| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said shortly [shortly]" |
| | dialogueSentences | 40 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.063 | | effectiveRatio | 0.05 | |
| 91.80% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1220 | | totalAiIsmAdverbs | 2 | | 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) | |
| 22.13% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1220 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "scanning" | | 1 | "silence" | | 2 | "charm" | | 3 | "stomach" | | 4 | "unreadable" | | 5 | "tension" | | 6 | "calculated" | | 7 | "eyebrow" | | 8 | "calculating" | | 9 | "echoing" | | 10 | "tracing" | | 11 | "familiar" | | 12 | "weight" | | 13 | "warmth" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "stomach dropped/sank" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 75 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 75 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 99 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1212 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 1002 | | uniqueNames | 11 | | maxNameDensity | 1.3 | | worstName | "Rory" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 13 | | Raven | 1 | | Nest | 1 | | Silas | 9 | | Glenfiddich | 1 | | Golden | 1 | | Empress | 1 | | Londoners | 1 | | Evan | 9 | | Cardiff | 3 | | Seeing | 1 |
| | persons | | 0 | "Rory" | | 1 | "Raven" | | 2 | "Silas" | | 3 | "Empress" | | 4 | "Londoners" | | 5 | "Evan" |
| | places | | | globalScore | 0.851 | | windowScore | 0.833 | |
| 30.95% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | glossingSentenceCount | 3 | | matches | | 0 | "felt like one long fight for autonomy s" | | 1 | "appeared beside her, refilling her glass without a word" | | 2 | "felt like she was still picking up the" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.825 | | wordCount | 1212 | | matches | | 0 | "not a bar,” Silas interjected, his voice calm but firm" |
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| 99.33% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 99 | | matches | | 0 | "knew that grin" | | 1 | "Buried that part" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 27.55 | | std | 19.98 | | cv | 0.725 | | sampleLengths | | 0 | 100 | | 1 | 8 | | 2 | 29 | | 3 | 16 | | 4 | 61 | | 5 | 5 | | 6 | 73 | | 7 | 10 | | 8 | 17 | | 9 | 22 | | 10 | 28 | | 11 | 58 | | 12 | 49 | | 13 | 16 | | 14 | 13 | | 15 | 14 | | 16 | 37 | | 17 | 15 | | 18 | 44 | | 19 | 19 | | 20 | 15 | | 21 | 18 | | 22 | 24 | | 23 | 18 | | 24 | 65 | | 25 | 10 | | 26 | 13 | | 27 | 12 | | 28 | 24 | | 29 | 34 | | 30 | 20 | | 31 | 12 | | 32 | 23 | | 33 | 22 | | 34 | 34 | | 35 | 12 | | 36 | 31 | | 37 | 29 | | 38 | 8 | | 39 | 20 | | 40 | 23 | | 41 | 13 | | 42 | 62 | | 43 | 36 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 75 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 178 | | matches | | 0 | "was polishing" | | 1 | "was trying" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 99 | | ratio | 0.061 | | matches | | 0 | "Rory admired that about him—he never let his guard down, even in retirement." | | 1 | "Silas always had a knack for reading people—and Rory wasn’t the only one who’d noticed the tension in the room." | | 2 | "She’d almost forgotten how much she used to love writing—short stories, poems, anything to escape the pressure of her father’s expectations." | | 3 | "Seeing him now, so polished and poised, only reminded her of how much she’d lost—and how much she still carried with her." | | 4 | "He lingered for a moment, his gaze searching her face for something she couldn’t—or wouldn’t—give." | | 5 | "She’d changed, just as Evan had—but where he’d become something harder, sharper, she felt like she was still picking up the pieces of who she used to be." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1010 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.03564356435643564 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.00891089108910891 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 99 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 99 | | mean | 12.24 | | std | 6.6 | | cv | 0.539 | | sampleLengths | | 0 | 18 | | 1 | 18 | | 2 | 28 | | 3 | 23 | | 4 | 13 | | 5 | 8 | | 6 | 26 | | 7 | 3 | | 8 | 10 | | 9 | 6 | | 10 | 15 | | 11 | 24 | | 12 | 22 | | 13 | 5 | | 14 | 20 | | 15 | 16 | | 16 | 15 | | 17 | 22 | | 18 | 3 | | 19 | 7 | | 20 | 17 | | 21 | 17 | | 22 | 5 | | 23 | 3 | | 24 | 19 | | 25 | 6 | | 26 | 29 | | 27 | 20 | | 28 | 7 | | 29 | 2 | | 30 | 13 | | 31 | 16 | | 32 | 20 | | 33 | 13 | | 34 | 3 | | 35 | 8 | | 36 | 5 | | 37 | 8 | | 38 | 6 | | 39 | 5 | | 40 | 16 | | 41 | 12 | | 42 | 4 | | 43 | 10 | | 44 | 5 | | 45 | 6 | | 46 | 15 | | 47 | 23 | | 48 | 4 | | 49 | 15 |
