| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.01% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1252 | | 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) | |
| 76.04% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1252 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "glinting" | | 1 | "flicked" | | 2 | "echoing" | | 3 | "traced" | | 4 | "weight" | | 5 | "familiar" |
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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 | 43 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 43 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 52 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 65 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1252 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 911 | | uniqueNames | 12 | | maxNameDensity | 0.55 | | worstName | "Eva" | | maxWindowNameDensity | 1 | | worstWindowName | "Eva" | | discoveredNames | | Eva | 5 | | Evan | 2 | | Ford | 1 | | Fiesta | 1 | | Moreau | 1 | | Marseille | 1 | | Ptolemy | 2 | | Lucien | 2 | | Shoreditch | 1 | | London | 1 | | Cardiff | 3 | | Sunday | 1 |
| | persons | | 0 | "Eva" | | 1 | "Evan" | | 2 | "Fiesta" | | 3 | "Moreau" | | 4 | "Ptolemy" | | 5 | "Lucien" |
| | places | | 0 | "Marseille" | | 1 | "Shoreditch" | | 2 | "London" | | 3 | "Cardiff" |
| | globalScore | 1 | | windowScore | 1 | |
| 18.42% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 38 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like a lifetime ago" | | 1 | "looked like it held smoke, fixed on mine" |
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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 | 1252 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 52 | | matches | | |
| 69.89% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 16 | | mean | 78.25 | | std | 30.88 | | cv | 0.395 | | sampleLengths | | 0 | 103 | | 1 | 97 | | 2 | 16 | | 3 | 89 | | 4 | 131 | | 5 | 69 | | 6 | 67 | | 7 | 83 | | 8 | 97 | | 9 | 58 | | 10 | 87 | | 11 | 74 | | 12 | 113 | | 13 | 103 | | 14 | 47 | | 15 | 18 |
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| 97.10% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 43 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 162 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 52 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 918 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.023965141612200435 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0010893246187363835 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 52 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 52 | | mean | 24.08 | | std | 15.21 | | cv | 0.632 | | sampleLengths | | 0 | 31 | | 1 | 37 | | 2 | 35 | | 3 | 15 | | 4 | 16 | | 5 | 25 | | 6 | 2 | | 7 | 29 | | 8 | 4 | | 9 | 6 | | 10 | 11 | | 11 | 5 | | 12 | 47 | | 13 | 24 | | 14 | 18 | | 15 | 32 | | 16 | 27 | | 17 | 27 | | 18 | 45 | | 19 | 7 | | 20 | 27 | | 21 | 28 | | 22 | 7 | | 23 | 51 | | 24 | 16 | | 25 | 5 | | 26 | 34 | | 27 | 44 | | 28 | 12 | | 29 | 9 | | 30 | 25 | | 31 | 14 | | 32 | 11 | | 33 | 26 | | 34 | 26 | | 35 | 32 | | 36 | 30 | | 37 | 57 | | 38 | 9 | | 39 | 65 | | 40 | 31 | | 41 | 39 | | 42 | 43 | | 43 | 20 | | 44 | 3 | | 45 | 48 | | 46 | 3 | | 47 | 29 | | 48 | 6 | | 49 | 11 |
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| 29.49% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3269230769230769 | | totalSentences | 52 | | uniqueOpeners | 17 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 42 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 42 | | matches | | 0 | "I fumbled the third deadbolt," | | 1 | "She’d run out ten minutes" | | 2 | "I hadn’t heard from him" | | 3 | "I gripped the lemon hard" | | 4 | "His voice was low, rough" | | 5 | "I stepped back, unable to" | | 6 | "I hauled the door shut" | | 7 | "I saw Lucien’s eyes lock" | | 8 | "He’d traced that scar once," | | 9 | "I’d told him about falling" | | 10 | "I nodded at the stack" | | 11 | "He didn’t move the cane." | | 12 | "He leaned his weight on" | | 13 | "I froze mid-step toward the" | | 14 | "I’d moved to London to" | | 15 | "I’d changed my number, deleted" | | 16 | "I’d fled Cardiff in the" | | 17 | "My voice came out sharper" | | 18 | "He stepped forward, the cane" | | 19 | "I laughed, and it came" |
| | ratio | 0.643 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 41 | | totalSentences | 42 | | matches | | 0 | "I fumbled the third deadbolt," | | 1 | "She’d run out ten minutes" | | 2 | "The lemon dripped sour juice" | | 3 | "Platinum blond hair, slicked back" | | 4 | "A tailored charcoal suit, no" | | 5 | "An ivory-handled cane planted firm" | | 6 | "The man who’d left a" | | 7 | "I hadn’t heard from him" | | 8 | "I gripped the lemon hard" | | 9 | "His voice was low, rough" | | 10 | "Ptolemy, Eva’s overgrown barn cat" | | 11 | "I stepped back, unable to" | | 12 | "I hauled the door shut" | | 13 | "Eva’s flat was never less" | | 14 | "The smell of garam masala" | | 15 | "I saw Lucien’s eyes lock" | | 16 | "He’d traced that scar once," | | 17 | "I’d told him about falling" | | 18 | "That night felt like a" | | 19 | "I nodded at the stack" |
| | ratio | 0.976 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 42 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 8 | | matches | | 0 | "The man who’d left a crumpled scrap of notebook paper on my kitchen counter two months prior, scrawled in his sharp, slanted handwriting: I can’t put you in dan…" | | 1 | "Ptolemy, Eva’s overgrown barn cat that hated every stranger that crossed the threshold, was purring so loud I could hear it from the door." | | 2 | "I stepped back, unable to block the door forever when the couple that ran the curry house below trundled past with a stack of takeaway boxes, side-eyeing the st…" | | 3 | "Eva’s flat was never less than cluttered, every surface covered in half-written notes, yellowed parchment, mugs of cold tea that had been left to stew for days." | | 4 | "He leaned his weight on it slightly, his boots sinking into the threadbare rug that covered the floorboards, and shrugged out of his suit jacket, draping it ove…" | | 5 | "I’d changed my number, deleted all my social media, taken a job that didn’t list my name anywhere public, and I’d thought I’d escaped him." | | 6 | "He closed the gap between us, and I could smell his cologne, sandalwood and old cigar smoke, the same scent that had clung to my sheets for weeks after he left." | | 7 | "He lifted a hand, brushed a strand of black hair that had fallen in my face behind my ear, and his palm lingered on my cheek, warm and calloused from years of h…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 78.57% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 14 | | tagDensity | 0.071 | | leniency | 0.143 | | rawRatio | 1 | | effectiveRatio | 0.143 | |