| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said quietly [quietly]" |
| | dialogueSentences | 60 | | tagDensity | 0.35 | | leniency | 0.7 | | rawRatio | 0.048 | | effectiveRatio | 0.033 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1237 | | 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) | |
| 91.92% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1237 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 63 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 62 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 9 | | totalWords | 1241 | | ratio | 0.007 | | matches | | 0 | "Alive. Don't look for me." | | 1 | "come and find me" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 27 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 710 | | uniqueNames | 7 | | maxNameDensity | 0.99 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 7 | | Moreau | 1 | | Water | 1 | | Eva | 2 | | Lucien | 6 | | Latin | 1 | | Ptolemy | 2 |
| | persons | | 0 | "Rory" | | 1 | "Moreau" | | 2 | "Water" | | 3 | "Eva" | | 4 | "Lucien" | | 5 | "Ptolemy" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 34 | | 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 | 1241 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 101 | | matches | | 0 | "let that sit" | | 1 | "hated that it" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 21.4 | | std | 21.13 | | cv | 0.988 | | sampleLengths | | 0 | 44 | | 1 | 11 | | 2 | 6 | | 3 | 54 | | 4 | 40 | | 5 | 43 | | 6 | 3 | | 7 | 28 | | 8 | 63 | | 9 | 3 | | 10 | 2 | | 11 | 31 | | 12 | 2 | | 13 | 41 | | 14 | 13 | | 15 | 2 | | 16 | 51 | | 17 | 4 | | 18 | 15 | | 19 | 13 | | 20 | 51 | | 21 | 57 | | 22 | 3 | | 23 | 7 | | 24 | 19 | | 25 | 21 | | 26 | 29 | | 27 | 9 | | 28 | 92 | | 29 | 14 | | 30 | 11 | | 31 | 15 | | 32 | 17 | | 33 | 24 | | 34 | 6 | | 35 | 30 | | 36 | 7 | | 37 | 5 | | 38 | 2 | | 39 | 2 | | 40 | 17 | | 41 | 60 | | 42 | 8 | | 43 | 9 | | 44 | 2 | | 45 | 2 | | 46 | 6 | | 47 | 48 | | 48 | 20 | | 49 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 63 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 126 | | matches | | |
| 1.41% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 1 | | flaggedSentences | 5 | | totalSentences | 101 | | ratio | 0.05 | | matches | | 0 | "That was the thing about him — he'd murder a man in a cellar and still knock the mud off before he crossed your threshold." | | 1 | "The curry house below was doing something with cumin and burnt onion; the smell came up through the floorboards, through the walls, and had lived in Rory's clothes for eight months." | | 2 | "\"I know. She's at the Whitechapel archive until midnight. She books the reading room under her mother's name, which is—\" he set the cane against the counter, \"—sweet, but not clever.\"" | | 3 | "\"You're not shouting.\" His mismatched eyes moved over her — the amber one caught the kitchen bulb and threw it back, the black one gave nothing." | | 4 | "\"They're in a drawer in Marylebone. I read them the next morning and each one said the same thing underneath, which was *come and find me*, and that would have killed you.\" He caught her wrist then — the left one, thumb landing exactly over the small crescent scar, the way it always had, like his hand had memorised it." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 639 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.02190923317683881 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.004694835680751174 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 12.29 | | std | 13.06 | | cv | 1.063 | | sampleLengths | | 0 | 24 | | 1 | 11 | | 2 | 9 | | 3 | 11 | | 4 | 6 | | 5 | 11 | | 6 | 35 | | 7 | 8 | | 8 | 6 | | 9 | 19 | | 10 | 15 | | 11 | 21 | | 12 | 1 | | 13 | 21 | | 14 | 3 | | 15 | 2 | | 16 | 1 | | 17 | 25 | | 18 | 4 | | 19 | 28 | | 20 | 31 | | 21 | 3 | | 22 | 2 | | 23 | 31 | | 24 | 2 | | 25 | 16 | | 26 | 3 | | 27 | 22 | | 28 | 10 | | 29 | 3 | | 30 | 2 | | 31 | 51 | | 32 | 4 | | 33 | 15 | | 34 | 4 | | 35 | 5 | | 36 | 4 | | 37 | 6 | | 38 | 45 | | 39 | 10 | | 40 | 47 | | 41 | 3 | | 42 | 7 | | 43 | 19 | | 44 | 8 | | 45 | 13 | | 46 | 26 | | 47 | 3 | | 48 | 4 | | 49 | 5 |
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| 65.68% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.43564356435643564 | | totalSentences | 101 | | uniqueOpeners | 44 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 23.77% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 53 | | matches | | 0 | "She'd expected the kid from" | | 1 | "She didn't move out of" | | 2 | "He let that sit" | | 3 | "he set the cane against" | | 4 | "He shrugged out of the" | | 5 | "He folded it over his" | | 6 | "His jaw worked" | | 7 | "She pointed at him, one" | | 8 | "His mismatched eyes moved over" | | 9 | "She got it open again." | | 10 | "It was true" | | 11 | "She hated that it was" | | 12 | "He picked at a thread" | | 13 | "she gestured at the bruise" | | 14 | "She had to move a" | | 15 | "She stood over him." | | 16 | "His skin was warm." | | 17 | "She wet the gauze" | | 18 | "He didn't flinch when she" | | 19 | "she said quietly" |
| | ratio | 0.491 | |
| 16.60% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 53 | | matches | | 0 | "The third deadbolt stuck, the" | | 1 | "She'd expected the kid from" | | 2 | "Vindaloo for a flat that" | | 3 | "Lucien Moreau stood on the" | | 4 | "She didn't move out of" | | 5 | "He let that sit" | | 6 | "Water dripped from the hem" | | 7 | "Ptolemy came down the hall" | | 8 | "Rory looked at the cat," | | 9 | "That was the thing about" | | 10 | "The flat swallowed him." | | 11 | "Eva's research stacks rose off" | | 12 | "The curry house below was" | | 13 | "he set the cane against" | | 14 | "He shrugged out of the" | | 15 | "He folded it over his" | | 16 | "Rory filled the kettle because" | | 17 | "His jaw worked" | | 18 | "The bruise moved with it." | | 19 | "The kettle started its low" |
| | ratio | 0.887 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 35.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 3 | | matches | | 0 | "Water dripped from the hem of his coat onto Eva's welcome mat, which said GO AWAY in curling script." | | 1 | "She had to move a stack of grimoires off the kitchen chair to make room, and when she came back with the first-aid tin from under the sink he'd gone still and o…" | | 2 | "He didn't flinch when she pressed it, which was its own kind of showing off, and she was close enough now to smell the rain in his hair and the cedar underneath…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 60 | | tagDensity | 0.133 | | leniency | 0.267 | | rawRatio | 0 | | effectiveRatio | 0 | |