| 69.57% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 3 | | adverbTags | | 0 | "he said softly [softly]" | | 1 | "His fingers trembled slightly [slightly]" | | 2 | "he said quietly [quietly]" |
| | dialogueSentences | 46 | | tagDensity | 0.391 | | leniency | 0.783 | | rawRatio | 0.167 | | effectiveRatio | 0.13 | |
| 69.97% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 999 | | totalAiIsmAdverbs | 6 | | found | | 0 | | | 1 | | adverb | "barely above a whisper" | | count | 1 |
| | 2 | | | 3 | | | 4 | |
| | highlights | | 0 | "softly" | | 1 | "barely above a whisper" | | 2 | "really" | | 3 | "slightly" | | 4 | "slowly" |
| |
| 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) | |
| 59.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 999 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "scanned" | | 1 | "tenderness" | | 2 | "flickered" | | 3 | "whisper" | | 4 | "stomach" | | 5 | "shattered" | | 6 | "trembled" | | 7 | "could feel" |
| |
| 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 | 72 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 72 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 100 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 997 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 53.58% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 726 | | uniqueNames | 7 | | maxNameDensity | 1.93 | | worstName | "Aurora" | | maxWindowNameDensity | 3 | | worstWindowName | "Aurora" | | discoveredNames | | Aurora | 14 | | Moreau | 1 | | Lucien | 6 | | Frenchman | 1 | | Marseille | 1 | | Evan | 1 | | Ptolemy | 2 |
| | persons | | 0 | "Aurora" | | 1 | "Moreau" | | 2 | "Lucien" | | 3 | "Evan" |
| | places | | | globalScore | 0.536 | | windowScore | 0.667 | |
| 53.85% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 2 | | matches | | 0 | "tasted like wine and secrets, the way he’" | | 1 | "felt like she wasn’t facing it alone" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 997 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 100 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 54 | | mean | 18.46 | | std | 10.49 | | cv | 0.568 | | sampleLengths | | 0 | 19 | | 1 | 25 | | 2 | 48 | | 3 | 15 | | 4 | 27 | | 5 | 27 | | 6 | 12 | | 7 | 16 | | 8 | 21 | | 9 | 10 | | 10 | 24 | | 11 | 15 | | 12 | 17 | | 13 | 43 | | 14 | 13 | | 15 | 23 | | 16 | 2 | | 17 | 12 | | 18 | 4 | | 19 | 7 | | 20 | 18 | | 21 | 27 | | 22 | 36 | | 23 | 6 | | 24 | 28 | | 25 | 34 | | 26 | 8 | | 27 | 19 | | 28 | 30 | | 29 | 16 | | 30 | 24 | | 31 | 3 | | 32 | 26 | | 33 | 10 | | 34 | 19 | | 35 | 22 | | 36 | 4 | | 37 | 6 | | 38 | 17 | | 39 | 2 | | 40 | 3 | | 41 | 29 | | 42 | 15 | | 43 | 7 | | 44 | 8 | | 45 | 23 | | 46 | 25 | | 47 | 28 | | 48 | 19 | | 49 | 35 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 72 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 147 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 100 | | ratio | 0.01 | | matches | | 0 | "His heterochromatic eyes—one amber, one black—scanned the cramped space with practiced precision, taking in the scattered books, the herbs drying from the ceiling, the tabby cat watching from the shadows." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 728 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.03296703296703297 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.012362637362637362 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 100 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 100 | | mean | 9.97 | | std | 6.61 | | cv | 0.663 | | sampleLengths | | 0 | 19 | | 1 | 11 | | 2 | 14 | | 3 | 18 | | 4 | 30 | | 5 | 9 | | 6 | 6 | | 7 | 16 | | 8 | 6 | | 9 | 5 | | 10 | 8 | | 11 | 19 | | 12 | 12 | | 13 | 8 | | 14 | 8 | | 15 | 5 | | 16 | 16 | | 17 | 3 | | 18 | 7 | | 19 | 8 | | 20 | 16 | | 21 | 12 | | 22 | 3 | | 23 | 6 | | 24 | 11 | | 25 | 13 | | 26 | 30 | | 27 | 13 | | 28 | 10 | | 29 | 9 | | 30 | 4 | | 31 | 2 | | 32 | 12 | | 33 | 2 | | 34 | 2 | | 35 | 3 | | 36 | 4 | | 37 | 8 | | 38 | 9 | | 39 | 1 | | 40 | 8 | | 41 | 15 | | 42 | 4 | | 43 | 3 | | 44 | 14 | | 45 | 19 | | 46 | 3 | | 47 | 3 | | 48 | 28 | | 49 | 9 |
