| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1430 | | 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) | |
| 61.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1430 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "pulse" | | 1 | "throb" | | 2 | "flickered" | | 3 | "weight" | | 4 | "silk" | | 5 | "footsteps" | | 6 | "echo" | | 7 | "desire" | | 8 | "vibrated" | | 9 | "chill" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 159 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 159 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 209 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1433 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 35.50% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 1048 | | uniqueNames | 8 | | maxNameDensity | 2.29 | | worstName | "Aurora" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Aurora" | | discoveredNames | | Park | 1 | | Aurora | 24 | | Shade | 1 | | Nyx | 10 | | Richmond | 2 | | Light | 3 | | Cold | 3 | | Isolde | 6 |
| | persons | | 0 | "Aurora" | | 1 | "Shade" | | 2 | "Nyx" | | 3 | "Light" | | 4 | "Cold" | | 5 | "Isolde" |
| | places | | | globalScore | 0.355 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 82 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.698 | | wordCount | 1433 | | matches | | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 209 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 101 | | mean | 14.19 | | std | 12.43 | | cv | 0.876 | | sampleLengths | | 0 | 36 | | 1 | 42 | | 2 | 32 | | 3 | 6 | | 4 | 8 | | 5 | 7 | | 6 | 27 | | 7 | 4 | | 8 | 37 | | 9 | 7 | | 10 | 8 | | 11 | 23 | | 12 | 12 | | 13 | 6 | | 14 | 6 | | 15 | 19 | | 16 | 6 | | 17 | 5 | | 18 | 22 | | 19 | 2 | | 20 | 3 | | 21 | 31 | | 22 | 2 | | 23 | 53 | | 24 | 27 | | 25 | 2 | | 26 | 22 | | 27 | 3 | | 28 | 24 | | 29 | 6 | | 30 | 11 | | 31 | 23 | | 32 | 21 | | 33 | 4 | | 34 | 5 | | 35 | 8 | | 36 | 44 | | 37 | 9 | | 38 | 2 | | 39 | 28 | | 40 | 6 | | 41 | 1 | | 42 | 3 | | 43 | 4 | | 44 | 5 | | 45 | 4 | | 46 | 17 | | 47 | 18 | | 48 | 5 | | 49 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 159 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 185 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 209 | | ratio | 0.014 | | matches | | 0 | "Smell flooded in — crushed stem, wet earth, wild garlic, honey." | | 1 | "The light tasted of orchard on her tongue — ripe plum, crushed grape, woodsmoke." | | 2 | "For a heartbeat she caught it — a vertical seam of distortion, taller than a door, shimmering at the rim." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1050 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.018095238095238095 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 209 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 209 | | mean | 6.86 | | std | 4.61 | | cv | 0.672 | | sampleLengths | | 0 | 10 | | 1 | 6 | | 2 | 20 | | 3 | 5 | | 4 | 23 | | 5 | 14 | | 6 | 5 | | 7 | 7 | | 8 | 12 | | 9 | 8 | | 10 | 6 | | 11 | 8 | | 12 | 7 | | 13 | 8 | | 14 | 8 | | 15 | 11 | | 16 | 4 | | 17 | 15 | | 18 | 8 | | 19 | 7 | | 20 | 7 | | 21 | 2 | | 22 | 5 | | 23 | 8 | | 24 | 7 | | 25 | 6 | | 26 | 5 | | 27 | 5 | | 28 | 8 | | 29 | 4 | | 30 | 6 | | 31 | 6 | | 32 | 3 | | 33 | 5 | | 34 | 2 | | 35 | 9 | | 36 | 6 | | 37 | 5 | | 38 | 10 | | 39 | 5 | | 40 | 2 | | 41 | 5 | | 42 | 2 | | 43 | 3 | | 44 | 8 | | 45 | 2 | | 46 | 2 | | 47 | 8 | | 48 | 11 | | 49 | 2 |
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| 78.85% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.49038461538461536 | | totalSentences | 208 | | uniqueOpeners | 102 | |
| 75.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 132 | | matches | | 0 | "Light bent and righted itself." | | 1 | "Then it dissolved between two" | | 2 | "Then it vanished." |
| | ratio | 0.023 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 132 | | matches | | 0 | "Its core held a dull" | | 1 | "It fell to her shoulders" | | 2 | "She scratched at the small" | | 3 | "Her bright blue eyes narrowed." | | 4 | "It warped the green behind" | | 5 | "She stepped close." | | 6 | "It sat dead and quiet." | | 7 | "Their form flickered at the" | | 8 | "She locked and unlocked it." | | 9 | "It landed on a thistle" | | 10 | "She wore a dress stitched" | | 11 | "Her feet left no mark" | | 12 | "Her hand found the pendant" | | 13 | "She moved without disturbance." | | 14 | "Her jaw set." | | 15 | "Her gaze dropped to the" | | 16 | "Her skin prickled." | | 17 | "She chewed, swallowed." | | 18 | "She dug fingers into leaf" | | 19 | "She unwrapped it with care." |
| | ratio | 0.189 | |
| 35.76% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 112 | | totalSentences | 132 | | matches | | 0 | "Richmond Park stretched flat and" | | 1 | "A jogger cut across the" | | 2 | "Nyx kept to the treeline." | | 3 | "Aurora tugged her jacket shut." | | 4 | "The pendant rested cold against" | | 5 | "Its core held a dull" | | 6 | "Wind carried the answer thin" | | 7 | "Aurora pushed straight black hair" | | 8 | "It fell to her shoulders" | | 9 | "She scratched at the small" | | 10 | "Oak trunks rose ahead, too" | | 11 | "Ivy matted each one from" | | 12 | "Lichen spread in pale maps" | | 13 | "Her bright blue eyes narrowed." | | 14 | "The ripple hung upright between" | | 15 | "It warped the green behind" | | 16 | "Colours ran at the rim." | | 17 | "Nyx poured back into shape" | | 18 | "She stepped close." | | 19 | "The shimmer threw no reflection." |
| | ratio | 0.848 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 132 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |