| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 941 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 89.37% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 941 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 84 | | matches | (empty) | |
| 91.84% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 84 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 85 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 927 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 924 | | uniqueNames | 10 | | maxNameDensity | 1.19 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Harlow | 1 | | Quinn | 11 | | Raven | 1 | | Nest | 1 | | Chinese | 1 | | Morris | 3 | | Tube | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Market" |
| | places | | | globalScore | 0.905 | | windowScore | 0.833 | |
| 75.37% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 2 | | matches | | 0 | "something like burnt hair and old spices" | | 1 | "dagger that seemed to swallow the light around it" |
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| 92.13% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.079 | | wordCount | 927 | | matches | | 0 | "Not as he’d been in life, laughing over a bad coffee, but as she’d found him" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 85 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 40.3 | | std | 22.67 | | cv | 0.563 | | sampleLengths | | 0 | 73 | | 1 | 37 | | 2 | 50 | | 3 | 57 | | 4 | 27 | | 5 | 17 | | 6 | 58 | | 7 | 81 | | 8 | 4 | | 9 | 52 | | 10 | 23 | | 11 | 22 | | 12 | 94 | | 13 | 33 | | 14 | 46 | | 15 | 61 | | 16 | 43 | | 17 | 35 | | 18 | 21 | | 19 | 29 | | 20 | 41 | | 21 | 11 | | 22 | 12 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 84 | | matches | (empty) | |
| 70.97% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 155 | | matches | | 0 | "was talking" | | 1 | "was handing" | | 2 | "was leaving" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 85 | | ratio | 0.071 | | matches | | 0 | "She kept her eyes locked on the figure thirty meters ahead—a man in a dark hoodie who’d bolted the moment she’d identified herself outside The Raven’s Nest." | | 1 | "He yanked the door open—a slice of darkness yawned—and vanished inside." | | 2 | "A faint, unnatural odor wafted up—ozone and something else, something like burnt hair and old spices." | | 3 | "A crowd moved through the space—a bizarre, whispered gathering." | | 4 | "He was talking urgently to a stallholder—an elderly individual with too many knuckles on each long-fingered hand." | | 5 | "The man might lead her to something bigger—the clique she suspected was moving weapons, or worse, through these channels." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 938 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.010660980810234541 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0042643923240938165 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 85 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 85 | | mean | 10.91 | | std | 5.72 | | cv | 0.524 | | sampleLengths | | 0 | 19 | | 1 | 13 | | 2 | 14 | | 3 | 27 | | 4 | 10 | | 5 | 18 | | 6 | 9 | | 7 | 6 | | 8 | 17 | | 9 | 21 | | 10 | 6 | | 11 | 11 | | 12 | 12 | | 13 | 4 | | 14 | 12 | | 15 | 18 | | 16 | 17 | | 17 | 7 | | 18 | 3 | | 19 | 6 | | 20 | 11 | | 21 | 8 | | 22 | 11 | | 23 | 6 | | 24 | 5 | | 25 | 12 | | 26 | 16 | | 27 | 13 | | 28 | 12 | | 29 | 10 | | 30 | 21 | | 31 | 8 | | 32 | 3 | | 33 | 14 | | 34 | 4 | | 35 | 5 | | 36 | 16 | | 37 | 13 | | 38 | 9 | | 39 | 9 | | 40 | 9 | | 41 | 9 | | 42 | 5 | | 43 | 17 | | 44 | 5 | | 45 | 13 | | 46 | 16 | | 47 | 9 | | 48 | 17 | | 49 | 18 |
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| 51.76% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.35294117647058826 | | totalSentences | 85 | | uniqueOpeners | 30 | |
| 41.15% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 81 | | matches | | 0 | "Then she stepped out of" |
| | ratio | 0.012 | |
| 91.60% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 81 | | matches | | 0 | "Her worn leather watch was" | | 1 | "She kept her eyes locked" | | 2 | "He’d been talking to someone" | | 3 | "Her shoes slapped against wet" | | 4 | "She ignored the burn in" | | 5 | "She saw him scramble over" | | 6 | "He was fast, athletic." | | 7 | "She dropped down into a" | | 8 | "He was already crossing the" | | 9 | "He yanked the door open—a" | | 10 | "Her hand went to her" | | 11 | "He’d followed a lead into" | | 12 | "They’d found his body two" | | 13 | "She’d seen the symbols scrawled" | | 14 | "She took the stairs." | | 15 | "She peered around the edge." | | 16 | "She’d heard whispers." | | 17 | "Her target was there, halfway" | | 18 | "He was younger than she’d" | | 19 | "He was talking urgently to" |
| | ratio | 0.321 | |
| 46.42% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 67 | | totalSentences | 81 | | matches | | 0 | "The rain fell in icy" | | 1 | "Detective Harlow Quinn’s breath came" | | 2 | "Her worn leather watch was" | | 3 | "She kept her eyes locked" | | 4 | "He’d been talking to someone" | | 5 | "Quinn hadn’t gotten a clear" | | 6 | "Her shoes slapped against wet" | | 7 | "She ignored the burn in" | | 8 | "The man cut left into" | | 9 | "Quinn followed, her sharp jaw" | | 10 | "The alley was narrow, cluttered" | | 11 | "She saw him scramble over" | | 12 | "He was fast, athletic." | | 13 | "Quinn hauled herself over, the" | | 14 | "She dropped down into a" | | 15 | "He was already crossing the" | | 16 | "Quinn sprinted, her coat flapping" | | 17 | "The man didn’t even glance" | | 18 | "He yanked the door open—a" | | 19 | "Quinn skidded to a halt" |
| | ratio | 0.827 | |
| 61.73% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 81 | | matches | | | ratio | 0.012 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 2 | | matches | | 0 | "She kept her eyes locked on the figure thirty meters ahead—a man in a dark hoodie who’d bolted the moment she’d identified herself outside The Raven’s Nest." | | 1 | "This place hummed with a wrongness that set her teeth on edge, the same feeling she’d had outside that warehouse three years ago." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |