| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 1 | | adverbTags | | 0 | "She glanced back [back]" |
| | dialogueSentences | 28 | | tagDensity | 0.357 | | leniency | 0.714 | | rawRatio | 0.1 | | effectiveRatio | 0.071 | |
| 88.92% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1354 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 74.15% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1354 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "silence" | | 1 | "familiar" | | 2 | "silk" | | 3 | "flicked" | | 4 | "glinting" | | 5 | "eyebrow" |
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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 | 1 | | narrationSentences | 49 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 49 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 67 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 58 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1354 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 890 | | uniqueNames | 14 | | maxNameDensity | 2.02 | | worstName | "Rory" | | maxWindowNameDensity | 4 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 18 | | Eva | 13 | | Cardiff | 2 | | Lisbon | 1 | | Pink | 1 | | Floyd | 1 | | British | 1 | | Isles | 1 | | Silas | 5 | | London | 1 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Evan | 1 |
| | persons | | 0 | "Rory" | | 1 | "Eva" | | 2 | "Isles" | | 3 | "Silas" | | 4 | "Raven" | | 5 | "Evan" |
| | places | | 0 | "Cardiff" | | 1 | "Lisbon" | | 2 | "Pink" | | 3 | "London" | | 4 | "Soho" |
| | globalScore | 0.489 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | 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 | 1354 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 67 | | matches | (empty) | |
| 93.70% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 48.36 | | std | 23.11 | | cv | 0.478 | | sampleLengths | | 0 | 85 | | 1 | 84 | | 2 | 17 | | 3 | 41 | | 4 | 51 | | 5 | 33 | | 6 | 44 | | 7 | 29 | | 8 | 71 | | 9 | 89 | | 10 | 14 | | 11 | 61 | | 12 | 56 | | 13 | 33 | | 14 | 51 | | 15 | 76 | | 16 | 77 | | 17 | 41 | | 18 | 76 | | 19 | 60 | | 20 | 52 | | 21 | 27 | | 22 | 57 | | 23 | 17 | | 24 | 47 | | 25 | 41 | | 26 | 19 | | 27 | 5 |
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| 98.10% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 49 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 136 | | matches | | 0 | "weren’t looking" | | 1 | "was sitting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 67 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 898 | | adjectiveStacks | 1 | | stackExamples | | 0 | "oil-stained plastic wrapper" |
| | adverbCount | 28 | | adverbRatio | 0.031180400890868598 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.0066815144766146995 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 67 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 67 | | mean | 20.21 | | std | 11.46 | | cv | 0.567 | | sampleLengths | | 0 | 24 | | 1 | 24 | | 2 | 22 | | 3 | 15 | | 4 | 14 | | 5 | 22 | | 6 | 48 | | 7 | 17 | | 8 | 19 | | 9 | 22 | | 10 | 8 | | 11 | 37 | | 12 | 6 | | 13 | 16 | | 14 | 17 | | 15 | 19 | | 16 | 22 | | 17 | 3 | | 18 | 15 | | 19 | 14 | | 20 | 6 | | 21 | 26 | | 22 | 39 | | 23 | 37 | | 24 | 32 | | 25 | 20 | | 26 | 8 | | 27 | 6 | | 28 | 17 | | 29 | 11 | | 30 | 33 | | 31 | 11 | | 32 | 33 | | 33 | 12 | | 34 | 16 | | 35 | 17 | | 36 | 9 | | 37 | 42 | | 38 | 32 | | 39 | 44 | | 40 | 8 | | 41 | 15 | | 42 | 23 | | 43 | 31 | | 44 | 12 | | 45 | 29 | | 46 | 18 | | 47 | 58 | | 48 | 11 | | 49 | 15 |
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| 59.20% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.373134328358209 | | totalSentences | 67 | | uniqueOpeners | 25 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 49 | | matches | | 0 | "Then she saw the scar" | | 1 | "Then she reached across the" |
| | ratio | 0.041 | |
| 81.22% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 49 | | matches | | 0 | "She didn’t look up until" | | 1 | "She turned, and for a" | | 2 | "She set the tumbler down" | | 3 | "She gestured to the empty" | | 4 | "She’d heard stories of Lisbon," | | 5 | "She glanced around the bar," | | 6 | "He’d watched them for the" | | 7 | "He extended his hand, and" | | 8 | "She glanced back at Rory," | | 9 | "She leaned forward, her elbows" | | 10 | "She’d never told anyone the" | | 11 | "He’d just found her sitting" | | 12 | "She’d mumbled something about a" | | 13 | "She slipped on her black" | | 14 | "She turned to wave at" | | 15 | "It was Evan." | | 16 | "He flicked the cigarette butt" |
| | ratio | 0.347 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 49 | | matches | | 0 | "The cloth snagged on the" | | 1 | "The crescent scar on her" | | 2 | "She didn’t look up until" | | 3 | "A beat of silence, then" | | 4 | "She turned, and for a" | | 5 | "She set the tumbler down" | | 6 | "Eva pushed off the doorframe," | | 7 | "Rory snorted, wiping her palms" | | 8 | "She gestured to the empty" | | 9 | "Eva slid onto the stool," | | 10 | "Rory poured the whisky, the" | | 11 | "The amber glow caught the" | | 12 | "Eva took a sip, her" | | 13 | "Rory nodded, leaning against the" | | 14 | "She’d heard stories of Lisbon," | | 15 | "The girl who’d once snuck" | | 16 | "She glanced around the bar," | | 17 | "Silas was sitting in the" | | 18 | "He’d watched them for the" | | 19 | "Rory said, nodding at him" |
| | ratio | 0.939 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 49 | | matches | (empty) | | ratio | 0 | |
| 58.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 4 | | matches | | 0 | "Then she saw the scar snaking along Eva’s jaw, the one they’d earned when they crashed their bikes down the lane behind their Cardiff terraces at 16, and the la…" | | 1 | "The girl who’d once snuck into the local zoo to feed the monkeys, who’d spray-painted slogans on the side of the town hall, who’d gotten them both suspended for…" | | 2 | "Eva laughed, a loud, genuine laugh that made Rory’s chest ache." | | 3 | "A man leaning against the green neon sign of The Raven’s Nest, a half-smoked cigarette dangling from his lips, a leather jacket slung over his shoulders, a scar…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 7 | | matches | | 0 | "Eva slid, her heels tapping the bar as she shifted forward" | | 1 | "Eva took, her throat bobbing as she swallowed" | | 2 | "She glanced back, her smile fading a little" | | 3 | "Eva raised, her voice soft" | | 4 | "Eva said, her voice quiet" | | 5 | "Rory said, the words slipping out before she could stop them" | | 6 | "Silas said, his voice soft, from the corner" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 28 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0 | | effectiveRatio | 0 | |