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| 63.30% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.40404040404040403 | | totalSentences | 99 | | uniqueOpeners | 40 | |
| 88.89% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 75 | | matches | | 0 | "Instead, he downed the rest" | | 1 | "Then he turned and walked" |
| | ratio | 0.027 | |
| 33.33% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 75 | | matches | | 0 | "She slid onto a stool" | | 1 | "He gave her a curt" | | 2 | "She’d spent the evening delivering" | | 3 | "He was tall, broad-shouldered, his" | | 4 | "His suit was tailored, expensive," | | 5 | "She knew that grin all" | | 6 | "Her voice came out sharper" | | 7 | "She didn’t move." | | 8 | "Her fingers tightened around the" | | 9 | "He slid onto the stool" | | 10 | "he said, nodding at the" | | 11 | "she replied, her tone flat" | | 12 | "He shrugged, the motion too" | | 13 | "She didn’t know, not anymore." | | 14 | "It made her uneasy." | | 15 | "he asked, his gaze flickering" | | 16 | "she said shortly, not elaborating" | | 17 | "She wasn’t about to tell" | | 18 | "He leaned back, studying her" | | 19 | "She’d almost forgotten how much" |
| | ratio | 0.467 | |
| 20.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 66 | | totalSentences | 75 | | matches | | 0 | "The neon green sign buzzed" | | 1 | "The bar was dimly lit," | | 2 | "She slid onto a stool" | | 3 | "He gave her a curt" | | 4 | "Rory admired that about him—he" | | 5 | "Silas asked, his gravelly voice" | | 6 | "Rory replied, slipping her fingertips" | | 7 | "Silas shrugged, pouring a measure" | | 8 | "She’d spent the evening delivering" | | 9 | "The door creaked open again," | | 10 | "Rory glanced up, her bright" | | 11 | "He was tall, broad-shouldered, his" | | 12 | "His suit was tailored, expensive," | | 13 | "Rory’s stomach dropped." | | 14 | "She knew that grin all" | | 15 | "Her voice came out sharper" | | 16 | "The man froze, mid-step, his" | | 17 | "She didn’t move." | | 18 | "Her fingers tightened around the" | | 19 | "Evan shot back, his tone" |
| | ratio | 0.88 | |
| 66.67% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 75 | | matches | | 0 | "Now, she just wanted to" |
| | ratio | 0.013 | |
| 82.07% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 4 | | matches | | 0 | "She slid onto a stool at the far end of the counter, her usual spot, and nodded at Silas, who was polishing a glass with a worn rag." | | 1 | "He gave her a curt nod in return, his hazel eyes scanning the room as though he were still on some covert op." | | 2 | "He was tall, broad-shouldered, his dark hair slicked back with a precision that screamed effort." | | 3 | "His suit was tailored, expensive, and his grin was all teeth, the kind that could charm or disarm depending on the situation." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 10 | | matches | | 0 | "she replied, her tone flat" | | 1 | "he asked, his gaze flickering over her face" | | 2 | "she said shortly, not elaborating" | | 3 | "Silas interjected, his voice calm but firm" | | 4 | "she said, her voice tight" | | 5 | "he asked, his tone soft but edged with something darker" | | 6 | "she asked, incredulous" | | 7 | "she said, her voice quieter now" | | 8 | "she said, though the words tasted bitter on her tongue" | | 9 | "he asked, his voice low" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 2 | | fancyTags | | 0 | "Silas interjected (interject)" | | 1 | "she muttered (mutter)" |
| | dialogueSentences | 40 | | tagDensity | 0.35 | | leniency | 0.7 | | rawRatio | 0.143 | | effectiveRatio | 0.1 | |