| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva's thumb moved once [once]" |
| | dialogueSentences | 52 | | tagDensity | 0.365 | | leniency | 0.731 | | rawRatio | 0.053 | | effectiveRatio | 0.038 | |
| 95.88% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1215 | | totalAiIsmAdverbs | 1 | | 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) | |
| 100.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1215 | | totalAiIsms | 0 | | found | (empty) | | highlights | (empty) | |
| 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 | 63 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 63 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 94 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1215 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 30 | | unquotedAttributions | 0 | | matches | (empty) | |
| 29.43% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 705 | | uniqueNames | 10 | | maxNameDensity | 2.41 | | worstName | "Eva" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Eva" | | discoveredNames | | Aurora | 1 | | Carter | 1 | | Holborn | 1 | | Raven | 1 | | Nest | 1 | | Eva | 17 | | Marsh | 1 | | Rory | 16 | | Silas | 3 | | Llandaff | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Carter" | | 2 | "Raven" | | 3 | "Eva" | | 4 | "Rory" | | 5 | "Silas" |
| | places | | | globalScore | 0.294 | | windowScore | 0.5 | |
| 80.56% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 36 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like, whether the sparkling water" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1215 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 94 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 23.82 | | std | 22.4 | | cv | 0.94 | | sampleLengths | | 0 | 58 | | 1 | 23 | | 2 | 10 | | 3 | 41 | | 4 | 6 | | 5 | 15 | | 6 | 4 | | 7 | 9 | | 8 | 66 | | 9 | 19 | | 10 | 26 | | 11 | 4 | | 12 | 10 | | 13 | 4 | | 14 | 50 | | 15 | 8 | | 16 | 4 | | 17 | 37 | | 18 | 5 | | 19 | 20 | | 20 | 51 | | 21 | 5 | | 22 | 65 | | 23 | 7 | | 24 | 51 | | 25 | 13 | | 26 | 4 | | 27 | 7 | | 28 | 78 | | 29 | 40 | | 30 | 7 | | 31 | 6 | | 32 | 3 | | 33 | 10 | | 34 | 27 | | 35 | 51 | | 36 | 27 | | 37 | 12 | | 38 | 62 | | 39 | 8 | | 40 | 46 | | 41 | 3 | | 42 | 30 | | 43 | 54 | | 44 | 5 | | 45 | 3 | | 46 | 3 | | 47 | 80 | | 48 | 10 | | 49 | 16 |
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| 94.12% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 63 | | matches | | 0 | "being asked" | | 1 | "was cropped" |
| |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 125 | | matches | | 0 | "were holding" | | 1 | "was filling" | | 2 | "was holding" | | 3 | "was telling" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 94 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 707 | | adjectiveStacks | 1 | | stackExamples | | 0 | "same bruised grey they" |
| | adverbCount | 18 | | adverbRatio | 0.02545968882602546 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.008486562942008486 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 94 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 94 | | mean | 12.93 | | std | 10.38 | | cv | 0.803 | | sampleLengths | | 0 | 33 | | 1 | 25 | | 2 | 3 | | 3 | 20 | | 4 | 4 | | 5 | 6 | | 6 | 5 | | 7 | 16 | | 8 | 20 | | 9 | 6 | | 10 | 6 | | 11 | 9 | | 12 | 4 | | 13 | 9 | | 14 | 7 | | 15 | 28 | | 16 | 2 | | 17 | 7 | | 18 | 22 | | 19 | 7 | | 20 | 12 | | 21 | 19 | | 22 | 4 | | 23 | 3 | | 24 | 4 | | 25 | 10 | | 26 | 4 | | 27 | 11 | | 28 | 39 | | 29 | 5 | | 30 | 3 | | 31 | 4 | | 32 | 8 | | 33 | 29 | | 34 | 5 | | 35 | 20 | | 36 | 6 | | 37 | 16 | | 38 | 23 | | 39 | 6 | | 40 | 5 | | 41 | 25 | | 42 | 40 | | 43 | 7 | | 44 | 31 | | 45 | 10 | | 46 | 10 | | 47 | 5 | | 48 | 8 | | 49 | 4 |
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| 53.90% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.39361702127659576 | | totalSentences | 94 | | uniqueOpeners | 37 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 52 | | matches | | 0 | "Then she reached across the" | | 1 | "Instead she said," |
| | ratio | 0.038 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 52 | | matches | | 0 | "She stopped under the green" | | 1 | "She folded the umbrella with" | | 2 | "He didn't go far." | | 3 | "He never did." | | 4 | "He lifted one hand from" | | 5 | "Her wrist, the one with" | | 6 | "She had not realised she" | | 7 | "He looked at Eva, then" | | 8 | "He moved off again before" | | 9 | "She wanted to ask what" | | 10 | "She wanted to ask whether" | | 11 | "she said, shaking her head" |
| | ratio | 0.231 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 48 | | totalSentences | 52 | | matches | | 0 | "The rain had followed Aurora" | | 1 | "She stopped under the green" | | 2 | "The woman turned." | | 3 | "The face was older, sharper" | | 4 | "Rory didn't answer right away." | | 5 | "She folded the umbrella with" | | 6 | "Silas glanced up from the" | | 7 | "Rory slid onto the stool" | | 8 | "A thin silver chain at" | | 9 | "Rory noticed the lack of" | | 10 | "Eva said, following her gaze" | | 11 | "Silas set a whisky down" | | 12 | "He didn't go far." | | 13 | "He never did." | | 14 | "Eva said it without heat," | | 15 | "The whisky burned going down." | | 16 | "Rory let it." | | 17 | "A brief, tight smile" | | 18 | "The bar was filling behind" | | 19 | "Someone laughed too loudly near" |
| | ratio | 0.923 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 52 | | matches | (empty) | | ratio | 0 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 2 | | matches | | 0 | "Up close, Eva's hair was cropped short and shot through with grey, and her hands rested flat on the wood as though she were holding the bar down." | | 1 | "Rory studied the photographs on the wall above the optics, black-and-white faces she didn't know, a harbour in some century that wasn't theirs." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 52 | | tagDensity | 0.269 | | leniency | 0.538 | | rawRatio | 0 | | effectiveRatio | 0 | |