| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 3 | | adverbTags | | 0 | "Eva turned fully [fully]" | | 1 | "Eva's voice cracked around [around]" | | 2 | "She turned back [back]" |
| | dialogueSentences | 70 | | tagDensity | 0.243 | | leniency | 0.486 | | rawRatio | 0.176 | | effectiveRatio | 0.086 | |
| 82.05% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1114 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "perfectly" | | 1 | "very" | | 2 | "really" |
| |
| 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) | |
| 77.56% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1114 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "flickered" | | 1 | "familiar" | | 2 | "gloom" | | 3 | "stomach" | | 4 | "shattered" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "stomach dropped/sank" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 75 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 75 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 128 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1111 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 632 | | uniqueNames | 9 | | maxNameDensity | 3.01 | | worstName | "Eva" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Rory | 15 | | Silas | 5 | | Private | 1 | | Cardiff | 1 | | Budapest | 2 | | Belfast | 1 | | Eva | 19 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Rory" | | 3 | "Silas" | | 4 | "Eva" |
| | places | | 0 | "Cardiff" | | 1 | "Budapest" | | 2 | "Belfast" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | 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 | 1111 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 128 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 67 | | mean | 16.58 | | std | 15.82 | | cv | 0.954 | | sampleLengths | | 0 | 56 | | 1 | 5 | | 2 | 48 | | 3 | 3 | | 4 | 6 | | 5 | 3 | | 6 | 42 | | 7 | 25 | | 8 | 34 | | 9 | 5 | | 10 | 10 | | 11 | 32 | | 12 | 39 | | 13 | 7 | | 14 | 35 | | 15 | 41 | | 16 | 3 | | 17 | 15 | | 18 | 5 | | 19 | 2 | | 20 | 16 | | 21 | 21 | | 22 | 12 | | 23 | 5 | | 24 | 16 | | 25 | 7 | | 26 | 13 | | 27 | 3 | | 28 | 4 | | 29 | 5 | | 30 | 11 | | 31 | 36 | | 32 | 2 | | 33 | 33 | | 34 | 16 | | 35 | 8 | | 36 | 1 | | 37 | 3 | | 38 | 14 | | 39 | 10 | | 40 | 14 | | 41 | 33 | | 42 | 6 | | 43 | 7 | | 44 | 8 | | 45 | 40 | | 46 | 30 | | 47 | 11 | | 48 | 4 | | 49 | 6 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 75 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 109 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 128 | | ratio | 0 | | matches | (empty) | |
| 91.81% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 547 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.04936014625228519 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.016453382084095063 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 128 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 128 | | mean | 8.68 | | std | 6.26 | | cv | 0.722 | | sampleLengths | | 0 | 11 | | 1 | 21 | | 2 | 4 | | 3 | 4 | | 4 | 16 | | 5 | 5 | | 6 | 26 | | 7 | 8 | | 8 | 14 | | 9 | 3 | | 10 | 6 | | 11 | 3 | | 12 | 26 | | 13 | 16 | | 14 | 6 | | 15 | 14 | | 16 | 5 | | 17 | 15 | | 18 | 9 | | 19 | 10 | | 20 | 5 | | 21 | 7 | | 22 | 3 | | 23 | 9 | | 24 | 7 | | 25 | 3 | | 26 | 8 | | 27 | 5 | | 28 | 6 | | 29 | 5 | | 30 | 1 | | 31 | 10 | | 32 | 17 | | 33 | 7 | | 34 | 26 | | 35 | 9 | | 36 | 6 | | 37 | 20 | | 38 | 9 | | 39 | 6 | | 40 | 3 | | 41 | 9 | | 42 | 6 | | 43 | 5 | | 44 | 2 | | 45 | 5 | | 46 | 8 | | 47 | 3 | | 48 | 11 | | 49 | 7 |
| |
| 75.52% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4765625 | | totalSentences | 128 | | uniqueOpeners | 61 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 71 | | matches | | 0 | "She stopped two steps inside." | | 1 | "Their eyes met." | | 2 | "He set the glass down" | | 3 | "She moved toward the bar" | | 4 | "Her face had changed too." | | 5 | "She wrapped her hands around" | | 6 | "His limp more pronounced than" | | 7 | "Her eyes catalogued everything." | | 8 | "Her hands moved with practiced" | | 9 | "She turned back, and her" | | 10 | "His hazel eyes carried something" | | 11 | "He went back to polishing" |
| | ratio | 0.169 | |
| 23.38% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 71 | | matches | | 0 | "The Raven's Nest smelled of" | | 1 | "Rory shouldered through the door" | | 2 | "The green neon above the" | | 3 | "She stopped two steps inside." | | 4 | "Eva sat at the far" | | 5 | "Designer coat draped over the" | | 6 | "Their eyes met." | | 7 | "Rory's stomach dropped through the" | | 8 | "Silas glanced up from polishing" | | 9 | "He set the glass down" | | 10 | "Rory's voice came out flat" | | 11 | "She moved toward the bar" | | 12 | "Eva's accent had changed, the" | | 13 | "Eva took a slow sip" | | 14 | "Rory slid onto a stool" | | 15 | "The photos on the walls" | | 16 | "Prague, Budapest, Belfast." | | 17 | "Silas had a thing for" | | 18 | "Eva turned fully now" | | 19 | "Her face had changed too." |
| | ratio | 0.873 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 35.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 3 | | matches | | 0 | "Rory shouldered through the door at half-past ten, still wearing the thermal bag straps that had left grooves across her shoulders." | | 1 | "Silas glanced up from polishing a tumbler, his hazel eyes moving between them with the careful assessment of a man who'd spent thirty years reading rooms." | | 2 | "His hazel eyes carried something that might have been sympathy." |
| |
| 66.18% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 2 | | matches | | 0 | "Eva's accent had, the edges smoothed away" | | 1 | "She turned back, and her eyes were bright in the dim light" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "Eva breathed (breathe)" | | 1 | "Eva laughed (laugh)" |
| | dialogueSentences | 70 | | tagDensity | 0.029 | | leniency | 0.057 | | rawRatio | 1 | | effectiveRatio | 0.057 | |