| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1363 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 66.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1363 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "flicked" | | 1 | "weight" | | 2 | "silence" | | 3 | "pulse" | | 4 | "chill" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 82 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 82 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 118 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 52 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1359 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 910 | | uniqueNames | 10 | | maxNameDensity | 0.77 | | worstName | "Lucien" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Lucien" | | discoveredNames | | Eva | 3 | | Ptolemy | 3 | | Moreau | 1 | | French-edged | 1 | | Cardiff | 1 | | Evan | 1 | | Lucien | 7 | | Brick | 1 | | Lane | 1 | | Rory | 3 |
| | persons | | 0 | "Eva" | | 1 | "Ptolemy" | | 2 | "Moreau" | | 3 | "Evan" | | 4 | "Lucien" | | 5 | "Rory" |
| | places | | 0 | "Cardiff" | | 1 | "Brick" | | 2 | "Lane" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | glossingSentenceCount | 1 | | matches | | 0 | "something like burnt sugar" |
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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 | 1359 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 118 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 77 | | mean | 17.65 | | std | 15.81 | | cv | 0.896 | | sampleLengths | | 0 | 8 | | 1 | 72 | | 2 | 39 | | 3 | 38 | | 4 | 23 | | 5 | 65 | | 6 | 13 | | 7 | 1 | | 8 | 25 | | 9 | 15 | | 10 | 5 | | 11 | 10 | | 12 | 15 | | 13 | 9 | | 14 | 18 | | 15 | 16 | | 16 | 13 | | 17 | 4 | | 18 | 4 | | 19 | 35 | | 20 | 13 | | 21 | 7 | | 22 | 7 | | 23 | 17 | | 24 | 21 | | 25 | 4 | | 26 | 1 | | 27 | 5 | | 28 | 31 | | 29 | 38 | | 30 | 1 | | 31 | 3 | | 32 | 7 | | 33 | 32 | | 34 | 4 | | 35 | 15 | | 36 | 26 | | 37 | 13 | | 38 | 6 | | 39 | 12 | | 40 | 33 | | 41 | 9 | | 42 | 4 | | 43 | 16 | | 44 | 14 | | 45 | 34 | | 46 | 6 | | 47 | 5 | | 48 | 11 | | 49 | 30 |
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| 96.71% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 82 | | matches | | 0 | "was slicked" | | 1 | "been paid" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 144 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 3 | | flaggedSentences | 6 | | totalSentences | 118 | | ratio | 0.051 | | matches | | 0 | "The third resisted; she put her shoulder into it and the metal gave with a clunk." | | 1 | "His platinum hair was slicked back, not a strand out of place, and his mismatched eyes—amber and void-black—traveled over her face like a man checking a wound for infection." | | 2 | "The name she'd shed after Cardiff—after Evan—the name Lucien had stitched onto a set of false papers and a real danger." | | 3 | "He shifted his weight, and the hallway light caught the blade hidden in the cane's shaft; the hint of steel showed at the collar." | | 4 | "His amber eye narrowed; the black one stayed fathomless." | | 5 | "She hated that he could still do that, hated the way her body remembered the exact pressure of his hand at the small of her back, the scrape of his teeth on her lower lip in the back of a black cab, the taste of him—rain and something like burnt sugar." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 921 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.02280130293159609 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.002171552660152009 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 118 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 118 | | mean | 11.52 | | std | 9.1 | | cv | 0.79 | | sampleLengths | | 0 | 8 | | 1 | 7 | | 2 | 16 | | 3 | 24 | | 4 | 25 | | 5 | 5 | | 6 | 20 | | 7 | 5 | | 8 | 3 | | 9 | 6 | | 10 | 5 | | 11 | 15 | | 12 | 2 | | 13 | 16 | | 14 | 14 | | 15 | 5 | | 16 | 4 | | 17 | 25 | | 18 | 11 | | 19 | 29 | | 20 | 7 | | 21 | 6 | | 22 | 1 | | 23 | 8 | | 24 | 4 | | 25 | 13 | | 26 | 8 | | 27 | 7 | | 28 | 5 | | 29 | 10 | | 30 | 15 | | 31 | 9 | | 32 | 18 | | 33 | 11 | | 34 | 4 | | 35 | 1 | | 36 | 13 | | 37 | 4 | | 38 | 4 | | 39 | 9 | | 40 | 10 | | 41 | 16 | | 42 | 13 | | 43 | 7 | | 44 | 7 | | 45 | 17 | | 46 | 21 | | 47 | 4 | | 48 | 1 | | 49 | 5 |
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| 47.18% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.3135593220338983 | | totalSentences | 118 | | uniqueOpeners | 37 | |
| 43.29% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 77 | | matches | | 0 | "Then she forced air past" |
| | ratio | 0.013 | |
| 1.82% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 77 | | matches | | 0 | "She had thrown all three" | | 1 | "She crossed the cramped hall," | | 2 | "Her delivery bag sat by" | | 3 | "She didn't look at it." | | 4 | "She opened the door." | | 5 | "His platinum hair was slicked" | | 6 | "Her fingers tightened on the" | | 7 | "He didn't step forward." | | 8 | "He waited, the way a" | | 9 | "His mouth curved, but the" | | 10 | "He glanced at the tea" | | 11 | "Her free hand found the" | | 12 | "She didn't throw it." | | 13 | "He shifted his weight." | | 14 | "Her shoulder pressed harder into" | | 15 | "She hadn't heard it spoken" | | 16 | "Her voice dropped." | | 17 | "He shifted his weight, and" | | 18 | "She had seen it slide" | | 19 | "He shifted his weight again," |
| | ratio | 0.545 | |
| 5.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 77 | | matches | | 0 | "The kettle had boiled when" | | 1 | "Rory stilled, mug halfway to" | | 2 | "Steam curled around her left" | | 3 | "The smell of cumin and" | | 4 | "Books covered the table, scrolls" | | 5 | "She had thrown all three" | | 6 | "The knock came again, firmer." | | 7 | "Rory set the mug down." | | 8 | "She crossed the cramped hall," | | 9 | "The third resisted; she put" | | 10 | "Her delivery bag sat by" | | 11 | "She didn't look at it." | | 12 | "She opened the door." | | 13 | "Lucien Moreau stood in the" | | 14 | "Rain dotted his charcoal suit," | | 15 | "His platinum hair was slicked" | | 16 | "Her fingers tightened on the" | | 17 | "The scar on her wrist" | | 18 | "The syllable dropped between them," | | 19 | "He didn't step forward." |
| | ratio | 0.909 | |
| 64.94% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 77 | | matches | | 0 | "If anything, he leaned in," |
| | ratio | 0.013 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |