| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 31 | | tagDensity | 0.258 | | leniency | 0.516 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.28% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1059 | | 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) | |
| 33.90% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1059 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "pulsed" | | 1 | "flickered" | | 2 | "glinting" | | 3 | "flicked" | | 4 | "stomach" | | 5 | "echoed" | | 6 | "lurched" | | 7 | "familiar" | | 8 | "silk" | | 9 | "tracing" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 90 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 90 | | filterMatches | (empty) | | hedgeMatches | | |
| 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 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 16 | | markdownWords | 17 | | totalWords | 1044 | | ratio | 0.016 | | matches | | 0 | "No time." | | 1 | "wrong" | | 2 | "shifted" | | 3 | "wrong" | | 4 | "Focus." | | 5 | "courier" | | 6 | "worse" | | 7 | "hung" | | 8 | "always" | | 9 | "click" | | 10 | "don’t" | | 11 | "teeth" | | 12 | "him" | | 13 | "wrong" | | 14 | "delightful" | | 15 | "click" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 79.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 849 | | uniqueNames | 11 | | maxNameDensity | 1.41 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 12 | | Camden | 1 | | Lock | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Met | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 1 | | Herrera | 9 |
| | persons | | 0 | "Quinn" | | 1 | "Market" | | 2 | "Met" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Tomás" | | 6 | "Herrera" |
| | places | (empty) | | globalScore | 0.793 | | windowScore | 0.833 | |
| 50.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | glossingSentenceCount | 2 | | matches | | 0 | "quite human, the wet *click* of too many joints moving at once" | | 1 | "looked like it had been tailored in the 1" |
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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 | 1044 | | 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 | 49 | | mean | 21.31 | | std | 18.31 | | cv | 0.86 | | sampleLengths | | 0 | 1 | | 1 | 76 | | 2 | 2 | | 3 | 47 | | 4 | 68 | | 5 | 47 | | 6 | 3 | | 7 | 43 | | 8 | 12 | | 9 | 24 | | 10 | 1 | | 11 | 63 | | 12 | 17 | | 13 | 37 | | 14 | 35 | | 15 | 19 | | 16 | 7 | | 17 | 28 | | 18 | 35 | | 19 | 21 | | 20 | 18 | | 21 | 5 | | 22 | 3 | | 23 | 21 | | 24 | 11 | | 25 | 12 | | 26 | 18 | | 27 | 21 | | 28 | 14 | | 29 | 9 | | 30 | 47 | | 31 | 40 | | 32 | 27 | | 33 | 27 | | 34 | 6 | | 35 | 4 | | 36 | 35 | | 37 | 11 | | 38 | 2 | | 39 | 2 | | 40 | 38 | | 41 | 3 | | 42 | 14 | | 43 | 22 | | 44 | 3 | | 45 | 7 | | 46 | 17 | | 47 | 4 | | 48 | 17 |
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| 97.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 90 | | matches | | 0 | "been trained" | | 1 | "been tailored" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 152 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 0 | | flaggedSentences | 12 | | totalSentences | 113 | | ratio | 0.106 | | matches | | 0 | "The suspect—tall, lean, moving like a man who knew the city’s veins better than its arteries—had vanished into the mouth of the abandoned Tube station." | | 1 | "The air inside the station hit her like a slap—thick, damp, and laced with the scent of wet stone and something older, something that made her nostrils flare." | | 2 | "The distant hum of voices reached her—too many, too close, too *wrong*." | | 3 | "Quinn reacted on instinct—elbow back, heel stomp, twist." | | 4 | "The grip broke with a grunt, and she spun, fist raised, ready to drive it into—" | | 5 | "Tomás Herrera—former paramedic, current den mother to the city’s supernatural underbelly—exhaled sharply." | | 6 | "The voices had sharpened, becoming distinct—laughter, haggling, the clink of glass." | | 7 | "She’d seen the reports—the missing persons, the empty flats, the way the air in those places *hung* wrong, like the world had taken a breath and forgotten to exhale." | | 8 | "But the missing persons cases flashed behind her eyes—faces, names, lives snuffed out like candles." | | 9 | "The voices grew louder, clearer—snatches of languages she didn’t recognize, the hiss of something that wasn’t quite human, the wet *click* of too many joints moving at once." | | 10 | "The scent hit her first—copper and rot, sweet decay, the sharp tang of ozone." | | 11 | "His face was all sharp angles, his eyes—" |
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| 82.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 864 | | adjectiveStacks | 3 | | stackExamples | | 0 | "suspect—tall, lean, moving like" | | 1 | "former paramedic, current den" | | 2 | "small, leather-bound book." |
