| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.538 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.59% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1561 | | totalAiIsmAdverbs | 2 | | 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) | |
| 55.16% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1561 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "weight" | | 1 | "footsteps" | | 2 | "familiar" | | 3 | "comfortable" | | 4 | "flickered" | | 5 | "echo" | | 6 | "silence" | | 7 | "methodical" | | 8 | "shimmered" | | 9 | "glinting" | | 10 | "warmth" | | 11 | "etched" |
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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 | 107 | | matches | (empty) | |
| 76.10% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 4 | | narrationSentences | 107 | | filterMatches | | | hedgeMatches | | 0 | "began to" | | 1 | "try to" | | 2 | "happened to" | | 3 | "begin to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 113 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1555 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1418 | | uniqueNames | 13 | | maxNameDensity | 0.99 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | London | 2 | | Harlow | 1 | | Quinn | 14 | | Soho | 2 | | Raven | 4 | | Nest | 4 | | Tube | 3 | | Morris | 2 | | Camden | 1 | | Saint | 1 | | Christopher | 1 | | Spain | 1 | | Tomás | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Morris" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Tomás" |
| | places | | 0 | "London" | | 1 | "Soho" | | 2 | "Tube" | | 3 | "Spain" |
| | globalScore | 1 | | windowScore | 1 | |
| 84.21% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 76 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like a trap dressed in the shape o" | | 1 | "looked like it had come from a blade" |
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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 | 1555 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 113 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 35.34 | | std | 21.77 | | cv | 0.616 | | sampleLengths | | 0 | 52 | | 1 | 44 | | 2 | 65 | | 3 | 19 | | 4 | 11 | | 5 | 67 | | 6 | 9 | | 7 | 52 | | 8 | 51 | | 9 | 11 | | 10 | 50 | | 11 | 41 | | 12 | 30 | | 13 | 64 | | 14 | 3 | | 15 | 51 | | 16 | 14 | | 17 | 30 | | 18 | 5 | | 19 | 53 | | 20 | 79 | | 21 | 2 | | 22 | 43 | | 23 | 39 | | 24 | 14 | | 25 | 75 | | 26 | 17 | | 27 | 66 | | 28 | 22 | | 29 | 49 | | 30 | 5 | | 31 | 70 | | 32 | 28 | | 33 | 18 | | 34 | 21 | | 35 | 3 | | 36 | 43 | | 37 | 51 | | 38 | 16 | | 39 | 26 | | 40 | 30 | | 41 | 57 | | 42 | 33 | | 43 | 26 |
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| 88.87% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 107 | | matches | | 0 | "been closed" | | 1 | "was supposed" | | 2 | "was connected" | | 3 | "was gone" | | 4 | "was connected" |
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| 91.16% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 245 | | matches | | 0 | "was training" | | 1 | "was losing" | | 2 | "was leading" | | 3 | "was slipping" |
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| 16.43% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 113 | | ratio | 0.044 | | matches | | 0 | "The suspect—a wiry man in a dark coat she'd only caught a glimpse of—darted left, cutting through an alley that reeked of rotting garbage and stale urine." | | 1 | "Quinn recognized the location from memory—an old entrance to a Tube station that had been closed for decades, boarded up and forgotten by everyone except the city's homeless and its predators." | | 2 | "He'd come from the Raven's Nest, and the Raven's Nest was connected to things she couldn't explain—disappearances, strange injuries, victims who told stories that didn't match the evidence." | | 3 | "She pushed through the crowd—and they were a crowd, dozens of figures pressing close, their faces hidden, their attention fixed on goods and conversations that she couldn't begin to understand." | | 4 | "Then he reached into his pocket and pulled out a small object—a token, carved from bone, etched with symbols she couldn't read." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1429 | | adjectiveStacks | 1 | | stackExamples | | 0 | "warmer, thicker, pressing against her" |
