| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 88.65% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1762 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slightly" | | 1 | "really" | | 2 | "slowly" |
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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) | |
| 60.27% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1762 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "dancing" | | 1 | "weight" | | 2 | "silence" | | 3 | "throbbed" | | 4 | "unreadable" | | 5 | "familiar" | | 6 | "flickered" | | 7 | "predator" | | 8 | "scanned" | | 9 | "tension" | | 10 | "whisper" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 313 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 313 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 313 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1760 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 95 | | wordCount | 1760 | | uniqueNames | 12 | | maxNameDensity | 1.82 | | worstName | "Rory" | | maxWindowNameDensity | 4 | | worstWindowName | "Lucien" | | discoveredNames | | Ptolemy | 5 | | Moreau | 1 | | Lucien | 31 | | Brick | 1 | | Lane | 1 | | Luc | 3 | | Rory | 32 | | Evan | 3 | | London | 1 | | You | 11 | | Tell | 3 | | One | 3 |
| | persons | | 0 | "Ptolemy" | | 1 | "Moreau" | | 2 | "Lucien" | | 3 | "Luc" | | 4 | "Rory" | | 5 | "Evan" | | 6 | "You" | | 7 | "One" |
| | places | | | globalScore | 0.591 | | windowScore | 0.333 | |
| 90.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 125 | | glossingSentenceCount | 3 | | matches | | 0 | "papers that seemed to have migrated from the desk to the floor and back again" | | 1 | "looked like a library had collapsed" | | 2 | "smelled like wet rain and expensive tobacc" |
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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 | 1760 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 313 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 142 | | mean | 12.39 | | std | 13.16 | | cv | 1.062 | | sampleLengths | | 0 | 78 | | 1 | 23 | | 2 | 61 | | 3 | 9 | | 4 | 60 | | 5 | 4 | | 6 | 47 | | 7 | 9 | | 8 | 29 | | 9 | 7 | | 10 | 33 | | 11 | 9 | | 12 | 34 | | 13 | 3 | | 14 | 10 | | 15 | 38 | | 16 | 7 | | 17 | 27 | | 18 | 10 | | 19 | 8 | | 20 | 31 | | 21 | 13 | | 22 | 6 | | 23 | 28 | | 24 | 3 | | 25 | 14 | | 26 | 4 | | 27 | 36 | | 28 | 12 | | 29 | 27 | | 30 | 6 | | 31 | 35 | | 32 | 7 | | 33 | 3 | | 34 | 3 | | 35 | 26 | | 36 | 9 | | 37 | 3 | | 38 | 28 | | 39 | 5 | | 40 | 57 | | 41 | 3 | | 42 | 3 | | 43 | 38 | | 44 | 4 | | 45 | 2 | | 46 | 8 | | 47 | 6 | | 48 | 24 | | 49 | 7 |
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| 99.66% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 313 | | matches | | 0 | "been worn" | | 1 | "was told" | | 2 | "was scuffed" | | 3 | "was gone" | | 4 | "gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 345 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 313 | | ratio | 0.003 | | matches | | 0 | "His heterochromatic eyes—one amber, one black—locked onto hers without blinking." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1766 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 57 | | adverbRatio | 0.03227633069082673 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.00622876557191393 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 313 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 313 | | mean | 5.62 | | std | 3.66 | | cv | 0.652 | | sampleLengths | | 0 | 19 | | 1 | 13 | | 2 | 18 | | 3 | 28 | | 4 | 8 | | 5 | 3 | | 6 | 12 | | 7 | 5 | | 8 | 5 | | 9 | 22 | | 10 | 8 | | 11 | 6 | | 12 | 4 | | 13 | 4 | | 14 | 4 | | 15 | 3 | | 16 | 9 | | 17 | 6 | | 18 | 8 | | 19 | 12 | | 20 | 9 | | 21 | 10 | | 22 | 15 | | 23 | 4 | | 24 | 6 | | 25 | 4 | | 26 | 3 | | 27 | 21 | | 28 | 13 | | 29 | 8 | | 30 | 1 | | 31 | 7 | | 32 | 15 | | 33 | 7 | | 34 | 7 | | 35 | 4 | | 36 | 6 | | 37 | 15 | | 38 | 8 | | 39 | 5 | | 40 | 4 | | 41 | 4 | | 42 | 13 | | 43 | 17 | | 44 | 3 | | 45 | 3 | | 46 | 7 | | 47 | 11 | | 48 | 8 | | 49 | 8 |
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| 36.90% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 41 | | diversityRatio | 0.16932907348242812 | | totalSentences | 313 | | uniqueOpeners | 53 | |
| 95.24% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 8 | | totalSentences | 280 | | matches | | 0 | "Then the middle one." | | 1 | "Then the latch." | | 2 | "Slowly, he withdrew a sealed" | | 3 | "Then get out." | | 4 | "Just a little." | | 5 | "Then I leave you to" | | 6 | "Then at Lucien." | | 7 | "Then he left." |
| | ratio | 0.029 | |
| 27.14% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 135 | | totalSentences | 280 | | matches | | 0 | "He didn’t meow." | | 1 | "She didn’t turn around immediately." | | 2 | "She walked toward the door," | | 3 | "She paused, hand hovering over" | | 4 | "She turned the bolt." | | 5 | "She cracked the door open" | | 6 | "His heterochromatic eyes—one amber, one" | | 7 | "He leaned on an ivory-handled" | | 8 | "You look ridiculous, Lucien." | | 9 | "He didn’t step inside." | | 10 | "He didn’t speak." | | 11 | "I was told you’d be" | | 12 | "Her left wrist ached where" | | 13 | "She didn’t move out of" | | 14 | "I’m not open for business" | | 15 | "He shifted his weight." | | 16 | "He wore a pair of" | | 17 | "I don’t want a deal." | | 18 | "I want to talk." | | 19 | "He stood too close for" |
| | ratio | 0.482 | |
| 22.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 245 | | totalSentences | 280 | | matches | | 0 | "The brass knocker on the" | | 1 | "Rory froze, the stack of" | | 2 | "Sunlight cut through the dust" | | 3 | "Every surface groaned under the" | | 4 | "Ptolemy, the tabby, sat on" | | 5 | "He didn’t meow." | | 6 | "The cat just lifted his" | | 7 | "Rory set the scrolls down." | | 8 | "She didn’t turn around immediately." | | 9 | "She walked toward the door," | | 10 | "She paused, hand hovering over" | | 11 | "She turned the bolt." | | 12 | "Metal scraped against metal." | | 13 | "She cracked the door open" | | 14 | "Lucien Moreau stood in the" | | 15 | "Water dripped from the brim" | | 16 | "The platinum blond hair slicked" | | 17 | "His heterochromatic eyes—one amber, one" | | 18 | "He leaned on an ivory-handled" | | 19 | "You look ridiculous, Lucien." |
| | ratio | 0.875 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 7 | | totalSentences | 280 | | matches | | 0 | "If you don’t want it," | | 1 | "Because Evan’s coming." | | 2 | "Because Evan isn’t human." | | 3 | "Because the trail leads back" | | 4 | "If you run, you’ll never" | | 5 | "If you leave, I won’t" | | 6 | "If I leave, you can’t" |
| | ratio | 0.025 | |
| 99.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 2 | | matches | | 0 | "Sunlight cut through the dust motes dancing above the piles of books that choked the living room floor." | | 1 | "She walked toward the door, her sneakers silent on the floorboards that had been worn smooth by Ptolemy’s claws over the years." |
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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 | |