| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 133 | | tagDensity | 0.045 | | leniency | 0.09 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1979 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 89.89% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1979 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "wavering" | | 1 | "racing" | | 2 | "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 | 115 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 115 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 242 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1979 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 23 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 90 | | wordCount | 1078 | | uniqueNames | 10 | | maxNameDensity | 3.71 | | worstName | "Eva" | | maxWindowNameDensity | 6 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Carter | 1 | | Yu-Fei | 2 | | Cheung | 1 | | Blackwood | 1 | | Aurora | 35 | | Eva | 40 | | One | 1 | | Silas | 7 |
| | persons | | 0 | "Raven" | | 1 | "Carter" | | 2 | "Yu-Fei" | | 3 | "Cheung" | | 4 | "Blackwood" | | 5 | "Aurora" | | 6 | "Eva" | | 7 | "Silas" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 89 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 98.94% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.011 | | wordCount | 1979 | | matches | | 0 | "neither laugh nor" | | 1 | "not closing but rearranging itself around an obligation" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 242 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 188 | | mean | 10.53 | | std | 11.51 | | cv | 1.094 | | sampleLengths | | 0 | 70 | | 1 | 21 | | 2 | 57 | | 3 | 5 | | 4 | 4 | | 5 | 6 | | 6 | 6 | | 7 | 1 | | 8 | 2 | | 9 | 61 | | 10 | 47 | | 11 | 2 | | 12 | 2 | | 13 | 6 | | 14 | 7 | | 15 | 18 | | 16 | 3 | | 17 | 2 | | 18 | 18 | | 19 | 48 | | 20 | 13 | | 21 | 2 | | 22 | 2 | | 23 | 2 | | 24 | 4 | | 25 | 4 | | 26 | 2 | | 27 | 11 | | 28 | 13 | | 29 | 14 | | 30 | 3 | | 31 | 4 | | 32 | 6 | | 33 | 1 | | 34 | 8 | | 35 | 5 | | 36 | 6 | | 37 | 6 | | 38 | 33 | | 39 | 3 | | 40 | 4 | | 41 | 5 | | 42 | 5 | | 43 | 4 | | 44 | 20 | | 45 | 10 | | 46 | 27 | | 47 | 5 | | 48 | 1 | | 49 | 10 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 115 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 174 | | matches | | 0 | "was ruining" | | 1 | "was showing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 3 | | flaggedSentences | 3 | | totalSentences | 242 | | ratio | 0.012 | | matches | | 0 | "At nineteen she had cut her hair with kitchen scissors whenever life bored her; now it lay in a smooth knot at the nape of her neck." | | 1 | "The laugh had altered something in her face; for an instant the suit fitted less well." | | 2 | "Aurora had forgotten the small gap between her front teeth; it appeared when a smile got away from her." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1081 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.014801110083256245 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0009250693802035153 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 242 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 242 | | mean | 8.18 | | std | 6.48 | | cv | 0.792 | | sampleLengths | | 0 | 17 | | 1 | 26 | | 2 | 13 | | 3 | 14 | | 4 | 13 | | 5 | 8 | | 6 | 18 | | 7 | 16 | | 8 | 6 | | 9 | 17 | | 10 | 5 | | 11 | 4 | | 12 | 6 | | 13 | 6 | | 14 | 1 | | 15 | 2 | | 16 | 2 | | 17 | 27 | | 18 | 8 | | 19 | 24 | | 20 | 6 | | 21 | 7 | | 22 | 14 | | 23 | 9 | | 24 | 11 | | 25 | 2 | | 26 | 2 | | 27 | 6 | | 28 | 7 | | 29 | 3 | | 30 | 15 | | 31 | 3 | | 32 | 2 | | 33 | 18 | | 34 | 12 | | 35 | 4 | | 36 | 19 | | 37 | 13 | | 38 | 13 | | 39 | 2 | | 40 | 2 | | 41 | 2 | | 42 | 4 | | 43 | 4 | | 44 | 2 | | 45 | 11 | | 46 | 6 | | 47 | 7 | | 48 | 14 | | 49 | 3 |
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| 46.69% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.2768595041322314 | | totalSentences | 242 | | uniqueOpeners | 67 | |
| 31.75% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 105 | | matches | | 0 | "Then she laughed, and two" |
| | ratio | 0.01 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 105 | | matches | | 0 | "He took in Aurora’s soaked" | | 1 | "His silver signet ring clicked" | | 2 | "She folded it in half," | | 3 | "It had creased school worksheets," | | 4 | "Her raincoat was folded over" | | 5 | "She rose without the old" | | 6 | "They met beside the crossword" | | 7 | "Her gaze moved over Aurora’s" | | 8 | "He gathered his newspaper and" | | 9 | "He had a talent for" | | 10 | "She turned her glass between" | | 11 | "She lifted her hand, and" | | 12 | "Their faces were pale ovals" | | 13 | "She had filled it with" | | 14 | "She caught it before it" | | 15 | "She set the ring straight" | | 16 | "She took a mouthful anyway." | | 17 | "She stopped and shook her" | | 18 | "He had abandoned the racing" | | 19 | "Her face changed, not closing" |
| | ratio | 0.219 | |
| 21.90% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 92 | | totalSentences | 105 | | matches | | 0 | "Rain had broken the green" | | 1 | "Aurora Carter pushed through the" | | 2 | "Silas Blackwood stood behind the" | | 3 | "He took in Aurora’s soaked" | | 4 | "His silver signet ring clicked" | | 5 | "She folded it in half," | | 6 | "Aurora recognised that small, exact" | | 7 | "It had creased school worksheets," | | 8 | "The woman looked up." | | 9 | "Aurora’s hand stilled inside the" | | 10 | "Her raincoat was folded over" | | 11 | "She rose without the old" | | 12 | "They met beside the crossword" | | 13 | "The delivery bag knocked against" | | 14 | "Aurora shifted it, and they" | | 15 | "Eva’s perfume caught at the" | | 16 | "Eva drew back." | | 17 | "Her gaze moved over Aurora’s" | | 18 | "Silas set a soda and" | | 19 | "Aurora glanced at him." |
| | ratio | 0.876 | |
| 47.62% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 105 | | matches | | | ratio | 0.01 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 133 | | tagDensity | 0.038 | | leniency | 0.075 | | rawRatio | 0 | | effectiveRatio | 0 | |