| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1335 | | totalAiIsmAdverbs | 1 | | 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) | |
| 43.82% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1335 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "echoed" | | 1 | "chaotic" | | 2 | "weight" | | 3 | "predator" | | 4 | "warmth" | | 5 | "perfect" | | 6 | "tracing" | | 7 | "vibrated" | | 8 | "scanned" | | 9 | "pounding" | | 10 | "fluttered" | | 11 | "could feel" | | 12 | "electric" | | 13 | "silence" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 184 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 184 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 184 | | 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 | 1335 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 1 | | matches | | 0 | "If I see you again, I call the police." |
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| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1335 | | uniqueNames | 11 | | maxNameDensity | 1.12 | | worstName | "Rory" | | maxWindowNameDensity | 3.5 | | worstWindowName | "You" | | discoveredNames | | Moreau | 1 | | London | 1 | | Council | 1 | | Silas | 2 | | Old | 1 | | Bailey | 1 | | Lucien | 12 | | Luc | 1 | | Rory | 15 | | You | 15 | | Ptolemy | 4 |
| | persons | | 0 | "Moreau" | | 1 | "Council" | | 2 | "Silas" | | 3 | "Old" | | 4 | "Bailey" | | 5 | "Lucien" | | 6 | "Luc" | | 7 | "Rory" | | 8 | "You" | | 9 | "Ptolemy" |
| | places | | | globalScore | 0.938 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 102 | | 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 | 1335 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 184 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 92 | | mean | 14.51 | | std | 13.37 | | cv | 0.922 | | sampleLengths | | 0 | 48 | | 1 | 62 | | 2 | 12 | | 3 | 4 | | 4 | 40 | | 5 | 7 | | 6 | 41 | | 7 | 5 | | 8 | 7 | | 9 | 56 | | 10 | 4 | | 11 | 20 | | 12 | 5 | | 13 | 14 | | 14 | 3 | | 15 | 50 | | 16 | 14 | | 17 | 4 | | 18 | 15 | | 19 | 13 | | 20 | 9 | | 21 | 13 | | 22 | 18 | | 23 | 41 | | 24 | 4 | | 25 | 4 | | 26 | 31 | | 27 | 5 | | 28 | 4 | | 29 | 15 | | 30 | 15 | | 31 | 15 | | 32 | 4 | | 33 | 3 | | 34 | 14 | | 35 | 12 | | 36 | 36 | | 37 | 5 | | 38 | 16 | | 39 | 3 | | 40 | 2 | | 41 | 21 | | 42 | 33 | | 43 | 8 | | 44 | 7 | | 45 | 30 | | 46 | 6 | | 47 | 14 | | 48 | 5 | | 49 | 13 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 184 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 252 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 184 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1336 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.019461077844311378 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0014970059880239522 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 184 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 184 | | mean | 7.26 | | std | 4.57 | | cv | 0.629 | | sampleLengths | | 0 | 16 | | 1 | 9 | | 2 | 23 | | 3 | 14 | | 4 | 18 | | 5 | 14 | | 6 | 16 | | 7 | 4 | | 8 | 8 | | 9 | 4 | | 10 | 10 | | 11 | 12 | | 12 | 18 | | 13 | 7 | | 14 | 6 | | 15 | 24 | | 16 | 11 | | 17 | 5 | | 18 | 7 | | 19 | 14 | | 20 | 6 | | 21 | 16 | | 22 | 4 | | 23 | 16 | | 24 | 4 | | 25 | 20 | | 26 | 5 | | 27 | 2 | | 28 | 12 | | 29 | 3 | | 30 | 1 | | 31 | 13 | | 32 | 10 | | 33 | 26 | | 34 | 3 | | 35 | 11 | | 36 | 4 | | 37 | 8 | | 38 | 7 | | 39 | 7 | | 40 | 6 | | 41 | 6 | | 42 | 3 | | 43 | 2 | | 44 | 11 | | 45 | 11 | | 46 | 7 | | 47 | 4 | | 48 | 13 | | 49 | 16 |
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| 36.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 24 | | diversityRatio | 0.19021739130434784 | | totalSentences | 184 | | uniqueOpeners | 35 | |
| 20.20% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 165 | | matches | | | ratio | 0.006 | |
| 4.24% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 89 | | totalSentences | 165 | | matches | | 0 | "His heterochromatic eyes locked onto" | | 1 | "He held an ivory-handled cane" | | 2 | "Her knuckles turned white against" | | 3 | "You locked the bolts." | | 4 | "He stepped past her before" | | 5 | "He looked at the scar" | | 6 | "He reached out, his fingers" | | 7 | "You left the mark behind." | | 8 | "I left a lot of" | | 9 | "She didn't want him to" | | 10 | "You shouldn't be here." | | 11 | "He leaned the cane against" | | 12 | "She kept her face neutral," | | 13 | "He moved with a predator's" | | 14 | "He stopped in front of" | | 15 | "He is missing." | | 16 | "They have him in the" | | 17 | "I don't know anything about" | | 18 | "I deliver food." | | 19 | "You delivered a package to" |
| | ratio | 0.539 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 156 | | totalSentences | 165 | | matches | | 0 | "The three deadbolts clicked into" | | 1 | "Aurora turned the handle and" | | 2 | "Rain slicked the landing, smelling" | | 3 | "Lucien Moreau wore a charcoal" | | 4 | "Platinum hair lay slicked back" | | 5 | "His heterochromatic eyes locked onto" | | 6 | "He held an ivory-handled cane" | | 7 | "Rory gripped the doorframe." | | 8 | "Her knuckles turned white against" | | 9 | "You locked the bolts." | | 10 | "He stepped past her before" | | 11 | "The smell of expensive cologne" | | 12 | "Ptolemy, the tabby cat, arched" | | 13 | "The deadbolts keep the wrong" | | 14 | "Lucien didn't look at the" | | 15 | "He looked at the scar" | | 16 | "He reached out, his fingers" | | 17 | "You left the mark behind." | | 18 | "I left a lot of" | | 19 | "Rory stepped back, forcing him" |
| | ratio | 0.945 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 6 | | totalSentences | 165 | | matches | | 0 | "Now it felt suffocating." | | 1 | "Because you know the layout" | | 2 | "Because you know where the" | | 3 | "Because I cannot enter the" | | 4 | "If I do this, I" | | 5 | "If I see you again," |
| | ratio | 0.036 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 2 | | matches | | 0 | "Lucien Moreau wore a charcoal suit that repelled the moisture clinging to the air." | | 1 | "He reached out, his fingers hovering near the mark without touching." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |