| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 31 | | tagDensity | 0.194 | | leniency | 0.387 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.29% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 854 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 18.03% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 854 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "stomach" | | 1 | "pulse" | | 2 | "unreadable" | | 3 | "gleaming" | | 4 | "silence" | | 5 | "reminder" | | 6 | "unspoken" | | 7 | "racing" | | 8 | "echoing" | | 9 | "pounding" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | 0 | "clenched into fists" | | 1 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 79 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 79 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 104 | | 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 | 2 | | markdownWords | 5 | | totalWords | 849 | | ratio | 0.006 | | matches | | 0 | "He’s not here." | | 1 | "Knock knock." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 78.88% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 703 | | uniqueNames | 7 | | maxNameDensity | 1.42 | | worstName | "Aurora" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Aurora" | | discoveredNames | | Eva | 4 | | Carter | 1 | | Rory | 1 | | Aurora | 10 | | Lucien | 9 | | Moreau | 1 | | Cardiff | 1 |
| | persons | | 0 | "Eva" | | 1 | "Carter" | | 2 | "Rory" | | 3 | "Aurora" | | 4 | "Lucien" | | 5 | "Moreau" |
| | places | | | globalScore | 0.789 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | 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 | 849 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 104 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 22.34 | | std | 16.27 | | cv | 0.728 | | sampleLengths | | 0 | 48 | | 1 | 73 | | 2 | 13 | | 3 | 38 | | 4 | 68 | | 5 | 14 | | 6 | 15 | | 7 | 44 | | 8 | 15 | | 9 | 36 | | 10 | 17 | | 11 | 21 | | 12 | 10 | | 13 | 9 | | 14 | 31 | | 15 | 18 | | 16 | 17 | | 17 | 10 | | 18 | 40 | | 19 | 7 | | 20 | 33 | | 21 | 9 | | 22 | 13 | | 23 | 26 | | 24 | 9 | | 25 | 7 | | 26 | 20 | | 27 | 35 | | 28 | 8 | | 29 | 10 | | 30 | 12 | | 31 | 10 | | 32 | 17 | | 33 | 11 | | 34 | 14 | | 35 | 17 | | 36 | 43 | | 37 | 11 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 79 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 121 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 104 | | ratio | 0.01 | | matches | | 0 | "Aurora Carter—Rory, to those who knew her well—hadn’t expected anyone." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 708 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped mark" |
| | adverbCount | 22 | | adverbRatio | 0.031073446327683617 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.007062146892655367 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 104 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 104 | | mean | 8.16 | | std | 6.95 | | cv | 0.851 | | sampleLengths | | 0 | 28 | | 1 | 10 | | 2 | 4 | | 3 | 6 | | 4 | 28 | | 5 | 16 | | 6 | 17 | | 7 | 9 | | 8 | 3 | | 9 | 11 | | 10 | 2 | | 11 | 3 | | 12 | 8 | | 13 | 12 | | 14 | 5 | | 15 | 5 | | 16 | 5 | | 17 | 10 | | 18 | 32 | | 19 | 26 | | 20 | 7 | | 21 | 7 | | 22 | 3 | | 23 | 5 | | 24 | 7 | | 25 | 4 | | 26 | 10 | | 27 | 30 | | 28 | 12 | | 29 | 3 | | 30 | 11 | | 31 | 15 | | 32 | 6 | | 33 | 4 | | 34 | 12 | | 35 | 5 | | 36 | 3 | | 37 | 3 | | 38 | 10 | | 39 | 5 | | 40 | 3 | | 41 | 7 | | 42 | 9 | | 43 | 9 | | 44 | 5 | | 45 | 5 | | 46 | 12 | | 47 | 13 | | 48 | 5 | | 49 | 3 |
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| 38.46% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.2692307692307692 | | totalSentences | 104 | | uniqueOpeners | 28 | |
| 45.66% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 73 | | matches | | 0 | "Instead, she crossed her arms," |
| | ratio | 0.014 | |
| 83.01% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 73 | | matches | | 0 | "She stood in the narrow" | | 1 | "She exhaled through her nose," | | 2 | "*He’s not here.*" | | 3 | "She hadn’t heard from Lucien" | | 4 | "She’d thought he’d moved on." | | 5 | "he said, his voice low," | | 6 | "She didn’t move." | | 7 | "It was the same as" | | 8 | "He smirked, the faintest hint" | | 9 | "She didn’t laugh." | | 10 | "His expression darkened." | | 11 | "She didn’t have to." | | 12 | "he said, his voice a" | | 13 | "Her childhood friend." | | 14 | "she whispered, her voice breaking" | | 15 | "He nodded, his expression grim." | | 16 | "She turned away, her back" | | 17 | "She didn’t look at him." | | 18 | "he said, his voice firm" | | 19 | "She wanted to scream." |
| | ratio | 0.342 | |
| 21.64% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 73 | | matches | | 0 | "The brass knocker scraped against" | | 1 | "Aurora Carter—Rory, to those who" | | 2 | "She stood in the narrow" | | 3 | "The flat smelled of old" | | 4 | "The deadbolts were engaged, the" | | 5 | "She exhaled through her nose," | | 6 | "*He’s not here.*" | | 7 | "Aurora’s pulse jumped." | | 8 | "She hadn’t heard from Lucien" | | 9 | "She’d thought he’d moved on." | | 10 | "That he’d found someone else." | | 11 | "That he’d forgotten her entirely." | | 12 | "The door creaked open before" | | 13 | "The light from the hallway" | | 14 | "Lucien stood there, his tailored" | | 15 | "he said, his voice low," | | 16 | "She didn’t move." | | 17 | "A beat of silence." | | 18 | "The kind that hung between" | | 19 | "The kind that made her" |
| | ratio | 0.877 | |
| 68.49% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 73 | | matches | | 0 | "To do anything but stand" |
| | ratio | 0.014 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 3 | | matches | | 0 | "The kind that hung between them like a half-remembered dream." | | 1 | "The kind that made her fingers twitch toward the scar on her wrist, the small crescent-shaped mark from a childhood accident, a reminder of the past she’d tried…" | | 2 | "The door clicked shut behind him, the sound echoing through the flat, sealing them in a moment of silence that was both heavy and light." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 6 | | matches | | 0 | "he said, his voice low, smooth" | | 1 | "he said, his voice a low growl" | | 2 | "she whispered, her voice breaking" | | 3 | "Lucien said, his voice gentle" | | 4 | "he said, his voice firm" | | 5 | "she said, her voice breaking" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 1 | | fancyTags | | 0 | "she whispered (whisper)" |
| | dialogueSentences | 31 | | tagDensity | 0.194 | | leniency | 0.387 | | rawRatio | 0.167 | | effectiveRatio | 0.065 | |