| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.45 | | leniency | 0.9 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 85.15% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1683 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "gently" | | 1 | "completely" | | 2 | "slightly" | | 3 | "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) | |
| 31.67% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1683 | | totalAiIsms | 23 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "maw" | | 1 | "measured" | | 2 | "etched" | | 3 | "intricate" | | 4 | "beacon" | | 5 | "scanned" | | 6 | "tension" | | 7 | "traced" | | 8 | "perfect" | | 9 | "weight" | | 10 | "etching" | | 11 | "gloom" | | 12 | "trembled" | | 13 | "structure" | | 14 | "raced" | | 15 | "shattered" | | 16 | "flickered" | | 17 | "aligned" | | 18 | "footsteps" |
| |
| 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 | 162 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 162 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 184 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1683 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 35.19% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 58 | | wordCount | 1350 | | uniqueNames | 10 | | maxNameDensity | 2.3 | | worstName | "Harlow" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Harlow" | | discoveredNames | | Quinn | 1 | | Camden | 1 | | Veil | 1 | | Market | 1 | | Tube | 1 | | Harlow | 31 | | Aris | 9 | | Kowalski | 1 | | Eva | 11 | | Shade | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Camden" | | 2 | "Market" | | 3 | "Harlow" | | 4 | "Aris" | | 5 | "Kowalski" | | 6 | "Eva" |
| | places | (empty) | | globalScore | 0.352 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 107 | | glossingSentenceCount | 1 | | matches | | 0 | "shadows that seemed to stretch toward the bodies lying on the tracks" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1683 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 184 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 33.66 | | std | 18.29 | | cv | 0.543 | | sampleLengths | | 0 | 65 | | 1 | 39 | | 2 | 31 | | 3 | 59 | | 4 | 55 | | 5 | 18 | | 6 | 32 | | 7 | 66 | | 8 | 4 | | 9 | 10 | | 10 | 26 | | 11 | 43 | | 12 | 29 | | 13 | 18 | | 14 | 60 | | 15 | 11 | | 16 | 11 | | 17 | 48 | | 18 | 11 | | 19 | 20 | | 20 | 51 | | 21 | 45 | | 22 | 7 | | 23 | 25 | | 24 | 51 | | 25 | 9 | | 26 | 46 | | 27 | 9 | | 28 | 20 | | 29 | 40 | | 30 | 69 | | 31 | 18 | | 32 | 41 | | 33 | 41 | | 34 | 34 | | 35 | 9 | | 36 | 39 | | 37 | 28 | | 38 | 35 | | 39 | 34 | | 40 | 27 | | 41 | 8 | | 42 | 16 | | 43 | 62 | | 44 | 39 | | 45 | 62 | | 46 | 24 | | 47 | 52 | | 48 | 33 | | 49 | 53 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 162 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 245 | | matches | | 0 | "was spinning" | | 1 | "was staring" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 184 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1352 | | adjectiveStacks | 1 | | stackExamples | | 0 | "revealing fresh, bright brass" |
| | adverbCount | 39 | | adverbRatio | 0.028846153846153848 | | lyAdverbCount | 19 | | lyAdverbRatio | 0.014053254437869823 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 184 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 184 | | mean | 9.15 | | std | 5.72 | | cv | 0.626 | | sampleLengths | | 0 | 14 | | 1 | 18 | | 2 | 12 | | 3 | 21 | | 4 | 12 | | 5 | 12 | | 6 | 2 | | 7 | 2 | | 8 | 11 | | 9 | 17 | | 10 | 14 | | 11 | 11 | | 12 | 5 | | 13 | 8 | | 14 | 5 | | 15 | 17 | | 16 | 3 | | 17 | 10 | | 18 | 7 | | 19 | 7 | | 20 | 7 | | 21 | 13 | | 22 | 2 | | 23 | 2 | | 24 | 17 | | 25 | 5 | | 26 | 13 | | 27 | 3 | | 28 | 6 | | 29 | 9 | | 30 | 6 | | 31 | 2 | | 32 | 1 | | 33 | 5 | | 34 | 13 | | 35 | 1 | | 36 | 1 | | 37 | 4 | | 38 | 17 | | 39 | 11 | | 40 | 19 | | 41 | 4 | | 42 | 4 | | 43 | 6 | | 44 | 6 | | 45 | 7 | | 46 | 13 | | 47 | 8 | | 48 | 23 | | 49 | 12 |
| |
| 41.85% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.29891304347826086 | | totalSentences | 184 | | uniqueOpeners | 55 | |
| 50.13% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 133 | | matches | | 0 | "Just seamless tiles and the" | | 1 | "Only one vendor in the" |
| | ratio | 0.015 | |
| 87.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 44 | | totalSentences | 133 | | matches | | 0 | "He stared at the platform" | | 1 | "She checked her left wrist." | | 2 | "Her gaze swept the perimeter." | | 3 | "She kept her hands visible," | | 4 | "She stopped three feet from" | | 5 | "She squinted at the man's" | | 6 | "She reached into the victim's" | | 7 | "She flipped it open." | | 8 | "It pointed directly toward the" | | 9 | "Her satchel thumped against her" | | 10 | "Her voice carried over the" | | 11 | "She ignored Aris completely." | | 12 | "Her green eyes scanned the" | | 13 | "She ran a gloved hand" | | 14 | "She traced the bruising on" | | 15 | "She examined the purple bruising." | | 16 | "She looked at the vertigris" | | 17 | "She wiped a thumb across" | | 18 | "She looked down at the" | | 19 | "Her gaze tracked upward to" |
| | ratio | 0.331 | |
| 1.35% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 122 | | totalSentences | 133 | | matches | | 0 | "Harlow Quinn stepped through the" | | 1 | "The scent of Camden's damp" | | 2 | "The Veil Market occupied the" | | 3 | "Aris adjusted his cap, water" | | 4 | "He stared at the platform" | | 5 | "Aris said, gesturing with a" | | 6 | "Harlow moved past him, boots" | | 7 | "She checked her left wrist." | | 8 | "The worn leather of her" | | 9 | "Her gaze swept the perimeter." | | 10 | "The crowd of hooded figures" | | 11 | "Harlow ignored them." | | 12 | "She kept her hands visible," | | 13 | "She stopped three feet from" | | 14 | "A man in a heavy" | | 15 | "Harlow leaned in." | | 16 | "She squinted at the man's" | | 17 | "Eyes wide, pupils blown so" | | 18 | "The tongue hung swollen and" | | 19 | "The market air circulated constantly." |
| | ratio | 0.917 | |
| 37.59% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 133 | | matches | | | ratio | 0.008 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 1 | | matches | | 0 | "Flickering lanterns hung from the tiled arches, casting long shadows that seemed to stretch toward the bodies lying on the tracks." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 75.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 17 | | fancyCount | 3 | | fancyTags | | 0 | "Aris muttered (mutter)" | | 1 | "Eva whispered (whisper)" | | 2 | "Harlow corrected (correct)" |
| | dialogueSentences | 40 | | tagDensity | 0.425 | | leniency | 0.85 | | rawRatio | 0.176 | | effectiveRatio | 0.15 | |