| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 60 | | tagDensity | 0.15 | | leniency | 0.3 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1440 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 82.64% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1440 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "crystal" | | 1 | "weight" | | 2 | "silence" |
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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 | 82 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 82 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 133 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 82 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1440 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 8 | | wordCount | 803 | | uniqueNames | 8 | | maxNameDensity | 0.12 | | worstName | "Raven" | | maxWindowNameDensity | 0 | | worstWindowName | (null) | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Golden | 1 | | Empress | 1 | | Blackwood | 1 | | Evan | 1 | | Prague | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Empress" | | 3 | "Blackwood" | | 4 | "Evan" |
| | places | | 0 | "Soho" | | 1 | "Golden" | | 2 | "Prague" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | 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 | 1440 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 133 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 81 | | mean | 17.78 | | std | 19.64 | | cv | 1.105 | | sampleLengths | | 0 | 104 | | 1 | 48 | | 2 | 65 | | 3 | 8 | | 4 | 4 | | 5 | 15 | | 6 | 1 | | 7 | 15 | | 8 | 1 | | 9 | 14 | | 10 | 4 | | 11 | 40 | | 12 | 13 | | 13 | 44 | | 14 | 2 | | 15 | 2 | | 16 | 8 | | 17 | 13 | | 18 | 22 | | 19 | 54 | | 20 | 5 | | 21 | 17 | | 22 | 13 | | 23 | 4 | | 24 | 2 | | 25 | 7 | | 26 | 24 | | 27 | 1 | | 28 | 23 | | 29 | 4 | | 30 | 38 | | 31 | 14 | | 32 | 1 | | 33 | 18 | | 34 | 3 | | 35 | 4 | | 36 | 7 | | 37 | 25 | | 38 | 8 | | 39 | 13 | | 40 | 16 | | 41 | 2 | | 42 | 13 | | 43 | 35 | | 44 | 60 | | 45 | 4 | | 46 | 48 | | 47 | 15 | | 48 | 4 | | 49 | 14 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 82 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 139 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 133 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 810 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.024691358024691357 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0012345679012345679 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 133 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 133 | | mean | 10.83 | | std | 10.17 | | cv | 0.939 | | sampleLengths | | 0 | 19 | | 1 | 16 | | 2 | 16 | | 3 | 4 | | 4 | 15 | | 5 | 16 | | 6 | 18 | | 7 | 6 | | 8 | 8 | | 9 | 13 | | 10 | 21 | | 11 | 6 | | 12 | 19 | | 13 | 11 | | 14 | 7 | | 15 | 11 | | 16 | 11 | | 17 | 8 | | 18 | 4 | | 19 | 2 | | 20 | 4 | | 21 | 9 | | 22 | 1 | | 23 | 4 | | 24 | 11 | | 25 | 1 | | 26 | 14 | | 27 | 4 | | 28 | 21 | | 29 | 19 | | 30 | 13 | | 31 | 11 | | 32 | 22 | | 33 | 11 | | 34 | 2 | | 35 | 2 | | 36 | 8 | | 37 | 13 | | 38 | 7 | | 39 | 15 | | 40 | 15 | | 41 | 39 | | 42 | 5 | | 43 | 13 | | 44 | 4 | | 45 | 13 | | 46 | 4 | | 47 | 2 | | 48 | 7 | | 49 | 3 |
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| 39.47% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.2857142857142857 | | totalSentences | 133 | | uniqueOpeners | 38 | |
| 41.67% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 80 | | matches | | 0 | "Somewhere behind the bookshelf, a" |
| | ratio | 0.013 | |
| 5.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 80 | | matches | | 0 | "She shook rain from her" | | 1 | "Her bright blue eyes adjusted" | | 2 | "Her left wrist caught the" | | 3 | "She wore her delivery jacket," | | 4 | "He polished a glass in" | | 5 | "He shifted his weight, and" | | 6 | "She set the bag on" | | 7 | "He lowered it to the" | | 8 | "She felt the flinch start" | | 9 | "His voice dropped the way" | | 10 | "She glanced at the ceiling," | | 11 | "He came around the bar," | | 12 | "He stopped a metre away," | | 13 | "He tapped the signet ring" | | 14 | "She let the bag sit" | | 15 | "Her hands stayed flat on" | | 16 | "He poured a measure of" | | 17 | "She didn't touch it." | | 18 | "Her hand moved to her" | | 19 | "She hadn't meant to do" |
| | ratio | 0.538 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 80 | | matches | | 0 | "The green neon sign above" | | 1 | "Rain slicked the Soho pavement" | | 2 | "Aurora pushed through the door" | | 3 | "The dim swallowed her." | | 4 | "Maps curled on the walls:" | | 5 | "She shook rain from her" | | 6 | "Her bright blue eyes adjusted" | | 7 | "Her left wrist caught the" | | 8 | "She wore her delivery jacket," | | 9 | "Silas Blackwood stood behind the" | | 10 | "He polished a glass in" | | 11 | "He shifted his weight, and" | | 12 | "A beard, trimmed close, covered" | | 13 | "Hazel eyes lifted from the" | | 14 | "She set the bag on" | | 15 | "The glass paused mid-wipe." | | 16 | "He lowered it to the" | | 17 | "The name landed wrong." | | 18 | "She felt the flinch start" | | 19 | "His voice dropped the way" |
| | ratio | 0.925 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 80 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 1 | | matches | | 0 | "Rain slicked the Soho pavement outside, smearing the reflection into something that moved when nothing did." |
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| 69.44% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 1 | | matches | | 0 | "He came around, each step announcing the limp" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |