| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 36 | | adverbTagCount | 3 | | adverbTags | | 0 | "She looked like [like]" | | 1 | "Rory drank again [again]" | | 2 | "Nia’s voice cracked just [just]" |
| | dialogueSentences | 85 | | tagDensity | 0.424 | | leniency | 0.847 | | rawRatio | 0.083 | | effectiveRatio | 0.071 | |
| 95.37% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2159 | | totalAiIsmAdverbs | 2 | | 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) | |
| 90.74% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2159 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "could feel" | | 2 | "coded" | | 3 | "flickered" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "let out a breath" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 149 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 149 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 195 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 21 | | totalWords | 2151 | | ratio | 0.01 | | matches | | 0 | "We’re going to be brilliant, you and me. We’ll be so brilliant they’ll name a building after us." | | 1 | "Come. Just come." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 31 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 92 | | wordCount | 1519 | | uniqueNames | 16 | | maxNameDensity | 2.57 | | worstName | "Nia" | | maxWindowNameDensity | 6 | | worstWindowName | "Nia" | | discoveredNames | | Rory | 31 | | Dean | 1 | | Street | 1 | | Raven | 1 | | Nest | 1 | | Golden | 1 | | Empress | 1 | | Silas | 8 | | Nia | 39 | | Prague | 1 | | Richmond | 1 | | Road | 1 | | Evan | 2 | | London | 1 | | Eva | 1 | | Cardiff | 1 |
| | persons | | 0 | "Rory" | | 1 | "Raven" | | 2 | "Empress" | | 3 | "Silas" | | 4 | "Nia" | | 5 | "Evan" | | 6 | "Eva" |
| | places | | 0 | "Dean" | | 1 | "Street" | | 2 | "Prague" | | 3 | "Richmond" | | 4 | "Road" | | 5 | "London" | | 6 | "Cardiff" |
| | globalScore | 0.216 | | windowScore | 0 | |
| 63.79% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 87 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like she had stepped out of a meet" | | 1 | "looked like she had never in her life bee" | | 2 | "felt like walking uphill with stones in" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 2151 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 195 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 75 | | mean | 28.68 | | std | 31.01 | | cv | 1.081 | | sampleLengths | | 0 | 75 | | 1 | 7 | | 2 | 82 | | 3 | 10 | | 4 | 65 | | 5 | 54 | | 6 | 1 | | 7 | 82 | | 8 | 13 | | 9 | 40 | | 10 | 5 | | 11 | 74 | | 12 | 8 | | 13 | 38 | | 14 | 29 | | 15 | 30 | | 16 | 19 | | 17 | 65 | | 18 | 11 | | 19 | 1 | | 20 | 25 | | 21 | 3 | | 22 | 12 | | 23 | 16 | | 24 | 5 | | 25 | 6 | | 26 | 100 | | 27 | 9 | | 28 | 2 | | 29 | 64 | | 30 | 47 | | 31 | 22 | | 32 | 2 | | 33 | 12 | | 34 | 2 | | 35 | 40 | | 36 | 4 | | 37 | 64 | | 38 | 21 | | 39 | 3 | | 40 | 5 | | 41 | 9 | | 42 | 46 | | 43 | 63 | | 44 | 5 | | 45 | 23 | | 46 | 94 | | 47 | 26 | | 48 | 3 | | 49 | 31 |
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| 93.49% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 149 | | matches | | 0 | "was surprised" | | 1 | "been taught" | | 2 | "was scored" | | 3 | "were stuck" | | 4 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 278 | | matches | | 0 | "was still trying" | | 1 | "was making" | | 2 | "was presenting" |
