| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.03% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1007 | | 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) | |
| 65.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1007 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "flicker" | | 1 | "gloom" | | 2 | "silence" | | 3 | "constructed" | | 4 | "dancing" | | 5 | "cacophony" | | 6 | "familiar" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "flicker of emotion" | | count | 1 |
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| | highlights | | 0 | "a flicker of recognition" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 72 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 72 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 72 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 10 | | totalWords | 995 | | ratio | 0.01 | | matches | | 0 | "“There’s a goblin selling magic candles in Camden Town station.”" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 972 | | uniqueNames | 14 | | maxNameDensity | 0.82 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 8 | | Tomás | 2 | | Herrera | 4 | | Raven | 1 | | Nest | 1 | | Camden | 1 | | Tube | 2 | | Morris | 2 | | Veil | 1 | | Market | 1 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Raven" | | 5 | "Morris" | | 6 | "Saint" | | 7 | "Christopher" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 1 | | matches | | 0 | "dagger that seemed to drink the light around it" |
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| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 2.01 | | wordCount | 995 | | matches | | 0 | "not into a collapsed sewer or a junkie’s den, but into a cavernous space" | | 1 | "not as a fugitive, but as a patron" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 72 | | matches | | |
| 96.49% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 19 | | mean | 52.37 | | std | 25.54 | | cv | 0.488 | | sampleLengths | | 0 | 72 | | 1 | 56 | | 2 | 57 | | 3 | 42 | | 4 | 68 | | 5 | 4 | | 6 | 67 | | 7 | 49 | | 8 | 33 | | 9 | 67 | | 10 | 7 | | 11 | 89 | | 12 | 36 | | 13 | 44 | | 14 | 93 | | 15 | 75 | | 16 | 6 | | 17 | 58 | | 18 | 72 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 72 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 144 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 72 | | ratio | 0.056 | | matches | | 0 | "Her eyes, sharp and brown, never left the back of the man fifty yards ahead—Tomás Herrera, his dark jacket a shifting shadow under the gaudy glow of neon signs." | | 1 | "The scar on his forearm, just visible when his sleeve rode up—a knife attack." | | 2 | "The memory of Morris, her partner, ghosted through her mind—a different kind of dark, an unexplained silence on the comms, a case file that made no sense no matter how many times she read it." | | 3 | "Figures moved through the space—some human, many decidedly not." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 342 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 7 | | adverbRatio | 0.02046783625730994 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 72 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 72 | | mean | 13.82 | | std | 8.59 | | cv | 0.622 | | sampleLengths | | 0 | 15 | | 1 | 28 | | 2 | 29 | | 3 | 7 | | 4 | 23 | | 5 | 5 | | 6 | 11 | | 7 | 10 | | 8 | 10 | | 9 | 12 | | 10 | 12 | | 11 | 12 | | 12 | 11 | | 13 | 10 | | 14 | 7 | | 15 | 1 | | 16 | 2 | | 17 | 2 | | 18 | 14 | | 19 | 6 | | 20 | 18 | | 21 | 6 | | 22 | 13 | | 23 | 31 | | 24 | 4 | | 25 | 7 | | 26 | 19 | | 27 | 14 | | 28 | 7 | | 29 | 14 | | 30 | 6 | | 31 | 5 | | 32 | 3 | | 33 | 35 | | 34 | 6 | | 35 | 23 | | 36 | 10 | | 37 | 8 | | 38 | 21 | | 39 | 14 | | 40 | 24 | | 41 | 7 | | 42 | 30 | | 43 | 9 | | 44 | 17 | | 45 | 19 | | 46 | 14 | | 47 | 5 | | 48 | 16 | | 49 | 6 |
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| 64.81% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4305555555555556 | | totalSentences | 72 | | uniqueOpeners | 31 | |
| 98.04% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 68 | | matches | | 0 | "Just another night, she’d thought." | | 1 | "Then he’d glanced back, a" |
| | ratio | 0.029 | |
| 67.06% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 68 | | matches | | 0 | "Her eyes, sharp and brown," | | 1 | "He’d been clean leaving The" | | 2 | "She shoved past a couple" | | 3 | "He was faster than she’d" | | 4 | "He knew how to handle" | | 5 | "He burst out of the" | | 6 | "She saw him glance over" | | 7 | "He vanished through it." | | 8 | "It led to a set" | | 9 | "She pulled her torch, the" | | 10 | "They descended into a deeper" | | 11 | "She could radio for backup." | | 12 | "She’d followed procedure that night," | | 13 | "It was a sound utterly" | | 14 | "She moved down the steps," | | 15 | "She’d heard whispers, always dismissed" | | 16 | "He paused at a stall" | | 17 | "Her training screamed at her" | | 18 | "They’d revoke her badge, and" | | 19 | "His death was in the" |
| | ratio | 0.382 | |
| 18.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 68 | | matches | | 0 | "The rain fell in icy" | | 1 | "Detective Harlow Quinn’s breath came" | | 2 | "Her eyes, sharp and brown," | | 3 | "He’d been clean leaving The" | | 4 | "Quinn had watched from a" | | 5 | "Herrera cut left into a" | | 6 | "Quinn swore under her breath," | | 7 | "She shoved past a couple" | | 8 | "The alley was a canyon" | | 9 | "The smell of wet brick" | | 10 | "He was faster than she’d" | | 11 | "A former paramedic, her notes" | | 12 | "The scar on his forearm," | | 13 | "He knew how to handle" | | 14 | "He burst out of the" | | 15 | "The warehouses here were dark," | | 16 | "Quinn followed, her jacket soaked" | | 17 | "She saw him glance over" | | 18 | "He vanished through it." | | 19 | "Quinn slowed, approaching the gate" |
| | ratio | 0.882 | |
| 73.53% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 68 | | matches | | 0 | "To follow was to abandon" |
| | ratio | 0.015 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 3 | | matches | | 0 | "Detective Harlow Quinn’s breath came in ragged white puffs, her boots slapping against the wet pavement as she pushed through the crowd spilling out of a late-n…" | | 1 | "The memory of Morris, her partner, ghosted through her mind—a different kind of dark, an unexplained silence on the comms, a case file that made no sense no mat…" | | 2 | "It was also, she knew with a cold, certain clarity, the only path forward that hadn’t already been swept clean of clues by men who wore better suits than hers." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |