| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.455 | | leniency | 0.909 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.18% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1038 | | 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) | |
| 80.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1038 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "warmth" | | 1 | "pulse" | | 2 | "pulsed" | | 3 | "resolved" |
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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 | 0 | | hedgeCount | 0 | | narrationSentences | 67 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 73 | | 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 | 1038 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 12 | | wordCount | 1002 | | uniqueNames | 7 | | maxNameDensity | 0.3 | | worstName | "Aurora" | | maxWindowNameDensity | 0.5 | | worstWindowName | "Aurora" | | discoveredNames | | Aurora | 3 | | Richmond | 3 | | Park | 1 | | Eva | 2 | | November | 1 | | Petersham | 1 | | Rory | 1 |
| | persons | | | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "November" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.963 | | wordCount | 1038 | | matches | | 0 | "not with wind, but the way a curtain bends" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 73 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 32.44 | | std | 26.61 | | cv | 0.82 | | sampleLengths | | 0 | 28 | | 1 | 94 | | 2 | 42 | | 3 | 10 | | 4 | 50 | | 5 | 63 | | 6 | 20 | | 7 | 7 | | 8 | 60 | | 9 | 5 | | 10 | 40 | | 11 | 6 | | 12 | 89 | | 13 | 5 | | 14 | 58 | | 15 | 17 | | 16 | 17 | | 17 | 68 | | 18 | 13 | | 19 | 37 | | 20 | 1 | | 21 | 3 | | 22 | 44 | | 23 | 2 | | 24 | 37 | | 25 | 58 | | 26 | 4 | | 27 | 31 | | 28 | 74 | | 29 | 36 | | 30 | 9 | | 31 | 10 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 67 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 162 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 73 | | ratio | 0 | | matches | (empty) | |
| 84.55% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1006 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 58 | | adverbRatio | 0.05765407554671968 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.009940357852882704 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 73 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 73 | | mean | 14.22 | | std | 11.27 | | cv | 0.793 | | sampleLengths | | 0 | 28 | | 1 | 31 | | 2 | 34 | | 3 | 5 | | 4 | 2 | | 5 | 17 | | 6 | 5 | | 7 | 29 | | 8 | 2 | | 9 | 11 | | 10 | 9 | | 11 | 1 | | 12 | 23 | | 13 | 7 | | 14 | 20 | | 15 | 8 | | 16 | 15 | | 17 | 40 | | 18 | 12 | | 19 | 8 | | 20 | 2 | | 21 | 5 | | 22 | 19 | | 23 | 16 | | 24 | 25 | | 25 | 5 | | 26 | 10 | | 27 | 1 | | 28 | 8 | | 29 | 21 | | 30 | 3 | | 31 | 3 | | 32 | 3 | | 33 | 24 | | 34 | 41 | | 35 | 3 | | 36 | 18 | | 37 | 5 | | 38 | 5 | | 39 | 13 | | 40 | 4 | | 41 | 18 | | 42 | 18 | | 43 | 17 | | 44 | 6 | | 45 | 11 | | 46 | 20 | | 47 | 29 | | 48 | 19 | | 49 | 13 |
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| 70.32% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4794520547945205 | | totalSentences | 73 | | uniqueOpeners | 35 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 61 | | matches | | 0 | "Just the stones, the flowers," | | 1 | "Twice she was certain she'd" | | 2 | "Somewhere off to her right," | | 3 | "Only the flowers answered, opening" |
| | ratio | 0.066 | |
| 75.74% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 61 | | matches | | 0 | "She'd followed that heat through" | | 1 | "She'd noticed that on her" | | 2 | "She stepped between two of" | | 3 | "she said, mostly to hear" | | 4 | "She'd come because the pendant" | | 5 | "she murmured, palm pressed flat" | | 6 | "She hadn't expected it to." | | 7 | "She picked her way deeper," | | 8 | "She glanced up once and" | | 9 | "She exhaled, and the breath" | | 10 | "She kept walking." | | 11 | "She lost track." | | 12 | "She told herself deer." | | 13 | "she said, and hated how" | | 14 | "She backed away from the" | | 15 | "She stopped breathing." | | 16 | "She should have run then." | | 17 | "She knew that later, turning" | | 18 | "She stepped towards it." | | 19 | "she said, steadier this time," |
| | ratio | 0.361 | |
| 91.15% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 45 | | totalSentences | 61 | | matches | | 0 | "The pendant burned hot against" | | 1 | "She'd followed that heat through" | | 2 | "The oaks here stood taller" | | 3 | "Wildflowers grew between the roots." | | 4 | "She'd noticed that on her" | | 5 | "She stepped between two of" | | 6 | "she said, mostly to hear" | | 7 | "The grove opened around her," | | 8 | "Flowers she couldn't name nodded" | | 9 | "The air tasted of crushed" | | 10 | "She'd come because the pendant" | | 11 | "That was the truth of" | | 12 | "she murmured, palm pressed flat" | | 13 | "She hadn't expected it to." | | 14 | "She picked her way deeper," | | 15 | "The moon hung directly overhead," | | 16 | "She glanced up once and" | | 17 | "A twig snapped behind her." | | 18 | "Aurora spun, pulse jumping, hand" | | 19 | "She exhaled, and the breath" |
| | ratio | 0.738 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 61 | | matches | (empty) | | ratio | 0 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 3 | | matches | | 0 | "She picked her way deeper, boots silent on grass that shouldn't have been this soft in autumn, this green." | | 1 | "Twice she was certain she'd looped back to where she started, and twice the pattern of flowers at her feet told her otherwise, a spray of violet ones on the lef…" | | 2 | "Only the flowers answered, opening again in a slow wave that rippled away from her towards the black mouth of the trees, as if something had walked through them…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 59.09% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 11 | | tagDensity | 0.455 | | leniency | 0.909 | | rawRatio | 0.2 | | effectiveRatio | 0.182 | |