| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 190 | | tagDensity | 0.074 | | leniency | 0.147 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.88% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2358 | | 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) | |
| 91.52% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2358 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "stomach" | | 1 | "weight" | | 2 | "silence" |
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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 | 0 | | narrationSentences | 162 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 162 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 338 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2358 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 42 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 101 | | wordCount | 1377 | | uniqueNames | 8 | | maxNameDensity | 3.12 | | worstName | "Eva" | | maxWindowNameDensity | 6 | | worstWindowName | "Eva" | | discoveredNames | | Nest | 2 | | Soho | 1 | | Greek | 1 | | Street | 1 | | London | 2 | | Aurora | 39 | | Eva | 43 | | Silas | 12 |
| | persons | | | places | | 0 | "Soho" | | 1 | "Greek" | | 2 | "Street" | | 3 | "London" |
| | globalScore | 0 | | windowScore | 0 | |
| 99.49% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 99 | | glossingSentenceCount | 2 | | matches | | 0 | "sounded like a dare and a punishment" | | 1 | "as if expecting someone else to come through it" |
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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 | 2358 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 338 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 266 | | mean | 8.86 | | std | 11.75 | | cv | 1.326 | | sampleLengths | | 0 | 22 | | 1 | 77 | | 2 | 40 | | 3 | 4 | | 4 | 2 | | 5 | 4 | | 6 | 5 | | 7 | 7 | | 8 | 1 | | 9 | 3 | | 10 | 12 | | 11 | 5 | | 12 | 8 | | 13 | 44 | | 14 | 62 | | 15 | 8 | | 16 | 2 | | 17 | 25 | | 18 | 1 | | 19 | 15 | | 20 | 1 | | 21 | 64 | | 22 | 9 | | 23 | 76 | | 24 | 4 | | 25 | 7 | | 26 | 4 | | 27 | 10 | | 28 | 16 | | 29 | 5 | | 30 | 3 | | 31 | 40 | | 32 | 8 | | 33 | 8 | | 34 | 3 | | 35 | 7 | | 36 | 2 | | 37 | 19 | | 38 | 9 | | 39 | 4 | | 40 | 2 | | 41 | 1 | | 42 | 29 | | 43 | 6 | | 44 | 4 | | 45 | 7 | | 46 | 11 | | 47 | 14 | | 48 | 24 | | 49 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 162 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 254 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 338 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1385 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.023104693140794223 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.001444043321299639 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 338 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 338 | | mean | 6.98 | | std | 5.83 | | cv | 0.836 | | sampleLengths | | 0 | 22 | | 1 | 20 | | 2 | 9 | | 3 | 16 | | 4 | 18 | | 5 | 4 | | 6 | 10 | | 7 | 17 | | 8 | 8 | | 9 | 15 | | 10 | 4 | | 11 | 2 | | 12 | 4 | | 13 | 5 | | 14 | 7 | | 15 | 1 | | 16 | 3 | | 17 | 12 | | 18 | 5 | | 19 | 8 | | 20 | 16 | | 21 | 11 | | 22 | 17 | | 23 | 13 | | 24 | 21 | | 25 | 9 | | 26 | 13 | | 27 | 6 | | 28 | 8 | | 29 | 2 | | 30 | 11 | | 31 | 3 | | 32 | 11 | | 33 | 1 | | 34 | 15 | | 35 | 1 | | 36 | 9 | | 37 | 10 | | 38 | 12 | | 39 | 24 | | 40 | 9 | | 41 | 9 | | 42 | 10 | | 43 | 16 | | 44 | 4 | | 45 | 7 | | 46 | 13 | | 47 | 26 | | 48 | 4 | | 49 | 7 |
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| 44.97% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.20414201183431951 | | totalSentences | 338 | | uniqueOpeners | 69 | |
| 45.98% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 145 | | matches | | 0 | "Then the woman by the" | | 1 | "Then Eva’s smile fell." |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 145 | | matches | | 0 | "His grey-streaked auburn hair caught" | | 1 | "He glanced at the empty" | | 2 | "She crossed towards the counter," | | 3 | "Her wineglass tipped." | | 4 | "It spun across the wood" | | 5 | "They were seventeen, sitting on" | | 6 | "Her face had narrowed." | | 7 | "She wore a charcoal suit" | | 8 | "His eyes moved between them," | | 9 | "She had always been easy" | | 10 | "Her eyebrows rose before she" | | 11 | "Her left shoulder lifted when" | | 12 | "She knew them still." | | 13 | "He placed a towel beside" | | 14 | "He filled a glass and" | | 15 | "she told him" | | 16 | "He returned to the bar," | | 17 | "She kept both hands around" | | 18 | "Her nails were short, unpainted," | | 19 | "It rose like a match" |
| | ratio | 0.2 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 137 | | totalSentences | 145 | | matches | | 0 | "The green neon raven above" | | 1 | "Aurora pushed through the entrance" | | 2 | "The smell of wet pavement" | | 3 | "Yu-Fei had sent her across" | | 4 | "Aurora had delivered two." | | 5 | "The third had vanished into" | | 6 | "Silas stood behind the bar," | | 7 | "His grey-streaked auburn hair caught" | | 8 | "The silver signet ring on" | | 9 | "He glanced at the empty" | | 10 | "Aurora hung her coat beside" | | 11 | "Rain had darkened the ends," | | 12 | "She crossed towards the counter," | | 13 | "A woman in a red" | | 14 | "The usual music murmured from" | | 15 | "Maps covered the walls: borders," | | 16 | "The place held its shadows" | | 17 | "The woman stood too quickly," | | 18 | "Her wineglass tipped." | | 19 | "It spun across the wood" |
| | ratio | 0.945 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 145 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 1 | | matches | | 0 | "She stood beneath the black-and-white photograph, wearing a suit that looked borrowed from a life she had outgrown." |
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| 89.29% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 1 | | matches | | 0 | "Eva repeated, and the words broke apart in her mouth" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 3 | | fancyTags | | 0 | "Eva murmured (murmur)" | | 1 | "she added (add)" | | 2 | "Eva repeated (repeat)" |
| | dialogueSentences | 190 | | tagDensity | 0.074 | | leniency | 0.147 | | rawRatio | 0.214 | | effectiveRatio | 0.032 | |