| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 80 | | tagDensity | 0.138 | | leniency | 0.275 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1479 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 93.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1479 | | 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 | 83 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 83 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 152 | | 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 | 1479 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 36.22% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 791 | | uniqueNames | 6 | | maxNameDensity | 2.28 | | worstName | "Aurora" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Aurora" | | discoveredNames | | Lucien | 13 | | Moreau | 1 | | Aurora | 18 | | Silas | 2 | | Eva | 1 | | Cardiff | 1 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Aurora" | | 3 | "Silas" | | 4 | "Eva" |
| | places | | | globalScore | 0.362 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | 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 | 1479 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 152 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 95 | | mean | 15.57 | | std | 13.33 | | cv | 0.856 | | sampleLengths | | 0 | 20 | | 1 | 43 | | 2 | 10 | | 3 | 30 | | 4 | 19 | | 5 | 9 | | 6 | 10 | | 7 | 58 | | 8 | 7 | | 9 | 2 | | 10 | 6 | | 11 | 12 | | 12 | 39 | | 13 | 13 | | 14 | 1 | | 15 | 4 | | 16 | 1 | | 17 | 14 | | 18 | 48 | | 19 | 14 | | 20 | 33 | | 21 | 3 | | 22 | 5 | | 23 | 7 | | 24 | 27 | | 25 | 13 | | 26 | 10 | | 27 | 37 | | 28 | 3 | | 29 | 2 | | 30 | 11 | | 31 | 38 | | 32 | 8 | | 33 | 42 | | 34 | 10 | | 35 | 10 | | 36 | 4 | | 37 | 4 | | 38 | 52 | | 39 | 6 | | 40 | 18 | | 41 | 1 | | 42 | 19 | | 43 | 1 | | 44 | 25 | | 45 | 17 | | 46 | 2 | | 47 | 1 | | 48 | 43 | | 49 | 6 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 138 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 152 | | ratio | 0.013 | | matches | | 0 | "His control lasted until she taped the final corner; then his shoulders dropped." | | 1 | "Her father had tied it for her before she left Cardiff; the knot remained clumsy and firm." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 794 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.013853904282115869 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 152 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 152 | | mean | 9.73 | | std | 6.83 | | cv | 0.702 | | sampleLengths | | 0 | 20 | | 1 | 11 | | 2 | 9 | | 3 | 23 | | 4 | 10 | | 5 | 12 | | 6 | 5 | | 7 | 13 | | 8 | 5 | | 9 | 14 | | 10 | 9 | | 11 | 10 | | 12 | 8 | | 13 | 5 | | 14 | 27 | | 15 | 18 | | 16 | 5 | | 17 | 2 | | 18 | 2 | | 19 | 6 | | 20 | 12 | | 21 | 12 | | 22 | 13 | | 23 | 14 | | 24 | 13 | | 25 | 1 | | 26 | 4 | | 27 | 1 | | 28 | 14 | | 29 | 3 | | 30 | 12 | | 31 | 8 | | 32 | 15 | | 33 | 10 | | 34 | 9 | | 35 | 5 | | 36 | 4 | | 37 | 7 | | 38 | 14 | | 39 | 8 | | 40 | 3 | | 41 | 5 | | 42 | 7 | | 43 | 27 | | 44 | 13 | | 45 | 9 | | 46 | 1 | | 47 | 6 | | 48 | 15 | | 49 | 16 |
| |
| 47.37% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.29605263157894735 | | totalSentences | 152 | | uniqueOpeners | 45 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 78 | | matches | (empty) | | ratio | 0 | |
| 35.38% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 78 | | matches | | 0 | "His platinum hair had come" | | 1 | "He held his ivory-handled cane" | | 2 | "His amber eye met hers." | | 3 | "she told him" | | 4 | "She set the noodles on" | | 5 | "She watched him do it." | | 6 | "His mouth tightened." | | 7 | "He set down his cane" | | 8 | "His fingers had landed against" | | 9 | "He braced his uninjured hand" | | 10 | "His breath stopped as she" | | 11 | "She reached for gauze" | | 12 | "He let the shirt fall" | | 13 | "His control lasted until she" | | 14 | "She put down the tape," | | 15 | "She knew the nick beside" | | 16 | "She had made it at" | | 17 | "She closed her fist around" | | 18 | "she told him" | | 19 | "Her father had tied it" |
| | ratio | 0.462 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 73 | | totalSentences | 78 | | matches | | 0 | "Aurora opened the door with" | | 1 | "Rain had darkened his charcoal" | | 2 | "His platinum hair had come" | | 3 | "He held his ivory-handled cane" | | 4 | "Aurora looked at the blood" | | 5 | "His amber eye met hers." | | 6 | "The black one caught the" | | 7 | "she told him" | | 8 | "She set the noodles on" | | 9 | "Lucien crossed the threshold without" | | 10 | "She watched him do it." | | 11 | "The flat above Silas’s bar" | | 12 | "Lucien stopped beside the sink" | | 13 | "Aurora turned on the tap." | | 14 | "Blood ran from Lucien’s palm" | | 15 | "A cut crossed the base" | | 16 | "Aurora pulled the first-aid box" | | 17 | "His mouth tightened." | | 18 | "He set down his cane" | | 19 | "His fingers had landed against" |
| | ratio | 0.936 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 78 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 80 | | tagDensity | 0.075 | | leniency | 0.15 | | rawRatio | 0 | | effectiveRatio | 0 | |