| 88.89% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 2 | | adverbTags | | 0 | "her thumb pressed hard [hard]" | | 1 | "Eva asked suddenly [suddenly]" |
| | dialogueSentences | 36 | | tagDensity | 0.389 | | leniency | 0.778 | | rawRatio | 0.143 | | effectiveRatio | 0.111 | |
| 85.15% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1010 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "very" | | 1 | "suddenly" | | 2 | "lightly" |
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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) | |
| 60.40% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1010 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "unreadable" | | 1 | "lilt" | | 2 | "unsettled" | | 3 | "efficient" | | 4 | "wavered" | | 5 | "warmth" | | 6 | "familiar" | | 7 | "flickered" |
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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 | 1 | | narrationSentences | 55 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 55 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 76 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 16 | | totalWords | 1010 | | ratio | 0.016 | | matches | | 0 | "go, just go, I'll send you the money, I'll tell them you've gone to your aunt's." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 755 | | uniqueNames | 15 | | maxNameDensity | 2.52 | | worstName | "Eva" | | maxWindowNameDensity | 5 | | worstWindowName | "Rory" | | discoveredNames | | Nest | 1 | | Aurora | 1 | | Golden | 1 | | Empress | 1 | | Thames | 1 | | Rory | 17 | | Cardiff | 2 | | Bay | 1 | | Eva | 19 | | Welsh | 2 | | London-polished | 1 | | Evan | 1 | | Big | 1 | | Ben | 1 | | Silas | 2 |
| | persons | | 0 | "Aurora" | | 1 | "Rory" | | 2 | "Eva" | | 3 | "Evan" | | 4 | "Big" | | 5 | "Ben" | | 6 | "Silas" |
| | places | | 0 | "Nest" | | 1 | "Thames" | | 2 | "Cardiff" | | 3 | "Bay" | | 4 | "London-polished" |
| | globalScore | 0.242 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | 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 | 1010 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 76 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 26.58 | | std | 25.53 | | cv | 0.961 | | sampleLengths | | 0 | 80 | | 1 | 81 | | 2 | 12 | | 3 | 81 | | 4 | 16 | | 5 | 37 | | 6 | 1 | | 7 | 34 | | 8 | 41 | | 9 | 13 | | 10 | 61 | | 11 | 5 | | 12 | 3 | | 13 | 8 | | 14 | 2 | | 15 | 12 | | 16 | 22 | | 17 | 5 | | 18 | 2 | | 19 | 101 | | 20 | 8 | | 21 | 30 | | 22 | 4 | | 23 | 1 | | 24 | 50 | | 25 | 31 | | 26 | 28 | | 27 | 4 | | 28 | 38 | | 29 | 5 | | 30 | 32 | | 31 | 14 | | 32 | 41 | | 33 | 13 | | 34 | 2 | | 35 | 35 | | 36 | 30 | | 37 | 27 |
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| 92.50% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 55 | | matches | | 0 | "been sanded" | | 1 | "was surprised" |
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| 99.75% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 133 | | matches | | 0 | "was trying" | | 1 | "was choosing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 76 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 759 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.03689064558629776 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.010540184453227932 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 76 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 76 | | mean | 13.29 | | std | 8.72 | | cv | 0.656 | | sampleLengths | | 0 | 30 | | 1 | 6 | | 2 | 23 | | 3 | 21 | | 4 | 9 | | 5 | 27 | | 6 | 18 | | 7 | 15 | | 8 | 12 | | 9 | 12 | | 10 | 24 | | 11 | 26 | | 12 | 31 | | 13 | 7 | | 14 | 9 | | 15 | 6 | | 16 | 19 | | 17 | 12 | | 18 | 1 | | 19 | 17 | | 20 | 17 | | 21 | 11 | | 22 | 27 | | 23 | 3 | | 24 | 4 | | 25 | 9 | | 26 | 2 | | 27 | 6 | | 28 | 22 | | 29 | 17 | | 30 | 14 | | 31 | 5 | | 32 | 3 | | 33 | 4 | | 34 | 4 | | 35 | 2 | | 36 | 12 | | 37 | 3 | | 38 | 19 | | 39 | 5 | | 40 | 2 | | 41 | 6 | | 42 | 30 | | 43 | 26 | | 44 | 17 | | 45 | 8 | | 46 | 14 | | 47 | 8 | | 48 | 19 | | 49 | 11 |
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| 62.28% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.42105263157894735 | | totalSentences | 76 | | uniqueOpeners | 32 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 50 | | matches | | 0 | "Instead she stood with her" | | 1 | "Then Eva stood, the chair" | | 2 | "Only her vowels gave her" |
| | ratio | 0.06 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 50 | | matches | | 0 | "She had meant to walk" | | 1 | "She had meant to go" | | 2 | "He glanced up, saw her," | | 3 | "He tilted his head toward" | | 4 | "She turned to follow his" | | 5 | "Her hair was cut short" | | 6 | "She wore a camel coat" | | 7 | "Her fingers found the small" | | 8 | "She noticed too that Eva" | | 9 | "She had sent one postcard," | | 10 | "They sat with that for" |
| | ratio | 0.22 | |
| 40.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 42 | | totalSentences | 50 | | matches | | 0 | "The green neon from the" | | 1 | "She had meant to walk" | | 2 | "She had meant to go" | | 3 | "The old maps on the" | | 4 | "Silas was behind the bar," | | 5 | "He glanced up, saw her," | | 6 | "He tilted his head toward" | | 7 | "She turned to follow his" | | 8 | "Eva sat at the small" | | 9 | "Her hair was cut short" | | 10 | "She wore a camel coat" | | 11 | "The Welsh lilt had been" | | 12 | "Rory heard it and felt" | | 13 | "Eva laughed, a short sound" | | 14 | "Rory set the paper bag" | | 15 | "Her fingers found the small" | | 16 | "Eva's eyebrows lifted" | | 17 | "The wine glass sweated between" | | 18 | "Rory noticed Eva's hands, the" | | 19 | "She noticed too that Eva" |
| | ratio | 0.84 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 3 | | matches | | 0 | "The Welsh lilt had been sanded down to something London-polished, something that had learned to sound unhurried in boardrooms." | | 1 | "Rory remembered the last time they had sat across from each other like this, in a Cardiff kitchen with the radio murmuring and Evan's car pulling into the stree…" | | 2 | "Across the room, the green neon flickered, and somewhere under the floorboards the old pipes knocked twice, as if the building itself had decided to speak up." |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 2 | | matches | | 0 | "Eva stood, the chair scraping, and said," | | 1 | "Eva looked up, eyes bright" |
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| 94.44% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 2 | | fancyTags | | 0 | "Eva laughed (laugh)" | | 1 | "her thumb pressed hard (press)" |
| | dialogueSentences | 36 | | tagDensity | 0.194 | | leniency | 0.389 | | rawRatio | 0.286 | | effectiveRatio | 0.111 | |