| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 99.68% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 46570 | | totalAiIsmAdverbs | 3 | | 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) | |
| 77.56% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 46570 | | totalAiIsms | 209 | | found | | | highlights | | 0 | "perfect" | | 1 | "flicker" | | 2 | "etched" | | 3 | "weight" | | 4 | "trembled" | | 5 | "echoing" | | 6 | "footsteps" | | 7 | "echoed" | | 8 | "pulsed" | | 9 | "charged" | | 10 | "silence" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 9394 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 2 | | narrationSentences | 9394 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 9394 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 18 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 46570 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 46570 | | uniqueNames | 12 | | maxNameDensity | 0.04 | | worstName | "Eva" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Eva" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Kowalski | 1 | | Veil | 3 | | Compass | 1 | | Shade | 2 | | Morris | 5 | | Market | 5 | | Detective | 1 | | Eva | 19 | | London | 1 | | You | 5 |
| | persons | | 0 | "Kowalski" | | 1 | "Morris" | | 2 | "Market" | | 3 | "Eva" | | 4 | "You" |
| | places | | | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | 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 | 46570 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 9394 | | matches | (empty) | |
| 25.75% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 9251 | | mean | 5.03 | | std | 1.21 | | cv | 0.24 | | sampleLengths | | 0 | 48 | | 1 | 26 | | 2 | 5 | | 3 | 16 | | 4 | 13 | | 5 | 33 | | 6 | 47 | | 7 | 3 | | 8 | 8 | | 9 | 6 | | 10 | 11 | | 11 | 2 | | 12 | 21 | | 13 | 34 | | 14 | 4 | | 15 | 9 | | 16 | 9 | | 17 | 26 | | 18 | 10 | | 19 | 3 | | 20 | 12 | | 21 | 13 | | 22 | 8 | | 23 | 4 | | 24 | 4 | | 25 | 30 | | 26 | 4 | | 27 | 3 | | 28 | 26 | | 29 | 1 | | 30 | 7 | | 31 | 22 | | 32 | 11 | | 33 | 5 | | 34 | 17 | | 35 | 10 | | 36 | 5 | | 37 | 12 | | 38 | 3 | | 39 | 12 | | 40 | 4 | | 41 | 6 | | 42 | 32 | | 43 | 8 | | 44 | 2 | | 45 | 15 | | 46 | 8 | | 47 | 3 | | 48 | 8 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 9394 | | matches | | 0 | "was etched" | | 1 | "was frayed" | | 2 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 18 | | totalVerbs | 9420 | | matches | | 0 | "was running" | | 1 | "was walking" | | 2 | "wasn't running" | | 3 | "was walking" | | 4 | "was spinning" | | 5 | "wasn't pointing" | | 6 | "was pointing" | | 7 | "was looking" | | 8 | "was shaking" | | 9 | "were hiding" | | 10 | "was still pointing" | | 11 | "was spinning" | | 12 | "was rising" | | 13 | "was burning" | | 14 | "were spinning" | | 15 | "were waiting" | | 16 | "were watching" | | 17 | "were waiting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 9394 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 46570 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 427 | | adverbRatio | 0.009168992913893064 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.00006441915396177797 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 9394 | | echoCount | 0 | | echoWords | (empty) | |
| 0.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 9394 | | mean | 4.96 | | std | 0.46 | | cv | 0.093 | | sampleLengths | | 0 | 10 | | 1 | 9 | | 2 | 16 | | 3 | 10 | | 4 | 3 | | 5 | 17 | | 6 | 9 | | 7 | 5 | | 8 | 4 | | 9 | 12 | | 10 | 4 | | 11 | 5 | | 12 | 4 | | 13 | 6 | | 14 | 9 | | 15 | 5 | | 16 | 6 | | 17 | 4 | | 18 | 3 | | 19 | 3 | | 20 | 12 | | 21 | 7 | | 22 | 7 | | 23 | 4 | | 24 | 7 | | 25 | 7 | | 26 | 3 | | 27 | 8 | | 28 | 4 | | 29 | 2 | | 30 | 5 | | 31 | 6 | | 32 | 2 | | 33 | 3 | | 34 | 18 | | 35 | 3 | | 36 | 6 | | 37 | 7 | | 38 | 11 | | 39 | 4 | | 40 | 3 | | 41 | 4 | | 42 | 2 | | 43 | 7 | | 44 | 4 | | 45 | 5 | | 46 | 7 | | 47 | 8 | | 48 | 4 | | 49 | 3 |
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| 25.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9170 | | diversityRatio | 0.005216095380029807 | | totalSentences | 9394 | | uniqueOpeners | 49 | |
| 0.36% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 9362 | | matches | | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 9254 | | totalSentences | 9362 | | matches | | 0 | "It was fresh, wet, and" | | 1 | "I wiped my gloved hand" | | 2 | "My joints popped." | | 3 | "She clutched her worn leather" | | 4 | "It isn't a ritual cut." | | 5 | "She tucked a strand of" | | 6 | "They form a sigil." | | 7 | "I looked at the body" | | 8 | "I walked closer." | | 9 | "I reached down and picked" | | 10 | "You shouldn't touch that." | | 11 | "I pocketed the brass object." | | 12 | "It felt cold against my" | | 13 | "It's a tool." | | 14 | "I ignored her." | | 15 | "I looked at the wound" | | 16 | "My partner, Morris, died three" | | 17 | "He found something in a" | | 18 | "He never told me." | | 19 | "He just vanished." |
| | ratio | 0.988 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 9345 | | totalSentences | 9362 | | matches | | 0 | "Blood pooled around the victim's" | | 1 | "The copper scent hit me" | | 2 | "It was fresh, wet, and" | | 3 | "I wiped my gloved hand" | | 4 | "My joints popped." | | 5 | "Eva Kowalski stood by the" | | 6 | "She clutched her worn leather" | | 7 | "It isn't a ritual cut." | | 8 | "Eva adjusted her glasses." | | 9 | "She tucked a strand of" | | 10 | "The geometry is precise." | | 11 | "They form a sigil." | | 12 | "I looked at the body" | | 13 | "The skin was pale, stretched" | | 14 | "The cuts were too clean." | | 15 | "A knife would leave drag" | | 16 | "A blade would slip." | | 17 | "This looked etched." | | 18 | "I walked closer." | | 19 | "The tiles beneath my boots" |
| | ratio | 0.998 | |
| 1.07% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 9362 | | matches | | 0 | "If you handle it without" | | 1 | "Because he knew what he" |
| | ratio | 0 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 13 | | technicalSentenceCount | 1 | | matches | | 0 | "If you handle it without protection, you might invite the attention of the Shade artisan who crafted it." |
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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 | |