| 59.65% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 26 | | adverbTagCount | 4 | | adverbTags | | 0 | "he said quietly [quietly]" | | 1 | "He gestured vaguely [vaguely]" | | 2 | "she said finally [finally]" | | 3 | "she said instead [instead]" |
| | dialogueSentences | 57 | | tagDensity | 0.456 | | leniency | 0.912 | | rawRatio | 0.154 | | effectiveRatio | 0.14 | |
| 88.39% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1723 | | totalAiIsmAdverbs | 4 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
| | 2 | | | 3 | |
| | highlights | | 0 | "softly" | | 1 | "deliberately" | | 2 | "slightly" | | 3 | "carefully" |
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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) | |
| 85.49% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1723 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "stomach" | | 1 | "flicked" | | 2 | "weight" | | 3 | "tension" | | 4 | "silence" |
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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 | 1 | | narrationSentences | 113 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 113 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 144 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 57 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 8 | | totalWords | 1723 | | ratio | 0.005 | | matches | | 0 | "I know you're here." | | 1 | "I miss you too" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 33 | | unquotedAttributions | 1 | | matches | | 0 | "Cool-headed, she told herself." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 1204 | | uniqueNames | 20 | | maxNameDensity | 0.5 | | worstName | "Lucien" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Aurora" | | discoveredNames | | Eva | 3 | | Aurora | 4 | | Tonight | 1 | | Brick | 2 | | Lane | 2 | | Golden | 2 | | Empress | 2 | | Lucien | 6 | | Moreau | 1 | | French | 1 | | Cool-headed | 1 | | Evan | 2 | | London | 1 | | Cardiff | 2 | | Yu-Fei | 1 | | Cheung | 1 | | Ptolemy | 4 | | Shoreditch | 1 | | English | 1 | | Marseilles | 1 |
| | persons | | 0 | "Eva" | | 1 | "Aurora" | | 2 | "Empress" | | 3 | "Lucien" | | 4 | "Moreau" | | 5 | "French" | | 6 | "Evan" | | 7 | "Yu-Fei" | | 8 | "Cheung" | | 9 | "Ptolemy" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "Golden" | | 3 | "London" | | 4 | "Cardiff" | | 5 | "Shoreditch" |
| | globalScore | 1 | | windowScore | 1 | |
| 50.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | glossingSentenceCount | 3 | | matches | | 0 | "as if showing her something invisible" | | 1 | "felt like a witness" | | 2 | "felt like safety" |
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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 | 1723 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 144 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 65 | | mean | 26.51 | | std | 22.35 | | cv | 0.843 | | sampleLengths | | 0 | 103 | | 1 | 80 | | 2 | 4 | | 3 | 24 | | 4 | 86 | | 5 | 23 | | 6 | 26 | | 7 | 4 | | 8 | 6 | | 9 | 28 | | 10 | 41 | | 11 | 19 | | 12 | 6 | | 13 | 62 | | 14 | 8 | | 15 | 1 | | 16 | 14 | | 17 | 15 | | 18 | 15 | | 19 | 17 | | 20 | 74 | | 21 | 35 | | 22 | 6 | | 23 | 7 | | 24 | 30 | | 25 | 16 | | 26 | 7 | | 27 | 19 | | 28 | 9 | | 29 | 79 | | 30 | 20 | | 31 | 12 | | 32 | 27 | | 33 | 27 | | 34 | 18 | | 35 | 6 | | 36 | 44 | | 37 | 7 | | 38 | 13 | | 39 | 60 | | 40 | 10 | | 41 | 31 | | 42 | 11 | | 43 | 59 | | 44 | 22 | | 45 | 13 | | 46 | 40 | | 47 | 22 | | 48 | 14 | | 49 | 34 |
