| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.52% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1435 | | 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) | |
| 16.38% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1435 | | totalAiIsms | 24 | | found | | | highlights | | 0 | "chill" | | 1 | "pulse" | | 2 | "shattered" | | 3 | "scanned" | | 4 | "stark" | | 5 | "oppressive" | | 6 | "structure" | | 7 | "constructed" | | 8 | "crystal" | | 9 | "stomach" | | 10 | "warmth" | | 11 | "whisper" | | 12 | "weight" | | 13 | "echoed" | | 14 | "pulsed" | | 15 | "flicked" | | 16 | "footsteps" |
| |
| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
| | 1 | | label | "knuckles turned white" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "knuckles turned white" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 198 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 198 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 198 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 19 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1429 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 1429 | | uniqueNames | 12 | | maxNameDensity | 1.33 | | worstName | "Aurora" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Aurora" | | discoveredNames | | Aurora | 19 | | Richmond | 1 | | Park | 1 | | London | 2 | | Veil | 1 | | Heartstone | 4 | | Fae-Forged | 2 | | Blade | 2 | | Nyx | 11 | | Belphegor | 1 | | Stone | 2 | | Dymas | 2 |
| | persons | | 0 | "Aurora" | | 1 | "Heartstone" | | 2 | "Nyx" | | 3 | "Belphegor" | | 4 | "Stone" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "London" | | 3 | "Veil" | | 4 | "Dymas" |
| | globalScore | 0.835 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 120 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like cured meat" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1429 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 198 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 60 | | mean | 23.82 | | std | 16.89 | | cv | 0.709 | | sampleLengths | | 0 | 49 | | 1 | 37 | | 2 | 4 | | 3 | 33 | | 4 | 41 | | 5 | 42 | | 6 | 1 | | 7 | 42 | | 8 | 43 | | 9 | 3 | | 10 | 56 | | 11 | 28 | | 12 | 5 | | 13 | 34 | | 14 | 11 | | 15 | 55 | | 16 | 51 | | 17 | 6 | | 18 | 42 | | 19 | 24 | | 20 | 5 | | 21 | 47 | | 22 | 2 | | 23 | 20 | | 24 | 10 | | 25 | 48 | | 26 | 17 | | 27 | 12 | | 28 | 53 | | 29 | 38 | | 30 | 15 | | 31 | 3 | | 32 | 37 | | 33 | 36 | | 34 | 32 | | 35 | 9 | | 36 | 3 | | 37 | 32 | | 38 | 4 | | 39 | 36 | | 40 | 2 | | 41 | 35 | | 42 | 42 | | 43 | 28 | | 44 | 6 | | 45 | 25 | | 46 | 4 | | 47 | 13 | | 48 | 10 | | 49 | 32 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 198 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 256 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 198 | | ratio | 0.005 | | matches | | 0 | "Food piled high on platters—whole roasted beasts, mountains of bread, cascades of honey." |
| |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1435 | | adjectiveStacks | 2 | | stackExamples | | 0 | "small crescent-shaped scar" | | 1 | "red hot against their" |
| | adverbCount | 23 | | adverbRatio | 0.01602787456445993 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0027874564459930314 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 198 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 198 | | mean | 7.22 | | std | 3.57 | | cv | 0.495 | | sampleLengths | | 0 | 9 | | 1 | 15 | | 2 | 17 | | 3 | 8 | | 4 | 15 | | 5 | 11 | | 6 | 11 | | 7 | 4 | | 8 | 4 | | 9 | 6 | | 10 | 13 | | 11 | 10 | | 12 | 4 | | 13 | 8 | | 14 | 8 | | 15 | 10 | | 16 | 5 | | 17 | 6 | | 18 | 7 | | 19 | 19 | | 20 | 5 | | 21 | 11 | | 22 | 1 | | 23 | 4 | | 24 | 9 | | 25 | 6 | | 26 | 11 | | 27 | 8 | | 28 | 4 | | 29 | 7 | | 30 | 13 | | 31 | 14 | | 32 | 9 | | 33 | 3 | | 34 | 10 | | 35 | 3 | | 36 | 11 | | 37 | 9 | | 38 | 12 | | 39 | 11 | | 40 | 8 | | 41 | 7 | | 42 | 13 | | 43 | 5 | | 44 | 4 | | 45 | 5 | | 46 | 5 | | 47 | 6 | | 48 | 4 | | 49 | 10 |
| |
| 45.12% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.3282828282828283 | | totalSentences | 198 | | uniqueOpeners | 65 | |
| 17.45% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 191 | | matches | | 0 | "Faintly glowing violet eyes fixed" |
| | ratio | 0.005 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 191 | | matches | | 0 | "She paused before the shimmering" | | 1 | "Her breath plumed in the" | | 2 | "She tucked the gem beneath" | | 3 | "She stumbled onto soft grass." | | 4 | "Her straight shoulder-length black hair" | | 5 | "She wiped sweat from her" | | 6 | "It coated her tongue." | | 7 | "She drew the Fae-Forged Blade" | | 8 | "Its leaf-shaped edge hummed, a" | | 9 | "Her bright blue eyes narrowed." | | 10 | "She kicked a fallen pomegranate." | | 11 | "They writhed before settling." | | 12 | "They walked deeper into the" | | 13 | "She gripped the Heartstone through" | | 14 | "They wore rags and carried" | | 15 | "They only reached for a" | | 16 | "She tightened her grip on" | | 17 | "Her knuckles turned white." | | 18 | "We need the source of" | | 19 | "It pulled toward the center" |
| | ratio | 0.225 | |
| 12.36% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 171 | | totalSentences | 191 | | matches | | 0 | "The crimson gem against Aurora's" | | 1 | "She paused before the shimmering" | | 2 | "The air rippled like heat" | | 3 | "Her breath plumed in the" | | 4 | "Nyx coalesced beside her, a" | | 5 | "The shadow's edges blurred, merging" | | 6 | "The Heartstone burns hotter." | | 7 | "Aurora lifted the chain." | | 8 | "The silver bit into her" | | 9 | "The pulse matched the beat" | | 10 | "She tucked the gem beneath" | | 11 | "Humidity wrapped around her like" | | 12 | "The grey sky shattered into" | | 13 | "Gravity shifted, pressing down on" | | 14 | "She stumbled onto soft grass." | | 15 | "The ground sponged under her" | | 16 | "Nyx flowed through the barrier" | | 17 | "The shadow solidified, taking on" | | 18 | "Violet eyes scanned the horizon." | | 19 | "The entity's form rippled, adjusting" |
| | ratio | 0.895 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 191 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 49 | | technicalSentenceCount | 1 | | matches | | 0 | "Its leaf-shaped edge hummed, a low vibration that traveled up her arm." |
| |
| 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 | |