| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 1 | | adverbTags | | 0 | "social smile snapped back [back]" |
| | dialogueSentences | 46 | | tagDensity | 0.413 | | leniency | 0.826 | | rawRatio | 0.053 | | effectiveRatio | 0.043 | |
| 91.82% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1222 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 59.08% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1222 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "familiar" | | 1 | "calculating" | | 2 | "glint" | | 3 | "silence" | | 4 | "could feel" | | 5 | "tension" | | 6 | "fractured" | | 7 | "calculated" | | 8 | "tracing" |
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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 | 98 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 98 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 122 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 11 | | totalWords | 1222 | | ratio | 0.009 | | matches | | 0 | "Eva Chen, Curator & Acquisitions, Finch & Associates." | | 1 | "Finch & Associates." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 65 | | wordCount | 925 | | uniqueNames | 19 | | maxNameDensity | 2.59 | | worstName | "Rory" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 24 | | Yu-Fei | 2 | | Sichuan | 1 | | Raven | 1 | | Nest | 1 | | Eva | 19 | | Silas | 4 | | Cardiff | 1 | | Evan | 1 | | Chen | 1 | | Curator | 1 | | Acquisitions | 1 | | Finch | 2 | | Thick | 1 | | Thames | 1 | | Soho | 1 | | Old | 1 | | Aurora | 1 | | Carter | 1 |
| | persons | | 0 | "Rory" | | 1 | "Yu-Fei" | | 2 | "Raven" | | 3 | "Eva" | | 4 | "Silas" | | 5 | "Evan" | | 6 | "Chen" | | 7 | "Aurora" | | 8 | "Carter" |
| | places | | 0 | "Sichuan" | | 1 | "Cardiff" | | 2 | "Thames" | | 3 | "Soho" |
| | globalScore | 0.203 | | windowScore | 0.167 | |
| 73.08% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like an old coat that no longer fi" | | 1 | "felt like a foreign currency" |
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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 | 1222 | | matches | (empty) | |
| 57.38% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 4 | | totalSentences | 122 | | matches | | 0 | "chose that moment" | | 1 | "remembered that song" | | 2 | "remembered that fury" | | 3 | "chose that moment" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 29.8 | | std | 19.81 | | cv | 0.665 | | sampleLengths | | 0 | 67 | | 1 | 65 | | 2 | 26 | | 3 | 18 | | 4 | 14 | | 5 | 15 | | 6 | 3 | | 7 | 34 | | 8 | 26 | | 9 | 34 | | 10 | 13 | | 11 | 52 | | 12 | 20 | | 13 | 22 | | 14 | 19 | | 15 | 38 | | 16 | 24 | | 17 | 48 | | 18 | 68 | | 19 | 23 | | 20 | 50 | | 21 | 14 | | 22 | 5 | | 23 | 45 | | 24 | 6 | | 25 | 83 | | 26 | 27 | | 27 | 27 | | 28 | 5 | | 29 | 31 | | 30 | 23 | | 31 | 11 | | 32 | 30 | | 33 | 39 | | 34 | 12 | | 35 | 2 | | 36 | 37 | | 37 | 35 | | 38 | 34 | | 39 | 9 | | 40 | 68 |
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| 87.36% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 98 | | matches | | 0 | "was locked" | | 1 | "was gone" | | 2 | "was gone" | | 3 | "was gone" | | 4 | "was swallowed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 152 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 122 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 929 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.02906350914962325 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.005382131324004306 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 122 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 122 | | mean | 10.02 | | std | 7.35 | | cv | 0.734 | | sampleLengths | | 0 | 14 | | 1 | 23 | | 2 | 5 | | 3 | 25 | | 4 | 2 | | 5 | 19 | | 6 | 17 | | 7 | 1 | | 8 | 1 | | 9 | 10 | | 10 | 15 | | 11 | 12 | | 12 | 14 | | 13 | 5 | | 14 | 8 | | 15 | 5 | | 16 | 11 | | 17 | 3 | | 18 | 4 | | 19 | 7 | | 20 | 4 | | 21 | 3 | | 22 | 9 | | 23 | 19 | | 24 | 6 | | 25 | 25 | | 26 | 1 | | 27 | 28 | | 28 | 6 | | 29 | 10 | | 30 | 3 | | 31 | 4 | | 32 | 19 | | 33 | 4 | | 34 | 20 | | 35 | 2 | | 36 | 3 | | 37 | 13 | | 38 | 6 | | 39 | 1 | | 40 | 6 | | 41 | 16 | | 42 | 16 | | 43 | 3 | | 44 | 6 | | 45 | 26 | | 46 | 6 | | 47 | 8 | | 48 | 10 | | 49 | 6 |
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| 57.10% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3770491803278688 | | totalSentences | 122 | | uniqueOpeners | 46 | |
| 37.88% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 88 | | matches | | 0 | "Too bright, too polished, a" |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 88 | | matches | | 0 | "She didn’t bend for it." | | 1 | "Her gaze was locked on" | | 2 | "Her eyes, once wide with" | | 3 | "She retrieved the bag, the" | | 4 | "It wasn’t a question." | | 5 | "She could feel Silas behind" | | 6 | "He knew enough not to" | | 7 | "They’d played it on a" | | 8 | "She kept her tone even," | | 9 | "Her voice dropped" | | 10 | "It felt like an old" | | 11 | "She’d fed on it, used" | | 12 | "She saw the girl who’d" | | 13 | "He placed a fresh glass" | | 14 | "She reached for her sleek" | | 15 | "She hesitated, then pulled a" | | 16 | "It felt like a foreign" | | 17 | "She turned and walked towards" | | 18 | "They’d been circling one of" | | 19 | "she said, her mind already" |
| | ratio | 0.261 | |
| 16.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 88 | | matches | | 0 | "The plastic bag slipped from" | | 1 | "She didn’t bend for it." | | 2 | "Her gaze was locked on" | | 3 | "The woman turned, auburn hair" | | 4 | "The soft curve of her" | | 5 | "Her eyes, once wide with" | | 6 | "The name hung there, a" | | 7 | "Eva’s hand stilled on her" | | 8 | "Rory’s voice was flat" | | 9 | "She retrieved the bag, the" | | 10 | "Eva’s smile didn’t reach her" | | 11 | "It wasn’t a question." | | 12 | "Rory placed the bag on" | | 13 | "Rory nodded, a single, sharp" | | 14 | "She could feel Silas behind" | | 15 | "He knew enough not to" | | 16 | "Eva’s gaze swept over Rory’s" | | 17 | "Rory’s eyes lingered on Eva’s" | | 18 | "Eva took a sip of" | | 19 | "An awkward beat stretched." |
| | ratio | 0.886 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 88 | | matches | | 0 | "Before everything fractured." | | 1 | "Now, looking at Eva’s immaculate" |
| | ratio | 0.023 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 1 | | matches | | 0 | "The woman who stood in her place had traded vulnerability for something harder, something sharper." |
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| 46.05% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 3 | | matches | | 0 | "Rory said, the words escaping before she could weigh them" | | 1 | "social smile snapped back, a mask clicking into position" | | 2 | "Rory said, not touching the card" |
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| 84.78% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 3 | | fancyTags | | 0 | "Rory repeated (repeat)" | | 1 | "social smile snapped back (snap)" | | 2 | "Rory murmured (murmur)" |
| | dialogueSentences | 46 | | tagDensity | 0.174 | | leniency | 0.348 | | rawRatio | 0.375 | | effectiveRatio | 0.13 | |