| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.475 | | leniency | 0.95 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 854 | | 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) | |
| 82.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 854 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "traced" | | 1 | "etched" | | 2 | "unwavering" |
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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 | 67 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 67 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 86 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 852 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 64.38% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 584 | | uniqueNames | 7 | | maxNameDensity | 1.71 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 10 | | Morris | 1 | | Eva | 8 | | Kowalski | 1 | | Quinn | 1 | | British | 1 | | Museum | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Morris" | | 2 | "Eva" | | 3 | "Kowalski" | | 4 | "Quinn" |
| | places | (empty) | | globalScore | 0.644 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | 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 | 852 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 86 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 21.85 | | std | 16.22 | | cv | 0.742 | | sampleLengths | | 0 | 41 | | 1 | 31 | | 2 | 22 | | 3 | 35 | | 4 | 24 | | 5 | 2 | | 6 | 32 | | 7 | 33 | | 8 | 12 | | 9 | 8 | | 10 | 38 | | 11 | 16 | | 12 | 3 | | 13 | 41 | | 14 | 2 | | 15 | 59 | | 16 | 42 | | 17 | 5 | | 18 | 11 | | 19 | 9 | | 20 | 25 | | 21 | 27 | | 22 | 12 | | 23 | 14 | | 24 | 30 | | 25 | 26 | | 26 | 37 | | 27 | 4 | | 28 | 7 | | 29 | 73 | | 30 | 6 | | 31 | 35 | | 32 | 17 | | 33 | 5 | | 34 | 20 | | 35 | 6 | | 36 | 19 | | 37 | 5 | | 38 | 18 |
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| 89.55% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 67 | | matches | | 0 | "been sealed" | | 1 | "been used" | | 2 | "been reactivated" | | 3 | "was etched" |
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| 67.99% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 101 | | matches | | 0 | "were standing" | | 1 | "was trying" |
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| 43.19% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 86 | | ratio | 0.035 | | matches | | 0 | "Eva shook her head.”But the symbols—they’re protective. If he was trying to get out, why would he activate a warding circle? That would keep him in.”" | | 1 | "Eva’s face paled.”But why? Who would—“" | | 2 | "”They left a calling card. The compass points to their exit. Not to a portal—to a location. Probably their bolt hole.”" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 405 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.022222222222222223 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0024691358024691358 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 86 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 86 | | mean | 9.91 | | std | 7.69 | | cv | 0.776 | | sampleLengths | | 0 | 16 | | 1 | 14 | | 2 | 1 | | 3 | 5 | | 4 | 2 | | 5 | 3 | | 6 | 14 | | 7 | 17 | | 8 | 12 | | 9 | 10 | | 10 | 5 | | 11 | 14 | | 12 | 4 | | 13 | 12 | | 14 | 12 | | 15 | 12 | | 16 | 2 | | 17 | 10 | | 18 | 22 | | 19 | 4 | | 20 | 20 | | 21 | 5 | | 22 | 4 | | 23 | 9 | | 24 | 3 | | 25 | 8 | | 26 | 20 | | 27 | 4 | | 28 | 4 | | 29 | 10 | | 30 | 16 | | 31 | 3 | | 32 | 7 | | 33 | 34 | | 34 | 2 | | 35 | 5 | | 36 | 22 | | 37 | 12 | | 38 | 20 | | 39 | 7 | | 40 | 9 | | 41 | 17 | | 42 | 9 | | 43 | 5 | | 44 | 11 | | 45 | 6 | | 46 | 3 | | 47 | 6 | | 48 | 19 | | 49 | 8 |
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| 76.74% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4883720930232558 | | totalSentences | 86 | | uniqueOpeners | 42 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 59 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 59 | | matches | | 0 | "She’d seen one before." | | 1 | "She didn’t look up from" | | 2 | "She stopped at his feet" | | 3 | "she traced the air above" | | 4 | "They were standing in an" | | 5 | "She nudged the hand aside" | | 6 | "She walked toward the north" | | 7 | "She held up the bone" | | 8 | "She knelt by the footprint," | | 9 | "”They left a calling card." | | 10 | "She walked toward the door," | | 11 | "She stepped into the corridor," |
| | ratio | 0.203 | |
| 53.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 48 | | totalSentences | 59 | | matches | | 0 | "The bone token was still" | | 1 | "The body lay at the" | | 2 | "The uniformed officer nodded at" | | 3 | "Harlow turned the token over." | | 4 | "She’d seen one before." | | 5 | "Eva Kowalski knelt by the" | | 6 | "She didn’t look up from" | | 7 | "The officer flipped his notebook" | | 8 | "Harlow circled the body." | | 9 | "The man lay on his" | | 10 | "She stopped at his feet" | | 11 | "The shoes were clean." | | 12 | "Eva stood, brushing dust from" | | 13 | "Eva pointed to the symbols" | | 14 | "she traced the air above" | | 15 | "Eva looked at the walls." | | 16 | "They were standing in an" | | 17 | "The symbols covered the floor," | | 18 | "Harlow crouched beside the victim’s" | | 19 | "Something glinted beneath the fingers," |
| | ratio | 0.814 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 59 | | matches | (empty) | | ratio | 0 | |
| 91.84% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 14 | | technicalSentenceCount | 1 | | matches | | 0 | "They were standing in an abandoned storage room beneath the British Museum, a forgotten chamber that had been sealed off for decades." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.075 | | leniency | 0.15 | | rawRatio | 0 | | effectiveRatio | 0 | |