| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 7 | | adverbTags | | 0 | "I said quietly [quietly]" | | 1 | "she said simply [simply]" | | 2 | "I said carefully [carefully]" | | 3 | "she said suddenly [suddenly]" | | 4 | "Eva's lips twitched just [just]" | | 5 | "I said softly [softly]" | | 6 | "she said finally [finally]" |
| | dialogueSentences | 41 | | tagDensity | 0.39 | | leniency | 0.78 | | rawRatio | 0.438 | | effectiveRatio | 0.341 | |
| 50.54% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1213 | | totalAiIsmAdverbs | 12 | | found | | | highlights | | 0 | "slowly" | | 1 | "carefully" | | 2 | "suddenly" | | 3 | "perfectly" | | 4 | "softly" | | 5 | "really" |
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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) | |
| 67.02% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1213 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "comforting" | | 1 | "silk" | | 2 | "traced" | | 3 | "weight" | | 4 | "flicker" | | 5 | "pang" | | 6 | "sanctuary" | | 7 | "familiar" |
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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 | 0 | | narrationSentences | 90 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 90 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 115 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1210 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 19 | | unquotedAttributions | 1 | | matches | | 0 | "But instead, I said the only thing that felt true in that moment." |
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| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 1003 | | uniqueNames | 6 | | maxNameDensity | 1.2 | | worstName | "Eva" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Friday | 1 | | Tuesday | 1 | | Eva | 12 | | London | 1 |
| | persons | | | places | | | globalScore | 0.902 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like she'd stepped out of the page" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.826 | | wordCount | 1210 | | matches | | 0 | "Not just in appearance, but in the way she carried herself" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 115 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 23.27 | | std | 14.56 | | cv | 0.626 | | sampleLengths | | 0 | 40 | | 1 | 37 | | 2 | 3 | | 3 | 36 | | 4 | 15 | | 5 | 1 | | 6 | 63 | | 7 | 19 | | 8 | 11 | | 9 | 13 | | 10 | 9 | | 11 | 8 | | 12 | 14 | | 13 | 62 | | 14 | 21 | | 15 | 19 | | 16 | 39 | | 17 | 20 | | 18 | 18 | | 19 | 7 | | 20 | 30 | | 21 | 12 | | 22 | 35 | | 23 | 10 | | 24 | 25 | | 25 | 50 | | 26 | 19 | | 27 | 31 | | 28 | 18 | | 29 | 20 | | 30 | 37 | | 31 | 10 | | 32 | 23 | | 33 | 33 | | 34 | 17 | | 35 | 18 | | 36 | 40 | | 37 | 2 | | 38 | 8 | | 39 | 44 | | 40 | 13 | | 41 | 8 | | 42 | 41 | | 43 | 9 | | 44 | 17 | | 45 | 32 | | 46 | 35 | | 47 | 37 | | 48 | 25 | | 49 | 34 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 90 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 189 | | matches | | 0 | "was wiping" | | 1 | "were going" |
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| 93.17% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 115 | | ratio | 0.017 | | matches | | 0 | "The clink of glasses, the low murmur of conversations, the occasional burst of laughter - all the comforting sounds of a neighbourhood bar that had seen generations come and go." | | 1 | "I knew what she meant - the dreams we'd shared, the plans we'd made to take on the world together." |
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| 93.25% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1006 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 48 | | adverbRatio | 0.04771371769383698 | | lyAdverbCount | 19 | | lyAdverbRatio | 0.018886679920477135 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 115 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 115 | | mean | 10.52 | | std | 7 | | cv | 0.665 | | sampleLengths | | 0 | 10 | | 1 | 30 | | 2 | 10 | | 3 | 2 | | 4 | 25 | | 5 | 3 | | 6 | 4 | | 7 | 4 | | 8 | 10 | | 9 | 18 | | 10 | 11 | | 11 | 4 | | 12 | 1 | | 13 | 6 | | 14 | 19 | | 15 | 18 | | 16 | 20 | | 17 | 12 | | 18 | 7 | | 19 | 7 | | 20 | 4 | | 21 | 9 | | 22 | 4 | | 23 | 5 | | 24 | 4 | | 25 | 5 | | 26 | 3 | | 27 | 10 | | 28 | 4 | | 29 | 15 | | 30 | 27 | | 31 | 20 | | 32 | 14 | | 33 | 7 | | 34 | 15 | | 35 | 4 | | 36 | 4 | | 37 | 3 | | 38 | 14 | | 39 | 18 | | 40 | 10 | | 41 | 10 | | 42 | 4 | | 43 | 14 | | 44 | 4 | | 45 | 3 | | 46 | 12 | | 47 | 1 | | 48 | 1 | | 49 | 16 |
