| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1276 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 49.06% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1276 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "silk" | | 1 | "footsteps" | | 2 | "measured" | | 3 | "traced" | | 4 | "crystal" | | 5 | "echoed" | | 6 | "trembled" | | 7 | "warmth" |
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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 | 168 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 168 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 168 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 22 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1276 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 85 | | wordCount | 1276 | | uniqueNames | 31 | | maxNameDensity | 1.49 | | worstName | "Rory" | | maxWindowNameDensity | 3 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 2 | | Nest | 2 | | Soho | 3 | | Mark | 1 | | Union | 1 | | Ireland | 1 | | Wales | 1 | | Liam | 14 | | London | 3 | | Golden | 1 | | Empress | 1 | | Davies | 3 | | Hughes | 3 | | Taff | 1 | | Valley | 1 | | Rory | 19 | | Kensington | 2 | | British | 1 | | Empire | 1 | | Tuesday | 1 | | Silas | 3 | | Pontcanna | 1 | | Cardiff | 3 | | Old | 1 | | Bailey | 1 | | Evan | 2 | | Brecon | 1 | | Beacons | 1 | | New | 1 | | York | 1 | | You | 8 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Mark" | | 3 | "Liam" | | 4 | "Davies" | | 5 | "Hughes" | | 6 | "Rory" | | 7 | "Empire" | | 8 | "Silas" | | 9 | "Old" | | 10 | "Bailey" | | 11 | "Evan" | | 12 | "You" |
| | places | | 0 | "Soho" | | 1 | "Union" | | 2 | "Ireland" | | 3 | "Wales" | | 4 | "London" | | 5 | "Golden" | | 6 | "Taff" | | 7 | "Valley" | | 8 | "Kensington" | | 9 | "Pontcanna" | | 10 | "Cardiff" | | 11 | "Brecon" | | 12 | "New" | | 13 | "York" |
| | globalScore | 0.755 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 99 | | 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 | 1276 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 168 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 76 | | mean | 16.79 | | std | 12.13 | | cv | 0.722 | | sampleLengths | | 0 | 50 | | 1 | 24 | | 2 | 3 | | 3 | 44 | | 4 | 15 | | 5 | 8 | | 6 | 1 | | 7 | 1 | | 8 | 32 | | 9 | 16 | | 10 | 3 | | 11 | 3 | | 12 | 34 | | 13 | 12 | | 14 | 2 | | 15 | 15 | | 16 | 16 | | 17 | 9 | | 18 | 11 | | 19 | 15 | | 20 | 29 | | 21 | 11 | | 22 | 14 | | 23 | 15 | | 24 | 10 | | 25 | 11 | | 26 | 20 | | 27 | 7 | | 28 | 14 | | 29 | 23 | | 30 | 7 | | 31 | 18 | | 32 | 21 | | 33 | 18 | | 34 | 21 | | 35 | 20 | | 36 | 3 | | 37 | 19 | | 38 | 12 | | 39 | 6 | | 40 | 23 | | 41 | 15 | | 42 | 25 | | 43 | 4 | | 44 | 2 | | 45 | 34 | | 46 | 40 | | 47 | 1 | | 48 | 4 | | 49 | 28 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 168 | | matches | | 0 | "was styled" | | 1 | "got married" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 220 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 168 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1282 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 14 | | adverbRatio | 0.0109204368174727 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.00078003120124805 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 168 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 168 | | mean | 7.6 | | std | 4.69 | | cv | 0.618 | | sampleLengths | | 0 | 19 | | 1 | 9 | | 2 | 9 | | 3 | 13 | | 4 | 6 | | 5 | 18 | | 6 | 3 | | 7 | 13 | | 8 | 12 | | 9 | 8 | | 10 | 11 | | 11 | 5 | | 12 | 10 | | 13 | 3 | | 14 | 5 | | 15 | 1 | | 16 | 1 | | 17 | 5 | | 18 | 11 | | 19 | 16 | | 20 | 5 | | 21 | 11 | | 22 | 3 | | 23 | 2 | | 24 | 1 | | 25 | 10 | | 26 | 11 | | 27 | 13 | | 28 | 7 | | 29 | 5 | | 30 | 2 | | 31 | 1 | | 32 | 7 | | 33 | 7 | | 34 | 4 | | 35 | 12 | | 36 | 2 | | 37 | 7 | | 38 | 5 | | 39 | 2 | | 40 | 4 | | 41 | 2 | | 42 | 13 | | 43 | 1 | | 44 | 10 | | 45 | 7 | | 46 | 11 | | 47 | 4 | | 48 | 7 | | 49 | 2 |
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| 39.09% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 21 | | diversityRatio | 0.30952380952380953 | | totalSentences | 168 | | uniqueOpeners | 52 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 146 | | matches | (empty) | | ratio | 0 | |
| 25.48% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 71 | | totalSentences | 146 | | matches | | 0 | "He folded a black umbrella" | | 1 | "His hair was styled with" | | 2 | "His eyes locked onto hers." | | 3 | "He walked toward the bar." | | 4 | "His footsteps clicked against the" | | 5 | "He stopped at the nearest" | | 6 | "She tossed it into the" | | 7 | "Her fingers brushed the small" | | 8 | "She poured the amber liquid" | | 9 | "He stared at the liquid." | | 10 | "He set the glass down" | | 11 | "I moved to London." | | 12 | "He gestured to the Golden" | | 13 | "I manage the books for" | | 14 | "You look like you own" | | 15 | "I made partner at Davies" | | 16 | "We provide legal counsel, Rory." | | 17 | "We don't judge the clients." | | 18 | "You used to chain yourself" | | 19 | "He looked past her, staring" |
| | ratio | 0.486 | |
| 21.64% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 128 | | totalSentences | 146 | | matches | | 0 | "The green neon sign of" | | 1 | "Rory dragged a damp cloth" | | 2 | "The bar smelled of spilled" | | 3 | "Rain lashed against the front" | | 4 | "The heavy oak door groaned" | | 5 | "A gust of damp wind" | | 6 | "Rory looked up." | | 7 | "A man stood in the" | | 8 | "He folded a black umbrella" | | 9 | "His hair was styled with" | | 10 | "A silk tie peeked out" | | 11 | "Rory stopped wiping the counter." | | 12 | "The cloth slipped from her" | | 13 | "The man turned." | | 14 | "His eyes locked onto hers." | | 15 | "He walked toward the bar." | | 16 | "His footsteps clicked against the" | | 17 | "He stopped at the nearest" | | 18 | "Rory picked up the cloth." | | 19 | "She tossed it into the" |
| | ratio | 0.877 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 146 | | matches | | 0 | "To a solicitor from his" | | 1 | "If you were still fragile." | | 2 | "To the way things were." | | 3 | "Before the firm." | | 4 | "Before we figured out that" |
| | ratio | 0.034 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 3 | | matches | | 0 | "You look like you own the bank that holds their mortgages." | | 1 | "I defended the people who made your life a living hell, thinking I could change the system from the inside." | | 2 | "I find loopholes that let executives walk away with golden parachutes while their workers lose their pensions." |
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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 | |