| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 30 | | adverbTagCount | 2 | | adverbTags | | 0 | "Eva started then [then]" | | 1 | "Rory said again [again]" |
| | dialogueSentences | 72 | | tagDensity | 0.417 | | leniency | 0.833 | | rawRatio | 0.067 | | effectiveRatio | 0.056 | |
| 88.54% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1309 | | totalAiIsmAdverbs | 3 | | 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) | |
| 84.72% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1309 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "flicker" | | 1 | "chill" | | 2 | "comfortable" | | 3 | "scanning" |
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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 | 94 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 94 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 135 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1305 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 30 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 67 | | wordCount | 929 | | uniqueNames | 8 | | maxNameDensity | 3.01 | | worstName | "Eva" | | maxWindowNameDensity | 5 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 3 | | Nest | 3 | | Rory | 22 | | October | 1 | | Eva | 28 | | Walsh | 2 | | Silas | 6 | | Cardiff | 2 |
| | persons | | 0 | "Nest" | | 1 | "Rory" | | 2 | "October" | | 3 | "Eva" | | 4 | "Walsh" | | 5 | "Silas" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.766 | | wordCount | 1305 | | matches | | 0 | "not with tears exactly, but with the effort of not having them" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 135 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 80 | | mean | 16.31 | | std | 16.29 | | cv | 0.999 | | sampleLengths | | 0 | 26 | | 1 | 52 | | 2 | 1 | | 3 | 6 | | 4 | 5 | | 5 | 59 | | 6 | 12 | | 7 | 13 | | 8 | 6 | | 9 | 71 | | 10 | 8 | | 11 | 3 | | 12 | 6 | | 13 | 3 | | 14 | 55 | | 15 | 14 | | 16 | 2 | | 17 | 35 | | 18 | 19 | | 19 | 5 | | 20 | 15 | | 21 | 5 | | 22 | 10 | | 23 | 50 | | 24 | 8 | | 25 | 28 | | 26 | 3 | | 27 | 5 | | 28 | 9 | | 29 | 16 | | 30 | 4 | | 31 | 24 | | 32 | 2 | | 33 | 5 | | 34 | 8 | | 35 | 43 | | 36 | 6 | | 37 | 7 | | 38 | 11 | | 39 | 3 | | 40 | 1 | | 41 | 7 | | 42 | 34 | | 43 | 68 | | 44 | 6 | | 45 | 3 | | 46 | 11 | | 47 | 23 | | 48 | 3 | | 49 | 28 |
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| 90.33% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 94 | | matches | | 0 | "were gone" | | 1 | "were drawn" | | 2 | "was gone" | | 3 | "being asked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 160 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 135 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 933 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.028938906752411574 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.012861736334405145 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 135 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 135 | | mean | 9.67 | | std | 7.69 | | cv | 0.796 | | sampleLengths | | 0 | 20 | | 1 | 6 | | 2 | 20 | | 3 | 15 | | 4 | 5 | | 5 | 12 | | 6 | 1 | | 7 | 6 | | 8 | 4 | | 9 | 1 | | 10 | 9 | | 11 | 17 | | 12 | 17 | | 13 | 16 | | 14 | 12 | | 15 | 13 | | 16 | 4 | | 17 | 2 | | 18 | 27 | | 19 | 11 | | 20 | 10 | | 21 | 23 | | 22 | 8 | | 23 | 3 | | 24 | 3 | | 25 | 3 | | 26 | 3 | | 27 | 22 | | 28 | 12 | | 29 | 14 | | 30 | 7 | | 31 | 8 | | 32 | 6 | | 33 | 2 | | 34 | 28 | | 35 | 7 | | 36 | 4 | | 37 | 3 | | 38 | 12 | | 39 | 5 | | 40 | 11 | | 41 | 4 | | 42 | 5 | | 43 | 7 | | 44 | 3 | | 45 | 6 | | 46 | 44 | | 47 | 8 | | 48 | 19 | | 49 | 9 |
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| 50.86% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.362962962962963 | | totalSentences | 135 | | uniqueOpeners | 49 | |
| 43.29% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 77 | | matches | | 0 | "Somewhere behind the wall of" |
| | ratio | 0.013 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 77 | | matches | | 0 | "She caught the woman staring" | | 1 | "She'd lost the gap between" | | 2 | "She wore the kind of" | | 3 | "They stood on the pavement" | | 4 | "He glanced up as they" | | 5 | "He moved back to his" | | 6 | "He knew when not to" | | 7 | "They hadn't spoken in fourteen" | | 8 | "She turned her glass." | | 9 | "She pulled her sleeve down." | | 10 | "She used to say it" | | 11 | "She wasn't saying it like" | | 12 | "Her eyes were bright, not" | | 13 | "she said, she stopped" | | 14 | "He found Eva at the" |
| | ratio | 0.195 | |
| 11.95% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 77 | | matches | | 0 | "The green neon sign outside" | | 1 | "Tonight, though, someone else noticed" | | 2 | "She caught the woman staring" | | 3 | "Rory nearly walked past her." | | 4 | "The name landed like a" | | 5 | "Rory stopped dead" | | 6 | "Eva Walsh looked the same" | | 7 | "The wild copper curls were" | | 8 | "She'd lost the gap between" | | 9 | "She wore the kind of" | | 10 | "They stood on the pavement" | | 11 | "Eva started, then gestured vaguely" | | 12 | "The Raven's Nest smelled the" | | 13 | "The old maps on the" | | 14 | "A couple in the corner" | | 15 | "He glanced up as they" | | 16 | "Silas set down the glass" | | 17 | "He moved back to his" | | 18 | "That was one of the" | | 19 | "He knew when not to" |
| | ratio | 0.896 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 8.93% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 6 | | matches | | 0 | "She'd lost the gap between her front teeth, which meant she'd finally caved to her mother's orthodontist." | | 1 | "She wore the kind of quiet, expensive clothes that said she'd stopped needing to be noticed." | | 2 | "At the far end of the bar, Silas polished a glass with the slow, deliberate patience of a man who had seen everything." | | 3 | "Rory knew the number because it was the same number of months since she'd left Cardiff, and Eva was the reason she'd left Cardiff, and sometimes those two facts…" | | 4 | "The couple in the corner finally started talking, low voices, something that had the shape of an argument about nothing, the kind of argument that was really ab…" | | 5 | "Rory looked at her oldest friend, this woman with borrowed composure and good shoes and a life that had moved forward without her, and felt something complicate…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 30 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 19 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 72 | | tagDensity | 0.264 | | leniency | 0.528 | | rawRatio | 0.053 | | effectiveRatio | 0.028 | |