| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 87.50% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 800 | | totalAiIsmAdverbs | 2 | | 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) | |
| 6.25% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 800 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "pulse" | | 1 | "loomed" | | 2 | "pulsed" | | 3 | "warmth" | | 4 | "footsteps" | | 5 | "echo" | | 6 | "flicker" | | 7 | "scanning" | | 8 | "silk" | | 9 | "searing" | | 10 | "whisper" | | 11 | "trembled" |
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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 | 95 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 95 | | filterMatches | (empty) | | hedgeMatches | | |
| 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 | 40 | | ratio | 0 | | matches | (empty) | |
| 75.95% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 25 | | markdownWords | 49 | | totalWords | 790 | | ratio | 0.062 | | matches | | 0 | "Just the cold" | | 1 | "Or the rain." | | 2 | "Isolde’s grove." | | 3 | "“Find what’s yours,”" | | 4 | "something" | | 5 | "moved" | | 6 | "between" | | 7 | "Logic" | | 8 | "I need to get to the other side." | | 9 | "shape" | | 10 | "wrong" | | 11 | "was" | | 12 | "smile" | | 13 | "things" | | 14 | "peel" | | 15 | "“You shouldn’t be here,”" | | 16 | "“The pendant doesn’t belong to you.”" | | 17 | "warped" | | 18 | "“You leave.”" | | 19 | "should" | | 20 | "smiled" | | 21 | "step" | | 22 | "“Take it.”" | | 23 | "ached" | | 24 | "vanished" |
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| 75.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 2 | | matches | | 0 | "*Just the cold*, she told herself." | | 1 | "*Logic*, she told herself." |
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| 77.82% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 762 | | uniqueNames | 6 | | maxNameDensity | 1.44 | | worstName | "Rory" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Rory" | | discoveredNames | | Richmond | 1 | | Park | 1 | | Eva | 2 | | Golden | 1 | | Empress | 1 | | Rory | 11 |
| | persons | | | places | | | globalScore | 0.778 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 49 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 73.42% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.266 | | wordCount | 790 | | matches | | 0 | "Not the usual damp earth and pine, but something sharp, like ozone after a storm, with an undercurr" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 100 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 27.24 | | std | 24.21 | | cv | 0.889 | | sampleLengths | | 0 | 83 | | 1 | 56 | | 2 | 5 | | 3 | 56 | | 4 | 4 | | 5 | 86 | | 6 | 11 | | 7 | 43 | | 8 | 2 | | 9 | 36 | | 10 | 1 | | 11 | 43 | | 12 | 47 | | 13 | 4 | | 14 | 45 | | 15 | 11 | | 16 | 3 | | 17 | 64 | | 18 | 8 | | 19 | 12 | | 20 | 23 | | 21 | 16 | | 22 | 28 | | 23 | 6 | | 24 | 19 | | 25 | 7 | | 26 | 34 | | 27 | 30 | | 28 | 7 |
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| 97.88% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 95 | | matches | | 0 | "been given" | | 1 | "being torn" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 122 | | matches | | |
| 28.57% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 100 | | ratio | 0.04 | | matches | | 0 | "She’d told herself it was just a shortcut—cutting through the park to avoid the rain-soaked streets—but the way the trees seemed to lean in closer, the way the path twisted like a living thing, told her otherwise." | | 1 | "The wildflowers along the path—pale bluebells, golden buttercups—bloomed too brightly, too suddenly, as if someone had turned up the light." | | 2 | "Rory had laughed it off—another of Eva’s wild schemes—but now, standing in the heart of the grove, she realised she’d been waiting for this moment." | | 3 | "Then—a rustle." |
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| 85.13% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 772 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 44 | | adverbRatio | 0.05699481865284974 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007772020725388601 | |
| 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 | 7.9 | | std | 6.65 | | cv | 0.841 | | sampleLengths | | 0 | 9 | | 1 | 23 | | 2 | 14 | | 3 | 37 | | 4 | 14 | | 5 | 15 | | 6 | 6 | | 7 | 12 | | 8 | 6 | | 9 | 3 | | 10 | 5 | | 11 | 7 | | 12 | 3 | | 13 | 3 | | 14 | 20 | | 15 | 3 | | 16 | 5 | | 17 | 6 | | 18 | 9 | | 19 | 4 | | 20 | 18 | | 21 | 28 | | 22 | 10 | | 23 | 25 | | 24 | 5 | | 25 | 11 | | 26 | 3 | | 27 | 2 | | 28 | 19 | | 29 | 2 | | 30 | 1 | | 31 | 1 | | 32 | 15 | | 33 | 2 | | 34 | 4 | | 35 | 4 | | 36 | 3 | | 37 | 16 | | 38 | 3 | | 39 | 6 | | 40 | 1 | | 41 | 4 | | 42 | 15 | | 43 | 7 | | 44 | 5 | | 45 | 4 | | 46 | 8 | | 47 | 4 | | 48 | 4 | | 49 | 14 |
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| 34.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.31 | | totalSentences | 100 | | uniqueOpeners | 31 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 83 | | matches | | 0 | "*Just the cold*, she told" | | 1 | "Just the hum of the" | | 2 | "Then she saw it." | | 3 | "Just *was*, like a stain" | | 4 | "Then the ground gave way" |
| | ratio | 0.06 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 83 | | matches | | 0 | "She’d told herself it was" | | 1 | "She’d passed them before, always" | | 2 | "She stepped between the stones." | | 3 | "She’d heard about this place." | | 4 | "She should’ve turned back." | | 5 | "She’d been given it in" | | 6 | "she’d said, before vanishing into" | | 7 | "They didn’t echo." | | 8 | "She turned slowly, scanning the" | | 9 | "She chose the deeper path." | | 10 | "*I need to get to" | | 11 | "She told herself it was" | | 12 | "It didn’t move." | | 13 | "She took a step forward." | | 14 | "It tilted its head, and" | | 15 | "It was a *peel*, like" | | 16 | "it said, and the words" | | 17 | "She could run." | | 18 | "She *should* run." | | 19 | "she asked, her voice raw" |
| | ratio | 0.277 | |
| 98.55% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 83 | | matches | | 0 | "The air in Richmond Park" | | 1 | "Rory adjusted the strap of" | | 2 | "She’d told herself it was" | | 3 | "The standing stones loomed ahead," | | 4 | "She’d passed them before, always" | | 5 | "The pendant at her collar" | | 6 | "She stepped between the stones." | | 7 | "The moment she crossed, the" | | 8 | "The wind died." | | 9 | "The air thickened." | | 10 | "The wildflowers along the path—pale" | | 11 | "Rory’s breath hitched." | | 12 | "She’d heard about this place." | | 13 | "*Isolde’s grove.* A pocket between" | | 14 | "A place where time didn’t" | | 15 | "She should’ve turned back." | | 16 | "She’d been given it in" | | 17 | "she’d said, before vanishing into" | | 18 | "Rory had laughed it off—another" | | 19 | "The first wrongness came in" |
| | ratio | 0.723 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 83 | | matches | | 0 | "Now, the grove swallowed the" | | 1 | "*Or the rain.*" |
| | ratio | 0.024 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 1 | | matches | | 0 | "The wildflowers along the path—pale bluebells, golden buttercups—bloomed too brightly, too suddenly, as if someone had turned up the light." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 2 | | matches | | 0 | "it said, and the words slithered into her skull, warm and thick as honey" | | 1 | "she asked, her voice raw" |
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| 25.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 8 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0.333 | | effectiveRatio | 0.25 | |