| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 26 | | tagDensity | 0.385 | | leniency | 0.769 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1075 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 86.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1075 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "velvet" | | 1 | "flickered" | | 2 | "shattered" |
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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 | 115 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 115 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 131 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1075 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 98.19% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 965 | | uniqueNames | 9 | | maxNameDensity | 1.04 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 10 | | Inverness | 1 | | Street | 1 | | Tube | 1 | | Camden | 2 | | Spanish | 1 | | London | 1 | | Tomás | 1 | | You | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Camden" | | 2 | "Tomás" | | 3 | "You" |
| | places | | 0 | "Inverness" | | 1 | "Street" | | 2 | "London" |
| | globalScore | 0.982 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 1 | | matches | | 0 | "as if toasting the spectacle" |
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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 | 1075 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 131 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 21.08 | | std | 16.53 | | cv | 0.784 | | sampleLengths | | 0 | 23 | | 1 | 45 | | 2 | 16 | | 3 | 2 | | 4 | 5 | | 5 | 51 | | 6 | 44 | | 7 | 10 | | 8 | 13 | | 9 | 7 | | 10 | 52 | | 11 | 6 | | 12 | 4 | | 13 | 17 | | 14 | 53 | | 15 | 7 | | 16 | 13 | | 17 | 30 | | 18 | 14 | | 19 | 43 | | 20 | 4 | | 21 | 33 | | 22 | 9 | | 23 | 58 | | 24 | 42 | | 25 | 5 | | 26 | 34 | | 27 | 2 | | 28 | 17 | | 29 | 2 | | 30 | 36 | | 31 | 20 | | 32 | 4 | | 33 | 38 | | 34 | 2 | | 35 | 46 | | 36 | 10 | | 37 | 9 | | 38 | 4 | | 39 | 43 | | 40 | 20 | | 41 | 12 | | 42 | 38 | | 43 | 27 | | 44 | 5 | | 45 | 25 | | 46 | 19 | | 47 | 5 | | 48 | 26 | | 49 | 9 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 115 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 193 | | matches | | 0 | "was going" | | 1 | "was joking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 131 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 969 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.030959752321981424 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0020639834881320948 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 131 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 131 | | mean | 8.21 | | std | 6.5 | | cv | 0.792 | | sampleLengths | | 0 | 23 | | 1 | 19 | | 2 | 2 | | 3 | 2 | | 4 | 22 | | 5 | 3 | | 6 | 5 | | 7 | 8 | | 8 | 2 | | 9 | 4 | | 10 | 1 | | 11 | 8 | | 12 | 14 | | 13 | 19 | | 14 | 10 | | 15 | 16 | | 16 | 16 | | 17 | 7 | | 18 | 5 | | 19 | 3 | | 20 | 1 | | 21 | 3 | | 22 | 3 | | 23 | 13 | | 24 | 5 | | 25 | 2 | | 26 | 16 | | 27 | 3 | | 28 | 13 | | 29 | 2 | | 30 | 2 | | 31 | 16 | | 32 | 6 | | 33 | 4 | | 34 | 8 | | 35 | 9 | | 36 | 6 | | 37 | 6 | | 38 | 5 | | 39 | 2 | | 40 | 27 | | 41 | 1 | | 42 | 1 | | 43 | 5 | | 44 | 7 | | 45 | 13 | | 46 | 3 | | 47 | 11 | | 48 | 16 | | 49 | 14 |
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| 68.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.45038167938931295 | | totalSentences | 131 | | uniqueOpeners | 59 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 93 | | matches | | 0 | "Just her, the rain, and" | | 1 | "Then he turned, and the" | | 2 | "Then he stepped backwards into" |
| | ratio | 0.032 | |
| 78.06% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 93 | | matches | | 0 | "She'd lost the radio two" | | 1 | "He didn't even flinch." | | 2 | "Her father had drilled that" | | 3 | "You finish what you start." | | 4 | "She slowed, drawing her baton," | | 5 | "His shoulders heaved." | | 6 | "He pressed something against the" | | 7 | "She threw herself forward, wedged" | | 8 | "She knew Camden had swallowed" | | 9 | "She went down." | | 10 | "She'd heard the rumours." | | 11 | "You'd love it, Quinn." | | 12 | "She'd thought he was joking." | | 13 | "He was wrong." | | 14 | "She pushed after him, and" | | 15 | "She understood that now." | | 16 | "She kept her eyes on" | | 17 | "He glanced back once, found" | | 18 | "He broke into a run" | | 19 | "They shattered, and pale gas" |
| | ratio | 0.355 | |
| 19.14% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 82 | | totalSentences | 93 | | matches | | 0 | "The suspect went left at" | | 1 | "She'd lost the radio two" | | 2 | "That bothered her." | | 3 | "People who ran blind tripped." | | 4 | "People who ran with purpose" | | 5 | "He didn't even flinch." | | 6 | "Camden at this hour wore" | | 7 | "The market stalls had shuttered" | | 8 | "A drunk slumped in a" | | 9 | "Neon bled across the wet" | | 10 | "The grey coat cut down" | | 11 | "Quinn followed, lungs burning, eighteen" | | 12 | "Her father had drilled that" | | 13 | "You finish what you start." | | 14 | "The alley narrowed." | | 15 | "A fire escape." | | 16 | "A dead end." | | 17 | "She slowed, drawing her baton," | | 18 | "The man stood with his" | | 19 | "His shoulders heaved." |
| | ratio | 0.882 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 93 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 1 | | matches | | 0 | "He yanked, hard, and she went down onto the filthy platform, dragging him half with her, the crowd parting around the brawl with the practised ease of people wh…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 73.08% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "the man shouted (shout)" | | 1 | "He pressed (press)" |
| | dialogueSentences | 26 | | tagDensity | 0.192 | | leniency | 0.385 | | rawRatio | 0.4 | | effectiveRatio | 0.154 | |