| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 35 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 82.71% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1157 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "very" | | 1 | "gently" | | 2 | "carefully" |
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| 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) | |
| 95.68% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1157 | | totalAiIsms | 1 | | 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 | 57 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 57 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 75 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 20 | | totalWords | 1157 | | ratio | 0.017 | | matches | | 0 | "You're not going back to him, do you hear me? You're getting on a train." | | 1 | "Still alive, don't come looking" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 871 | | uniqueNames | 15 | | maxNameDensity | 1.84 | | worstName | "Rory" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Eva" | | discoveredNames | | Rory | 16 | | Old | 2 | | Compton | 1 | | Street | 1 | | Nest | 1 | | Golden | 1 | | Empress | 1 | | Italian | 1 | | Cathays | 1 | | Taf | 1 | | Inn | 1 | | Cardiff | 1 | | Eva | 15 | | Silas | 4 | | Sunday | 1 |
| | persons | | 0 | "Rory" | | 1 | "Nest" | | 2 | "Eva" | | 3 | "Silas" |
| | places | | 0 | "Old" | | 1 | "Compton" | | 2 | "Street" | | 3 | "Italian" | | 4 | "Cathays" | | 5 | "Cardiff" |
| | globalScore | 0.582 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | 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 | 1157 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 75 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 34.03 | | std | 32.43 | | cv | 0.953 | | sampleLengths | | 0 | 92 | | 1 | 86 | | 2 | 2 | | 3 | 28 | | 4 | 130 | | 5 | 4 | | 6 | 1 | | 7 | 28 | | 8 | 5 | | 9 | 19 | | 10 | 25 | | 11 | 4 | | 12 | 28 | | 13 | 68 | | 14 | 23 | | 15 | 4 | | 16 | 37 | | 17 | 75 | | 18 | 21 | | 19 | 19 | | 20 | 3 | | 21 | 76 | | 22 | 37 | | 23 | 31 | | 24 | 103 | | 25 | 19 | | 26 | 3 | | 27 | 17 | | 28 | 56 | | 29 | 14 | | 30 | 42 | | 31 | 13 | | 32 | 36 | | 33 | 8 |
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| 92.95% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 57 | | matches | | 0 | "been paid" | | 1 | "was cropped" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 145 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 75 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 875 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.036571428571428574 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.009142857142857144 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 75 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 75 | | mean | 15.43 | | std | 12.34 | | cv | 0.8 | | sampleLengths | | 0 | 49 | | 1 | 10 | | 2 | 33 | | 3 | 12 | | 4 | 31 | | 5 | 19 | | 6 | 24 | | 7 | 2 | | 8 | 28 | | 9 | 7 | | 10 | 21 | | 11 | 31 | | 12 | 34 | | 13 | 12 | | 14 | 25 | | 15 | 4 | | 16 | 1 | | 17 | 4 | | 18 | 24 | | 19 | 5 | | 20 | 17 | | 21 | 2 | | 22 | 9 | | 23 | 16 | | 24 | 4 | | 25 | 7 | | 26 | 21 | | 27 | 12 | | 28 | 10 | | 29 | 46 | | 30 | 6 | | 31 | 17 | | 32 | 4 | | 33 | 11 | | 34 | 26 | | 35 | 25 | | 36 | 50 | | 37 | 7 | | 38 | 14 | | 39 | 7 | | 40 | 8 | | 41 | 4 | | 42 | 3 | | 43 | 17 | | 44 | 11 | | 45 | 48 | | 46 | 29 | | 47 | 8 | | 48 | 11 | | 49 | 20 |
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| 63.56% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.4533333333333333 | | totalSentences | 75 | | uniqueOpeners | 34 | |
| 68.03% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 49 | | matches | | | ratio | 0.02 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 49 | | matches | | 0 | "He looked up, caught her" | | 1 | "You're getting on a train.*" | | 2 | "Her coat, folded over the" | | 3 | "She stood with the bag" | | 4 | "It came out flat, and" | | 5 | "She lifted the water glass" | | 6 | "Her hand was steady, but" | | 7 | "He came around the end" | | 8 | "He said nothing." | | 9 | "He did not need to." | | 10 | "She thought of Eva on" | | 11 | "she said at last" | | 12 | "She picked up the glass." |
| | ratio | 0.265 | |
| 51.84% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 40 | | totalSentences | 49 | | matches | | 0 | "The rain had found the" | | 1 | "Yu-Fei would have scolded her" | | 2 | "Rory had taken it anyway," | | 3 | "The bar was warm and" | | 4 | "Silas stood behind the bar" | | 5 | "He looked up, caught her" | | 6 | "Eva had always been the" | | 7 | "You're getting on a train.*" | | 8 | "A slim gold band caught" | | 9 | "Her coat, folded over the" | | 10 | "Rory did not sit." | | 11 | "She stood with the bag" | | 12 | "It came out flat, and" | | 13 | "Eva turned the ring with" | | 14 | "A thin smile" | | 15 | "Rory sat down then, because" | | 16 | "The vinyl of the booth" | | 17 | "Eva's voice softened, and that" | | 18 | "The bar was quiet except" | | 19 | "Rory looked at the scar" |
| | ratio | 0.816 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 49 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 1 | | matches | | 0 | "Across the room Silas set a short glass of whisky on the bar and did not bring it over, though his eyes stayed on the booth with the patience of a man who had s…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 35 | | tagDensity | 0.257 | | leniency | 0.514 | | rawRatio | 0 | | effectiveRatio | 0 | |