| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 60.16% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 502 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slowly" | | 1 | "precisely" | | 2 | "suddenly" | | 3 | "slightly" |
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| 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) | |
| 30.28% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 502 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "flickered" | | 1 | "familiar" | | 2 | "weight" | | 3 | "scanning" | | 4 | "methodical" | | 5 | "navigate" | | 6 | "tension" |
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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 | 54 | | matches | (empty) | |
| 89.95% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 54 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 56 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 499 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 98.13% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 15 | | wordCount | 482 | | uniqueNames | 8 | | maxNameDensity | 1.04 | | worstName | "Aurora" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Evan" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Cardiff | 1 | | London | 1 | | Evan | 4 | | Silas | 1 | | Blackwood | 1 | | Aurora | 5 |
| | persons | | 0 | "Raven" | | 1 | "Evan" | | 2 | "Silas" | | 3 | "Blackwood" | | 4 | "Aurora" |
| | places | | | globalScore | 0.981 | | windowScore | 1 | |
| 18.42% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 38 | | glossingSentenceCount | 2 | | matches | | 0 | "walls that seemed to hold more stories than anyone could count" | | 1 | "sounded like an apology and an accusation" |
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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 | 499 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 56 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 20 | | mean | 24.95 | | std | 17.34 | | cv | 0.695 | | sampleLengths | | 0 | 63 | | 1 | 50 | | 2 | 1 | | 3 | 16 | | 4 | 45 | | 5 | 20 | | 6 | 9 | | 7 | 19 | | 8 | 39 | | 9 | 16 | | 10 | 53 | | 11 | 5 | | 12 | 29 | | 13 | 34 | | 14 | 7 | | 15 | 11 | | 16 | 14 | | 17 | 34 | | 18 | 8 | | 19 | 26 |
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| 92.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 54 | | matches | | 0 | "been replaced" | | 1 | "were squared" |
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| 43.14% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 85 | | matches | | 0 | "were adjusting" | | 1 | "was willing" |
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| 40.82% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 56 | | ratio | 0.036 | | matches | | 0 | "He looked different—older, certainly, but more than that." | | 1 | "And Aurora—strong, resilient Aurora—would decide how much of that past she was willing to carry forward." |
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| 84.17% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 488 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 25 | | adverbRatio | 0.05122950819672131 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.014344262295081968 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 56 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 56 | | mean | 8.91 | | std | 6.02 | | cv | 0.676 | | sampleLengths | | 0 | 22 | | 1 | 16 | | 2 | 25 | | 3 | 6 | | 4 | 20 | | 5 | 24 | | 6 | 1 | | 7 | 5 | | 8 | 11 | | 9 | 16 | | 10 | 8 | | 11 | 6 | | 12 | 14 | | 13 | 1 | | 14 | 9 | | 15 | 6 | | 16 | 2 | | 17 | 3 | | 18 | 9 | | 19 | 9 | | 20 | 3 | | 21 | 4 | | 22 | 3 | | 23 | 6 | | 24 | 7 | | 25 | 8 | | 26 | 18 | | 27 | 5 | | 28 | 11 | | 29 | 5 | | 30 | 13 | | 31 | 11 | | 32 | 24 | | 33 | 5 | | 34 | 3 | | 35 | 7 | | 36 | 7 | | 37 | 12 | | 38 | 3 | | 39 | 6 | | 40 | 9 | | 41 | 3 | | 42 | 1 | | 43 | 12 | | 44 | 7 | | 45 | 8 | | 46 | 3 | | 47 | 7 | | 48 | 7 | | 49 | 7 |
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| 77.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5178571428571429 | | totalSentences | 56 | | uniqueOpeners | 29 | |
| 69.44% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 48 | | matches | | 0 | "Maybe something deeper." |
| | ratio | 0.021 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 48 | | matches | | 0 | "She didn't notice him at" | | 1 | "Her eyes were adjusting to" | | 2 | "He looked different—older, certainly, but" | | 3 | "Her first instinct was to" | | 4 | "she said, her voice flat" | | 5 | "He gestured to the empty" | | 6 | "She knew how to navigate" | | 7 | "It sounded like an apology" | | 8 | "She knew what he saw." | | 9 | "Her shoulders were squared, her" | | 10 | "They both knew why she'd" | | 11 | "He looked tired." | | 12 | "His hazel eyes narrowed slightly," | | 13 | "His fingers, adorned with the" |
| | ratio | 0.292 | |
| 64.17% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 38 | | totalSentences | 48 | | matches | | 0 | "The neon green sign flickered" | | 1 | "Aurora pressed her palm against" | | 2 | "The bar smelled of whiskey" | | 3 | "She didn't notice him at" | | 4 | "Her eyes were adjusting to" | | 5 | "The bar was nearly empty," | | 6 | "The voice stopped her mid-stride." | | 7 | "A voice she hadn't heard" | | 8 | "Evan sat at a corner" | | 9 | "He looked different—older, certainly, but" | | 10 | "Something in his posture had" | | 11 | "The confidence that once bordered" | | 12 | "Her first instinct was to" | | 13 | "she said, her voice flat" | | 14 | "He gestured to the empty" | | 15 | "Something in between." | | 16 | "Aurora moved slowly, each step" | | 17 | "She knew how to navigate" | | 18 | "The small crescent-shaped scar on" | | 19 | "It sounded like an apology" |
| | ratio | 0.792 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 48 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 2 | | matches | | 0 | "The bar smelled of whiskey and old paper, maps and photographs blurring together on walls that seemed to hold more stories than anyone could count." | | 1 | "The black hair that used to fall across her face was now cut precisely to her shoulders, framing features that had hardened with experience." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, her voice flat and controlled" |
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| 25.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 6 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0.25 | | effectiveRatio | 0.25 | |