| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 23 | | adverbTagCount | 2 | | adverbTags | | 0 | "Quinn said softly [softly]" | | 1 | "she gestured around [around]" |
| | dialogueSentences | 45 | | tagDensity | 0.511 | | leniency | 1 | | rawRatio | 0.087 | | effectiveRatio | 0.087 | |
| 77.14% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1531 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "precisely" | | 1 | "slightly" | | 2 | "lazily" | | 3 | "perfectly" | | 4 | "quickly" | | 5 | "gently" | | 6 | "softly" |
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| 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) | |
| 41.21% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1531 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "silence" | | 1 | "perfect" | | 2 | "echoed" | | 3 | "scanned" | | 4 | "velvet" | | 5 | "shattered" | | 6 | "glint" | | 7 | "etched" | | 8 | "intricate" | | 9 | "quivered" | | 10 | "raced" | | 11 | "weight" | | 12 | "eyebrow" | | 13 | "echoing" | | 14 | "gloom" | | 15 | "churning" |
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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 | 0 | | narrationSentences | 138 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 161 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 6 | | markdownWords | 25 | | totalWords | 1519 | | ratio | 0.016 | | matches | | 0 | "plink" | | 1 | "They’re not decorations, Harlow. They’re cages." | | 2 | "Points to the nearest rift. For those who know how to shop off the grid." | | 3 | "price" | | 4 | "plink" | | 5 | "plink-plink-plink" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 74.39% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 1058 | | uniqueNames | 13 | | maxNameDensity | 1.51 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 16 | | Tube | 2 | | Camden | 1 | | Davies | 12 | | Eva | 5 | | Harlow | 2 | | Veil | 2 | | Compass | 1 | | Market | 2 | | Morris | 1 | | Northern | 1 | | Line | 1 | | London | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Davies" | | 2 | "Eva" | | 3 | "Harlow" | | 4 | "Morris" |
| | places | | 0 | "Compass" | | 1 | "Market" | | 2 | "London" |
| | globalScore | 0.744 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 1 | | matches | | 0 | "smelled like spoiled dates and arcane ince" |
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| 68.33% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.317 | | wordCount | 1519 | | matches | | 0 | "not as he was in life, but as she’d last seen him in the morgue, a similar, inexplicabl" | | 1 | "not a wallet, but a single, oblong token" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 161 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 33.76 | | std | 21.82 | | cv | 0.647 | | sampleLengths | | 0 | 48 | | 1 | 16 | | 2 | 57 | | 3 | 42 | | 4 | 21 | | 5 | 53 | | 6 | 35 | | 7 | 49 | | 8 | 7 | | 9 | 72 | | 10 | 12 | | 11 | 38 | | 12 | 47 | | 13 | 21 | | 14 | 48 | | 15 | 28 | | 16 | 42 | | 17 | 7 | | 18 | 43 | | 19 | 3 | | 20 | 73 | | 21 | 20 | | 22 | 10 | | 23 | 46 | | 24 | 52 | | 25 | 66 | | 26 | 7 | | 27 | 63 | | 28 | 22 | | 29 | 11 | | 30 | 5 | | 31 | 47 | | 32 | 11 | | 33 | 29 | | 34 | 54 | | 35 | 6 | | 36 | 66 | | 37 | 8 | | 38 | 44 | | 39 | 39 | | 40 | 7 | | 41 | 7 | | 42 | 71 | | 43 | 56 | | 44 | 10 |
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| 90.01% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 138 | | matches | | 0 | "was carved" | | 1 | "was littered" | | 2 | "was etched" | | 3 | "was wedged" | | 4 | "was scratched" | | 5 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 173 | | matches | | |
