| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 108 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.46% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1805 | | totalAiIsmAdverbs | 2 | | 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) | |
| 94.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1805 | | 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 | 1 | | narrationSentences | 123 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 123 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 214 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1805 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 34 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 1157 | | uniqueNames | 9 | | maxNameDensity | 1.56 | | worstName | "Lucien" | | maxWindowNameDensity | 3 | | worstWindowName | "Lucien" | | discoveredNames | | Lucien | 18 | | Moreau | 1 | | Ptolemy | 5 | | Aurora | 17 | | Eva | 5 | | Friday | 1 | | Golden | 1 | | Empress | 1 | | Evan | 1 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Ptolemy" | | 3 | "Aurora" | | 4 | "Eva" | | 5 | "Evan" |
| | places | | | globalScore | 0.722 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 84 | | glossingSentenceCount | 1 | | matches | | 0 | "seemed smaller then, its stacks of paper suddenly less like clutter and more like kindling" |
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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 | 1805 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 214 | | matches | | 0 | "over that afternoon" | | 1 | "hated that he" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 134 | | mean | 13.47 | | std | 14.8 | | cv | 1.099 | | sampleLengths | | 0 | 20 | | 1 | 51 | | 2 | 37 | | 3 | 5 | | 4 | 5 | | 5 | 3 | | 6 | 1 | | 7 | 4 | | 8 | 22 | | 9 | 3 | | 10 | 39 | | 11 | 54 | | 12 | 11 | | 13 | 41 | | 14 | 11 | | 15 | 6 | | 16 | 2 | | 17 | 3 | | 18 | 5 | | 19 | 48 | | 20 | 4 | | 21 | 39 | | 22 | 8 | | 23 | 3 | | 24 | 12 | | 25 | 7 | | 26 | 3 | | 27 | 38 | | 28 | 6 | | 29 | 1 | | 30 | 1 | | 31 | 3 | | 32 | 4 | | 33 | 2 | | 34 | 47 | | 35 | 5 | | 36 | 4 | | 37 | 3 | | 38 | 1 | | 39 | 1 | | 40 | 10 | | 41 | 17 | | 42 | 8 | | 43 | 2 | | 44 | 18 | | 45 | 40 | | 46 | 3 | | 47 | 8 | | 48 | 4 | | 49 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 123 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 213 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 214 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1047 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.033428844317096466 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.0057306590257879654 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 214 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 214 | | mean | 8.43 | | std | 6.27 | | cv | 0.744 | | sampleLengths | | 0 | 20 | | 1 | 7 | | 2 | 17 | | 3 | 27 | | 4 | 10 | | 5 | 18 | | 6 | 9 | | 7 | 5 | | 8 | 5 | | 9 | 3 | | 10 | 1 | | 11 | 4 | | 12 | 3 | | 13 | 7 | | 14 | 3 | | 15 | 9 | | 16 | 3 | | 17 | 10 | | 18 | 15 | | 19 | 14 | | 20 | 4 | | 21 | 4 | | 22 | 22 | | 23 | 15 | | 24 | 9 | | 25 | 9 | | 26 | 2 | | 27 | 14 | | 28 | 17 | | 29 | 6 | | 30 | 4 | | 31 | 11 | | 32 | 6 | | 33 | 2 | | 34 | 3 | | 35 | 5 | | 36 | 20 | | 37 | 9 | | 38 | 19 | | 39 | 4 | | 40 | 6 | | 41 | 14 | | 42 | 9 | | 43 | 10 | | 44 | 6 | | 45 | 2 | | 46 | 3 | | 47 | 12 | | 48 | 7 | | 49 | 3 |
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| 53.58% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3411214953271028 | | totalSentences | 214 | | uniqueOpeners | 73 | |
| 92.59% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 108 | | matches | | 0 | "Barely pressure at all, but" | | 1 | "Too straight for broken glass." | | 2 | "Perhaps he had been afraid" |
| | ratio | 0.028 | |
| 38.52% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 49 | | totalSentences | 108 | | matches | | 0 | "His platinum hair was slicked" | | 1 | "She shut the door." | | 2 | "His hand came up against" | | 3 | "She had never heard him" | | 4 | "They had made a joke" | | 5 | "She slid the chain free" | | 6 | "He leaned his cane against" | | 7 | "He remembered where Eva kept" | | 8 | "He glanced at the red" | | 9 | "He watched her without speaking" | | 10 | "She set out gauze and" | | 11 | "His brows lifted." | | 12 | "He took off the coat," | | 13 | "She cleaned the wound." | | 14 | "He held still, though the" | | 15 | "His mismatched eyes followed her" | | 16 | "She kept her attention on" | | 17 | "He took a breath." | | 18 | "He glanced at the books" | | 19 | "Her phone lay face down" |
| | ratio | 0.454 | |
| 43.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 90 | | totalSentences | 108 | | matches | | 0 | "Aurora opened the door on" | | 1 | "Lucien looked as immaculate as" | | 2 | "His platinum hair was slicked" | | 3 | "She shut the door." | | 4 | "His hand came up against" | | 5 | "The word caught her harder" | | 6 | "Lucien could make a request" | | 7 | "She had never heard him" | | 8 | "Aurora looked past him." | | 9 | "The landing was empty." | | 10 | "Eva would be across town" | | 11 | "They had made a joke" | | 12 | "She slid the chain free" | | 13 | "Lucien stepped inside, bringing cold" | | 14 | "He leaned his cane against" | | 15 | "He remembered where Eva kept" | | 16 | "That irritated her too." | | 17 | "Ptolemy approached, sniffed his trouser" | | 18 | "He glanced at the red" | | 19 | "Aurora fetched the first-aid box" |
| | ratio | 0.833 | |
| 46.30% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 108 | | matches | | 0 | "Now she was grateful for" |
| | ratio | 0.009 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 1 | | matches | | 0 | "Eva would be across town until morning, cataloguing a collection for a woman who paid in cash and refused to be photographed." |
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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 | | totalTags | 14 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 108 | | tagDensity | 0.13 | | leniency | 0.259 | | rawRatio | 0 | | effectiveRatio | 0 | |