| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 22 | | tagDensity | 0.545 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.69% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1203 | | 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) | |
| 12.72% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1203 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | word | "practiced ease" | | count | 1 |
| | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | |
| | highlights | | 0 | "rhythmic" | | 1 | "reminder" | | 2 | "maw" | | 3 | "practiced ease" | | 4 | "unraveling" | | 5 | "scanned" | | 6 | "gloom" | | 7 | "scanning" | | 8 | "imposing" | | 9 | "velvet" | | 10 | "intensity" | | 11 | "whisper" | | 12 | "echoing" | | 13 | "pulse" | | 14 | "oppressive" | | 15 | "chilling" | | 16 | "silence" | | 17 | "palpable" | | 18 | "sense of" | | 19 | "echo" |
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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 | 76 | | matches | (empty) | |
| 67.67% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 76 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 87 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 3 | | totalWords | 1213 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.01% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 1045 | | uniqueNames | 15 | | maxNameDensity | 1.34 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Raven | 2 | | Nest | 2 | | Harlow | 1 | | Quinn | 14 | | Morris | 2 | | Fairy | 1 | | Tomás | 1 | | Herrera | 5 | | Veil | 1 | | Market | 1 | | Seraphina | 3 | | Silas | 1 | | Five | 3 | | Three | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Morris" | | 5 | "Tomás" | | 6 | "Herrera" | | 7 | "Seraphina" | | 8 | "Silas" |
| | places | | | globalScore | 0.83 | | windowScore | 0.833 | |
| 75.37% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 2 | | matches | | 0 | "answers that seemed to be hiding just beyond the veil" | | 1 | "appeared incongruously placed at the back of the bar" |
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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 | 1213 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 87 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 24 | | mean | 50.54 | | std | 39.71 | | cv | 0.786 | | sampleLengths | | 0 | 136 | | 1 | 111 | | 2 | 143 | | 3 | 25 | | 4 | 19 | | 5 | 17 | | 6 | 4 | | 7 | 41 | | 8 | 94 | | 9 | 94 | | 10 | 15 | | 11 | 19 | | 12 | 16 | | 13 | 69 | | 14 | 15 | | 15 | 59 | | 16 | 16 | | 17 | 72 | | 18 | 9 | | 19 | 60 | | 20 | 26 | | 21 | 59 | | 22 | 36 | | 23 | 58 |
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| 96.03% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 76 | | matches | | 0 | "was involved" | | 1 | "were lined" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 176 | | matches | | 0 | "was arguing" | | 1 | "wasn’t letting" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 4 | | flaggedSentences | 14 | | totalSentences | 87 | | ratio | 0.161 | | matches | | 0 | "A greasy film slicked the cobblestones of Soho, reflecting the neon bleed of The Raven’s Nest sign – a distinctive green glow smeared across the wet pavement." | | 1 | "It wasn’t an apology for the weather, of course; it was a reminder that time, as always, was a commodity she couldn’t afford to waste." | | 2 | "The scent of stale beer and something vaguely medicinal – Tomás’ signature – clung to the air." | | 3 | "This wasn't a straightforward pursuit; it was a tangled thread woven with occult whispers and the unsettling implications of DS Morris’s last case." | | 4 | "The air immediately shifted, colder and heavier, laced with the distinct aroma of damp earth and something metallic – the scent of blood, perhaps." | | 5 | "The stairs spiralled down into a labyrinth of dimly lit corridors – the secret room." | | 6 | "He was arguing with a tall, imposing man clad in a crimson velvet coat – a known antiquities dealer with a penchant for procuring rare and dangerous artifacts." | | 7 | "Herrera, startled, instinctively reached for a small, intricately carved bone token – a bone token, Quinn knew from her research, used as an entry requirement for The Veil Market." | | 8 | "As she moved to apprehend him, a figure emerged from the shadows – a young woman, her eyes glittering with unnerving intensity." | | 9 | "The smell of gunpowder mingled with the other scents of the room – the musty odor of the market, the metallic tang of blood, and something else..." | | 10 | "The secret room wasn’t just a meeting place; it was a nexus, a conduit to something far darker than she’d anticipated." | | 11 | "“The Veil Market,” Quinn murmured, recognizing the signs – the bone token, the ritualistic symbols, the palpable sense of dread." | | 12 | "This wasn’t just a criminal operation; this was a gateway." | | 13 | "“I’ve been expecting you.” As a blinding blue light emanated from the pit, Quinn heard a voice echo around the room– a voice she knew all too well." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 792 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.030303030303030304 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.015151515151515152 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 87 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 87 | | mean | 13.94 | | std | 7.29 | | cv | 0.523 | | sampleLengths | | 0 | 8 | | 1 | 27 | | 2 | 24 | | 3 | 25 | | 4 | 24 | | 5 | 28 | | 6 | 15 | | 7 | 15 | | 8 | 17 | | 9 | 18 | | 10 | 13 | | 11 | 16 | | 12 | 2 | | 13 | 5 | | 14 | 10 | | 15 | 8 | | 16 | 23 | | 17 | 2 | | 18 | 12 | | 19 | 22 | | 20 | 21 | | 21 | 17 | | 22 | 21 | | 23 | 17 | | 24 | 11 | | 25 | 14 | | 26 | 11 | | 27 | 8 | | 28 | 6 | | 29 | 11 | | 30 | 4 | | 31 | 23 | | 32 | 18 | | 33 | 23 | | 34 | 16 | | 35 | 15 | | 36 | 16 | | 37 | 24 | | 38 | 15 | | 39 | 15 | | 40 | 18 | | 41 | 14 | | 42 | 4 | | 43 | 28 | | 44 | 10 | | 45 | 5 | | 46 | 12 | | 47 | 7 | | 48 | 3 | | 49 | 11 |
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| 62.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.4482758620689655 | | totalSentences | 87 | | uniqueOpeners | 39 | |
| 45.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 74 | | matches | | 0 | "Suddenly, the figure in crimson" |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 74 | | matches | | 0 | "It wasn’t an apology for" | | 1 | "She crossed the street, the" | | 2 | "He’d been meeting with someone," | | 3 | "She scanned the entrance, the" | | 4 | "she muttered, pulling her collar" | | 5 | "She pushed open the door" | | 6 | "he asked, his voice barely" | | 7 | "He fidgeted, wiping down the" | | 8 | "She ran her hand along" | | 9 | "It was there, a pressure" | | 10 | "He was arguing with a" | | 11 | "She burst into the room," | | 12 | "He fumbled with it, dropping" | | 13 | "She wore a dark, flowing" | | 14 | "It was Seraphina, a known" | | 15 | "he said, his voice dripping" |
| | ratio | 0.216 | |
| 74.86% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 57 | | totalSentences | 74 | | matches | | 0 | "The rain smelled of burnt" | | 1 | "A greasy film slicked the" | | 2 | "Detective Harlow Quinn adjusted the" | | 3 | "It wasn’t an apology for" | | 4 | "Quinn moved, a military precision" | | 5 | "She crossed the street, the" | | 6 | "The scent of stale beer" | | 7 | "He’d been meeting with someone," | | 8 | "She scanned the entrance, the" | | 9 | "she muttered, pulling her collar" | | 10 | "This wasn't a straightforward pursuit;" | | 11 | "She pushed open the door" | | 12 | "The bar was dimly lit," | | 13 | "A low hum of conversation," | | 14 | "A waiter, a thin man" | | 15 | "he asked, his voice barely" | | 16 | "Quinn stated, her brown eyes" | | 17 | "The waiter hesitated, glancing around" | | 18 | "He fidgeted, wiping down the" | | 19 | "Quinn ignored the jab, pushing" |
| | ratio | 0.77 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 74 | | matches | | 0 | "Now, only the rain remained," | | 1 | "Before Quinn could react, Seraphina" |
| | ratio | 0.027 | |
| 96.27% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 3 | | matches | | 0 | "Three years since the unexplained, sickeningly sweet rain that preceded Morris’s disappearance." | | 1 | "Three years since she'd started hunting for answers she didn't yet understand, answers that seemed to be hiding just beyond the veil." | | 2 | "Quinn ignored the jab, pushing past the waiter and heading towards a bookshelf that appeared incongruously placed at the back of the bar." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 7 | | matches | | 0 | "he asked, his voice barely audible above the music" | | 1 | "Quinn stated, her brown eyes scanning the room" | | 2 | "Herrera hissed, his voice strained" | | 3 | "the man replied, his voice a low growl" | | 4 | "Seraphina said, her voice a silken whisper" | | 5 | "Quinn said, her voice grim as she moved to retreat" | | 6 | "he said, his voice dripping with satisfaction" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 5 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "Quinn stated (state)" | | 2 | "Herrera hissed (hiss)" | | 3 | "the dealer shouted (shout)" | | 4 | "Quinn murmured (murmur)" |
| | dialogueSentences | 22 | | tagDensity | 0.455 | | leniency | 0.909 | | rawRatio | 0.5 | | effectiveRatio | 0.455 | |