| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 27 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva followed close [close]" |
| | dialogueSentences | 62 | | tagDensity | 0.435 | | leniency | 0.871 | | rawRatio | 0.037 | | effectiveRatio | 0.032 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1793 | | 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) | |
| 74.90% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1793 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "constructed" | | 1 | "shattered" | | 2 | "standard" | | 3 | "traced" | | 4 | "trembled" | | 5 | "magnetic" | | 6 | "scanned" | | 7 | "crystallized" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 121 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 121 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 155 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1785 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 59 | | wordCount | 982 | | uniqueNames | 13 | | maxNameDensity | 3.05 | | worstName | "Harlow" | | maxWindowNameDensity | 5 | | worstWindowName | "Harlow" | | discoveredNames | | Quinn | 1 | | Victorian | 1 | | Camden | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Metropolitan | 1 | | Police | 1 | | Kowalski | 1 | | Harlow | 30 | | Caucasian | 1 | | Eva | 17 | | Morris | 2 |
| | persons | | 0 | "Quinn" | | 1 | "Police" | | 2 | "Kowalski" | | 3 | "Harlow" | | 4 | "Eva" | | 5 | "Morris" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 90 | | 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 | 1785 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 155 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 34.33 | | std | 18.9 | | cv | 0.551 | | sampleLengths | | 0 | 95 | | 1 | 55 | | 2 | 11 | | 3 | 46 | | 4 | 35 | | 5 | 32 | | 6 | 41 | | 7 | 56 | | 8 | 39 | | 9 | 27 | | 10 | 4 | | 11 | 65 | | 12 | 41 | | 13 | 10 | | 14 | 33 | | 15 | 71 | | 16 | 14 | | 17 | 57 | | 18 | 13 | | 19 | 31 | | 20 | 17 | | 21 | 52 | | 22 | 1 | | 23 | 20 | | 24 | 8 | | 25 | 4 | | 26 | 35 | | 27 | 19 | | 28 | 38 | | 29 | 26 | | 30 | 40 | | 31 | 61 | | 32 | 56 | | 33 | 38 | | 34 | 28 | | 35 | 27 | | 36 | 40 | | 37 | 30 | | 38 | 32 | | 39 | 20 | | 40 | 19 | | 41 | 46 | | 42 | 17 | | 43 | 51 | | 44 | 48 | | 45 | 25 | | 46 | 37 | | 47 | 4 | | 48 | 48 | | 49 | 37 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 121 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 152 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 155 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 987 | | adjectiveStacks | 1 | | stackExamples | | 0 | "heavy leather-bound tome" |
| | adverbCount | 8 | | adverbRatio | 0.008105369807497468 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.00303951367781155 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 155 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 155 | | mean | 11.52 | | std | 7.71 | | cv | 0.67 | | sampleLengths | | 0 | 17 | | 1 | 10 | | 2 | 12 | | 3 | 8 | | 4 | 16 | | 5 | 11 | | 6 | 14 | | 7 | 7 | | 8 | 9 | | 9 | 4 | | 10 | 16 | | 11 | 9 | | 12 | 11 | | 13 | 6 | | 14 | 11 | | 15 | 7 | | 16 | 16 | | 17 | 10 | | 18 | 13 | | 19 | 23 | | 20 | 12 | | 21 | 8 | | 22 | 6 | | 23 | 6 | | 24 | 12 | | 25 | 13 | | 26 | 28 | | 27 | 2 | | 28 | 8 | | 29 | 7 | | 30 | 7 | | 31 | 6 | | 32 | 11 | | 33 | 6 | | 34 | 9 | | 35 | 20 | | 36 | 19 | | 37 | 7 | | 38 | 11 | | 39 | 9 | | 40 | 4 | | 41 | 3 | | 42 | 8 | | 43 | 27 | | 44 | 27 | | 45 | 7 | | 46 | 6 | | 47 | 11 | | 48 | 4 | | 49 | 5 |
| |
| 42.15% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3032258064516129 | | totalSentences | 155 | | uniqueOpeners | 47 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 114 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 114 | | matches | | 0 | "She crossed the platform with" | | 1 | "Her sharp jaw set as" | | 2 | "She pushed her round glasses" | | 3 | "She tucked a rogue strand" | | 4 | "She examined the victim's heavy" | | 5 | "She shifted her gaze along" | | 6 | "She inspected the metal without" | | 7 | "She traced the air above" | | 8 | "She checked the delicate iron" | | 9 | "She checked her own worn" | | 10 | "She brushed the grit from" | | 11 | "She tucked another red curl" | | 12 | "She pointed to the dark" | | 13 | "Her green eyes widened behind" | | 14 | "She spotted a small object" | | 15 | "It ignored the corpse's chest." | | 16 | "It swung hard to the" | | 17 | "She gathered the data." | | 18 | "She flipped the thick parchment" | | 19 | "Her missing partner, DS Morris," |
| | ratio | 0.263 | |
| 8.25% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 103 | | totalSentences | 114 | | matches | | 0 | "Harlow Quinn descended the cracked" | | 1 | "The damp air smelled of" | | 2 | "Dust motes danced in the" | | 3 | "Baskets of crushed quartz and" | | 4 | "The locals called it the" | | 5 | "Uniforms milled about the perimeter" | | 6 | "Harlow ignored their chatter." | | 7 | "She crossed the platform with" | | 8 | "Her sharp jaw set as" | | 9 | "The Metropolitan Police treated the" | | 10 | "Harlow saw the contradictions at" | | 11 | "A body lay with limbs" | | 12 | "Eva Kowalski crouched next to" | | 13 | "A worn leather satchel hung" | | 14 | "She pushed her round glasses" | | 15 | "She tucked a rogue strand" | | 16 | "Eva gestured to the scattered" | | 17 | "Harlow studied the victim" | | 18 | "A Caucasian male in his" | | 19 | "A savage laceration severed his" |
| | ratio | 0.904 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 114 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 1 | | matches | | 0 | "Shattered glass vials leaked a luminescent blue liquid that hissed against the stone floor." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 27 | | uselessAdditionCount | 1 | | matches | | 0 | "Harlow set, her military bearing returning as the mission crystallized" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |