| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.07% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1007 | | 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) | |
| 50.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1007 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "pulse" | | 1 | "loomed" | | 2 | "pulsed" | | 3 | "silence" | | 4 | "weight" | | 5 | "resolved" | | 6 | "familiar" | | 7 | "perfect" | | 8 | "gleaming" |
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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 | 95 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 95 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 103 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1007 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 968 | | uniqueNames | 6 | | maxNameDensity | 0.93 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Eva" | | discoveredNames | | Aurora | 9 | | October | 1 | | London | 1 | | Eva | 8 | | Park | 1 | | Camden | 1 |
| | persons | | 0 | "Aurora" | | 1 | "October" | | 2 | "Eva" |
| | places | | 0 | "London" | | 1 | "Park" | | 2 | "Camden" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like a personal record given the w" |
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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 | 1007 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 103 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 23.42 | | std | 23.6 | | cv | 1.008 | | sampleLengths | | 0 | 15 | | 1 | 43 | | 2 | 8 | | 3 | 90 | | 4 | 31 | | 5 | 10 | | 6 | 1 | | 7 | 44 | | 8 | 11 | | 9 | 10 | | 10 | 10 | | 11 | 74 | | 12 | 20 | | 13 | 5 | | 14 | 6 | | 15 | 38 | | 16 | 1 | | 17 | 4 | | 18 | 50 | | 19 | 4 | | 20 | 4 | | 21 | 64 | | 22 | 5 | | 23 | 60 | | 24 | 8 | | 25 | 3 | | 26 | 30 | | 27 | 3 | | 28 | 59 | | 29 | 13 | | 30 | 28 | | 31 | 19 | | 32 | 4 | | 33 | 1 | | 34 | 51 | | 35 | 4 | | 36 | 8 | | 37 | 50 | | 38 | 5 | | 39 | 67 | | 40 | 13 | | 41 | 24 | | 42 | 9 |
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| 97.88% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 95 | | matches | | 0 | "was meant" | | 1 | "were sketched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 159 | | matches | | 0 | "was screaming" | | 1 | "was burning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 103 | | ratio | 0 | | matches | (empty) | |
| 94.37% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 969 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 45 | | adverbRatio | 0.04643962848297214 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.007223942208462332 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 103 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 103 | | mean | 9.78 | | std | 9.05 | | cv | 0.925 | | sampleLengths | | 0 | 15 | | 1 | 2 | | 2 | 28 | | 3 | 1 | | 4 | 1 | | 5 | 11 | | 6 | 3 | | 7 | 5 | | 8 | 16 | | 9 | 21 | | 10 | 1 | | 11 | 1 | | 12 | 12 | | 13 | 18 | | 14 | 21 | | 15 | 12 | | 16 | 19 | | 17 | 10 | | 18 | 1 | | 19 | 4 | | 20 | 6 | | 21 | 20 | | 22 | 12 | | 23 | 1 | | 24 | 1 | | 25 | 11 | | 26 | 10 | | 27 | 1 | | 28 | 9 | | 29 | 8 | | 30 | 22 | | 31 | 6 | | 32 | 23 | | 33 | 3 | | 34 | 12 | | 35 | 20 | | 36 | 5 | | 37 | 2 | | 38 | 1 | | 39 | 3 | | 40 | 4 | | 41 | 20 | | 42 | 3 | | 43 | 11 | | 44 | 1 | | 45 | 4 | | 46 | 5 | | 47 | 7 | | 48 | 13 | | 49 | 4 |
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| 76.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.5048543689320388 | | totalSentences | 103 | | uniqueOpeners | 52 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 74 | | matches | | 0 | "Just a wash of deep" | | 1 | "Just the soundless wind moving" | | 2 | "Just flowers, swaying." |
| | ratio | 0.041 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 74 | | matches | | 0 | "She let the stone go" | | 1 | "Her voice didn't carry." | | 2 | "She'd shouted across plenty of" | | 3 | "She tried again, quieter this" | | 4 | "She'd read about places like" | | 5 | "She'd stopped assuming about an" | | 6 | "She told herself it was" | | 7 | "She almost laughed." | | 8 | "She walked on, faster now," | | 9 | "It came from everywhere and" | | 10 | "It was the loveliest thing" | | 11 | "Her voice cracked on the" | | 12 | "She didn't turn around." | | 13 | "She stared at the dark" | | 14 | "it said, in Eva's voice," |
| | ratio | 0.203 | |
| 88.38% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 74 | | matches | | 0 | "The pendant turned warm against" | | 1 | "The grove spread out before" | | 2 | "Things she didn't have names" | | 3 | "The grass glowed pale silver," | | 4 | "Eva's note had said come" | | 5 | "She let the stone go" | | 6 | "Her voice didn't carry." | | 7 | "That was the first wrong" | | 8 | "She'd shouted across plenty of" | | 9 | "Here it dropped at her" | | 10 | "She tried again, quieter this" | | 11 | "Aurora picked her way deeper" | | 12 | "The standing stones loomed behind" | | 13 | "She'd read about places like" | | 14 | "Eva had talked about them" | | 15 | "Pockets between worlds." | | 16 | "Doors that only opened at" | | 17 | "She'd stopped assuming about an" | | 18 | "Something rustled to her left." | | 19 | "The whole field moved one" |
| | ratio | 0.743 | |
| 67.57% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 74 | | matches | | 0 | "As if something low had" |
| | ratio | 0.014 | |
| 86.47% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 3 | | matches | | 0 | "A fox was a perfectly reasonable explanation for grass moving in a magical clearing that existed in a fold between dimensions." | | 1 | "In the silence that followed, Aurora became aware of her own breathing, loud and ragged, and underneath it, just at the edge of hearing, another breath that did…" | | 2 | "Some animal part of her brain had taken the wheel, and it knew, with absolute certainty, that turning around was the wrong move." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 66.67% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 12 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0.5 | | effectiveRatio | 0.167 | |