| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 58 | | tagDensity | 0.276 | | leniency | 0.552 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.56% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1344 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 77.68% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1344 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "warmth" | | 2 | "weight" | | 3 | "pulse" | | 4 | "silence" |
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| 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 | 2 | | narrationSentences | 134 | | matches | | 0 | "d with fury" | | 1 | "was surprised" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 134 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 174 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 24 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 5 | | totalWords | 1343 | | ratio | 0.004 | | matches | | 0 | "out" | | 1 | "Marcus Holt, Private Investigator." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 19 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 970 | | uniqueNames | 14 | | maxNameDensity | 0.93 | | worstName | "Aurora" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Aurora" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Carter | 1 | | Golden | 1 | | Empress | 1 | | Silas | 5 | | Aurora | 9 | | Scotch | 3 | | Cardiff | 2 | | Eva | 1 | | Holt | 2 | | Marcus | 9 | | Private | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Carter" | | 3 | "Silas" | | 4 | "Aurora" | | 5 | "Eva" | | 6 | "Holt" | | 7 | "Marcus" |
| | places | | | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | 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 | 1343 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 174 | | matches | | 0 | "knew that voice" | | 1 | "spoken that name" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 73 | | mean | 18.4 | | std | 16.14 | | cv | 0.877 | | sampleLengths | | 0 | 52 | | 1 | 50 | | 2 | 26 | | 3 | 4 | | 4 | 35 | | 5 | 26 | | 6 | 10 | | 7 | 11 | | 8 | 10 | | 9 | 44 | | 10 | 7 | | 11 | 13 | | 12 | 41 | | 13 | 1 | | 14 | 32 | | 15 | 3 | | 16 | 70 | | 17 | 1 | | 18 | 32 | | 19 | 28 | | 20 | 18 | | 21 | 6 | | 22 | 4 | | 23 | 16 | | 24 | 6 | | 25 | 7 | | 26 | 8 | | 27 | 27 | | 28 | 3 | | 29 | 4 | | 30 | 27 | | 31 | 8 | | 32 | 4 | | 33 | 37 | | 34 | 8 | | 35 | 2 | | 36 | 32 | | 37 | 4 | | 38 | 18 | | 39 | 14 | | 40 | 10 | | 41 | 10 | | 42 | 1 | | 43 | 59 | | 44 | 36 | | 45 | 3 | | 46 | 3 | | 47 | 31 | | 48 | 12 | | 49 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 134 | | matches | | 0 | "was surprised" | | 1 | "being asked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 169 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 174 | | ratio | 0.006 | | matches | | 0 | "His eyes—still that deep amber-brown—carried a heaviness that hadn’t been there before." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 978 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.02556237218813906 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.00408997955010225 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 174 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 174 | | mean | 7.72 | | std | 5.27 | | cv | 0.682 | | sampleLengths | | 0 | 20 | | 1 | 8 | | 2 | 12 | | 3 | 12 | | 4 | 21 | | 5 | 13 | | 6 | 16 | | 7 | 14 | | 8 | 12 | | 9 | 4 | | 10 | 16 | | 11 | 19 | | 12 | 4 | | 13 | 17 | | 14 | 5 | | 15 | 10 | | 16 | 11 | | 17 | 4 | | 18 | 6 | | 19 | 11 | | 20 | 1 | | 21 | 11 | | 22 | 21 | | 23 | 3 | | 24 | 3 | | 25 | 1 | | 26 | 13 | | 27 | 4 | | 28 | 8 | | 29 | 18 | | 30 | 11 | | 31 | 1 | | 32 | 5 | | 33 | 4 | | 34 | 1 | | 35 | 22 | | 36 | 3 | | 37 | 10 | | 38 | 11 | | 39 | 12 | | 40 | 8 | | 41 | 9 | | 42 | 12 | | 43 | 8 | | 44 | 1 | | 45 | 12 | | 46 | 20 | | 47 | 4 | | 48 | 4 | | 49 | 20 |
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| 56.51% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.39080459770114945 | | totalSentences | 174 | | uniqueOpeners | 68 | |
| 60.61% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 110 | | matches | | 0 | "Somewhere behind them, the door" | | 1 | "Instead, he reached into his" |
| | ratio | 0.018 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 110 | | matches | | 0 | "She pulled the day’s delivery" | | 1 | "He dried a glass with" | | 2 | "She pushed the receipts toward" | | 3 | "He gave a near-imperceptible nod" | | 4 | "He moved to the bar" | | 5 | "He ordered Scotch." | | 6 | "She knew that voice." | | 7 | "She’d heard it crack jokes" | | 8 | "Her own voice surprised her." | | 9 | "It came out level." | | 10 | "His dark hair had retreated" | | 11 | "His eyes—still that deep amber-brown-carried" | | 12 | "He said it like a" | | 13 | "His face betrayed nothing." | | 14 | "He drifted to the other" | | 15 | "He touched his jaw." | | 16 | "He took a slow sip." | | 17 | "She felt her pulse in" | | 18 | "She didn’t know what he’d" | | 19 | "He set a glass of" |
| | ratio | 0.291 | |
| 64.55% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 110 | | matches | | 0 | "The green neon of The" | | 1 | "Aurora Carter shook rain from" | | 2 | "She pulled the day’s delivery" | | 3 | "The Golden Empress logo on" | | 4 | "Silas emerged from the back," | | 5 | "He dried a glass with" | | 6 | "She pushed the receipts toward" | | 7 | "Silas’s hazel eyes crinkled." | | 8 | "The silver signet ring on" | | 9 | "He gave a near-imperceptible nod" | | 10 | "The door swung open." | | 11 | "A man stood in the" | | 12 | "He moved to the bar" | | 13 | "He ordered Scotch." | | 14 | "The voice landed in her" | | 15 | "She knew that voice." | | 16 | "She’d heard it crack jokes" | | 17 | "Her own voice surprised her." | | 18 | "It came out level." | | 19 | "Nothing like the girl who’d" |
| | ratio | 0.791 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 110 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 2 | | matches | | 0 | "Broad-shouldered in a way that spoke of discipline rather than genetics." | | 1 | "His eyes—still that deep amber-brown—carried a heaviness that hadn’t been there before." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 1 | | fancyTags | | 0 | "Marcus continued (continue)" |
| | dialogueSentences | 58 | | tagDensity | 0.155 | | leniency | 0.31 | | rawRatio | 0.111 | | effectiveRatio | 0.034 | |