| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 1 | | adverbTags | | 0 | "she asked again [again]" |
| | dialogueSentences | 173 | | tagDensity | 0.081 | | leniency | 0.162 | | rawRatio | 0.071 | | effectiveRatio | 0.012 | |
| 83.53% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2428 | | totalAiIsmAdverbs | 8 | | found | | | highlights | | 0 | "sharply" | | 1 | "quickly" | | 2 | "very" | | 3 | "lightly" | | 4 | "perfectly" | | 5 | "utterly" |
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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) | |
| 75.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2428 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "unreadable" | | 1 | "efficient" | | 2 | "footsteps" | | 3 | "whisper" | | 4 | "warmth" | | 5 | "familiar" | | 6 | "stomach" | | 7 | "tenderness" | | 8 | "pulse" | | 9 | "measured" | | 10 | "absolutely" |
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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 | 1 | | narrationSentences | 168 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 168 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 327 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 52 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2428 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 26 | | unquotedAttributions | 1 | | matches | | 0 | "This time, he answered without a smile." |
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| 76.86% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 60 | | wordCount | 1504 | | uniqueNames | 11 | | maxNameDensity | 1.46 | | worstName | "Lucien" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Lucien" | | discoveredNames | | Lucien | 22 | | Moreau | 1 | | Golden | 3 | | Empress | 3 | | Rory | 1 | | Welsh | 1 | | Avaros | 1 | | Ptolemy | 7 | | Brick | 1 | | Lane | 1 | | Aurora | 19 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Rory" | | 3 | "Ptolemy" | | 4 | "Aurora" |
| | places | | 0 | "Golden" | | 1 | "Avaros" | | 2 | "Brick" | | 3 | "Lane" |
| | globalScore | 0.769 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 107 | | glossingSentenceCount | 1 | | matches | | 0 | "seemed offensive to the cramped landing" |
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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 | 2428 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 327 | | matches | | 0 | "hated that he" | | 1 | "remembered that sound" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 229 | | mean | 10.6 | | std | 12.8 | | cv | 1.208 | | sampleLengths | | 0 | 18 | | 1 | 55 | | 2 | 24 | | 3 | 10 | | 4 | 4 | | 5 | 15 | | 6 | 15 | | 7 | 3 | | 8 | 3 | | 9 | 2 | | 10 | 20 | | 11 | 12 | | 12 | 14 | | 13 | 13 | | 14 | 8 | | 15 | 3 | | 16 | 3 | | 17 | 67 | | 18 | 14 | | 19 | 16 | | 20 | 4 | | 21 | 4 | | 22 | 2 | | 23 | 4 | | 24 | 2 | | 25 | 5 | | 26 | 10 | | 27 | 1 | | 28 | 1 | | 29 | 1 | | 30 | 25 | | 31 | 24 | | 32 | 62 | | 33 | 6 | | 34 | 4 | | 35 | 8 | | 36 | 4 | | 37 | 3 | | 38 | 4 | | 39 | 4 | | 40 | 10 | | 41 | 14 | | 42 | 5 | | 43 | 7 | | 44 | 42 | | 45 | 10 | | 46 | 7 | | 47 | 13 | | 48 | 5 | | 49 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 168 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 267 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 327 | | ratio | 0.006 | | matches | | 0 | "Lucien’s amber eye moved over her face; his black eye remained unreadable." | | 1 | "His mouth was warm; his restraint broke in measured increments, never taking more than she gave, always asking with the pressure of his fingers." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1508 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 57 | | adverbRatio | 0.03779840848806366 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.009283819628647215 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 327 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 327 | | mean | 7.43 | | std | 6.74 | | cv | 0.907 | | sampleLengths | | 0 | 18 | | 1 | 7 | | 2 | 28 | | 3 | 20 | | 4 | 6 | | 5 | 7 | | 6 | 11 | | 7 | 10 | | 8 | 4 | | 9 | 12 | | 10 | 3 | | 11 | 8 | | 12 | 7 | | 13 | 3 | | 14 | 3 | | 15 | 2 | | 16 | 13 | | 17 | 7 | | 18 | 7 | | 19 | 5 | | 20 | 14 | | 21 | 13 | | 22 | 8 | | 23 | 3 | | 24 | 3 | | 25 | 11 | | 26 | 19 | | 27 | 12 | | 28 | 25 | | 29 | 14 | | 30 | 12 | | 31 | 4 | | 32 | 4 | | 33 | 4 | | 34 | 2 | | 35 | 4 | | 36 | 2 | | 37 | 5 | | 38 | 6 | | 39 | 4 | | 40 | 1 | | 41 | 1 | | 42 | 1 | | 43 | 7 | | 44 | 13 | | 45 | 5 | | 46 | 12 | | 47 | 12 | | 48 | 7 | | 49 | 9 |