| | adverbCount | 38 | | adverbRatio | 0.026592022393282014 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0034989503149055285 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 113 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 113 | | mean | 13.76 | | std | 8.71 | | cv | 0.633 | | sampleLengths | | 0 | 12 | | 1 | 22 | | 2 | 18 | | 3 | 11 | | 4 | 33 | | 5 | 24 | | 6 | 27 | | 7 | 14 | | 8 | 19 | | 9 | 4 | | 10 | 4 | | 11 | 3 | | 12 | 20 | | 13 | 28 | | 14 | 19 | | 15 | 6 | | 16 | 3 | | 17 | 7 | | 18 | 25 | | 19 | 20 | | 20 | 15 | | 21 | 4 | | 22 | 2 | | 23 | 30 | | 24 | 11 | | 25 | 19 | | 26 | 31 | | 27 | 7 | | 28 | 2 | | 29 | 32 | | 30 | 20 | | 31 | 10 | | 32 | 14 | | 33 | 30 | | 34 | 3 | | 35 | 1 | | 36 | 16 | | 37 | 3 | | 38 | 18 | | 39 | 18 | | 40 | 3 | | 41 | 12 | | 42 | 3 | | 43 | 11 | | 44 | 3 | | 45 | 10 | | 46 | 1 | | 47 | 16 | | 48 | 5 | | 49 | 4 |
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| 53.39% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.36283185840707965 | | totalSentences | 113 | | uniqueOpeners | 41 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 94 | | matches | | 0 | "Of course he didn't." | | 1 | "Too many witnesses, too many" | | 2 | "Then he reached into his" |
| | ratio | 0.032 | |
| 79.57% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 94 | | matches | | 0 | "She cared about the wet" | | 1 | "Her lungs burned with the" | | 2 | "Her voice came out raw," | | 3 | "They never did." | | 4 | "She'd picked him up outside" | | 5 | "It was training." | | 6 | "He knew the short cuts," | | 7 | "She hadn't drawn it." | | 8 | "She stopped, breathing hard, and" | | 9 | "It was warm." | | 10 | "It smelled of incense and" | | 11 | "She'd worked enough cases to" | | 12 | "Her radio crackled." | | 13 | "She didn't answer." | | 14 | "Her thumb found the button," | | 15 | "She could go back." | | 16 | "He'd come from the Raven's" | | 17 | "Her partner, DS Morris, had" | | 18 | "It had been a Tube" | | 19 | "She could see the remnants" |
| | ratio | 0.351 | |
| 45.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 94 | | matches | | 0 | "The rain had a way" | | 1 | "Something that remembered gas lamps" | | 2 | "Detective Harlow Quinn didn't give" | | 3 | "She cared about the wet" | | 4 | "Her lungs burned with the" | | 5 | "The suspect—a wiry man in" | | 6 | "Quinn followed without hesitation, her" | | 7 | "Her voice came out raw," | | 8 | "The man didn't stop." | | 9 | "They never did." | | 10 | "She'd picked him up outside" | | 11 | "The green neon sign above" | | 12 | "The man had seen her" | | 13 | "That kind of reaction wasn't" | | 14 | "It was training." | | 15 | "The man had a sprinter's" | | 16 | "He knew the short cuts," | | 17 | "Quinn's hand went instinctively to" | | 18 | "She hadn't drawn it." | | 19 | "The man veered suddenly, cutting" |
| | ratio | 0.83 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 94 | | matches | (empty) | | ratio | 0 | |
| 36.25% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 67 | | technicalSentenceCount | 10 | | matches | | 0 | "Something that remembered gas lamps and fog and the weight of unsolved murders pressing down on its bones." | | 1 | "The suspect—a wiry man in a dark coat she'd only caught a glimpse of—darted left, cutting through an alley that reeked of rotting garbage and stale urine." | | 2 | "The green neon sign above its entrance flickered like a dying insect, and the clientele moved with a collective wariness that told her more than any informant c…" | | 3 | "Too many witnesses, too many cameras, too many ways for a chase to turn into a shooting and a shooting into a stack of paperwork that would bury her desk." | | 4 | "Quinn recognized the location from memory—an old entrance to a Tube station that had been closed for decades, boarded up and forgotten by everyone except the ci…" | | 5 | "The tile walls were stained with decades of grime and water damage, and the air that rose from below carried a smell that didn't belong to a forgotten railway s…" | | 6 | "It smelled of incense and copper and something that made the back of her throat tighten." | | 7 | "A hidden market, operating beneath the streets of Camden in a place that shouldn't exist." | | 8 | "A woman with eyes that were too large and too black offered vials of liquid that shimmered like trapped starlight." | | 9 | "Behind her, the man named Tomás watched her go, his hand touching the medallion at his throat, his lips moving in a prayer she couldn't hear." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 73.08% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 13 | | tagDensity | 0.308 | | leniency | 0.615 | | rawRatio | 0.25 | | effectiveRatio | 0.154 | |