| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 26 | | adverbTagCount | 2 | | adverbTags | | 0 | "His voice softened just [just]" | | 1 | "The word tasted like [like]" |
| | dialogueSentences | 64 | | tagDensity | 0.406 | | leniency | 0.813 | | rawRatio | 0.077 | | effectiveRatio | 0.063 | |
| 73.46% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1319 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "slightly" | | 1 | "really" | | 2 | "sharply" | | 3 | "carefully" |
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| 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) | |
| 43.14% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1319 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "glinting" | | 1 | "etched" | | 2 | "flicked" | | 3 | "tension" | | 4 | "sanctuary" | | 5 | "weight" | | 6 | "familiar" | | 7 | "trembled" | | 8 | "pulse" | | 9 | "warmth" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 2 | | found | | 0 | | label | "weight of words/silence" | | count | 2 |
| | 1 | | label | "hung in the air" | | count | 1 |
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| | highlights | | 0 | "the weight of his words" | | 1 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 95 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 95 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 133 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 7 | | totalWords | 1314 | | ratio | 0.005 | | matches | | 0 | "We need to talk. It’s about Evan." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 886 | | uniqueNames | 9 | | maxNameDensity | 0.9 | | worstName | "Silas" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Silas" | | discoveredNames | | Blackwood | 2 | | Cardiff | 3 | | Raven | 1 | | Nest | 1 | | Silas | 8 | | London | 1 | | Rory | 1 | | Carter | 1 | | Aurora | 2 |
| | persons | | 0 | "Blackwood" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Silas" | | 4 | "Rory" | | 5 | "Carter" | | 6 | "Aurora" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 68.03% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like expensive whiskey" | | 1 | "tasted like a lie" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1314 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 133 | | matches | | 0 | "left that version" | | 1 | "running, that she" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 63 | | mean | 20.86 | | std | 14.49 | | cv | 0.695 | | sampleLengths | | 0 | 33 | | 1 | 6 | | 2 | 17 | | 3 | 73 | | 4 | 34 | | 5 | 20 | | 6 | 32 | | 7 | 13 | | 8 | 28 | | 9 | 2 | | 10 | 31 | | 11 | 56 | | 12 | 7 | | 13 | 8 | | 14 | 18 | | 15 | 25 | | 16 | 7 | | 17 | 31 | | 18 | 39 | | 19 | 10 | | 20 | 9 | | 21 | 49 | | 22 | 16 | | 23 | 18 | | 24 | 22 | | 25 | 21 | | 26 | 8 | | 27 | 40 | | 28 | 35 | | 29 | 5 | | 30 | 12 | | 31 | 27 | | 32 | 18 | | 33 | 11 | | 34 | 48 | | 35 | 17 | | 36 | 3 | | 37 | 11 | | 38 | 34 | | 39 | 8 | | 40 | 24 | | 41 | 8 | | 42 | 28 | | 43 | 44 | | 44 | 7 | | 45 | 19 | | 46 | 29 | | 47 | 11 | | 48 | 40 | | 49 | 8 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 95 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 175 | | matches | | 0 | "was bracing" | | 1 | "wasn’t running" |
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| 35.45% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 133 | | ratio | 0.038 | | matches | | 0 | "The limp, though—new." | | 1 | "The Raven’s Nest hadn’t changed much—same green neon sign outside, same maps and black-and-white photos on the walls." | | 2 | "This was the Silas she remembered—the man who could read a room before he entered it, who could strip a person bare with a glance." | | 3 | "Instead, he studied her—the dark circles under her bright blue eyes, the way her shoulders were tense, like she was bracing for a fight." | | 4 | "The handwriting was familiar—too familiar." |
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| 91.94% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 894 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 44 | | adverbRatio | 0.049217002237136466 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.0145413870246085 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 133 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 133 | | mean | 9.88 | | std | 6.22 | | cv | 0.63 | | sampleLengths | | 0 | 11 | | 1 | 11 | | 2 | 11 | | 3 | 6 | | 4 | 2 | | 5 | 13 | | 6 | 2 | | 7 | 29 | | 8 | 17 | | 9 | 18 | | 10 | 3 | | 11 | 6 | | 12 | 8 | | 13 | 8 | | 14 | 18 | | 15 | 6 | | 16 | 14 | | 17 | 5 | | 18 | 8 | | 19 | 19 | | 20 | 13 | | 21 | 18 | | 22 | 10 | | 23 | 2 | | 24 | 23 | | 25 | 8 | | 26 | 23 | | 27 | 18 | | 28 | 15 | | 29 | 7 | | 30 | 6 | | 31 | 2 | | 32 | 6 | | 33 | 12 | | 34 | 15 | | 35 | 10 | | 36 | 4 | | 37 | 3 | | 38 | 19 | | 39 | 12 | | 40 | 9 | | 41 | 15 | | 42 | 15 | | 43 | 10 | | 44 | 7 | | 45 | 2 | | 46 | 5 | | 47 | 13 | | 48 | 25 | | 49 | 6 |
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| 59.15% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.3684210526315789 | | totalSentences | 133 | | uniqueOpeners | 49 | |
| 76.63% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 87 | | matches | | 0 | "Instead, he studied her—the dark" | | 1 | "Then her phone buzzed." |
| | ratio | 0.023 | |
| 3.91% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 47 | | totalSentences | 87 | | matches | | 0 | "His auburn hair, now streaked" | | 1 | "She set the glass down" | | 2 | "She flinched at the nickname." | | 3 | "she corrected, though the word" | | 4 | "He pushed off the bar," | | 5 | "He stopped a respectful distance" | | 6 | "He raised his glass slightly" | | 7 | "She tapped her untouched drink" | | 8 | "He took a sip, eyes" | | 9 | "She crossed her arms." | | 10 | "His gaze flicked to her" | | 11 | "She’d thought she’d outgrown that," | | 12 | "she shot back" | | 13 | "His voice softened, just a" | | 14 | "She wanted to look away," | | 15 | "she said, because she needed" | | 16 | "She laughed, but it came" | | 17 | "His signet ring tapped against" | | 18 | "He didn’t answer right away." | | 19 | "She wanted to deny it." |
| | ratio | 0.54 | |
| 51.95% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 87 | | matches | | 0 | "The glass slipped from her" | | 1 | "Aurora barely caught it, the" | | 2 | "The piano’s discordant note hung" | | 3 | "Silas Blackwood leaned against the" | | 4 | "The years had etched lines" | | 5 | "His auburn hair, now streaked" | | 6 | "The limp, though—new." | | 7 | "The name tasted foreign on" | | 8 | "She set the glass down" | | 9 | "A smirk tugged at his" | | 10 | "She flinched at the nickname." | | 11 | "she corrected, though the word" | | 12 | "He pushed off the bar," | | 13 | "He stopped a respectful distance" | | 14 | "The bar’s usual hum of" | | 15 | "The Raven’s Nest hadn’t changed" | | 16 | "He raised his glass slightly" | | 17 | "She tapped her untouched drink" | | 18 | "He took a sip, eyes" | | 19 | "She crossed her arms." |
| | ratio | 0.816 | |
| 57.47% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 87 | | matches | | 0 | "Because he wasn’t wrong." |
| | ratio | 0.011 | |
| 66.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 3 | | matches | | 0 | "Low, rough, the kind that carried across crowded rooms without raising an inch." | | 1 | "But here he was, Silas bloody Blackwood, pulling the thread that unravelled her in seconds." | | 2 | "Silas paused at the door, as if he’d heard her thoughts." |
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| 67.31% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 26 | | uselessAdditionCount | 3 | | matches | | 0 | "she corrected, though the word felt too formal, too heavy between them" | | 1 | "He took, eyes never leaving hers" | | 2 | "He leaned, his voice dropping to a murmur" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 3 | | fancyTags | | 0 | "she corrected (correct)" | | 1 | "he agreed (agree)" | | 2 | "she muttered (mutter)" |
| | dialogueSentences | 64 | | tagDensity | 0.094 | | leniency | 0.188 | | rawRatio | 0.5 | | effectiveRatio | 0.094 | |