| 88.89% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 24 | | adverbTagCount | 3 | | adverbTags | | 0 | "she offered instead [instead]" | | 1 | "Her throat tightened around [around]" | | 2 | "he said quietly [quietly]" |
| | dialogueSentences | 54 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0.125 | | effectiveRatio | 0.111 | |
| 87.04% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1157 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "carefully" | | 1 | "slowly" | | 2 | "very" |
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| 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) | |
| 91.36% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1157 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 83 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 68 | | 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 | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 12 | | wordCount | 648 | | uniqueNames | 6 | | maxNameDensity | 0.62 | | worstName | "Daniel" | | maxWindowNameDensity | 1 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Silas | 3 | | Daniel | 4 | | Eva | 1 | | Aurora | 2 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Daniel" | | 4 | "Eva" | | 5 | "Aurora" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 85.90% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.864 | | wordCount | 1157 | | matches | | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 27.55 | | std | 21.92 | | cv | 0.796 | | sampleLengths | | 0 | 62 | | 1 | 27 | | 2 | 1 | | 3 | 66 | | 4 | 13 | | 5 | 14 | | 6 | 53 | | 7 | 29 | | 8 | 30 | | 9 | 4 | | 10 | 22 | | 11 | 42 | | 12 | 16 | | 13 | 18 | | 14 | 70 | | 15 | 17 | | 16 | 4 | | 17 | 22 | | 18 | 50 | | 19 | 2 | | 20 | 65 | | 21 | 24 | | 22 | 6 | | 23 | 43 | | 24 | 3 | | 25 | 40 | | 26 | 54 | | 27 | 15 | | 28 | 2 | | 29 | 52 | | 30 | 27 | | 31 | 10 | | 32 | 4 | | 33 | 92 | | 34 | 21 | | 35 | 28 | | 36 | 9 | | 37 | 16 | | 38 | 32 | | 39 | 15 | | 40 | 8 | | 41 | 29 |
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| 98.88% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 55 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 106 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 83 | | ratio | 0.012 | | matches | | 0 | "The old Daniel would have laughed; he'd laughed at everything, even the things that should have broken him, the dead father, the mother who drank, the flat with the boiler that never worked." |
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| 96.02% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 651 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.0445468509984639 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.01228878648233487 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 83 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 83 | | mean | 13.94 | | std | 10.5 | | cv | 0.753 | | sampleLengths | | 0 | 18 | | 1 | 12 | | 2 | 32 | | 3 | 16 | | 4 | 11 | | 5 | 1 | | 6 | 7 | | 7 | 17 | | 8 | 20 | | 9 | 22 | | 10 | 10 | | 11 | 3 | | 12 | 9 | | 13 | 5 | | 14 | 12 | | 15 | 10 | | 16 | 31 | | 17 | 10 | | 18 | 19 | | 19 | 12 | | 20 | 18 | | 21 | 4 | | 22 | 14 | | 23 | 8 | | 24 | 10 | | 25 | 8 | | 26 | 24 | | 27 | 9 | | 28 | 7 | | 29 | 13 | | 30 | 5 | | 31 | 3 | | 32 | 33 | | 33 | 13 | | 34 | 21 | | 35 | 7 | | 36 | 10 | | 37 | 4 | | 38 | 17 | | 39 | 5 | | 40 | 12 | | 41 | 38 | | 42 | 2 | | 43 | 34 | | 44 | 31 | | 45 | 15 | | 46 | 9 | | 47 | 6 | | 48 | 17 | | 49 | 20 |
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| 73.90% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4939759036144578 | | totalSentences | 83 | | uniqueOpeners | 41 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 51 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 51 | | matches | | 0 | "He went still in a" | | 1 | "She knew it before she" | | 2 | "She said it carefully, like" | | 3 | "He lifted his glass an" | | 4 | "She crossed the floor because" | | 5 | "She didn't mean to ask" | | 6 | "He turned his glass a" | | 7 | "His mouth moved, not quite" | | 8 | "She caught Silas's eye and" | | 9 | "He poured her something amber" | | 10 | "She wrapped both hands around" | | 11 | "He didn't laugh." | | 12 | "He'd made jokes in the" | | 13 | "She studied his profile, the" | | 14 | "He shrugged, and the shrug" | | 15 | "He finally looked at her," | | 16 | "She turned away from that," | | 17 | "He pressed his thumb into" | | 18 | "He shook his head slowly" | | 19 | "She wanted to be angry." |
| | ratio | 0.588 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 51 | | matches | | 0 | "The green neon hummed above" | | 1 | "Aurora shook the rain from" | | 2 | "A man at the far" | | 3 | "He went still in a" | | 4 | "The voice landed somewhere beneath" | | 5 | "She knew it before she" | | 6 | "The boy she'd known had" | | 7 | "This man sat squared and" | | 8 | "She said it carefully, like" | | 9 | "He lifted his glass an" | | 10 | "She crossed the floor because" | | 11 | "The stool beside him scraped" | | 12 | "Silas worked the other end," | | 13 | "She didn't mean to ask" | | 14 | "The question came out anyway," | | 15 | "He turned his glass a" | | 16 | "His mouth moved, not quite" | | 17 | "She caught Silas's eye and" | | 18 | "He poured her something amber" | | 19 | "Daniel exhaled through his nose" |
| | ratio | 0.922 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 51 | | matches | (empty) | | ratio | 0 | |
| 74.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 2 | | matches | | 0 | "Silas worked the other end, polishing a tumbler that didn't need polishing, his hazel eyes flicking over the pair of them once before he gave them the privacy o…" | | 1 | "The old Daniel would have laughed; he'd laughed at everything, even the things that should have broken him, the dead father, the mother who drank, the flat with…" |
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| 83.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 24 | | uselessAdditionCount | 2 | | matches | | 0 | "His mouth moved, not quite a smile" | | 1 | "She lifted, an offering, a truce" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 54 | | tagDensity | 0.093 | | leniency | 0.185 | | rawRatio | 0.2 | | effectiveRatio | 0.037 | |