| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.316 | | leniency | 0.632 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 451 | | 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) | |
| 33.48% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 451 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "etched" | | 1 | "furrowed" | | 2 | "intricate" | | 3 | "tinged" | | 4 | "racing" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 58.33% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 25 | | matches | | 0 | "d in confusion" | | 1 | "d with determination" |
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| 28.57% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 25 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 38 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 447 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 15.90% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 261 | | uniqueNames | 6 | | maxNameDensity | 2.68 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 2 | | Quinn | 7 | | Tube | 1 | | Camden | 1 | | Liam | 1 | | Jacobs | 5 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Liam" | | 3 | "Jacobs" |
| | places | (empty) | | globalScore | 0.159 | | windowScore | 0.667 | |
| 11.11% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 18 | | glossingSentenceCount | 1 | | matches | | 0 | "sigils that seemed to shimmer faintly in the dim light" |
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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 | 447 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 38 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 19 | | mean | 23.53 | | std | 14.34 | | cv | 0.61 | | sampleLengths | | 0 | 55 | | 1 | 26 | | 2 | 13 | | 3 | 46 | | 4 | 10 | | 5 | 28 | | 6 | 8 | | 7 | 31 | | 8 | 29 | | 9 | 16 | | 10 | 32 | | 11 | 6 | | 12 | 26 | | 13 | 6 | | 14 | 33 | | 15 | 5 | | 16 | 14 | | 17 | 45 | | 18 | 18 |
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| 91.23% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 25 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 46 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 262 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 8 | | adverbRatio | 0.030534351145038167 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.007633587786259542 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 38 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 38 | | mean | 11.76 | | std | 7.28 | | cv | 0.619 | | sampleLengths | | 0 | 14 | | 1 | 24 | | 2 | 17 | | 3 | 18 | | 4 | 8 | | 5 | 6 | | 6 | 7 | | 7 | 12 | | 8 | 16 | | 9 | 13 | | 10 | 5 | | 11 | 7 | | 12 | 3 | | 13 | 11 | | 14 | 17 | | 15 | 4 | | 16 | 4 | | 17 | 6 | | 18 | 25 | | 19 | 12 | | 20 | 17 | | 21 | 16 | | 22 | 5 | | 23 | 27 | | 24 | 3 | | 25 | 3 | | 26 | 26 | | 27 | 6 | | 28 | 10 | | 29 | 13 | | 30 | 10 | | 31 | 5 | | 32 | 5 | | 33 | 9 | | 34 | 16 | | 35 | 29 | | 36 | 4 | | 37 | 14 |
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| 100.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.631578947368421 | | totalSentences | 38 | | uniqueOpeners | 24 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 25 | | matches | (empty) | | ratio | 0 | |
| 76.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 9 | | totalSentences | 25 | | matches | | 0 | "She adjusted her watch as" | | 1 | "She knelt down, examining a" | | 2 | "Its face was etched with" | | 3 | "she muttered, more to herself" | | 4 | "She stood up, her gaze" | | 5 | "she held it up" | | 6 | "She began pacing the platform," | | 7 | "She tapped the compass" | | 8 | "She pocketed the compass and" |
| | ratio | 0.36 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 23 | | totalSentences | 25 | | matches | | 0 | "Detective Harlow Quinn surveyed the" | | 1 | "The dim flickering of the" | | 2 | "She adjusted her watch as" | | 3 | "Constable Liam Jacobs approached her," | | 4 | "Quinn nodded, her sharp jawline" | | 5 | "She knelt down, examining a" | | 6 | "Its face was etched with" | | 7 | "she muttered, more to herself" | | 8 | "Jacobs leaned in for a" | | 9 | "She stood up, her gaze" | | 10 | "she held it up" | | 11 | "The constable's eyes widened." | | 12 | "Quinn's lips thinned into a" | | 13 | "She began pacing the platform," | | 14 | "Jacobs asked, his voice tinged" | | 15 | "Quinn paused, her eyes narrowing." | | 16 | "Jacobs shifted uncomfortably." | | 17 | "Quinn cut him off, She" | | 18 | "A wry smile tugged at" | | 19 | "She tapped the compass" |
| | ratio | 0.92 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 25 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 11 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 1 | | matches | | 0 | "she muttered, more to herself than to Jacobs" |
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| 97.37% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 19 | | tagDensity | 0.105 | | leniency | 0.211 | | rawRatio | 0.5 | | effectiveRatio | 0.105 | |