| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 39 | | tagDensity | 0.205 | | leniency | 0.41 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.32% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1863 | | 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) | |
| 75.85% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1863 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "flickered" | | 1 | "electric" | | 2 | "scanned" | | 3 | "footsteps" | | 4 | "silence" | | 5 | "velvet" | | 6 | "scanning" | | 7 | "unreadable" | | 8 | "pulsed" |
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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 | 185 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 185 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 217 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1863 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 1489 | | uniqueNames | 12 | | maxNameDensity | 0.74 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Morris" | | discoveredNames | | Camden | 3 | | High | 1 | | Street | 1 | | Morris | 5 | | Lock | 1 | | Victorian | 1 | | Quinn | 11 | | Herrera | 2 | | Raven | 1 | | Nest | 1 | | London | 1 | | Three | 3 |
| | persons | | 0 | "Morris" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Raven" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 22.45% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 98 | | glossingSentenceCount | 5 | | matches | | 0 | "looked like they meant something to someo" | | 1 | "smelled like a garden in another country," | | 2 | "looked like a human tooth, except it was" | | 3 | "sounded like gravel in a tin cup" | | 4 | "looked like human skin" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.537 | | wordCount | 1863 | | matches | | 0 | "not electric but something warmer, more erratic" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 217 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 85 | | mean | 21.92 | | std | 18.7 | | cv | 0.853 | | sampleLengths | | 0 | 36 | | 1 | 2 | | 2 | 7 | | 3 | 60 | | 4 | 47 | | 5 | 56 | | 6 | 29 | | 7 | 33 | | 8 | 6 | | 9 | 64 | | 10 | 35 | | 11 | 2 | | 12 | 2 | | 13 | 50 | | 14 | 22 | | 15 | 5 | | 16 | 78 | | 17 | 5 | | 18 | 66 | | 19 | 64 | | 20 | 29 | | 21 | 23 | | 22 | 15 | | 23 | 21 | | 24 | 7 | | 25 | 13 | | 26 | 31 | | 27 | 11 | | 28 | 2 | | 29 | 26 | | 30 | 2 | | 31 | 18 | | 32 | 11 | | 33 | 5 | | 34 | 5 | | 35 | 47 | | 36 | 15 | | 37 | 1 | | 38 | 5 | | 39 | 37 | | 40 | 7 | | 41 | 10 | | 42 | 7 | | 43 | 17 | | 44 | 1 | | 45 | 39 | | 46 | 28 | | 47 | 15 | | 48 | 11 | | 49 | 2 |
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| 95.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 185 | | matches | | 0 | "were shuttered" | | 1 | "been closed" | | 2 | "been defaced" | | 3 | "been transformed" | | 4 | "been scared" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 245 | | matches | | 0 | "was disappearing" | | 1 | "was standing" | | 2 | "was still watching" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 217 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1127 | | adjectiveStacks | 2 | | stackExamples | | 0 | "ahead, all dark hoodie" | | 1 | "lay deeper under Camden," |
| | adverbCount | 43 | | adverbRatio | 0.038154392191659274 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0044365572315882874 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 217 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 217 | | mean | 8.59 | | std | 7.03 | | cv | 0.818 | | sampleLengths | | 0 | 15 | | 1 | 16 | | 2 | 5 | | 3 | 2 | | 4 | 4 | | 5 | 3 | | 6 | 4 | | 7 | 31 | | 8 | 4 | | 9 | 15 | | 10 | 3 | | 11 | 3 | | 12 | 14 | | 13 | 4 | | 14 | 6 | | 15 | 23 | | 16 | 8 | | 17 | 18 | | 18 | 6 | | 19 | 22 | | 20 | 1 | | 21 | 1 | | 22 | 13 | | 23 | 5 | | 24 | 11 | | 25 | 2 | | 26 | 3 | | 27 | 15 | | 28 | 9 | | 29 | 4 | | 30 | 3 | | 31 | 3 | | 32 | 6 | | 33 | 18 | | 34 | 16 | | 35 | 5 | | 36 | 19 | | 37 | 2 | | 38 | 1 | | 39 | 10 | | 40 | 10 | | 41 | 12 | | 42 | 2 | | 43 | 2 | | 44 | 5 | | 45 | 11 | | 46 | 4 | | 47 | 2 | | 48 | 6 | | 49 | 1 |
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| 66.21% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.4470046082949309 | | totalSentences | 217 | | uniqueOpeners | 97 | |
| 84.39% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 158 | | matches | | 0 | "Just Morris on a warehouse" | | 1 | "At least, she thought they" | | 2 | "Just the drip of water" | | 3 | "Too many teeth." |
| | ratio | 0.025 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 47 | | totalSentences | 158 | | matches | | 0 | "She pumped her arms harder." | | 1 | "They never did." | | 2 | "Her leather watch slipped against" | | 3 | "Her lungs burned in the" | | 4 | "She caught a flash of" | | 5 | "She wrinkled her nose." | | 6 | "Her phone buzzed." | | 7 | "She ignored it." | | 8 | "Her shadow stretched long and" | | 9 | "She could still turn back." | | 10 | "She'd read his case file" | | 11 | "She took the ninth step." | | 12 | "She scanned the market floor" | | 13 | "She looked down." | | 14 | "It smiled at her with" | | 15 | "Its voice sounded like gravel" | | 16 | "She showed her badge" | | 17 | "It held out a small," | | 18 | "Its smile hadn't changed." | | 19 | "She could hear, faintly, the" |
| | ratio | 0.297 | |
| 80.25% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 120 | | totalSentences | 158 | | matches | | 0 | "Quinn's boots hit the pavement" | | 1 | "The figure ahead, all dark" | | 2 | "She pumped her arms harder." | | 3 | "The figure didn't stop." | | 4 | "They never did." | | 5 | "Rain needled her face." | | 6 | "Her leather watch slipped against" | | 7 | "Flesh and blood." | | 8 | "The suspect knocked over a" | | 9 | "Metal clanged against brick." | | 10 | "Quinn hurdled it without breaking" | | 11 | "Her lungs burned in the" | | 12 | "The alley spat her out" | | 13 | "The market stalls were shuttered," | | 14 | "The canal beyond was black" | | 15 | "She caught a flash of" | | 16 | "Someone had done this recently." | | 17 | "The metal edges still gleamed" | | 18 | "The tunnel mouth breathed damp" | | 19 | "Something organic, like incense burning" |
| | ratio | 0.759 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 158 | | matches | (empty) | | ratio | 0 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 6 | | matches | | 0 | "Someone had scratched symbols over them, concentric circles and jagged lines that looked like they meant something to someone." | | 1 | "Just Morris on a warehouse floor with his eyes wide open and his skin cold, and the coroner's report that listed his core temperature as fourteen degrees below …" | | 2 | "Her suspect was disappearing into whatever lay deeper under Camden, and she was standing at a supernatural toll booth run by something that wanted to eat her pa…" | | 3 | "The crowd parted for her, faces turning, eyes catching light in ways that human eyes shouldn't." | | 4 | "She found it set into the canal wall, half-hidden by overgrown ivy that shouldn't have survived a London winter." | | 5 | "Stone grinding against stone, revealing a passage that glowed faintly blue, a cold and sourceless light that made Quinn's chest tighten." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |