| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 63 | | tagDensity | 0.206 | | leniency | 0.413 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.06% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1793 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "slowly" | | 1 | "perfectly" | | 2 | "quickly" | | 3 | "slightly" |
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| 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) | |
| 72.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1793 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "comforting" | | 1 | "pulse" | | 2 | "whisper" | | 3 | "flickered" | | 4 | "warmth" | | 5 | "trembled" | | 6 | "silence" |
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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 | 181 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 181 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 231 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1788 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 48.03% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 64 | | wordCount | 1520 | | uniqueNames | 9 | | maxNameDensity | 2.04 | | worstName | "Aurora" | | maxWindowNameDensity | 3 | | worstWindowName | "Aurora" | | discoveredNames | | Richmond | 1 | | Park | 1 | | Aurora | 31 | | Isolde | 16 | | Fae-forged | 1 | | Heartstone | 1 | | Nyx | 11 | | London | 1 | | Seer | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Isolde" | | 2 | "Nyx" | | 3 | "Seer" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "London" |
| | globalScore | 0.48 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 113 | | 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 | 1788 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 231 | | matches | | 0 | "hated that answer" | | 1 | "knew that stance" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 131 | | mean | 13.65 | | std | 13.36 | | cv | 0.979 | | sampleLengths | | 0 | 8 | | 1 | 71 | | 2 | 8 | | 3 | 53 | | 4 | 32 | | 5 | 8 | | 6 | 2 | | 7 | 8 | | 8 | 8 | | 9 | 44 | | 10 | 3 | | 11 | 40 | | 12 | 10 | | 13 | 22 | | 14 | 9 | | 15 | 24 | | 16 | 7 | | 17 | 7 | | 18 | 6 | | 19 | 3 | | 20 | 10 | | 21 | 7 | | 22 | 8 | | 23 | 12 | | 24 | 4 | | 25 | 24 | | 26 | 5 | | 27 | 3 | | 28 | 41 | | 29 | 56 | | 30 | 27 | | 31 | 40 | | 32 | 5 | | 33 | 5 | | 34 | 12 | | 35 | 5 | | 36 | 4 | | 37 | 7 | | 38 | 12 | | 39 | 23 | | 40 | 3 | | 41 | 6 | | 42 | 27 | | 43 | 23 | | 44 | 10 | | 45 | 6 | | 46 | 3 | | 47 | 10 | | 48 | 9 | | 49 | 51 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 181 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 263 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 231 | | ratio | 0.013 | | matches | | 0 | "Wildflowers crowded their roots—bluebells, poppies, little star-shaped blooms Aurora didn’t recognise—bright as spilled paint despite the cold." | | 1 | "Aurora drew a breath through her nose, caught the scent of flowers and something sharper beneath it—iron, perhaps, or frost." | | 2 | "Moonlight—though there was no moon—ran along its leaf-shaped edge." |
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| 93.54% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1530 | | adjectiveStacks | 1 | | stackExamples | | 0 | "little star-shaped blooms" |
| | adverbCount | 62 | | adverbRatio | 0.040522875816993466 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.00849673202614379 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 231 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 231 | | mean | 7.74 | | std | 5.23 | | cv | 0.676 | | sampleLengths | | 0 | 8 | | 1 | 31 | | 2 | 8 | | 3 | 32 | | 4 | 8 | | 5 | 19 | | 6 | 17 | | 7 | 17 | | 8 | 5 | | 9 | 11 | | 10 | 5 | | 11 | 11 | | 12 | 8 | | 13 | 2 | | 14 | 8 | | 15 | 7 | | 16 | 1 | | 17 | 15 | | 18 | 12 | | 19 | 17 | | 20 | 3 | | 21 | 14 | | 22 | 17 | | 23 | 9 | | 24 | 5 | | 25 | 5 | | 26 | 6 | | 27 | 16 | | 28 | 5 | | 29 | 4 | | 30 | 5 | | 31 | 4 | | 32 | 15 | | 33 | 7 | | 34 | 7 | | 35 | 6 | | 36 | 3 | | 37 | 6 | | 38 | 4 | | 39 | 4 | | 40 | 3 | | 41 | 8 | | 42 | 8 | | 43 | 4 | | 44 | 4 | | 45 | 4 | | 46 | 20 | | 47 | 5 | | 48 | 3 | | 49 | 10 |
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| 46.54% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.2554112554112554 | | totalSentences | 231 | | uniqueOpeners | 59 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 162 | | matches | | 0 | "Only a gap, dark with" | | 1 | "Then the pressure released, and" | | 2 | "Only a wall of trees," | | 3 | "Then the bird called again." | | 4 | "Then seven again." | | 5 | "Then it darted into her" | | 6 | "Then the pool went black." |
| | ratio | 0.043 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 37 | | totalSentences | 162 | | matches | | 0 | "Their trunks were wider than" | | 1 | "Her silver hair fell to" | | 2 | "She had no shoes on." | | 3 | "Their violet eyes glimmered faintly" | | 4 | "It had been warm since" | | 5 | "She touched it through her" | | 6 | "She wished Nyx would say" | | 7 | "She stepped between the stones." | | 8 | "Their outline softened at the" | | 9 | "She resisted the urge to" | | 10 | "Her bare feet passed over" | | 11 | "She kept her attention on" | | 12 | "she asked Isolde" | | 13 | "Their delicate faces followed her" | | 14 | "She took another step, and" | | 15 | "She backed away." | | 16 | "She spun, blade already half" | | 17 | "They moved on." | | 18 | "Their trunks were smooth and" | | 19 | "It stopped level with her" |
| | ratio | 0.228 | |
| 58.77% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 130 | | totalSentences | 162 | | matches | | 0 | "The standing stones were not" | | 1 | "Aurora had checked twice before" | | 2 | "The path behind them had" | | 3 | "Their trunks were wider than" | | 4 | "Wildflowers crowded their roots—bluebells, poppies," | | 5 | "Isolde waited between two stones." | | 6 | "Her silver hair fell to" | | 7 | "She had no shoes on." | | 8 | "Aurora glanced at the grass" | | 9 | "Isolde’s pale lavender eyes rested" | | 10 | "Nyx stood a few paces" | | 11 | "Their violet eyes glimmered faintly" | | 12 | "The shadows under the trees" | | 13 | "The Fae-forged blade sat cold" | | 14 | "The Heartstone pendant rested at" | | 15 | "It had been warm since" | | 16 | "She touched it through her" | | 17 | "The pulse beneath her fingers" | | 18 | "Isolde lifted one pale hand." | | 19 | "Aurora looked between the stones." |
| | ratio | 0.802 | |
| 61.73% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 162 | | matches | | 0 | "Now the phone showed a" | | 1 | "Whether it would let them" |
| | ratio | 0.012 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 61 | | technicalSentenceCount | 2 | | matches | | 0 | "Aurora had checked twice before they left the path, phone held up to the grey sky as if better reception might make the ancient oaks appear in the park’s offici…" | | 1 | "She kept her attention on them as they walked, cataloguing what she could: the water’s direction, the sloping ground, the trees that repeated in patterns too pr…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 2 | | fancyTags | | 0 | "Aurora muttered (mutter)" | | 1 | "Nyx murmured (murmur)" |
| | dialogueSentences | 63 | | tagDensity | 0.206 | | leniency | 0.413 | | rawRatio | 0.154 | | effectiveRatio | 0.063 | |