| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 1 | | adverbTags | | 0 | "She knelt again [again]" |
| | dialogueSentences | 32 | | tagDensity | 0.688 | | leniency | 1 | | rawRatio | 0.045 | | effectiveRatio | 0.045 | |
| 93.81% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 808 | | 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) | |
| 25.74% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 808 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "scanning" | | 1 | "gloom" | | 2 | "tinged" | | 3 | "apprehension" | | 4 | "traced" | | 5 | "echoing" | | 6 | "furrowed" | | 7 | "dancing" | | 8 | "etched" | | 9 | "weight" | | 10 | "determined" |
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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 | 38 | | matches | (empty) | |
| 67.67% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 38 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 47 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 806 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 57.02% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 484 | | uniqueNames | 11 | | maxNameDensity | 1.86 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Eva" | | discoveredNames | | Veil | 3 | | Market | 2 | | Camden | 1 | | Northern | 1 | | Line | 1 | | Harlow | 9 | | Quinn | 2 | | Tube | 1 | | Kowalski | 1 | | Eva | 9 | | Compass | 1 |
| | persons | | 0 | "Market" | | 1 | "Camden" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Kowalski" | | 5 | "Eva" | | 6 | "Compass" |
| | places | (empty) | | globalScore | 0.57 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 32 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 806 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 47 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 13 | | mean | 62 | | std | 35.85 | | cv | 0.578 | | sampleLengths | | 0 | 126 | | 1 | 20 | | 2 | 79 | | 3 | 61 | | 4 | 52 | | 5 | 55 | | 6 | 37 | | 7 | 55 | | 8 | 12 | | 9 | 73 | | 10 | 16 | | 11 | 96 | | 12 | 124 |
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| 77.56% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 38 | | matches | | 0 | "was pulled" | | 1 | "was hushed" | | 2 | "was determined" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 84 | | matches | (empty) | |
| 21.28% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 47 | | ratio | 0.043 | | matches | | 0 | "“The blood spatter pattern on the wall above the body. It’s not just splatter; it’s a spray pattern, like something was thrown upwards. But the victim was standing. How?”" | | 1 | "“Towards the dirt. Towards something buried. Something supernatural. The Veil Market isn’t just a black market; it’s a gateway. And whoever did this knows it. They’re marking their territory, or hiding something they don’t want found.” She snapped the compass shut, tucking it away." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 486 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, leather-bound notebook" |
| | adverbCount | 15 | | adverbRatio | 0.030864197530864196 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.01646090534979424 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 47 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 47 | | mean | 17.15 | | std | 10.46 | | cv | 0.61 | | sampleLengths | | 0 | 30 | | 1 | 28 | | 2 | 9 | | 3 | 25 | | 4 | 17 | | 5 | 17 | | 6 | 16 | | 7 | 4 | | 8 | 9 | | 9 | 31 | | 10 | 13 | | 11 | 15 | | 12 | 11 | | 13 | 10 | | 14 | 35 | | 15 | 16 | | 16 | 9 | | 17 | 18 | | 18 | 25 | | 19 | 4 | | 20 | 19 | | 21 | 22 | | 22 | 10 | | 23 | 13 | | 24 | 5 | | 25 | 4 | | 26 | 15 | | 27 | 6 | | 28 | 20 | | 29 | 29 | | 30 | 10 | | 31 | 2 | | 32 | 11 | | 33 | 24 | | 34 | 26 | | 35 | 12 | | 36 | 11 | | 37 | 5 | | 38 | 7 | | 39 | 37 | | 40 | 21 | | 41 | 16 | | 42 | 15 | | 43 | 10 | | 44 | 44 | | 45 | 47 | | 46 | 23 |
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| 77.30% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.48936170212765956 | | totalSentences | 47 | | uniqueOpeners | 23 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 37 | | matches | (empty) | | ratio | 0 | |
| 36.22% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 37 | | matches | | 0 | "Her sharp jawline was set," | | 1 | "She clutched a worn leather" | | 2 | "She gestured towards the far" | | 3 | "She traced a finger through" | | 4 | "She pulled a small, leather-bound" | | 5 | "She pointed to the blade" | | 6 | "She stood, brushing dirt from" | | 7 | "She walked towards the platform’s" | | 8 | "She pointed towards the ceiling," | | 9 | "She knelt again, this time" | | 10 | "She scraped at the surface" | | 11 | "She pulled her worn leather" | | 12 | "She reached into her own" | | 13 | "It was the Veil Compass," | | 14 | "She flipped it open, the" | | 15 | "She snapped the compass shut," | | 16 | "Her sharp gaze swept the" |
| | ratio | 0.459 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 37 | | totalSentences | 37 | | matches | | 0 | "The Veil Market’s entrance wasa" | | 1 | "Detective Harlow Quinn’s worn leather" | | 2 | "Her sharp jawline was set," | | 3 | "Eva Kowalski, her childhood friend" | | 4 | "Eva’s curly red hair was" | | 5 | "She clutched a worn leather" | | 6 | "Eva’s voice was hushed, tinged" | | 7 | "Harlow confirmed, her voice low" | | 8 | "She gestured towards the far" | | 9 | "A man in a cheap" | | 10 | "A single, precise stab wound" | | 11 | "Blood had pooled around him," | | 12 | "Harlow continued, crouching beside the" | | 13 | "She traced a finger through" | | 14 | "Eva knelt beside her, peering" | | 15 | "She pulled a small, leather-bound" | | 16 | "She pointed to the blade" | | 17 | "She stood, brushing dirt from" | | 18 | "She walked towards the platform’s" | | 19 | "Eva followed, her satchel swaying." |
| | ratio | 1 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 37 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 12 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 34.09% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 4 | | matches | | 0 | "Harlow confirmed, her voice low and gravelly" | | 1 | "Eva suggested, her voice rising slightly" | | 2 | "She knelt again, this time examining the concrete floor near the body’s feet" | | 3 | "Harlow stated, her voice low" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 6 | | fancyTags | | 0 | "Harlow confirmed (confirm)" | | 1 | "Harlow continued (continue)" | | 2 | "Eva suggested (suggest)" | | 3 | "Harlow admitted (admit)" | | 4 | "Harlow stated (state)" | | 5 | "She snapped (snap)" |
| | dialogueSentences | 32 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0.75 | | effectiveRatio | 0.375 | |