| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 23 | | tagDensity | 0.348 | | leniency | 0.696 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 78.49% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 930 | | totalAiIsmAdverbs | 4 | | found | | 0 | | | 1 | | adverb | "barely above a whisper" | | count | 1 |
| | 2 | |
| | highlights | | 0 | "carefully" | | 1 | "barely above a whisper" | | 2 | "really" |
| |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 8.60% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 930 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "echo" | | 1 | "reminder" | | 2 | "determined" | | 3 | "echoed" | | 4 | "scanning" | | 5 | "stark" | | 6 | "measured" | | 7 | "furrowed" | | 8 | "etched" | | 9 | "racing" | | 10 | "tangible" | | 11 | "intricate" | | 12 | "whisper" | | 13 | "processed" |
| |
| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "eyes widened" | | 1 | "The air was thick with" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 53 | | matches | | |
| 61.99% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 53 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 68 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 930 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 18.62% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 647 | | uniqueNames | 10 | | maxNameDensity | 2.63 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 2 | | Quinn | 17 | | Tube | 2 | | Veil | 1 | | Market | 1 | | Eva | 12 | | Kowalski | 1 | | Morris | 2 | | Thompson | 5 | | Detective | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Eva" | | 3 | "Kowalski" | | 4 | "Morris" | | 5 | "Thompson" | | 6 | "Detective" |
| | places | | | globalScore | 0.186 | | windowScore | 0.333 | |
| 90.48% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 42 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 930 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 68 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 25 | | mean | 37.2 | | std | 23.82 | | cv | 0.64 | | sampleLengths | | 0 | 90 | | 1 | 80 | | 2 | 58 | | 3 | 11 | | 4 | 42 | | 5 | 9 | | 6 | 21 | | 7 | 9 | | 8 | 33 | | 9 | 79 | | 10 | 8 | | 11 | 32 | | 12 | 28 | | 13 | 60 | | 14 | 32 | | 15 | 64 | | 16 | 16 | | 17 | 36 | | 18 | 10 | | 19 | 26 | | 20 | 37 | | 21 | 40 | | 22 | 9 | | 23 | 58 | | 24 | 42 |
| |
| 72.16% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 53 | | matches | | 0 | "was littered" | | 1 | "was tucked" | | 2 | "were torn" | | 3 | "been shrouded" | | 4 | "was determined" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 111 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 68 | | ratio | 0 | | matches | (empty) | |
| 99.95% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 648 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.033950617283950615 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.020061728395061727 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 68 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 68 | | mean | 13.68 | | std | 8.15 | | cv | 0.596 | | sampleLengths | | 0 | 21 | | 1 | 17 | | 2 | 17 | | 3 | 11 | | 4 | 24 | | 5 | 14 | | 6 | 23 | | 7 | 24 | | 8 | 19 | | 9 | 25 | | 10 | 17 | | 11 | 16 | | 12 | 6 | | 13 | 5 | | 14 | 9 | | 15 | 33 | | 16 | 3 | | 17 | 6 | | 18 | 12 | | 19 | 9 | | 20 | 7 | | 21 | 2 | | 22 | 4 | | 23 | 29 | | 24 | 7 | | 25 | 14 | | 26 | 12 | | 27 | 16 | | 28 | 16 | | 29 | 14 | | 30 | 8 | | 31 | 9 | | 32 | 23 | | 33 | 7 | | 34 | 21 | | 35 | 10 | | 36 | 15 | | 37 | 17 | | 38 | 13 | | 39 | 5 | | 40 | 17 | | 41 | 9 | | 42 | 6 | | 43 | 15 | | 44 | 17 | | 45 | 9 | | 46 | 10 | | 47 | 13 | | 48 | 16 | | 49 | 3 |
| |
| 55.88% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.35294117647058826 | | totalSentences | 68 | | uniqueOpeners | 24 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 99.25% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 53 | | matches | | 0 | "Her worn leather watch ticked" | | 1 | "She glanced around, her sharp" | | 2 | "She paused, her gaze narrowing" | | 3 | "She picked one up, turning" | | 4 | "Her freckled complexion was pale," | | 5 | "She turned her attention back" | | 6 | "Her clothes were torn, and" | | 7 | "She knelt beside the body," | | 8 | "They were crude, but they" | | 9 | "She picked up a small" | | 10 | "She frowned, her mind racing." | | 11 | "They came to a small" | | 12 | "It was a map, hand-drawn" | | 13 | "She turned to Thompson." | | 14 | "She had never understood what" | | 15 | "It was a web of" |
| | ratio | 0.302 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 51 | | totalSentences | 53 | | matches | | 0 | "Detective Harlow Quinn stepped out" | | 1 | "The air was thick with" | | 2 | "Her worn leather watch ticked" | | 3 | "She glanced around, her sharp" | | 4 | "The place was eerie, a" | | 5 | "Detective Quinn’s boots echoed as" | | 6 | "The platform was littered with" | | 7 | "She paused, her gaze narrowing" | | 8 | "She picked one up, turning" | | 9 | "The voice was hesitant, and" | | 10 | "Eva’s curly red hair was" | | 11 | "Her freckled complexion was pale," | | 12 | "Quinn acknowledged, her tone measured" | | 13 | "Eva adjusted her glasses and" | | 14 | "Quinn’s brow furrowed." | | 15 | "Eva explained, avoiding Quinn’s gaze" | | 16 | "Quinn repeated, her voice flat" | | 17 | "Eva bit her lip." | | 18 | "Quinn nodded, though she didn’t" | | 19 | "She turned her attention back" |
| | ratio | 0.962 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 1 | | matches | | 0 | "The case that had taken him had been shrouded in the same kind of supernatural elements." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 4 | | matches | | 0 | "Quinn acknowledged, her tone measured" | | 1 | "Quinn repeated, her voice flat" | | 2 | "Eva asked, her voice low" | | 3 | "Quinn replied, her eyes scanning the area" |
| |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 4 | | fancyTags | | 0 | "Quinn acknowledged (acknowledge)" | | 1 | "Eva explained (explain)" | | 2 | "Quinn repeated (repeat)" | | 3 | "A voice called out (call out)" |
| | dialogueSentences | 23 | | tagDensity | 0.304 | | leniency | 0.609 | | rawRatio | 0.571 | | effectiveRatio | 0.348 | |