| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 3 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 83.80% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 926 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "lightly" | | 1 | "sharply" | | 2 | "very" |
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| 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) | |
| 46.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 926 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "loomed" | | 1 | "unreadable" | | 2 | "could feel" | | 3 | "flicker" | | 4 | "pulse" | | 5 | "echoing" | | 6 | "trembled" | | 7 | "silk" | | 8 | "echoed" |
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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 | 84 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 84 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 86 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 5 | | totalWords | 920 | | ratio | 0.005 | | matches | | 0 | "moved" | | 1 | "wrong" | | 2 | "going somewhere" | | 3 | "moved" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 910 | | uniqueNames | 9 | | maxNameDensity | 0.99 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 9 | | Metropolitan | 1 | | Museum | 1 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Veil | 2 | | Market | 2 | | Knew | 4 |
| | persons | | 0 | "Quinn" | | 1 | "Museum" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Market" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 66.67% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like they belonged in a different" | | 1 | "felt like the very air was pressing aga" |
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| 91.30% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.087 | | wordCount | 920 | | matches | | 0 | "not the sound of a clock, but something older, something" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 86 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 25.56 | | std | 23.89 | | cv | 0.935 | | sampleLengths | | 0 | 65 | | 1 | 70 | | 2 | 82 | | 3 | 6 | | 4 | 4 | | 5 | 93 | | 6 | 33 | | 7 | 43 | | 8 | 18 | | 9 | 31 | | 10 | 37 | | 11 | 24 | | 12 | 4 | | 13 | 36 | | 14 | 5 | | 15 | 29 | | 16 | 58 | | 17 | 11 | | 18 | 49 | | 19 | 37 | | 20 | 11 | | 21 | 6 | | 22 | 5 | | 23 | 15 | | 24 | 5 | | 25 | 33 | | 26 | 8 | | 27 | 30 | | 28 | 6 | | 29 | 6 | | 30 | 11 | | 31 | 34 | | 32 | 6 | | 33 | 4 | | 34 | 3 | | 35 | 2 |
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| 84.38% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 84 | | matches | | 0 | "was propped" | | 1 | "was muffled" | | 2 | "were lined" | | 3 | "was gone" | | 4 | "were made" | | 5 | "were paid" |
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| 32.29% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 159 | | matches | | 0 | "was leading" | | 1 | "wasn’t just running" | | 2 | "was *going" | | 3 | "was happening" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 86 | | ratio | 0.07 | | matches | | 0 | "She had been tracking him for hours—ever since the call came in about the stolen artifact, the one that had been sitting in the Metropolitan Museum’s restricted vaults for a century." | | 1 | "Inside, the hum of low voices and the clink of glasses was muffled, but the air smelled of stale whiskey and something older—something that didn’t belong in a human world." | | 2 | "They turned as she entered, but their expressions were unreadable—just the kind of blank indifference that made her skin prickle." | | 3 | "Then she saw it—a flicker of movement behind the bookshelf." | | 4 | "She smiled—a slow, knowing thing." | | 5 | "Somewhere in the distance, a bell tolled—not the sound of a clock, but something older, something that made her teeth ache." |
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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 | 32 | | adverbRatio | 0.03474484256243214 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004343105320304018 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 86 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 86 | | mean | 10.7 | | std | 7.11 | | cv | 0.664 | | sampleLengths | | 0 | 18 | | 1 | 15 | | 2 | 32 | | 3 | 31 | | 4 | 8 | | 5 | 7 | | 6 | 22 | | 7 | 2 | | 8 | 17 | | 9 | 16 | | 10 | 30 | | 11 | 2 | | 12 | 4 | | 13 | 3 | | 14 | 10 | | 15 | 6 | | 16 | 4 | | 17 | 16 | | 18 | 18 | | 19 | 21 | | 20 | 18 | | 21 | 20 | | 22 | 12 | | 23 | 4 | | 24 | 5 | | 25 | 12 | | 26 | 10 | | 27 | 11 | | 28 | 9 | | 29 | 6 | | 30 | 7 | | 31 | 11 | | 32 | 5 | | 33 | 2 | | 34 | 17 | | 35 | 11 | | 36 | 3 | | 37 | 3 | | 38 | 6 | | 39 | 20 | | 40 | 8 | | 41 | 20 | | 42 | 4 | | 43 | 4 | | 44 | 4 | | 45 | 16 | | 46 | 16 | | 47 | 5 | | 48 | 10 | | 49 | 10 |
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| 32.56% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.2558139534883721 | | totalSentences | 86 | | uniqueOpeners | 22 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 80 | | matches | | 0 | "Then she saw it—a flicker" | | 1 | "Then she turned back to" | | 2 | "Somewhere in the distance, a" |
| | ratio | 0.038 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 80 | | matches | | 0 | "She crouched low, her breath" | | 1 | "She had been tracking him" | | 2 | "She knew the place." | | 3 | "She didn’t hesitate long." | | 4 | "They turned as she entered," | | 5 | "She’d seen doors like this" | | 6 | "She reached out, her fingers" | | 7 | "Her pulse spiked." | | 8 | "She knew about the Veil" | | 9 | "He was *going somewhere*." | | 10 | "She could turn back." | | 11 | "She stepped forward, her boots" | | 12 | "He stood with his back" | | 13 | "He wasn’t alone." | | 14 | "She had been wrong." | | 15 | "She smiled—a slow, knowing thing." | | 16 | "she said, her voice like" | | 17 | "She wasn’t here to arrest" | | 18 | "She was here to find" |
| | ratio | 0.238 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 57 | | totalSentences | 80 | | matches | | 0 | "The rain fell in sheets," | | 1 | "She crouched low, her breath" | | 2 | "The neon glow of a" | | 3 | "She had been tracking him" | | 4 | "The kind of thing that" | | 5 | "The kind of thing that" | | 6 | "The Raven’s Nest loomed ahead," | | 7 | "The bar’s door was propped" | | 8 | "She knew the place." | | 9 | "She didn’t hesitate long." | | 10 | "The moment she stepped over" | | 11 | "The bar was dim, the" | | 12 | "The walls were lined with" | | 13 | "They turned as she entered," | | 14 | "Quinn moved toward the back," | | 15 | "The suspect was gone." | | 16 | "The air shifted where he’d" | | 17 | "A hidden door, barely visible" | | 18 | "The kind of thing that" | | 19 | "She’d seen doors like this" |
| | ratio | 0.713 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 80 | | matches | (empty) | | ratio | 0 | |
| 59.80% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 5 | | matches | | 0 | "Inside, the hum of low voices and the clink of glasses was muffled, but the air smelled of stale whiskey and something older—something that didn’t belong in a h…" | | 1 | "The walls were lined with yellowed maps and black-and-white photographs of people who looked like they belonged in a different century." | | 2 | "They turned as she entered, but their expressions were unreadable—just the kind of blank indifference that made her skin prickle." | | 3 | "He stood with his back to her, his dark hair damp from the rain that had followed her down." | | 4 | "Somewhere in the distance, a bell tolled—not the sound of a clock, but something older, something that made her teeth ache." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 3 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |