| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 23 | | adverbTagCount | 2 | | adverbTags | | 0 | "Harlow turned back [back]" | | 1 | "Eva said quietly [quietly]" |
| | dialogueSentences | 53 | | tagDensity | 0.434 | | leniency | 0.868 | | rawRatio | 0.087 | | effectiveRatio | 0.075 | |
| 91.34% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1155 | | 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) | |
| 61.04% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1155 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "trembled" | | 1 | "scanned" | | 2 | "etched" | | 3 | "measured" | | 4 | "perfect" | | 5 | "standard" | | 6 | "predator" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 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 | 1 | | hedgeCount | 0 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 113 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1155 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 674 | | uniqueNames | 8 | | maxNameDensity | 3.12 | | worstName | "Eva" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Eva" | | discoveredNames | | Greek | 1 | | Eva | 21 | | Oxford | 1 | | Harlow | 19 | | Tube | 1 | | Camden | 1 | | Veil | 1 | | Market | 1 |
| | persons | | | places | | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.866 | | wordCount | 1155 | | matches | | 0 | "not your shoes, but the size is close" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 113 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 23.57 | | std | 16.44 | | cv | 0.697 | | sampleLengths | | 0 | 4 | | 1 | 11 | | 2 | 37 | | 3 | 14 | | 4 | 30 | | 5 | 34 | | 6 | 71 | | 7 | 25 | | 8 | 28 | | 9 | 8 | | 10 | 43 | | 11 | 35 | | 12 | 11 | | 13 | 28 | | 14 | 56 | | 15 | 21 | | 16 | 53 | | 17 | 8 | | 18 | 30 | | 19 | 28 | | 20 | 32 | | 21 | 37 | | 22 | 37 | | 23 | 19 | | 24 | 62 | | 25 | 22 | | 26 | 29 | | 27 | 17 | | 28 | 6 | | 29 | 27 | | 30 | 37 | | 31 | 9 | | 32 | 32 | | 33 | 8 | | 34 | 4 | | 35 | 5 | | 36 | 2 | | 37 | 46 | | 38 | 6 | | 39 | 24 | | 40 | 32 | | 41 | 13 | | 42 | 19 | | 43 | 6 | | 44 | 3 | | 45 | 25 | | 46 | 3 | | 47 | 13 | | 48 | 5 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 83 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 123 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 113 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 678 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.038348082595870206 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.01032448377581121 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 113 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 113 | | mean | 10.22 | | std | 6.59 | | cv | 0.645 | | sampleLengths | | 0 | 4 | | 1 | 5 | | 2 | 6 | | 3 | 3 | | 4 | 22 | | 5 | 12 | | 6 | 14 | | 7 | 23 | | 8 | 7 | | 9 | 2 | | 10 | 12 | | 11 | 20 | | 12 | 14 | | 13 | 26 | | 14 | 20 | | 15 | 11 | | 16 | 15 | | 17 | 10 | | 18 | 5 | | 19 | 23 | | 20 | 3 | | 21 | 5 | | 22 | 12 | | 23 | 14 | | 24 | 8 | | 25 | 9 | | 26 | 8 | | 27 | 12 | | 28 | 15 | | 29 | 6 | | 30 | 5 | | 31 | 8 | | 32 | 20 | | 33 | 22 | | 34 | 13 | | 35 | 14 | | 36 | 7 | | 37 | 3 | | 38 | 8 | | 39 | 10 | | 40 | 6 | | 41 | 10 | | 42 | 37 | | 43 | 4 | | 44 | 4 | | 45 | 5 | | 46 | 25 | | 47 | 10 | | 48 | 10 | | 49 | 8 |
| |
| 77.88% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.4778761061946903 | | totalSentences | 113 | | uniqueOpeners | 54 | |
| 49.75% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 67 | | matches | | 0 | "Then the darkness swallowed everything." |
| | ratio | 0.015 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 67 | | matches | | 0 | "Her fingers trembled against the" | | 1 | "Her closely cropped salt-and-pepper hair" | | 2 | "They stood in the abandoned" | | 3 | "His eyes stared upward, bloodshot" | | 4 | "Her breath smelled of old" | | 5 | "She walked to the pillar" | | 6 | "It came away sticky, dark" | | 7 | "She turned slowly, her sharp" | | 8 | "She shone her torch along" | | 9 | "Her gloved fingers closed around" | | 10 | "She pulled out a bone" | | 11 | "She looked up at Eva." | | 12 | "She pointed her torch at" | | 13 | "She examined the coat lapels," | | 14 | "Her hand went to her" | | 15 | "It hit the tile with" | | 16 | "Her voice had lost its" |
| | ratio | 0.254 | |
| 19.70% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 59 | | totalSentences | 67 | | matches | | 0 | "The sigil is wrong." | | 1 | "Eva adjusted her round glasses." | | 2 | "Her fingers trembled against the" | | 3 | "Harlow crouched lower." | | 4 | "The dead man's throat bore" | | 5 | "A protective Greek ward turned" | | 6 | "Harlow's tone carried the flat" | | 7 | "Eva tucked her curly red" | | 8 | "Her closely cropped salt-and-pepper hair" | | 9 | "The platform stretched around them," | | 10 | "They stood in the abandoned" | | 11 | "The air smelled of rusted" | | 12 | "Eva stepped closer, her worn" | | 13 | "Harlow scanned the dark" | | 14 | "Harlow knelt beside the victim" | | 15 | "The young man's coat was" | | 16 | "His eyes stared upward, bloodshot" | | 17 | "Eva pointed to the floor" | | 18 | "A dark smear coated the" | | 19 | "Harlow reminded her" |
| | ratio | 0.881 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 23 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 74.53% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 4 | | fancyTags | | 0 | "Eva suggested (suggest)" | | 1 | "Eva whispered (whisper)" | | 2 | "Harlow agreed (agree)" | | 3 | "Harlow warned (warn)" |
| | dialogueSentences | 53 | | tagDensity | 0.208 | | leniency | 0.415 | | rawRatio | 0.364 | | effectiveRatio | 0.151 | |