| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.41% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1394 | | 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) | |
| 46.20% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1394 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "electric" | | 1 | "etched" | | 2 | "silence" | | 3 | "stark" | | 4 | "dance" | | 5 | "magnetic" | | 6 | "stomach" | | 7 | "shimmered" | | 8 | "measured" | | 9 | "firmly" | | 10 | "scanned" | | 11 | "resolve" | | 12 | "weight" | | 13 | "scanning" |
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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 | 162 | | matches | (empty) | |
| 81.13% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 2 | | narrationSentences | 162 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 162 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1393 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 22 | | unquotedAttributions | 13 | | matches | | 0 | "Traffic, Eva replied, not looking up." | | 1 | "There was a body down the tunnel, Quinn said, keeping her voice steady." | | 2 | "Look at the shadows, Eva said, pointing the lantern toward the wall behind the body." | | 3 | "If he was stabbed here, Quinn said, looking back at Eva, where is the weapon?" | | 4 | "Gone, Eva said." | | 5 | "You think this is the work of the clique, Quinn asked." | | 6 | "If this is a market, Quinn said, her voice low, there should be a price." | | 7 | "A token, Quinn said." | | 8 | "And someone just walked through it, Eva whispered." | | 9 | "Get your gear, Quinn said, turning to Eva." | | 10 | "Then we make sure the door stays shut, Quinn said." | | 11 | "Down, Eva said." | | 12 | "Stay close, Quinn ordered." |
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| 35.14% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 72 | | wordCount | 1393 | | uniqueNames | 14 | | maxNameDensity | 2.3 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Detective | 1 | | Harlow | 3 | | Quinn | 32 | | Tube | 1 | | London | 1 | | Morris | 5 | | Eva | 20 | | Kowalski | 1 | | Oxford | 1 | | Veil | 2 | | Compass | 1 | | Shade | 1 | | Market | 2 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Morris" | | 4 | "Eva" | | 5 | "Kowalski" | | 6 | "Market" |
| | places | | 0 | "London" | | 1 | "Oxford" | | 2 | "Compass" |
| | globalScore | 0.351 | | windowScore | 0.5 | |
| 77.18% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 103 | | glossingSentenceCount | 3 | | matches | | 0 | "smelled like ozone, sharp and electric, cl" | | 1 | "It was as if the ground had rejected the life" | | 2 | "looked like a silhouette of something wit" |
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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 | 1393 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 162 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 27.86 | | std | 21.4 | | cv | 0.768 | | sampleLengths | | 0 | 95 | | 1 | 44 | | 2 | 65 | | 3 | 7 | | 4 | 32 | | 5 | 40 | | 6 | 60 | | 7 | 6 | | 8 | 47 | | 9 | 15 | | 10 | 25 | | 11 | 58 | | 12 | 21 | | 13 | 8 | | 14 | 54 | | 15 | 15 | | 16 | 47 | | 17 | 8 | | 18 | 57 | | 19 | 52 | | 20 | 26 | | 21 | 15 | | 22 | 6 | | 23 | 43 | | 24 | 11 | | 25 | 31 | | 26 | 21 | | 27 | 20 | | 28 | 13 | | 29 | 45 | | 30 | 4 | | 31 | 17 | | 32 | 53 | | 33 | 14 | | 34 | 8 | | 35 | 38 | | 36 | 13 | | 37 | 13 | | 38 | 30 | | 39 | 31 | | 40 | 22 | | 41 | 3 | | 42 | 36 | | 43 | 4 | | 44 | 75 | | 45 | 18 | | 46 | 10 | | 47 | 6 | | 48 | 10 | | 49 | 1 |
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| 94.43% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 162 | | matches | | 0 | "was etched" | | 1 | "was crafted" | | 2 | "was stabbed" | | 3 | "was brought" | | 4 | "was stabbed" |
