| 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 | 1606 | | 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) | |
| 93.77% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1606 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 71 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 71 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 171 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1606 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 36.23% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 835 | | uniqueNames | 6 | | maxNameDensity | 2.28 | | worstName | "Rory" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 19 | | Eva | 8 | | Ptolemy | 4 | | Lucien | 15 | | French | 1 | | Blood | 3 |
| | persons | | 0 | "Rory" | | 1 | "Eva" | | 2 | "Ptolemy" | | 3 | "Lucien" | | 4 | "Blood" |
| | places | | | globalScore | 0.362 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | 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 | 1606 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 171 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 142 | | mean | 11.31 | | std | 10.67 | | cv | 0.943 | | sampleLengths | | 0 | 24 | | 1 | 68 | | 2 | 7 | | 3 | 3 | | 4 | 11 | | 5 | 6 | | 6 | 9 | | 7 | 10 | | 8 | 8 | | 9 | 17 | | 10 | 12 | | 11 | 6 | | 12 | 5 | | 13 | 7 | | 14 | 23 | | 15 | 4 | | 16 | 7 | | 17 | 14 | | 18 | 2 | | 19 | 14 | | 20 | 7 | | 21 | 10 | | 22 | 43 | | 23 | 3 | | 24 | 24 | | 25 | 22 | | 26 | 7 | | 27 | 3 | | 28 | 6 | | 29 | 8 | | 30 | 5 | | 31 | 10 | | 32 | 11 | | 33 | 29 | | 34 | 12 | | 35 | 24 | | 36 | 3 | | 37 | 11 | | 38 | 4 | | 39 | 9 | | 40 | 3 | | 41 | 7 | | 42 | 45 | | 43 | 15 | | 44 | 4 | | 45 | 4 | | 46 | 4 | | 47 | 10 | | 48 | 18 | | 49 | 4 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 71 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 132 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 171 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 837 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.013142174432497013 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 171 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 171 | | mean | 9.39 | | std | 6.32 | | cv | 0.673 | | sampleLengths | | 0 | 24 | | 1 | 37 | | 2 | 18 | | 3 | 7 | | 4 | 6 | | 5 | 7 | | 6 | 3 | | 7 | 11 | | 8 | 6 | | 9 | 9 | | 10 | 10 | | 11 | 8 | | 12 | 10 | | 13 | 7 | | 14 | 12 | | 15 | 6 | | 16 | 5 | | 17 | 7 | | 18 | 23 | | 19 | 4 | | 20 | 7 | | 21 | 14 | | 22 | 2 | | 23 | 14 | | 24 | 7 | | 25 | 10 | | 26 | 16 | | 27 | 15 | | 28 | 12 | | 29 | 3 | | 30 | 24 | | 31 | 8 | | 32 | 14 | | 33 | 7 | | 34 | 3 | | 35 | 6 | | 36 | 8 | | 37 | 5 | | 38 | 10 | | 39 | 11 | | 40 | 3 | | 41 | 12 | | 42 | 14 | | 43 | 12 | | 44 | 24 | | 45 | 3 | | 46 | 11 | | 47 | 4 | | 48 | 9 | | 49 | 3 |
| |
| 53.61% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.3391812865497076 | | totalSentences | 171 | | uniqueOpeners | 58 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 70 | | matches | (empty) | | ratio | 0 | |
| 94.29% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 70 | | matches | | 0 | "His amber eye caught the" | | 1 | "His jaw worked." | | 2 | "He lifted the cane and" | | 3 | "He set the cane against" | | 4 | "Her shoulder brushed his chest." | | 5 | "She stepped clear and pointed" | | 6 | "He sat and peeled his" | | 7 | "His shirt clung to the" | | 8 | "She put the bowl on" | | 9 | "She pushed his shirt up" | | 10 | "She pressed a towel to" | | 11 | "Her hand stopped above his" | | 12 | "His tail knocked a scroll" | | 13 | "His muscles jumped under her" | | 14 | "She pinched the edges of" | | 15 | "His fingers closed round the" | | 16 | "He loosened his hold and" | | 17 | "He reached into his inside" | | 18 | "She pulled the thread through" | | 19 | "He took the towel and" |
| | ratio | 0.314 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 70 | | matches | | 0 | "The third deadbolt scraped back" | | 1 | "Lucien stood on the landing" | | 2 | "Curry steam drifted up from" | | 3 | "His amber eye caught the" | | 4 | "The black one gave nothing" | | 5 | "Rory kept one foot against" | | 6 | "Ptolemy pushed his head past" | | 7 | "Lucien shifted his weight and" | | 8 | "Blood darkened the cloth between" | | 9 | "Rory looked at the stain," | | 10 | "Rory pushed Ptolemy back with" | | 11 | "Rain struck the window at" | | 12 | "Lucien took his hand from" | | 13 | "A thin blade of sweat" | | 14 | "Lucien dropped his gaze to" | | 15 | "The crescent scar showed white" | | 16 | "Rory laughed once, without humour" | | 17 | "His jaw worked." | | 18 | "He lifted the cane and" | | 19 | "The wards in Eva's paintwork" |
| | ratio | 0.986 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 70 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | 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 | |