| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 44 | | tagDensity | 0.341 | | leniency | 0.682 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 76.08% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1254 | | totalAiIsmAdverbs | 6 | | found | | 0 | | | 1 | | | 2 | | | 3 | | adverb | "reluctantly" | | count | 1 |
| | 4 | | | 5 | |
| | highlights | | 0 | "gently" | | 1 | "suddenly" | | 2 | "perfectly" | | 3 | "reluctantly" | | 4 | "warily" | | 5 | "very" |
| |
| 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) | |
| 8.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1254 | | totalAiIsms | 23 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "flickered" | | 1 | "dancing" | | 2 | "fluttered" | | 3 | "scanned" | | 4 | "footsteps" | | 5 | "echoed" | | 6 | "unwavering" | | 7 | "furrowed" | | 8 | "racing" | | 9 | "tracing" | | 10 | "quickened" | | 11 | "ensure" | | 12 | "raced" | | 13 | "traced" | | 14 | "intricate" | | 15 | "navigated" | | 16 | "etched" | | 17 | "sinister" | | 18 | "resolve" |
| |
| 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 widened" | | 1 | "fists clenched" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 72 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 72 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 100 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1247 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 22.11% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 821 | | uniqueNames | 11 | | maxNameDensity | 2.56 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 21 | | Tube | 3 | | Kowalski | 1 | | Eva | 14 | | Veil | 5 | | Market | 5 | | Finley | 4 | | Compass | 1 | | Suddenly | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Eva" | | 4 | "Market" | | 5 | "Finley" | | 6 | "Compass" | | 7 | "Morris" |
| | places | | | globalScore | 0.221 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | glossingSentenceCount | 1 | | matches | | 0 | "seemed louder the patrons oblivious to the sinister undercurrent beneath their very feet" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1247 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 100 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 28.34 | | std | 14.46 | | cv | 0.51 | | sampleLengths | | 0 | 50 | | 1 | 53 | | 2 | 45 | | 3 | 13 | | 4 | 30 | | 5 | 12 | | 6 | 29 | | 7 | 55 | | 8 | 36 | | 9 | 38 | | 10 | 16 | | 11 | 41 | | 12 | 51 | | 13 | 45 | | 14 | 18 | | 15 | 24 | | 16 | 37 | | 17 | 35 | | 18 | 17 | | 19 | 35 | | 20 | 28 | | 21 | 13 | | 22 | 18 | | 23 | 37 | | 24 | 31 | | 25 | 56 | | 26 | 20 | | 27 | 40 | | 28 | 24 | | 29 | 20 | | 30 | 10 | | 31 | 14 | | 32 | 13 | | 33 | 47 | | 34 | 3 | | 35 | 11 | | 36 | 11 | | 37 | 12 | | 38 | 19 | | 39 | 28 | | 40 | 9 | | 41 | 22 | | 42 | 36 | | 43 | 45 |
| |
| 95.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 72 | | matches | | 0 | "was contorted" | | 1 | "was meant" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 139 | | matches | | |
| 85.71% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 100 | | ratio | 0.02 | | matches | | 0 | "It was marked with protective sigils, the casing patinaed with verdigris – a Veil Compass." | | 1 | "The unexplained supernatural undertones had always haunted her – and now they were resurfacing." |
| |
| 97.22% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 600 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.02666666666666667 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.023333333333333334 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 100 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 100 | | mean | 12.47 | | std | 6.27 | | cv | 0.503 | | sampleLengths | | 0 | 13 | | 1 | 18 | | 2 | 19 | | 3 | 18 | | 4 | 17 | | 5 | 18 | | 6 | 7 | | 7 | 25 | | 8 | 13 | | 9 | 9 | | 10 | 4 | | 11 | 15 | | 12 | 15 | | 13 | 7 | | 14 | 5 | | 15 | 4 | | 16 | 25 | | 17 | 13 | | 18 | 18 | | 19 | 24 | | 20 | 18 | | 21 | 18 | | 22 | 16 | | 23 | 15 | | 24 | 7 | | 25 | 9 | | 26 | 7 | | 27 | 12 | | 28 | 20 | | 29 | 9 | | 30 | 11 | | 31 | 25 | | 32 | 15 | | 33 | 3 | | 34 | 7 | | 35 | 10 | | 36 | 12 | | 37 | 13 | | 38 | 8 | | 39 | 10 | | 40 | 6 | | 41 | 18 | | 42 | 12 | | 43 | 25 | | 44 | 16 | | 45 | 12 | | 46 | 7 | | 47 | 7 | | 48 | 10 | | 49 | 21 |
| |
| 74.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.47 | | totalSentences | 100 | | uniqueOpeners | 47 | |
| 47.62% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 70 | | matches | | 0 | "Suddenly, Eva’s eyes widened, and" |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 70 | | matches | | 0 | "It was a young man," | | 1 | "His face was contorted in" | | 2 | "She nodded, her vision narrowing" | | 3 | "It was marked with protective" | | 4 | "She motioned for Eva to" | | 5 | "She pointed to an engraved" | | 6 | "It made sense, in a" | | 7 | "she urged, flipping to a" | | 8 | "Her thoughts raced back to" | | 9 | "Their hurried footsteps echoed once" | | 10 | "They emerged into a hidden" | | 11 | "They stepped inside the small" | | 12 | "his voice oily with feigned" | | 13 | "he said, lowering his voice" |
| | ratio | 0.2 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 70 | | matches | | 0 | "The harsh fluorescent light flickered" | | 1 | "Detective Harlow Quinn crouched beside" | | 2 | "Quinn’s sharp brown eyes scanned" | | 3 | "A worn leather watch encircled" | | 4 | "Footsteps echoed behind her, drawing" | | 5 | "A petite woman with curly" | | 6 | "Eva Kowalski, an occult researcher" | | 7 | "Quinn's voice was steady, her" | | 8 | "Eva replied, tucking a stray" | | 9 | "Quinn stood, surveying the scene" | | 10 | "Eva bit her lip." | | 11 | "Quinn murmured, shifting her attention" | | 12 | "It was a young man," | | 13 | "His face was contorted in" | | 14 | "Sergeant Finley, a burly man" | | 15 | "She nodded, her vision narrowing" | | 16 | "It was marked with protective" | | 17 | "A tool crafted to locate" | | 18 | "Eva whispered, her eyes wide" | | 19 | "Quinn replied, her mind racing" |
| | ratio | 0.929 | |
| 71.43% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 70 | | matches | | 0 | "Whoever did this knew they" |
| | ratio | 0.014 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 1 | | matches | | 0 | "It was a young man, mid-20s, dressed in a stylish suit that clashed bizarrely with the grim setting." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 5 | | matches | | 0 | "Eva whispered, her eyes wide" | | 1 | "Quinn replied, her mind racing" | | 2 | "Eva interjected, her eyes shining with the thrill of discovery" | | 3 | "Quinn said, her mind already assembling the pieces" | | 4 | "Quinn said, urgency sharpening her tone" |
| |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 7 | | fancyTags | | 0 | "Quinn murmured (murmur)" | | 1 | "Eva whispered (whisper)" | | 2 | "Eva interjected (interject)" | | 3 | "she urged (urge)" | | 4 | "Quinn ordered (order)" | | 5 | "Quinn snapped (snap)" | | 6 | "Quinn demanded (demand)" |
| | dialogueSentences | 44 | | tagDensity | 0.273 | | leniency | 0.545 | | rawRatio | 0.583 | | effectiveRatio | 0.318 | |