| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.34% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1367 | | 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) | |
| 48.79% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1367 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "effortless" | | 1 | "scanning" | | 2 | "intensity" | | 3 | "could feel" | | 4 | "silence" | | 5 | "unreadable" | | 6 | "flicker" | | 7 | "tracing" | | 8 | "weight" | | 9 | "reminder" |
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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 | 196 | | matches | (empty) | |
| 77.26% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 9 | | hedgeCount | 0 | | narrationSentences | 196 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 196 | | 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 | 0 | | markdownWords | 0 | | totalWords | 1367 | | ratio | 0 | | matches | (empty) | |
| 56.82% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 3 | | matches | | 0 | "You look tired, he said." | | 1 | "I saw the report, he said, his voice dropping." | | 2 | "The chain is still on the door, he said." |
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| 80.50% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 61 | | wordCount | 1367 | | uniqueNames | 10 | | maxNameDensity | 1.39 | | worstName | "You" | | maxWindowNameDensity | 2.5 | | worstWindowName | "You" | | discoveredNames | | Moreau | 1 | | London | 3 | | Marseille | 2 | | Ptolemy | 4 | | Rory | 12 | | East | 1 | | Cardiff | 1 | | Lucien | 15 | | Evan | 3 | | You | 19 |
| | persons | | 0 | "Moreau" | | 1 | "Ptolemy" | | 2 | "Rory" | | 3 | "Lucien" | | 4 | "Evan" | | 5 | "You" |
| | places | | 0 | "London" | | 1 | "Marseille" | | 2 | "East" | | 3 | "Cardiff" |
| | globalScore | 0.805 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 100 | | 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 | 1367 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 196 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 89 | | mean | 15.36 | | std | 14.91 | | cv | 0.971 | | sampleLengths | | 0 | 57 | | 1 | 16 | | 2 | 73 | | 3 | 7 | | 4 | 1 | | 5 | 34 | | 6 | 18 | | 7 | 5 | | 8 | 54 | | 9 | 11 | | 10 | 8 | | 11 | 39 | | 12 | 4 | | 13 | 11 | | 14 | 26 | | 15 | 31 | | 16 | 9 | | 17 | 48 | | 18 | 5 | | 19 | 14 | | 20 | 5 | | 21 | 14 | | 22 | 38 | | 23 | 14 | | 24 | 3 | | 25 | 2 | | 26 | 39 | | 27 | 11 | | 28 | 14 | | 29 | 14 | | 30 | 28 | | 31 | 16 | | 32 | 4 | | 33 | 24 | | 34 | 6 | | 35 | 36 | | 36 | 4 | | 37 | 7 | | 38 | 8 | | 39 | 6 | | 40 | 30 | | 41 | 19 | | 42 | 2 | | 43 | 14 | | 44 | 2 | | 45 | 36 | | 46 | 3 | | 47 | 15 | | 48 | 3 | | 49 | 14 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 196 | | matches | | 0 | "was slicked" | | 1 | "was sent" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 283 | | matches | | 0 | "was bleeding" | | 1 | "was sleeping" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 196 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1367 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.020482809070958303 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.002926115581565472 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 196 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 196 | | mean | 6.97 | | std | 4.48 | | cv | 0.643 | | sampleLengths | | 0 | 18 | | 1 | 11 | | 2 | 18 | | 3 | 5 | | 4 | 2 | | 5 | 3 | | 6 | 4 | | 7 | 12 | | 8 | 9 | | 9 | 33 | | 10 | 15 | | 11 | 16 | | 12 | 7 | | 13 | 1 | | 14 | 3 | | 15 | 6 | | 16 | 6 | | 17 | 19 | | 18 | 5 | | 19 | 13 | | 20 | 5 | | 21 | 3 | | 22 | 5 | | 23 | 15 | | 24 | 16 | | 25 | 15 | | 26 | 11 | | 27 | 4 | | 28 | 4 | | 29 | 4 | | 30 | 11 | | 31 | 16 | | 32 | 8 | | 33 | 4 | | 34 | 11 | | 35 | 15 | | 36 | 11 | | 37 | 8 | | 38 | 10 | | 39 | 2 | | 40 | 2 | | 41 | 9 | | 42 | 9 | | 43 | 3 | | 44 | 5 | | 45 | 10 | | 46 | 7 | | 47 | 9 | | 48 | 4 | | 49 | 10 |
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| 31.63% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 36 | | diversityRatio | 0.17857142857142858 | | totalSentences | 196 | | uniqueOpeners | 35 | |
| 37.66% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 177 | | matches | | 0 | "Then why are you shaking?" | | 1 | "Then I'll change the locks." |
| | ratio | 0.011 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 107 | | totalSentences | 177 | | matches | | 0 | "She adjusted the strap of" | | 1 | "She hadn't locked the chain." | | 2 | "His charcoal suit swallowed the" | | 3 | "His platinum blond hair was" | | 4 | "He smelled of expensive tobacco" | | 5 | "She hadn’t seen him since" | | 6 | "She kept her arm close" | | 7 | "You look tired, he said." | | 8 | "His voice was smooth, a" | | 9 | "You look like a mistake." | | 10 | "He didn’t ask for permission." | | 11 | "It’s not your home, he" | | 12 | "It’s where I stay." | | 13 | "You know the rules." | | 14 | "I know you’re hiding." | | 15 | "He walked past her, the" | | 16 | "He moved to the window," | | 17 | "He dropped his cane to" | | 18 | "I’m here because you called" | | 19 | "He gestured to the stacks" |
| | ratio | 0.605 | |
| 5.20% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 161 | | totalSentences | 177 | | matches | | 0 | "The deadbolt slid back with" | | 1 | "Rory gripped the handle, her" | | 2 | "She adjusted the strap of" | | 3 | "She hadn't locked the chain." | | 4 | "The chain ratcheted open." | | 5 | "The door swung inward to" | | 6 | "Lucien Moreau leaned against the" | | 7 | "His charcoal suit swallowed the" | | 8 | "His platinum blond hair was" | | 9 | "He smelled of expensive tobacco" | | 10 | "Rory’s throat tightened." | | 11 | "She hadn’t seen him since" | | 12 | "She kept her arm close" | | 13 | "You look tired, he said." | | 14 | "His voice was smooth, a" | | 15 | "You look like a mistake." | | 16 | "Lucien stepped inside." | | 17 | "He didn’t ask for permission." | | 18 | "The smell of ozone intensified," | | 19 | "Books and scrolls stacked in" |
| | ratio | 0.91 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 177 | | matches | | 0 | "If you’re here to kill" | | 1 | "If you walk away from" | | 2 | "If you stay, you die." | | 3 | "If you leave, you get" | | 4 | "If I leave, I get" |
| | ratio | 0.028 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |