| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 3 | | adverbTags | | 0 | "Aurora said dryly [dryly]" | | 1 | "she said flatly [flatly]" | | 2 | "She pushed away [away]" |
| | dialogueSentences | 20 | | tagDensity | 0.55 | | leniency | 1 | | rawRatio | 0.273 | | effectiveRatio | 0.273 | |
| 92.32% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 651 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 53.92% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 651 | | totalAiIsms | 6 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | word | "the last thing" | | count | 1 |
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| | highlights | | 0 | "scanned" | | 1 | "glint" | | 2 | "unreadable" | | 3 | "depths" | | 4 | "eyebrow" | | 5 | "the last thing" |
| |
| 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 | 44 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 44 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 53 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 645 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 480 | | uniqueNames | 9 | | maxNameDensity | 1.67 | | worstName | "Evan" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Evan" | | discoveredNames | | Raven | 1 | | Nest | 1 | | London | 1 | | Carter | 1 | | Evan | 8 | | Jones | 1 | | Cardiff | 1 | | Aurora | 8 | | Silas | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Carter" | | 3 | "Evan" | | 4 | "Jones" | | 5 | "Aurora" | | 6 | "Silas" |
| | places | | | globalScore | 0.667 | | windowScore | 0.5 | |
| 82.43% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 37 | | glossingSentenceCount | 1 | | matches | | 0 | "something like regret flickering across his" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 645 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 53 | | matches | (empty) | |
| 62.33% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 18 | | mean | 35.83 | | std | 13.19 | | cv | 0.368 | | sampleLengths | | 0 | 45 | | 1 | 49 | | 2 | 50 | | 3 | 35 | | 4 | 16 | | 5 | 16 | | 6 | 63 | | 7 | 31 | | 8 | 24 | | 9 | 32 | | 10 | 18 | | 11 | 37 | | 12 | 26 | | 13 | 42 | | 14 | 23 | | 15 | 50 | | 16 | 41 | | 17 | 47 |
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| 97.29% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 44 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 88 | | matches | (empty) | |
| 88.95% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 53 | | ratio | 0.019 | | matches | | 0 | "She noticed the slight limp in his left leg as he moved - an old injury, if memory served." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 485 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.02268041237113402 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.010309278350515464 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 53 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 53 | | mean | 12.17 | | std | 5.88 | | cv | 0.483 | | sampleLengths | | 0 | 15 | | 1 | 20 | | 2 | 10 | | 3 | 19 | | 4 | 20 | | 5 | 10 | | 6 | 17 | | 7 | 14 | | 8 | 19 | | 9 | 15 | | 10 | 7 | | 11 | 13 | | 12 | 9 | | 13 | 6 | | 14 | 1 | | 15 | 10 | | 16 | 6 | | 17 | 15 | | 18 | 11 | | 19 | 5 | | 20 | 27 | | 21 | 5 | | 22 | 9 | | 23 | 22 | | 24 | 13 | | 25 | 4 | | 26 | 7 | | 27 | 8 | | 28 | 24 | | 29 | 11 | | 30 | 7 | | 31 | 11 | | 32 | 16 | | 33 | 10 | | 34 | 14 | | 35 | 12 | | 36 | 16 | | 37 | 10 | | 38 | 16 | | 39 | 13 | | 40 | 7 | | 41 | 3 | | 42 | 9 | | 43 | 17 | | 44 | 24 | | 45 | 2 | | 46 | 16 | | 47 | 15 | | 48 | 8 | | 49 | 17 |
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| 83.02% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5094339622641509 | | totalSentences | 53 | | uniqueOpeners | 27 | |
| 77.52% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 43 | | matches | | 0 | "Suddenly, the man turned, his" |
| | ratio | 0.023 | |
| 61.86% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 43 | | matches | | 0 | "She slid onto a worn" | | 1 | "Her bright blue eyes scanned" | | 2 | "she said to the bartender," | | 3 | "He nodded, his hazel eyes" | | 4 | "She noticed the slight limp" | | 5 | "She squinted, trying to make" | | 6 | "He slid off his stool," | | 7 | "His eyes roved over her" | | 8 | "She cleared her throat." | | 9 | "He signaled the bartender for" | | 10 | "she shot back, her cool" | | 11 | "he said, wiping his eyes" | | 12 | "she said flatly" | | 13 | "He sighed, swirling the whiskey" | | 14 | "She pushed away her half-finished" | | 15 | "she called to the bartender" | | 16 | "She strode out of the" |
| | ratio | 0.395 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 40 | | totalSentences | 43 | | matches | | 0 | "The distinctive green neon sign" | | 1 | "Aurora Carter, her straight shoulder-length" | | 2 | "The dimly lit interior enveloped" | | 3 | "She slid onto a worn" | | 4 | "Her bright blue eyes scanned" | | 5 | "The place hadn't changed a" | | 6 | "she said to the bartender," | | 7 | "He nodded, his hazel eyes" | | 8 | "She noticed the slight limp" | | 9 | "Aurora's attention was drawn to" | | 10 | "Something about the set of" | | 11 | "She squinted, trying to make" | | 12 | "Aurora's breath caught in her" | | 13 | "The man blinked, his rugged" | | 14 | "He slid off his stool," | | 15 | "Aurora stiffened, memories flooding back." | | 16 | "The last time she'd seen" | | 17 | "That was three years ago." | | 18 | "Evan pulled back, his hands" | | 19 | "His eyes roved over her" |
| | ratio | 0.93 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 43 | | matches | | 0 | "Before she could react, he" |
| | ratio | 0.023 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 34.09% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 2 | | matches | | 0 | "His eyes roved, a glint of something unreadable in their depths" | | 1 | "she shot back, her cool mask slipping" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 20 | | tagDensity | 0.35 | | leniency | 0.7 | | rawRatio | 0.143 | | effectiveRatio | 0.1 | |