| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 95 | | tagDensity | 0.053 | | leniency | 0.105 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2040 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 75.49% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2040 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "flicked" | | 1 | "silence" | | 2 | "rhythmic" | | 3 | "pulse" | | 4 | "weight" | | 5 | "warmth" | | 6 | "charged" | | 7 | "charm" | | 8 | "familiar" |
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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 | 190 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 190 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 279 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2040 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 88 | | wordCount | 1505 | | uniqueNames | 15 | | maxNameDensity | 1.79 | | worstName | "Aurora" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Aurora" | | discoveredNames | | Aurora | 27 | | Price | 1 | | Eva | 19 | | Evan | 16 | | Cardiff | 3 | | Saint | 1 | | Brigid | 1 | | Transit | 1 | | Ledger | 1 | | London | 3 | | Soho | 1 | | Roman | 1 | | Lucien | 8 | | Moreau | 2 | | One | 3 |
| | persons | | 0 | "Aurora" | | 1 | "Price" | | 2 | "Eva" | | 3 | "Evan" | | 4 | "Saint" | | 5 | "Brigid" | | 6 | "Roman" | | 7 | "Lucien" | | 8 | "Moreau" |
| | places | | 0 | "Cardiff" | | 1 | "London" | | 2 | "Soho" | | 3 | "One" |
| | globalScore | 0.603 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 101 | | 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 | 2040 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 279 | | matches | | 0 | "knew that look" | | 1 | "hated that she" | | 2 | "let that sit" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 182 | | mean | 11.21 | | std | 14.55 | | cv | 1.298 | | sampleLengths | | 0 | 37 | | 1 | 19 | | 2 | 64 | | 3 | 13 | | 4 | 1 | | 5 | 4 | | 6 | 14 | | 7 | 2 | | 8 | 3 | | 9 | 6 | | 10 | 24 | | 11 | 8 | | 12 | 7 | | 13 | 5 | | 14 | 25 | | 15 | 15 | | 16 | 2 | | 17 | 2 | | 18 | 15 | | 19 | 6 | | 20 | 44 | | 21 | 3 | | 22 | 5 | | 23 | 2 | | 24 | 2 | | 25 | 5 | | 26 | 47 | | 27 | 1 | | 28 | 6 | | 29 | 5 | | 30 | 12 | | 31 | 29 | | 32 | 4 | | 33 | 19 | | 34 | 5 | | 35 | 7 | | 36 | 5 | | 37 | 24 | | 38 | 9 | | 39 | 26 | | 40 | 14 | | 41 | 14 | | 42 | 5 | | 43 | 4 | | 44 | 1 | | 45 | 7 | | 46 | 2 | | 47 | 5 | | 48 | 3 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 190 | | matches | | 0 | "been told" | | 1 | "got offered" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 247 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 279 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1508 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 45 | | adverbRatio | 0.029840848806366047 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.001326259946949602 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 279 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 279 | | mean | 7.31 | | std | 6.58 | | cv | 0.9 | | sampleLengths | | 0 | 37 | | 1 | 19 | | 2 | 9 | | 3 | 6 | | 4 | 11 | | 5 | 24 | | 6 | 14 | | 7 | 13 | | 8 | 1 | | 9 | 4 | | 10 | 6 | | 11 | 8 | | 12 | 2 | | 13 | 3 | | 14 | 6 | | 15 | 9 | | 16 | 11 | | 17 | 3 | | 18 | 1 | | 19 | 8 | | 20 | 7 | | 21 | 5 | | 22 | 15 | | 23 | 10 | | 24 | 15 | | 25 | 2 | | 26 | 2 | | 27 | 15 | | 28 | 6 | | 29 | 27 | | 30 | 2 | | 31 | 1 | | 32 | 4 | | 33 | 6 | | 34 | 4 | | 35 | 3 | | 36 | 5 | | 37 | 2 | | 38 | 2 | | 39 | 5 | | 40 | 47 | | 41 | 1 | | 42 | 6 | | 43 | 5 | | 44 | 3 | | 45 | 9 | | 46 | 9 | | 47 | 5 | | 48 | 7 | | 49 | 8 |
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| 50.90% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3333333333333333 | | totalSentences | 279 | | uniqueOpeners | 93 | |
| 42.74% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 156 | | matches | | 0 | "Then he withdrew, slow, making" | | 1 | "Of course he did." |
| | ratio | 0.013 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 156 | | matches | | 0 | "Her fingers tightened on the" | | 1 | "He gave her the same" | | 2 | "She slammed the door." | | 3 | "His hand shot into the" | | 4 | "She shifted the paper sack" | | 5 | "His face tightened." | | 6 | "His eyes flicked over her" | | 7 | "He was checking exits, obstacles," | | 8 | "He had always hated her." | | 9 | "It had taken Aurora eleven" | | 10 | "She didn't raise her voice." | | 11 | "She didn't need to." | | 12 | "He always liked brinkmanship." | | 13 | "She braced one palm on" | | 14 | "His voice dropped lower, which" | | 15 | "She remembered a different door," | | 16 | "She remembered the sound of" | | 17 | "Her name turned into a" | | 18 | "He spoke again, smoother now." | | 19 | "She hated that she did." |
| | ratio | 0.25 | |
| 33.72% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 133 | | totalSentences | 156 | | matches | | 0 | "The first deadbolt clacked back," | | 1 | "Evan Price stood on Eva's" | | 2 | "The grey wool of his" | | 3 | "The little nick on his" | | 4 | "The stale aftershave that sat" | | 5 | "Her fingers tightened on the" | | 6 | "He gave her the same" | | 7 | "She slammed the door." | | 8 | "His hand shot into the" | | 9 | "The wood hit bone with" | | 10 | "Aurora set the menus on" | | 11 | "She shifted the paper sack" | | 12 | "His face tightened." | | 13 | "Ptolemy streaked between her ankles," | | 14 | "The cat had better instincts" | | 15 | "Evan leaned in, lowering his" | | 16 | "His eyes flicked over her" | | 17 | "Aurora knew that look." | | 18 | "He was checking exits, obstacles," | | 19 | "Control wore many costumes." |
| | ratio | 0.853 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 156 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 3 | | matches | | 0 | "The stale aftershave that sat under the stronger smell of the curry house downstairs, cumin and frying onion climbing the stairwell in hot waves." | | 1 | "In London, after goblins in Soho, after a woman with silver eyes paid for noodles in old Roman coins, after Lucien Moreau corrected her pronunciation of a demon…" | | 2 | "Behind him came two broad-shouldered women in dark coats, both strangers, both carrying themselves like professionals who charged by the broken rib." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 95 | | tagDensity | 0.042 | | leniency | 0.084 | | rawRatio | 0.25 | | effectiveRatio | 0.021 | |