| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said quietly [quietly]" |
| | dialogueSentences | 40 | | tagDensity | 0.325 | | leniency | 0.65 | | rawRatio | 0.077 | | effectiveRatio | 0.05 | |
| 85.54% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1037 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slowly" | | 1 | "suddenly" | | 2 | "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) | |
| 56.61% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1037 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "familiar" | | 1 | "footsteps" | | 2 | "eyebrow" | | 3 | "warmth" | | 4 | "traced" | | 5 | "pang" | | 6 | "flicker" | | 7 | "flicked" |
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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 | 81 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 81 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 109 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1026 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 85.97% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 859 | | uniqueNames | 10 | | maxNameDensity | 1.28 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Thursday | 1 | | Rory | 11 | | Silas | 9 | | Neat | 1 | | Assessing | 1 | | Evan | 6 | | Three | 1 | | Berlin | 1 |
| | persons | | 0 | "Nest" | | 1 | "Rory" | | 2 | "Silas" | | 3 | "Evan" |
| | places | | | globalScore | 0.86 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared beside her, his limp more pronounced as he moved" |
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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 | 1026 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 109 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 20.94 | | std | 18.36 | | cv | 0.877 | | sampleLengths | | 0 | 77 | | 1 | 53 | | 2 | 17 | | 3 | 63 | | 4 | 19 | | 5 | 33 | | 6 | 59 | | 7 | 8 | | 8 | 23 | | 9 | 39 | | 10 | 14 | | 11 | 13 | | 12 | 45 | | 13 | 14 | | 14 | 6 | | 15 | 15 | | 16 | 5 | | 17 | 10 | | 18 | 52 | | 19 | 40 | | 20 | 10 | | 21 | 7 | | 22 | 3 | | 23 | 10 | | 24 | 10 | | 25 | 39 | | 26 | 5 | | 27 | 6 | | 28 | 2 | | 29 | 5 | | 30 | 10 | | 31 | 32 | | 32 | 7 | | 33 | 25 | | 34 | 2 | | 35 | 2 | | 36 | 10 | | 37 | 10 | | 38 | 32 | | 39 | 37 | | 40 | 42 | | 41 | 9 | | 42 | 12 | | 43 | 8 | | 44 | 7 | | 45 | 23 | | 46 | 6 | | 47 | 39 | | 48 | 11 |
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| 96.60% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 81 | | matches | | 0 | "was gone" | | 1 | "been, hoped" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 158 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 109 | | ratio | 0.055 | | matches | | 0 | "The bar was quiet for a Thursday night—just a few regulars hunched over their drinks, the low murmur of conversation barely rising above the crackle of vinyl spinning on Silas’ ancient record player." | | 1 | "She knew that voice—had known it since they were kids trading sweets behind the schoolyard." | | 2 | "His fingers—still long and elegant, she noted absently—traced the rim of the glass before lifting it to his lips." | | 3 | "The sound of her nickname in his voice—the old one, the one from before—sent an unexpected pang through her chest." | | 4 | "His eyes met hers, and for the first time, she saw something flicker there—something that might have been regret." | | 5 | "Rory studied his face—the lines deeper than she remembered, the silver in his auburn hair more pronounced." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 874 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.03546910755148741 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.010297482837528604 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 109 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 109 | | mean | 9.41 | | std | 6.95 | | cv | 0.739 | | sampleLengths | | 0 | 21 | | 1 | 23 | | 2 | 33 | | 3 | 25 | | 4 | 14 | | 5 | 14 | | 6 | 6 | | 7 | 11 | | 8 | 11 | | 9 | 8 | | 10 | 16 | | 11 | 16 | | 12 | 12 | | 13 | 14 | | 14 | 5 | | 15 | 6 | | 16 | 15 | | 17 | 9 | | 18 | 3 | | 19 | 16 | | 20 | 26 | | 21 | 17 | | 22 | 6 | | 23 | 2 | | 24 | 12 | | 25 | 9 | | 26 | 2 | | 27 | 25 | | 28 | 13 | | 29 | 1 | | 30 | 12 | | 31 | 2 | | 32 | 9 | | 33 | 4 | | 34 | 12 | | 35 | 5 | | 36 | 19 | | 37 | 9 | | 38 | 13 | | 39 | 1 | | 40 | 4 | | 41 | 2 | | 42 | 11 | | 43 | 4 | | 44 | 5 | | 45 | 6 | | 46 | 4 | | 47 | 3 | | 48 | 4 | | 49 | 25 |
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| 74.62% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.45871559633027525 | | totalSentences | 109 | | uniqueOpeners | 50 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 75 | | matches | | 0 | "Slowly, she turned." | | 1 | "Instead, he reached into his" | | 2 | "Then he was gone, tossing" |
| | ratio | 0.04 | |
| 60.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 75 | | matches | | 0 | "She slid onto a stool" | | 1 | "he asked, setting the glass" | | 2 | "He knew she didn’t drink" | | 3 | "She wrapped her fingers around" | | 4 | "Her fingers tightened around the" | | 5 | "She knew that voice—had known" | | 6 | "His hair was shorter, military-short," | | 7 | "He wore a leather jacket" | | 8 | "she said, her voice steady" | | 9 | "he said, signaling Silas for" | | 10 | "He laughed, but there was" | | 11 | "His fingers—still long and elegant," | | 12 | "he said after a moment," | | 13 | "she asked, though she knew" | | 14 | "It wasn’t a question." | | 15 | "She nodded anyway." | | 16 | "His fingers stilled on the" | | 17 | "She looked away, focusing on" | | 18 | "He exhaled, long and slow." | | 19 | "She turned back to him." |
| | ratio | 0.4 | |
| 20.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 66 | | totalSentences | 75 | | matches | | 0 | "The green neon sign buzzed" | | 1 | "Rory pushed open the heavy" | | 2 | "The bar was quiet for" | | 3 | "She slid onto a stool" | | 4 | "Silas glanced up from polishing" | | 5 | "he asked, setting the glass" | | 6 | "Rory said, shrugging" | | 7 | "Silas chuckled, pouring her a" | | 8 | "He knew she didn’t drink" | | 9 | "She wrapped her fingers around" | | 10 | "A draft curled through the" | | 11 | "Rory didn’t look up until" | | 12 | "Her fingers tightened around the" | | 13 | "She knew that voice—had known" | | 14 | "Evan stood there, taller than" | | 15 | "His hair was shorter, military-short," | | 16 | "He wore a leather jacket" | | 17 | "she said, her voice steady" | | 18 | "Silas had gone still behind" | | 19 | "Rory gave him the slightest" |
| | ratio | 0.88 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 81.63% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 3 | | matches | | 0 | "His hair was shorter, military-short, and there was a thin scar running through his left eyebrow that hadn’t been there the last time she’d seen him." | | 1 | "He wore a leather jacket that looked both expensive and well-worn, the kind that didn’t come cheap." | | 2 | "His eyes met hers, and for the first time, she saw something flicker there—something that might have been regret." |
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| 9.62% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 3 | | matches | | 0 | "she said, her voice steady" | | 1 | "he said, his gaze raking over her" | | 2 | "He cut, jaw tightening" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 2 | | fancyTags | | 0 | "she pressed (press)" | | 1 | "he murmured (murmur)" |
| | dialogueSentences | 40 | | tagDensity | 0.275 | | leniency | 0.55 | | rawRatio | 0.182 | | effectiveRatio | 0.1 | |