| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 1 | | adverbTags | | 0 | "His accent thickened around [around]" |
| | dialogueSentences | 129 | | tagDensity | 0.14 | | leniency | 0.279 | | rawRatio | 0.056 | | effectiveRatio | 0.016 | |
| 97.38% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1907 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 71.16% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1907 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "traced" | | 1 | "weight" | | 2 | "stomach" | | 3 | "pulse" | | 4 | "warmth" | | 5 | "could feel" | | 6 | "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 | 0 | | narrationSentences | 143 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 143 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 254 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1907 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 99.61% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 1290 | | uniqueNames | 12 | | maxNameDensity | 1.01 | | worstName | "Lucien" | | maxWindowNameDensity | 2 | | worstWindowName | "Lucien" | | discoveredNames | | Lucien | 13 | | Moreau | 1 | | London | 1 | | Marseille | 1 | | Eva | 4 | | Aurora | 4 | | Soho | 1 | | Silas | 1 | | Evan | 2 | | Hale | 1 | | One | 3 | | Ptolemy | 3 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Eva" | | 3 | "Aurora" | | 4 | "Silas" | | 5 | "Evan" | | 6 | "Hale" | | 7 | "One" | | 8 | "Ptolemy" |
| | places | | 0 | "London" | | 1 | "Marseille" | | 2 | "Soho" |
| | globalScore | 0.996 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 96 | | glossingSentenceCount | 1 | | matches | | 0 | "as if asking permission after the fact" |
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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 | 1907 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 254 | | matches | | 0 | "trusted that steadiness" | | 1 | "traced that mark" | | 2 | "gone that night" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 162 | | mean | 11.77 | | std | 14.16 | | cv | 1.203 | | sampleLengths | | 0 | 49 | | 1 | 34 | | 2 | 1 | | 3 | 6 | | 4 | 29 | | 5 | 16 | | 6 | 37 | | 7 | 5 | | 8 | 4 | | 9 | 3 | | 10 | 44 | | 11 | 1 | | 12 | 22 | | 13 | 4 | | 14 | 4 | | 15 | 5 | | 16 | 2 | | 17 | 4 | | 18 | 43 | | 19 | 7 | | 20 | 22 | | 21 | 5 | | 22 | 7 | | 23 | 16 | | 24 | 1 | | 25 | 1 | | 26 | 16 | | 27 | 15 | | 28 | 2 | | 29 | 1 | | 30 | 5 | | 31 | 7 | | 32 | 9 | | 33 | 35 | | 34 | 46 | | 35 | 5 | | 36 | 2 | | 37 | 32 | | 38 | 2 | | 39 | 26 | | 40 | 7 | | 41 | 4 | | 42 | 7 | | 43 | 8 | | 44 | 5 | | 45 | 57 | | 46 | 4 | | 47 | 41 | | 48 | 3 | | 49 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 143 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 219 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 254 | | ratio | 0.004 | | matches | | 0 | "The amber eye held the kitchen light; the black one swallowed it." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 757 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.022457067371202115 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.002642007926023778 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 254 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 254 | | mean | 7.51 | | std | 6.17 | | cv | 0.822 | | sampleLengths | | 0 | 18 | | 1 | 7 | | 2 | 24 | | 3 | 8 | | 4 | 10 | | 5 | 16 | | 6 | 1 | | 7 | 6 | | 8 | 11 | | 9 | 4 | | 10 | 14 | | 11 | 8 | | 12 | 8 | | 13 | 8 | | 14 | 5 | | 15 | 24 | | 16 | 5 | | 17 | 4 | | 18 | 3 | | 19 | 8 | | 20 | 26 | | 21 | 6 | | 22 | 4 | | 23 | 1 | | 24 | 7 | | 25 | 15 | | 26 | 4 | | 27 | 4 | | 28 | 5 | | 29 | 2 | | 30 | 4 | | 31 | 7 | | 32 | 10 | | 33 | 2 | | 34 | 24 | | 35 | 7 | | 36 | 9 | | 37 | 7 | | 38 | 6 | | 39 | 5 | | 40 | 7 | | 41 | 6 | | 42 | 10 | | 43 | 1 | | 44 | 1 | | 45 | 16 | | 46 | 8 | | 47 | 7 | | 48 | 2 | | 49 | 1 |
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| 46.06% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.25196850393700787 | | totalSentences | 254 | | uniqueOpeners | 64 | |
| 24.69% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 135 | | matches | | 0 | "Instead she unhooked the chain" |
| | ratio | 0.007 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 81 | | totalSentences | 135 | | matches | | 0 | "His hair, slicked back platinum," | | 1 | "She began to close the" | | 2 | "She stared at it." | | 3 | "Her voice came out low" | | 4 | "She hated how much she" | | 5 | "His accent thickened around the" | | 6 | "He looked past her shoulder" | | 7 | "His odd eyes took in" | | 8 | "He wound around her legs," | | 9 | "She tightened her grip on" | | 10 | "He had traced that mark" | | 11 | "She nodded at his side" | | 12 | "His hand brushed the charcoal" | | 13 | "It would have been a" | | 14 | "She should have shut the" | | 15 | "She should have called Eva." | | 16 | "She should have thrown the" | | 17 | "He crossed the threshold." | | 18 | "He moved with the old" | | 19 | "She left please off" |
| | ratio | 0.6 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 125 | | totalSentences | 135 | | matches | | 0 | "The door opened, and Lucien" | | 1 | "Aurora kept her fingers on" | | 2 | "His hair, slicked back platinum," | | 3 | "The charcoal suit fit him" | | 4 | "She began to close the" | | 5 | "The ivory head of his" | | 6 | "She stared at it." | | 7 | "The handle was smooth, innocent," | | 8 | "Her voice came out low" | | 9 | "She hated how much she" | | 10 | "His accent thickened around the" | | 11 | "Marseille had never left him." | | 12 | "He looked past her shoulder" | | 13 | "His odd eyes took in" | | 14 | "The other stayed black." | | 15 | "The tabby had appeared between" | | 16 | "He wound around her legs," | | 17 | "She tightened her grip on" | | 18 | "The small crescent scar on" | | 19 | "He had traced that mark" |
| | ratio | 0.926 | |
| 37.04% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 135 | | matches | | 0 | "Now, close enough to count" |
| | ratio | 0.007 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 3 | | matches | | 0 | "The charcoal suit fit him as if London had cut it to make her memory worse." | | 1 | "She remembered the night she had dropped a bag of noodles outside the restaurant and seen Evan across the road, one shoulder leaning against a delivery van, smi…" | | 2 | "His thumb rested over her pulse as if asking permission after the fact." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |