| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said softly [softly]" |
| | dialogueSentences | 41 | | tagDensity | 0.488 | | leniency | 0.976 | | rawRatio | 0.05 | | effectiveRatio | 0.049 | |
| 83.04% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1474 | | totalAiIsmAdverbs | 5 | | 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) | |
| 18.59% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1474 | | totalAiIsms | 24 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "familiar" | | 1 | "shattered" | | 2 | "silence" | | 3 | "pounding" | | 4 | "measured" | | 5 | "sanctuary" | | 6 | "intensity" | | 7 | "vibrated" | | 8 | "weight" | | 9 | "reminder" | | 10 | "magnetic" | | 11 | "palpable" | | 12 | "warmth" | | 13 | "echoing" | | 14 | "whisper" | | 15 | "fractured" | | 16 | "charged" | | 17 | "tension" | | 18 | "could feel" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "sent a shiver through" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 96 | | matches | | |
| 98.21% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 96 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 117 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1474 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 97.83% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1150 | | uniqueNames | 10 | | maxNameDensity | 1.04 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Aurora" | | discoveredNames | | Aurora | 12 | | Eva | 3 | | Golden | 1 | | Empress | 1 | | Ptolemy | 2 | | Moreau | 1 | | Shoreditch | 1 | | Lucien | 11 | | Avaros | 1 | | London | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Eva" | | 2 | "Ptolemy" | | 3 | "Moreau" | | 4 | "Lucien" |
| | places | | 0 | "Golden" | | 1 | "Shoreditch" | | 2 | "London" |
| | globalScore | 0.978 | | windowScore | 1 | |
| 85.90% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 78 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like a trap" | | 1 | "looked like love, before he turned his ba" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1474 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 117 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 46 | | mean | 32.04 | | std | 20.76 | | cv | 0.648 | | sampleLengths | | 0 | 78 | | 1 | 5 | | 2 | 64 | | 3 | 49 | | 4 | 14 | | 5 | 36 | | 6 | 84 | | 7 | 18 | | 8 | 21 | | 9 | 42 | | 10 | 48 | | 11 | 23 | | 12 | 50 | | 13 | 51 | | 14 | 10 | | 15 | 48 | | 16 | 5 | | 17 | 5 | | 18 | 56 | | 19 | 10 | | 20 | 75 | | 21 | 21 | | 22 | 9 | | 23 | 41 | | 24 | 39 | | 25 | 11 | | 26 | 50 | | 27 | 4 | | 28 | 36 | | 29 | 35 | | 30 | 26 | | 31 | 33 | | 32 | 27 | | 33 | 47 | | 34 | 30 | | 35 | 64 | | 36 | 7 | | 37 | 9 | | 38 | 21 | | 39 | 22 | | 40 | 32 | | 41 | 11 | | 42 | 28 | | 43 | 10 | | 44 | 28 | | 45 | 41 |
| |
| 97.95% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 96 | | matches | | 0 | "was cramped" | | 1 | "was gone" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 205 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 117 | | ratio | 0.009 | | matches | | 0 | "She glanced at the window; the street outside was dark, save for the flickering neon of a kebab shop across the way." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1160 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.02413793103448276 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.010344827586206896 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 117 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 117 | | mean | 12.6 | | std | 6.48 | | cv | 0.515 | | sampleLengths | | 0 | 22 | | 1 | 6 | | 2 | 28 | | 3 | 22 | | 4 | 5 | | 5 | 23 | | 6 | 11 | | 7 | 8 | | 8 | 22 | | 9 | 13 | | 10 | 14 | | 11 | 12 | | 12 | 5 | | 13 | 5 | | 14 | 14 | | 15 | 3 | | 16 | 10 | | 17 | 23 | | 18 | 21 | | 19 | 23 | | 20 | 21 | | 21 | 19 | | 22 | 3 | | 23 | 15 | | 24 | 12 | | 25 | 9 | | 26 | 18 | | 27 | 24 | | 28 | 17 | | 29 | 15 | | 30 | 12 | | 31 | 4 | | 32 | 14 | | 33 | 9 | | 34 | 10 | | 35 | 14 | | 36 | 22 | | 37 | 4 | | 38 | 8 | | 39 | 18 | | 40 | 11 | | 41 | 14 | | 42 | 8 | | 43 | 2 | | 44 | 11 | | 45 | 27 | | 46 | 10 | | 47 | 5 | | 48 | 5 | | 49 | 12 |
