| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1319 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 20.39% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1319 | | totalAiIsms | 21 | | found | | | highlights | | 0 | "glint" | | 1 | "pulsed" | | 2 | "weight" | | 3 | "grave" | | 4 | "echoed" | | 5 | "pulse" | | 6 | "fleeting" | | 7 | "rhythmic" | | 8 | "flicker" | | 9 | "constructed" | | 10 | "cacophony" | | 11 | "velvet" | | 12 | "profound" | | 13 | "synchronized" | | 14 | "warmth" | | 15 | "reminder" | | 16 | "navigated" |
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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 | 68 | | matches | (empty) | |
| 58.82% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 68 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 78 | | 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 | 1317 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 1154 | | uniqueNames | 10 | | maxNameDensity | 0.52 | | worstName | "Harlow" | | maxWindowNameDensity | 1 | | worstWindowName | "Morris" | | discoveredNames | | Soho | 1 | | Quinn | 1 | | Tube | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 3 | | Veil | 1 | | Market | 1 | | Herrera | 4 | | Harlow | 6 |
| | persons | | 0 | "Quinn" | | 1 | "Saint" | | 2 | "Christopher" | | 3 | "Morris" | | 4 | "Market" | | 5 | "Herrera" | | 6 | "Harlow" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 22.88% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 3 | | matches | | 0 | "tasted like copper and wet stone" | | 1 | "felt like sandpaper against her ears" | | 2 | "looked like layers of wet leather, leaned" |
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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 | 1317 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 78 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 48.78 | | std | 32.34 | | cv | 0.663 | | sampleLengths | | 0 | 106 | | 1 | 9 | | 2 | 74 | | 3 | 41 | | 4 | 117 | | 5 | 23 | | 6 | 109 | | 7 | 40 | | 8 | 4 | | 9 | 90 | | 10 | 69 | | 11 | 69 | | 12 | 9 | | 13 | 66 | | 14 | 29 | | 15 | 22 | | 16 | 62 | | 17 | 29 | | 18 | 22 | | 19 | 65 | | 20 | 21 | | 21 | 50 | | 22 | 69 | | 23 | 11 | | 24 | 30 | | 25 | 13 | | 26 | 68 |
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| 94.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 68 | | matches | | 0 | "was trained" | | 1 | "being pulled" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 185 | | matches | | |
| 32.97% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 2 | | flaggedSentences | 3 | | totalSentences | 78 | | ratio | 0.038 | | matches | | 0 | "Figures—some too tall, some shifting in ways that defied human anatomy—shouldered through the murk of the Veil Market." | | 1 | "This wasn't a crime scene she was trained to investigate; it was an intersection of realities she had spent years pretending didn't exist." | | 2 | "He wasn't a kingpin; he was a desperate man hunting for a cure that no NHS hospital would ever authorize." |
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| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1158 | | adjectiveStacks | 2 | | stackExamples | | 0 | "jagged, bleach-white token." | | 1 | "heavy, moth-eaten velvet." |
| | adverbCount | 22 | | adverbRatio | 0.018998272884283247 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004317789291882556 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 78 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 78 | | mean | 16.88 | | std | 7.37 | | cv | 0.436 | | sampleLengths | | 0 | 20 | | 1 | 18 | | 2 | 28 | | 3 | 21 | | 4 | 19 | | 5 | 9 | | 6 | 20 | | 7 | 3 | | 8 | 31 | | 9 | 20 | | 10 | 14 | | 11 | 14 | | 12 | 13 | | 13 | 21 | | 14 | 24 | | 15 | 16 | | 16 | 17 | | 17 | 30 | | 18 | 5 | | 19 | 4 | | 20 | 23 | | 21 | 10 | | 22 | 19 | | 23 | 17 | | 24 | 22 | | 25 | 18 | | 26 | 23 | | 27 | 15 | | 28 | 4 | | 29 | 21 | | 30 | 4 | | 31 | 2 | | 32 | 23 | | 33 | 17 | | 34 | 24 | | 35 | 24 | | 36 | 3 | | 37 | 24 | | 38 | 20 | | 39 | 22 | | 40 | 11 | | 41 | 20 | | 42 | 18 | | 43 | 20 | | 44 | 9 | | 45 | 8 | | 46 | 19 | | 47 | 5 | | 48 | 34 | | 49 | 29 |
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| 56.41% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.38461538461538464 | | totalSentences | 78 | | uniqueOpeners | 30 | |
| 49.75% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 67 | | matches | | 0 | "Maybe the weight of his" |
| | ratio | 0.015 | |
| 46.87% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 67 | | matches | | 0 | "He didn't turn." | | 1 | "His shoulders hunched against the" | | 2 | "He glanced back, breath hitching," | | 3 | "He descended into the black" | | 4 | "She stood at the edge," | | 5 | "She remembered the empty stare" | | 6 | "She drew a breath, the" | | 7 | "She thought of Morris." | | 8 | "She closed her eyes for" | | 9 | "Her pulse hammered against her" | | 10 | "She kept her back pressed" | | 11 | "It was a sprawling, subterranean" | | 12 | "She spotted him near a" | | 13 | "He looked haggard, his curly" | | 14 | "He wasn't a kingpin; he" | | 15 | "He turned, the movement fluid" | | 16 | "He didn't bolt this time." | | 17 | "She stepped forward, the heel" | | 18 | "He clutched the vials to" | | 19 | "She ignored the prickle on" |
| | ratio | 0.433 | |
| 12.24% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 67 | | matches | | 0 | "Rain drummed a frantic rhythm" | | 1 | "Harlow Quinn ignored the soak" | | 2 | "Harlow adjusted the pace, the" | | 3 | "The man reached the iron" | | 4 | "He didn't turn." | | 5 | "His shoulders hunched against the" | | 6 | "He glanced back, breath hitching," | | 7 | "The heavy grating groaned, retracting" | | 8 | "He descended into the black" | | 9 | "Harlow skidded to a halt" | | 10 | "The stairs dropped into an" | | 11 | "She stood at the edge," | | 12 | "She remembered the empty stare" | | 13 | "This was the same scent." | | 14 | "The same impossible gravity." | | 15 | "The voice echoed back, thin" | | 16 | "She drew a breath, the" | | 17 | "A hundred yards down, the" | | 18 | "Figures—some too tall, some shifting" | | 19 | "This wasn't a crime scene" |
| | ratio | 0.896 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 30.08% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 9 | | matches | | 0 | "His shoulders hunched against the gale, the Saint Christopher medallion hanging from his neck catching a glint as he twisted his frame, exposing the jagged scar…" | | 1 | "The stairs dropped into an abyss where the usual city grime gave way to something older, something that tasted like copper and wet stone." | | 2 | "A hundred yards down, the violet pulse revealed the silhouette of stalls packed with crates that hummed with a low, sub-audible vibration." | | 3 | "Her pulse hammered against her throat, a frantic, steady rhythm that set the pace for her descent." | | 4 | "Every strike of her heel echoed against the vaulted ceiling of the underground tunnel, a declaration of presence in a space that demanded invisibility." | | 5 | "It was a sprawling, subterranean scar, a jagged subterranean cavern constructed of rusted girders and crates marked with symbols that made her vision swim." | | 6 | "The air thrummed with the sound of a thousand muttered conversations, a cacophony of languages that felt like sandpaper against her ears." | | 7 | "Herrera took a frantic step backward, his back hitting a crate that groaned under his weight." | | 8 | "She moved forward, closing the distance, her focus narrowed to the man who held the missing pieces of Morris’s final hour." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |