| 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 | 2265 | | 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) | |
| 86.75% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2265 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "glint" | | 1 | "weight" | | 2 | "flicked" | | 3 | "charged" | | 4 | "traced" |
| |
| 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 | 149 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 149 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 177 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2265 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 39.47% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 126 | | wordCount | 1900 | | uniqueNames | 11 | | maxNameDensity | 2.21 | | worstName | "Rory" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Rory" | | discoveredNames | | Lucien | 38 | | Moreau | 1 | | Brick | 2 | | Lane | 2 | | Rory | 42 | | Eva | 12 | | Ptolemy | 18 | | Avaros | 4 | | Golden | 3 | | Empress | 3 | | Cheung | 1 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Rory" | | 3 | "Eva" | | 4 | "Ptolemy" | | 5 | "Cheung" |
| | places | | | globalScore | 0.395 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 147 | | 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 | 2265 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 177 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 57 | | mean | 39.74 | | std | 27.67 | | cv | 0.696 | | sampleLengths | | 0 | 106 | | 1 | 3 | | 2 | 64 | | 3 | 11 | | 4 | 69 | | 5 | 18 | | 6 | 62 | | 7 | 20 | | 8 | 63 | | 9 | 14 | | 10 | 64 | | 11 | 13 | | 12 | 56 | | 13 | 8 | | 14 | 70 | | 15 | 9 | | 16 | 62 | | 17 | 17 | | 18 | 64 | | 19 | 21 | | 20 | 57 | | 21 | 12 | | 22 | 70 | | 23 | 23 | | 24 | 59 | | 25 | 4 | | 26 | 63 | | 27 | 15 | | 28 | 63 | | 29 | 7 | | 30 | 63 | | 31 | 16 | | 32 | 66 | | 33 | 10 | | 34 | 80 | | 35 | 16 | | 36 | 70 | | 37 | 13 | | 38 | 53 | | 39 | 19 | | 40 | 41 | | 41 | 16 | | 42 | 64 | | 43 | 17 | | 44 | 64 | | 45 | 28 | | 46 | 61 | | 47 | 11 | | 48 | 60 | | 49 | 9 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 149 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 299 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 177 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1905 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.007349081364829396 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 177 | | echoCount | 0 | | echoWords | (empty) | |
| 74.33% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 177 | | mean | 12.8 | | std | 4.3 | | cv | 0.336 | | sampleLengths | | 0 | 24 | | 1 | 18 | | 2 | 22 | | 3 | 16 | | 4 | 8 | | 5 | 11 | | 6 | 7 | | 7 | 3 | | 8 | 15 | | 9 | 7 | | 10 | 9 | | 11 | 17 | | 12 | 16 | | 13 | 11 | | 14 | 16 | | 15 | 19 | | 16 | 7 | | 17 | 15 | | 18 | 12 | | 19 | 18 | | 20 | 7 | | 21 | 13 | | 22 | 15 | | 23 | 11 | | 24 | 16 | | 25 | 20 | | 26 | 9 | | 27 | 10 | | 28 | 9 | | 29 | 20 | | 30 | 15 | | 31 | 14 | | 32 | 18 | | 33 | 9 | | 34 | 13 | | 35 | 13 | | 36 | 11 | | 37 | 13 | | 38 | 13 | | 39 | 13 | | 40 | 5 | | 41 | 11 | | 42 | 14 | | 43 | 8 | | 44 | 17 | | 45 | 14 | | 46 | 14 | | 47 | 14 | | 48 | 11 | | 49 | 9 |
| |
| 48.31% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.1977401129943503 | | totalSentences | 177 | | uniqueOpeners | 35 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 149 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 149 | | matches | | 0 | "His cane tip tapped the" | | 1 | "Her straight black hair brushed" | | 2 | "Her bright blue eyes stayed" | | 3 | "She counted the three deadbolts" | | 4 | "He set the mug on" | | 5 | "His black eye caught the" | | 6 | "She straightened with the paper" | | 7 | "His tailored suit hung heavy" | | 8 | "She read the name twice." | | 9 | "He set the mug down" | | 10 | "She held the note up" | | 11 | "She set the book on" | | 12 | "He opened the damp envelope" | | 13 | "He laid the sheet beside" | | 14 | "She had carried that bag" | | 15 | "He flicked his wrist and" | | 16 | "Her sleeve caught on Lucien's" | | 17 | "His suit fabric clung to" | | 18 | "His chin cleared the top" | | 19 | "His arm brushed her shoulder" |
| | ratio | 0.255 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 146 | | totalSentences | 149 | | matches | | 0 | "The door opened and Lucien" | | 1 | "Curry steam rose from the" | | 2 | "Ptolemy the tabby crouched on" | | 3 | "Lucien's platinum hair lay slicked" | | 4 | "His cane tip tapped the" | | 5 | "Rory braced her shoulder in" | | 6 | "Her straight black hair brushed" | | 7 | "Her bright blue eyes stayed" | | 8 | "She counted the three deadbolts" | | 9 | "Ptolemy slipped past her ankle," | | 10 | "Lucien reached past Rory and" | | 11 | "He set the mug on" | | 12 | "Rory moved back to guard" | | 13 | "Hall light struck her left" | | 14 | "Lucien looked at the scar" | | 15 | "Rory's hand tightened on the" | | 16 | "Paper notes shifted under her" | | 17 | "Ptolemy leapt from the stack," | | 18 | "Lucien kept his cane upright" | | 19 | "His black eye caught the" |
| | ratio | 0.98 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 149 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 115 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |