| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn corrected flatly [flatly]" |
| | dialogueSentences | 33 | | tagDensity | 0.364 | | leniency | 0.727 | | rawRatio | 0.083 | | effectiveRatio | 0.061 | |
| 96.33% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1363 | | 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) | |
| 41.31% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1363 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "echoed" | | 1 | "velvet" | | 2 | "scanned" | | 3 | "glinting" | | 4 | "complex" | | 5 | "etched" | | 6 | "quivered" | | 7 | "silence" | | 8 | "echoing" | | 9 | "measured" | | 10 | "intensity" | | 11 | "mechanical" |
| |
| 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 | 95 | | matches | (empty) | |
| 97.74% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 95 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 115 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 56 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 4 | | totalWords | 1363 | | ratio | 0.003 | | matches | | 0 | "is" | | 1 | "aggravated robbery" | | 2 | "things" |
| |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 887 | | uniqueNames | 6 | | maxNameDensity | 1.47 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 13 | | Camden | 1 | | Tube | 1 | | Kowalski | 1 | | Eva | 11 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Eva" |
| | places | (empty) | | globalScore | 0.767 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like a child’s story told to comfo" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.734 | | wordCount | 1363 | | matches | | 0 | "not looking for footprints or blood spatter, but for the spaces between things" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 115 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 34.95 | | std | 24.15 | | cv | 0.691 | | sampleLengths | | 0 | 54 | | 1 | 5 | | 2 | 18 | | 3 | 6 | | 4 | 98 | | 5 | 29 | | 6 | 40 | | 7 | 9 | | 8 | 38 | | 9 | 52 | | 10 | 2 | | 11 | 98 | | 12 | 20 | | 13 | 51 | | 14 | 15 | | 15 | 22 | | 16 | 33 | | 17 | 43 | | 18 | 12 | | 19 | 9 | | 20 | 40 | | 21 | 65 | | 22 | 25 | | 23 | 2 | | 24 | 52 | | 25 | 26 | | 26 | 22 | | 27 | 71 | | 28 | 44 | | 29 | 21 | | 30 | 62 | | 31 | 26 | | 32 | 22 | | 33 | 39 | | 34 | 74 | | 35 | 26 | | 36 | 4 | | 37 | 47 | | 38 | 41 |
| |
| 79.41% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 95 | | matches | | 0 | "been sheared" | | 1 | "were made" | | 2 | "being brought" | | 3 | "were polished" | | 4 | "was fixed" | | 5 | "was etched" | | 6 | "was gone" |
| |
| 29.06% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 156 | | matches | | 0 | "was staring" | | 1 | "wasn’t pointing" | | 2 | "was already scribbling" | | 3 | "was only beginning" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 115 | | ratio | 0.009 | | matches | | 0 | "The constable’s words—*aggravated robbery*—felt like a child’s story told to comfort themselves in the dark." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 891 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.02356902356902357 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.007856341189674524 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 115 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 115 | | mean | 11.85 | | std | 8.66 | | cv | 0.731 | | sampleLengths | | 0 | 9 | | 1 | 24 | | 2 | 13 | | 3 | 8 | | 4 | 5 | | 5 | 7 | | 6 | 11 | | 7 | 4 | | 8 | 2 | | 9 | 6 | | 10 | 9 | | 11 | 17 | | 12 | 10 | | 13 | 22 | | 14 | 10 | | 15 | 13 | | 16 | 11 | | 17 | 13 | | 18 | 9 | | 19 | 7 | | 20 | 15 | | 21 | 10 | | 22 | 15 | | 23 | 9 | | 24 | 10 | | 25 | 28 | | 26 | 9 | | 27 | 8 | | 28 | 25 | | 29 | 10 | | 30 | 2 | | 31 | 8 | | 32 | 5 | | 33 | 22 | | 34 | 14 | | 35 | 49 | | 36 | 6 | | 37 | 14 | | 38 | 17 | | 39 | 8 | | 40 | 5 | | 41 | 9 | | 42 | 12 | | 43 | 5 | | 44 | 10 | | 45 | 8 | | 46 | 10 | | 47 | 4 | | 48 | 13 | | 49 | 9 |
| |
| 52.46% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3826086956521739 | | totalSentences | 115 | | uniqueOpeners | 44 | |
| 75.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 88 | | matches | | 0 | "Instead, she studied his neck." | | 1 | "Just the wound." |
| | ratio | 0.023 | |
| 83.64% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 88 | | matches | | 0 | "her voice echoed" | | 1 | "She clicked her tongue." | | 2 | "Her boots, made for city" | | 3 | "They were made of mismatched" | | 4 | "His eyes were open, staring" | | 5 | "She pointed at the man’s" | | 6 | "His wallet, half-peeking from his" | | 7 | "She didn’t touch the body." | | 8 | "Her military precision made each" | | 9 | "She scanned the nearby stalls." | | 10 | "She was staring at the" | | 11 | "She pulled a worn journal" | | 12 | "His shoes were polished, his" | | 13 | "She knelt again, ignoring Eva’s" | | 14 | "She leaned over the body," | | 15 | "His gaze was fixed on" | | 16 | "She didn’t follow it." | | 17 | "His skin tone was normal," | | 18 | "It was the wound, then." | | 19 | "Her eyes dropped to his" |
| | ratio | 0.341 | |
| 28.18% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 88 | | matches | | 0 | "The air tasted of damp" | | 1 | "Detective Harlow Quinn ducked under" | | 2 | "The entrance to the abandoned" | | 3 | "her voice echoed" | | 4 | "The constable nodded, not meeting" | | 5 | "She clicked her tongue." | | 6 | "The stairs were slick with" | | 7 | "Her boots, made for city" | | 8 | "The old platform was there," | | 9 | "They were made of mismatched" | | 10 | "A faint, sweet-rot smell of" | | 11 | "His eyes were open, staring" | | 12 | "A single, precise wound darkened" | | 13 | "A figure knelt by the" | | 14 | "Eva Kowalski, her leather satchel" | | 15 | "Eva tucked a stray red" | | 16 | "Quinn walked a slow perimeter," | | 17 | "The dirt floor was a" | | 18 | "She pointed at the man’s" | | 19 | "His wallet, half-peeking from his" |
| | ratio | 0.864 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 2 | | matches | | 0 | "At the bottom, another lamp cast a sickly yellow circle over a cavern that shouldn’t have existed." | | 1 | "She looked at Eva, who was already scribbling notes, her freckled face pale in the lamplight." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 3 | | matches | | 0 | "Quinn crouched, her sharp jaw tightening" | | 1 | "Eva murmured, her voice dropping" | | 2 | "Quinn said, her voice echoing in the vast chamber" |
| |
| 59.09% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 3 | | fancyTags | | 0 | "Eva murmured (murmur)" | | 1 | "Eva whispered (whisper)" | | 2 | "Quinn corrected flatly (correct)" |
| | dialogueSentences | 33 | | tagDensity | 0.182 | | leniency | 0.364 | | rawRatio | 0.5 | | effectiveRatio | 0.182 | |