| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 92.96% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1420 | | 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) | |
| 40.14% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1420 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "pulsed" | | 1 | "silence" | | 2 | "flickered" | | 3 | "racing" | | 4 | "pulse" | | 5 | "echo" | | 6 | "rhythmic" | | 7 | "dance" | | 8 | "echoed" | | 9 | "whisper" | | 10 | "resolved" |
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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 | 158 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 158 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 164 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1416 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 1390 | | uniqueNames | 14 | | maxNameDensity | 0.86 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Evan" | | discoveredNames | | Richmond | 2 | | Park | 1 | | Cardiff | 2 | | Heartstone | 3 | | Camden | 1 | | Pen | 1 | | Ponds | 1 | | November | 1 | | London | 1 | | Evan | 3 | | Golden | 1 | | Empress | 1 | | Rory | 12 | | Eva | 3 |
| | persons | | 0 | "Heartstone" | | 1 | "Evan" | | 2 | "Rory" | | 3 | "Eva" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Cardiff" | | 3 | "Pen" | | 4 | "London" | | 5 | "Golden" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.706 | | wordCount | 1416 | | matches | | 0 | "Not circling anymore but approaching, the sound of movement coming from directly behi" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 164 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 31.47 | | std | 20.11 | | cv | 0.639 | | sampleLengths | | 0 | 11 | | 1 | 49 | | 2 | 58 | | 3 | 54 | | 4 | 39 | | 5 | 65 | | 6 | 64 | | 7 | 16 | | 8 | 40 | | 9 | 3 | | 10 | 46 | | 11 | 29 | | 12 | 4 | | 13 | 27 | | 14 | 4 | | 15 | 50 | | 16 | 11 | | 17 | 56 | | 18 | 52 | | 19 | 8 | | 20 | 30 | | 21 | 47 | | 22 | 48 | | 23 | 4 | | 24 | 27 | | 25 | 11 | | 26 | 48 | | 27 | 3 | | 28 | 47 | | 29 | 40 | | 30 | 33 | | 31 | 21 | | 32 | 52 | | 33 | 39 | | 34 | 2 | | 35 | 5 | | 36 | 43 | | 37 | 46 | | 38 | 35 | | 39 | 54 | | 40 | 29 | | 41 | 3 | | 42 | 53 | | 43 | 6 | | 44 | 4 |
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| 98.60% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 158 | | matches | | 0 | "been seven" | | 1 | "was gone" | | 2 | "were curled" |
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| 59.94% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 238 | | matches | | 0 | "was breathing" | | 1 | "was playing" | | 2 | "was working" | | 3 | "was taking" | | 4 | "were drowning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 164 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 475 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.029473684210526315 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.008421052631578947 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 164 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 164 | | mean | 8.63 | | std | 7.29 | | cv | 0.845 | | sampleLengths | | 0 | 11 | | 1 | 4 | | 2 | 1 | | 3 | 12 | | 4 | 5 | | 5 | 27 | | 6 | 17 | | 7 | 2 | | 8 | 20 | | 9 | 4 | | 10 | 3 | | 11 | 4 | | 12 | 8 | | 13 | 5 | | 14 | 6 | | 15 | 2 | | 16 | 7 | | 17 | 2 | | 18 | 3 | | 19 | 25 | | 20 | 4 | | 21 | 7 | | 22 | 22 | | 23 | 10 | | 24 | 1 | | 25 | 3 | | 26 | 23 | | 27 | 9 | | 28 | 24 | | 29 | 5 | | 30 | 7 | | 31 | 3 | | 32 | 10 | | 33 | 22 | | 34 | 8 | | 35 | 1 | | 36 | 13 | | 37 | 7 | | 38 | 9 | | 39 | 6 | | 40 | 11 | | 41 | 2 | | 42 | 6 | | 43 | 15 | | 44 | 3 | | 45 | 6 | | 46 | 8 | | 47 | 3 | | 48 | 12 | | 49 | 5 |
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| 46.14% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.3353658536585366 | | totalSentences | 164 | | uniqueOpeners | 55 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 131 | | matches | | 0 | "Just those words printed on" | | 1 | "Of course there was no" | | 2 | "Then her phone went dark." | | 3 | "Of course it hadn't moved." |
| | ratio | 0.031 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 131 | | matches | | 0 | "She was certain of it." | | 1 | "She adjusted the strap of" | | 2 | "She'd known that." | | 3 | "She touched the Heartstone through" | | 4 | "She was late." | | 5 | "She'd seen precisely one other" | | 6 | "It was November." | | 7 | "She crouched, brushing her fingers" | | 8 | "Her phone buzzed in her" | | 9 | "WE SEE YOU." | | 10 | "Her thumb hovered over the" | | 11 | "Her messages app opened to" | | 12 | "It had been at sixty" | | 13 | "She was breathing too fast." | | 14 | "She turned to leave." | | 15 | "She'd made certain of that" | | 16 | "She'd told no one about" | | 17 | "She looked right." | | 18 | "She started walking toward the" | | 19 | "She was sure of it." |
| | ratio | 0.26 | |
| 63.05% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 104 | | totalSentences | 131 | | matches | | 0 | "The standing stones rose against" | | 1 | "Rory counted them again." | | 2 | "There had been seven when" | | 3 | "She was certain of it." | | 4 | "She adjusted the strap of" | | 5 | "The moon hung fat and" | | 6 | "Time moved strangely here." | | 7 | "She'd known that." | | 8 | "Isolde had warned her." | | 9 | "The note had been specific." | | 10 | "The pendant will show you" | | 11 | "Dust and old secrets." | | 12 | "She touched the Heartstone through" | | 13 | "The gem pulsed against her" | | 14 | "She was late." | | 15 | "The ride from Camden had" | | 16 | "Rangers didn't patrol this far" | | 17 | "She'd seen precisely one other" | | 18 | "Neither had acknowledged the other." | | 19 | "The wildflowers should not have" |
| | ratio | 0.794 | |
| 76.34% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 131 | | matches | | 0 | "Now it thrummed with a" | | 1 | "Before she could tap it," |
| | ratio | 0.015 | |
| 21.56% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 9 | | matches | | 0 | "Just those words printed on cream-colored paper that smelled of woodsmoke and something else, something that made her think of her grandmother's attic in Cardif…" | | 1 | "The gem pulsed against her sternum, a second heartbeat that had grown stronger with every step she took past the park's boundary." | | 2 | "She'd seen precisely one other living soul, a man walking his dog near the Pen Ponds, his flashlight swinging wild arcs across the grass." | | 3 | "The trunks stood shoulder to shoulder, their branches interlocking overhead in a lattice that blocked even the strange orange moon." | | 4 | "When she glanced down, they were already closing behind her, stems straightening, petals unbruised." | | 5 | "The pendant seared through her shirt, through her skin, a brand of heat that made her vision swim." | | 6 | "Her heel caught on something, and she went down hard, her spine cracking against a root that hadn't been there a moment ago." | | 7 | "The pendant flew from her grip, its chain slithering across the wildflowers, and came to rest three feet away, pulsing faster now, its glow approaching a steady…" | | 8 | "The ground shifted beneath her palms, the wildflowers pressing down, flattening themselves against the earth as if bowing." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |