| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 1 | | adverbTags | | 0 | "She turned back [back]" |
| | dialogueSentences | 20 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 0.167 | | effectiveRatio | 0.1 | |
| 85.12% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1344 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slightly" | | 1 | "slowly" | | 2 | "perfectly" | | 3 | "truly" |
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| 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) | |
| 40.48% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1344 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "pulsed" | | 1 | "rhythmic" | | 2 | "pulse" | | 3 | "echo" | | 4 | "silence" | | 5 | "sense of" | | 6 | "navigating" | | 7 | "weight" | | 8 | "could feel" | | 9 | "footsteps" | | 10 | "familiar" | | 11 | "trembled" | | 12 | "flickered" |
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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 | 162 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 162 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 175 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1341 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 1172 | | uniqueNames | 10 | | maxNameDensity | 0.94 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Aurora" | | discoveredNames | | Richmond | 1 | | Park | 1 | | English | 1 | | Golden | 1 | | Empress | 1 | | Cardiff | 1 | | Valleys | 1 | | Evan | 1 | | London | 1 | | Aurora | 11 |
| | persons | | | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "English" | | 3 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 76 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like holes in the world" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.746 | | wordCount | 1341 | | matches | | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 175 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 76 | | mean | 17.64 | | std | 15.46 | | cv | 0.876 | | sampleLengths | | 0 | 6 | | 1 | 57 | | 2 | 2 | | 3 | 66 | | 4 | 1 | | 5 | 37 | | 6 | 4 | | 7 | 35 | | 8 | 8 | | 9 | 10 | | 10 | 4 | | 11 | 32 | | 12 | 49 | | 13 | 6 | | 14 | 23 | | 15 | 32 | | 16 | 9 | | 17 | 2 | | 18 | 2 | | 19 | 17 | | 20 | 16 | | 21 | 18 | | 22 | 38 | | 23 | 4 | | 24 | 5 | | 25 | 16 | | 26 | 7 | | 27 | 6 | | 28 | 44 | | 29 | 8 | | 30 | 5 | | 31 | 24 | | 32 | 12 | | 33 | 2 | | 34 | 15 | | 35 | 6 | | 36 | 38 | | 37 | 2 | | 38 | 47 | | 39 | 2 | | 40 | 2 | | 41 | 3 | | 42 | 3 | | 43 | 26 | | 44 | 2 | | 45 | 35 | | 46 | 16 | | 47 | 30 | | 48 | 8 | | 49 | 19 |
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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 | 1 | | totalVerbs | 200 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 175 | | ratio | 0 | | matches | (empty) | |
| 79.85% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1175 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 66 | | adverbRatio | 0.05617021276595745 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.00851063829787234 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 175 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 175 | | mean | 7.66 | | std | 7.05 | | cv | 0.92 | | sampleLengths | | 0 | 6 | | 1 | 13 | | 2 | 12 | | 3 | 26 | | 4 | 6 | | 5 | 2 | | 6 | 16 | | 7 | 24 | | 8 | 13 | | 9 | 1 | | 10 | 1 | | 11 | 11 | | 12 | 1 | | 13 | 8 | | 14 | 2 | | 15 | 2 | | 16 | 25 | | 17 | 4 | | 18 | 10 | | 19 | 2 | | 20 | 3 | | 21 | 7 | | 22 | 7 | | 23 | 6 | | 24 | 2 | | 25 | 6 | | 26 | 7 | | 27 | 3 | | 28 | 4 | | 29 | 12 | | 30 | 2 | | 31 | 2 | | 32 | 16 | | 33 | 3 | | 34 | 5 | | 35 | 30 | | 36 | 11 | | 37 | 6 | | 38 | 6 | | 39 | 2 | | 40 | 6 | | 41 | 5 | | 42 | 4 | | 43 | 2 | | 44 | 1 | | 45 | 14 | | 46 | 10 | | 47 | 1 | | 48 | 4 | | 49 | 2 |
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| 53.90% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.38857142857142857 | | totalSentences | 175 | | uniqueOpeners | 68 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 8 | | totalSentences | 125 | | matches | | 0 | "Just the sudden, ringing silence" | | 1 | "Just grey bark and dark" | | 2 | "More like the darkness had" | | 3 | "Just trees and shadows and" | | 4 | "Then, from somewhere to her" | | 5 | "Too many joints in the" | | 6 | "All tall, all thin, all" | | 7 | "Too many notes, too many" |
| | ratio | 0.064 | |
| 98.40% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 125 | | matches | | 0 | "She'd followed its pull from" | | 1 | "She stood at the edge" | | 2 | "Her voice died faster than" | | 3 | "She'd expected that." | | 4 | "She'd left the Golden Empress" | | 5 | "Her phone now read 2:47" | | 6 | "She'd walked for maybe fifteen" | | 7 | "She turned back toward the" | | 8 | "She was a Cardiff girl." | | 9 | "She'd grown up navigating the" | | 10 | "She knew how to find" | | 11 | "She picked a direction and" | | 12 | "They should have been soft," | | 13 | "She didn't look down." | | 14 | "Her eyes watered again." | | 15 | "She kept her voice level" | | 16 | "She could ignore it." | | 17 | "She could feel it pressing" | | 18 | "She caught fragments that sounded" | | 19 | "Her hand found the pendant" |
| | ratio | 0.304 | |
| 56.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 101 | | totalSentences | 125 | | matches | | 0 | "The pendant burned against her" | | 1 | "Aurora's hand flew to the" | | 2 | "Heat pulsed through her palm," | | 3 | "She'd followed its pull from" | | 4 | "The pulse had led her" | | 5 | "She stood at the edge" | | 6 | "Wildflowers carpeted the ground in" | | 7 | "A blue so deep it" | | 8 | "Her voice died faster than" | | 9 | "The pendant cooled slightly." | | 10 | "Aurora let go of the" | | 11 | "She'd expected that." | | 12 | "She'd left the Golden Empress" | | 13 | "Her phone now read 2:47" | | 14 | "She'd walked for maybe fifteen" | | 15 | "She turned back toward the" | | 16 | "The path was gone." | | 17 | "Aurora's jaw tightened." | | 18 | "She was a Cardiff girl." | | 19 | "She'd grown up navigating the" |
| | ratio | 0.808 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 125 | | matches | (empty) | | ratio | 0 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 4 | | matches | | 0 | "She'd grown up navigating the Valleys on weekends with her da before the arguments started, before law school became the only acceptable future, before Evan and…" | | 1 | "But here they were, circling her again, their symbols blazing with faint silver light." | | 2 | "Too many notes, too many harmonics, like several people laughing at once through a mouth that had forgotten how." | | 3 | "For one instant, she saw the grove as it truly was, no glamour, no disguise, and the thing that reached for her had a face after all, a face made of writhing ro…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |