| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 94.38% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1780 | | 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) | |
| 52.25% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1780 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "simmering" | | 2 | "calculate" | | 3 | "silence" | | 4 | "traced" | | 5 | "porcelain" | | 6 | "database" | | 7 | "potential" | | 8 | "pulse" | | 9 | "synthetic" | | 10 | "electric" | | 11 | "fractured" |
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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 | 292 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 292 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 292 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1780 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 77 | | wordCount | 1780 | | uniqueNames | 25 | | maxNameDensity | 1.24 | | worstName | "You" | | maxWindowNameDensity | 3 | | worstWindowName | "You" | | discoveredNames | | Lane | 4 | | Marseille | 3 | | Lucien | 2 | | Brick | 4 | | Italian | 1 | | Heathrow | 1 | | Calais | 1 | | Whitechapel | 1 | | Interpol | 1 | | Lisbon | 1 | | Cardiff | 1 | | Avaros | 4 | | French | 1 | | Canary | 1 | | Wharf | 1 | | Helvetica | 1 | | Rory | 1 | | Amber | 3 | | Black | 3 | | You | 22 | | Didn | 4 | | Let | 7 | | Felt | 3 | | Reclaimed | 3 | | Clearances | 3 |
| | persons | | 0 | "Lucien" | | 1 | "Avaros" | | 2 | "Rory" | | 3 | "Amber" | | 4 | "You" |
| | places | | 0 | "Lane" | | 1 | "Marseille" | | 2 | "Brick" | | 3 | "Heathrow" | | 4 | "Calais" | | 5 | "Interpol" | | 6 | "Lisbon" | | 7 | "Cardiff" | | 8 | "French" | | 9 | "Canary" |
| | globalScore | 0.882 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 125 | | 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 | 1780 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 292 | | matches | (empty) | |
| 77.04% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 61.38 | | std | 25.76 | | cv | 0.42 | | sampleLengths | | 0 | 36 | | 1 | 61 | | 2 | 52 | | 3 | 15 | | 4 | 60 | | 5 | 31 | | 6 | 46 | | 7 | 71 | | 8 | 32 | | 9 | 53 | | 10 | 29 | | 11 | 39 | | 12 | 58 | | 13 | 48 | | 14 | 53 | | 15 | 68 | | 16 | 53 | | 17 | 78 | | 18 | 86 | | 19 | 79 | | 20 | 78 | | 21 | 44 | | 22 | 99 | | 23 | 69 | | 24 | 89 | | 25 | 84 | | 26 | 143 | | 27 | 86 | | 28 | 40 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 292 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 387 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 292 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1787 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.019026301063234472 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0016787912702853946 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 292 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 292 | | mean | 6.1 | | std | 3.98 | | cv | 0.653 | | sampleLengths | | 0 | 2 | | 1 | 7 | | 2 | 10 | | 3 | 17 | | 4 | 14 | | 5 | 26 | | 6 | 4 | | 7 | 4 | | 8 | 8 | | 9 | 5 | | 10 | 5 | | 11 | 10 | | 12 | 5 | | 13 | 16 | | 14 | 16 | | 15 | 4 | | 16 | 11 | | 17 | 4 | | 18 | 6 | | 19 | 9 | | 20 | 8 | | 21 | 14 | | 22 | 4 | | 23 | 1 | | 24 | 8 | | 25 | 6 | | 26 | 3 | | 27 | 16 | | 28 | 4 | | 29 | 3 | | 30 | 5 | | 31 | 2 | | 32 | 11 | | 33 | 8 | | 34 | 2 | | 35 | 3 | | 36 | 3 | | 37 | 6 | | 38 | 11 | | 39 | 4 | | 40 | 8 | | 41 | 8 | | 42 | 6 | | 43 | 9 | | 44 | 10 | | 45 | 5 | | 46 | 4 | | 47 | 17 | | 48 | 9 | | 49 | 5 |
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| 60.84% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 54 | | diversityRatio | 0.476027397260274 | | totalSentences | 292 | | uniqueOpeners | 139 | |
| 51.68% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 258 | | matches | | 0 | "Then I followed the trail." | | 1 | "Then you tore out the" | | 2 | "Just enough to break the" | | 3 | "Forever or fractured." |
| | ratio | 0.016 | |
| 55.66% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 106 | | totalSentences | 258 | | matches | | 0 | "I stopped breathing for a" | | 1 | "They had carved them deeper." | | 2 | "He stepped inside without asking." | | 3 | "You picked the locks." | | 4 | "His voice carried the low" | | 5 | "You taught me how." | | 6 | "I closed the door behind" | | 7 | "I leaned against the wood," | | 8 | "I rubbed it once." | | 9 | "He ran a leather-gloved hand" | | 10 | "I delivered them." | | 11 | "I pushed off the doorframe" | | 12 | "You track people for a" | | 13 | "He set his ivory cane" | | 14 | "He turned slowly, charcoal trousers" | | 15 | "You never throw things away" | | 16 | "You keep receipts in shoeboxes." | | 17 | "You alphabetize takeout menus." | | 18 | "You build fortresses out of" | | 19 | "I opened a drawer and" |
| | ratio | 0.411 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 161 | | totalSentences | 258 | | matches | | 0 | "Deadbolts slid back with three" | | 1 | "The door pushed outward and" | | 2 | "I stopped breathing for a" | | 3 | "Lucien stood in the threshold," | | 4 | "Brick Lane streetlight bled through" | | 5 | "Amber on the left." | | 6 | "Years hadn’t blurred the angles" | | 7 | "They had carved them deeper." | | 8 | "He stepped inside without asking." | | 9 | "Boots scuffed linoleum that smelled" | | 10 | "The flat shrank around us." | | 11 | "Shelves bowed under stacked law" | | 12 | "Ptolemy vaulted from the windowsill" | | 13 | "You picked the locks." | | 14 | "His voice carried the low" | | 15 | "You taught me how." | | 16 | "I closed the door behind" | | 17 | "The deadbolts fell back into" | | 18 | "I leaned against the wood," | | 19 | "The crescent scar on my" |
| | ratio | 0.624 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 9 | | totalSentences | 258 | | matches | | 0 | "Because the algorithm failed." | | 1 | "Because data doesn’t account for" | | 2 | "Because consulting me would mean" | | 3 | "Because trusting me meant admitting" | | 4 | "Because your name surfaced next" | | 5 | "Because I couldn’t breathe when" | | 6 | "Before the runners arrive." | | 7 | "Before the ledger flips." | | 8 | "Before I stop knocking and" |
| | ratio | 0.035 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 1 | | matches | | 0 | "An honest conversation about bloodlines, debts, and territories that demand fealty." |
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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 | |