| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 1 | | adverbTags | | 0 | "he corrected gently [gently]" |
| | dialogueSentences | 40 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.05 | | effectiveRatio | 0.05 | |
| 84.60% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1299 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "gently" | | 1 | "really" | | 2 | "softly" | | 3 | "slightly" |
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| 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) | |
| 49.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1299 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "echoed" | | 1 | "chill" | | 2 | "predator" | | 3 | "intensity" | | 4 | "testament" | | 5 | "weight" | | 6 | "raced" | | 7 | "sanctuary" | | 8 | "charged" | | 9 | "tension" | | 10 | "traced" | | 11 | "racing" |
| |
| 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 | 1 | | narrationSentences | 84 | | matches | | |
| 91.84% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 84 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 104 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1299 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 26 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 932 | | uniqueNames | 10 | | maxNameDensity | 1.07 | | worstName | "Lucien" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Lucien" | | discoveredNames | | Carter | 1 | | Tuesday | 1 | | East | 1 | | London | 1 | | Moreau | 1 | | Marseille | 1 | | Eva | 4 | | Lucien | 10 | | Aurora | 10 | | Ptolemy | 3 |
| | persons | | 0 | "Carter" | | 1 | "Moreau" | | 2 | "Eva" | | 3 | "Lucien" | | 4 | "Aurora" |
| | places | | 0 | "East" | | 1 | "London" | | 2 | "Marseille" |
| | globalScore | 0.964 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1299 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 104 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 30.93 | | std | 22.24 | | cv | 0.719 | | sampleLengths | | 0 | 68 | | 1 | 21 | | 2 | 100 | | 3 | 21 | | 4 | 16 | | 5 | 41 | | 6 | 56 | | 7 | 68 | | 8 | 41 | | 9 | 44 | | 10 | 4 | | 11 | 59 | | 12 | 5 | | 13 | 29 | | 14 | 39 | | 15 | 3 | | 16 | 16 | | 17 | 46 | | 18 | 53 | | 19 | 7 | | 20 | 29 | | 21 | 44 | | 22 | 8 | | 23 | 36 | | 24 | 14 | | 25 | 15 | | 26 | 4 | | 27 | 22 | | 28 | 34 | | 29 | 9 | | 30 | 40 | | 31 | 5 | | 32 | 23 | | 33 | 30 | | 34 | 20 | | 35 | 9 | | 36 | 15 | | 37 | 9 | | 38 | 41 | | 39 | 61 | | 40 | 70 | | 41 | 24 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 84 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 174 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 104 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 933 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.028938906752411574 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.011789924973204717 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 104 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 104 | | mean | 12.49 | | std | 6.93 | | cv | 0.554 | | sampleLengths | | 0 | 16 | | 1 | 15 | | 2 | 37 | | 3 | 21 | | 4 | 13 | | 5 | 10 | | 6 | 19 | | 7 | 13 | | 8 | 9 | | 9 | 14 | | 10 | 22 | | 11 | 4 | | 12 | 17 | | 13 | 3 | | 14 | 13 | | 15 | 3 | | 16 | 26 | | 17 | 12 | | 18 | 5 | | 19 | 8 | | 20 | 18 | | 21 | 25 | | 22 | 12 | | 23 | 8 | | 24 | 13 | | 25 | 18 | | 26 | 17 | | 27 | 11 | | 28 | 9 | | 29 | 21 | | 30 | 3 | | 31 | 14 | | 32 | 20 | | 33 | 7 | | 34 | 4 | | 35 | 20 | | 36 | 16 | | 37 | 23 | | 38 | 5 | | 39 | 12 | | 40 | 4 | | 41 | 13 | | 42 | 16 | | 43 | 23 | | 44 | 3 | | 45 | 5 | | 46 | 11 | | 47 | 13 | | 48 | 11 | | 49 | 22 |
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| 37.50% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.2980769230769231 | | totalSentences | 104 | | uniqueOpeners | 31 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 70 | | matches | (empty) | | ratio | 0 | |
| 2.86% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 70 | | matches | | 0 | "She hadn't expected visitors, not" | | 1 | "She pulled the door open," | | 2 | "He looked exactly as she" | | 3 | "His hair, slicked back and" | | 4 | "They held a predator's stillness," | | 5 | "She held her ground, her" | | 6 | "His voice was smooth, accented" | | 7 | "He didn't move to enter." | | 8 | "He didn't offer a greeting" | | 9 | "He simply leaned on his" | | 10 | "she said, keeping her voice" | | 11 | "She glanced over her shoulder" | | 12 | "He shifted his weight, the" | | 13 | "She stepped back, gripping the" | | 14 | "He didn't walk so much" | | 15 | "He stopped in the center" | | 16 | "He turned, the amber eye" | | 17 | "he corrected gently" | | 18 | "He took a step closer." | | 19 | "She could see the fine" |
| | ratio | 0.543 | |
| 2.86% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 70 | | matches | | 0 | "The third deadbolt slid home" | | 1 | "Aurora Carter hesitated, her hand" | | 2 | "She hadn't expected visitors, not" | | 3 | "She pulled the door open," | | 4 | "Lucien Moreau stood on the" | | 5 | "He looked exactly as she" | | 6 | "The tailored charcoal suit fit" | | 7 | "His hair, slicked back and" | | 8 | "They held a predator's stillness," | | 9 | "Aurora didn't step back." | | 10 | "She held her ground, her" | | 11 | "The name felt heavy, weighted" | | 12 | "His voice was smooth, accented" | | 13 | "He didn't move to enter." | | 14 | "He didn't offer a greeting" | | 15 | "He simply leaned on his" | | 16 | "Aurora knew the cane concealed" | | 17 | "she said, keeping her voice" | | 18 | "She glanced over her shoulder" | | 19 | "The room was a disaster" |
| | ratio | 0.914 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 70 | | matches | | 0 | "If she let him in," | | 1 | "If she didn't, he might" | | 2 | "Before she could respond, he" |
| | ratio | 0.043 | |
| 93.02% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 3 | | matches | | 0 | "They held a predator's stillness, fixed on her with an intensity that made the small crescent scar on her left wrist prickle." | | 1 | "His voice was smooth, accented with the faintest trace of Marseille, a sound that used to make her feel safe and now made her feel hunted." | | 2 | "She looked at Ptolemy, who had stood up and was now arching his back, hissing softly at the corner of the room." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, her voice trembling slightly" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 19 | | fancyCount | 6 | | fancyTags | | 0 | "he observed (observe)" | | 1 | "Aurora snapped (snap)" | | 2 | "he corrected gently (correct)" | | 3 | "she lied (lie)" | | 4 | "she whispered (whisper)" | | 5 | "he agreed (agree)" |
| | dialogueSentences | 40 | | tagDensity | 0.475 | | leniency | 0.95 | | rawRatio | 0.316 | | effectiveRatio | 0.3 | |