| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 92.02% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1253 | | 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) | |
| 16.20% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1253 | | totalAiIsms | 21 | | found | | | highlights | | 0 | "shimmered" | | 1 | "pulse" | | 2 | "warmth" | | 3 | "traced" | | 4 | "oppressive" | | 5 | "silence" | | 6 | "whisper" | | 7 | "pulsed" | | 8 | "rhythmic" | | 9 | "throb" | | 10 | "weight" | | 11 | "vibrated" |
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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 | 188 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 188 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 188 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1253 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 94.13% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 1253 | | uniqueNames | 5 | | maxNameDensity | 1.12 | | worstName | "Aurora" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Nyx" | | discoveredNames | | Veil | 1 | | Heartstone | 2 | | Fae | 2 | | Nyx | 7 | | Aurora | 14 |
| | persons | | | places | | | globalScore | 0.941 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 90 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like swallowing smoke" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.798 | | wordCount | 1253 | | matches | | 0 | "Not a beam, but a pulse" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 188 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 29.14 | | std | 21.1 | | cv | 0.724 | | sampleLengths | | 0 | 52 | | 1 | 79 | | 2 | 36 | | 3 | 29 | | 4 | 4 | | 5 | 16 | | 6 | 6 | | 7 | 66 | | 8 | 16 | | 9 | 9 | | 10 | 76 | | 11 | 11 | | 12 | 28 | | 13 | 29 | | 14 | 25 | | 15 | 44 | | 16 | 16 | | 17 | 9 | | 18 | 25 | | 19 | 19 | | 20 | 16 | | 21 | 24 | | 22 | 7 | | 23 | 43 | | 24 | 53 | | 25 | 2 | | 26 | 55 | | 27 | 40 | | 28 | 10 | | 29 | 22 | | 30 | 7 | | 31 | 58 | | 32 | 4 | | 33 | 39 | | 34 | 47 | | 35 | 52 | | 36 | 3 | | 37 | 3 | | 38 | 31 | | 39 | 10 | | 40 | 55 | | 41 | 23 | | 42 | 54 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 188 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 221 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 188 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1256 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.02388535031847134 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0023885350318471337 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 188 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 188 | | mean | 6.66 | | std | 4.53 | | cv | 0.679 | | sampleLengths | | 0 | 6 | | 1 | 18 | | 2 | 8 | | 3 | 20 | | 4 | 7 | | 5 | 2 | | 6 | 17 | | 7 | 14 | | 8 | 5 | | 9 | 7 | | 10 | 5 | | 11 | 7 | | 12 | 15 | | 13 | 2 | | 14 | 4 | | 15 | 16 | | 16 | 2 | | 17 | 6 | | 18 | 6 | | 19 | 4 | | 20 | 13 | | 21 | 4 | | 22 | 8 | | 23 | 4 | | 24 | 8 | | 25 | 5 | | 26 | 3 | | 27 | 3 | | 28 | 3 | | 29 | 2 | | 30 | 21 | | 31 | 4 | | 32 | 12 | | 33 | 11 | | 34 | 16 | | 35 | 6 | | 36 | 2 | | 37 | 2 | | 38 | 6 | | 39 | 9 | | 40 | 6 | | 41 | 27 | | 42 | 16 | | 43 | 12 | | 44 | 7 | | 45 | 8 | | 46 | 2 | | 47 | 9 | | 48 | 6 | | 49 | 8 |
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| 52.66% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.35106382978723405 | | totalSentences | 188 | | uniqueOpeners | 66 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 160 | | matches | | 0 | "Just a seamless dome of" | | 1 | "Just a drape of ivy" | | 2 | "Away from the tables." | | 3 | "Then a gap." | | 4 | "Then three again." | | 5 | "Then follow the warmth." | | 6 | "Just the ones that remember" |
| | ratio | 0.044 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 160 | | matches | | 0 | "Her left wrist ached." | | 1 | "They stood perfectly still." | | 2 | "It knows we’re close." | | 3 | "They were calcified meat and" | | 4 | "They crested a rise and" | | 5 | "Their fingers were stained purple" | | 6 | "Their bellies distended but their" | | 7 | "They crouched, letting their knees" | | 8 | "They never stop." | | 9 | "They eat to keep the" | | 10 | "It’s a machine." | | 11 | "Her mind stripped the romance" | | 12 | "She stepped down the first" | | 13 | "She poked it with her" | | 14 | "They didn’t look away from" | | 15 | "It feeds on words." | | 16 | "She adjusted her grip on" | | 17 | "She kept her mouth shut." | | 18 | "They turned toward a grove" | | 19 | "Their branches trailed into a" |
| | ratio | 0.238 | |
| 56.87% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 129 | | totalSentences | 160 | | matches | | 0 | "The air split like wet" | | 1 | "Aurora stepped through the tear" | | 2 | "Nyx slipped through after her," | | 3 | "The sky burned amber, low" | | 4 | "Fruit hung in heavy clusters." | | 5 | "Grapes the size of fists." | | 6 | "Peaches dripping gold sap onto" | | 7 | "The ground gave slightly under" | | 8 | "Her left wrist ached." | | 9 | "The crescent scar pulled tight" | | 10 | "Warmth radiated through fabric and" | | 11 | "Nyx tilted their head." | | 12 | "The violet in their eyes" | | 13 | "They stood perfectly still." | | 14 | "Shadows pooled around their boots," | | 15 | "It knows we’re close." | | 16 | "Aurora traced the pendant’s edge" | | 17 | "Metal pressed against her ribs." | | 18 | "The path wound between stone" | | 19 | "The carvings weren’t stone." |
| | ratio | 0.806 | |
| 62.50% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 160 | | matches | | 0 | "To the center." | | 1 | "To the table." |
| | ratio | 0.013 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 4 | | matches | | 0 | "Giant trees arched overhead, their trunks wrapped in bark that shimmered like hammered bronze." | | 1 | "Long tables of polished obsidian stretched for hundreds of yards, laden with mountains of bread, roasted beasts, towers of cheese, rivers of wine that sloshed w…" | | 2 | "Servants in ash-gray livery moved with stiff, practiced steps, plating food that steamed with unnatural vigor." | | 3 | "They vibrated against her skin, pulling her fingers forward, drawing her hand deeper into the bowl’s center." |
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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 | |