| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.07% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1271 | | totalAiIsmAdverbs | 1 | | 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) | |
| 84.26% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1271 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "vibrated" | | 1 | "stomach" | | 2 | "echoing" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "stomach dropped/sank" | | count | 1 |
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| | highlights | | 0 | "eyes widened" | | 1 | "stomach dropped" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 55 | | matches | | |
| 64.94% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 55 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 55 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 52 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1265 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 1252 | | uniqueNames | 19 | | maxNameDensity | 0.64 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 8 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Thames | 1 | | Tomás | 2 | | Herrera | 8 | | Saint | 1 | | Christopher | 1 | | London | 4 | | Old | 1 | | Compton | 1 | | Street | 1 | | Morris | 3 | | East | 2 | | Veil | 1 | | Market | 1 | | Camden | 1 | | Tube | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Thames" | | 2 | "London" | | 3 | "Old" | | 4 | "Compton" | | 5 | "Street" | | 6 | "East" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | 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 | 1265 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 55 | | matches | | |
| 21.33% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 14 | | mean | 90.36 | | std | 20.3 | | cv | 0.225 | | sampleLengths | | 0 | 117 | | 1 | 113 | | 2 | 102 | | 3 | 71 | | 4 | 67 | | 5 | 84 | | 6 | 82 | | 7 | 63 | | 8 | 106 | | 9 | 96 | | 10 | 132 | | 11 | 70 | | 12 | 85 | | 13 | 77 |
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| 98.88% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 55 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 211 | | matches | | 0 | "was running" | | 1 | "was standing" | | 2 | "was hurrying" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 55 | | ratio | 0.091 | | matches | | 0 | "He’d lost his NHS paramedic license two years prior, after administering unapproved IV treatments to a teenager who’d been hurt in a supernatural gang fight—something Quinn had only found out about when the teenager’s mother had filed a complaint that had led back to Herrera’s off-the-books clinic." | | 1 | "Herrera paused at the top of the stairs, fumbling in his jacket pocket before pulling out a small, chipped white bone—femur, she thought, based on the size—and pressed it to a rusted metal plate set into the concrete wall beside the stairs." | | 2 | "She raised her left wrist, checking her watch—quarter past one a.m." | | 3 | "It wasn’t music, not exactly—there was a high, thin note layered over the stringed instruments, something that made the fine hairs on her neck stand on end." | | 4 | "She thought about the clique’s recent robberies, the way the victims had described a mist that had slipped through their windows, the way valuables had vanished without a trace—something only possible with enchanted tools." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1106 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.018083182640144666 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.0054249547920434 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 55 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 55 | | mean | 23 | | std | 11.17 | | cv | 0.486 | | sampleLengths | | 0 | 25 | | 1 | 21 | | 2 | 21 | | 3 | 50 | | 4 | 20 | | 5 | 12 | | 6 | 32 | | 7 | 21 | | 8 | 28 | | 9 | 24 | | 10 | 47 | | 11 | 31 | | 12 | 19 | | 13 | 30 | | 14 | 22 | | 15 | 42 | | 16 | 25 | | 17 | 7 | | 18 | 6 | | 19 | 28 | | 20 | 18 | | 21 | 17 | | 22 | 3 | | 23 | 5 | | 24 | 38 | | 25 | 37 | | 26 | 7 | | 27 | 11 | | 28 | 44 | | 29 | 3 | | 30 | 5 | | 31 | 31 | | 32 | 30 | | 33 | 18 | | 34 | 27 | | 35 | 18 | | 36 | 28 | | 37 | 29 | | 38 | 21 | | 39 | 29 | | 40 | 25 | | 41 | 33 | | 42 | 27 | | 43 | 18 | | 44 | 17 | | 45 | 24 | | 46 | 29 | | 47 | 27 | | 48 | 34 | | 49 | 24 |
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| 51.52% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.36363636363636365 | | totalSentences | 55 | | uniqueOpeners | 20 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 52.73% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 55 | | matches | | 0 | "Her left wrist’s worn leather" | | 1 | "She held her pace with" | | 2 | "She recognized him before the" | | 3 | "His Saint Christopher medallion peeked" | | 4 | "He hauled a frayed canvas" | | 5 | "She’d been tailing him for" | | 6 | "He’d lost his NHS paramedic" | | 7 | "She’d seen that bone token" | | 8 | "He’d called her that night," | | 9 | "She’d told him to hurry" | | 10 | "He never did." | | 11 | "His body was never found." | | 12 | "She’d spent every day since" | | 13 | "She raised her left wrist," | | 14 | "She took a step toward" | | 15 | "It wasn’t music, not exactly—there" | | 16 | "She hurried the last few" | | 17 | "His eyes widened, and he" | | 18 | "She thought about Morris, his" | | 19 | "She thought about the clique’s" |
| | ratio | 0.418 | |
| 23.64% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 48 | | totalSentences | 55 | | matches | | 0 | "The rain bit through Quinn’s" | | 1 | "Her left wrist’s worn leather" | | 2 | "She held her pace with" | | 3 | "A taxi honked a sharp" | | 4 | "Quinn flinched only slightly, her" | | 5 | "She recognized him before the" | | 6 | "His Saint Christopher medallion peeked" | | 7 | "He hauled a frayed canvas" | | 8 | "She’d been tailing him for" | | 9 | "He’d lost his NHS paramedic" | | 10 | "The chase twisted down a" | | 11 | "Tomás darted through a gap" | | 12 | "Herrera paused at the top" | | 13 | "The metal grumbled, sliding inward" | | 14 | "Quinn froze halfway through the" | | 15 | "She’d seen that bone token" | | 16 | "He’d called her that night," | | 17 | "She’d told him to hurry" | | 18 | "He never did." | | 19 | "His body was never found." |
| | ratio | 0.873 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 11 | | matches | | 0 | "Two blocks back, she’d spotted him slipping out of the Raven’s Nest, the Soho bar lined with old maps and black-and-white photos that she’d been staking out for…" | | 1 | "He’d lost his NHS paramedic license two years prior, after administering unapproved IV treatments to a teenager who’d been hurt in a supernatural gang fight—som…" | | 2 | "The metal grumbled, sliding inward to reveal a narrow, dripping tunnel, and Herrera did not hesitate before descending, his boots thudding against the stone ste…" | | 3 | "She’d spent every day since then digging into supernatural circles in London, chasing every lead that might lead to whatever had taken Morris, and this was the …" | | 4 | "But now she was standing at the top of a hidden stairwell, rain dripping off the brim of her trench coat onto the stone below, staring into a tunnel that led in…" | | 5 | "It wasn’t music, not exactly—there was a high, thin note layered over the stringed instruments, something that made the fine hairs on her neck stand on end." | | 6 | "She hurried the last few steps, her breath coming faster now, and stepped into the vast, vaulted underground space that had once been a Camden Tube station plat…" | | 7 | "A man with horns curling from his forehead leaned against a stack of old newspapers, smoking a cigarette that curled purple smoke into the air." | | 8 | "She thought about Morris, his laugh echoing in her ears as they’d driven to that East London warehouse, saying he’d have the lead wrapped up by morning." | | 9 | "She thought about the clique’s recent robberies, the way the victims had described a mist that had slipped through their windows, the way valuables had vanished…" | | 10 | "She pulled her baton from her belt, clicking it open with a sharp snap, and stepped into the crowd, the market’s whispers falling silent as the non-human vendor…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |