| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.342 | | leniency | 0.684 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 83.15% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1484 | | totalAiIsmAdverbs | 5 | | 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) | |
| 86.52% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1484 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "scanning" | | 1 | "warmth" | | 2 | "familiar" | | 3 | "eyebrow" |
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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 | 58 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 58 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 84 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 123 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1473 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 54.88% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 841 | | uniqueNames | 17 | | maxNameDensity | 1.9 | | worstName | "Eva" | | maxWindowNameDensity | 3 | | worstWindowName | "Eva" | | discoveredNames | | Nest | 1 | | Thursday | 1 | | Silas | 5 | | Eva | 16 | | Marsh | 1 | | Cardiff | 2 | | Rory | 14 | | Queen | 1 | | Street | 1 | | Hong | 1 | | Kong | 1 | | Berlin | 1 | | London | 1 | | Evan | 1 | | Manchester | 1 | | Rain | 3 | | Four | 3 |
| | persons | | 0 | "Nest" | | 1 | "Silas" | | 2 | "Eva" | | 3 | "Rory" | | 4 | "Queen" | | 5 | "Evan" | | 6 | "Rain" |
| | places | | 0 | "Marsh" | | 1 | "Cardiff" | | 2 | "Street" | | 3 | "Hong" | | 4 | "Kong" | | 5 | "Berlin" | | 6 | "London" | | 7 | "Manchester" |
| | globalScore | 0.549 | | windowScore | 0.667 | |
| 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 | 0 | | per1kWords | 0 | | wordCount | 1473 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 84 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 33.48 | | std | 31.33 | | cv | 0.936 | | sampleLengths | | 0 | 87 | | 1 | 44 | | 2 | 39 | | 3 | 1 | | 4 | 37 | | 5 | 28 | | 6 | 6 | | 7 | 52 | | 8 | 57 | | 9 | 7 | | 10 | 74 | | 11 | 6 | | 12 | 19 | | 13 | 6 | | 14 | 9 | | 15 | 41 | | 16 | 91 | | 17 | 43 | | 18 | 11 | | 19 | 5 | | 20 | 2 | | 21 | 61 | | 22 | 6 | | 23 | 6 | | 24 | 21 | | 25 | 24 | | 26 | 69 | | 27 | 8 | | 28 | 3 | | 29 | 60 | | 30 | 23 | | 31 | 120 | | 32 | 13 | | 33 | 120 | | 34 | 27 | | 35 | 44 | | 36 | 3 | | 37 | 1 | | 38 | 68 | | 39 | 48 | | 40 | 36 | | 41 | 4 | | 42 | 2 | | 43 | 41 |
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| 99.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 58 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 144 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 1 | | flaggedSentences | 9 | | totalSentences | 84 | | ratio | 0.107 | | matches | | 0 | "The Nest sat half-empty at eleven on a Thursday—two old men hunched over a chessboard by the window, a couple whispering in the corner booth, Silas polishing glasses with the patience of a man who had wiped the same spot for a decade." | | 1 | "What stopped her breath was the hair—white-blonde now, cropped close at the jaw, a color Eva Marsh would once have scorched with lighter fluid rather than wear." | | 2 | "For a second the old face surfaced through the new one—the wide mouth, the freckles the Cardiff sun had never been strong enough to burn off—and then it submerged behind something composed and careful." | | 3 | "Her rings caught the light—four of them, slim and gold, worn on fingers that used to wear a single plastic skull ring from a joke shop on Queen Street." | | 4 | "Eva looked at her, and something moved behind the composure—a door opened an inch on a dark room." | | 5 | "Outside, a bus hissed past and its lights swept the photographs on the wall—Hong Kong, 1962; Berlin, some year nobody had ever asked about." | | 6 | "Somewhere behind her eyes the careful thing was doing arithmetic—what to spend, what to keep." | | 7 | "She didn't take Eva's hand—she took the crooked finger itself, lifted it, turned it once in the light the way a jeweller might, and set it back down on the wood with all its rings and all its history." | | 8 | "Then she looked at Rory, and for one unguarded second the composed woman was gone and something much younger sat on the stool in her place—freckles, wide mouth, handbrake half on, engine running." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 599 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.0333889816360601 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 84 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 84 | | mean | 17.54 | | std | 19.75 | | cv | 1.126 | | sampleLengths | | 0 | 17 | | 1 | 27 | | 2 | 43 | | 3 | 8 | | 4 | 36 | | 5 | 6 | | 6 | 6 | | 7 | 27 | | 8 | 1 | | 9 | 3 | | 10 | 34 | | 11 | 12 | | 12 | 16 | | 13 | 6 | | 14 | 19 | | 15 | 15 | | 16 | 18 | | 17 | 18 | | 18 | 21 | | 19 | 18 | | 20 | 4 | | 21 | 3 | | 22 | 9 | | 23 | 29 | | 24 | 36 | | 25 | 6 | | 26 | 18 | | 27 | 1 | | 28 | 6 | | 29 | 9 | | 30 | 11 | | 31 | 6 | | 32 | 24 | | 33 | 7 | | 34 | 1 | | 35 | 36 | | 36 | 47 | | 37 | 9 | | 38 | 15 | | 39 | 19 | | 40 | 9 | | 41 | 2 | | 42 | 5 | | 43 | 2 | | 44 | 42 | | 45 | 19 | | 46 | 6 | | 47 | 6 | | 48 | 10 | | 49 | 11 |
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| 65.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.4166666666666667 | | totalSentences | 84 | | uniqueOpeners | 35 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 53 | | matches | | 0 | "Somewhere behind her eyes the" | | 1 | "Then she looked at Rory," |
| | ratio | 0.038 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 7 | | totalSentences | 53 | | matches | | 0 | "She knew the posture was" | | 1 | "She delivered it like a" | | 2 | "Her ginger wine was still" | | 3 | "Her rings caught the light—four" | | 4 | "She looked at Eva's left" | | 5 | "She addressed the olive" | | 6 | "She didn't take Eva's hand—she" |
| | ratio | 0.132 | |
| 82.64% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 40 | | totalSentences | 53 | | matches | | 0 | "Rain ticked against the green" | | 1 | "Rory peeled off her cycling" | | 2 | "The Nest sat half-empty at" | | 3 | "The door opened on a" | | 4 | "A woman stepped in, shook" | | 5 | "Rory knew the coat was" | | 6 | "She knew the posture was" | | 7 | "The woman turned." | | 8 | "She delivered it like a" | | 9 | "Eva hung her coat over" | | 10 | "Silas came down the bar" | | 11 | "Eva ordered a gin martini," | | 12 | "Her ginger wine was still" | | 13 | "Eva turned her phone face-down" | | 14 | "Her rings caught the light—four" | | 15 | "Eva looked at her, and" | | 16 | "Silas set down the martini" | | 17 | "Chess pieces clicked by the" | | 18 | "Rory counted the years without" | | 19 | "She looked at Eva's left" |
| | ratio | 0.755 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 23 | | technicalSentenceCount | 1 | | matches | | 0 | "The Nest sat half-empty at eleven on a Thursday—two old men hunched over a chessboard by the window, a couple whispering in the corner booth, Silas polishing gl…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 97.37% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "Eva ordered (order)" | | 1 | "She addressed (address)" |
| | dialogueSentences | 38 | | tagDensity | 0.079 | | leniency | 0.158 | | rawRatio | 0.667 | | effectiveRatio | 0.105 | |