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| 92.84% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 113 | | matches | | 0 | "were stacked" | | 1 | "was weighted" | | 2 | "was fixed" | | 3 | "been built" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 221 | | matches | | 0 | "was getting" | | 1 | "was shaking" | | 2 | "was thinking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 144 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 450 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.03111111111111111 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0022222222222222222 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 144 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 144 | | mean | 11.97 | | std | 9.68 | | cv | 0.809 | | sampleLengths | | 0 | 39 | | 1 | 5 | | 2 | 11 | | 3 | 16 | | 4 | 16 | | 5 | 16 | | 6 | 25 | | 7 | 18 | | 8 | 29 | | 9 | 8 | | 10 | 4 | | 11 | 7 | | 12 | 4 | | 13 | 13 | | 14 | 21 | | 15 | 13 | | 16 | 22 | | 17 | 12 | | 18 | 18 | | 19 | 10 | | 20 | 5 | | 21 | 8 | | 22 | 26 | | 23 | 4 | | 24 | 6 | | 25 | 21 | | 26 | 4 | | 27 | 3 | | 28 | 8 | | 29 | 4 | | 30 | 1 | | 31 | 3 | | 32 | 18 | | 33 | 7 | | 34 | 14 | | 35 | 5 | | 36 | 6 | | 37 | 11 | | 38 | 12 | | 39 | 22 | | 40 | 17 | | 41 | 8 | | 42 | 1 | | 43 | 5 | | 44 | 9 | | 45 | 6 | | 46 | 3 | | 47 | 6 | | 48 | 8 | | 49 | 7 |
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| 38.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.25 | | totalSentences | 144 | | uniqueOpeners | 36 | |
| 66.01% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 101 | | matches | | 0 | "Instead she stepped back, just" | | 1 | "Then she thought of Evan's" |
| | ratio | 0.02 | |
| 10.10% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 53 | | totalSentences | 101 | | matches | | 0 | "She was in Eva's flat" | | 1 | "She had meant to shower," | | 2 | "It had just said: *I" | | 3 | "She froze, hand halfway to" | | 4 | "His slicked-back platinum blond hair" | | 5 | "He held his ivory-handled cane" | | 6 | "She knew his face too" | | 7 | "Her thumb found the crescent" | | 8 | "She unlatched the chain." | | 9 | "He said her name like" | | 10 | "Her voice was level" | | 11 | "It was getting her through" | | 12 | "He tilted his head, rain" | | 13 | "He stepped forward when she" | | 14 | "He was in socks, so" | | 15 | "he asked, nodding toward the" | | 16 | "He smiled, thin and tired." | | 17 | "She had only undone the" | | 18 | "He'd known about the underworld" | | 19 | "He'd offered her work, a" |
| | ratio | 0.525 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 94 | | totalSentences | 101 | | matches | | 0 | "The three deadbolts were a" | | 1 | "Tonight she was not alone." | | 2 | "She was in Eva's flat" | | 3 | "Books were stacked on the" | | 4 | "A scroll of yellowed vellum" | | 5 | "Ptolemy had claimed the one" | | 6 | "The bell over the front" | | 7 | "Aurora had come straight from" | | 8 | "She had meant to shower," | | 9 | "It had just said: *I" | | 10 | "She froze, hand halfway to" | | 11 | "Ptolemy opened one eye." | | 12 | "The sound was too polite" | | 13 | "Aurora moved through the narrow" | | 14 | "The hallway outside was dim," | | 15 | "Lucien Moreau stood there in" | | 16 | "His slicked-back platinum blond hair" | | 17 | "He held his ivory-handled cane" | | 18 | "She knew his face too" | | 19 | "The left one always caught" |
| | ratio | 0.931 | |
| 49.50% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 101 | | matches | | 0 | "Now the doorbell rang." |
| | ratio | 0.01 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 2 | | matches | | 0 | "Lucien Moreau stood there in the rain-slicked stairwell, shoulders hunched under a tailored charcoal coat that was too expensive for the building." | | 1 | "She found herself studying the details she had memorized once and then tried to forget: the way his hair fell back from his forehead when he was thinking, the f…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 26 | | uselessAdditionCount | 1 | | matches | | 0 | "He took, the cane tip scraping the floor" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 15 | | fancyCount | 2 | | fancyTags | | 0 | "he admitted (admit)" | | 1 | "he admitted (admit)" |
| | dialogueSentences | 57 | | tagDensity | 0.263 | | leniency | 0.526 | | rawRatio | 0.133 | | effectiveRatio | 0.07 | |