| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 44 | | tagDensity | 0.295 | | leniency | 0.591 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.81% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 964 | | 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) | |
| 6.64% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 964 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "glinting" | | 1 | "etched" | | 2 | "calculating" | | 3 | "whisper" | | 4 | "tracing" | | 5 | "intricate" | | 6 | "standard" | | 7 | "furrowed" | | 8 | "tension" | | 9 | "palpable" | | 10 | "scanned" | | 11 | "glint" | | 12 | "amidst" | | 13 | "familiar" | | 14 | "determined" |
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| 0.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 5 | | maxInWindow | 4 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 4 |
| | 1 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "eyes widened" | | 2 | "The air was heavy with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 54 | | matches | (empty) | |
| 89.95% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 54 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 84 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 962 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 51 | | wordCount | 564 | | uniqueNames | 11 | | maxNameDensity | 3.55 | | worstName | "Quinn" | | maxWindowNameDensity | 5 | | worstWindowName | "Quinn" | | discoveredNames | | Veil | 3 | | Market | 1 | | Camden | 1 | | Harlow | 1 | | Quinn | 20 | | Detective | 2 | | Lachlan | 1 | | Davies | 10 | | Kowalski | 1 | | Compass | 2 | | Eva | 9 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Lachlan" | | 3 | "Davies" | | 4 | "Kowalski" | | 5 | "Compass" | | 6 | "Eva" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0 | |
| 87.50% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 40 | | glossingSentenceCount | 1 | | matches | | 0 | "as if searching for an escape" |
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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 | 962 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 84 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 25.32 | | std | 12.75 | | cv | 0.504 | | sampleLengths | | 0 | 56 | | 1 | 20 | | 2 | 33 | | 3 | 9 | | 4 | 13 | | 5 | 31 | | 6 | 52 | | 7 | 18 | | 8 | 25 | | 9 | 28 | | 10 | 19 | | 11 | 45 | | 12 | 7 | | 13 | 26 | | 14 | 7 | | 15 | 31 | | 16 | 40 | | 17 | 16 | | 18 | 4 | | 19 | 34 | | 20 | 35 | | 21 | 26 | | 22 | 25 | | 23 | 26 | | 24 | 26 | | 25 | 20 | | 26 | 48 | | 27 | 40 | | 28 | 10 | | 29 | 28 | | 30 | 9 | | 31 | 34 | | 32 | 18 | | 33 | 23 | | 34 | 31 | | 35 | 8 | | 36 | 27 | | 37 | 14 |
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| 98.77% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 54 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 99 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 84 | | ratio | 0.012 | | matches | | 0 | "The scene before her was something out of a nightmare — a grotesque tableau that defied all logic and reason." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 565 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.023008849557522124 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.008849557522123894 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 84 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 84 | | mean | 11.45 | | std | 7.09 | | cv | 0.619 | | sampleLengths | | 0 | 18 | | 1 | 18 | | 2 | 20 | | 3 | 20 | | 4 | 20 | | 5 | 13 | | 6 | 3 | | 7 | 6 | | 8 | 8 | | 9 | 5 | | 10 | 11 | | 11 | 20 | | 12 | 24 | | 13 | 7 | | 14 | 21 | | 15 | 13 | | 16 | 5 | | 17 | 12 | | 18 | 13 | | 19 | 3 | | 20 | 13 | | 21 | 12 | | 22 | 2 | | 23 | 15 | | 24 | 2 | | 25 | 3 | | 26 | 18 | | 27 | 24 | | 28 | 5 | | 29 | 2 | | 30 | 11 | | 31 | 15 | | 32 | 2 | | 33 | 5 | | 34 | 11 | | 35 | 12 | | 36 | 8 | | 37 | 18 | | 38 | 16 | | 39 | 6 | | 40 | 13 | | 41 | 3 | | 42 | 2 | | 43 | 2 | | 44 | 27 | | 45 | 7 | | 46 | 18 | | 47 | 17 | | 48 | 21 | | 49 | 5 |
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| 74.21% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.4523809523809524 | | totalSentences | 84 | | uniqueOpeners | 38 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 51 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 10 | | totalSentences | 51 | | matches | | 0 | "she growled, her sharp jaw" | | 1 | "They rounded a corner, and" | | 2 | "She glanced up at him," | | 3 | "He hesitated, his voice dropping" | | 4 | "She turned her attention back" | | 5 | "Her voice trailed off as" | | 6 | "Her sharp eyes scanned the" | | 7 | "She picked it up, turning" | | 8 | "She stepped closer, her sharp" | | 9 | "She gestured to the glowing" |
| | ratio | 0.196 | |
| 9.02% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 51 | | matches | | 0 | "The Veil Market was a" | | 1 | "Detective Harlow Quinn strode down" | | 2 | "The air was heavy with" | | 3 | "she growled, her sharp jaw" | | 4 | "Quinn's eyes narrowed." | | 5 | "Davies hesitated, the nerves evident" | | 6 | "They rounded a corner, and" | | 7 | "The scene before her was" | | 8 | "Quinn knelt beside the body," | | 9 | "Davies adjusted his glasses, a" | | 10 | "Quinn's lips thinned." | | 11 | "She glanced up at him," | | 12 | "He hesitated, his voice dropping" | | 13 | "Quinn's jaw tightened." | | 14 | "She turned her attention back" | | 15 | "Her voice trailed off as" | | 16 | "Davies waited, the tension palpable." | | 17 | "Quinn's gaze snapped up to" | | 18 | "Quinn didn't answer, her focus" | | 19 | "Her sharp eyes scanned the" |
| | ratio | 0.902 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 51 | | matches | (empty) | | ratio | 0 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 2 | | matches | | 0 | "The scene before her was something out of a nightmare — a grotesque tableau that defied all logic and reason." | | 1 | "Eva swallowed hard, her gaze darting around the chamber as if searching for an escape." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 5 | | matches | | 0 | "Quinn knelt, her experienced gaze cataloging the details" | | 1 | "Davies adjusted, a frown creasing his brow" | | 2 | "She glanced, her brown eyes cold and calculating" | | 3 | "He hesitated, his voice dropping to a near-whisper" | | 4 | "She turned, her fingers tracing the intricate markings on the floor" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 44 | | tagDensity | 0.023 | | leniency | 0.045 | | rawRatio | 1 | | effectiveRatio | 0.045 | |