| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.538 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 558 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 19.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 558 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "chill" | | 1 | "warmth" | | 2 | "scanned" | | 3 | "pulsed" | | 4 | "velvet" | | 5 | "whisper" | | 6 | "crystal" | | 7 | "structure" | | 8 | "navigated" |
| |
| 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 | 23 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 23 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 29 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 558 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 59.09% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 440 | | uniqueNames | 9 | | maxNameDensity | 1.82 | | worstName | "Aurora" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Aurora" | | discoveredNames | | London | 1 | | Heartstone | 1 | | Pendant | 1 | | Nyx | 5 | | Dymas | 1 | | Aurora | 8 | | Cardiff | 1 | | Fae-forged | 1 | | Isolde | 1 |
| | persons | | | places | | 0 | "London" | | 1 | "Dymas" | | 2 | "Cardiff" |
| | globalScore | 0.591 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 23 | | 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 | 558 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 29 | | matches | (empty) | |
| 57.57% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 15 | | mean | 37.2 | | std | 13.08 | | cv | 0.351 | | sampleLengths | | 0 | 57 | | 1 | 13 | | 2 | 42 | | 3 | 35 | | 4 | 25 | | 5 | 40 | | 6 | 67 | | 7 | 22 | | 8 | 44 | | 9 | 40 | | 10 | 34 | | 11 | 40 | | 12 | 43 | | 13 | 30 | | 14 | 26 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 23 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 71 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 29 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 448 | | adjectiveStacks | 2 | | stackExamples | | 0 | "silent, floating silver platters" | | 1 | "ice-cold against her" |
| | adverbCount | 12 | | adverbRatio | 0.026785714285714284 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.013392857142857142 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 29 | | echoCount | 0 | | echoWords | (empty) | |
| 85.74% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 29 | | mean | 19.24 | | std | 7.01 | | cv | 0.364 | | sampleLengths | | 0 | 21 | | 1 | 18 | | 2 | 18 | | 3 | 13 | | 4 | 23 | | 5 | 19 | | 6 | 9 | | 7 | 26 | | 8 | 21 | | 9 | 4 | | 10 | 26 | | 11 | 14 | | 12 | 23 | | 13 | 18 | | 14 | 26 | | 15 | 22 | | 16 | 23 | | 17 | 21 | | 18 | 28 | | 19 | 12 | | 20 | 34 | | 21 | 30 | | 22 | 10 | | 23 | 30 | | 24 | 13 | | 25 | 14 | | 26 | 16 | | 27 | 14 | | 28 | 12 |
| |
| 96.55% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.6551724137931034 | | totalSentences | 29 | | uniqueOpeners | 19 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 23 | | matches | | 0 | "Faintly glowing violet eyes scanned" |
| | ratio | 0.043 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 2 | | totalSentences | 23 | | matches | | 0 | "They moved deeper into Dymas," | | 1 | "They navigated a labyrinth of" |
| | ratio | 0.087 | |
| 46.96% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 19 | | totalSentences | 23 | | matches | | 0 | "Aurora crossed the threshold past" | | 1 | "The London chill vanished instantly," | | 2 | "Nyx detached from the bark" | | 3 | "Aurora touched the Heartstone Pendant" | | 4 | "The deep crimson gemstone pulsed" | | 5 | "Aurora said, kneeling to scoop" | | 6 | "Nyx murmured, their voice sounding" | | 7 | "They moved deeper into Dymas," | | 8 | "Aurora noted, gripping the silver" | | 9 | "Nyx replied, sinking knee-deep into" | | 10 | "Aurora unzipped her delivery jacket," | | 11 | "The sound of their wings" | | 12 | "Aurora said, checking the crescent-shaped" | | 13 | "Nyx pointed a shadowy finger" | | 14 | "They navigated a labyrinth of" | | 15 | "Every surface hummed with a" | | 16 | "Aurora whispered, drawing the Fae-forged" | | 17 | "The moonsilver dagger remained ice-cold" | | 18 | "Nyx melted into the shadows" |
| | ratio | 0.826 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 23 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 15 | | technicalSentenceCount | 5 | | matches | | 0 | "Aurora crossed the threshold past the ancient oak standing stones, boots sinking into soil that smelled of crushed mint and ozone." | | 1 | "They moved deeper into Dymas, the air growing thick with the scent of caramelized citrus and roasting meats that lingered on the tongue." | | 2 | "No diners populated the vast banquet halls carved directly into the living rock of the hills, only silent, floating silver platters that rotated endlessly in mi…" | | 3 | "They navigated a labyrinth of towering tomato vines that climbed into the amber clouds, the leaves whispering secrets in languages Aurora almost recognized from…" | | 4 | "Every surface hummed with a low, vibrational frequency that made her teeth ache." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 3 | | fancyTags | | 0 | "Nyx murmured (murmur)" | | 1 | "Aurora noted (note)" | | 2 | "Aurora whispered (whisper)" |
| | dialogueSentences | 13 | | tagDensity | 0.462 | | leniency | 0.923 | | rawRatio | 0.5 | | effectiveRatio | 0.462 | |