| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 179 | | tagDensity | 0.067 | | leniency | 0.134 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.74% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2213 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 93.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2213 | | totalAiIsms | 3 | | found | | | highlights | | |
| 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 | 151 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 151 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 318 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2213 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 40 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 104 | | wordCount | 1328 | | uniqueNames | 6 | | maxNameDensity | 3.54 | | worstName | "Eva" | | maxWindowNameDensity | 6.5 | | worstWindowName | "Eva" | | discoveredNames | | Nest | 2 | | Carter | 1 | | Aurora | 42 | | Cardiff | 1 | | Eva | 47 | | Silas | 11 |
| | persons | | 0 | "Carter" | | 1 | "Aurora" | | 2 | "Eva" | | 3 | "Silas" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 93 | | 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 | 2213 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 318 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 253 | | mean | 8.75 | | std | 9.72 | | cv | 1.111 | | sampleLengths | | 0 | 22 | | 1 | 45 | | 2 | 7 | | 3 | 2 | | 4 | 7 | | 5 | 5 | | 6 | 7 | | 7 | 19 | | 8 | 10 | | 9 | 57 | | 10 | 6 | | 11 | 13 | | 12 | 5 | | 13 | 63 | | 14 | 36 | | 15 | 6 | | 16 | 1 | | 17 | 8 | | 18 | 19 | | 19 | 1 | | 20 | 8 | | 21 | 15 | | 22 | 12 | | 23 | 4 | | 24 | 9 | | 25 | 5 | | 26 | 4 | | 27 | 32 | | 28 | 14 | | 29 | 3 | | 30 | 1 | | 31 | 9 | | 32 | 5 | | 33 | 1 | | 34 | 32 | | 35 | 4 | | 36 | 4 | | 37 | 26 | | 38 | 5 | | 39 | 5 | | 40 | 4 | | 41 | 34 | | 42 | 7 | | 43 | 3 | | 44 | 3 | | 45 | 7 | | 46 | 3 | | 47 | 8 | | 48 | 5 | | 49 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 151 | | matches | | 0 | "been assembled" | | 1 | "been opened" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 239 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 318 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1329 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.01730624529721595 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0015048908954100827 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 318 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 318 | | mean | 6.96 | | std | 5.84 | | cv | 0.839 | | sampleLengths | | 0 | 22 | | 1 | 14 | | 2 | 7 | | 3 | 24 | | 4 | 7 | | 5 | 2 | | 6 | 7 | | 7 | 5 | | 8 | 7 | | 9 | 10 | | 10 | 9 | | 11 | 10 | | 12 | 11 | | 13 | 21 | | 14 | 25 | | 15 | 6 | | 16 | 13 | | 17 | 5 | | 18 | 15 | | 19 | 25 | | 20 | 14 | | 21 | 9 | | 22 | 36 | | 23 | 6 | | 24 | 1 | | 25 | 8 | | 26 | 5 | | 27 | 14 | | 28 | 1 | | 29 | 2 | | 30 | 6 | | 31 | 15 | | 32 | 12 | | 33 | 4 | | 34 | 6 | | 35 | 3 | | 36 | 5 | | 37 | 4 | | 38 | 7 | | 39 | 25 | | 40 | 14 | | 41 | 3 | | 42 | 1 | | 43 | 4 | | 44 | 5 | | 45 | 5 | | 46 | 1 | | 47 | 8 | | 48 | 5 | | 49 | 19 |
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| 42.77% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 23 | | diversityRatio | 0.1792452830188679 | | totalSentences | 318 | | uniqueOpeners | 57 | |
| 25.06% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 133 | | matches | | 0 | "Then the woman lifted her" |
| | ratio | 0.008 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 133 | | matches | | 0 | "Her black hair clung to" | | 1 | "She crossed the bar and" | | 2 | "Her hair, once a thick" | | 3 | "She held a glass of" | | 4 | "His gaze moved from one" | | 5 | "They looked at each other" | | 6 | "She had always possessed a" | | 7 | "His hazel eyes sharpened." | | 8 | "It smelled of black leaves" | | 9 | "It ran down the window" | | 10 | "She had no idea who" | | 11 | "She envied the certainty of" | | 12 | "He looked at Eva." | | 13 | "His silver signet ring caught" | | 14 | "It had healed badly, pale" | | 15 | "She pressed her thumb against" | | 16 | "He stood near the shelves," | | 17 | "His eyes remained on the" | | 18 | "She lifted the whisky and" | | 19 | "She placed it on the" |
| | ratio | 0.18 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 126 | | totalSentences | 133 | | matches | | 0 | "The green neon raven above" | | 1 | "Aurora Carter shouldered through the" | | 2 | "Her black hair clung to" | | 3 | "A paper receipt had stuck" | | 4 | "Silas looked up from polishing" | | 5 | "Silas glanced at the takeaway" | | 6 | "She crossed the bar and" | | 7 | "The Nest held its usual" | | 8 | "Aurora peeled off her wet" | | 9 | "A woman at the far" | | 10 | "The movement stopped Aurora cold." | | 11 | "The woman wore a charcoal" | | 12 | "Her hair, once a thick" | | 13 | "A pale line ran from" | | 14 | "She held a glass of" | | 15 | "The receipt beneath Aurora’s boot" | | 16 | "Silas set down the glass." | | 17 | "His gaze moved from one" | | 18 | "The stool legs scraped the" | | 19 | "They looked at each other" |
| | ratio | 0.947 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 133 | | matches | (empty) | | ratio | 0 | |
| 76.41% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 4 | | matches | | 0 | "Old maps covered the walls between framed photographs of men and women who had stared into cameras with the strained stillness of people carrying secrets." | | 1 | "Her hair, once a thick brown braid that had reached the middle of her back, now fell in a sharp blond cut around her jaw." | | 2 | "For a moment, Aurora saw the girl she had known at nineteen: bare feet on Cardiff pavement, stolen chips wrapped in newspaper, laughter spilling out of her as i…" | | 3 | "Aurora remembered those hands covered in blue ink, waving through the air while Eva argued about politics, poetry, or whether the university’s entire faculty ha…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 179 | | tagDensity | 0.061 | | leniency | 0.123 | | rawRatio | 0.091 | | effectiveRatio | 0.011 | |