| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 958 | | 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) | |
| 68.68% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 958 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "echoed" | | 1 | "weight" | | 2 | "traced" | | 3 | "could feel" | | 4 | "tension" | | 5 | "silence" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 209 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 0 | | narrationSentences | 209 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 209 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 19 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 958 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 958 | | uniqueNames | 10 | | maxNameDensity | 1.25 | | worstName | "You" | | maxWindowNameDensity | 2.5 | | worstWindowName | "You" | | discoveredNames | | Moreau | 1 | | Lucien | 10 | | Shoreditch | 1 | | Brick | 1 | | Lane | 1 | | Rory | 9 | | Three | 4 | | You | 12 | | Ptolemy | 3 | | Evan | 4 |
| | persons | | 0 | "Moreau" | | 1 | "Lucien" | | 2 | "Rory" | | 3 | "You" | | 4 | "Ptolemy" | | 5 | "Evan" |
| | places | | | globalScore | 0.874 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | 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 | 958 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 209 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 108 | | mean | 8.87 | | std | 10.43 | | cv | 1.175 | | sampleLengths | | 0 | 60 | | 1 | 39 | | 2 | 5 | | 3 | 9 | | 4 | 2 | | 5 | 36 | | 6 | 5 | | 7 | 35 | | 8 | 6 | | 9 | 2 | | 10 | 3 | | 11 | 24 | | 12 | 6 | | 13 | 3 | | 14 | 6 | | 15 | 46 | | 16 | 5 | | 17 | 5 | | 18 | 30 | | 19 | 5 | | 20 | 3 | | 21 | 29 | | 22 | 3 | | 23 | 1 | | 24 | 5 | | 25 | 9 | | 26 | 2 | | 27 | 26 | | 28 | 1 | | 29 | 11 | | 30 | 8 | | 31 | 6 | | 32 | 9 | | 33 | 13 | | 34 | 5 | | 35 | 3 | | 36 | 20 | | 37 | 3 | | 38 | 5 | | 39 | 15 | | 40 | 7 | | 41 | 4 | | 42 | 19 | | 43 | 16 | | 44 | 3 | | 45 | 4 | | 46 | 2 | | 47 | 2 | | 48 | 2 | | 49 | 32 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 209 | | matches | | 0 | "was fogged" | | 1 | "was gone" | | 2 | "was scarred" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 206 | | matches | | 0 | "was staying" | | 1 | "were coming" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 209 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 958 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.011482254697286013 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0010438413361169101 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 209 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 209 | | mean | 4.58 | | std | 2.73 | | cv | 0.595 | | sampleLengths | | 0 | 4 | | 1 | 15 | | 2 | 11 | | 3 | 6 | | 4 | 8 | | 5 | 16 | | 6 | 5 | | 7 | 8 | | 8 | 11 | | 9 | 3 | | 10 | 12 | | 11 | 5 | | 12 | 6 | | 13 | 3 | | 14 | 2 | | 15 | 5 | | 16 | 6 | | 17 | 12 | | 18 | 4 | | 19 | 6 | | 20 | 3 | | 21 | 5 | | 22 | 2 | | 23 | 11 | | 24 | 4 | | 25 | 6 | | 26 | 12 | | 27 | 6 | | 28 | 2 | | 29 | 3 | | 30 | 5 | | 31 | 4 | | 32 | 7 | | 33 | 8 | | 34 | 6 | | 35 | 3 | | 36 | 6 | | 37 | 5 | | 38 | 7 | | 39 | 7 | | 40 | 9 | | 41 | 3 | | 42 | 7 | | 43 | 8 | | 44 | 5 | | 45 | 5 | | 46 | 6 | | 47 | 4 | | 48 | 6 | | 49 | 9 |
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| 41.39% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.20574162679425836 | | totalSentences | 209 | | uniqueOpeners | 43 | |
| 40.16% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 166 | | matches | | 0 | "Then open the door." | | 1 | "Then at his face." |
| | ratio | 0.012 | |
| 15.18% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 85 | | totalSentences | 166 | | matches | | 0 | "She didn't want to open" | | 1 | "She knew who stood on" | | 2 | "His charcoal suit absorbed the" | | 3 | "He didn't smile." | | 4 | "His eyes, one amber and" | | 5 | "You didn't answer your phone." | | 6 | "I was busy." | | 7 | "He stepped over the threshold." | | 8 | "He closed the door behind" | | 9 | "He walked past her, his" | | 10 | "You heard wrong." | | 11 | "He stopped by the window." | | 12 | "He wiped a streak with" | | 13 | "You left without a word," | | 14 | "You know why." | | 15 | "I know what you told" | | 16 | "He turned back to her." | | 17 | "She tucked her hand into" | | 18 | "I didn't run from you." | | 19 | "You ran from the truth." |
| | ratio | 0.512 | |
| 26.27% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 144 | | totalSentences | 166 | | matches | | 0 | "The chain lock rattled." | | 1 | "Rory gripped the handle, her" | | 2 | "She didn't want to open" | | 3 | "She knew who stood on" | | 4 | "The smell of rain and" | | 5 | "Lucien Moreau filled the frame." | | 6 | "His charcoal suit absorbed the" | | 7 | "The ivory handle of his" | | 8 | "He didn't smile." | | 9 | "His eyes, one amber and" | | 10 | "You didn't answer your phone." | | 11 | "Rory stepped back, letting him" | | 12 | "I was busy." | | 13 | "He stepped over the threshold." | | 14 | "The floorboards groaned under his" | | 15 | "Ptolemy, the tabby cat, arched" | | 16 | "Lucien ignored the animal." | | 17 | "He closed the door behind" | | 18 | "The lock clicked." | | 19 | "The room smelled of old" |
| | ratio | 0.867 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 166 | | matches | | 0 | "Because you are the only" | | 1 | "Because he has your name." | | 2 | "If I take it, I'm" | | 3 | "To the safe house." | | 4 | "Because he is at the" |
| | ratio | 0.03 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 10 | | technicalSentenceCount | 1 | | matches | | 0 | "Three deadbolts slid back with a heavy metallic thud that echoed in the narrow hallway." |
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| 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 | |