| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 267 | | 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) | |
| 62.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 267 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 27 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 27 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 30 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 263 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 3 | | wordCount | 245 | | uniqueNames | 3 | | maxNameDensity | 0.41 | | worstName | "Eva" | | maxWindowNameDensity | 0 | | worstWindowName | (null) | | discoveredNames | | | persons | | | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 16 | | 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 | 263 | | matches | (empty) | |
| 55.56% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 30 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 12 | | mean | 21.92 | | std | 13.7 | | cv | 0.625 | | sampleLengths | | 0 | 45 | | 1 | 4 | | 2 | 31 | | 3 | 47 | | 4 | 13 | | 5 | 11 | | 6 | 18 | | 7 | 30 | | 8 | 16 | | 9 | 17 | | 10 | 27 | | 11 | 4 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 27 | | matches | (empty) | |
| 24.56% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 38 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 30 | | ratio | 0.1 | | matches | | 0 | "Before her brain could catch up, her body was already moving—setting down the yellowed manuscript, sliding between stacks of books that teetered precariously on every available surface." | | 1 | "His mismatched eyes—one amber, one black—swept over her with an intensity that made her breath catch." | | 2 | "His gaze dropped to the small crescent-shaped scar on her left wrist—a childhood accident he'd heard about but never seen." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 254 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 6 | | adverbRatio | 0.023622047244094488 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.007874015748031496 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 30 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 30 | | mean | 8.77 | | std | 6.57 | | cv | 0.75 | | sampleLengths | | 0 | 17 | | 1 | 14 | | 2 | 14 | | 3 | 2 | | 4 | 2 | | 5 | 4 | | 6 | 27 | | 7 | 13 | | 8 | 18 | | 9 | 16 | | 10 | 8 | | 11 | 3 | | 12 | 2 | | 13 | 6 | | 14 | 4 | | 15 | 1 | | 16 | 7 | | 17 | 6 | | 18 | 5 | | 19 | 19 | | 20 | 11 | | 21 | 5 | | 22 | 10 | | 23 | 1 | | 24 | 5 | | 25 | 8 | | 26 | 4 | | 27 | 20 | | 28 | 7 | | 29 | 4 |
| |
| 93.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.6333333333333333 | | totalSentences | 30 | | uniqueOpeners | 19 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 21 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 6 | | totalSentences | 21 | | matches | | 0 | "She knew that rhythm." | | 1 | "His mismatched eyes—one amber, one" | | 2 | "She hadn't seen him in" | | 3 | "His French accent threaded through" | | 4 | "Her hand gripped the doorframe." | | 5 | "His gaze dropped to the" |
| | ratio | 0.286 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 15 | | totalSentences | 21 | | matches | | 0 | "The knock came sharp and" | | 1 | "Aurora's fingers paused mid-page, a" | | 2 | "Ptolemy, the tabby cat, lifted" | | 3 | "She knew that rhythm." | | 4 | "Lucien stood in the hallway" | | 5 | "Charcoal suit crisp, platinum hair" | | 6 | "His mismatched eyes—one amber, one" | | 7 | "A corner of his mouth" | | 8 | "She hadn't seen him in" | | 9 | "The hallway behind him was" | | 10 | "Ptolemy wound between her ankles," | | 11 | "His French accent threaded through" | | 12 | "Her hand gripped the doorframe." | | 13 | "His gaze dropped to the" | | 14 | "A intimacy that hung between" |
| | ratio | 0.714 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 21 | | matches | | 0 | "Before her brain could catch" |
| | ratio | 0.048 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 11 | | technicalSentenceCount | 3 | | matches | | 0 | "The knock came sharp and sudden, three precise raps that cut through the quiet of Eva's flat." | | 1 | "Before her brain could catch up, her body was already moving—setting down the yellowed manuscript, sliding between stacks of books that teetered precariously on…" | | 2 | "His mismatched eyes—one amber, one black—swept over her with an intensity that made her breath catch." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |