| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.214 | | leniency | 0.429 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.13% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 564 | | 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) | |
| 11.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 564 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "maw" | | 1 | "implication" | | 2 | "flicked" | | 3 | "echoed" | | 4 | "pulse" | | 5 | "stomach" | | 6 | "lurched" | | 7 | "silence" | | 8 | "etched" |
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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 | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 55 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 55 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 66 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 4 | | totalWords | 558 | | ratio | 0.007 | | matches | | 0 | "uncooperative" | | 1 | "less" | | 2 | "wrong" | | 3 | "screamed" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 8.01% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 493 | | uniqueNames | 6 | | maxNameDensity | 2.84 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Veil | 5 | | Market | 5 | | Camden | 1 | | Quinn | 14 | | Kowalski | 1 | | Eva | 8 |
| | persons | | 0 | "Market" | | 1 | "Camden" | | 2 | "Quinn" | | 3 | "Kowalski" | | 4 | "Eva" |
| | places | (empty) | | globalScore | 0.08 | | windowScore | 0.5 | |
| 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 | 558 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 66 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 25.36 | | std | 20.17 | | cv | 0.795 | | sampleLengths | | 0 | 88 | | 1 | 44 | | 2 | 2 | | 3 | 14 | | 4 | 10 | | 5 | 6 | | 6 | 47 | | 7 | 18 | | 8 | 13 | | 9 | 8 | | 10 | 29 | | 11 | 33 | | 12 | 51 | | 13 | 12 | | 14 | 29 | | 15 | 10 | | 16 | 38 | | 17 | 15 | | 18 | 42 | | 19 | 6 | | 20 | 9 | | 21 | 34 |
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| 98.88% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 55 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 83 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 66 | | ratio | 0.076 | | matches | | 0 | "Detective Quinn’s boots crunched on the rusted metal grates as she stepped inside, the air thick with the scent of damp earth and something older—something that clung to the shadows like a second skin." | | 1 | "She turned, her pulse hammering in her throat, and saw it then—the body." | | 2 | "She knelt, brushing back the man’s hair to reveal a mark on his neck—a sigil, etched into his flesh." | | 3 | "The one that didn’t just point to rifts—it *screamed* them." | | 4 | "His heart was still beating, but there was something else—something pulsing beneath the skin, like a second heartbeat." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 499 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.03807615230460922 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.01002004008016032 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 66 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 66 | | mean | 8.45 | | std | 6.32 | | cv | 0.748 | | sampleLengths | | 0 | 19 | | 1 | 34 | | 2 | 27 | | 3 | 4 | | 4 | 4 | | 5 | 21 | | 6 | 16 | | 7 | 7 | | 8 | 2 | | 9 | 10 | | 10 | 4 | | 11 | 5 | | 12 | 5 | | 13 | 6 | | 14 | 17 | | 15 | 10 | | 16 | 11 | | 17 | 1 | | 18 | 8 | | 19 | 7 | | 20 | 11 | | 21 | 7 | | 22 | 6 | | 23 | 3 | | 24 | 5 | | 25 | 8 | | 26 | 16 | | 27 | 5 | | 28 | 17 | | 29 | 5 | | 30 | 10 | | 31 | 1 | | 32 | 13 | | 33 | 18 | | 34 | 8 | | 35 | 3 | | 36 | 4 | | 37 | 5 | | 38 | 6 | | 39 | 6 | | 40 | 3 | | 41 | 19 | | 42 | 3 | | 43 | 2 | | 44 | 2 | | 45 | 10 | | 46 | 12 | | 47 | 9 | | 48 | 3 | | 49 | 4 |
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| 45.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.2727272727272727 | | totalSentences | 66 | | uniqueOpeners | 18 | |
| 65.36% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 51 | | matches | | | ratio | 0.02 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 9 | | totalSentences | 51 | | matches | | 0 | "She didn’t ask questions." | | 1 | "She didn’t need to." | | 2 | "She didn’t turn around when" | | 3 | "She turned, her pulse hammering" | | 4 | "His eyes were wide, glassy," | | 5 | "She knelt, brushing back the" | | 6 | "His heart was still beating," | | 7 | "She reached out, her fingers" | | 8 | "It was a prison." |
| | ratio | 0.176 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 48 | | totalSentences | 51 | | matches | | 0 | "The Veil Market’s entrance yawned" | | 1 | "Detective Quinn’s boots crunched on" | | 2 | "The bone token in her" | | 3 | "She didn’t ask questions." | | 4 | "She didn’t need to." | | 5 | "A figure in a long," | | 6 | "Eva Kowalski’s curly red hair" | | 7 | "She didn’t turn around when" | | 8 | "Eva exhaled through her nose," | | 9 | "Quinn didn’t lower her voice." | | 10 | "A murmur rippled through the" | | 11 | "Quinn’s fingers tightened around the" | | 12 | "The Veil Market was always" | | 13 | "Quinn asked, stepping closer" | | 14 | "The air smelled of ozone," | | 15 | "Eva’s voice was low, almost" | | 16 | "Quinn’s jaw tightened." | | 17 | "The words hung between them," | | 18 | "Eva’s gaze flicked toward the" | | 19 | "A sharp crack echoed through" |
| | ratio | 0.941 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 51 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 1 | | matches | | 0 | "Detective Quinn’s boots crunched on the rusted metal grates as she stepped inside, the air thick with the scent of damp earth and something older—something that…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn demanded, her voice raw" | | 1 | "Eva whispered, her voice trembling" |
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| 7.14% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "Quinn demanded (demand)" | | 1 | "Eva whispered (whisper)" |
| | dialogueSentences | 14 | | tagDensity | 0.214 | | leniency | 0.429 | | rawRatio | 0.667 | | effectiveRatio | 0.286 | |