| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 71.67% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 353 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 15.01% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 353 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "charged" | | 1 | "measured" | | 2 | "pounding" | | 3 | "footsteps" | | 4 | "echoing" | | 5 | "scanned" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "The air was heavy with" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 24 | | matches | (empty) | |
| 83.33% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 24 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 24 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 349 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 47.96% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 343 | | uniqueNames | 12 | | maxNameDensity | 2.04 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 7 | | Herrera | 5 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Saint | 1 | | Christopher | 1 | | Tube | 1 | | Camden | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" |
| | places | | | globalScore | 0.48 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 22 | | 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 | 349 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 24 | | matches | (empty) | |
| 41.39% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 8 | | mean | 43.63 | | std | 12.86 | | cv | 0.295 | | sampleLengths | | |
| 90.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 24 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 50 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 24 | | ratio | 0 | | matches | (empty) | |
| 92.65% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 347 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.0345821325648415 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.02881844380403458 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 24 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 24 | | mean | 14.54 | | std | 7.22 | | cv | 0.497 | | sampleLengths | | 0 | 18 | | 1 | 28 | | 2 | 23 | | 3 | 11 | | 4 | 29 | | 5 | 16 | | 6 | 13 | | 7 | 20 | | 8 | 14 | | 9 | 22 | | 10 | 5 | | 11 | 6 | | 12 | 10 | | 13 | 11 | | 14 | 23 | | 15 | 7 | | 16 | 10 | | 17 | 17 | | 18 | 12 | | 19 | 10 | | 20 | 7 | | 21 | 14 | | 22 | 22 | | 23 | 1 |
| |
| 93.06% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5833333333333334 | | totalSentences | 24 | | uniqueOpeners | 14 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 23 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 3 | | totalSentences | 23 | | matches | | 0 | "She followed him inside, her" | | 1 | "She scanned the crowd, searching" | | 2 | "He had to be here" |
| | ratio | 0.13 | |
| 46.96% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 19 | | totalSentences | 23 | | matches | | 0 | "Detective Harlow Quinn charged through" | | 1 | "The relentless downpour plastered her" | | 2 | "Tomás Herrera, a former paramedic" | | 3 | "Quinn had tailed him from" | | 4 | "The suspect veered sharply into" | | 5 | "The alley was narrow, the" | | 6 | "Herrera's Saint Christopher medallion glinted" | | 7 | "Quinn shouted, her voice cutting" | | 8 | "Herrera glanced back, his warm" | | 9 | "Quinn hesitated for a moment." | | 10 | "This was unfamiliar territory, potentially" | | 11 | "She followed him inside, her" | | 12 | "The air was heavy with" | | 13 | "Quinn moved cautiously, keeping to" | | 14 | "The Veil Market, a supernatural" | | 15 | "Quinn had only heard rumors" | | 16 | "She scanned the crowd, searching" | | 17 | "He had to be here" | | 18 | "A hooded figure bumped into" |
| | ratio | 0.826 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 23 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 1 | | matches | | 0 | "The Veil Market, a supernatural black market that moved every full moon." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn shouted, her voice cutting through the rain" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 1 | | effectiveRatio | 1 | |