| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 26 | | tagDensity | 0.154 | | leniency | 0.308 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.42% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 583 | | totalAiIsmAdverbs | 1 | | 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) | |
| 65.69% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 583 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "scanned" | | 1 | "silence" | | 2 | "tracing" | | 3 | "weight" |
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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 | 0 | | narrationSentences | 48 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 48 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 69 | | 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 | 579 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 9 | | wordCount | 453 | | uniqueNames | 5 | | maxNameDensity | 0.66 | | worstName | "Rory" | | maxWindowNameDensity | 1 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Didn | 1 | | Rory | 3 | | Silas | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Rory" | | 3 | "Silas" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 33 | | 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 | 579 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 69 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 16.54 | | std | 14.35 | | cv | 0.868 | | sampleLengths | | 0 | 55 | | 1 | 4 | | 2 | 64 | | 3 | 2 | | 4 | 1 | | 5 | 26 | | 6 | 10 | | 7 | 19 | | 8 | 14 | | 9 | 23 | | 10 | 1 | | 11 | 11 | | 12 | 7 | | 13 | 13 | | 14 | 18 | | 15 | 25 | | 16 | 19 | | 17 | 6 | | 18 | 12 | | 19 | 11 | | 20 | 10 | | 21 | 9 | | 22 | 7 | | 23 | 48 | | 24 | 12 | | 25 | 11 | | 26 | 7 | | 27 | 35 | | 28 | 8 | | 29 | 12 | | 30 | 28 | | 31 | 11 | | 32 | 6 | | 33 | 21 | | 34 | 13 |
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| 97.95% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 48 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 82 | | matches | (empty) | |
| 18.63% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 69 | | ratio | 0.043 | | matches | | 0 | "She glanced at the clock—nearly midnight." | | 1 | "She remembered the last time they’d spoken—her, fresh out of university, him, already deep in the shadows of MI6." | | 2 | "A small, worn photograph slid across the counter—a younger version of them, grinning in some long-forgotten pub." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 457 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.037199124726477024 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0087527352297593 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 69 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 69 | | mean | 8.39 | | std | 6.12 | | cv | 0.73 | | sampleLengths | | 0 | 10 | | 1 | 23 | | 2 | 16 | | 3 | 6 | | 4 | 4 | | 5 | 9 | | 6 | 26 | | 7 | 29 | | 8 | 2 | | 9 | 1 | | 10 | 13 | | 11 | 13 | | 12 | 3 | | 13 | 7 | | 14 | 10 | | 15 | 9 | | 16 | 10 | | 17 | 4 | | 18 | 7 | | 19 | 13 | | 20 | 3 | | 21 | 1 | | 22 | 9 | | 23 | 2 | | 24 | 6 | | 25 | 1 | | 26 | 13 | | 27 | 4 | | 28 | 6 | | 29 | 8 | | 30 | 7 | | 31 | 18 | | 32 | 13 | | 33 | 6 | | 34 | 3 | | 35 | 3 | | 36 | 6 | | 37 | 6 | | 38 | 10 | | 39 | 1 | | 40 | 5 | | 41 | 5 | | 42 | 6 | | 43 | 2 | | 44 | 1 | | 45 | 4 | | 46 | 3 | | 47 | 11 | | 48 | 19 | | 49 | 14 |
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| 68.60% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.42028985507246375 | | totalSentences | 69 | | uniqueOpeners | 29 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 44 | | matches | | 0 | "Then he smiled." | | 1 | "Then he laughed, sharp and" | | 2 | "Then he was gone, the" |
| | ratio | 0.068 | |
| 10.91% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 44 | | matches | | 0 | "She glanced at the clock—nearly" | | 1 | "His auburn hair, streaked with" | | 2 | "He moved with a slight" | | 3 | "His gaze landed on her," | | 4 | "She exhaled, forcing her fingers" | | 5 | "He chuckled, low and rough," | | 6 | "She poured him a whiskey" | | 7 | "She pushed the drink toward" | | 8 | "His fingers stilled around the" | | 9 | "She leaned against the counter," | | 10 | "He took a sip, eyes" | | 11 | "Her jaw tightened." | | 12 | "His voice was quiet, probing" | | 13 | "She looked away, fingers tracing" | | 14 | "He didn’t press" | | 15 | "She remembered the last time" | | 16 | "She’d been angry then, furious" | | 17 | "He’d been distant, untouchable." | | 18 | "He swirled his drink." | | 19 | "She wanted to ask what" |
| | ratio | 0.523 | |
| 39.55% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 37 | | totalSentences | 44 | | matches | | 0 | "The Raven’s Nest smelled of" | | 1 | "Rory wiped her hands on" | | 2 | "The evening crowd had thinned," | | 3 | "She glanced at the clock—nearly" | | 4 | "The door creaked open." | | 5 | "A man stepped inside, shaking" | | 6 | "His auburn hair, streaked with" | | 7 | "He moved with a slight" | | 8 | "His gaze landed on her," | | 9 | "The years between them stretched" | | 10 | "She exhaled, forcing her fingers" | | 11 | "He chuckled, low and rough," | | 12 | "She poured him a whiskey" | | 13 | "The amber liquid sloshed into" | | 14 | "She pushed the drink toward" | | 15 | "His fingers stilled around the" | | 16 | "A beat of silence." | | 17 | "She leaned against the counter," | | 18 | "He took a sip, eyes" | | 19 | "Her jaw tightened." |
| | ratio | 0.841 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 44 | | matches | | | ratio | 0.023 | |
| 74.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 2 | | matches | | 0 | "His auburn hair, streaked with grey, was slick with moisture, and his hazel eyes scanned the room with the quiet precision of someone who missed nothing." | | 1 | "He moved with a slight limp, favoring his left leg, and his right hand bore a silver signet ring that glinted under the neon glow of the bar’s sign." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 1 | | matches | | 0 | "He took, eyes never leaving hers" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 26 | | tagDensity | 0.038 | | leniency | 0.077 | | rawRatio | 0 | | effectiveRatio | 0 | |