 0 | "Miller muttered (mutter)" | | 1 | "Miller noted (note)" |
| | dialogueSentences | 23 | | tagDensity | 0.174 | | leniency | 0.348 | | rawRatio | 0.5 | | effectiveRatio | 0.174 | |