| 49.06% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 4 | | adverbTags | | 0 | "Silas said mildly [mildly]" | | 1 | "Meg said finally [finally]" | | 2 | "She laughed again [again]" | | 3 | "she said thickly [thickly]" |
| | dialogueSentences | 53 | | tagDensity | 0.415 | | leniency | 0.83 | | rawRatio | 0.182 | | effectiveRatio | 0.151 | |
| 85.89% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1417 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "very" | | 1 | "quickly" | | 2 | "really" |
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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) | |
| 85.89% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1417 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "warmth" | | 1 | "weight" | | 2 | "could feel" | | 3 | "pulse" |
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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 | 73 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 73 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 104 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 62 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 8 | | markdownWords | 29 | | totalWords | 1417 | | ratio | 0.02 | | matches | | 0 | "the usual" | | 1 | "you're late" | | 2 | "there's someone here you'll want to see." | | 3 | "is" | | 4 | "Do not go gentle" | | 5 | "your mum, your dad, are you still in touch with Cerys" | | 6 | "you" | | 7 | "charming" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 20 | | unquotedAttributions | 0 | | matches | (empty) | |
| 55.46% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 952 | | uniqueNames | 10 | | maxNameDensity | 1.89 | | worstName | "Meg" | | maxWindowNameDensity | 3 | | worstWindowName | "Meg" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Greek | 1 | | Street | 1 | | Meg | 18 | | Rory | 16 | | Silas | 4 | | Bloomsbury | 1 | | Yu-Fei | 2 | | Pressed | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Meg" | | 3 | "Rory" | | 4 | "Silas" | | 5 | "Yu-Fei" |
| | places | | 0 | "Greek" | | 1 | "Street" | | 2 | "Bloomsbury" |
| | globalScore | 0.555 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | glossingSentenceCount | 1 | | matches | | 0 | "as if saying it enough would wear a groove in the world" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.706 | | wordCount | 1417 | | matches | | 0 | "not forgiveness, nothing so tidy, but a slackening, like a rope let out an inch" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 104 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 29.52 | | std | 30.09 | | cv | 1.019 | | sampleLengths | | 0 | 126 | | 1 | 10 | | 2 | 66 | | 3 | 8 | | 4 | 36 | | 5 | 29 | | 6 | 28 | | 7 | 30 | | 8 | 113 | | 9 | 32 | | 10 | 5 | | 11 | 5 | | 12 | 8 | | 13 | 1 | | 14 | 31 | | 15 | 39 | | 16 | 35 | | 17 | 30 | | 18 | 5 | | 19 | 51 | | 20 | 2 | | 21 | 10 | | 22 | 23 | | 23 | 4 | | 24 | 4 | | 25 | 68 | | 26 | 33 | | 27 | 2 | | 28 | 34 | | 29 | 1 | | 30 | 50 | | 31 | 26 | | 32 | 117 | | 33 | 13 | | 34 | 1 | | 35 | 28 | | 36 | 4 | | 37 | 71 | | 38 | 28 | | 39 | 4 | | 40 | 45 | | 41 | 14 | | 42 | 3 | | 43 | 5 | | 44 | 55 | | 45 | 15 | | 46 | 55 | | 47 | 14 |
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| 95.65% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 73 | | matches | | 0 | "been allotted" | | 1 | "being asked" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 154 | | matches | | 0 | "was reading" | | 1 | "was smiling" | | 2 | "was going" | | 3 | "was holding" | | 4 | "was crying" | | 5 | "was studiously counting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 104 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 666 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.03153153153153153 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.006006006006006006 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 104 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 104 | | mean | 13.63 | | std | 11.5 | | cv | 0.844 | | sampleLengths | | 0 | 21 | | 1 | 30 | | 2 | 6 | | 3 | 10 | | 4 | 31 | | 5 | 28 | | 6 | 10 | | 7 | 22 | | 8 | 10 | | 9 | 10 | | 10 | 24 | | 11 | 8 | | 12 | 3 | | 13 | 3 | | 14 | 26 | | 15 | 4 | | 16 | 10 | | 17 | 13 | | 18 | 6 | | 19 | 16 | | 20 | 12 | | 21 | 30 | | 22 | 4 | | 23 | 15 | | 24 | 62 | | 25 | 9 | | 26 | 23 | | 27 | 10 | | 28 | 6 | | 29 | 16 | | 30 | 5 | | 31 | 5 | | 32 | 4 | | 33 | 4 | | 34 | 1 | | 35 | 21 | | 36 | 4 | | 37 | 6 | | 38 | 5 | | 39 | 26 | | 40 | 8 | | 41 | 35 | | 42 | 10 | | 43 | 10 | | 44 | 10 | | 45 | 5 | | 46 | 25 | | 47 | 7 | | 48 | 7 | | 49 | 8 |
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| 68.27% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4326923076923077 | | totalSentences | 104 | | uniqueOpeners | 45 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 67 | | matches | | 0 | "Then she looked up, and" | | 1 | "Then it folded away." | | 2 | "Somewhere in Bloomsbury, a bowl" | | 3 | "Then she touched Meg's hand," |
| | ratio | 0.06 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 67 | | matches | | 0 | "She followed it to the" | | 1 | "She was reading the label" | | 2 | "She stood too quickly and" | | 3 | "Her voice came out lighter" | | 4 | "They stood a moment." | | 5 | "She had said it constantly," | | 6 | "Her nails were clear-varnished and" | | 7 | "She sat like someone who" | | 8 | "she said, and took the" | | 9 | "She turned the tumbler a" | | 10 | "She did not pull her" | | 11 | "She'd stopped doing that a" | | 12 | "It was such an odd," | | 13 | "Her hand had found the" | | 14 | "She laughed again, and this" | | 15 | "It came out gentler than" | | 16 | "She turned the wineglass" | | 17 | "She didn't wipe at it." | | 18 | "she said thickly" | | 19 | "She stood, slung the bag" |
| | ratio | 0.299 | |
| 49.55% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 67 | | matches | | 0 | "The green neon over the" | | 1 | "Rory shouldered through the door" | | 2 | "Silas stood behind the bar" | | 3 | "That meant *the usual*, and" | | 4 | "She followed it to the" | | 5 | "The woman sitting there had" | | 6 | "A glass of something clear" | | 7 | "She was reading the label" | | 8 | "She stood too quickly and" | | 9 | "Rory set the bag down" | | 10 | "Her voice came out lighter" | | 11 | "Meg gave a short laugh" | | 12 | "Silas said mildly, to no" | | 13 | "They stood a moment." | | 14 | "Rory catalogued her, because it" | | 15 | "The Meg she'd known had" | | 16 | "That Meg had wanted to" | | 17 | "She had said it constantly," | | 18 | "This woman wore a wedding" | | 19 | "Her nails were clear-varnished and" |
| | ratio | 0.821 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 50.69% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 4 | | matches | | 0 | "The woman sitting there had her back half-turned, a charcoal coat folded over the neighboring stool as if it might be stolen." | | 1 | "She was reading the label on a bottle of tonic with the concentration of someone who wanted very much to be seen reading it." | | 2 | "Silas set a glass of red in front of her without being asked, and a second, smaller measure of something amber in front of Meg, who opened her mouth to refuse i…" | | 3 | "Meg sat alone with the sparkling water she'd abandoned and the whisky she hadn't, turning the glass a quarter turn, then another, as though it were a dial she m…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 1 | | fancyTags | | 0 | "She laughed again (laugh)" |
| | dialogueSentences | 53 | | tagDensity | 0.189 | | leniency | 0.377 | | rawRatio | 0.1 | | effectiveRatio | 0.038 | |