| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 172 | | tagDensity | 0.058 | | leniency | 0.116 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.69% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2321 | | 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) | |
| 76.30% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2321 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "stomach" | | 1 | "measured" | | 2 | "perfect" | | 3 | "velvet" | | 4 | "weight" | | 5 | "silence" | | 6 | "comforting" | | 7 | "familiar" |
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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 | 160 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 160 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 322 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2321 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 30 | | unquotedAttributions | 0 | | matches | (empty) | |
| 52.62% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 66 | | wordCount | 1335 | | uniqueNames | 8 | | maxNameDensity | 1.95 | | worstName | "Lucien" | | maxWindowNameDensity | 3 | | worstWindowName | "Lucien" | | discoveredNames | | Moreau | 1 | | Lucien | 26 | | Eva | 3 | | Rory | 25 | | Golden | 1 | | Empress | 1 | | London | 1 | | Ptolemy | 8 |
| | persons | | 0 | "Moreau" | | 1 | "Lucien" | | 2 | "Eva" | | 3 | "Rory" | | 4 | "Ptolemy" |
| | places | | | globalScore | 0.526 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 102 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 2321 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 322 | | matches | | 0 | "hated that some" | | 1 | "used that name" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 240 | | mean | 9.67 | | std | 9.94 | | cv | 1.028 | | sampleLengths | | 0 | 11 | | 1 | 45 | | 2 | 51 | | 3 | 6 | | 4 | 1 | | 5 | 29 | | 6 | 2 | | 7 | 18 | | 8 | 6 | | 9 | 8 | | 10 | 3 | | 11 | 25 | | 12 | 5 | | 13 | 27 | | 14 | 6 | | 15 | 6 | | 16 | 37 | | 17 | 5 | | 18 | 15 | | 19 | 4 | | 20 | 5 | | 21 | 2 | | 22 | 13 | | 23 | 2 | | 24 | 14 | | 25 | 18 | | 26 | 1 | | 27 | 31 | | 28 | 7 | | 29 | 5 | | 30 | 3 | | 31 | 29 | | 32 | 41 | | 33 | 3 | | 34 | 7 | | 35 | 6 | | 36 | 3 | | 37 | 2 | | 38 | 10 | | 39 | 18 | | 40 | 3 | | 41 | 7 | | 42 | 15 | | 43 | 7 | | 44 | 2 | | 45 | 2 | | 46 | 4 | | 47 | 2 | | 48 | 9 | | 49 | 1 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 160 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 227 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 322 | | ratio | 0.006 | | matches | | 0 | "One eye held amber like whisky held to a flame; the other gave nothing back." | | 1 | "A man cursed in a voice that did not carry human shape; each syllable rasped like metal dragged over stone." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1339 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 38 | | adverbRatio | 0.028379387602688575 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.002987303958177745 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 322 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 322 | | mean | 7.21 | | std | 5.41 | | cv | 0.75 | | sampleLengths | | 0 | 11 | | 1 | 25 | | 2 | 20 | | 3 | 15 | | 4 | 14 | | 5 | 7 | | 6 | 15 | | 7 | 6 | | 8 | 1 | | 9 | 29 | | 10 | 2 | | 11 | 18 | | 12 | 6 | | 13 | 8 | | 14 | 3 | | 15 | 7 | | 16 | 18 | | 17 | 5 | | 18 | 6 | | 19 | 21 | | 20 | 6 | | 21 | 6 | | 22 | 7 | | 23 | 9 | | 24 | 21 | | 25 | 5 | | 26 | 15 | | 27 | 4 | | 28 | 5 | | 29 | 2 | | 30 | 13 | | 31 | 2 | | 32 | 14 | | 33 | 7 | | 34 | 7 | | 35 | 4 | | 36 | 1 | | 37 | 7 | | 38 | 4 | | 39 | 20 | | 40 | 7 | | 41 | 5 | | 42 | 3 | | 43 | 5 | | 44 | 15 | | 45 | 9 | | 46 | 12 | | 47 | 7 | | 48 | 6 | | 49 | 16 |
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| 47.52% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.2546583850931677 | | totalSentences | 322 | | uniqueOpeners | 82 | |
| 91.95% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 145 | | matches | | 0 | "Somewhere below, the curry house" | | 1 | "Then she found something hard" | | 2 | "Already, the purple had begun" | | 3 | "Then a voice drifted through" |
| | ratio | 0.028 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 145 | | matches | | 0 | "His ivory-handled cane rested against" | | 1 | "His mouth shifted, not quite" | | 2 | "It used to pull something" | | 3 | "It still did, which annoyed" | | 4 | "His black one stayed fixed" | | 5 | "He stepped over the threshold." | | 6 | "His shoulders nearly brushed the" | | 7 | "His cane nudged a pile" | | 8 | "He set the journals down" | | 9 | "He looked at her for" | | 10 | "He did it smoothly, but" | | 11 | "She snatched up the scissors." | | 12 | "His eyebrows rose." | | 13 | "His fingers shook once." | | 14 | "He hid it by pulling" | | 15 | "She had not expected the" | | 16 | "His chest rose slowly, each" | | 17 | "Her anger found a new" | | 18 | "She leaned close." | | 19 | "She tore open a packet" |
| | ratio | 0.283 | |
| 11.72% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 130 | | totalSentences | 145 | | matches | | 0 | "The first deadbolt scraped back," | | 1 | "Aurora opened the door with" | | 2 | "Ptolemy shot between her ankles" | | 3 | "Lucien Moreau stood beneath the" | | 4 | "Water gathered along the sharp" | | 5 | "His ivory-handled cane rested against" | | 6 | "Rory’s fingers tightened round the" | | 7 | "Lucien looked past her at" | | 8 | "Ptolemy hissed at Lucien’s polished" | | 9 | "Lucien bent, held out two" | | 10 | "Ptolemy slapped them with one" | | 11 | "Rory did not move aside." | | 12 | "His mouth shifted, not quite" | | 13 | "It used to pull something" | | 14 | "It still did, which annoyed" | | 15 | "Lucien’s amber eye lowered for" | | 16 | "His black one stayed fixed" | | 17 | "A crash sounded from the" | | 18 | "Something heavy struck plaster." | | 19 | "A man cursed in a" |
| | ratio | 0.897 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 145 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 1 | | matches | | 0 | "The way he had taken her helmet after a delivery shift and kissed the rain from her hair outside Golden Empress, as if London had built its whole filthy skyline…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 2 | | fancyTags | | 0 | "she snapped (snap)" | | 1 | "Lucien murmured (murmur)" |
| | dialogueSentences | 172 | | tagDensity | 0.047 | | leniency | 0.093 | | rawRatio | 0.25 | | effectiveRatio | 0.023 | |