| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 0 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.15% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1383 | | totalAiIsmAdverbs | 3 | | 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) | |
| 89.15% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1383 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "standard" | | 1 | "footsteps" | | 2 | "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 | 138 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 138 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 167 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1381 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 1123 | | uniqueNames | 11 | | maxNameDensity | 1.16 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 13 | | Holloway | 1 | | King | 1 | | Cross | 1 | | Eleanor | 3 | | Marsh | 3 | | Morris | 3 | | Tube | 2 | | Camden | 3 | | Veil | 1 | | Detective | 1 |
| | persons | | 0 | "Quinn" | | 1 | "King" | | 2 | "Cross" | | 3 | "Morris" | | 4 | "Camden" |
| | places | | | globalScore | 0.921 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 77 | | glossingSentenceCount | 1 | | matches | | 0 | "smelled like piss and stale chip grease" |
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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 | 1381 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 167 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 28.77 | | std | 18.63 | | cv | 0.648 | | sampleLengths | | 0 | 23 | | 1 | 64 | | 2 | 34 | | 3 | 2 | | 4 | 5 | | 5 | 43 | | 6 | 2 | | 7 | 55 | | 8 | 24 | | 9 | 45 | | 10 | 28 | | 11 | 50 | | 12 | 40 | | 13 | 23 | | 14 | 2 | | 15 | 50 | | 16 | 42 | | 17 | 30 | | 18 | 69 | | 19 | 16 | | 20 | 5 | | 21 | 11 | | 22 | 45 | | 23 | 16 | | 24 | 24 | | 25 | 33 | | 26 | 18 | | 27 | 42 | | 28 | 4 | | 29 | 49 | | 30 | 25 | | 31 | 35 | | 32 | 48 | | 33 | 47 | | 34 | 46 | | 35 | 5 | | 36 | 28 | | 37 | 41 | | 38 | 55 | | 39 | 31 | | 40 | 16 | | 41 | 1 | | 42 | 23 | | 43 | 1 | | 44 | 52 | | 45 | 19 | | 46 | 6 | | 47 | 8 |
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| 90.01% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 138 | | matches | | 0 | "been seen" | | 1 | "was faded" | | 2 | "been removed" | | 3 | "were arranged" | | 4 | "is connected" | | 5 | "meant" |
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| 63.95% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 196 | | matches | | 0 | "was widening" | | 1 | "was being" | | 2 | "was getting" | | 3 | "was waiting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 167 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 121 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 1 | | adverbRatio | 0.008264462809917356 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 167 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 167 | | mean | 8.27 | | std | 6.46 | | cv | 0.781 | | sampleLengths | | 0 | 23 | | 1 | 3 | | 2 | 25 | | 3 | 16 | | 4 | 20 | | 5 | 3 | | 6 | 15 | | 7 | 9 | | 8 | 7 | | 9 | 1 | | 10 | 1 | | 11 | 5 | | 12 | 19 | | 13 | 13 | | 14 | 4 | | 15 | 1 | | 16 | 1 | | 17 | 1 | | 18 | 4 | | 19 | 2 | | 20 | 14 | | 21 | 19 | | 22 | 8 | | 23 | 14 | | 24 | 4 | | 25 | 20 | | 26 | 6 | | 27 | 12 | | 28 | 1 | | 29 | 9 | | 30 | 17 | | 31 | 1 | | 32 | 10 | | 33 | 3 | | 34 | 6 | | 35 | 8 | | 36 | 17 | | 37 | 12 | | 38 | 7 | | 39 | 2 | | 40 | 2 | | 41 | 10 | | 42 | 5 | | 43 | 11 | | 44 | 2 | | 45 | 22 | | 46 | 9 | | 47 | 14 | | 48 | 2 | | 49 | 8 |
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| 58.28% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.40718562874251496 | | totalSentences | 167 | | uniqueOpeners | 68 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 113 | | matches | | 0 | "Just the steady drip of" | | 1 | "Dark purple, deep red, black" | | 2 | "Sometimes people don't want to" | | 3 | "Sometimes they're not missing at" | | 4 | "Sometimes they're exactly where they're" | | 5 | "Somewhere in that darkness, footsteps" |
| | ratio | 0.053 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 113 | | matches | | 0 | "Her lungs burned." | | 1 | "She'd spotted him leaving a" | | 2 | "He ducked left." | | 3 | "She blinked it away and" | | 4 | "He didn't even glance back." | | 5 | "Her torch beam caught his" | | 6 | "She counted the seconds." | | 7 | "She pushed harder, her worn" | | 8 | "She'd lost track of the" | | 9 | "Her breath came in sharp" | | 10 | "She could wait for backup." | | 11 | "Her torch cut through the" | | 12 | "She'd have to ask someone" | | 13 | "She reached the bottom and" | | 14 | "She wore layers of mismatched" | | 15 | "Her smile showed too many" | | 16 | "I'm pursuing a suspect who" | | 17 | "It's a market." | | 18 | "We sell things." | | 19 | "I need to find" |
| | ratio | 0.265 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 113 | | matches | | 0 | "The man in the grey" | | 1 | "Her lungs burned." | | 2 | "She'd spotted him leaving a" | | 3 | "The same limp that belonged" | | 4 | "He ducked left." | | 5 | "Quinn followed, her shoulder clipping" | | 6 | "Rain drove sideways into her" | | 7 | "She blinked it away and" | | 8 | "He didn't even glance back." | | 9 | "The chase carried them past" | | 10 | "Her torch beam caught his" | | 11 | "She counted the seconds." | | 12 | "The gap was widening." | | 13 | "She pushed harder, her worn" | | 14 | "Morris would've had something to" | | 15 | "Morris was in the ground," | | 16 | "The suspect vanished around another" | | 17 | "She'd lost track of the" | | 18 | "A flash of grey disappearing" | | 19 | "Tube station entrance." |
| | ratio | 0.673 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 113 | | matches | (empty) | | ratio | 0 | |
| 17.86% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 7 | | matches | | 0 | "The same limp that belonged to the man who'd been seen speaking to Eleanor Marsh three hours before she disappeared." | | 1 | "Quinn followed, her shoulder clipping a bin and sending rubbish spilling across the wet asphalt." | | 2 | "The chase carried them past shuttered shopfronts and closed pubs, through puddles that soaked her trousers to the knee." | | 3 | "The kind of thing she'd written in countless reports, the kind of thing that kept you alive and kept your pension intact." | | 4 | "Her torch cut through the darkness, illuminating graffiti that looked older than her career." | | 5 | "Copper and herbs and an undertone of rot that clung to the back of her throat." | | 6 | "Goods were arranged on tables and hanging from frames: bottles filled with liquids that shifted and moved on their own, jewelry that hummed at frequencies just …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |