| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 85.41% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1713 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "suddenly" | | 1 | "really" | | 2 | "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) | |
| 47.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1713 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "throbbed" | | 1 | "weight" | | 2 | "calculate" | | 3 | "charm" | | 4 | "fractured" | | 5 | "silence" | | 6 | "pulsed" | | 7 | "traced" | | 8 | "warmth" | | 9 | "pulse" | | 10 | "tension" | | 11 | "echoed" |
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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 | 294 | | matches | (empty) | |
| 79.69% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 11 | | hedgeCount | 2 | | narrationSentences | 294 | | filterMatches | | 0 | "look" | | 1 | "know" | | 2 | "know think" | | 3 | "see" |
| | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 294 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1712 | | ratio | 0 | | matches | (empty) | |
| 35.71% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 5 | | matches | | 0 | "You locked three times, he said." | | 1 | "Tell me about the cracks, she said." | | 2 | "You memorized the alleys, he said." | | 3 | "Three degrees will tear the roof off, she said." | | 4 | "Say something, he said." |
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| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 66 | | wordCount | 1712 | | uniqueNames | 19 | | maxNameDensity | 1.52 | | worstName | "You" | | maxWindowNameDensity | 4 | | worstWindowName | "You" | | discoveredNames | | Marseille | 2 | | London | 2 | | Luc | 1 | | Brick | 2 | | Lane | 2 | | Cardiff | 1 | | Lucien | 3 | | Rory | 1 | | Golden | 1 | | Empress | 1 | | Silas | 3 | | Commercial | 1 | | Road | 1 | | Aurora | 3 | | Rain | 3 | | You | 26 | | Close | 4 | | Ptolemy | 5 | | Didn | 4 |
| | persons | | 0 | "Luc" | | 1 | "Lucien" | | 2 | "Rory" | | 3 | "Empress" | | 4 | "Silas" | | 5 | "Aurora" | | 6 | "Rain" | | 7 | "You" | | 8 | "Ptolemy" |
| | places | | 0 | "Marseille" | | 1 | "London" | | 2 | "Brick" | | 3 | "Lane" | | 4 | "Cardiff" | | 5 | "Golden" | | 6 | "Commercial" | | 7 | "Road" |
| | globalScore | 0.741 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 136 | | glossingSentenceCount | 2 | | matches | | 0 | "smelled like lavender and regret" | | 1 | "felt like a fortress" |
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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 | 1712 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 294 | | matches | (empty) | |
| 95.26% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 54 | | mean | 31.7 | | std | 15.33 | | cv | 0.483 | | sampleLengths | | 0 | 68 | | 1 | 46 | | 2 | 20 | | 3 | 28 | | 4 | 34 | | 5 | 46 | | 6 | 64 | | 7 | 28 | | 8 | 28 | | 9 | 45 | | 10 | 15 | | 11 | 46 | | 12 | 24 | | 13 | 55 | | 14 | 39 | | 15 | 31 | | 16 | 58 | | 17 | 26 | | 18 | 39 | | 19 | 43 | | 20 | 41 | | 21 | 26 | | 22 | 30 | | 23 | 13 | | 24 | 18 | | 25 | 22 | | 26 | 38 | | 27 | 51 | | 28 | 34 | | 29 | 19 | | 30 | 10 | | 31 | 40 | | 32 | 16 | | 33 | 57 | | 34 | 31 | | 35 | 50 | | 36 | 6 | | 37 | 11 | | 38 | 32 | | 39 | 9 | | 40 | 22 | | 41 | 23 | | 42 | 31 | | 43 | 21 | | 44 | 25 | | 45 | 48 | | 46 | 26 | | 47 | 31 | | 48 | 16 | | 49 | 54 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 294 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 360 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 294 | | ratio | 0.003 | | matches | | 0 | "The air thickened with cardamom and something else—ozone, old paper, the faint metallic tang of the supernatural underworld." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1719 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 56 | | adverbRatio | 0.03257707969749855 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.005817335660267597 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 294 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 294 | | mean | 5.82 | | std | 3.76 | | cv | 0.645 | | sampleLengths | | 0 | 14 | | 1 | 7 | | 2 | 15 | | 3 | 9 | | 4 | 11 | | 5 | 7 | | 6 | 5 | | 7 | 9 | | 8 | 14 | | 9 | 6 | | 10 | 4 | | 11 | 13 | | 12 | 6 | | 13 | 14 | | 14 | 2 | | 15 | 2 | | 16 | 4 | | 17 | 14 | | 18 | 6 | | 19 | 8 | | 20 | 6 | | 21 | 5 | | 22 | 4 | | 23 | 4 | | 24 | 7 | | 25 | 4 | | 26 | 7 | | 27 | 7 | | 28 | 18 | | 29 | 8 | | 30 | 2 | | 31 | 8 | | 32 | 5 | | 33 | 5 | | 34 | 10 | | 35 | 10 | | 36 | 12 | | 37 | 14 | | 38 | 3 | | 39 | 8 | | 40 | 7 | | 41 | 1 | | 42 | 9 | | 43 | 2 | | 44 | 4 | | 45 | 8 | | 46 | 14 | | 47 | 2 | | 48 | 5 | | 49 | 5 |
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| 43.31% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 36 | | diversityRatio | 0.3333333333333333 | | totalSentences | 294 | | uniqueOpeners | 98 | |
| 67.48% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 247 | | matches | | 0 | "Of course he did." | | 1 | "Then we rebuild them." | | 2 | "Just near enough that the" | | 3 | "Then walk it." | | 4 | "Just stared at him." |
| | ratio | 0.02 | |
| 40.24% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 111 | | totalSentences | 247 | | matches | | 0 | "He leaned on an ivory-handled" | | 1 | "She kept her left hand" | | 2 | "He took up the entire" | | 3 | "You locked three times, he" | | 4 | "His voice carried the Marseille" | | 5 | "She didn’t step back." | | 6 | "I figured Brick Lane made" | | 7 | "He shifted his weight." | | 8 | "She finally exhaled, shoulders dropping" | | 9 | "You haven’t been to London" | | 10 | "He stepped past her before" | | 11 | "He remembers me." | | 12 | "I’d rather not be bitten" | | 13 | "She closed the door." | | 14 | "You look like you sleep" | | 15 | "He turned to face her." | | 16 | "I stopped doing most of" | | 17 | "Her fingers brushed her wrist." | | 18 | "He didn’t move." | | 19 | "He stayed rooted, hands loose" |
| | ratio | 0.449 | |
| 55.14% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 200 | | totalSentences | 247 | | matches | | 0 | "The latch clicked, and the" | | 1 | "Aurora blinked against the hallway’s" | | 2 | "Lucien stood in the narrow" | | 3 | "Rain slicked the platinum sweep" | | 4 | "He leaned on an ivory-handled" | | 5 | "The other swallowed it whole." | | 6 | "She kept her left hand" | | 7 | "The crescent scar there throbbed" | | 8 | "He took up the entire" | | 9 | "You locked three times, he" | | 10 | "His voice carried the Marseille" | | 11 | "She didn’t step back." | | 12 | "The hallway air smelled of" | | 13 | "Silas mentioned you were staying" | | 14 | "I figured Brick Lane made" | | 15 | "He shifted his weight." | | 16 | "The cane clicked against the" | | 17 | "Silas talks too much." | | 18 | "She finally exhaled, shoulders dropping" | | 19 | "The door remained wedged in" |
| | ratio | 0.81 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 7 | | totalSentences | 247 | | matches | | 0 | "Because the cracks are widening." | | 1 | "Because the ledger’s bleeding." | | 2 | "Because every time I close" | | 3 | "However long you need it" | | 4 | "If I don’t correct them" | | 5 | "Because I couldn’t sleep, he" | | 6 | "Because I kept rehearsing every" |
| | ratio | 0.028 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 1 | | matches | | 0 | "You delivered half-truths and a one-way ticket to a safe house that smelled like lavender and regret." |
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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 | |