| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 3 | | adverbTags | | 0 | "Eva said finally [finally]" | | 1 | "Rory leaned back [back]" | | 2 | "Rory said instead [instead]" |
| | dialogueSentences | 62 | | tagDensity | 0.306 | | leniency | 0.613 | | rawRatio | 0.158 | | effectiveRatio | 0.097 | |
| 86.88% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1143 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 69.38% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1143 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "silk" | | 1 | "silence" | | 2 | "weight" | | 3 | "traced" | | 4 | "pristine" | | 5 | "familiar" |
| |
| 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 | 79 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 79 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 122 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1136 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 66 | | wordCount | 800 | | uniqueNames | 15 | | maxNameDensity | 3.38 | | worstName | "Eva" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Golden | 1 | | Empress | 1 | | Friday | 1 | | Silas | 6 | | Prague | 1 | | Berlin | 1 | | Eva | 27 | | London | 1 | | Rory | 21 | | Cardiff | 1 | | Silence | 1 | | Were | 1 | | Evan | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Empress" | | 3 | "Silas" | | 4 | "Eva" | | 5 | "Rory" | | 6 | "Silence" | | 7 | "Were" | | 8 | "Evan" |
| | places | | 0 | "Prague" | | 1 | "Berlin" | | 2 | "London" | | 3 | "Cardiff" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1136 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 122 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 69 | | mean | 16.46 | | std | 16.12 | | cv | 0.979 | | sampleLengths | | 0 | 48 | | 1 | 48 | | 2 | 41 | | 3 | 33 | | 4 | 6 | | 5 | 68 | | 6 | 9 | | 7 | 1 | | 8 | 34 | | 9 | 23 | | 10 | 11 | | 11 | 3 | | 12 | 12 | | 13 | 8 | | 14 | 3 | | 15 | 25 | | 16 | 2 | | 17 | 29 | | 18 | 30 | | 19 | 19 | | 20 | 10 | | 21 | 7 | | 22 | 4 | | 23 | 1 | | 24 | 36 | | 25 | 5 | | 26 | 2 | | 27 | 5 | | 28 | 21 | | 29 | 14 | | 30 | 9 | | 31 | 56 | | 32 | 2 | | 33 | 39 | | 34 | 7 | | 35 | 10 | | 36 | 9 | | 37 | 11 | | 38 | 25 | | 39 | 42 | | 40 | 7 | | 41 | 3 | | 42 | 3 | | 43 | 61 | | 44 | 3 | | 45 | 5 | | 46 | 7 | | 47 | 18 | | 48 | 5 | | 49 | 3 |
| |
| 91.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 79 | | matches | | 0 | "been slammed" | | 1 | "were gone" | | 2 | "was ashamed" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 147 | | matches | (empty) | |
| 72.60% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 122 | | ratio | 0.025 | | matches | | 0 | "Rory thought of her own existence—delivering chow mein in the rain, living in a flat small enough that she could touch both walls with her arms spread wide." | | 1 | "Rory saw Eva's eyes flick over her—the worn jacket, the grease stain on her jeans, the calluses on her hands from bicycle handlebars." | | 2 | "Eva checked her watch—sleek, silver, probably worth more than Rory's bicycle." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 804 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.029850746268656716 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0037313432835820895 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 122 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 122 | | mean | 9.31 | | std | 6.98 | | cv | 0.75 | | sampleLengths | | 0 | 23 | | 1 | 18 | | 2 | 7 | | 3 | 8 | | 4 | 22 | | 5 | 18 | | 6 | 4 | | 7 | 12 | | 8 | 9 | | 9 | 16 | | 10 | 3 | | 11 | 7 | | 12 | 23 | | 13 | 3 | | 14 | 3 | | 15 | 5 | | 16 | 10 | | 17 | 24 | | 18 | 13 | | 19 | 16 | | 20 | 8 | | 21 | 1 | | 22 | 1 | | 23 | 13 | | 24 | 21 | | 25 | 13 | | 26 | 10 | | 27 | 8 | | 28 | 3 | | 29 | 3 | | 30 | 12 | | 31 | 6 | | 32 | 2 | | 33 | 3 | | 34 | 17 | | 35 | 8 | | 36 | 2 | | 37 | 19 | | 38 | 10 | | 39 | 28 | | 40 | 2 | | 41 | 5 | | 42 | 14 | | 43 | 8 | | 44 | 2 | | 45 | 2 | | 46 | 5 | | 47 | 4 | | 48 | 1 | | 49 | 13 |
| |
| 59.02% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.39344262295081966 | | totalSentences | 122 | | uniqueOpeners | 48 | |
| 47.62% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 70 | | matches | | 0 | "Then Eva stood, and Rory" |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 8 | | totalSentences | 70 | | matches | | 0 | "Her trainers squelched against the" | | 1 | "She'd come for a quiet" | | 2 | "She hadn't seen Eva in" | | 3 | "Their eyes met." | | 4 | "She wore a charcoal suit" | | 5 | "They stood there, separated by" | | 6 | "She paused with her hand" | | 7 | "She didn't pick up the" |
| | ratio | 0.114 | |
| 24.29% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 70 | | matches | | 0 | "The Raven's Nest smelled of" | | 1 | "Rory pushed through the door," | | 2 | "Her trainers squelched against the" | | 3 | "She'd come for a quiet" | | 4 | "The Golden Empress had been" | | 5 | "The bar was half-empty." | | 6 | "A couple occupied the corner" | | 7 | "An old man nursed something" | | 8 | "Rory's chest tightened." | | 9 | "She hadn't seen Eva in" | | 10 | "Eva looked up." | | 11 | "Their eyes met." | | 12 | "The wild auburn curls Eva" | | 13 | "She wore a charcoal suit" | | 14 | "Nothing like the ripped band" | | 15 | "Eva's voice was softer than" | | 16 | "They stood there, separated by" | | 17 | "Silas emerged from behind the" | | 18 | "Eva gestured at the bar" | | 19 | "Rory jerked her thumb toward" |
| | ratio | 0.871 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 70 | | matches | | 0 | "Because you'd have seen the" | | 1 | "Because you'd have asked questions" | | 2 | "Because I was ashamed and" |
| | ratio | 0.043 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 1 | | matches | | 0 | "The wild auburn curls Eva had always refused to tame were gone, replaced by a sleek blonde bob that ended just below her chin." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 62 | | tagDensity | 0.113 | | leniency | 0.226 | | rawRatio | 0 | | effectiveRatio | 0 | |