| 33.33% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 3 | | adverbTags | | 0 | "he said quietly [quietly]" | | 1 | "Now he moved slowly [slowly]" | | 2 | "she asked softly [softly]" |
| | dialogueSentences | 36 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0.333 | | effectiveRatio | 0.167 | |
| 49.20% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 689 | | totalAiIsmAdverbs | 7 | | found | | 0 | | | 1 | | | 2 | | | 3 | | adverb | "deliberately" | | count | 1 |
| | 4 | | | 5 | | | 6 | |
| | highlights | | 0 | "quickly" | | 1 | "really" | | 2 | "slowly" | | 3 | "deliberately" | | 4 | "slightly" | | 5 | "suddenly" | | 6 | "softly" |
| |
| 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) | |
| 56.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 689 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "predictable" | | 1 | "silence" | | 2 | "weight" | | 3 | "tension" | | 4 | "velvet" | | 5 | "unspoken" |
| |
| 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 | 56 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 56 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 82 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 686 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 99.70% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 13 | | wordCount | 497 | | uniqueNames | 6 | | maxNameDensity | 1.01 | | worstName | "Lucien" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Lucien" | | discoveredNames | | Eva | 1 | | Lucien | 5 | | Moreau | 1 | | Brick | 1 | | Lane | 1 | | Aurora | 4 |
| | persons | | 0 | "Eva" | | 1 | "Lucien" | | 2 | "Moreau" | | 3 | "Aurora" |
| | places | | | globalScore | 0.997 | | windowScore | 1 | |
| 85.90% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like dust stirred anew by his pres" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 686 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 82 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 15.59 | | std | 13.65 | | cv | 0.876 | | sampleLengths | | 0 | 15 | | 1 | 36 | | 2 | 17 | | 3 | 49 | | 4 | 10 | | 5 | 13 | | 6 | 4 | | 7 | 8 | | 8 | 20 | | 9 | 9 | | 10 | 2 | | 11 | 57 | | 12 | 29 | | 13 | 7 | | 14 | 3 | | 15 | 13 | | 16 | 21 | | 17 | 28 | | 18 | 8 | | 19 | 23 | | 20 | 7 | | 21 | 16 | | 22 | 5 | | 23 | 23 | | 24 | 9 | | 25 | 37 | | 26 | 16 | | 27 | 24 | | 28 | 45 | | 29 | 4 | | 30 | 3 | | 31 | 4 | | 32 | 7 | | 33 | 4 | | 34 | 5 | | 35 | 15 | | 36 | 2 | | 37 | 3 | | 38 | 4 | | 39 | 41 | | 40 | 6 | | 41 | 17 | | 42 | 11 | | 43 | 6 |
| |
| 86.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 56 | | matches | | 0 | "being stalked" | | 1 | "was hidden" | | 2 | "was said" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 103 | | matches | (empty) | |
| 38.33% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 82 | | ratio | 0.037 | | matches | | 0 | "He smelled of sandalwood and something darker—like smoke off stone." | | 1 | "She thought about the first time she’d seen it—how it had felt wrong in its use, like violence wrapped in velvet." | | 2 | "All the things she’d left behind—every choice, every betrayal, every silent departure—felt like dust stirred anew by his presence." |
| |
| 86.30% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 501 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.05189620758483034 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.023952095808383235 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 82 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 82 | | mean | 8.37 | | std | 5.46 | | cv | 0.653 | | sampleLengths | | 0 | 15 | | 1 | 24 | | 2 | 9 | | 3 | 3 | | 4 | 17 | | 5 | 13 | | 6 | 10 | | 7 | 15 | | 8 | 11 | | 9 | 10 | | 10 | 7 | | 11 | 6 | | 12 | 4 | | 13 | 4 | | 14 | 4 | | 15 | 13 | | 16 | 7 | | 17 | 4 | | 18 | 5 | | 19 | 2 | | 20 | 3 | | 21 | 13 | | 22 | 24 | | 23 | 13 | | 24 | 3 | | 25 | 1 | | 26 | 8 | | 27 | 10 | | 28 | 7 | | 29 | 4 | | 30 | 7 | | 31 | 3 | | 32 | 13 | | 33 | 17 | | 34 | 4 | | 35 | 16 | | 36 | 12 | | 37 | 6 | | 38 | 2 | | 39 | 18 | | 40 | 5 | | 41 | 5 | | 42 | 2 | | 43 | 12 | | 44 | 4 | | 45 | 5 | | 46 | 23 | | 47 | 3 | | 48 | 6 | | 49 | 9 |
| |
| 65.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.4146341463414634 | | totalSentences | 82 | | uniqueOpeners | 34 | |
| 64.10% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 52 | | matches | | | ratio | 0.019 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 52 | | matches | | 0 | "He was leaning against the" | | 1 | "It always did." | | 2 | "he said, stepping past her" | | 3 | "She shut the door quickly" | | 4 | "He’d already taken up most" | | 5 | "His tailored charcoal suit looked" | | 6 | "He turned to face her" | | 7 | "His amber eye gleamed." | | 8 | "It circled them like prey" | | 9 | "She didn’t answer." | | 10 | "She wasn't even sure how" | | 11 | "He smelled of sandalwood and" | | 12 | "he said quietly" | | 13 | "Her breath caught a beat" | | 14 | "His voice was low, almost" | | 15 | "She took a sharp step" | | 16 | "It creaked under her weight." | | 17 | "He tilted his head slightly," | | 18 | "She swallowed hard." | | 19 | "He hesitated, then extended the" |
| | ratio | 0.558 | |
| 36.92% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 44 | | totalSentences | 52 | | matches | | 0 | "Aurora opened the door of" | | 1 | "He was leaning against the" | | 2 | "The air shifted when he" | | 3 | "It always did." | | 4 | "he said, stepping past her" | | 5 | "She shut the door quickly" | | 6 | "He’d already taken up most" | | 7 | "His tailored charcoal suit looked" | | 8 | "Stacks of parchment teetered over" | | 9 | "He turned to face her" | | 10 | "His amber eye gleamed." | | 11 | "Ptolemy meowed from the top" | | 12 | "It circled them like prey" | | 13 | "Aurora folded her arms." | | 14 | "She didn’t answer." | | 15 | "There had been no parting" | | 16 | "She wasn't even sure how" | | 17 | "Lucien stepped closer, his presence" | | 18 | "He smelled of sandalwood and" | | 19 | "he said quietly" |
| | ratio | 0.846 | |
| 96.15% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 52 | | matches | | 0 | "Now he moved slowly, deliberately," |
| | ratio | 0.019 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 19 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 94.44% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 2 | | fancyTags | | 0 | "she whispered (whisper)" | | 1 | "she murmured (murmur)" |
| | dialogueSentences | 36 | | tagDensity | 0.194 | | leniency | 0.389 | | rawRatio | 0.286 | | effectiveRatio | 0.111 | |