| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.28% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1060 | | 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) | |
| 66.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1060 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "fractured" | | 1 | "footfall" | | 2 | "echoing" | | 3 | "electric" | | 4 | "weight" | | 5 | "warmth" | | 6 | "echo" |
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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 | 60 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 60 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 62 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1053 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 1036 | | uniqueNames | 17 | | maxNameDensity | 0.68 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Camden" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | London | 1 | | Herrera | 1 | | Camden | 2 | | High | 1 | | Street | 1 | | Town | 1 | | Quinn | 7 | | Vice | 1 | | Cornwall | 1 | | Croft-losing-his-marbles | 1 | | Morris | 3 | | July | 2 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Nest" | | 1 | "Herrera" | | 2 | "Quinn" | | 3 | "Morris" | | 4 | "Market" |
| | places | | 0 | "Raven" | | 1 | "Soho" | | 2 | "London" | | 3 | "Camden" | | 4 | "High" | | 5 | "Street" | | 6 | "Town" | | 7 | "Cornwall" | | 8 | "July" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1053 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 62 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 39 | | std | 26.87 | | cv | 0.689 | | sampleLengths | | 0 | 65 | | 1 | 2 | | 2 | 25 | | 3 | 71 | | 4 | 2 | | 5 | 50 | | 6 | 7 | | 7 | 55 | | 8 | 9 | | 9 | 63 | | 10 | 46 | | 11 | 20 | | 12 | 22 | | 13 | 46 | | 14 | 20 | | 15 | 21 | | 16 | 62 | | 17 | 37 | | 18 | 12 | | 19 | 78 | | 20 | 94 | | 21 | 16 | | 22 | 60 | | 23 | 16 | | 24 | 91 | | 25 | 46 | | 26 | 17 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 60 | | matches | (empty) | |
| 38.38% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 165 | | matches | | 0 | "was standing" | | 1 | "weren't running" | | 2 | "weren't waiting" | | 3 | "wasn't walking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 62 | | ratio | 0.065 | | matches | | 0 | "The suspect—she'd only caught the edge of his face outside the bar, enough to know he wasn't one of Herrera's usual crowd—vaulted a low fence with an ease that made her chest tighten." | | 1 | "But someone had been walking them recently—she could see it in the way the dust pattern broke, footprints heading down into the black mouth of the platform level." | | 2 | "Now the smell of clove and copper filled her throat, and the sound of bartering in half a dozen languages—some she recognized, some she'd swear weren't human—drifted up from the tunnel where the tracks should have been." | | 3 | "Whatever waited in that tunnel—whoever her suspect had run to, whatever this Veil Market truly was—she intended to walk out the other side with answers." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1053 | | adjectiveStacks | 1 | | stackExamples | | 0 | "under Croft-losing-his-" |
| | adverbCount | 27 | | adverbRatio | 0.02564102564102564 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.006647673314339981 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 62 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 62 | | mean | 16.98 | | std | 10.09 | | cv | 0.594 | | sampleLengths | | 0 | 20 | | 1 | 21 | | 2 | 24 | | 3 | 2 | | 4 | 8 | | 5 | 17 | | 6 | 33 | | 7 | 13 | | 8 | 25 | | 9 | 2 | | 10 | 19 | | 11 | 6 | | 12 | 25 | | 13 | 4 | | 14 | 3 | | 15 | 20 | | 16 | 2 | | 17 | 33 | | 18 | 9 | | 19 | 15 | | 20 | 12 | | 21 | 16 | | 22 | 20 | | 23 | 18 | | 24 | 28 | | 25 | 15 | | 26 | 5 | | 27 | 2 | | 28 | 20 | | 29 | 23 | | 30 | 1 | | 31 | 1 | | 32 | 21 | | 33 | 20 | | 34 | 2 | | 35 | 19 | | 36 | 5 | | 37 | 27 | | 38 | 22 | | 39 | 8 | | 40 | 37 | | 41 | 12 | | 42 | 19 | | 43 | 29 | | 44 | 30 | | 45 | 7 | | 46 | 7 | | 47 | 30 | | 48 | 9 | | 49 | 13 |
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| 96.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.5967741935483871 | | totalSentences | 62 | | uniqueOpeners | 37 | |
| 61.73% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 54 | | matches | | 0 | "Just the drip of water" |
| | ratio | 0.019 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 54 | | matches | | 0 | "They never did anymore, not" | | 1 | "She followed anyway, palms scraping" | | 2 | "She slowed at the threshold," | | 3 | "Her torch beam cut through" | | 4 | "she called out, voice flat" | | 5 | "She started down the escalator" | | 6 | "It smelled of copper and" | | 7 | "She'd heard whispers about this." | | 8 | "She'd filed it under Croft-losing-his-marbles" | | 9 | "Her hoodied suspect had vanished" | | 10 | "She thought of Morris, three" | | 11 | "She'd buried those details in" | | 12 | "she muttered, echo swallowing the" | | 13 | "She started down the last" |
| | ratio | 0.259 | |
| 80.37% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 41 | | totalSentences | 54 | | matches | | 0 | "Quinn's boots hit the wet" | | 1 | "The words meant nothing to" | | 2 | "They never did anymore, not" | | 3 | "The suspect—she'd only caught the" | | 4 | "Nobody moved like that after" | | 5 | "She followed anyway, palms scraping" | | 6 | "The alley spat them out" | | 7 | "Neon signs bled color into" | | 8 | "Quinn caught a flash of" | | 9 | "The chain hung open." | | 10 | "She slowed at the threshold," | | 11 | "Morris used to tease her" | | 12 | "Morris wasn't around to tease" | | 13 | "Quinn ducked through the gap" | | 14 | "Dust motes hung suspended in" | | 15 | "Her torch beam cut through" | | 16 | "The escalators weren't running, hadn't" | | 17 | "she called out, voice flat" | | 18 | "She started down the escalator" | | 19 | "It smelled of copper and" |
| | ratio | 0.759 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 54 | | matches | | 0 | "Now the smell of clove" | | 1 | "If this market held answers," |
| | ratio | 0.037 | |
| 57.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 5 | | matches | | 0 | "Rain hammered the pavement outside the Raven's Nest, turning Soho's cobblestones into black mirrors that fractured the green neon overhead." | | 1 | "The suspect—she'd only caught the edge of his face outside the bar, enough to know he wasn't one of Herrera's usual crowd—vaulted a low fence with an ease that …" | | 2 | "The platform level opened up ahead, and Quinn's torch beam caught the edge of something that made her stop cold." | | 3 | "Quinn checked her weapon, confirmed the extra clip in her jacket pocket, and let out a slow breath that fogged in front of her face despite the odd warmth radia…" | | 4 | "Lantern light catching on scales, on too-sharp teeth, on eyes that reflected wrong in the dark." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 1 | | matches | | 0 | "she called out, voice flat against the tiled walls" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "she called out (call out)" | | 1 | "she muttered (mutter)" |
| | dialogueSentences | 4 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 1 | | effectiveRatio | 1 | |