| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 4 | | adverbTags | | 0 | "Eva said quietly [quietly]" | | 1 | "Daniels finished reluctantly [reluctantly]" | | 2 | "she said tersely [tersely]" | | 3 | "Eva hesitated then [then]" |
| | dialogueSentences | 39 | | tagDensity | 0.41 | | leniency | 0.821 | | rawRatio | 0.25 | | effectiveRatio | 0.205 | |
| 59.10% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 978 | | totalAiIsmAdverbs | 8 | | found | | 0 | | | 1 | | | 2 | | | 3 | | adverb | "reluctantly" | | count | 1 |
| | 4 | | | 5 | |
| | highlights | | 0 | "nervously" | | 1 | "carefully" | | 2 | "gently" | | 3 | "reluctantly" | | 4 | "precisely" | | 5 | "slowly" |
| |
| 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) | |
| 38.65% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 978 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "scanned" | | 1 | "familiar" | | 2 | "furrowing" | | 3 | "glint" | | 4 | "etched" | | 5 | "fascinating" | | 6 | "furrowed" | | 7 | "otherworldly" | | 8 | "complex" | | 9 | "anticipation" |
| |
| 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 | 1 | | narrationSentences | 58 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 58 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 81 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 979 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 551 | | uniqueNames | 5 | | maxNameDensity | 3.63 | | worstName | "Harlow" | | maxWindowNameDensity | 5 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 20 | | Quinn | 1 | | Tube | 1 | | Eva | 15 | | Kowalski | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Eva" | | 3 | "Kowalski" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0 | |
| 85.90% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | glossingSentenceCount | 1 | | matches | | 0 | "n erratically, seemingly drawn toward the bo" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 979 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 81 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 25.1 | | std | 14.22 | | cv | 0.567 | | sampleLengths | | 0 | 54 | | 1 | 25 | | 2 | 23 | | 3 | 23 | | 4 | 21 | | 5 | 29 | | 6 | 36 | | 7 | 46 | | 8 | 21 | | 9 | 28 | | 10 | 31 | | 11 | 17 | | 12 | 34 | | 13 | 5 | | 14 | 4 | | 15 | 30 | | 16 | 19 | | 17 | 14 | | 18 | 10 | | 19 | 18 | | 20 | 11 | | 21 | 33 | | 22 | 5 | | 23 | 13 | | 24 | 30 | | 25 | 10 | | 26 | 32 | | 27 | 20 | | 28 | 27 | | 29 | 11 | | 30 | 14 | | 31 | 25 | | 32 | 40 | | 33 | 33 | | 34 | 79 | | 35 | 25 | | 36 | 17 | | 37 | 40 | | 38 | 26 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 58 | | matches | (empty) | |
| 18.18% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 110 | | matches | | 0 | "was meticulously cataloging" | | 1 | "was standing" | | 2 | "was turning" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 81 | | ratio | 0 | | matches | (empty) | |
| 69.31% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 554 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.0631768953068592 | | lyAdverbCount | 18 | | lyAdverbRatio | 0.032490974729241874 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 81 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 81 | | mean | 12.09 | | std | 7.58 | | cv | 0.627 | | sampleLengths | | 0 | 23 | | 1 | 31 | | 2 | 13 | | 3 | 12 | | 4 | 14 | | 5 | 9 | | 6 | 12 | | 7 | 11 | | 8 | 8 | | 9 | 13 | | 10 | 19 | | 11 | 10 | | 12 | 14 | | 13 | 22 | | 14 | 3 | | 15 | 24 | | 16 | 19 | | 17 | 10 | | 18 | 11 | | 19 | 18 | | 20 | 10 | | 21 | 14 | | 22 | 6 | | 23 | 10 | | 24 | 1 | | 25 | 17 | | 26 | 18 | | 27 | 16 | | 28 | 5 | | 29 | 4 | | 30 | 3 | | 31 | 27 | | 32 | 19 | | 33 | 10 | | 34 | 4 | | 35 | 10 | | 36 | 3 | | 37 | 4 | | 38 | 11 | | 39 | 4 | | 40 | 7 | | 41 | 19 | | 42 | 14 | | 43 | 3 | | 44 | 2 | | 45 | 5 | | 46 | 8 | | 47 | 5 | | 48 | 22 | | 49 | 3 |
| |
| 81.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.49382716049382713 | | totalSentences | 81 | | uniqueOpeners | 40 | |
| 59.52% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 56 | | matches | | 0 | "Just when she thought her" |
| | ratio | 0.018 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 56 | | matches | | 0 | "Her sharp eyes scanned the" | | 1 | "she called out, her voice" | | 2 | "She knew all too well" | | 3 | "she asked the lead forensics" | | 4 | "She trailed off, brow furrowing." | | 5 | "She glanced nervously at Eva," | | 6 | "She should have known." | | 7 | "she said tersely" | | 8 | "Her eyes narrowed." | | 9 | "She examined the compass, watching" | | 10 | "Her frown deepened." | | 11 | "she asked, glancing at Eva" | | 12 | "she murmured, almost to herself" | | 13 | "She tucked the compass into" | | 14 | "She glanced at the covered" | | 15 | "She paused, tucking a stray" |
| | ratio | 0.286 | |
| 40.36% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 56 | | matches | | 0 | "Detective Harlow Quinn ducked under" | | 1 | "Her sharp eyes scanned the" | | 2 | "Harlow's gaze narrowed as she" | | 3 | "she called out, her voice" | | 4 | "Eva Kowalski startled at the" | | 5 | "Harlow strode over, arms crossed" | | 6 | "Eva swallowed hard, her green" | | 7 | "Harlow cut her off, lip" | | 8 | "Eva said quietly, adjusting the" | | 9 | "Harlow's jaw tightened." | | 10 | "She knew all too well" | | 11 | "Eva nodded gratefully and followed" | | 12 | "Harlow crouched beside the body," | | 13 | "The victim was a middle-aged" | | 14 | "Harlow frowned, gently examining the" | | 15 | "she asked the lead forensics" | | 16 | "the tech reported, tucking a" | | 17 | "She trailed off, brow furrowing." | | 18 | "The tech hesitated." | | 19 | "She glanced nervously at Eva," |
| | ratio | 0.839 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 56 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 1 | | matches | | 0 | "She glanced nervously at Eva, who was standing a respectful distance away, observing the proceedings with a pensive expression." |
| |
| 31.25% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 3 | | matches | | 0 | "she called out, her voice clipped" | | 1 | "she murmured, almost to herself" | | 2 | "She tucked, her mind whirring" |
| |
| 73.08% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 3 | | fancyTags | | 0 | "she called out (call out)" | | 1 | "the tech reported (report)" | | 2 | "she murmured (murmur)" |
| | dialogueSentences | 39 | | tagDensity | 0.179 | | leniency | 0.359 | | rawRatio | 0.429 | | effectiveRatio | 0.154 | |