| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.09% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1278 | | 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) | |
| 80.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1278 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "lilt" | | 1 | "echoed" | | 2 | "echoing" | | 3 | "racing" |
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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 | 1 | | narrationSentences | 51 | | matches | | |
| 86.83% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 51 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 58 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 85 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1278 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1020 | | uniqueNames | 18 | | maxNameDensity | 0.69 | | worstName | "Tomás" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Tomás" | | discoveredNames | | Camden | 2 | | High | 1 | | Street | 2 | | Royal | 1 | | College | 1 | | Raven | 1 | | Nest | 1 | | Morris | 1 | | Thames | 1 | | Tomás | 7 | | Harlow | 7 | | London | 1 | | Road | 1 | | Veil | 2 | | Market | 2 | | Saint | 1 | | Christopher | 1 | | One | 1 |
| | persons | | 0 | "Morris" | | 1 | "Tomás" | | 2 | "Harlow" | | 3 | "Saint" | | 4 | "Christopher" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Raven" | | 4 | "Thames" | | 5 | "London" | | 6 | "Road" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 49 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.782 | | wordCount | 1278 | | matches | | 0 | "not like a rotting plank, but like a hidden door, the smell of frankincense, burnt sugar" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 58 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 14 | | mean | 91.29 | | std | 55.26 | | cv | 0.605 | | sampleLengths | | 0 | 151 | | 1 | 191 | | 2 | 155 | | 3 | 152 | | 4 | 83 | | 5 | 80 | | 6 | 119 | | 7 | 87 | | 8 | 82 | | 9 | 42 | | 10 | 14 | | 11 | 94 | | 12 | 8 | | 13 | 20 |
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| 84.62% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 51 | | matches | | 0 | "been crushed" | | 1 | "been swept" | | 2 | "been boarded" |
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| 36.40% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 163 | | matches | | 0 | "wasn’t just chasing" | | 1 | "was running" | | 2 | "was really happening" | | 3 | "was seeing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 58 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 261 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 6 | | adverbRatio | 0.022988505747126436 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0038314176245210726 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 58 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 58 | | mean | 22.03 | | std | 13.98 | | cv | 0.634 | | sampleLengths | | 0 | 23 | | 1 | 8 | | 2 | 34 | | 3 | 34 | | 4 | 41 | | 5 | 11 | | 6 | 24 | | 7 | 28 | | 8 | 31 | | 9 | 29 | | 10 | 33 | | 11 | 30 | | 12 | 16 | | 13 | 37 | | 14 | 15 | | 15 | 7 | | 16 | 26 | | 17 | 24 | | 18 | 32 | | 19 | 14 | | 20 | 38 | | 21 | 21 | | 22 | 21 | | 23 | 17 | | 24 | 42 | | 25 | 6 | | 26 | 7 | | 27 | 57 | | 28 | 26 | | 29 | 23 | | 30 | 27 | | 31 | 30 | | 32 | 13 | | 33 | 21 | | 34 | 85 | | 35 | 12 | | 36 | 27 | | 37 | 33 | | 38 | 3 | | 39 | 12 | | 40 | 7 | | 41 | 5 | | 42 | 15 | | 43 | 11 | | 44 | 13 | | 45 | 17 | | 46 | 6 | | 47 | 8 | | 48 | 17 | | 49 | 25 |
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| 29.31% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3103448275862069 | | totalSentences | 58 | | uniqueOpeners | 18 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 51 | | matches | (empty) | | ratio | 0 | |
| 0.39% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 51 | | matches | | 0 | "She didn’t pause to brush" | | 1 | "She pushed off the bin’s" | | 2 | "She’d tracked him for six" | | 3 | "She’d never gotten close enough" | | 4 | "She’d flashed her badge, called" | | 5 | "She was forty one, but" | | 6 | "She gained five yards on" | | 7 | "She could see the scar" | | 8 | "He glanced over his shoulder" | | 9 | "He was running from something" | | 10 | "She’d spent three years obsessing" | | 11 | "His throat had been crushed" | | 12 | "They were the only ones" | | 13 | "She came to a stop" | | 14 | "He pulled out a small" | | 15 | "She’d never seen it in" | | 16 | "She’d never thought she’d get" | | 17 | "She kept her taser trained" | | 18 | "He tapped the bone token" | | 19 | "She could hear distant chatter," |
| | ratio | 0.549 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 51 | | matches | | 0 | "Harlow’s shoulder crunched into an" | | 1 | "She didn’t pause to brush" | | 2 | "She pushed off the bin’s" | | 3 | "She’d tracked him for six" | | 4 | "She’d never gotten close enough" | | 5 | "She’d flashed her badge, called" | | 6 | "The rain hit a minute" | | 7 | "Harlow’s closely cropped salt and" | | 8 | "She was forty one, but" | | 9 | "She gained five yards on" | | 10 | "She could see the scar" | | 11 | "He glanced over his shoulder" | | 12 | "He was running from something" | | 13 | "She’d spent three years obsessing" | | 14 | "The coroner had ruled his" | | 15 | "His throat had been crushed" | | 16 | "The case had been swept" | | 17 | "The clique was the only" | | 18 | "They were the only ones" | | 19 | "Tomás veered off the high" |
| | ratio | 0.922 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 51 | | matches | (empty) | | ratio | 0 | |
| 14.65% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 7 | | matches | | 0 | "She pushed off the bin’s dented side, her lungs burning, her vision locked on the dark curly head bobbing thirty yards ahead of her, weaving through the last st…" | | 1 | "She could see the scar on his left forearm, a thin pale line that peeked out of his torn sleeve, the same scar she’d noted in the surveillance photos from the R…" | | 2 | "Tomás veered off the high street, slipping down a narrow unlit alley that ran alongside the abandoned Camden Road tube station, the site that had been boarded u…" | | 3 | "Harlow followed, her boots slapping on the wet asphalt, the rain muffling all sound but the thud of her own heart." | | 4 | "He pulled out a small yellowed knucklebone, worn smooth at the edges, the entry token she’d read about in old reports of the Veil Market, the supernatural black…" | | 5 | "She kept her taser trained on his chest, her sharp jaw set in the line that had made every rookie and every suspect in her custody flinch." | | 6 | "She could hear distant chatter, the clink of glasses, the call of a vendor haggling over a price, all echoing up from the dark tunnel that led down into the old…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |