| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.8 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 248 | | 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) | |
| 19.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 248 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "jaw clenched" | | 1 | "standard" | | 2 | "flickered" | | 3 | "calculated" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "jaw clenched" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 20 | | matches | (empty) | |
| 71.43% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 20 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 21 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 240 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 30.38% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 13 | | wordCount | 209 | | uniqueNames | 7 | | maxNameDensity | 2.39 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Detective | 1 | | Harlow | 1 | | Quinn | 5 | | Eva | 3 | | Kowalski | 1 | | Morris | 1 |
| | persons | | 0 | "Detective" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Eva" | | 4 | "Kowalski" | | 5 | "Morris" |
| | places | (empty) | | globalScore | 0.304 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 13 | | 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 | 240 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 21 | | matches | (empty) | |
| 91.06% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 9 | | mean | 26.67 | | std | 12.5 | | cv | 0.469 | | sampleLengths | | 0 | 38 | | 1 | 40 | | 2 | 6 | | 3 | 37 | | 4 | 18 | | 5 | 44 | | 6 | 17 | | 7 | 22 | | 8 | 18 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 20 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 35 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 21 | | ratio | 0.238 | | matches | | 0 | "The underground Tube station reeked of stale air and something else—something metallic that caught in the back of Detective Harlow Quinn's throat." | | 1 | "The victim—male, mid-thirties—lay twisted at an unnatural angle, his limbs splayed like a broken marionette." | | 2 | "She tucked a strand of curly red hair behind her ear—a nervous habit Quinn had known since they first worked together." | | 3 | "Her mind flickered to DS Morris—her former partner who vanished three years ago under circumstances that still haunted her dreams." | | 4 | "Faint marks—almost like ritualistic etchings—were barely visible against the pale skin." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 217 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 5 | | adverbRatio | 0.02304147465437788 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.013824884792626729 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 21 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 21 | | mean | 11.43 | | std | 6.82 | | cv | 0.597 | | sampleLengths | | 0 | 22 | | 1 | 16 | | 2 | 10 | | 3 | 15 | | 4 | 15 | | 5 | 6 | | 6 | 16 | | 7 | 21 | | 8 | 18 | | 9 | 4 | | 10 | 20 | | 11 | 20 | | 12 | 17 | | 13 | 4 | | 14 | 11 | | 15 | 2 | | 16 | 2 | | 17 | 3 | | 18 | 7 | | 19 | 7 | | 20 | 4 |
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| 82.54% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5238095238095238 | | totalSentences | 21 | | uniqueOpeners | 11 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 16 | | matches | (empty) | | ratio | 0 | |
| 95.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 5 | | totalSentences | 16 | | matches | | 0 | "Her worn leather watch glinted" | | 1 | "She tucked a strand of" | | 2 | "She'd seen enough unexplained cases" | | 3 | "Her mind flickered to DS" | | 4 | "Her military precision bleeding into" |
| | ratio | 0.313 | |
| 22.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 14 | | totalSentences | 16 | | matches | | 0 | "The underground Tube station reeked" | | 1 | "Her worn leather watch glinted" | | 2 | "The victim—male, mid-thirties-lay twisted at" | | 3 | "Quinn's sharp jaw clenched as" | | 4 | "Eva Kowalski shifted beside her," | | 5 | "She tucked a strand of" | | 6 | "Eva said, her round glasses" | | 7 | "Quinn's brown eyes narrowed." | | 8 | "She'd seen enough unexplained cases" | | 9 | "Her mind flickered to DS" | | 10 | "Eva continued, pointing with a" | | 11 | "The detective leaned closer." | | 12 | "Something else entirely." | | 13 | "Her military precision bleeding into" |
| | ratio | 0.875 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 16 | | matches | (empty) | | ratio | 0 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 10 | | technicalSentenceCount | 1 | | matches | | 0 | "Her mind flickered to DS Morris—her former partner who vanished three years ago under circumstances that still haunted her dreams." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 1 | | matches | | 0 | "Eva said, her round glasses reflecting the harsh forensic lights," |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "Eva continued (continue)" |
| | dialogueSentences | 5 | | tagDensity | 0.8 | | leniency | 1 | | rawRatio | 0.5 | | effectiveRatio | 0.5 | |