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| 50.19% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 267 | | matches | | 0 | "was fiddling" | | 1 | "was spinning" | | 2 | "was already thinning" | | 3 | "were moving" | | 4 | "wasn't just looking" | | 5 | "was looking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 162 | | ratio | 0.012 | | matches | | 0 | "The damp here didn't smell like rain or sewage; it smelled like ozone, sharp and electric, clinging to the back of her throat." | | 1 | "She had seen the look in Morris’s eyes before he died—a look of absolute terror at something unseen." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1395 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.024372759856630826 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.005017921146953405 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 162 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 162 | | mean | 8.6 | | std | 5.71 | | cv | 0.664 | | sampleLengths | | 0 | 24 | | 1 | 4 | | 2 | 23 | | 3 | 20 | | 4 | 24 | | 5 | 4 | | 6 | 20 | | 7 | 2 | | 8 | 18 | | 9 | 19 | | 10 | 21 | | 11 | 25 | | 12 | 7 | | 13 | 6 | | 14 | 13 | | 15 | 10 | | 16 | 3 | | 17 | 5 | | 18 | 6 | | 19 | 25 | | 20 | 4 | | 21 | 4 | | 22 | 15 | | 23 | 11 | | 24 | 14 | | 25 | 16 | | 26 | 6 | | 27 | 9 | | 28 | 5 | | 29 | 21 | | 30 | 12 | | 31 | 11 | | 32 | 2 | | 33 | 2 | | 34 | 7 | | 35 | 7 | | 36 | 4 | | 37 | 7 | | 38 | 5 | | 39 | 14 | | 40 | 6 | | 41 | 5 | | 42 | 18 | | 43 | 6 | | 44 | 4 | | 45 | 13 | | 46 | 5 | | 47 | 3 | | 48 | 8 | | 49 | 2 |
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| 34.36% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 26 | | diversityRatio | 0.30246913580246915 | | totalSentences | 162 | | uniqueOpeners | 49 | |
| 45.98% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 145 | | matches | | 0 | "Just the victim, and the" | | 1 | "Then we make sure the" |
| | ratio | 0.014 | |
| 40.69% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 65 | | totalSentences | 145 | | matches | | 0 | "She adjusted the collar of" | | 1 | "She pushed the thought down," | | 2 | "She found Eva Kowalski standing" | | 3 | "She was fiddling with the" | | 4 | "You’re late, Quinn said, her" | | 5 | "She adjusted her round glasses," | | 6 | "She didn’t like the word" | | 7 | "She didn’t like the way" | | 8 | "Her green eyes were wide," | | 9 | "She reached into her satchel" | | 10 | "It was a brass compass," | | 11 | "She looked at the compass." | | 12 | "It pointed toward the void" | | 13 | "It’s magnetic interference, Quinn said," | | 14 | "It’s a Veil Compass, Eva" | | 15 | "It was crafted by a" | | 16 | "It points to rifts." | | 17 | "She had spent three years" | | 18 | "She had seen the look" | | 19 | "She hadn’t found a gas" |
| | ratio | 0.448 | |
| 42.76% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 121 | | totalSentences | 145 | | matches | | 0 | "The air beneath Camden tasted" | | 1 | "The damp here didn't smell" | | 2 | "She adjusted the collar of" | | 3 | "The abandoned Tube station was" | | 4 | "Quinn checked her watch." | | 5 | "The leather strap was worn" | | 6 | "She pushed the thought down," | | 7 | "She found Eva Kowalski standing" | | 8 | "The younger woman looked out" | | 9 | "She was fiddling with the" | | 10 | "You’re late, Quinn said, her" | | 11 | "Traffic, Eva replied, not looking" | | 12 | "She adjusted her round glasses," | | 13 | "The wards shift." | | 14 | "Quinn stopped three feet away." | | 15 | "She didn’t like the word" | | 16 | "She didn’t like the way" | | 17 | "Eva finally looked up." | | 18 | "Her green eyes were wide," | | 19 | "She reached into her satchel" |
| | ratio | 0.834 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 145 | | matches | | 0 | "To places where the world" | | 1 | "If he was stabbed here," | | 2 | "If this is a market," |
| | ratio | 0.021 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 1 | | matches | | 0 | "She was fiddling with the strap of her worn leather satchel, her fingers working the buckle with a nervous energy that betrayed her Oxford pedigree." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |