| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 0 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.30% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1493 | | totalAiIsmAdverbs | 2 | | 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) | |
| 69.86% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1493 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "weight" | | 1 | "silence" | | 2 | "warmth" | | 3 | "chill" | | 4 | "tension" |
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| 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 | 217 | | matches | (empty) | |
| 77.02% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 8 | | hedgeCount | 2 | | narrationSentences | 217 | | filterMatches | | 0 | "look" | | 1 | "know" | | 2 | "listen" | | 3 | "think" |
| | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 217 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1488 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 65 | | wordCount | 1475 | | uniqueNames | 13 | | maxNameDensity | 1.56 | | worstName | "You" | | maxWindowNameDensity | 3.5 | | worstWindowName | "You" | | discoveredNames | | Cardiff | 2 | | Lucien | 16 | | Southwark | 1 | | Evan | 2 | | Carter | 2 | | Avaros | 1 | | Golden | 1 | | Empress | 1 | | Rory | 1 | | Eva | 3 | | Aurora | 9 | | You | 23 | | Don | 3 |
| | persons | | 0 | "Lucien" | | 1 | "Evan" | | 2 | "Carter" | | 3 | "Rory" | | 4 | "Eva" | | 5 | "Aurora" | | 6 | "You" |
| | places | | 0 | "Cardiff" | | 1 | "Southwark" | | 2 | "Avaros" | | 3 | "Golden" |
| | globalScore | 0.72 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 106 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like a violation and a comfort all" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1488 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 217 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 109 | | mean | 13.65 | | std | 16.62 | | cv | 1.218 | | sampleLengths | | 0 | 78 | | 1 | 5 | | 2 | 45 | | 3 | 8 | | 4 | 4 | | 5 | 44 | | 6 | 5 | | 7 | 41 | | 8 | 5 | | 9 | 4 | | 10 | 4 | | 11 | 46 | | 12 | 3 | | 13 | 1 | | 14 | 2 | | 15 | 7 | | 16 | 39 | | 17 | 3 | | 18 | 25 | | 19 | 7 | | 20 | 4 | | 21 | 3 | | 22 | 3 | | 23 | 46 | | 24 | 4 | | 25 | 10 | | 26 | 8 | | 27 | 3 | | 28 | 5 | | 29 | 9 | | 30 | 25 | | 31 | 27 | | 32 | 23 | | 33 | 24 | | 34 | 7 | | 35 | 17 | | 36 | 19 | | 37 | 32 | | 38 | 1 | | 39 | 1 | | 40 | 2 | | 41 | 4 | | 42 | 21 | | 43 | 6 | | 44 | 46 | | 45 | 5 | | 46 | 11 | | 47 | 4 | | 48 | 29 | | 49 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 217 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 300 | | matches | | 0 | "were collapsing" | | 1 | "was building" | | 2 | "was watching" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 217 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 108 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 1 | | adverbRatio | 0.009259259259259259 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.009259259259259259 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 217 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 217 | | mean | 6.86 | | std | 4.51 | | cv | 0.657 | | sampleLengths | | 0 | 11 | | 1 | 11 | | 2 | 17 | | 3 | 19 | | 4 | 7 | | 5 | 13 | | 6 | 5 | | 7 | 5 | | 8 | 22 | | 9 | 18 | | 10 | 5 | | 11 | 3 | | 12 | 4 | | 13 | 10 | | 14 | 13 | | 15 | 21 | | 16 | 5 | | 17 | 5 | | 18 | 15 | | 19 | 3 | | 20 | 18 | | 21 | 5 | | 22 | 4 | | 23 | 4 | | 24 | 9 | | 25 | 15 | | 26 | 16 | | 27 | 6 | | 28 | 3 | | 29 | 1 | | 30 | 2 | | 31 | 7 | | 32 | 3 | | 33 | 8 | | 34 | 18 | | 35 | 10 | | 36 | 3 | | 37 | 2 | | 38 | 12 | | 39 | 11 | | 40 | 4 | | 41 | 3 | | 42 | 4 | | 43 | 3 | | 44 | 3 | | 45 | 5 | | 46 | 7 | | 47 | 12 | | 48 | 9 | | 49 | 3 |
| |
| 34.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 34 | | diversityRatio | 0.2350230414746544 | | totalSentences | 217 | | uniqueOpeners | 51 | |
| 50.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 197 | | matches | | 0 | "Just like him." | | 1 | "Then I will stand." | | 2 | "Only Eva called her that." |
| | ratio | 0.015 | |
| 18.98% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 99 | | totalSentences | 197 | | matches | | 0 | "He wore a charcoal suit" | | 1 | "He leaned on the ivory-handled" | | 2 | "You should not be here." | | 3 | "He closed the umbrella and" | | 4 | "She crossed her arms over" | | 5 | "He turned to face her." | | 6 | "He looked tired." | | 7 | "You stopped answering the phone." | | 8 | "I changed the number." | | 9 | "You changed the number." | | 10 | "He repeated the words, testing" | | 11 | "He walked further into the" | | 12 | "He stopped near the small" | | 13 | "You look well." | | 14 | "It warmed the cold draft" | | 15 | "I never pretended." | | 16 | "You walked out of the" | | 17 | "I had to find my" | | 18 | "You left me there." | | 19 | "I saved you." |
| | ratio | 0.503 | |
| 23.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 172 | | totalSentences | 197 | | matches | | 0 | "The lock clicked, a sharp" | | 1 | "Aurora pulled the door open" | | 2 | "Lucien stood on the threshold," | | 3 | "He wore a charcoal suit" | | 4 | "Water darkened the fabric at" | | 5 | "He leaned on the ivory-handled" | | 6 | "You should not be here." | | 7 | "Lucien stepped forward without invitation." | | 8 | "The scent of ozone and" | | 9 | "He closed the umbrella and" | | 10 | "The lock held three deadbolts." | | 11 | "Eva installed them." | | 12 | "Eva is not here." | | 13 | "Aurora moved back into the" | | 14 | "The flat was small, cramped" | | 15 | "She crossed her arms over" | | 16 | "He turned to face her." | | 17 | "The overhead light caught the" | | 18 | "He looked tired." | | 19 | "The usual immaculate slick of" |
| | ratio | 0.873 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 197 | | matches | | 0 | "Because of information I provided." | | 1 | "If I told you, you" | | 2 | "If you stay, you stay" | | 3 | "Before I change my mind" |
| | ratio | 0.02 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 1 | | matches | | 0 | "He wore a charcoal suit that cost more than her monthly rent, tailored to fit his shoulders with precision." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |