| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 1 | | adverbTags | | 0 | "Herrera said quietly [quietly]" |
| | dialogueSentences | 20 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.125 | | effectiveRatio | 0.1 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 829 | | 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) | |
| 93.97% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 829 | | totalAiIsms | 1 | | 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 | 54 | | matches | (empty) | |
| 63.49% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 54 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 66 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 829 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 71.88% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 640 | | uniqueNames | 9 | | maxNameDensity | 1.56 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 10 | | Camden | 1 | | Lock | 1 | | Herrera | 7 | | Chalk | 1 | | Farm | 1 | | Road | 1 | | Spanish | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" |
| | places | | 0 | "Chalk" | | 1 | "Farm" | | 2 | "Road" | | 3 | "Spanish" |
| | globalScore | 0.719 | | windowScore | 0.833 | |
| 84.21% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 38 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a carved piece of bone, threa" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 829 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 66 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 36.04 | | std | 25.09 | | cv | 0.696 | | sampleLengths | | 0 | 62 | | 1 | 6 | | 2 | 73 | | 3 | 16 | | 4 | 73 | | 5 | 22 | | 6 | 60 | | 7 | 64 | | 8 | 37 | | 9 | 38 | | 10 | 7 | | 11 | 11 | | 12 | 82 | | 13 | 6 | | 14 | 65 | | 15 | 37 | | 16 | 5 | | 17 | 21 | | 18 | 4 | | 19 | 22 | | 20 | 56 | | 21 | 28 | | 22 | 34 |
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| 92.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 54 | | matches | | 0 | "was fogged" | | 1 | "was crouched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 108 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 66 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 641 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.014040561622464899 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0031201248049922 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 66 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 66 | | mean | 12.56 | | std | 7.92 | | cv | 0.63 | | sampleLengths | | 0 | 22 | | 1 | 6 | | 2 | 16 | | 3 | 18 | | 4 | 6 | | 5 | 4 | | 6 | 25 | | 7 | 21 | | 8 | 5 | | 9 | 18 | | 10 | 10 | | 11 | 3 | | 12 | 3 | | 13 | 25 | | 14 | 5 | | 15 | 21 | | 16 | 22 | | 17 | 10 | | 18 | 6 | | 19 | 6 | | 20 | 3 | | 21 | 24 | | 22 | 21 | | 23 | 12 | | 24 | 27 | | 25 | 4 | | 26 | 15 | | 27 | 18 | | 28 | 10 | | 29 | 16 | | 30 | 11 | | 31 | 4 | | 32 | 8 | | 33 | 11 | | 34 | 15 | | 35 | 7 | | 36 | 11 | | 37 | 6 | | 38 | 14 | | 39 | 21 | | 40 | 18 | | 41 | 23 | | 42 | 3 | | 43 | 3 | | 44 | 25 | | 45 | 40 | | 46 | 8 | | 47 | 5 | | 48 | 9 | | 49 | 15 |
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| 75.25% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.4696969696969697 | | totalSentences | 66 | | uniqueOpeners | 31 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 51 | | matches | (empty) | | ratio | 0 | |
| 55.29% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 51 | | matches | | 0 | "Her watch read twenty past" | | 1 | "He did not stop." | | 2 | "He vaulted a low railing," | | 3 | "Her knee complained." | | 4 | "She ignored it." | | 5 | "They spilled onto Chalk Farm" | | 6 | "she called out" | | 7 | "Her breath came short and" | | 8 | "He glanced back." | | 9 | "His voice had the bruised" | | 10 | "He veered left onto a" | | 11 | "She followed the sound." | | 12 | "He lifted his hands." | | 13 | "It looked like a carved" | | 14 | "He nodded at the grate" | | 15 | "It carried the smell of" | | 16 | "He slid the bone token" | | 17 | "It seemed to be waiting," | | 18 | "Her watch ticked against her" | | 19 | "She took one step toward" |
| | ratio | 0.412 | |
| 9.02% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 51 | | matches | | 0 | "The rain came sideways off" | | 1 | "Her watch read twenty past" | | 2 | "The leather strap had gone" | | 3 | "He did not stop." | | 4 | "Tomás Herrera had never once" | | 5 | "He vaulted a low railing," | | 6 | "The sleeve had ridden up." | | 7 | "Quinn went over the railing" | | 8 | "Her knee complained." | | 9 | "She ignored it." | | 10 | "They spilled onto Chalk Farm" | | 11 | "A cab swerved, horn bellowing." | | 12 | "Herrera shoulder-checked a woman carrying" | | 13 | "Quinn shoved past the woman," | | 14 | "she called out" | | 15 | "Her breath came short and" | | 16 | "He glanced back." | | 17 | "The medallion around his neck" | | 18 | "His voice had the bruised" | | 19 | "He veered left onto a" |
| | ratio | 0.902 | |
| 98.04% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 51 | | matches | | 0 | "Even in the sodium glow" |
| | ratio | 0.02 | |
| 89.95% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 2 | | matches | | 0 | "Herrera shoulder-checked a woman carrying a bag of shopping, muttered an apology in Spanish that sounded almost sincere, and kept running." | | 1 | "He stood, the bone token swinging from his fingers, the saint on his chest rising and falling with each breath." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |