| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 4 | | adverbTags | | 0 | "Quinn said firmly [firmly]" | | 1 | "Eva glanced around [around]" | | 2 | "Quinn said slowly [slowly]" | | 3 | "she said quietly [quietly]" |
| | dialogueSentences | 40 | | tagDensity | 0.45 | | leniency | 0.9 | | rawRatio | 0.222 | | effectiveRatio | 0.2 | |
| 62.19% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1058 | | totalAiIsmAdverbs | 8 | | found | | 0 | | | 1 | | | 2 | | | 3 | | adverb | "barely above a whisper" | | count | 1 |
| | 4 | | | 5 | | | 6 | |
| | highlights | | 0 | "slightly" | | 1 | "carefully" | | 2 | "very" | | 3 | "barely above a whisper" | | 4 | "perfectly" | | 5 | "slowly" | | 6 | "really" |
| |
| 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) | |
| 52.74% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1058 | | totalAiIsms | 10 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | word | "down her spine" | | count | 1 |
| | 6 | | | 7 | | | 8 | |
| | highlights | | 0 | "shattered" | | 1 | "familiar" | | 2 | "firmly" | | 3 | "traced" | | 4 | "chill" | | 5 | "down her spine" | | 6 | "whisper" | | 7 | "framework" | | 8 | "flickered" |
| |
| 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 | 57 | | matches | | |
| 67.67% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 57 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 79 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1064 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 596 | | uniqueNames | 8 | | maxNameDensity | 3.19 | | worstName | "Quinn" | | maxWindowNameDensity | 5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 19 | | Tube | 1 | | Sanders | 5 | | Kowalski | 1 | | British | 1 | | Museum | 1 | | Eva | 11 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sanders" | | 3 | "Kowalski" | | 4 | "Eva" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | 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 | 1064 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 79 | | matches | (empty) | |
| 91.45% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 33.25 | | std | 15.63 | | cv | 0.47 | | sampleLengths | | 0 | 44 | | 1 | 23 | | 2 | 52 | | 3 | 13 | | 4 | 17 | | 5 | 46 | | 6 | 41 | | 7 | 15 | | 8 | 48 | | 9 | 56 | | 10 | 35 | | 11 | 16 | | 12 | 14 | | 13 | 15 | | 14 | 28 | | 15 | 11 | | 16 | 51 | | 17 | 23 | | 18 | 39 | | 19 | 40 | | 20 | 33 | | 21 | 2 | | 22 | 44 | | 23 | 61 | | 24 | 22 | | 25 | 20 | | 26 | 35 | | 27 | 31 | | 28 | 52 | | 29 | 58 | | 30 | 44 | | 31 | 35 |
| |
| 92.95% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 57 | | matches | | 0 | "been touched" | | 1 | "been sealed" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 103 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 79 | | ratio | 0.013 | | matches | | 0 | "And then there was the matter of location – this station had been sealed off for decades." |
| |
| 83.79% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 598 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small leather-bound notebook." |
| | adverbCount | 28 | | adverbRatio | 0.046822742474916385 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.02508361204013378 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 79 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 79 | | mean | 13.47 | | std | 9.07 | | cv | 0.673 | | sampleLengths | | 0 | 20 | | 1 | 3 | | 2 | 21 | | 3 | 12 | | 4 | 11 | | 5 | 3 | | 6 | 5 | | 7 | 14 | | 8 | 13 | | 9 | 17 | | 10 | 13 | | 11 | 17 | | 12 | 7 | | 13 | 8 | | 14 | 11 | | 15 | 20 | | 16 | 7 | | 17 | 20 | | 18 | 14 | | 19 | 15 | | 20 | 15 | | 21 | 19 | | 22 | 14 | | 23 | 4 | | 24 | 31 | | 25 | 19 | | 26 | 2 | | 27 | 21 | | 28 | 10 | | 29 | 4 | | 30 | 6 | | 31 | 10 | | 32 | 8 | | 33 | 6 | | 34 | 10 | | 35 | 5 | | 36 | 5 | | 37 | 8 | | 38 | 9 | | 39 | 6 | | 40 | 11 | | 41 | 6 | | 42 | 45 | | 43 | 21 | | 44 | 2 | | 45 | 24 | | 46 | 15 | | 47 | 6 | | 48 | 24 | | 49 | 10 |
| |
| 89.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.569620253164557 | | totalSentences | 79 | | uniqueOpeners | 45 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 55 | | matches | | 0 | "All sealed tight, no signs" | | 1 | "Once Sanders had stepped away," |
| | ratio | 0.036 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 55 | | matches | | 0 | "His wallet remained in his" | | 1 | "she muttered, more to herself" | | 2 | "She stood, her movements precise" | | 3 | "Her round glasses had slipped" | | 4 | "She'd been working with the" | | 5 | "She tucked a strand of" | | 6 | "Her green eyes widened as" | | 7 | "she asked, carefully taking the" | | 8 | "Her fingers traced the elaborate" | | 9 | "It wasn't the first time" | | 10 | "She thought of her partner," | | 11 | "She checked her watch again" | | 12 | "she said quietly" | | 13 | "She glanced down at the" | | 14 | "She nodded toward the body" |
| | ratio | 0.273 | |
| 32.73% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 55 | | matches | | 0 | "Detective Harlow Quinn crouched beside" | | 1 | "The abandoned Tube station's emergency" | | 2 | "Detective Sanders said from behind" | | 3 | "Quinn's jaw tightened." | | 4 | "Nothing about this was straightforward." | | 5 | "The victim lay spread-eagled on" | | 6 | "His wallet remained in his" | | 7 | "she muttered, more to herself" | | 8 | "She stood, her movements precise" | | 9 | "The victim's phone lay shattered" | | 10 | "A flash of brass caught" | | 11 | "Eva Kowalski hurried down the" | | 12 | "Her round glasses had slipped" | | 13 | "The young woman's worn leather" | | 14 | "She'd been working with the" | | 15 | "Eva's knowledge of obscure historical" | | 16 | "She tucked a strand of" | | 17 | "Her green eyes widened as" | | 18 | "Sanders shot Quinn a questioning" | | 19 | "Quinn said firmly" |
| | ratio | 0.855 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 4 | | matches | | 0 | "The abandoned Tube station's emergency lighting cast sickly shadows across the victim's face, making the already peculiar scene even more unsettling." | | 1 | "The victim lay spread-eagled on the platform, wearing expensive clothes that hadn't been touched." | | 2 | "In the eighteen months they'd worked together, she'd learned to trust Eva's instincts, even when they ventured into territory that challenged Quinn's rational m…" | | 3 | "It wasn't the first time she'd encountered something that defied explanation, but it was the first time someone had offered her a framework to understand it." |
| |
| 69.44% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 2 | | matches | | 0 | "she muttered, more to herself than Sanders" | | 1 | "She glanced, its needle spinning wildly" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "Quinn acknowledged (acknowledge)" |
| | dialogueSentences | 40 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0.25 | | effectiveRatio | 0.1 | |