| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 2 | | adverbTags | | 0 | "Aurora's voice cracked slightly [slightly]" | | 1 | "Eva said quietly [quietly]" |
| | dialogueSentences | 41 | | tagDensity | 0.366 | | leniency | 0.732 | | rawRatio | 0.133 | | effectiveRatio | 0.098 | |
| 67.62% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 772 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "perfectly" | | 1 | "slightly" | | 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) | |
| 54.66% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 772 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "fractured" | | 1 | "stark" | | 2 | "perfect" | | 3 | "silence" | | 4 | "footsteps" | | 5 | "familiar" | | 6 | "weight" |
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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 | 39 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 39 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 66 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 769 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 491 | | uniqueNames | 8 | | maxNameDensity | 3.26 | | worstName | "Aurora" | | maxWindowNameDensity | 6 | | worstWindowName | "Eva" | | discoveredNames | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Silas | 1 | | Eva | 15 | | Welsh | 1 | | Aurora | 16 | | London | 1 |
| | persons | | 0 | "Silas" | | 1 | "Eva" | | 2 | "Welsh" | | 3 | "Aurora" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 30 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like she'd stepped out of a fashio" | | 1 | "quite hide the dark circles under her eyes, the slight tremor in her hand as she lifted the glass again" |
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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 | 769 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 66 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 20.78 | | std | 15.96 | | cv | 0.768 | | sampleLengths | | 0 | 75 | | 1 | 52 | | 2 | 8 | | 3 | 24 | | 4 | 44 | | 5 | 23 | | 6 | 36 | | 7 | 20 | | 8 | 21 | | 9 | 46 | | 10 | 9 | | 11 | 15 | | 12 | 3 | | 13 | 2 | | 14 | 12 | | 15 | 20 | | 16 | 5 | | 17 | 10 | | 18 | 28 | | 19 | 34 | | 20 | 3 | | 21 | 29 | | 22 | 12 | | 23 | 5 | | 24 | 36 | | 25 | 21 | | 26 | 15 | | 27 | 3 | | 28 | 3 | | 29 | 27 | | 30 | 7 | | 31 | 15 | | 32 | 30 | | 33 | 8 | | 34 | 10 | | 35 | 24 | | 36 | 34 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 39 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 83 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 66 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 421 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.028503562945368172 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.014251781472684086 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 66 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 66 | | mean | 11.65 | | std | 7.72 | | cv | 0.662 | | sampleLengths | | 0 | 23 | | 1 | 23 | | 2 | 29 | | 3 | 17 | | 4 | 10 | | 5 | 25 | | 6 | 8 | | 7 | 11 | | 8 | 3 | | 9 | 10 | | 10 | 11 | | 11 | 33 | | 12 | 23 | | 13 | 11 | | 14 | 18 | | 15 | 7 | | 16 | 15 | | 17 | 5 | | 18 | 10 | | 19 | 11 | | 20 | 18 | | 21 | 28 | | 22 | 5 | | 23 | 4 | | 24 | 8 | | 25 | 7 | | 26 | 3 | | 27 | 2 | | 28 | 6 | | 29 | 6 | | 30 | 20 | | 31 | 5 | | 32 | 10 | | 33 | 8 | | 34 | 20 | | 35 | 24 | | 36 | 10 | | 37 | 3 | | 38 | 5 | | 39 | 24 | | 40 | 12 | | 41 | 5 | | 42 | 13 | | 43 | 23 | | 44 | 8 | | 45 | 13 | | 46 | 10 | | 47 | 5 | | 48 | 3 | | 49 | 3 |
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| 71.72% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.48484848484848486 | | totalSentences | 66 | | uniqueOpeners | 32 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 39 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 4 | | totalSentences | 39 | | matches | | 0 | "Her eyes widened." | | 1 | "She perched on a stool," | | 2 | "It was the same drink" | | 3 | "She hesitated, then pulled out" |
| | ratio | 0.103 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 36 | | totalSentences | 39 | | matches | | 0 | "The winter rain had driven" | | 1 | "Aurora wiped down the bar" | | 2 | "The evening crowd hadn't arrived" | | 3 | "The door creaked open, letting" | | 4 | "Aurora looked up, the glass" | | 5 | "The woman who walked in" | | 6 | "Aurora's voice cracked slightly on" | | 7 | "Eva Welsh turned, her manicured" | | 8 | "Her eyes widened." | | 9 | "The glass made a dull" | | 10 | "Aurora's fingers found the crescent" | | 11 | "Eva moved to the bar," | | 12 | "She perched on a stool," | | 13 | "Aurora grabbed another glass, needing" | | 14 | "Eva's perfectly lined lips pressed" | | 15 | "Aurora poured two fingers of" | | 16 | "It was the same drink" | | 17 | "Eva stared at the glass." | | 18 | "Aurora leaned against the bar" | | 19 | "Eva took a careful sip" |
| | ratio | 0.923 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 39 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 4 | | matches | | 0 | "The winter rain had driven most of Soho's usual crowd indoors, leaving puddles that reflected the neon signs in fractured ribbons of color." | | 1 | "The evening crowd hadn't arrived yet, and Silas was in his office doing inventory, leaving her alone with her thoughts and the steady drum of raindrops against …" | | 2 | "The woman who walked in shook water from her umbrella, her designer coat and perfectly styled hair a stark contrast to the bar's weathered interior." | | 3 | "She perched on a stool, maintaining the perfect posture that would have made their old PE teacher proud." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 41 | | tagDensity | 0.049 | | leniency | 0.098 | | rawRatio | 0.5 | | effectiveRatio | 0.049 | |