| 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 | |
| 83.50% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 303 | | totalAiIsmAdverbs | 1 | | 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) | |
| 17.49% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 303 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "flickered" | | 1 | "calculated" | | 2 | "etched" | | 3 | "traced" | | 4 | "intricate" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 23 | | matches | (empty) | |
| 18.63% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 23 | | filterMatches | | | hedgeMatches | | |
| 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 | 24 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 300 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 22.26% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 274 | | uniqueNames | 8 | | maxNameDensity | 2.55 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 2 | | Harlow | 1 | | Quinn | 7 | | Eva | 3 | | Kowalski | 1 | | Morris | 1 | | Camden | 1 | | Shade | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Eva" | | 3 | "Kowalski" | | 4 | "Morris" |
| | places | (empty) | | globalScore | 0.223 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 17 | | 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 | 300 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 24 | | matches | (empty) | |
| 99.24% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 10 | | mean | 30 | | std | 14.92 | | cv | 0.497 | | sampleLengths | | 0 | 44 | | 1 | 39 | | 2 | 6 | | 3 | 39 | | 4 | 43 | | 5 | 29 | | 6 | 15 | | 7 | 34 | | 8 | 46 | | 9 | 5 |
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| 74.75% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 23 | | matches | | 0 | "was arranged" | | 1 | "were etched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 44 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 24 | | ratio | 0.125 | | matches | | 0 | "\"The positioning is deliberate,\" Eva said, tucking a stray curl of red hair behind her ear—a nervous habit Quinn had known for years." | | 1 | "Her military-precise posture remained rigid, but her fingers traced a protective sigil on her belt—a gesture learned from her late partner, Morris." | | 2 | "Quinn recognized the signs now—the way shadows seemed to pool unnaturally, the faint scent of ozone and burned herbs." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 278 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 8 | | adverbRatio | 0.02877697841726619 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.014388489208633094 | |
| 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 | 12.5 | | std | 5.58 | | cv | 0.446 | | sampleLengths | | 0 | 15 | | 1 | 15 | | 2 | 14 | | 3 | 17 | | 4 | 16 | | 5 | 6 | | 6 | 6 | | 7 | 16 | | 8 | 23 | | 9 | 4 | | 10 | 14 | | 11 | 8 | | 12 | 17 | | 13 | 7 | | 14 | 22 | | 15 | 5 | | 16 | 10 | | 17 | 10 | | 18 | 19 | | 19 | 5 | | 20 | 17 | | 21 | 13 | | 22 | 16 | | 23 | 5 |
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| 97.22% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.5833333333333334 | | totalSentences | 24 | | uniqueOpeners | 14 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 20 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 3 | | totalSentences | 20 | | matches | | 0 | "Her leather watch caught the" | | 1 | "Her military-precise posture remained rigid," | | 2 | "She pulled out an evidence" |
| | ratio | 0.15 | |
| 35.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 17 | | totalSentences | 20 | | matches | | 0 | "The fluorescent lights flickered, casting" | | 1 | "Detective Harlow Quinn crouched, her" | | 2 | "Blood pooled around the victim's" | | 3 | "Her leather watch caught the" | | 4 | "This scene, however, whispered something" | | 5 | "Eva Kowalski shifted behind her," | | 6 | "Eva said, tucking a stray" | | 7 | "Quinn's brown eyes narrowed." | | 8 | "The body was arranged in" | | 9 | "Her military-precise posture remained rigid," | | 10 | "Eva adjusted her round glasses." | | 11 | "The abandoned Camden Tube station" | | 12 | "Quinn recognized the signs now—the" | | 13 | "Something supernatural had happened here." | | 14 | "She pulled out an evidence" | | 15 | "A small brass compass with" | | 16 | "Quinn recognized it immediately: a" |
| | ratio | 0.85 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 20 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 14 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "Quinn murmured (murmur)" |
| | dialogueSentences | 5 | | tagDensity | 0.8 | | leniency | 1 | | rawRatio | 0.5 | | effectiveRatio | 0.5 | |