| 94.74% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 2 | | adverbTags | | 0 | "Thomas gestured vaguely [vaguely]" | | 1 | "the scotch wincing slightly [slightly]" |
| | dialogueSentences | 38 | | tagDensity | 0.368 | | leniency | 0.737 | | rawRatio | 0.143 | | effectiveRatio | 0.105 | |
| 78.79% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 943 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slightly" | | 1 | "very" | | 2 | "slowly" |
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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) | |
| 52.28% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 943 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "flickered" | | 1 | "shattered" | | 2 | "flicked" | | 3 | "eyebrow" | | 4 | "tension" | | 5 | "silence" | | 6 | "weight" | | 7 | "familiar" |
| |
| 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 | 43 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 43 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 67 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 943 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 606 | | uniqueNames | 8 | | maxNameDensity | 1.82 | | worstName | "Thomas" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Thomas" | | discoveredNames | | Soho | 1 | | Silas | 6 | | London | 1 | | Welsh | 1 | | Aurora | 7 | | Cardiff | 2 | | Thomas | 11 | | Eva | 1 |
| | persons | | 0 | "Silas" | | 1 | "Aurora" | | 2 | "Thomas" | | 3 | "Eva" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Cardiff" |
| | globalScore | 0.592 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 37 | | 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 | 943 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 67 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 21.43 | | std | 18.3 | | cv | 0.854 | | sampleLengths | | 0 | 23 | | 1 | 74 | | 2 | 10 | | 3 | 3 | | 4 | 4 | | 5 | 39 | | 6 | 1 | | 7 | 66 | | 8 | 6 | | 9 | 15 | | 10 | 21 | | 11 | 38 | | 12 | 1 | | 13 | 3 | | 14 | 20 | | 15 | 21 | | 16 | 39 | | 17 | 8 | | 18 | 35 | | 19 | 28 | | 20 | 9 | | 21 | 16 | | 22 | 21 | | 23 | 15 | | 24 | 6 | | 25 | 1 | | 26 | 2 | | 27 | 49 | | 28 | 8 | | 29 | 32 | | 30 | 45 | | 31 | 5 | | 32 | 1 | | 33 | 7 | | 34 | 46 | | 35 | 15 | | 36 | 7 | | 37 | 38 | | 38 | 2 | | 39 | 53 | | 40 | 32 | | 41 | 26 | | 42 | 24 | | 43 | 28 |
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| 97.10% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 43 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 99 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 67 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 610 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 20 | | adverbRatio | 0.03278688524590164 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.01639344262295082 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 67 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 67 | | mean | 14.07 | | std | 9.82 | | cv | 0.698 | | sampleLengths | | 0 | 23 | | 1 | 10 | | 2 | 25 | | 3 | 20 | | 4 | 19 | | 5 | 10 | | 6 | 3 | | 7 | 4 | | 8 | 19 | | 9 | 11 | | 10 | 9 | | 11 | 1 | | 12 | 18 | | 13 | 22 | | 14 | 26 | | 15 | 6 | | 16 | 15 | | 17 | 21 | | 18 | 4 | | 19 | 22 | | 20 | 12 | | 21 | 1 | | 22 | 3 | | 23 | 17 | | 24 | 3 | | 25 | 21 | | 26 | 11 | | 27 | 28 | | 28 | 8 | | 29 | 20 | | 30 | 15 | | 31 | 28 | | 32 | 6 | | 33 | 3 | | 34 | 3 | | 35 | 13 | | 36 | 21 | | 37 | 15 | | 38 | 6 | | 39 | 1 | | 40 | 2 | | 41 | 21 | | 42 | 28 | | 43 | 8 | | 44 | 29 | | 45 | 3 | | 46 | 9 | | 47 | 36 | | 48 | 5 | | 49 | 1 |
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| 93.03% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5671641791044776 | | totalSentences | 67 | | uniqueOpeners | 38 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 40 | | matches | (empty) | | ratio | 0 | |
| 90.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 40 | | matches | | 0 | "She caught it mid-air, wiping" | | 1 | "She froze, the damp rag" | | 2 | "She stared at the man" | | 3 | "His straight blonde hair was" | | 4 | "He slid onto a stool" | | 5 | "she said, keeping her voice" | | 6 | "His gaze locked onto her" | | 7 | "she said, sliding the heavy" | | 8 | "He just offered a thin," | | 9 | "He finally took a sip" | | 10 | "It cracked against the wood," | | 11 | "It wasn't fast, but it" | | 12 | "He stepped between them, his" |
| | ratio | 0.325 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 40 | | totalSentences | 40 | | matches | | 0 | "The distinct green neon sign" | | 1 | "Aurora dodged the wet rag" | | 2 | "She caught it mid-air, wiping" | | 3 | "The brass bell above the" | | 4 | "A man stepped inside, shaking" | | 5 | "Silas didn't look up from" | | 6 | "The voice landed heavy across" | | 7 | "She froze, the damp rag" | | 8 | "She stared at the man" | | 9 | "His straight blonde hair was" | | 10 | "A jagged white line cut" | | 11 | "He slid onto a stool" | | 12 | "Aurora forced her hand to" | | 13 | "she said, keeping her voice" | | 14 | "Thomas turned his head." | | 15 | "His gaze locked onto her" | | 16 | "The small crescent-shaped scar caught" | | 17 | "Thomas tapped a manicured fingernail" | | 18 | "she said, sliding the heavy" | | 19 | "Thomas picked up the glass," |
| | ratio | 1 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 40 | | matches | (empty) | | ratio | 0 | |
| 45.45% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 22 | | technicalSentenceCount | 3 | | matches | | 0 | "A jagged white line cut through his left eyebrow, pulling the skin tight over an eye that looked too sharp, too dead." | | 1 | "His gaze locked onto her face, tracking every line of tension around her bright blue eyes before dropping to her left wrist." | | 2 | "He just offered a thin, humourless smile that didn't reach his cold eyes." |
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| 89.29% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 1 | | matches | | 0 | "Thomas said, his voice dropping an octave, losing its conversational edge entirely," |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | 0 | "she whispered (whisper)" |
| | dialogueSentences | 38 | | tagDensity | 0.184 | | leniency | 0.368 | | rawRatio | 0.143 | | effectiveRatio | 0.053 | |