| | adverbCount | 34 | | adverbRatio | 0.03935185185185185 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.009259259259259259 | |
| 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 | 9.23 | | std | 8.44 | | cv | 0.914 | | sampleLengths | | 0 | 31 | | 1 | 25 | | 2 | 20 | | 3 | 2 | | 4 | 3 | | 5 | 44 | | 6 | 28 | | 7 | 13 | | 8 | 3 | | 9 | 24 | | 10 | 10 | | 11 | 2 | | 12 | 2 | | 13 | 33 | | 14 | 3 | | 15 | 11 | | 16 | 12 | | 17 | 15 | | 18 | 4 | | 19 | 1 | | 20 | 12 | | 21 | 8 | | 22 | 16 | | 23 | 1 | | 24 | 16 | | 25 | 29 | | 26 | 7 | | 27 | 2 | | 28 | 9 | | 29 | 10 | | 30 | 7 | | 31 | 12 | | 32 | 25 | | 33 | 15 | | 34 | 11 | | 35 | 2 | | 36 | 7 | | 37 | 5 | | 38 | 14 | | 39 | 4 | | 40 | 3 | | 41 | 5 | | 42 | 23 | | 43 | 3 | | 44 | 29 | | 45 | 3 | | 46 | 3 | | 47 | 18 | | 48 | 12 | | 49 | 6 |
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| 49.26% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.3805309734513274 | | totalSentences | 113 | | uniqueOpeners | 43 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 73 | | matches | | 0 | "Just a Saint Christopher medallion" | | 1 | "Too many eyes." | | 2 | "Too many *teeth*." | | 3 | "Then he looked up." | | 4 | "Directly at Quinn." |
| | ratio | 0.068 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 73 | | matches | | 0 | "She didn’t hesitate." | | 1 | "She stepped forward." | | 2 | "She clenched her jaw." | | 3 | "He held up his hands," | | 4 | "she hissed, lowering her fist" | | 5 | "She bared her teeth." | | 6 | "His voice dropped" | | 7 | "She’d seen the reports—the missing" | | 8 | "he said, voice quiet now" | | 9 | "They moved forward, the tunnel" | | 10 | "Her stomach lurched." | | 11 | "She swallowed hard." | | 12 | "He was heading toward a" | | 13 | "His face was all sharp" | | 14 | "His eyes were *wrong*." | | 15 | "he said, his voice like" |
| | ratio | 0.219 | |
| 28.49% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 73 | | matches | | 0 | "The bone token burned cold" | | 1 | "The suspect—tall, lean, moving like" | | 2 | "The entrance yawned like a" | | 3 | "She didn’t hesitate." | | 4 | "The token was her only" | | 5 | "The air inside the station" | | 6 | "The token pulsed once, sharp" | | 7 | "The walls themselves seemed to" | | 8 | "Quinn’s fingers twitched toward her" | | 9 | "The Veil Market didn’t play" | | 10 | "She stepped forward." | | 11 | "The tunnel sloped downward, the" | | 12 | "The distant hum of voices" | | 13 | "The token’s glow flickered, casting" | | 14 | "She clenched her jaw." | | 15 | "A hand shot out from" | | 16 | "Quinn reacted on instinct—elbow back," | | 17 | "The grip broke with a" | | 18 | "The voice was low, rough," | | 19 | "A man stood there, barely" |
| | ratio | 0.863 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 73 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 8 | | matches | | 0 | "The bone token burned cold against Quinn’s palm as she skidded around the corner of Camden Lock, her boots splashing through puddles that reflected the sickly g…" | | 1 | "The suspect—tall, lean, moving like a man who knew the city’s veins better than its arteries—had vanished into the mouth of the abandoned Tube station." | | 2 | "The token was her only ticket in, and if she lost him now, she’d lose the thread that connected three missing persons cases, all with the same eerie signature: …" | | 3 | "The air inside the station hit her like a slap—thick, damp, and laced with the scent of wet stone and something older, something that made her nostrils flare." | | 4 | "The Veil Market didn’t play by Met rules, and if she called it in, she’d be explaining to the brass why she’d chased a suspect into a black site that didn’t off…" | | 5 | "The voices grew louder, clearer—snatches of languages she didn’t recognize, the hiss of something that wasn’t quite human, the wet *click* of too many joints mo…" | | 6 | "The market sprawled before them, a labyrinth of stalls and shadows, lit by flickering lanterns that cast long, grasping fingers across the stone." | | 7 | "He was heading toward a stall draped in black silk, its wares hidden behind a curtain that rippled as if touched by an unseen wind." |
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| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 1 | | matches | | 0 | "he said, voice quiet now" |
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| 53.23% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 3 | | fancyTags | | 0 | "she hissed (hiss)" | | 1 | "he snapped (snap)" | | 2 | "he murmured (murmur)" |
| | dialogueSentences | 31 | | tagDensity | 0.226 | | leniency | 0.452 | | rawRatio | 0.429 | | effectiveRatio | 0.194 | |