| |
| 55.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.36 | | totalSentences | 100 | | uniqueOpeners | 36 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 66 | | matches | | 0 | "Then he turned, grabbed his" | | 1 | "Then she sank onto the" | | 2 | "Somewhere in the distance, thunder" |
| | ratio | 0.045 | |
| 38.18% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 66 | | matches | | 0 | "she said, crossing her arms" | | 1 | "His heterochromatic eyes—one amber, one" | | 2 | "he said, stepping past her" | | 3 | "Her heartbeat hammered in her" | | 4 | "he said, more to himself" | | 5 | "Her skin prickled." | | 6 | "He tilted his head, studying" | | 7 | "he said softly" | | 8 | "she asked, her voice barely" | | 9 | "He set his cane against" | | 10 | "She stumbled backward, her hand" | | 11 | "he said, but his eyes" | | 12 | "He knelt beside her, his" | | 13 | "She remembered the fever dreams," | | 14 | "She’d convinced herself it was" | | 15 | "She looked up at him," | | 16 | "His cane leaned against the" | | 17 | "Her fingers twitched toward it." | | 18 | "he said, his voice breaking" | | 19 | "She remembered the last time" |
| | ratio | 0.455 | |
| 28.18% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 57 | | totalSentences | 66 | | matches | | 0 | "The key rattled against the" | | 1 | "she said, crossing her arms" | | 2 | "The words came out sharper" | | 3 | "Lucien Moreau stood in the" | | 4 | "His heterochromatic eyes—one amber, one" | | 5 | "he said, stepping past her" | | 6 | "Aurora replied, stepping closer" | | 7 | "Her heartbeat hammered in her" | | 8 | "Ptolemy wound himself around Lucien’s" | | 9 | "The cat rubbed against his" | | 10 | "he said, more to himself" | | 11 | "Lucien’s laugh was low, dangerous." | | 12 | "Her skin prickled." | | 13 | "He tilted his head, studying" | | 14 | "Aurora’s hand flew to her" | | 15 | "he said softly" | | 16 | "The air between them crackled" | | 17 | "Aurora remembered the night they’d" | | 18 | "she asked, her voice barely" | | 19 | "He set his cane against" |
| | ratio | 0.864 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 66 | | matches | (empty) | | ratio | 0 | |
| 89.95% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 2 | | matches | | 0 | "He knelt beside her, his hand hovering over her abdomen as if afraid to touch." | | 1 | "She could feel it spreading through her veins, protecting her, binding her to him in ways she didn’t understand." |
| |
| 13.89% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 4 | | matches | | 0 | "he said, more to himself than to her" | | 1 | "she asked, her voice barely above a whisper" | | 2 | "he said, but his eyes betrayed him" | | 3 | "he said, his voice breaking slightly" |
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| 63.04% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 15 | | fancyCount | 4 | | fancyTags | | 0 | "Aurora snapped (snap)" | | 1 | "she whispered (whisper)" | | 2 | "she whispered (whisper)" | | 3 | "she whispered (whisper)" |
| | dialogueSentences | 46 | | tagDensity | 0.326 | | leniency | 0.652 | | rawRatio | 0.267 | | effectiveRatio | 0.174 | |