| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 125 | | tagDensity | 0.072 | | leniency | 0.144 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.45% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2200 | | 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) | |
| 88.64% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2200 | | totalAiIsms | 5 | | found | | 0 | | | 1 | | word | "the last thing" | | count | 1 |
| | 2 | | | 3 | | | 4 | |
| | highlights | | 0 | "silk" | | 1 | "the last thing" | | 2 | "flickered" | | 3 | "pulse" | | 4 | "stomach" |
| |
| 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 | 145 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 145 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 261 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 57 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2200 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 20 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 66 | | wordCount | 1392 | | uniqueNames | 11 | | maxNameDensity | 1.8 | | worstName | "Aurora" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Aurora" | | discoveredNames | | Moreau | 1 | | Brick | 1 | | Lane | 1 | | Eva | 6 | | London | 1 | | Marseille | 1 | | French | 1 | | Aurora | 25 | | Lucien | 23 | | Mercedes | 1 | | Ptolemy | 5 |
| | persons | | 0 | "Moreau" | | 1 | "Eva" | | 2 | "Aurora" | | 3 | "Lucien" | | 4 | "Mercedes" | | 5 | "Ptolemy" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "London" | | 3 | "Marseille" |
| | globalScore | 0.602 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 94 | | 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 | 2200 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 261 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 186 | | mean | 11.83 | | std | 14.05 | | cv | 1.188 | | sampleLengths | | 0 | 15 | | 1 | 68 | | 2 | 11 | | 3 | 2 | | 4 | 6 | | 5 | 31 | | 6 | 9 | | 7 | 3 | | 8 | 8 | | 9 | 7 | | 10 | 3 | | 11 | 5 | | 12 | 6 | | 13 | 13 | | 14 | 12 | | 15 | 59 | | 16 | 5 | | 17 | 12 | | 18 | 1 | | 19 | 6 | | 20 | 2 | | 21 | 7 | | 22 | 6 | | 23 | 48 | | 24 | 11 | | 25 | 7 | | 26 | 15 | | 27 | 5 | | 28 | 14 | | 29 | 7 | | 30 | 6 | | 31 | 5 | | 32 | 9 | | 33 | 47 | | 34 | 5 | | 35 | 3 | | 36 | 4 | | 37 | 5 | | 38 | 11 | | 39 | 7 | | 40 | 5 | | 41 | 4 | | 42 | 26 | | 43 | 10 | | 44 | 2 | | 45 | 3 | | 46 | 4 | | 47 | 1 | | 48 | 1 | | 49 | 3 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 145 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 218 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 261 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1395 | | adjectiveStacks | 1 | | stackExamples | | 0 | "clean beneath pale skin." |
| | adverbCount | 41 | | adverbRatio | 0.02939068100358423 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.002867383512544803 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 261 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 261 | | mean | 8.43 | | std | 6.95 | | cv | 0.824 | | sampleLengths | | 0 | 15 | | 1 | 24 | | 2 | 8 | | 3 | 13 | | 4 | 17 | | 5 | 6 | | 6 | 11 | | 7 | 2 | | 8 | 6 | | 9 | 16 | | 10 | 15 | | 11 | 5 | | 12 | 4 | | 13 | 3 | | 14 | 8 | | 15 | 7 | | 16 | 3 | | 17 | 5 | | 18 | 6 | | 19 | 7 | | 20 | 6 | | 21 | 12 | | 22 | 18 | | 23 | 13 | | 24 | 9 | | 25 | 19 | | 26 | 5 | | 27 | 7 | | 28 | 5 | | 29 | 1 | | 30 | 6 | | 31 | 2 | | 32 | 7 | | 33 | 6 | | 34 | 3 | | 35 | 7 | | 36 | 38 | | 37 | 11 | | 38 | 7 | | 39 | 3 | | 40 | 12 | | 41 | 5 | | 42 | 14 | | 43 | 7 | | 44 | 6 | | 45 | 5 | | 46 | 9 | | 47 | 8 | | 48 | 20 | | 49 | 10 |
| |
| 43.10% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.24904214559386972 | | totalSentences | 261 | | uniqueOpeners | 65 | |
| 48.31% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 138 | | matches | | 0 | "Even soaked by London rain," | | 1 | "Just her name, worn down" |
| | ratio | 0.014 | |
| 75.07% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 50 | | totalSentences | 138 | | matches | | 0 | "His platinum hair lay slicked" | | 1 | "He rested both hands over" | | 2 | "Her grip tightened around the" | | 3 | "He sniffed at the crack" | | 4 | "He lowered his gaze to" | | 5 | "His jaw set." | | 6 | "She had once known every" | | 7 | "She bent and scooped him" | | 8 | "His striped body settled against" | | 9 | "He fixed Lucien with green" | | 10 | "She should have shut the" | | 11 | "She should have slid the" | | 12 | "she told him" | | 13 | "His amber eye warmed." | | 14 | "He entered with a slight" | | 15 | "His eyes moved to her." | | 16 | "She saw Lucien’s hand around" | | 17 | "She saw the black leather" | | 18 | "Her own name inside it." | | 19 | "Her father’s chambers." |
| | ratio | 0.362 | |
| 17.97% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 122 | | totalSentences | 138 | | matches | | 0 | "Aurora drew back the third" | | 1 | "Lucien Moreau filled the gap" | | 2 | "Rain darkened the shoulders of" | | 3 | "His platinum hair lay slicked" | | 4 | "The other held nothing but" | | 5 | "He rested both hands over" | | 6 | "Her grip tightened around the" | | 7 | "Ptolemy sprang from the arm" | | 8 | "He sniffed at the crack" | | 9 | "Aurora kept her face flat." | | 10 | "Lucien’s mouth shifted, not quite" | | 11 | "A burst of laughter rose" | | 12 | "The corridor smelt of wet" | | 13 | "Lucien looked too clean for" | | 14 | "Aurora hated that she remembered." | | 15 | "He lowered his gaze to" | | 16 | "His jaw set." | | 17 | "The movement ran clean beneath" | | 18 | "She had once known every" | | 19 | "Aurora laughed once." |
| | ratio | 0.884 | |
| 36.23% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 138 | | matches | | | ratio | 0.007 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 4 | | matches | | 0 | "Lucien Moreau filled the gap between the frame and the hall’s peeling wallpaper as though Brick Lane had built itself around him by mistake." | | 1 | "She had once known every small sign that he was losing patience: the way his thumb pressed against the ivory handle, the French syllables that sharpened at the …" | | 2 | "Eva’s kitchen contained tea bags, instant coffee, three jars of herbs that might have been seasoning or spell components, and a packet of biscuits Ptolemy had c…" | | 3 | "So did the memory of his mouth against hers in the back of a taxi, his hand warm at the nape of her neck, the way he had looked at her as if she had caught him …" |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 2 | | fancyTags | | 0 | "Lucien noted (note)" | | 1 | "Aurora murmured (murmur)" |
| | dialogueSentences | 125 | | tagDensity | 0.048 | | leniency | 0.096 | | rawRatio | 0.333 | | effectiveRatio | 0.032 | |