| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said quietly [quietly]" |
| | dialogueSentences | 24 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.125 | | effectiveRatio | 0.083 | |
| 80.60% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1031 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "really" | | 1 | "slowly" | | 2 | "very" |
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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) | |
| 90.30% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1031 | | totalAiIsms | 2 | | 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 | 48 | | matches | (empty) | |
| 53.57% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 48 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 64 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 52 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 16 | | totalWords | 1031 | | ratio | 0.016 | | matches | | 0 | "You're going to London. You're going to be someone who doesn't have to explain the bruises." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 65.36% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 827 | | uniqueNames | 12 | | maxNameDensity | 1.69 | | worstName | "Rory" | | maxWindowNameDensity | 3 | | worstWindowName | "Eva" | | discoveredNames | | October | 1 | | Rory | 14 | | London | 2 | | Cardiff | 2 | | Splott | 1 | | Eva | 14 | | Old | 1 | | Compton | 1 | | Street | 1 | | Canton | 1 | | Evan | 1 | | Silas | 2 |
| | persons | | 0 | "Rory" | | 1 | "Eva" | | 2 | "Evan" | | 3 | "Silas" |
| | places | | 0 | "London" | | 1 | "Cardiff" | | 2 | "Old" | | 3 | "Compton" | | 4 | "Street" | | 5 | "Canton" |
| | globalScore | 0.654 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 40 | | 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 | 1031 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 64 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 25 | | mean | 41.24 | | std | 36.45 | | cv | 0.884 | | sampleLengths | | 0 | 107 | | 1 | 1 | | 2 | 81 | | 3 | 68 | | 4 | 2 | | 5 | 4 | | 6 | 34 | | 7 | 34 | | 8 | 47 | | 9 | 37 | | 10 | 1 | | 11 | 8 | | 12 | 105 | | 13 | 111 | | 14 | 6 | | 15 | 69 | | 16 | 104 | | 17 | 17 | | 18 | 34 | | 19 | 3 | | 20 | 42 | | 21 | 63 | | 22 | 14 | | 23 | 22 | | 24 | 17 |
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| 83.33% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 48 | | matches | | 0 | "been polished" | | 1 | "was gone" | | 2 | "was cropped" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 134 | | matches | | |
| 98.21% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 64 | | ratio | 0.016 | | matches | | 0 | "He never did when someone she loved was in the room; he gave her the space to decide what she wanted to carry." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 832 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.027644230769230768 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006009615384615385 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 64 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 64 | | mean | 16.11 | | std | 11.68 | | cv | 0.725 | | sampleLengths | | 0 | 17 | | 1 | 28 | | 2 | 26 | | 3 | 36 | | 4 | 1 | | 5 | 5 | | 6 | 19 | | 7 | 41 | | 8 | 16 | | 9 | 18 | | 10 | 24 | | 11 | 10 | | 12 | 16 | | 13 | 2 | | 14 | 4 | | 15 | 29 | | 16 | 5 | | 17 | 26 | | 18 | 8 | | 19 | 13 | | 20 | 5 | | 21 | 7 | | 22 | 9 | | 23 | 8 | | 24 | 5 | | 25 | 15 | | 26 | 22 | | 27 | 1 | | 28 | 8 | | 29 | 10 | | 30 | 43 | | 31 | 52 | | 32 | 7 | | 33 | 24 | | 34 | 26 | | 35 | 4 | | 36 | 11 | | 37 | 39 | | 38 | 6 | | 39 | 7 | | 40 | 42 | | 41 | 20 | | 42 | 31 | | 43 | 11 | | 44 | 28 | | 45 | 21 | | 46 | 13 | | 47 | 9 | | 48 | 8 | | 49 | 19 |
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| 63.02% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.40625 | | totalSentences | 64 | | uniqueOpeners | 26 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 46 | | matches | | 0 | "Then the light shifted, and" | | 1 | "Then the memory swung round" | | 2 | "Then she had stopped." | | 3 | "Somewhere behind the bar, through" |
| | ratio | 0.087 | |
| 98.26% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 46 | | matches | | 0 | "She was wiping down the" | | 1 | "She set the cloth down." | | 2 | "She wore a charcoal coat" | | 3 | "Her hands knew this work." | | 4 | "Her mind kept snagging on" | | 5 | "It was a long time" | | 6 | "She had come for her" | | 7 | "You're going to be someone" | | 8 | "It had not been a" | | 9 | "She set the glass down" | | 10 | "Her left wrist ached faintly" | | 11 | "she said quietly" | | 12 | "He would not come out." | | 13 | "He never did when someone" |
| | ratio | 0.304 | |
| 57.83% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 37 | | totalSentences | 46 | | matches | | 0 | "The door opened on a" | | 1 | "She was wiping down the" | | 2 | "Silas had taught her to" | | 3 | "She set the cloth down." | | 4 | "Eva stood just inside the" | | 5 | "Eva's hair was cropped short," | | 6 | "She wore a charcoal coat" | | 7 | "Rory noticed it the way" | | 8 | "Rory heard how brittle it" | | 9 | "Eva took a stool near" | | 10 | "Rory reached for the sparkling" | | 11 | "Her hands knew this work." | | 12 | "Her mind kept snagging on" | | 13 | "It was a long time" | | 14 | "Eva turned the glass slowly" | | 15 | "Rory laughed, despite herself, and" | | 16 | "Eva had come for her" | | 17 | "She had come for her" | | 18 | "You're going to be someone" | | 19 | "It had not been a" |
| | ratio | 0.804 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 46 | | matches | (empty) | | ratio | 0 | |
| 23.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 5 | | matches | | 0 | "She was wiping down the far end of the bar, the cloth moving in slow circles over old oak that had been polished by forty years of elbows." | | 1 | "Eva's hair was cropped short, close to the skull, which made her face look older and more deliberate." | | 2 | "For a second the old current ran between them, quick and easy, and she remembered the two of them on the back step of Eva's mum's house, sharing a stolen cigare…" | | 3 | "Then the memory swung round and showed her the other side of it: a flat in Canton, a locked bathroom door, Evan's voice on the stairs, and Eva's car idling in t…" | | 4 | "The bar's old clock ticked above the photographs, black-and-white faces of men and women who had drunk and argued and died in this room long before either of th…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 24 | | tagDensity | 0.125 | | leniency | 0.25 | | rawRatio | 0 | | effectiveRatio | 0 | |