| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 156 | | tagDensity | 0.109 | | leniency | 0.218 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1061 | | 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) | |
| 81.15% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1061 | | totalAiIsms | 4 | | found | | 0 | | word | "carried the weight" | | count | 1 |
| | 1 | | | 2 | | | 3 | |
| | highlights | | 0 | "carried the weight" | | 1 | "wavered" | | 2 | "weight" | | 3 | "flickered" |
| |
| 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 | 180 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 180 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 319 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 12 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1061 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 81 | | wordCount | 689 | | uniqueNames | 4 | | maxNameDensity | 7.55 | | worstName | "Harlow" | | maxWindowNameDensity | 12 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 52 | | Camden | 1 | | Eva | 27 | | Kowalski | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Camden" | | 2 | "Eva" | | 3 | "Kowalski" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 32 | | 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 | 1061 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 319 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 127 | | mean | 8.35 | | std | 7.94 | | cv | 0.951 | | sampleLengths | | 0 | 65 | | 1 | 25 | | 2 | 6 | | 3 | 19 | | 4 | 7 | | 5 | 35 | | 6 | 6 | | 7 | 23 | | 8 | 13 | | 9 | 17 | | 10 | 4 | | 11 | 7 | | 12 | 15 | | 13 | 7 | | 14 | 9 | | 15 | 9 | | 16 | 20 | | 17 | 9 | | 18 | 3 | | 19 | 7 | | 20 | 2 | | 21 | 4 | | 22 | 16 | | 23 | 4 | | 24 | 11 | | 25 | 5 | | 26 | 12 | | 27 | 3 | | 28 | 4 | | 29 | 2 | | 30 | 12 | | 31 | 12 | | 32 | 15 | | 33 | 35 | | 34 | 9 | | 35 | 2 | | 36 | 11 | | 37 | 11 | | 38 | 8 | | 39 | 19 | | 40 | 3 | | 41 | 1 | | 42 | 7 | | 43 | 20 | | 44 | 8 | | 45 | 5 | | 46 | 7 | | 47 | 15 | | 48 | 3 | | 49 | 14 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 180 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 177 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 319 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 690 | | adjectiveStacks | 1 | | stackExamples | | 0 | "behind thick yellow rims." |
| | adverbCount | 5 | | adverbRatio | 0.007246376811594203 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 319 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 319 | | mean | 3.33 | | std | 1.89 | | cv | 0.569 | | sampleLengths | | 0 | 11 | | 1 | 10 | | 2 | 6 | | 3 | 7 | | 4 | 4 | | 5 | 12 | | 6 | 6 | | 7 | 2 | | 8 | 7 | | 9 | 2 | | 10 | 8 | | 11 | 8 | | 12 | 7 | | 13 | 6 | | 14 | 7 | | 15 | 4 | | 16 | 8 | | 17 | 7 | | 18 | 10 | | 19 | 7 | | 20 | 7 | | 21 | 6 | | 22 | 5 | | 23 | 6 | | 24 | 4 | | 25 | 8 | | 26 | 4 | | 27 | 3 | | 28 | 4 | | 29 | 3 | | 30 | 2 | | 31 | 6 | | 32 | 2 | | 33 | 3 | | 34 | 5 | | 35 | 2 | | 36 | 2 | | 37 | 5 | | 38 | 4 | | 39 | 2 | | 40 | 5 | | 41 | 6 | | 42 | 4 | | 43 | 3 | | 44 | 2 | | 45 | 3 | | 46 | 4 | | 47 | 2 | | 48 | 7 | | 49 | 3 |
| |
| 40.91% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 29 | | diversityRatio | 0.22884012539184953 | | totalSentences | 319 | | uniqueOpeners | 73 | |
| 27.78% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 120 | | matches | | 0 | "All scattered on the tracks." |
| | ratio | 0.008 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 120 | | matches | | 0 | "Her watch on the left" | | 1 | "She carried the weight of" | | 2 | "She propped her elbows on" | | 3 | "Her shadow stretched long across" | | 4 | "She lifted the corner of" | | 5 | "It held a matte finish," | | 6 | "She pulled a leather satchel" | | 7 | "She lifted the toothpick sharpness" | | 8 | "She touched the skin." | | 9 | "It smelled sharp." | | 10 | "She wiped dust from her" | | 11 | "It sat on the table." | | 12 | "She reached into the satchel." | | 13 | "She picked it up." | | 14 | "She tightened the grip on" | | 15 | "She walked to the body." | | 16 | "It was confused." | | 17 | "She pulled the flashlight." | | 18 | "She held the flashlight tighter." | | 19 | "She pulled out a book." |
| | ratio | 0.333 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 114 | | totalSentences | 120 | | matches | | 0 | "Floodlights swung on swivel heads" | | 1 | "The platform beneath Camden smelled" | | 2 | "Dust motes danced in the" | | 3 | "Her watch on the left" | | 4 | "She carried the weight of" | | 5 | "The body lay on the" | | 6 | "She propped her elbows on" | | 7 | "Her shadow stretched long across" | | 8 | "The light reflected in her" | | 9 | "She lifted the corner of" | | 10 | "The stain was dry." | | 11 | "It held a matte finish," | | 12 | "a voice said" | | 13 | "Eva Kowalski knelt on the" | | 14 | "She pulled a leather satchel" | | 15 | "Books spilled onto the platform." | | 16 | "Eva shook a hand." | | 17 | "She lifted the toothpick sharpness" | | 18 | "She touched the skin." | | 19 | "It smelled sharp." |
| | ratio | 0.95 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 120 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 4 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 16 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 156 | | tagDensity | 0.103 | | leniency | 0.205 | | rawRatio | 0 | | effectiveRatio | 0 | |