| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 29 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 92 | | tagDensity | 0.315 | | leniency | 0.63 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.04% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1884 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "very" | | 1 | "quickly" | | 2 | "gently" |
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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) | |
| 92.04% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1884 | | totalAiIsms | 3 | | 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 | 161 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 161 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 224 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1884 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 40 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1357 | | uniqueNames | 15 | | maxNameDensity | 1.33 | | worstName | "Rory" | | maxWindowNameDensity | 3 | | worstWindowName | "Rory" | | discoveredNames | | Lucien | 9 | | Moreau | 1 | | Rory | 18 | | Golden | 1 | | Empress | 1 | | Yu-Fei | 1 | | Silas | 2 | | Man | 2 | | Eva | 3 | | Brick | 1 | | Lane | 1 | | Ptolemy | 2 | | Cardiff | 1 | | London | 1 | | Vale | 1 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Rory" | | 3 | "Yu-Fei" | | 4 | "Silas" | | 5 | "Man" | | 6 | "Eva" | | 7 | "Ptolemy" | | 8 | "Vale" |
| | places | | 0 | "Golden" | | 1 | "Brick" | | 2 | "Lane" | | 3 | "Cardiff" | | 4 | "London" |
| | globalScore | 0.837 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 101 | | 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 | 1884 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 224 | | matches | | 0 | "hated that he" | | 1 | "kept that hand" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 121 | | mean | 15.57 | | std | 13.42 | | cv | 0.862 | | sampleLengths | | 0 | 19 | | 1 | 39 | | 2 | 9 | | 3 | 3 | | 4 | 18 | | 5 | 9 | | 6 | 18 | | 7 | 25 | | 8 | 4 | | 9 | 1 | | 10 | 28 | | 11 | 3 | | 12 | 43 | | 13 | 14 | | 14 | 6 | | 15 | 52 | | 16 | 21 | | 17 | 3 | | 18 | 12 | | 19 | 6 | | 20 | 4 | | 21 | 21 | | 22 | 6 | | 23 | 23 | | 24 | 9 | | 25 | 7 | | 26 | 22 | | 27 | 6 | | 28 | 5 | | 29 | 29 | | 30 | 14 | | 31 | 4 | | 32 | 32 | | 33 | 12 | | 34 | 34 | | 35 | 5 | | 36 | 48 | | 37 | 9 | | 38 | 5 | | 39 | 4 | | 40 | 16 | | 41 | 22 | | 42 | 35 | | 43 | 21 | | 44 | 8 | | 45 | 26 | | 46 | 6 | | 47 | 2 | | 48 | 6 | | 49 | 12 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 161 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 256 | | matches | | 0 | "was looking" | | 1 | "was watching" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 224 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1361 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 49 | | adverbRatio | 0.03600293901542983 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.0073475385745775165 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 224 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 224 | | mean | 8.41 | | std | 6.14 | | cv | 0.73 | | sampleLengths | | 0 | 19 | | 1 | 7 | | 2 | 10 | | 3 | 12 | | 4 | 10 | | 5 | 9 | | 6 | 3 | | 7 | 8 | | 8 | 3 | | 9 | 7 | | 10 | 9 | | 11 | 6 | | 12 | 12 | | 13 | 4 | | 14 | 4 | | 15 | 17 | | 16 | 4 | | 17 | 1 | | 18 | 6 | | 19 | 14 | | 20 | 8 | | 21 | 3 | | 22 | 6 | | 23 | 11 | | 24 | 9 | | 25 | 17 | | 26 | 14 | | 27 | 6 | | 28 | 6 | | 29 | 31 | | 30 | 15 | | 31 | 8 | | 32 | 4 | | 33 | 9 | | 34 | 3 | | 35 | 6 | | 36 | 6 | | 37 | 6 | | 38 | 4 | | 39 | 14 | | 40 | 7 | | 41 | 4 | | 42 | 2 | | 43 | 9 | | 44 | 6 | | 45 | 8 | | 46 | 5 | | 47 | 4 | | 48 | 7 | | 49 | 7 |
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| 46.43% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.3080357142857143 | | totalSentences | 224 | | uniqueOpeners | 69 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 135 | | matches | | 0 | "Then he descended, stepping wide" | | 1 | "Only then did her hand" | | 2 | "Then she dialled emergency services" | | 3 | "Then she called the police" | | 4 | "Then, gently, they closed around" |
| | ratio | 0.037 | |
| 30.37% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 64 | | totalSentences | 135 | | matches | | 0 | "His platinum hair, usually combed" | | 1 | "she said, and began to" | | 2 | "He caught the frame with" | | 3 | "He left her room to" | | 4 | "She hated that he answered" | | 5 | "It left her with no" | | 6 | "She caught his sleeve and" | | 7 | "He went, favouring his left" | | 8 | "His ivory-handled cane struck the" | | 9 | "Her flat was small enough" | | 10 | "She slipped the pot into" | | 11 | "He was looking at her" | | 12 | "He came up one more" | | 13 | "She could feel Lucien’s blood" | | 14 | "She kept that hand behind" | | 15 | "Hers was the only one" | | 16 | "His attention settled on her" | | 17 | "His eyes were an unremarkable" | | 18 | "His brow tightened." | | 19 | "She flung the oil past" |
| | ratio | 0.474 | |
| 60.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 108 | | totalSentences | 135 | | matches | | 0 | "Aurora opened the door to" | | 1 | "The bar downstairs sent up" | | 2 | "His platinum hair, usually combed" | | 3 | "she said, and began to" | | 4 | "He caught the frame with" | | 5 | "He left her room to" | | 6 | "That stopped her more effectively" | | 7 | "Rory looked past him." | | 8 | "The landing was empty." | | 9 | "She hated that he answered" | | 10 | "It left her with no" | | 11 | "She caught his sleeve and" | | 12 | "He went, favouring his left" | | 13 | "His ivory-handled cane struck the" | | 14 | "Rory closed the door quietly" | | 15 | "Her flat was small enough" | | 16 | "A footstep sounded on the" | | 17 | "Rory crossed to the kitchen" | | 18 | "She slipped the pot into" | | 19 | "A man stood halfway up" |
| | ratio | 0.8 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 135 | | matches | | 0 | "By the time she reached" | | 1 | "Before the kiss." | | 2 | "Before the envelope." |
| | ratio | 0.022 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 3 | | matches | | 0 | "She could feel Lucien’s blood drying on the fingers that had grabbed his sleeve." | | 1 | "Rory kept looking at the spilled oil, furious at it, like any woman who had just made a mess outside her own door." | | 2 | "It had arrived the morning after he kissed her in Eva’s kitchen, while Eva slept and Ptolemy sat on the research notes as if he understood their importance." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 29 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 22 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 92 | | tagDensity | 0.239 | | leniency | 0.478 | | rawRatio | 0 | | effectiveRatio | 0 | |