| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 2 | | adverbTags | | 0 | "Rory said quietly [quietly]" | | 1 | "Eva tossed back [back]" |
| | dialogueSentences | 50 | | tagDensity | 0.36 | | leniency | 0.72 | | rawRatio | 0.111 | | effectiveRatio | 0.08 | |
| 95.95% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1235 | | 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) | |
| 67.61% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1235 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "warmth" | | 1 | "silk" | | 2 | "chill" | | 3 | "shattered" | | 4 | "fractured" | | 5 | "whisper" | | 6 | "mechanical" | | 7 | "familiar" |
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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 | 83 | | matches | (empty) | |
| 91.22% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 116 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 33 | | totalWords | 1232 | | ratio | 0.027 | | matches | | 0 | "Do you know this woman? Is there danger?" | | 1 | "thank you, Eva, for the train ticket, for lying to your parents, for keeping Evan occupied while I packed" | | 2 | "I'm safe. Don't look for me." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 11.27% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 53 | | wordCount | 865 | | uniqueNames | 8 | | maxNameDensity | 2.77 | | worstName | "Rory" | | maxWindowNameDensity | 4 | | worstWindowName | "Rory" | | discoveredNames | | November | 1 | | Rory | 24 | | Cardiff | 1 | | Eva | 18 | | Prague | 1 | | Golden | 1 | | Empress | 1 | | Silas | 6 |
| | persons | | | places | | | globalScore | 0.113 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | 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 | 1232 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 116 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 54 | | mean | 22.81 | | std | 18.43 | | cv | 0.808 | | sampleLengths | | 0 | 36 | | 1 | 1 | | 2 | 33 | | 3 | 1 | | 4 | 60 | | 5 | 28 | | 6 | 4 | | 7 | 31 | | 8 | 16 | | 9 | 47 | | 10 | 4 | | 11 | 1 | | 12 | 4 | | 13 | 33 | | 14 | 48 | | 15 | 22 | | 16 | 48 | | 17 | 6 | | 18 | 7 | | 19 | 3 | | 20 | 25 | | 21 | 4 | | 22 | 5 | | 23 | 3 | | 24 | 36 | | 25 | 38 | | 26 | 7 | | 27 | 3 | | 28 | 3 | | 29 | 7 | | 30 | 19 | | 31 | 13 | | 32 | 24 | | 33 | 32 | | 34 | 9 | | 35 | 45 | | 36 | 3 | | 37 | 65 | | 38 | 27 | | 39 | 8 | | 40 | 38 | | 41 | 50 | | 42 | 5 | | 43 | 43 | | 44 | 9 | | 45 | 49 | | 46 | 19 | | 47 | 48 | | 48 | 27 | | 49 | 16 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 141 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 116 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 881 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.024971623155505107 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.011350737797956867 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 116 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 116 | | mean | 10.62 | | std | 7.14 | | cv | 0.672 | | sampleLengths | | 0 | 18 | | 1 | 18 | | 2 | 1 | | 3 | 7 | | 4 | 3 | | 5 | 23 | | 6 | 1 | | 7 | 9 | | 8 | 23 | | 9 | 14 | | 10 | 14 | | 11 | 6 | | 12 | 4 | | 13 | 10 | | 14 | 8 | | 15 | 4 | | 16 | 20 | | 17 | 11 | | 18 | 5 | | 19 | 9 | | 20 | 2 | | 21 | 15 | | 22 | 19 | | 23 | 13 | | 24 | 4 | | 25 | 1 | | 26 | 4 | | 27 | 2 | | 28 | 25 | | 29 | 6 | | 30 | 19 | | 31 | 18 | | 32 | 3 | | 33 | 5 | | 34 | 3 | | 35 | 6 | | 36 | 16 | | 37 | 15 | | 38 | 14 | | 39 | 11 | | 40 | 8 | | 41 | 6 | | 42 | 7 | | 43 | 3 | | 44 | 21 | | 45 | 4 | | 46 | 4 | | 47 | 5 | | 48 | 3 | | 49 | 7 |
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| 52.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.35344827586206895 | | totalSentences | 116 | | uniqueOpeners | 41 | |
| 88.89% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 75 | | matches | | 0 | "Only one person still called" | | 1 | "Somewhere in the kitchen, a" |
| | ratio | 0.027 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 75 | | matches | | 0 | "She wore a camel coat" | | 1 | "It wasn't a question." | | 2 | "She stepped inside, letting the" | | 3 | "It rang against the wood," | | 4 | "He carried himself like a" | | 5 | "His silver signet ring glinted" | | 6 | "he asked Eva" | | 7 | "She hadn't planned on drinking" | | 8 | "He slid the glasses across" | | 9 | "She shrugged off the coat," | | 10 | "It scraped against the quiet" | | 11 | "She pulled the fabric down." | | 12 | "Her eyes remained clear, bright," | | 13 | "She looked down at her" | | 14 | "His eyes remained on Eva," | | 15 | "She reached into her bag," | | 16 | "She withdrew a photograph and" |
| | ratio | 0.227 | |
| 26.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 75 | | matches | | 0 | "The door didn't swing open" | | 1 | "Rory looked up from the" | | 2 | "Rory's hand stalled." | | 3 | "The glass caught the light" | | 4 | "The woman in the doorway" | | 5 | "The wild auburn curls Rory" | | 6 | "She wore a camel coat" | | 7 | "It wasn't a question." | | 8 | "She stepped inside, letting the" | | 9 | "Eva approached the bar, her" | | 10 | "Rory set the glass down." | | 11 | "It rang against the wood," | | 12 | "Silas emerged from the back" | | 13 | "He carried himself like a" | | 14 | "His silver signet ring glinted" | | 15 | "he asked Eva" | | 16 | "She hadn't planned on drinking" | | 17 | "Silas poured two measures with" | | 18 | "He slid the glasses across" | | 19 | "A silent question." |
| | ratio | 0.867 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 1 | | matches | | 0 | "She wore a camel coat that cost more than three months of Rory's rent." |
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| 69.44% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 2 | | matches | | 0 | "Rory gestured, the hair, the veneer of success" | | 1 | "Eva touched, but her fingers found only skin" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 50 | | tagDensity | 0.18 | | leniency | 0.36 | | rawRatio | 0 | | effectiveRatio | 0 | |