| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 2 | | adverbTags | | 0 | "She glanced around [around]" | | 1 | "Silas said quietly [quietly]" |
| | dialogueSentences | 50 | | tagDensity | 0.32 | | leniency | 0.64 | | rawRatio | 0.125 | | effectiveRatio | 0.08 | |
| 78.39% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1157 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "very" | | 1 | "suddenly" | | 2 | "slightly" |
| |
| 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) | |
| 52.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1157 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "crystal" | | 1 | "silk" | | 2 | "weight" | | 3 | "silence" | | 4 | "eyebrow" | | 5 | "tracing" | | 6 | "whisper" | | 7 | "calculating" | | 8 | "footsteps" |
| |
| 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 | 55 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 55 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 89 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 58 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1157 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 78.57% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 630 | | uniqueNames | 8 | | maxNameDensity | 1.43 | | worstName | "Carys" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Silas" | | discoveredNames | | Hughes | 1 | | Silas | 6 | | Blackwood | 1 | | Carys | 9 | | Cardiff | 1 | | University | 1 | | Evan | 1 | | Mercedes | 1 |
| | persons | | 0 | "Hughes" | | 1 | "Silas" | | 2 | "Blackwood" | | 3 | "Carys" | | 4 | "Evan" | | 5 | "Mercedes" |
| | places | | | globalScore | 0.786 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 47 | | 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 | 1157 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 89 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 19.95 | | std | 18.74 | | cv | 0.94 | | sampleLengths | | 0 | 13 | | 1 | 8 | | 2 | 13 | | 3 | 30 | | 4 | 10 | | 5 | 50 | | 6 | 1 | | 7 | 42 | | 8 | 2 | | 9 | 37 | | 10 | 9 | | 11 | 13 | | 12 | 77 | | 13 | 8 | | 14 | 13 | | 15 | 32 | | 16 | 12 | | 17 | 14 | | 18 | 15 | | 19 | 11 | | 20 | 51 | | 21 | 3 | | 22 | 11 | | 23 | 36 | | 24 | 12 | | 25 | 7 | | 26 | 8 | | 27 | 4 | | 28 | 68 | | 29 | 5 | | 30 | 9 | | 31 | 11 | | 32 | 12 | | 33 | 11 | | 34 | 36 | | 35 | 9 | | 36 | 9 | | 37 | 2 | | 38 | 60 | | 39 | 4 | | 40 | 3 | | 41 | 14 | | 42 | 14 | | 43 | 15 | | 44 | 50 | | 45 | 73 | | 46 | 3 | | 47 | 44 | | 48 | 18 | | 49 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 55 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 92 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 89 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 634 | | adjectiveStacks | 2 | | stackExamples | | 0 | "razor-sharp against her" | | 1 | "tall broad-shouldered men" |
| | adverbCount | 9 | | adverbRatio | 0.014195583596214511 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.007886435331230283 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 89 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 89 | | mean | 13 | | std | 8.86 | | cv | 0.682 | | sampleLengths | | 0 | 13 | | 1 | 8 | | 2 | 13 | | 3 | 10 | | 4 | 20 | | 5 | 10 | | 6 | 23 | | 7 | 8 | | 8 | 6 | | 9 | 13 | | 10 | 1 | | 11 | 19 | | 12 | 23 | | 13 | 2 | | 14 | 26 | | 15 | 11 | | 16 | 9 | | 17 | 13 | | 18 | 20 | | 19 | 14 | | 20 | 23 | | 21 | 20 | | 22 | 8 | | 23 | 13 | | 24 | 14 | | 25 | 7 | | 26 | 11 | | 27 | 3 | | 28 | 9 | | 29 | 14 | | 30 | 15 | | 31 | 11 | | 32 | 27 | | 33 | 24 | | 34 | 3 | | 35 | 11 | | 36 | 15 | | 37 | 9 | | 38 | 12 | | 39 | 12 | | 40 | 7 | | 41 | 4 | | 42 | 4 | | 43 | 4 | | 44 | 30 | | 45 | 5 | | 46 | 7 | | 47 | 26 | | 48 | 5 | | 49 | 9 |
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| 83.90% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.5168539325842697 | | totalSentences | 89 | | uniqueOpeners | 46 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 44.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 50 | | matches | | 0 | "I froze with the rag" | | 1 | "She wore an immaculate charcoal" | | 2 | "Her blonde bob sat razor-sharp" | | 3 | "She glanced around the room," | | 4 | "I swept the broken glass" | | 5 | "His hazel eyes shifted toward" | | 6 | "He dragged his bad left" | | 7 | "His voice possessed that calm," | | 8 | "Her eyes stayed locked on" | | 9 | "He reached for a fresh" | | 10 | "She looked at the hem" | | 11 | "Her eyes lingered on it." | | 12 | "She knew where that scar" | | 13 | "She had sat in the" | | 14 | "She sipped her drink, the" | | 15 | "She didn't flinch." | | 16 | "Her gaze drifted to the" | | 17 | "I leaned across the counter," | | 18 | "she countered, her voice dropping" | | 19 | "She picked up her bag" |
| | ratio | 0.44 | |
| 20.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 44 | | totalSentences | 50 | | matches | | 0 | "The heavy crystal tumbler slipped" | | 1 | "Vodka and crushed ice splattered" | | 2 | "The voice hit me like" | | 3 | "I froze with the rag" | | 4 | "Carys Hughes stood on the" | | 5 | "She wore an immaculate charcoal" | | 6 | "Her blonde bob sat razor-sharp" | | 7 | "Carys slid onto a stool," | | 8 | "She glanced around the room," | | 9 | "I swept the broken glass" | | 10 | "His hazel eyes shifted toward" | | 11 | "He dragged his bad left" | | 12 | "His voice possessed that calm," | | 13 | "Carys said, without looking at" | | 14 | "Her eyes stayed locked on" | | 15 | "Silas didn't blink." | | 16 | "He reached for a fresh" | | 17 | "Carys pulled off a black" | | 18 | "The silence hung between us," | | 19 | "Silas placed the martini glass" |
| | ratio | 0.88 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 22 | | technicalSentenceCount | 2 | | matches | | 0 | "She wore an immaculate charcoal wool coat over an ivory silk blouse that cost more than my monthly rent on the room upstairs." | | 1 | "His voice possessed that calm, flat weight that made people straighten their spines." |
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| 31.25% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 3 | | matches | | 0 | "Carys slid, her leather handbag resting against the polished timber" | | 1 | "Carys said, without looking at him" | | 2 | "she countered, her voice dropping into that smooth, courtroom cadence" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 2 | | fancyTags | | 0 | "she murmured (murmur)" | | 1 | "Carys whispered (whisper)" |
| | dialogueSentences | 50 | | tagDensity | 0.16 | | leniency | 0.32 | | rawRatio | 0.25 | | effectiveRatio | 0.08 | |