| |
| 34.62% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.26495726495726496 | | totalSentences | 117 | | uniqueOpeners | 31 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 50 | | totalSentences | 88 | | matches | | 0 | "She sat cross-legged on the" | | 1 | "It wasn’t the frantic pounding" | | 2 | "It was measured, deliberate, and" | | 3 | "She glanced at the window;" | | 4 | "She slid off the floor," | | 5 | "She peered through the peephole." | | 6 | "She hadn’t expected to see" | | 7 | "She turned the handle and" | | 8 | "He looked exactly as he" | | 9 | "He wore a tailored charcoal" | | 10 | "His eyes, one amber and" | | 11 | "His voice was smooth, a" | | 12 | "She didn’t step back, but" | | 13 | "He shifted his weight, the" | | 14 | "It was a stupid, childish" | | 15 | "He knew the way she" | | 16 | "He knew too much." | | 17 | "she said, her voice steady" | | 18 | "He smelled of expensive cologne," | | 19 | "It was a scent that" |
| | ratio | 0.568 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 85 | | totalSentences | 88 | | matches | | 0 | "The smell of cardamom and" | | 1 | "Tonight, it felt like a" | | 2 | "She sat cross-legged on the" | | 3 | "The flat was cramped, every" | | 4 | "A knock shattered the silence." | | 5 | "It wasn’t the frantic pounding" | | 6 | "It was measured, deliberate, and" | | 7 | "Aurora froze, her pen hovering" | | 8 | "She glanced at the window;" | | 9 | "She slid off the floor," | | 10 | "The tabby cat, Ptolemy, blinked" | | 11 | "Aurora’s hand went to the" | | 12 | "The metal clacked with finality." | | 13 | "She peered through the peephole." | | 14 | "The distortion made the figure" | | 15 | "Aurora’s breath hitched." | | 16 | "She hadn’t expected to see" | | 17 | "She turned the handle and" | | 18 | "Lucien Moreau stood in the" | | 19 | "He looked exactly as he" |
| | ratio | 0.966 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 37.04% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 8 | | matches | | 0 | "She sat cross-legged on the floor of Eva’s flat, surrounded by a fortress of paperbacks and half-unrolled scrolls, trying to decipher a rune that refused to mak…" | | 1 | "He wore a tailored charcoal suit that cost more than Aurora’s monthly rent, and his ivory-handled cane rested against his leg." | | 2 | "His eyes, one amber and one black, locked onto hers with a predatory intensity that made her skin prickle." | | 3 | "Lucien’s lips curved into a smile that didn’t reach his eyes." | | 4 | "The way he had looked at her, betrayal warring with something that looked like love, before he turned his back." | | 5 | "He reached out, his fingers brushing the fabric of her sleeve, stopping just short of her skin." | | 6 | "The heat radiating from him was palpable, a warmth that felt unnatural against the cool London night." | | 7 | "He reached out again, this time gripping her hand, his fingers interlacing with hers." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 6 | | matches | | 0 | "He shifted, the tip of his cane tapping the linoleum" | | 1 | "she said, her voice steady despite the hammering of her heart" | | 2 | "she said, though the words lacked conviction" | | 3 | "she asked, her voice barely a whisper" | | 4 | "she said, her voice trembling slightly" | | 5 | "Lucien said, his voice low" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 41 | | tagDensity | 0.317 | | leniency | 0.634 | | rawRatio | 0.077 | | effectiveRatio | 0.049 | |