| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said finally [finally]" |
| | dialogueSentences | 26 | | tagDensity | 0.654 | | leniency | 1 | | rawRatio | 0.059 | | effectiveRatio | 0.059 | |
| 81.75% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1096 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slightly" | | 1 | "tightly" | | 2 | "quickly" |
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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) | |
| 36.13% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1096 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "flickered" | | 1 | "scanning" | | 2 | "testament" | | 3 | "familiar" | | 4 | "gloom" | | 5 | "glint" | | 6 | "flicker" | | 7 | "apprehension" | | 8 | "clenching" | | 9 | "comforting" | | 10 | "pounding" | | 11 | "racing" | | 12 | "eyebrow" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 59 | | matches | | 0 | "a flicker of apprehension" |
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| 94.43% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 59 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 68 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1099 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 75.69% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 942 | | uniqueNames | 4 | | maxNameDensity | 1.49 | | worstName | "Aurora" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Silas" | | discoveredNames | | Cardiff | 1 | | Aurora | 14 | | Silas | 10 | | Evan | 6 |
| | persons | | | places | | | globalScore | 0.757 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | 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 | 1099 | | matches | (empty) | |
| 68.63% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 68 | | matches | | 0 | "held that glint" | | 1 | "realized that he" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 25 | | mean | 43.96 | | std | 25.28 | | cv | 0.575 | | sampleLengths | | 0 | 96 | | 1 | 78 | | 2 | 78 | | 3 | 38 | | 4 | 78 | | 5 | 1 | | 6 | 60 | | 7 | 11 | | 8 | 33 | | 9 | 48 | | 10 | 65 | | 11 | 83 | | 12 | 8 | | 13 | 34 | | 14 | 40 | | 15 | 38 | | 16 | 53 | | 17 | 55 | | 18 | 6 | | 19 | 27 | | 20 | 29 | | 21 | 52 | | 22 | 21 | | 23 | 28 | | 24 | 39 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 59 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 170 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 68 | | ratio | 0.015 | | matches | | 0 | "As the night wore on, the bar began to fill with the usual assortment of patrons - lonely hearts nursing their sorrows, groups of friends celebrating a recent promotion or commiserating over a failed relationship." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 877 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 27 | | adverbRatio | 0.03078677309007982 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.010262257696693273 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 68 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 68 | | mean | 16.16 | | std | 8.07 | | cv | 0.499 | | sampleLengths | | 0 | 17 | | 1 | 25 | | 2 | 29 | | 3 | 25 | | 4 | 19 | | 5 | 16 | | 6 | 27 | | 7 | 16 | | 8 | 22 | | 9 | 4 | | 10 | 35 | | 11 | 17 | | 12 | 19 | | 13 | 19 | | 14 | 35 | | 15 | 17 | | 16 | 26 | | 17 | 1 | | 18 | 29 | | 19 | 13 | | 20 | 18 | | 21 | 11 | | 22 | 15 | | 23 | 12 | | 24 | 6 | | 25 | 27 | | 26 | 15 | | 27 | 6 | | 28 | 18 | | 29 | 20 | | 30 | 18 | | 31 | 9 | | 32 | 15 | | 33 | 18 | | 34 | 19 | | 35 | 31 | | 36 | 8 | | 37 | 15 | | 38 | 12 | | 39 | 7 | | 40 | 14 | | 41 | 16 | | 42 | 10 | | 43 | 11 | | 44 | 27 | | 45 | 10 | | 46 | 14 | | 47 | 15 | | 48 | 14 | | 49 | 25 |
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| 42.16% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3235294117647059 | | totalSentences | 68 | | uniqueOpeners | 22 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 13.10% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 58 | | matches | | 0 | "she murmured to herself, her" | | 1 | "It had been years since" | | 2 | "He looked older, more tired," | | 3 | "She needn't have worried." | | 4 | "he said, his voice low" | | 5 | "She took a sip, savoring" | | 6 | "It was then that she" | | 7 | "Her heart seized in her" | | 8 | "It was Silas who broke" | | 9 | "he said, his voice laced" | | 10 | "she whispered, her voice trembling" | | 11 | "he said, his voice low" | | 12 | "She needed closure, needed to" | | 13 | "She straightened her shoulders, downing" | | 14 | "she said, her voice stronger" | | 15 | "His hair was perhaps a" | | 16 | "she said, her voice steady" | | 17 | "He turned, his eyes widening" | | 18 | "he breathed, a smile tugging" | | 19 | "she asked, her voice dripping" |
| | ratio | 0.517 | |
| 37.59% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 49 | | totalSentences | 58 | | matches | | 0 | "The green neon sign above" | | 1 | "Aurora glanced up at it," | | 2 | "she murmured to herself, her" | | 3 | "It had been years since" | | 4 | "The dim lighting and hushed" | | 5 | "He looked older, more tired," | | 6 | "Aurora felt a flicker of" | | 7 | "She needn't have worried." | | 8 | "Silas's face broke into a" | | 9 | "he said, his voice low" | | 10 | "Aurora returned his smile, relief" | | 11 | "She took a sip, savoring" | | 12 | "Aurora watched them all, her" | | 13 | "It was then that she" | | 14 | "Her heart seized in her" | | 15 | "It was Silas who broke" | | 16 | "he said, his voice laced" | | 17 | "Aurora took a deep, shuddering" | | 18 | "she whispered, her voice trembling" | | 19 | "Silas's eyes narrowed as he" |
| | ratio | 0.845 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 35.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 6 | | matches | | 0 | "As her eyes adjusted to the gloom, she spotted Silas behind the bar, his neatly trimmed grey-streaked auburn beard matching the hair that receded from his foreh…" | | 1 | "It was Silas who broke the spell, his gentle touch on her arm pulling her back to reality." | | 2 | "Silas's eyes narrowed as he followed her gaze, his jaw clenching as he took in the sight of the man who had caused Aurora so much pain." | | 3 | "She straightened her shoulders, downing the last of her whiskey before setting the glass back on the bar." | | 4 | "Silas nodded, his hand squeezing her arm once in silent support before letting her go." | | 5 | "Aurora felt her anger rising, the old wounds resurfacing as she looked at him." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 11 | | matches | | 0 | "he said, his voice low and gravelly," | | 1 | "she whispered, her voice trembling slightly" | | 2 | "he said, his voice low and comforting" | | 3 | "she said, her voice stronger now," | | 4 | "she said, her voice steady and clear" | | 5 | "he breathed, a smile tugging at the corners of his mouth" | | 6 | "she asked, her voice dripping with disdain" | | 7 | "he said, his voice taking on a pleading edge," | | 8 | "she said, her voice low and dangerous" | | 9 | "Silas asked, his voice gentle" | | 10 | "he said, his voice warm and sincere" |
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| 56.25% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 16 | | fancyCount | 3 | | fancyTags | | 0 | "she murmured (murmur)" | | 1 | "she whispered (whisper)" | | 2 | "he breathed (breathe)" |
| | dialogueSentences | 26 | | tagDensity | 0.615 | | leniency | 1 | | rawRatio | 0.188 | | effectiveRatio | 0.188 | |