| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 1 | | adverbTags | | 0 | "She smiled back [back]" |
| | dialogueSentences | 22 | | tagDensity | 0.409 | | leniency | 0.818 | | rawRatio | 0.111 | | effectiveRatio | 0.091 | |
| 91.79% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 609 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 34.32% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 609 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "weight" | | 1 | "familiar" | | 2 | "lurching" | | 3 | "stark" | | 4 | "desire" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 94.14% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 27 | | matches | | |
| 89.95% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 27 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 40 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 601 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 26.03% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 19 | | wordCount | 363 | | uniqueNames | 7 | | maxNameDensity | 2.48 | | worstName | "Rory" | | maxWindowNameDensity | 4 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 1 | | Nest | 1 | | London | 1 | | Rory | 9 | | Silas | 1 | | Patrick | 5 | | Cardiff | 1 |
| | persons | | 0 | "Raven" | | 1 | "Rory" | | 2 | "Silas" | | 3 | "Patrick" |
| | places | | | globalScore | 0.26 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 23 | | 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 | 601 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 40 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 28.62 | | std | 15.13 | | cv | 0.529 | | sampleLengths | | 0 | 76 | | 1 | 39 | | 2 | 38 | | 3 | 20 | | 4 | 40 | | 5 | 36 | | 6 | 23 | | 7 | 16 | | 8 | 37 | | 9 | 6 | | 10 | 31 | | 11 | 30 | | 12 | 19 | | 13 | 23 | | 14 | 39 | | 15 | 17 | | 16 | 18 | | 17 | 42 | | 18 | 4 | | 19 | 26 | | 20 | 21 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 27 | | matches | (empty) | |
| 92.47% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 62 | | matches | | |
| 71.43% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 40 | | ratio | 0.025 | | matches | | 0 | "She made her way past the jovial clusters of people, nodding greetings to regular patrons, her gaze skimming over the familiar faces of London's underbelly—small-time drug peddlers, petty criminals, and the odd lawyer from the courthouse across the street." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 308 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.032467532467532464 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.012987012987012988 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 40 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 40 | | mean | 15.03 | | std | 6.73 | | cv | 0.448 | | sampleLengths | | 0 | 20 | | 1 | 17 | | 2 | 22 | | 3 | 17 | | 4 | 39 | | 5 | 25 | | 6 | 13 | | 7 | 20 | | 8 | 15 | | 9 | 25 | | 10 | 22 | | 11 | 14 | | 12 | 11 | | 13 | 12 | | 14 | 10 | | 15 | 6 | | 16 | 7 | | 17 | 18 | | 18 | 12 | | 19 | 6 | | 20 | 15 | | 21 | 16 | | 22 | 21 | | 23 | 9 | | 24 | 7 | | 25 | 12 | | 26 | 10 | | 27 | 13 | | 28 | 22 | | 29 | 17 | | 30 | 15 | | 31 | 2 | | 32 | 18 | | 33 | 13 | | 34 | 16 | | 35 | 13 | | 36 | 4 | | 37 | 13 | | 38 | 13 | | 39 | 21 |
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| 87.50% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.525 | | totalSentences | 40 | | uniqueOpeners | 21 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 26 | | matches | (empty) | | ratio | 0 | |
| 81.54% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 9 | | totalSentences | 26 | | matches | | 0 | "It was a tired, old" | | 1 | "She made her way past" | | 2 | "He quipped, glancing up with" | | 3 | "She smiled back, loading the" | | 4 | "His grey eyes twinkled, casting" | | 5 | "It was a stark contrast" | | 6 | "Her voice cracked with surprise" | | 7 | "She missed this version of" | | 8 | "He nodded, a smile softening" |
| | ratio | 0.346 | |
| 56.15% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 21 | | totalSentences | 26 | | matches | | 0 | "Rory felt the weight of" | | 1 | "The familiar smoky atmosphere washed" | | 2 | "It was a tired, old" | | 3 | "She made her way past" | | 4 | "The man had a way" | | 5 | "He quipped, glancing up with" | | 6 | "She smiled back, loading the" | | 7 | "His grey eyes twinkled, casting" | | 8 | "Rory snorted, leaning against the" | | 9 | "Rory turned, heart lurching as" | | 10 | "It was a stark contrast" | | 11 | "Her voice cracked with surprise" | | 12 | "Patrick's face broke into a" | | 13 | "Rory looked around, suddenly aware" | | 14 | "Patrick's expression grew concerned" | | 15 | "Rory shifted uncomfortably, wishing the" | | 16 | "Patrick glanced at his watch" | | 17 | "Rory hesitated, torn between the" | | 18 | "Rory nodded, feeling the weight" | | 19 | "She missed this version of" |
| | ratio | 0.808 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 26 | | matches | | 0 | "Before Silas could retort, a" | | 1 | "Before her stood a tall," |
| | ratio | 0.077 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 17 | | technicalSentenceCount | 1 | | matches | | 0 | "She made her way past the jovial clusters of people, nodding greetings to regular patrons, her gaze skimming over the familiar faces of London's underbelly—smal…" |
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| 13.89% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 2 | | matches | | 0 | "Patrick's face broke, his blue eyes sparkling with familiar humor" | | 1 | "He nodded, a smile softening his face" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 22 | | tagDensity | 0.045 | | leniency | 0.091 | | rawRatio | 1 | | effectiveRatio | 0.091 | |