| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 3 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 76.08% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 209 | | 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) | |
| 52.15% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 209 | | 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 | 19 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 19 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 21 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 213 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 5 | | wordCount | 201 | | uniqueNames | 4 | | maxNameDensity | 1 | | worstName | "Eva" | | maxWindowNameDensity | 1 | | worstWindowName | "Eva" | | discoveredNames | | Eva | 2 | | Moreau | 1 | | French-accented | 1 | | English | 1 |
| | persons | | | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 13 | | 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 | 213 | | matches | (empty) | |
| 7.94% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 21 | | matches | | |
| 93.13% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 9 | | mean | 23.67 | | std | 11.26 | | cv | 0.476 | | sampleLengths | | 0 | 24 | | 1 | 37 | | 2 | 5 | | 3 | 17 | | 4 | 33 | | 5 | 43 | | 6 | 16 | | 7 | 21 | | 8 | 17 |
| |
| 86.80% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 19 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 30 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 21 | | ratio | 0.095 | | matches | | 0 | "Her hand hesitated on the deadbolt — one of three securing Eva's door — before turning it with deliberate slowness." | | 1 | "Lucien stood precisely as she remembered: immaculate charcoal suit, platinum hair slicked back, those mismatched eyes — one amber, one black — studying her with surgical precision." |
| |
| 82.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 202 | | adjectiveStacks | 3 | | stackExamples | | 0 | "small flat, past stacks" | | 1 | "precise French-accented English." | | 2 | "small crescent-shaped scar" |
| | adverbCount | 3 | | adverbRatio | 0.01485148514851485 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.009900990099009901 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 21 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 21 | | mean | 10.14 | | std | 6.71 | | cv | 0.661 | | sampleLengths | | 0 | 8 | | 1 | 4 | | 2 | 12 | | 3 | 18 | | 4 | 19 | | 5 | 2 | | 6 | 3 | | 7 | 9 | | 8 | 8 | | 9 | 13 | | 10 | 20 | | 11 | 27 | | 12 | 16 | | 13 | 5 | | 14 | 3 | | 15 | 8 | | 16 | 16 | | 17 | 5 | | 18 | 5 | | 19 | 8 | | 20 | 4 |
| |
| 100.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.6666666666666666 | | totalSentences | 21 | | uniqueOpeners | 14 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 17 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 5 | | totalSentences | 17 | | matches | | 0 | "Her breath caught in her" | | 1 | "She knew who it was" | | 2 | "Her hand hesitated on the" | | 3 | "His ivory-handled cane rested against" | | 4 | "Her fingers unconsciously traced the" |
| | ratio | 0.294 | |
| 18.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 15 | | totalSentences | 17 | | matches | | 0 | "The knock came three times," | | 1 | "Aurora knew that rhythm." | | 2 | "Her breath caught in her" | | 3 | "Ptolemy, Eva's tabby cat, lifted" | | 4 | "The flat smelled of old" | | 5 | "She knew who it was" | | 6 | "Lucien Moreau didn't make social" | | 7 | "Aurora crossed the small flat," | | 8 | "Her hand hesitated on the" | | 9 | "Lucien stood precisely as she" | | 10 | "His ivory-handled cane rested against" | | 11 | "A statement delivered in that" | | 12 | "Her fingers unconsciously traced the" | | 13 | "A ghost of a smile." | | 14 | "The kind that didn't reach" |
| | ratio | 0.882 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 17 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 8 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 3 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |