| 50.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 3 | | adverbTags | | 0 | "Quinn mused aloud [aloud]" | | 1 | "Eva added grimly [grimly]" | | 2 | "she replied cryptically [cryptically]" |
| | dialogueSentences | 36 | | tagDensity | 0.556 | | leniency | 1 | | rawRatio | 0.15 | | effectiveRatio | 0.15 | |
| 92.84% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 698 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 78.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 698 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "charged" | | 2 | "scanned" |
| |
| 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 | 43 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 43 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 59 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 698 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 411 | | uniqueNames | 6 | | maxNameDensity | 3.41 | | worstName | "Quinn" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 14 | | Entrance | 1 | | Camden | 1 | | Eva | 9 | | Kowalski | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Eva" | | 3 | "Kowalski" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 30 | | 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 | 698 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 59 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 25.85 | | std | 13.83 | | cv | 0.535 | | sampleLengths | | 0 | 53 | | 1 | 35 | | 2 | 31 | | 3 | 42 | | 4 | 49 | | 5 | 19 | | 6 | 37 | | 7 | 6 | | 8 | 27 | | 9 | 17 | | 10 | 16 | | 11 | 7 | | 12 | 27 | | 13 | 40 | | 14 | 21 | | 15 | 9 | | 16 | 45 | | 17 | 22 | | 18 | 27 | | 19 | 34 | | 20 | 7 | | 21 | 12 | | 22 | 32 | | 23 | 9 | | 24 | 19 | | 25 | 11 | | 26 | 44 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 43 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 86 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 59 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 411 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.021897810218978103 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.012165450121654502 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 59 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 59 | | mean | 11.83 | | std | 6.16 | | cv | 0.521 | | sampleLengths | | 0 | 32 | | 1 | 21 | | 2 | 5 | | 3 | 13 | | 4 | 17 | | 5 | 16 | | 6 | 6 | | 7 | 9 | | 8 | 10 | | 9 | 14 | | 10 | 12 | | 11 | 4 | | 12 | 2 | | 13 | 8 | | 14 | 22 | | 15 | 19 | | 16 | 13 | | 17 | 6 | | 18 | 19 | | 19 | 5 | | 20 | 13 | | 21 | 4 | | 22 | 2 | | 23 | 9 | | 24 | 18 | | 25 | 10 | | 26 | 7 | | 27 | 6 | | 28 | 10 | | 29 | 5 | | 30 | 2 | | 31 | 12 | | 32 | 15 | | 33 | 17 | | 34 | 23 | | 35 | 10 | | 36 | 11 | | 37 | 9 | | 38 | 12 | | 39 | 24 | | 40 | 9 | | 41 | 9 | | 42 | 13 | | 43 | 15 | | 44 | 12 | | 45 | 9 | | 46 | 25 | | 47 | 7 | | 48 | 6 | | 49 | 6 |
| |
| 98.31% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.6101694915254238 | | totalSentences | 59 | | uniqueOpeners | 36 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 39 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 39 | | matches | | 0 | "She'd followed it's needle through" | | 1 | "she asked, flashing her badge" | | 2 | "He looked green around the" | | 3 | "she replied, kneeling beside the" | | 4 | "She pulled on a pair" | | 5 | "Her round glasses reflected the" | | 6 | "He swallowed hard and nodded." | | 7 | "She frowned at the body" | | 8 | "She stood, stripping off her" | | 9 | "she replied cryptically" | | 10 | "She scanned the shadows, her" |
| | ratio | 0.282 | |
| 24.10% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 34 | | totalSentences | 39 | | matches | | 0 | "Detective Harlow Quinn ducked under" | | 1 | "She'd followed it's needle through" | | 2 | "This was no ordinary murder." | | 3 | "The victim lay sprawled across" | | 4 | "she asked, flashing her badge" | | 5 | "He looked green around the" | | 6 | "she replied, kneeling beside the" | | 7 | "She pulled on a pair" | | 8 | "The sigils were too precise" | | 9 | "a voice called from the" | | 10 | "Quinn looked up to see" | | 11 | "Her round glasses reflected the" | | 12 | "Quinn said, gesturing at the" | | 13 | "Eva crouched beside her, tucking" | | 14 | "Quinn raised an eyebrow." | | 15 | "Eva explained, pointing to each" | | 16 | "The uniformed officer piped up," | | 17 | "Quinn shot him a sharp" | | 18 | "He swallowed hard and nodded." | | 19 | "Quinn mused aloud, turning to" |
| | ratio | 0.872 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 39 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 14 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 6 | | matches | | 0 | "officer piped up, eyes wide" | | 1 | "Quinn mused aloud, turning to survey the station platform" | | 2 | "Quinn supplied, the pieces clicking into place" | | 3 | "Quinn said, a plan forming in her mind" | | 4 | "Eva said, her voice low" | | 5 | "Eva said, her tone resolute" |
| |
| 11.11% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 15 | | fancyCount | 5 | | fancyTags | | 0 | "she agreed (agree)" | | 1 | "Eva explained (explain)" | | 2 | "Eva murmured (murmur)" | | 3 | "Eva added grimly (add)" | | 4 | "Quinn confirmed (confirm)" |
| | dialogueSentences | 36 | | tagDensity | 0.417 | | leniency | 0.833 | | rawRatio | 0.333 | | effectiveRatio | 0.278 | |