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| 46.64% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.22018348623853212 | | totalSentences | 327 | | uniqueOpeners | 72 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 148 | | matches | | 0 | "Then she had learned that" | | 1 | "Somewhere below, a delivery scooter" | | 2 | "Instead she stood there in" | | 3 | "Of course he did." | | 4 | "Instead she reached up and" | | 5 | "Then his hand came to" |
| | ratio | 0.041 | |
| 71.35% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 55 | | totalSentences | 148 | | matches | | 0 | "His platinum hair was slicked" | | 1 | "She kept the knife angled" | | 2 | "It arrived in the old" | | 3 | "She had known the subtle" | | 4 | "His gaze dipped to the" | | 5 | "It was the same almost-smile" | | 6 | "She hated it on sight." | | 7 | "His amber eye warmed." | | 8 | "She should have shut the" | | 9 | "she asked again" | | 10 | "His expression hardened." | | 11 | "She hated that he had" | | 12 | "She stepped back." | | 13 | "He entered, folding his tall" | | 14 | "His shoulder brushed hers as" | | 15 | "She shut the door and" | | 16 | "She took the paper bag" | | 17 | "Her stomach tightened with hunger." | | 18 | "She had eaten half a" | | 19 | "She carried the food to" |
| | ratio | 0.372 | |
| 37.70% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 125 | | totalSentences | 148 | | matches | | 0 | "Aurora opened the door with" | | 1 | "Ptolemy hissed from somewhere behind" | | 2 | "The curry house below had" | | 3 | "His platinum hair was slicked" | | 4 | "Rain jeweled the shoulders of" | | 5 | "The other held a paper" | | 6 | "Aurora stared at him." | | 7 | "Lucien’s amber eye moved over" | | 8 | "She kept the knife angled" | | 9 | "Ptolemy slipped past her, wound" | | 10 | "The cat’s betrayal was immediate" | | 11 | "Aurora tightened her grip on" | | 12 | "The familiarity of his concern" | | 13 | "It arrived in the old" | | 14 | "She had known the subtle" | | 15 | "Aurora opened the door another" | | 16 | "His gaze dipped to the" | | 17 | "The corner of his mouth" | | 18 | "It was the same almost-smile" | | 19 | "She hated it on sight." |
| | ratio | 0.845 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 148 | | matches | (empty) | | ratio | 0 | |
| 64.94% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 6 | | matches | | 0 | "Instead she stood there in an old Golden Empress T-shirt and black leggings, hair loose around her shoulders, knife in hand, heart beating too quickly for a wom…" | | 1 | "She had eaten half a packet of crisps at noon and spent the evening bent over a translation of a seventeenth-century Welsh manuscript that contained three contr…" | | 2 | "Aurora’s mind shifted despite herself, assembling fragments: the repeated symbol in the margins, the missing folio, the ink that had reacted to saltwater." | | 3 | "Beneath it, his body held rigid, as if restraint were the only thing keeping him upright." | | 4 | "Then his hand came to her waist, careful and devastatingly familiar, and he kissed her as though he had been starving." | | 5 | "Inside the cramped flat, surrounded by books, secrets, and the fragile beginning of a conversation that should have happened months ago, Aurora sat beside him." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 3 | | fancyTags | | 0 | "Aurora muttered (mutter)" | | 1 | "he continued (continue)" | | 2 | "she whispered (whisper)" |
| | dialogueSentences | 173 | | tagDensity | 0.075 | | leniency | 0.15 | | rawRatio | 0.231 | | effectiveRatio | 0.035 | |