| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.75 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1043 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 18.50% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1043 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "fractured" | | 1 | "whisper" | | 2 | "hulking" | | 3 | "glint" | | 4 | "echoed" | | 5 | "echoing" | | 6 | "vibrated" | | 7 | "intricate" | | 8 | "familiar" | | 9 | "weight" | | 10 | "gloom" | | 11 | "tapestry" | | 12 | "chaotic" | | 13 | "silence" | | 14 | "profound" | | 15 | "flicked" |
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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 | 99 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 99 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 99 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 6 | | totalWords | 1042 | | ratio | 0.006 | | matches | | 0 | "back-up" | | 1 | "Supernatural origins." | | 2 | "The Veil Market." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 1030 | | uniqueNames | 17 | | maxNameDensity | 0.58 | | worstName | "Herrera" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 2 | | Quinn | 5 | | Raven | 1 | | Nest | 1 | | Tommy | 2 | | Herrera | 6 | | Camden | 1 | | Market | 3 | | Morris | 3 | | Saint | 1 | | Christopher | 1 | | Tube | 1 | | Sunlight | 1 | | Soap | 1 | | Ovaltine | 1 | | Veil | 2 | | Detective | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Tommy" | | 4 | "Herrera" | | 5 | "Market" | | 6 | "Morris" | | 7 | "Saint" | | 8 | "Christopher" | | 9 | "Ovaltine" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 5.07% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 4 | | matches | | 0 | "symbols that seemed to shift if she looked too long" | | 1 | "quite" | | 2 | "felt like a child’s toy here" | | 3 | "felt like a pressure on her eardrums" |
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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 | 1042 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 99 | | matches | (empty) | |
| 96.69% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 45.3 | | std | 22.13 | | cv | 0.488 | | sampleLengths | | 0 | 57 | | 1 | 75 | | 2 | 53 | | 3 | 56 | | 4 | 68 | | 5 | 41 | | 6 | 35 | | 7 | 10 | | 8 | 48 | | 9 | 69 | | 10 | 56 | | 11 | 62 | | 12 | 86 | | 13 | 26 | | 14 | 36 | | 15 | 11 | | 16 | 43 | | 17 | 73 | | 18 | 51 | | 19 | 44 | | 20 | 23 | | 21 | 15 | | 22 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 99 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 152 | | matches | | |
| 85.14% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 99 | | ratio | 0.02 | | matches | | 0 | "Around the corner, the tunnel opened into a vast, vaulted chamber—the cavernous ghost of a platform." | | 1 | "A creature with moth wings and a human face offered her a glimpse into a shimmering orb; she brushed it aside." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1041 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, olive-skinned man" |
| | adverbCount | 26 | | adverbRatio | 0.024975984630163303 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0038424591738712775 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 99 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 99 | | mean | 10.53 | | std | 6.92 | | cv | 0.658 | | sampleLengths | | 0 | 26 | | 1 | 12 | | 2 | 4 | | 3 | 15 | | 4 | 19 | | 5 | 14 | | 6 | 10 | | 7 | 23 | | 8 | 1 | | 9 | 2 | | 10 | 6 | | 11 | 7 | | 12 | 10 | | 13 | 19 | | 14 | 17 | | 15 | 8 | | 16 | 19 | | 17 | 3 | | 18 | 11 | | 19 | 4 | | 20 | 4 | | 21 | 7 | | 22 | 11 | | 23 | 8 | | 24 | 4 | | 25 | 3 | | 26 | 16 | | 27 | 4 | | 28 | 22 | | 29 | 16 | | 30 | 10 | | 31 | 1 | | 32 | 1 | | 33 | 7 | | 34 | 3 | | 35 | 3 | | 36 | 5 | | 37 | 3 | | 38 | 16 | | 39 | 11 | | 40 | 10 | | 41 | 10 | | 42 | 22 | | 43 | 16 | | 44 | 18 | | 45 | 10 | | 46 | 24 | | 47 | 10 | | 48 | 7 | | 49 | 22 |
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| 63.64% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.42424242424242425 | | totalSentences | 99 | | uniqueOpeners | 42 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 94 | | matches | | 0 | "Then she thought of Morris." | | 1 | "Then another set of stairs," | | 2 | "Probably the regular kind too." | | 3 | "Just the smell of stone" | | 4 | "Somewhere in the dark ahead," |
| | ratio | 0.053 | |
| 96.60% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 94 | | matches | | 0 | "Her quarry, a wiry man" | | 1 | "He didn’t need to." | | 2 | "He ducked right, down a" | | 3 | "He was fast, but she" | | 4 | "She wanted answers about DS" | | 5 | "He didn’t slow." | | 6 | "His hand flew to his" | | 7 | "She’d seen it in his" | | 8 | "He dodged a lorry, its" | | 9 | "She stopped at the top" | | 10 | "His empty desk." | | 11 | "she muttered, and took the" | | 12 | "Her torch beam danced ahead," | | 13 | "She walked past a toppled" | | 14 | "She killed her torch." | | 15 | "She’d never believed." | | 16 | "Her hand brushed her service" | | 17 | "She saw scales beneath a" | | 18 | "She thought of Morris’s empty" | | 19 | "She thought of his funeral." |
| | ratio | 0.309 | |
| 71.70% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 73 | | totalSentences | 94 | | matches | | 0 | "The slick pavement reflected fractured" | | 1 | "Her quarry, a wiry man" | | 2 | "He didn’t need to." | | 3 | "The thud of her boots" | | 4 | "The words ripped from her" | | 5 | "He ducked right, down a" | | 6 | "Quinn followed, her worn leather" | | 7 | "The chase had started with" | | 8 | "He was fast, but she" | | 9 | "The alley spat them out" | | 10 | "Herrera veered left, aiming for" | | 11 | "She wanted answers about DS" | | 12 | "He didn’t slow." | | 13 | "His hand flew to his" | | 14 | "The glint of metal." | | 15 | "A Saint Christopher medallion." | | 16 | "She’d seen it in his" | | 17 | "He dodged a lorry, its" | | 18 | "A set of steps led" | | 19 | "An abandoned Tube entrance." |
| | ratio | 0.777 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 94 | | matches | | 0 | "Now a ghost in the" | | 1 | "To follow him into that" |
| | ratio | 0.021 | |
| 65.22% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 5 | | matches | | 0 | "The air smelled different here, of damp earth and ozone and something coppery that made the hairs on her arms stand up." | | 1 | "Her torch beam danced ahead, catching on graffiti that wasn’t tags but intricate, flowing symbols that seemed to shift if she looked too long." | | 2 | "She saw scales beneath a cloak, a face with too many angles, eyes that reflected her torch beam with an internal luminescence." | | 3 | "And there, on the far side, slipping between a stall displaying jars of glowing viscera and another selling blades that drank the light, was Tommy Herrera." | | 4 | "Taking a breath that tasted of ancient dust and dark magic, Detective Harlow Quinn stepped away from the pillar and into the chaotic, kaleidoscopic throng of Th…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 1 | | effectiveRatio | 0.5 | |