| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 25 | | tagDensity | 0.32 | | leniency | 0.64 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.43% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 946 | | totalAiIsmAdverbs | 2 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 946 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "familiar" | | 1 | "whisper" | | 2 | "scanned" | | 3 | "testament" | | 4 | "comforting" | | 5 | "sanctuary" | | 6 | "stark" | | 7 | "reminder" | | 8 | "glint" | | 9 | "pang" | | 10 | "warmth" | | 11 | "echoed" | | 12 | "tension" | | 13 | "weight" | | 14 | "unspoken" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "couldn't help but" | | count | 1 |
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| | highlights | | 0 | "couldn't help but think" |
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| 81.14% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 3 | | narrationSentences | 57 | | matches | | 0 | "was bitter" | | 1 | "d with excitement" | | 2 | "felt a lump" |
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| 67.67% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 57 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 74 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 940 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 702 | | uniqueNames | 10 | | maxNameDensity | 2.71 | | worstName | "Rory" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Carter | 1 | | Rory | 19 | | London | 2 | | Golden | 1 | | Empress | 1 | | Blackwood | 1 | | Eva | 15 | | Silas | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Carter" | | 3 | "Rory" | | 4 | "Blackwood" | | 5 | "Eva" | | 6 | "Silas" |
| | places | | | globalScore | 0.147 | | windowScore | 0 | |
| 98.98% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 49 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 940 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 74 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 30.32 | | std | 19.01 | | cv | 0.627 | | sampleLengths | | 0 | 46 | | 1 | 92 | | 2 | 54 | | 3 | 17 | | 4 | 20 | | 5 | 13 | | 6 | 74 | | 7 | 16 | | 8 | 18 | | 9 | 47 | | 10 | 18 | | 11 | 12 | | 12 | 20 | | 13 | 45 | | 14 | 22 | | 15 | 21 | | 16 | 15 | | 17 | 34 | | 18 | 22 | | 19 | 14 | | 20 | 24 | | 21 | 22 | | 22 | 9 | | 23 | 50 | | 24 | 39 | | 25 | 47 | | 26 | 24 | | 27 | 16 | | 28 | 30 | | 29 | 16 | | 30 | 43 |
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| 92.95% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 57 | | matches | | 0 | "was tied" | | 1 | "were interrupted" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 108 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 74 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 708 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 13 | | adverbRatio | 0.018361581920903956 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.002824858757062147 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 74 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 74 | | mean | 12.7 | | std | 5.67 | | cv | 0.446 | | sampleLengths | | 0 | 16 | | 1 | 13 | | 2 | 17 | | 3 | 29 | | 4 | 13 | | 5 | 25 | | 6 | 25 | | 7 | 13 | | 8 | 21 | | 9 | 20 | | 10 | 17 | | 11 | 10 | | 12 | 10 | | 13 | 13 | | 14 | 17 | | 15 | 15 | | 16 | 10 | | 17 | 14 | | 18 | 7 | | 19 | 11 | | 20 | 16 | | 21 | 11 | | 22 | 7 | | 23 | 6 | | 24 | 11 | | 25 | 17 | | 26 | 13 | | 27 | 12 | | 28 | 6 | | 29 | 6 | | 30 | 6 | | 31 | 10 | | 32 | 10 | | 33 | 28 | | 34 | 17 | | 35 | 5 | | 36 | 17 | | 37 | 4 | | 38 | 17 | | 39 | 4 | | 40 | 11 | | 41 | 3 | | 42 | 23 | | 43 | 8 | | 44 | 5 | | 45 | 17 | | 46 | 7 | | 47 | 7 | | 48 | 11 | | 49 | 13 |
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| 59.91% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3918918918918919 | | totalSentences | 74 | | uniqueOpeners | 29 | |
| 58.48% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 57 | | matches | | 0 | "Then, a smile spread across" |
| | ratio | 0.018 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 57 | | matches | | 0 | "She scanned the room, her" | | 1 | "Her shoulder-length black hair was" | | 2 | "She had been working a" | | 3 | "His hazel eyes crinkled at" | | 4 | "he said, a hint of" | | 5 | "She laughed, a sound that" | | 6 | "he replied, winking as he" | | 7 | "Her thoughts were interrupted by" | | 8 | "Her eyes, a deep green," | | 9 | "She took a deep breath," | | 10 | "he said, raising his glass" | | 11 | "They sipped their drinks, the" | | 12 | "They clinked glasses again, the" |
| | ratio | 0.228 | |
| 30.18% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 49 | | totalSentences | 57 | | matches | | 0 | "The neon sign above The" | | 1 | "The walls, adorned with old" | | 2 | "Aurora Carter, known to her" | | 3 | "She scanned the room, her" | | 4 | "Her shoulder-length black hair was" | | 5 | "She had been working a" | | 6 | "Silas Blackwood, the owner of" | | 7 | "His hazel eyes crinkled at" | | 8 | "he said, a hint of" | | 9 | "She laughed, a sound that" | | 10 | "he replied, winking as he" | | 11 | "Rory took a seat at" | | 12 | "The place was as comforting" | | 13 | "Her thoughts were interrupted by" | | 14 | "Rory's voice was a mix" | | 15 | "Eva's smile widened, and she" | | 16 | "Rory stood, the two women" | | 17 | "Eva's presence was a shock," | | 18 | "Eva's hair was still a" | | 19 | "Her eyes, a deep green," |
| | ratio | 0.86 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 57 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 1 | | matches | | 0 | "Rory hesitated, her mind flashing back to the long hours at the restaurant, the empty flat above the bar, the occasional delivery that lingered a bit too long." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 3 | | matches | | 0 | "he said, a hint of concern in his voice" | | 1 | "She took, trying to shake off the memories" | | 2 | "Eva said, her voice low" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 25 | | tagDensity | 0.24 | | leniency | 0.48 | | rawRatio | 0 | | effectiveRatio | 0 | |