| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 31 | | tagDensity | 0.226 | | leniency | 0.452 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.33% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1874 | | 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) | |
| 33.30% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1874 | | totalAiIsms | 25 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "cascading" | | 1 | "flickered" | | 2 | "glint" | | 3 | "rhythmic" | | 4 | "pulse" | | 5 | "echoed" | | 6 | "stomach" | | 7 | "pulsed" | | 8 | "shimmered" | | 9 | "gloom" | | 10 | "weight" | | 11 | "velvet" | | 12 | "sturdy" | | 13 | "silence" | | 14 | "scanning" | | 15 | "flicker" | | 16 | "tracing" | | 17 | "echoing" |
| |
| 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 | 139 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 139 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 163 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1861 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 73 | | wordCount | 1527 | | uniqueNames | 14 | | maxNameDensity | 2.23 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 4 | | Harlow | 1 | | Quinn | 34 | | Raven | 2 | | Nest | 2 | | Herrera | 18 | | Saint | 2 | | Christopher | 2 | | Morris | 1 | | Camden | 1 | | Tube | 1 | | Market | 3 | | Veil | 1 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" | | 8 | "Market" |
| | places | | | globalScore | 0.387 | | windowScore | 0.333 | |
| 57.41% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 108 | | glossingSentenceCount | 4 | | matches | | 0 | "felt like the breath of a furnace" | | 1 | "looked like a velvet curtain waiting to b" | | 2 | "tasted like lilies" | | 3 | "quite suppress" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1861 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 163 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 70 | | mean | 26.59 | | std | 16.14 | | cv | 0.607 | | sampleLengths | | 0 | 60 | | 1 | 44 | | 2 | 15 | | 3 | 12 | | 4 | 50 | | 5 | 39 | | 6 | 76 | | 7 | 8 | | 8 | 45 | | 9 | 44 | | 10 | 48 | | 11 | 16 | | 12 | 40 | | 13 | 25 | | 14 | 22 | | 15 | 17 | | 16 | 17 | | 17 | 54 | | 18 | 46 | | 19 | 9 | | 20 | 15 | | 21 | 32 | | 22 | 52 | | 23 | 25 | | 24 | 1 | | 25 | 28 | | 26 | 29 | | 27 | 10 | | 28 | 8 | | 29 | 2 | | 30 | 32 | | 31 | 15 | | 32 | 29 | | 33 | 2 | | 34 | 37 | | 35 | 33 | | 36 | 15 | | 37 | 41 | | 38 | 39 | | 39 | 20 | | 40 | 17 | | 41 | 35 | | 42 | 32 | | 43 | 19 | | 44 | 29 | | 45 | 11 | | 46 | 7 | | 47 | 17 | | 48 | 44 | | 49 | 26 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 139 | | matches | | |
| 14.08% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 251 | | matches | | 0 | "was shrinking" | | 1 | "were speaking" | | 2 | "was cloying" | | 3 | "was heading" | | 4 | "was weighing" | | 5 | "was still falling" | | 6 | "was still flickering" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 3 | | flaggedSentences | 14 | | totalSentences | 163 | | ratio | 0.086 | | matches | | 0 | "A glint of silver caught the streetlamp—the Saint Christopher medallion swinging against his chest." | | 1 | "The clean, sharp ozone of the storm vanished, replaced by the scent of ancient dust, wet copper, and something sweet—like rotting lilies." | | 2 | "The gate didn't snap shut; it lingered, vibrating with a low-frequency hum that set Quinn’s teeth on edge." | | 3 | "Colors that had no business being in an abandoned subway station—violet, charcoal, a bruised gold—shimmered in the air." | | 4 | "Figures moved through the space—some too tall, some moving with too many joints." | | 5 | "He wore a coat stitched from different types of leather—some of it looked suspiciously like human palms." | | 6 | "People—if they were people—skittered out of her way, their whispers rising like a swarm of locusts." | | 7 | "He grabbed a ceramic vial from the counter, tossing a second bone token to the merchant—a woman whose fingers were too long by half." | | 8 | "Herrera looked at her watch—the worn leather strap she’d had since her first year on the force." | | 9 | "She looked at the exit—the iron gate was a pinprick of normal, gray light in a sea of impossible colors." | | 10 | "One touched the sleeve of Quinn’s jacket; she barked a sharp command, and the figure recoiled, its face a mask of sudden, sharp teeth." | | 11 | "It was warm—unnaturally warm—against her chest." | | 12 | "The pavement wasn't just stone; it was a roof." | | 13 | "The Saint Christopher medallion Herrera had been wearing flashed in her mind—a protector of travelers." