| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.231 | | leniency | 0.462 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.49% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 908 | | 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) | |
| 22.91% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 908 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "pulsed" | | 1 | "pulse" | | 2 | "velvet" | | 3 | "whisper" | | 4 | "gleaming" | | 5 | "quickened" | | 6 | "flicker" | | 7 | "familiar" | | 8 | "silk" | | 9 | "porcelain" | | 10 | "searing" | | 11 | "silence" |
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| 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 | 118 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 118 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 128 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 11 | | totalWords | 898 | | ratio | 0.012 | | matches | | 0 | "RUN" | | 1 | "5 mins late. 10 mins late. Walked out?" | | 2 | "lord" | | 3 | "rupture" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 855 | | uniqueNames | 7 | | maxNameDensity | 0.94 | | worstName | "Aurora" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Aurora" | | discoveredNames | | Aurora | 8 | | Fae | 1 | | Grove | 1 | | Evan | 1 | | Silas | 2 | | Victorian | 1 | | Eva | 3 |
| | persons | | 0 | "Aurora" | | 1 | "Evan" | | 2 | "Silas" | | 3 | "Eva" |
| | places | | 0 | "Fae" | | 1 | "Grove" | | 2 | "Victorian" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | 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 | 898 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 128 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 28.97 | | std | 26.89 | | cv | 0.928 | | sampleLengths | | 0 | 107 | | 1 | 79 | | 2 | 1 | | 3 | 66 | | 4 | 1 | | 5 | 81 | | 6 | 54 | | 7 | 41 | | 8 | 10 | | 9 | 51 | | 10 | 9 | | 11 | 29 | | 12 | 3 | | 13 | 14 | | 14 | 36 | | 15 | 30 | | 16 | 10 | | 17 | 3 | | 18 | 39 | | 19 | 13 | | 20 | 35 | | 21 | 54 | | 22 | 11 | | 23 | 28 | | 24 | 37 | | 25 | 10 | | 26 | 3 | | 27 | 2 | | 28 | 28 | | 29 | 7 | | 30 | 6 |
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| 99.32% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 118 | | matches | | 0 | "been siphoned" | | 1 | "been when" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 143 | | matches | (empty) | |
| 8.93% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 128 | | ratio | 0.047 | | matches | | 0 | "She’d walked this path a dozen times before—twice last week alone—but tonight the Fae Grove felt tighter, as though the air had been siphoned off and replaced with something thicker, syrupy." | | 1 | "Her phone buzzed in her pocket—a text from Silas at the bar, asking if she’d forgotten her shift." | | 2 | "Its face was a blur, a watercolour smudge, and its dress—a white frock with frilled sleeves like something from a Victorian orphan—hung off its body as though it were too small." | | 3 | "The words were Eva’s, but the tone was wrong—flat, robotic, as if spoken through a broken recorder." | | 4 | "The figure—Eva—whirled." | | 5 | "The figure—no, the thing—screeched." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 872 | | adjectiveStacks | 1 | | stackExamples | | 0 | "over frost-kissed leaves." |
| | adverbCount | 28 | | adverbRatio | 0.03211009174311927 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0045871559633027525 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 128 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 128 | | mean | 7.02 | | std | 6.14 | | cv | 0.875 | | sampleLengths | | 0 | 16 | | 1 | 13 | | 2 | 31 | | 3 | 6 | | 4 | 3 | | 5 | 2 | | 6 | 7 | | 7 | 29 | | 8 | 12 | | 9 | 24 | | 10 | 2 | | 11 | 2 | | 12 | 21 | | 13 | 12 | | 14 | 6 | | 15 | 1 | | 16 | 12 | | 17 | 2 | | 18 | 8 | | 19 | 11 | | 20 | 12 | | 21 | 11 | | 22 | 9 | | 23 | 1 | | 24 | 1 | | 25 | 7 | | 26 | 1 | | 27 | 1 | | 28 | 20 | | 29 | 8 | | 30 | 20 | | 31 | 6 | | 32 | 18 | | 33 | 4 | | 34 | 2 | | 35 | 1 | | 36 | 13 | | 37 | 9 | | 38 | 11 | | 39 | 6 | | 40 | 8 | | 41 | 18 | | 42 | 13 | | 43 | 10 | | 44 | 4 | | 45 | 6 | | 46 | 6 | | 47 | 1 | | 48 | 1 | | 49 | 3 |
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| 51.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3671875 | | totalSentences | 128 | | uniqueOpeners | 47 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 88 | | matches | | 0 | "Just the pendulum-thump of her" | | 1 | "Just the black grass and" | | 2 | "Then the whisper again, closer" | | 3 | "Then a third." | | 4 | "Then a voice that was" |
| | ratio | 0.057 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 88 | | matches | | 0 | "Its crimson heart pulsed faintly," | | 1 | "She’d walked this path a" | | 2 | "Her boots crunched over frost-kissed" | | 3 | "She dropped to her haunches," | | 4 | "She dug her fingers into" | | 5 | "Her breath misted, but the" | | 6 | "She stood, spine rigid, and" | | 7 | "She turned sharply, arm knocking" | | 8 | "Her phone buzzed in her" | | 9 | "She stared at it, the" | | 10 | "It hadn’t been five minutes" | | 11 | "Its face was a blur," | | 12 | "Its teeth were too large." | | 13 | "She fell, the pendant flying" | | 14 | "It struck the ground, its" | | 15 | "Its form rippled, fraying at" | | 16 | "She scrabbled for the pendant," | | 17 | "It lunged at the tear," | | 18 | "She took a step toward" | | 19 | "She reached it, pressed her" |
| | ratio | 0.239 | |
| 45.23% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 73 | | totalSentences | 88 | | matches | | 0 | "The pendant dug into Aurora’s" | | 1 | "Its crimson heart pulsed faintly," | | 2 | "She’d walked this path a" | | 3 | "Her boots crunched over frost-kissed" | | 4 | "She dropped to her haunches," | | 5 | "The silver chain caught the" | | 6 | "She dug her fingers into" | | 7 | "A mirror fragment, its surface" | | 8 | "The air smelled of burnt" | | 9 | "The voice was a whisper," | | 10 | "The trees beyond the clearing" | | 11 | "A minute ago, there’d been" | | 12 | "Her breath misted, but the" | | 13 | "This time the voice had" | | 14 | "The same age she’d been" | | 15 | "She stood, spine rigid, and" | | 16 | "The grass grew darker the" | | 17 | "The pendulum-thump in her palm" | | 18 | "The pendant was warm now," | | 19 | "A flicker of movement." |
| | ratio | 0.83 | |
| 56.82% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 88 | | matches | | 0 | "Now they were straight and" |
| | ratio | 0.011 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 6 | | matches | | 0 | "She’d walked this path a dozen times before—twice last week alone—but tonight the Fae Grove felt tighter, as though the air had been siphoned off and replaced w…" | | 1 | "A mirror fragment, its surface warped as though liquid had frozen mid-puddle." | | 2 | "Now they were straight and featureless, their bark gleaming as if polished." | | 3 | "Its face was a blur, a watercolour smudge, and its dress—a white frock with frilled sleeves like something from a Victorian orphan—hung off its body as though i…" | | 4 | "The words were Eva’s, but the tone was wrong—flat, robotic, as if spoken through a broken recorder." | | 5 | "The figure’s wails crescendoed, a sound that made her teeth ache." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 73.08% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | 0 | "she whispered (whisper)" |
| | dialogueSentences | 13 | | tagDensity | 0.154 | | leniency | 0.308 | | rawRatio | 0.5 | | effectiveRatio | 0.154 | |