| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 54 | | tagDensity | 0.148 | | leniency | 0.296 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.77% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2074 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "very" | | 1 | "sharply" | | 2 | "quickly" |
| |
| 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) | |
| 95.18% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2074 | | totalAiIsms | 2 | | 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 | 1 | | narrationSentences | 234 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 234 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 280 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2074 | | ratio | 0 | | matches | (empty) | |
| 97.22% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 1 | | matches | | 0 | "Ahead, Herrera breathed hard." |
| |
| 71.71% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 62 | | wordCount | 1852 | | uniqueNames | 9 | | maxNameDensity | 1.57 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 29 | | Herrera | 22 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Camden | 1 | | Veil | 1 | | Market | 1 | | Morris | 5 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Morris" |
| | places | | | globalScore | 0.717 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 140 | | 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 | 2074 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 280 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 176 | | mean | 11.78 | | std | 11.31 | | cv | 0.96 | | sampleLengths | | 0 | 18 | | 1 | 24 | | 2 | 8 | | 3 | 2 | | 4 | 36 | | 5 | 2 | | 6 | 4 | | 7 | 63 | | 8 | 25 | | 9 | 8 | | 10 | 18 | | 11 | 6 | | 12 | 40 | | 13 | 15 | | 14 | 31 | | 15 | 3 | | 16 | 19 | | 17 | 7 | | 18 | 31 | | 19 | 4 | | 20 | 4 | | 21 | 15 | | 22 | 2 | | 23 | 15 | | 24 | 8 | | 25 | 46 | | 26 | 11 | | 27 | 10 | | 28 | 32 | | 29 | 4 | | 30 | 12 | | 31 | 6 | | 32 | 6 | | 33 | 5 | | 34 | 18 | | 35 | 4 | | 36 | 10 | | 37 | 1 | | 38 | 4 | | 39 | 6 | | 40 | 29 | | 41 | 29 | | 42 | 29 | | 43 | 4 | | 44 | 4 | | 45 | 22 | | 46 | 5 | | 47 | 14 | | 48 | 54 | | 49 | 4 |
| |
| 96.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 234 | | matches | | 0 | "been boarded" | | 1 | "been found" | | 2 | "been scraped" | | 3 | "were buried" | | 4 | "were filmed" | | 5 | "been rolled" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 349 | | matches | | 0 | "wasn’t touching" | | 1 | "was coughing" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 280 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1857 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 56 | | adverbRatio | 0.030156165858912225 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.004846526655896607 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 280 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 280 | | mean | 7.41 | | std | 4.7 | | cv | 0.634 | | sampleLengths | | 0 | 18 | | 1 | 5 | | 2 | 11 | | 3 | 8 | | 4 | 4 | | 5 | 4 | | 6 | 2 | | 7 | 5 | | 8 | 13 | | 9 | 18 | | 10 | 2 | | 11 | 4 | | 12 | 16 | | 13 | 6 | | 14 | 14 | | 15 | 27 | | 16 | 14 | | 17 | 5 | | 18 | 6 | | 19 | 8 | | 20 | 18 | | 21 | 2 | | 22 | 4 | | 23 | 9 | | 24 | 11 | | 25 | 16 | | 26 | 4 | | 27 | 3 | | 28 | 12 | | 29 | 3 | | 30 | 13 | | 31 | 9 | | 32 | 6 | | 33 | 3 | | 34 | 6 | | 35 | 6 | | 36 | 5 | | 37 | 2 | | 38 | 7 | | 39 | 9 | | 40 | 2 | | 41 | 2 | | 42 | 5 | | 43 | 13 | | 44 | 4 | | 45 | 4 | | 46 | 15 | | 47 | 2 | | 48 | 15 | | 49 | 8 |
| |
| 53.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3392857142857143 | | totalSentences | 280 | | uniqueOpeners | 95 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 219 | | matches | | 0 | "Sometimes he carried a medical" | | 1 | "Sometimes he left through the" | | 2 | "Then a clipped acknowledgment." | | 3 | "Then she pictured Morris’s hands." | | 4 | "Just his fingernails torn down" | | 5 | "Somewhere beneath her, machinery hummed." | | 6 | "Then he pushed through a" |
| | ratio | 0.032 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 55 | | totalSentences | 219 | | matches | | 0 | "His right hand tightened on" | | 1 | "She cleared its bonnet and" | | 2 | "He didn’t look back." | | 3 | "She had spent six nights" | | 4 | "Her shoes had grip enough" | | 5 | "She shortened her stride." | | 6 | "He looked frightened, but not" | | 7 | "She reached the corner and" | | 8 | "She repeated it, then crossed" | | 9 | "She knew it from the" | | 10 | "She wore a red wool" | | 11 | "He plunged down the stairs." | | 12 | "It had settled against her" | | 13 | "She had seen one before." | | 14 | "She had discovered it herself" | | 15 | "She still carried it." | | 16 | "She could admit that much." | | 17 | "She had no warrant for" | | 18 | "She could justify entry in" | | 19 | "She tried again." |
| | ratio | 0.251 | |
| 85.57% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 164 | | totalSentences | 219 | | matches | | 0 | "The man came out of" | | 1 | "Rain rattled against the pharmacy" | | 2 | "His right hand tightened on" | | 3 | "Quinn stepped into the road." | | 4 | "A taxi braked hard enough" | | 5 | "She cleared its bonnet and" | | 6 | "He didn’t look back." | | 7 | "She had spent six nights" | | 8 | "Tonight he had gone to" | | 9 | "The owner was upstairs now," | | 10 | "Quinn touched the radio clipped" | | 11 | "Quinn took the gap a" | | 12 | "Her shoes had grip enough" | | 13 | "She shortened her stride." | | 14 | "Herrera glanced back." | | 15 | "Streetlight caught his olive face" | | 16 | "He looked frightened, but not" | | 17 | "Quinn checked behind as she" | | 18 | "A bus lumbered through standing" | | 19 | "A cyclist swore at it." |
| | ratio | 0.749 | |
| 22.83% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 219 | | matches | | 0 | "To the worn leather watch." |
| | ratio | 0.005 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 67 | | technicalSentenceCount | 2 | | matches | | 0 | "She cleared its bonnet and caught sight of Herrera turning north, his trainers throwing water from the pavement." | | 1 | "Just his fingernails torn down to the quick, as though he had spent his last minutes trying to open something." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 94.44% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 3 | | fancyTags | | 0 | "the woman repeated (repeat)" | | 1 | "She heard (hear)" | | 2 | "Herrera whispered (whisper)" |
| | dialogueSentences | 54 | | tagDensity | 0.148 | | leniency | 0.296 | | rawRatio | 0.375 | | effectiveRatio | 0.111 | |