| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.11% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 849 | | 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) | |
| 76.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 849 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "footsteps" | | 2 | "weight" | | 3 | "echoed" |
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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 | 1 | | narrationSentences | 47 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 47 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 59 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 849 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 654 | | uniqueNames | 9 | | maxNameDensity | 0.76 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Tomás" | | discoveredNames | | Quinn | 5 | | Tomás | 5 | | Herrera | 1 | | Camden | 1 | | High | 1 | | Street | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Tomás" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" |
| | globalScore | 1 | | windowScore | 1 | |
| 85.90% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | glossingSentenceCount | 1 | | matches | | 0 | "not quite bone and not quite wood" | | 1 | "not quite wood" |
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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 | 849 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 59 | | matches | | 0 | "searched that basement" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 32.65 | | std | 22.79 | | cv | 0.698 | | sampleLengths | | 0 | 64 | | 1 | 21 | | 2 | 56 | | 3 | 5 | | 4 | 71 | | 5 | 59 | | 6 | 53 | | 7 | 30 | | 8 | 57 | | 9 | 4 | | 10 | 9 | | 11 | 45 | | 12 | 24 | | 13 | 28 | | 14 | 8 | | 15 | 7 | | 16 | 52 | | 17 | 27 | | 18 | 78 | | 19 | 27 | | 20 | 6 | | 21 | 1 | | 22 | 6 | | 23 | 40 | | 24 | 38 | | 25 | 33 |
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| 90.33% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 47 | | matches | | 0 | "been painted" | | 1 | "being dragged" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 102 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 59 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 654 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.0382262996941896 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0015290519877675841 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 59 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 59 | | mean | 14.39 | | std | 9.14 | | cv | 0.635 | | sampleLengths | | 0 | 24 | | 1 | 23 | | 2 | 7 | | 3 | 10 | | 4 | 12 | | 5 | 9 | | 6 | 3 | | 7 | 26 | | 8 | 27 | | 9 | 5 | | 10 | 12 | | 11 | 18 | | 12 | 8 | | 13 | 33 | | 14 | 8 | | 15 | 20 | | 16 | 9 | | 17 | 22 | | 18 | 16 | | 19 | 24 | | 20 | 13 | | 21 | 7 | | 22 | 11 | | 23 | 12 | | 24 | 31 | | 25 | 9 | | 26 | 17 | | 27 | 4 | | 28 | 9 | | 29 | 12 | | 30 | 33 | | 31 | 18 | | 32 | 6 | | 33 | 7 | | 34 | 5 | | 35 | 16 | | 36 | 8 | | 37 | 7 | | 38 | 22 | | 39 | 19 | | 40 | 11 | | 41 | 6 | | 42 | 21 | | 43 | 5 | | 44 | 41 | | 45 | 9 | | 46 | 6 | | 47 | 6 | | 48 | 11 | | 49 | 9 |
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| 74.58% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.4576271186440678 | | totalSentences | 59 | | uniqueOpeners | 27 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 43 | | matches | | 0 | "Somewhere behind her a generator" | | 1 | "Then down, into the tunnel." | | 2 | "Then she looked down the" |
| | ratio | 0.07 | |
| 61.86% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 43 | | matches | | 0 | "He hadn't stopped to look" | | 1 | "Her voice bounced off the" | | 2 | "He didn't answer." | | 3 | "He swung left, past the" | | 4 | "She swore and kept moving." | | 5 | "She stopped at the mouth" | | 6 | "He turned around, and in" | | 7 | "His accent softened the vowels," | | 8 | "She stepped onto the first" | | 9 | "He glanced past her, toward" | | 10 | "She watched him weigh it," | | 11 | "He reached into his jacket" | | 12 | "It was a token, pale" | | 13 | "Her left wrist ached where" | | 14 | "She had searched that basement" | | 15 | "She had never written any" | | 16 | "He turned and went down" |
| | ratio | 0.395 | |
| 29.77% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 37 | | totalSentences | 43 | | matches | | 0 | "The rain had soaked through" | | 1 | "He hadn't stopped to look" | | 2 | "Men who looked back were" | | 3 | "Her voice bounced off the" | | 4 | "He didn't answer." | | 5 | "He swung left, past the" | | 6 | "Quinn followed, her boots skidding" | | 7 | "She swore and kept moving." | | 8 | "The market crowds had thinned" | | 9 | "Neon from the chicken shop" | | 10 | "The rain drummed on the" | | 11 | "She stopped at the mouth" | | 12 | "The sign above the old" | | 13 | "Someone had hung a plastic" | | 14 | "Tomás stood on the third" | | 15 | "He turned around, and in" | | 16 | "The Saint Christopher medallion swung" | | 17 | "His accent softened the vowels," | | 18 | "She stepped onto the first" | | 19 | "The metal edge gave a" |
| | ratio | 0.86 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 43 | | matches | (empty) | | ratio | 0 | |
| 89.95% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 2 | | matches | | 0 | "Beneath it, the stairs curved down into the dark, slick with rainwater that ran in a steady stream toward the tiles below." | | 1 | "It was a token, pale and smooth, carved from something that was not quite bone and not quite wood." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0 | | effectiveRatio | 0 | |