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| 78.84% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.5043478260869565 | | totalSentences | 115 | | uniqueOpeners | 58 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 82 | | matches | | 0 | "Then her expression shuttered, becoming" | | 1 | "Of course she wasn't." | | 2 | "Especially the ones that look" |
| | ratio | 0.037 | |
| 5.37% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 44 | | totalSentences | 82 | | matches | | 0 | "I was wiping down the" | | 1 | "My hands froze mid-wipe." | | 2 | "I knew that voice." | | 3 | "I turned slowly, my heart" | | 4 | "She wore a tailored blazer" | | 5 | "She looked up, and for" | | 6 | "she said, her voice carefully" | | 7 | "I set down the cloth," | | 8 | "she finished for me" | | 9 | "I said quietly" | | 10 | "She nodded, her gaze drifting" | | 11 | "I watched as she traced" | | 12 | "I asked, falling back on" | | 13 | "She took a sip of" | | 14 | "We hadn't spoken in years," | | 15 | "I cleared my throat, trying" | | 16 | "she said simply" | | 17 | "My eyebrows shot up." | | 18 | "She shrugged, but there was" | | 19 | "It was hard to tell" |
| | ratio | 0.537 | |
| 88.05% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 82 | | matches | | 0 | "The Raven's Nest buzzed with" | | 1 | "The clink of glasses, the" | | 2 | "I was wiping down the" | | 3 | "The one I'd last heard" | | 4 | "My hands froze mid-wipe." | | 5 | "I knew that voice." | | 6 | "The kind of voice that" | | 7 | "I turned slowly, my heart" | | 8 | "This Eva had cut her" | | 9 | "She wore a tailored blazer" | | 10 | "The casual jeans and band" | | 11 | "She looked up, and for" | | 12 | "she said, her voice carefully" | | 13 | "I set down the cloth," | | 14 | "she finished for me" | | 15 | "I said quietly" | | 16 | "She nodded, her gaze drifting" | | 17 | "I watched as she traced" | | 18 | "This wasn't the Eva who'd" | | 19 | "This Eva looked like she'd" |
| | ratio | 0.744 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 82 | | matches | | 0 | "Because I wasn't sure anymore." | | 1 | "If any of us really" |
| | ratio | 0.024 | |
| 53.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 6 | | matches | | 0 | "The clink of glasses, the low murmur of conversations, the occasional burst of laughter - all the comforting sounds of a neighbourhood bar that had seen generat…" | | 1 | "This Eva had cut her once-flowing chestnut hair into a sleek bob that framed her face in sharp angles." | | 2 | "She wore a tailored blazer over a silk blouse, the kind of outfit that screamed money and power." | | 3 | "This wasn't the Eva who'd stayed up all night helping me study for exams, sharing a family-sized bag of crisps and making terrible jokes about our professors." | | 4 | "That tiny, cramped flat where we'd spent countless nights talking about our futures, painting each other's nails, and dreaming of the lives we'd lead." | | 5 | "I watched as she turned to leave, that familiar walk now carrying the confidence of someone who knew exactly where they were going in life." |
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| 93.75% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, her voice carefully controlled" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 2 | | fancyTags | | 0 | "she repeated (repeat)" | | 1 | "she continued (continue)" |
| | dialogueSentences | 41 | | tagDensity | 0.317 | | leniency | 0.634 | | rawRatio | 0.154 | | effectiveRatio | 0.098 | |