| 71.87% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 161 | | ratio | 0.025 | | matches | | 0 | "Davies was right about one thing; it was a mess." | | 1 | "One in the centre—a triangle bisected by a wavy line—was carved into the brick, not painted." | | 2 | "Morris’s face flashed before her eyes—not as he was in life, but as she’d last seen him in the morgue, a similar, inexplicable calm to his features." | | 3 | "The knife, the compass, the token—they weren’t just evidence." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 511 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.029354207436399216 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0019569471624266144 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 161 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 161 | | mean | 9.43 | | std | 7.45 | | cv | 0.79 | | sampleLengths | | 0 | 25 | | 1 | 15 | | 2 | 8 | | 3 | 11 | | 4 | 5 | | 5 | 15 | | 6 | 8 | | 7 | 22 | | 8 | 7 | | 9 | 2 | | 10 | 2 | | 11 | 1 | | 12 | 21 | | 13 | 21 | | 14 | 9 | | 15 | 4 | | 16 | 8 | | 17 | 12 | | 18 | 1 | | 19 | 11 | | 20 | 4 | | 21 | 7 | | 22 | 3 | | 23 | 15 | | 24 | 8 | | 25 | 27 | | 26 | 7 | | 27 | 8 | | 28 | 15 | | 29 | 5 | | 30 | 14 | | 31 | 4 | | 32 | 2 | | 33 | 1 | | 34 | 31 | | 35 | 13 | | 36 | 3 | | 37 | 10 | | 38 | 15 | | 39 | 12 | | 40 | 8 | | 41 | 4 | | 42 | 5 | | 43 | 2 | | 44 | 8 | | 45 | 2 | | 46 | 8 | | 47 | 1 | | 48 | 7 | | 49 | 10 |
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| 60.25% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.42857142857142855 | | totalSentences | 161 | | uniqueOpeners | 69 | |
| 84.03% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 119 | | matches | | 0 | "Just a dark, perfect circle" | | 1 | "Then rolled down the blade." | | 2 | "Just a single, impossible wound." |
| | ratio | 0.025 | |
| 75.46% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 119 | | matches | | 0 | "She just didn't understand why" | | 1 | "She ignored Davies and studied" | | 2 | "She didn’t look up." | | 3 | "She shone her light at" | | 4 | "She moved her light to" | | 5 | "She didn’t touch it." | | 6 | "She looked at the hands." | | 7 | "She turned to the wall" | | 8 | "Her eyes scanned the periphery" | | 9 | "It wasn’t a random dumping" | | 10 | "It was a stall." | | 11 | "Her torch beam caught a" | | 12 | "She nudged the fabric aside" | | 13 | "*They’re not decorations, Harlow." | | 14 | "she said, her voice tight" | | 15 | "She used her pen to" | | 16 | "It was cold." | | 17 | "It quivered, spinning lazily before" | | 18 | "She knew what this was." | | 19 | "It moved every full moon." |
| | ratio | 0.361 | |
| 39.83% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 100 | | totalSentences | 119 | | matches | | 0 | "The knife hung between two" | | 1 | "Harlow Quinn knew it was" | | 2 | "She just didn't understand why" | | 3 | "The abandoned Tube station beneath" | | 4 | "The air tasted of rust" | | 5 | "Quinn’s worn leather watch felt" | | 6 | "She ignored Davies and studied" | | 7 | "Quinn said, her voice flat" | | 8 | "She didn’t look up." | | 9 | "The body lay seven feet" | | 10 | "A single, clean incision below" | | 11 | "Davies continued, warming to his" | | 12 | "Quinn finally stood, her knees" | | 13 | "She shone her light at" | | 14 | "A steady, fat drip of" | | 15 | "She moved her light to" | | 16 | "The flagstones were dusty, marked" | | 17 | "Quinn walked to the body" | | 18 | "She didn’t touch it." | | 19 | "She looked at the hands." |
| | ratio | 0.84 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 119 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 1 | | matches | | 0 | "Well-dressed in a suit that cost more than Quinn’s monthly rent." |
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| 81.52% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 23 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn said, her voice flat" | | 1 | "she said, her voice tight" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 2 | | fancyTags | | 0 | "Davies continued (continue)" | | 1 | "Davies protested (protest)" |
| | dialogueSentences | 45 | | tagDensity | 0.244 | | leniency | 0.489 | | rawRatio | 0.182 | | effectiveRatio | 0.089 | |