| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 91.21% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 569 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 569 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "warmth" | | 1 | "weight" | | 2 | "silk" | | 3 | "profound" | | 4 | "silence" | | 5 | "velvet" | | 6 | "rhythmic" | | 7 | "vibrated" | | 8 | "pulse" | | 9 | "echoed" | | 10 | "symphony" | | 11 | "crystal" | | 12 | "footsteps" |
| |
| 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 | 44 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 44 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 44 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 569 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 97.28% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 16 | | wordCount | 569 | | uniqueNames | 8 | | maxNameDensity | 1.05 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Aurora" | | discoveredNames | | London | 2 | | Heartstone | 1 | | Nyx | 3 | | Richmond | 1 | | Aurora | 6 | | Fae-forged | 1 | | Prince | 1 | | Gluttony | 1 |
| | persons | | 0 | "Nyx" | | 1 | "Aurora" | | 2 | "Gluttony" |
| | places | | 0 | "London" | | 1 | "Heartstone" | | 2 | "Richmond" |
| | globalScore | 0.973 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 40 | | 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 | 569 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 44 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 18 | | mean | 31.61 | | std | 23.04 | | cv | 0.729 | | sampleLengths | | 0 | 73 | | 1 | 12 | | 2 | 37 | | 3 | 40 | | 4 | 2 | | 5 | 9 | | 6 | 50 | | 7 | 58 | | 8 | 10 | | 9 | 22 | | 10 | 77 | | 11 | 55 | | 12 | 18 | | 13 | 17 | | 14 | 23 | | 15 | 48 | | 16 | 7 | | 17 | 11 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 44 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 96 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 44 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 575 | | adjectiveStacks | 1 | | stackExamples | | 0 | "long, shadow-woven finger" |
| | adverbCount | 11 | | adverbRatio | 0.019130434782608695 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.01217391304347826 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 44 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 44 | | mean | 12.93 | | std | 5.59 | | cv | 0.432 | | sampleLengths | | 0 | 15 | | 1 | 11 | | 2 | 20 | | 3 | 18 | | 4 | 9 | | 5 | 12 | | 6 | 18 | | 7 | 5 | | 8 | 14 | | 9 | 5 | | 10 | 14 | | 11 | 5 | | 12 | 16 | | 13 | 2 | | 14 | 9 | | 15 | 10 | | 16 | 7 | | 17 | 21 | | 18 | 12 | | 19 | 12 | | 20 | 12 | | 21 | 8 | | 22 | 8 | | 23 | 18 | | 24 | 10 | | 25 | 22 | | 26 | 10 | | 27 | 23 | | 28 | 9 | | 29 | 19 | | 30 | 16 | | 31 | 9 | | 32 | 21 | | 33 | 25 | | 34 | 18 | | 35 | 8 | | 36 | 9 | | 37 | 16 | | 38 | 7 | | 39 | 10 | | 40 | 21 | | 41 | 17 | | 42 | 7 | | 43 | 11 |
| |
| 86.36% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.5454545454545454 | | totalSentences | 44 | | uniqueOpeners | 24 | |
| 77.52% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 43 | | matches | | 0 | "Faintly glowing violet eyes swept" |
| | ratio | 0.023 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 4 | | totalSentences | 43 | | matches | | 0 | "Her fingers brushed the silver" | | 1 | "Her bare feet skimmed the" | | 2 | "They bloomed in dense clusters" | | 3 | "She reached out to brush" |
| | ratio | 0.093 | |
| 29.77% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 37 | | totalSentences | 43 | | matches | | 0 | "The oak trees thickened into" | | 1 | "Aurora pulled the collar of" | | 2 | "Her fingers brushed the silver" | | 3 | "Step past the second standing" | | 4 | "Isolde's voice drifted through the" | | 5 | "The half-fae woman glided forward." | | 6 | "Her bare feet skimmed the" | | 7 | "Aurora crossed the invisible threshold." | | 8 | "The ambient roar of Richmond" | | 9 | "Warmth bloomed against her skin." | | 10 | "The cold London air surrendered" | | 11 | "Nyx pointed a long, shadow-woven" | | 12 | "A sky of bruised amber" | | 13 | "Twin suns hung suspended in" | | 14 | "The air felt thick, almost" | | 15 | "Wildflowers blanketed the forest floor" | | 16 | "They bloomed in dense clusters" | | 17 | "Aurora knelt beside a patch" | | 18 | "She reached out to brush" | | 19 | "The blossom recoiled instantly, folding" |
| | ratio | 0.86 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 43 | | matches | (empty) | | ratio | 0 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 2 | | matches | | 0 | "Twin suns hung suspended in the amber ceiling, casting double shadows that twisted in opposite directions whenever Aurora shifted her weight." | | 1 | "Terraced vineyards climbed the rolling hillsides, heavy with translucent grapes that glowed from within like clusters of stained glass." |
| |
| 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 | |