| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.636 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 85.85% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1060 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "lightly" | | 1 | "slightly" | | 2 | "softly" |
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| 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) | |
| 76.42% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1060 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "silence" | | 1 | "lilt" | | 2 | "weight" | | 3 | "etched" | | 4 | "echoes" |
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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 | 41 | | matches | (empty) | |
| 38.33% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 41 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 45 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 55 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1060 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 917 | | uniqueNames | 11 | | maxNameDensity | 0.44 | | worstName | "Eva" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Camden" | | discoveredNames | | Golden | 2 | | Empress | 2 | | Thursday | 1 | | Brick | 1 | | Lane | 1 | | Marseille | 1 | | Camden | 3 | | Eva | 4 | | Ptolemy | 3 | | Avaros | 1 | | Lucien | 2 |
| | persons | | 0 | "Eva" | | 1 | "Ptolemy" | | 2 | "Lucien" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "Marseille" | | 3 | "Camden" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | 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 | 1060 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 45 | | matches | (empty) | |
| 75.68% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 16 | | mean | 66.25 | | std | 27.49 | | cv | 0.415 | | sampleLengths | | 0 | 17 | | 1 | 77 | | 2 | 103 | | 3 | 78 | | 4 | 40 | | 5 | 121 | | 6 | 101 | | 7 | 53 | | 8 | 44 | | 9 | 42 | | 10 | 64 | | 11 | 54 | | 12 | 97 | | 13 | 52 | | 14 | 43 | | 15 | 74 |
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| 88.15% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 41 | | matches | | 0 | "was tied" | | 1 | "get dragged" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 148 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 924 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.03463203463203463 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.008658008658008658 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 45 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 45 | | mean | 23.56 | | std | 10.72 | | cv | 0.455 | | sampleLengths | | 0 | 17 | | 1 | 33 | | 2 | 25 | | 3 | 19 | | 4 | 28 | | 5 | 27 | | 6 | 48 | | 7 | 20 | | 8 | 24 | | 9 | 34 | | 10 | 14 | | 11 | 19 | | 12 | 7 | | 13 | 29 | | 14 | 28 | | 15 | 33 | | 16 | 31 | | 17 | 11 | | 18 | 19 | | 19 | 4 | | 20 | 55 | | 21 | 12 | | 22 | 18 | | 23 | 35 | | 24 | 13 | | 25 | 31 | | 26 | 7 | | 27 | 35 | | 28 | 18 | | 29 | 23 | | 30 | 23 | | 31 | 28 | | 32 | 26 | | 33 | 19 | | 34 | 13 | | 35 | 10 | | 36 | 29 | | 37 | 26 | | 38 | 20 | | 39 | 21 | | 40 | 11 | | 41 | 18 | | 42 | 25 | | 43 | 49 | | 44 | 25 |
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| 48.15% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.35555555555555557 | | totalSentences | 45 | | uniqueOpeners | 16 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 41 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 41 | | matches | | 0 | "I’d been sorting crumpled Golden" | | 1 | "I’d fumbled the first two" | | 2 | "He’s clad in tailored charcoal" | | 3 | "It’s been six months since" | | 4 | "I flex my left hand" | | 5 | "he says, his voice low," | | 6 | "He shifts his weight, and" | | 7 | "I don’t move to let" | | 8 | "He steps closer, one boot" | | 9 | "His tailored sleeve rides up" | | 10 | "He moves to push the" | | 11 | "he says, straightening up" | | 12 | "He reaches into his suit" | | 13 | "I don’t take it." | | 14 | "He nods past me, to" | | 15 | "I say, my voice tighter" | | 16 | "I haven’t spoken his last" | | 17 | "He lets out a quiet" | | 18 | "I snort, but my throat" | | 19 | "He nods, like he expected" |
| | ratio | 0.707 | |
| 8.78% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 37 | | totalSentences | 41 | | matches | | 0 | "The door swings fully open," | | 1 | "I’d been sorting crumpled Golden" | | 2 | "I’d fumbled the first two" | | 3 | "He’s clad in tailored charcoal" | | 4 | "It’s been six months since" | | 5 | "I flex my left hand" | | 6 | "Ptolemy rubs against my calf," | | 7 | "The smell of spiced vegetable" | | 8 | "he says, his voice low," | | 9 | "He shifts his weight, and" | | 10 | "I don’t move to let" | | 11 | "He steps closer, one boot" | | 12 | "His tailored sleeve rides up" | | 13 | "He moves to push the" | | 14 | "The tremor in his hand" | | 15 | "he says, straightening up" | | 16 | "He reaches into his suit" | | 17 | "I don’t take it." | | 18 | "He nods past me, to" | | 19 | "I say, my voice tighter" |
| | ratio | 0.902 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 41 | | matches | | 0 | "Now the person standing on" | | 1 | "If they’re tracking people who" |
| | ratio | 0.049 | |
| 27.65% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 5 | | matches | | 0 | "It’s been six months since I last saw him, six months since he left me holding a starlight ward that seared the crescent scar into my left wrist, six months sin…" | | 1 | "If they’re tracking people who touched it, I’m next too." | | 2 | "But now the threat is sitting on my friend’s stairwell, looking like he hasn’t slept in a week, and I can’t just walk away from it." | | 3 | "I step back from the door, holding it open wider, and Ptolemy darted around my legs, rubbing against Lucien’s calf." | | 4 | "He nods, and I start to turn toward the main room, where the stack of Eva’s research notebooks sits beside the empty delivery receipt stack, when a loud crash e…" |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 1 | | matches | | 0 | "I say, my voice tighter than I intend it to be" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.455 | | leniency | 0.909 | | rawRatio | 0 | | effectiveRatio | 0 | |