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| 84.25% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 195 | | ratio | 0.021 | | matches | | 0 | "A woman stepped in, shaking out a black umbrella, and for a moment Rory only saw the coat—good wool, well cut, the kind of thing you wore when you wanted people to take you seriously." | | 1 | "She came forward, and Rory saw her shoes—polished leather, low heels—and the leather satchel over her shoulder." | | 2 | "The bar’s old maps hung behind him, their edges curled, and one of the black-and-white photographs—Prague, Rory thought, though she had never asked—showed a bridge in fog." | | 3 | "There was so much Rory wanted to say—about Evan, about the flat, about the years, about how she had missed Nia more than she had let herself admit." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 880 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.03409090909090909 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.005681818181818182 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 195 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 195 | | mean | 11.03 | | std | 8.98 | | cv | 0.814 | | sampleLengths | | 0 | 30 | | 1 | 24 | | 2 | 17 | | 3 | 4 | | 4 | 7 | | 5 | 34 | | 6 | 8 | | 7 | 7 | | 8 | 33 | | 9 | 10 | | 10 | 3 | | 11 | 18 | | 12 | 7 | | 13 | 5 | | 14 | 28 | | 15 | 4 | | 16 | 9 | | 17 | 35 | | 18 | 10 | | 19 | 1 | | 20 | 3 | | 21 | 8 | | 22 | 37 | | 23 | 20 | | 24 | 6 | | 25 | 8 | | 26 | 3 | | 27 | 5 | | 28 | 5 | | 29 | 13 | | 30 | 27 | | 31 | 5 | | 32 | 19 | | 33 | 17 | | 34 | 10 | | 35 | 28 | | 36 | 8 | | 37 | 23 | | 38 | 13 | | 39 | 2 | | 40 | 6 | | 41 | 13 | | 42 | 3 | | 43 | 7 | | 44 | 9 | | 45 | 14 | | 46 | 7 | | 47 | 11 | | 48 | 8 | | 49 | 5 |
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| 36.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 26 | | diversityRatio | 0.24102564102564103 | | totalSentences | 195 | | uniqueOpeners | 47 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 121 | | matches | | 0 | "Then the door opened." | | 1 | "Then the woman turned, and" | | 2 | "Just come.* She thought about" | | 3 | "Then she reached into her" | | 4 | "Then she was gone, and" |
| | ratio | 0.041 | |
| 77.85% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 121 | | matches | | 0 | "He didn’t look up." | | 1 | "She had left the radiator" | | 2 | "She slid onto the stool" | | 3 | "It was Nia." | | 4 | "Her eyes were the same," | | 5 | "Her voice came out flat." | | 6 | "She put the glass down." | | 7 | "She came forward, and Rory" | | 8 | "She looked like she had" | | 9 | "She looked like she had" | | 10 | "He looked at Nia, then" | | 11 | "It wasn’t a question." | | 12 | "She had changed in more" | | 13 | "She remembered Nia saying, *We’re" | | 14 | "We’ll be so brilliant they’ll" | | 15 | "Her thumb moved along the" | | 16 | "She could feel the scar" | | 17 | "It was old, childhood, a" | | 18 | "She let out a breath" | | 19 | "He limped slightly, the old" |
| | ratio | 0.355 | |
| 38.51% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 102 | | totalSentences | 121 | | matches | | 0 | "The rain had followed Rory" | | 1 | "The green neon sign outside" | | 2 | "Silas was behind the counter," | | 3 | "He didn’t look up." | | 4 | "Rory peeled off her jacket," | | 5 | "The flat above the bar" | | 6 | "She had left the radiator" | | 7 | "She slid onto the stool" | | 8 | "The stout was dark and" | | 9 | "The bar was almost empty." | | 10 | "The bell above it gave" | | 11 | "A woman stepped in, shaking" | | 12 | "It was Nia." | | 13 | "The Nia Rory remembered had" | | 14 | "This woman’s hair was dark" | | 15 | "Her eyes were the same," | | 16 | "Her voice came out flat." | | 17 | "She put the glass down." | | 18 | "Nia stood just inside the" | | 19 | "Nia laughed, but it was" |
| | ratio | 0.843 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 121 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 1 | | matches | | 0 | "Silas was behind the counter, polishing a glass with a cloth that had gone soft with age." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 36 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 20 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 85 | | tagDensity | 0.235 | | leniency | 0.471 | | rawRatio | 0.05 | | effectiveRatio | 0.024 | |