| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said finally [finally]" |
| | dialogueSentences | 24 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0.111 | | effectiveRatio | 0.083 | |
| 89.43% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 946 | | totalAiIsmAdverbs | 2 | | 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) | |
| 84.14% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 946 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "echoed" | | 1 | "echo" | | 2 | "could feel" |
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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 | 58 | | matches | (empty) | |
| 68.97% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 58 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 72 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 937 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 18 | | wordCount | 677 | | uniqueNames | 10 | | maxNameDensity | 0.74 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Harlow | 1 | | Quinn | 5 | | Tube | 1 | | Useless | 1 | | Met | 1 | | Morris | 3 | | Veil | 1 | | Market | 1 | | Herrera | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Herrera" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 42 | | 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 | 937 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 72 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 26.77 | | std | 18.47 | | cv | 0.69 | | sampleLengths | | 0 | 50 | | 1 | 40 | | 2 | 42 | | 3 | 13 | | 4 | 45 | | 5 | 5 | | 6 | 70 | | 7 | 26 | | 8 | 2 | | 9 | 67 | | 10 | 19 | | 11 | 14 | | 12 | 44 | | 13 | 11 | | 14 | 25 | | 15 | 8 | | 16 | 34 | | 17 | 20 | | 18 | 32 | | 19 | 6 | | 20 | 46 | | 21 | 45 | | 22 | 6 | | 23 | 42 | | 24 | 3 | | 25 | 11 | | 26 | 41 | | 27 | 11 | | 28 | 2 | | 29 | 12 | | 30 | 31 | | 31 | 28 | | 32 | 43 | | 33 | 36 | | 34 | 7 |
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| 87.11% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 58 | | matches | | 0 | "been closed" | | 1 | "been approved" | | 2 | "were lined" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 126 | | matches | (empty) | |
| 23.81% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 72 | | ratio | 0.042 | | matches | | 0 | "Cold air rushed up from below, carrying the smell of damp stone and something else—incense, maybe, or burning herbs." | | 1 | "The walls were lined with stalls—impossible, there shouldn't be stalls here—but there they were." | | 2 | "Something shifted in his expression—recognition, maybe, or the memory of someone else who'd been stubborn enough to walk this far and not turn back." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 685 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.027737226277372264 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.01167883211678832 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 72 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 72 | | mean | 13.01 | | std | 8.55 | | cv | 0.657 | | sampleLengths | | 0 | 12 | | 1 | 19 | | 2 | 19 | | 3 | 4 | | 4 | 12 | | 5 | 24 | | 6 | 7 | | 7 | 18 | | 8 | 17 | | 9 | 7 | | 10 | 2 | | 11 | 4 | | 12 | 9 | | 13 | 17 | | 14 | 19 | | 15 | 5 | | 16 | 6 | | 17 | 38 | | 18 | 11 | | 19 | 15 | | 20 | 6 | | 21 | 20 | | 22 | 2 | | 23 | 19 | | 24 | 14 | | 25 | 11 | | 26 | 23 | | 27 | 3 | | 28 | 16 | | 29 | 14 | | 30 | 23 | | 31 | 13 | | 32 | 8 | | 33 | 5 | | 34 | 6 | | 35 | 19 | | 36 | 6 | | 37 | 8 | | 38 | 16 | | 39 | 18 | | 40 | 20 | | 41 | 4 | | 42 | 28 | | 43 | 6 | | 44 | 12 | | 45 | 34 | | 46 | 3 | | 47 | 13 | | 48 | 8 | | 49 | 21 |
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| 81.48% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5138888888888888 | | totalSentences | 72 | | uniqueOpeners | 37 | |
| 65.36% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 51 | | matches | | 0 | "Somewhere in this maze, the" |
| | ratio | 0.02 | |
| 78.82% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 51 | | matches | | 0 | "She grabbed the iron railing" | | 1 | "Her flashlight cut through the" | | 2 | "Her voice echoed off the" | | 3 | "She heard the thud of" | | 4 | "She'd heard rumors about this" | | 5 | "Her partner, Morris, had been" | | 6 | "He wore a medallion around" | | 7 | "She pulled her arm free." | | 8 | "He gestured at the market" | | 9 | "He blocked her path" | | 10 | "She pushed past him, but" | | 11 | "He grabbed her arm again," | | 12 | "She stopped walking." | | 13 | "She could feel eyes on" | | 14 | "he said finally" | | 15 | "She didn't wait for Herrera" | | 16 | "She'd spent three years chasing" | | 17 | "Her hand found the shelf." |
| | ratio | 0.353 | |
| 58.04% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 41 | | totalSentences | 51 | | matches | | 0 | "The rain turned the streets" | | 1 | "Detective Harlow Quinn sprinted around" | | 2 | "Quinn didn't slow down." | | 3 | "She grabbed the iron railing" | | 4 | "The station had been closed" | | 5 | "Her flashlight cut through the" | | 6 | "Her voice echoed off the" | | 7 | "She heard the thud of" | | 8 | "The beam of her torch" | | 9 | "Quinn hesitated at the threshold." | | 10 | "She'd heard rumors about this" | | 11 | "Her partner, Morris, had been" | | 12 | "The darkness below called to" | | 13 | "The stairs led to a" | | 14 | "The walls were lined with" | | 15 | "Tables covered in bottles and" | | 16 | "People moved between them, their" | | 17 | "The Veil Market." | | 18 | "The name surfaced from a" | | 19 | "A hand shot out from" |
| | ratio | 0.804 | |
| 98.04% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 51 | | matches | | | ratio | 0.02 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 1 | | matches | | 0 | "Detective Harlow Quinn sprinted around the corner, her boots splashing through puddles that reflected the orange glow of streetlights." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 24 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0 | | effectiveRatio | 0 | |