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1548 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 44 | | adverbRatio | 0.028423772609819122 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.008397932816537468 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 163 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 163 | | mean | 11.42 | | std | 6.8 | | cv | 0.595 | | sampleLengths | | 0 | 18 | | 1 | 22 | | 2 | 20 | | 3 | 8 | | 4 | 10 | | 5 | 14 | | 6 | 4 | | 7 | 8 | | 8 | 6 | | 9 | 9 | | 10 | 12 | | 11 | 4 | | 12 | 12 | | 13 | 11 | | 14 | 8 | | 15 | 2 | | 16 | 13 | | 17 | 4 | | 18 | 27 | | 19 | 8 | | 20 | 7 | | 21 | 20 | | 22 | 10 | | 23 | 3 | | 24 | 11 | | 25 | 5 | | 26 | 20 | | 27 | 8 | | 28 | 8 | | 29 | 20 | | 30 | 3 | | 31 | 14 | | 32 | 3 | | 33 | 13 | | 34 | 6 | | 35 | 22 | | 36 | 7 | | 37 | 16 | | 38 | 13 | | 39 | 12 | | 40 | 16 | | 41 | 11 | | 42 | 9 | | 43 | 20 | | 44 | 8 | | 45 | 17 | | 46 | 22 | | 47 | 17 | | 48 | 17 | | 49 | 5 |
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| 39.57% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.26380368098159507 | | totalSentences | 163 | | uniqueOpeners | 43 | |
| 24.69% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 135 | | matches | | 0 | "Soft, heatless fire burned in" |
| | ratio | 0.007 | |
| 77.78% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 48 | | totalSentences | 135 | | matches | | 0 | "He pulled his collar up," | | 1 | "He didn't look back." | | 2 | "He bolted toward the mouth" | | 3 | "Her boots hit the asphalt" | | 4 | "He rounded a corner, his" | | 5 | "She checked the leather strap" | | 6 | "Her sharp jaw tightened." | | 7 | "She’d spent three years chasing" | | 8 | "She wouldn't lose him to" | | 9 | "He moved with a frantic" | | 10 | "She didn't wait." | | 11 | "She scrambled over the hood," | | 12 | "He didn't stop." | | 13 | "He ducked into a stairwell" | | 14 | "Her military background kept her" | | 15 | "She reached the bottom of" | | 16 | "He reached into his pocket" | | 17 | "He pressed the bone token" | | 18 | "She reached for the holster" | | 19 | "He slipped through the opening." |
| | ratio | 0.356 | |
| 0.74% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 124 | | totalSentences | 135 | | matches | | 0 | "Detective Harlow Quinn adjusted the" | | 1 | "Tomás Herrera stepped out of" | | 2 | "He pulled his collar up," | | 3 | "A glint of silver caught" | | 4 | "He didn't look back." | | 5 | "He bolted toward the mouth" | | 6 | "Quinn shoved off the brick" | | 7 | "Her boots hit the asphalt" | | 8 | "The man didn't slow." | | 9 | "He rounded a corner, his" | | 10 | "Quinn’s lungs pulled in the" | | 11 | "She checked the leather strap" | | 12 | "The city felt hollow at" | | 13 | "Her sharp jaw tightened." | | 14 | "She’d spent three years chasing" | | 15 | "She wouldn't lose him to" | | 16 | "Herrera vaulted a stack of" | | 17 | "He moved with a frantic" | | 18 | "A delivery truck hissed as" | | 19 | "She didn't wait." |
| | ratio | 0.919 | |
| 74.07% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 135 | | matches | | 0 | "To her left, a merchant" | | 1 | "To her right, a creature" |
| | ratio | 0.015 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 72 | | technicalSentenceCount | 3 | | matches | | 0 | "She’d spent three years chasing shadows since Morris vanished, and Herrera was the first solid lead who didn’t end in a dead-end file or a redacted report." | | 1 | "Herrera glanced back, his warm brown eyes wide with a cocktail of terror and something that looked uncomfortably like pity." | | 2 | "Colors that had no business being in an abandoned subway station—violet, charcoal, a bruised gold—shimmered in the air." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn said, her voice tight" | | 1 | "Quinn told, its blue lights slicing through the Soho gloom" |
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| 85.48% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 2 | | fancyTags | | 0 | "the man murmured (murmur)" | | 1 | "Quinn whispered (whisper)" |
| | dialogueSentences | 31 | | tagDensity | 0.194 | | leniency | 0.387 | | rawRatio | 0.333 | | effectiveRatio | 0.129 | |